test: reproduce resource churn without per-user hook override

test: run generated asset hook inside MSI fixture
fix: preserve frontend resources during per-user MSI build
2026-09-07 16:44:20 +05:30 · 2026-09-07 16:43:48 +05:30 · 2026-09-07 16:41:41 +05:30 · 2026-09-07 16:39:33 +05:30 · 2026-09-07 15:51:52 +05:30 · 2026-09-07 15:30:30 +05:30
647 changed files with 85335 additions and 10770 deletions
@@ -5,6 +5,9 @@ description: "Local TTS, voice cloning, voice design, and video dubbing via the

 # VoiceStudio

+The canonical cross-agent package lives at `skills/omnivoice/SKILL.md`. This
+Claude-specific package retains the MCP lifecycle helpers and references.
+
 ## Overview

 Generate audio locally via the VoiceStudio MCP server. Tools: `generate_speech`, `list_voices`, `list_personalities`, `list_languages`, `check_health`. Resources: `voice://{id}`, `history://recent`.
@@ -166,4 +169,4 @@ The MCP server does not expose the dubbing endpoint. The full transcribe → tra

 Backend Swagger / OpenAPI: `http://127.0.0.1:3900/docs` (when backend is up).

-Upstream: github.com/debpalash/VoiceStudio — FSL-1.1-ALv2 (free for personal/internal/non-commercial; auto-converts to Apache-2.0 two years after each release).
+Upstream: github.com/debpalash/VoiceStudio. The app uses AGPL-3.0-only; optional engines and downloaded models retain their own licenses. See `LICENSE-NOTICE.md` in the repository.
@@ -1,6 +1,6 @@
 #!/usr/bin/env bash
 # Start the OmniVoice FastAPI backend on 127.0.0.1:3900, detached, idempotent.
-# Honors $OMNIVOICE_HOME (default ~/OmniVoice-Studio).
+# Honors $OMNIVOICE_HOME (default ~/VoiceStudio).
 #
 # Exit codes:
 #   0   success (already running, or freshly started + healthy within 60s)
@@ -11,7 +11,7 @@

 set -euo pipefail

-HOME_DIR="${OMNIVOICE_HOME:-$HOME/OmniVoice-Studio}"
+HOME_DIR="${OMNIVOICE_HOME:-$HOME/VoiceStudio}"
 URL="${OMNIVOICE_API_URL:-http://127.0.0.1:3900}"
 LOG="$HOME_DIR/backend.log"

@@ -40,6 +40,7 @@ sudo apt-get install -y \
  libwebkit2gtk-4.1-dev libgtk-3-dev libpango1.0-dev libcairo2-dev \
  libsoup-3.0-dev libgdk-pixbuf-2.0-dev \
  libayatana-appindicator3-dev librsvg2-dev libssl-dev libxdo-dev \
+  gstreamer1.0-plugins-good \
  libasound2-dev build-essential curl wget file
 ```

@@ -4,7 +4,7 @@

 | Version | Supported |
 |---------|-----------|
-| 0.3.x (latest release + `main` previews) | ✅ Current — all fixes land here |
+| 0.5.x (latest release + `main` previews) | ✅ Current — all fixes land here |
 | 0.2.7 | ⚠️ Legacy stable — security fixes only, upgrade recommended |
 | < 0.2.7 | ❌ No longer supported |

@@ -51,6 +51,13 @@ jobs:
          - os: ubuntu-latest
            platform: linux-x86_64
            experimental: false
+          - os: ubuntu-24.04-arm
+            platform: linux-aarch64
+            # Apple Silicon under Asahi Linux. Experimental: the Vulkan
+            # (Honeykrisp GPU) build path is new and the hosted arm64
+            # runner has no GPU — it validates that the binary builds;
+            # on-host Vulkan acceleration is exercised by users.
+            experimental: true
          - os: windows-latest
            platform: windows-x86_64
            experimental: false
@@ -80,11 +87,18 @@ jobs:
      # Linux-only: upstream `buildcpu.sh` enables `-DGGML_BLAS=ON` which
      # requires a system BLAS implementation at cmake configure time.
      - name: Linux system deps (BLAS for ggml-blas backend)
-        if: matrix.platform == 'linux-x86_64'
+        if: startsWith(matrix.platform, 'linux')
        run: |
          sudo apt-get update
          sudo apt-get install -y libopenblas-dev pkg-config

+      # linux-aarch64: let the build script's Vulkan path (Honeykrisp GPU
+      # on Asahi) engage instead of silently falling back to CPU.
+      - name: Vulkan dev deps (linux-aarch64 GPU backend)
+        if: matrix.platform == 'linux-aarch64'
+        run: |
+          sudo apt-get install -y glslc libvulkan-dev spirv-headers
+
      - name: Build omnivoice-tts
        shell: bash
        # Pass values through env (quoted) rather than ${{ }} interpolation
@@ -11,6 +11,11 @@ on:
  push:
    branches: [main]
  workflow_dispatch:
+    inputs:
+      windows_wix_diagnostic:
+        description: Run only the tiny nonpublishing Windows MSI authoring diagnostic
+        type: boolean
+        default: false

 permissions:
  contents: read
@@ -21,6 +26,7 @@ env:

 jobs:
  test:
+    if: ${{ !inputs.windows_wix_diagnostic }}
    name: Tests (backend + frontend)
    runs-on: ubuntu-22.04
    env:
@@ -189,6 +195,7 @@ jobs:
  # and `cargo test --lib` runs the shell's unit tests natively on each OS.
  # Full bundling stays in release.yml on tag push.
  tauri-cross-platform:
+    if: ${{ !inputs.windows_wix_diagnostic }}
    name: Tauri shell check (${{ matrix.label }})
    needs: test
    strategy:
@@ -289,6 +296,7 @@ jobs:
  # job above misses. Narrow scope (tests/smoke/ only) — full pytest stays
  # on Linux until Phase 1's INST-01 lands setuptools for WhisperX.
  smoke-matrix:
+    if: ${{ !inputs.windows_wix_diagnostic }}
    name: Smoke (${{ matrix.label }})
    needs: test
    strategy:
@@ -428,8 +436,9 @@ jobs:
          PY

      - name: Run smoke tests
+        # Exercise credential paths on native Windows as well as POSIX hosts.
        if: matrix.backend_supported
-        run: uv run --no-sync pytest tests/smoke/ -q --tb=short
+        run: uv run --no-sync pytest tests/smoke/ tests/test_hf_token_cache_paths.py -q --tb=short
        env:
          HF_HUB_OFFLINE: "1"  # same no-silent-downloads guard as the main pytest job
          HF_HUB_CACHE: ${{ runner.temp }}/pockettts-empty-hf-cache
@@ -442,3 +451,24 @@ jobs:
        env:
          HF_HUB_OFFLINE: "1"
          HF_HUB_CACHE: ${{ runner.temp }}/worker-artifact-empty-hf-cache
+
+  windows-wix-diagnostic:
+    name: Windows MSI authoring (no publishing)
+    needs: test
+    if: ${{ !cancelled() && (inputs.windows_wix_diagnostic || needs.test.result == 'success') }}
+    runs-on: windows-2022
+    timeout-minutes: 15
+    steps:
+      - uses: actions/checkout@v4
+      - uses: oven-sh/setup-bun@v1
+      - name: Bundle canonical system and per-user templates with a tiny payload
+        shell: pwsh
+        run: ./scripts/diagnose-windows-wix.ps1
+      - name: Preserve verbose linker output and rendered authoring
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: windows-wix-diagnostic
+          path: wix-diagnostic-artifacts/
+          if-no-files-found: warn
+          retention-days: 3
@@ -0,0 +1,127 @@
+# Installer smoke — runs scripts/install.sh / scripts/install.ps1 end-to-end
+# on all three desktop platforms so the one-liner installers can't rot.
+#
+# Gated by `paths` because a cold run downloads multi-GB wheels (torch) and
+# takes ~15-30 min per OS; it only needs to fire when an installer or this
+# workflow changes. The heavy Tauri bundles stay in release.yml (tag push).
+
+name: Install smoke
+
+on:
+  pull_request:
+    paths:
+      - "scripts/install.sh"
+      - "scripts/install.ps1"
+      - ".github/workflows/install-smoke.yml"
+  push:
+    branches: [main]
+    paths:
+      - "scripts/install.sh"
+      - "scripts/install.ps1"
+      - ".github/workflows/install-smoke.yml"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+env:
+  FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
+
+jobs:
+  install:
+    name: Install (${{ matrix.os }})
+    runs-on: ${{ matrix.os }}
+    timeout-minutes: 60
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-22.04, macos-latest, windows-latest]
+    steps:
+      - uses: actions/checkout@v4
+
+      # Running `sh scripts/install.sh` from the repo root exercises the
+      # repo-root resolution (script dir is scripts/, project root one level
+      # up) — the exact bug that made a local run clone a duplicate repo.
+      # Binary mode is the default: prebuilt release asset, checksum verified.
+      - name: Run installer — binary (macOS/Linux)
+        if: runner.os != 'Windows'
+        run: sh scripts/install.sh
+
+      - name: Verify install — binary (macOS/Linux)
+        if: runner.os != 'Windows'
+        run: |
+          if [ "$(uname)" = "Darwin" ]; then
+            test -d "/Applications/VoiceStudio.app" || { echo "::error::VoiceStudio.app missing from /Applications"; exit 1; }
+            echo "✓ VoiceStudio.app installed in /Applications"
+          else
+            test -x "$HOME/.local/bin/VoiceStudio" || { echo "::error::AppImage missing from ~/.local/bin"; exit 1; }
+            "$HOME/.local/bin/VoiceStudio" --appimage-help >/dev/null 2>&1 || true
+            echo "✓ AppImage installed and executable"
+          fi
+
+      # Source mode stays covered end-to-end behind --source.
+      - name: Run installer — source (macOS/Linux)
+        if: runner.os != 'Windows'
+        run: sh scripts/install.sh --source
+
+      - name: Verify install — source (macOS/Linux)
+        if: runner.os != 'Windows'
+        working-directory: ${{ github.workspace }}
+        run: |
+          test -d .venv || { echo "::error::.venv missing"; exit 1; }
+          test -f frontend/dist/index.html || { echo "::error::frontend build missing"; exit 1; }
+          echo "✓ venv + frontend bundle present"
+
+      # Binary mode is the default; CI runs msiexec silently.
+      - name: Run installer — binary (Windows)
+        if: runner.os == 'Windows'
+        env:
+          CI: true
+        shell: pwsh
+        run: '& { $ErrorActionPreference = "Stop"; & "${{ github.workspace }}\scripts\install.ps1" }'
+
+      - name: Verify install — binary (Windows)
+        if: runner.os == 'Windows'
+        shell: pwsh
+        run: |
+          $paths = @(
+            "HKLM:\Software\Microsoft\Windows\CurrentVersion\Uninstall\*",
+            "HKLM:\Software\WOW6432Node\Microsoft\Windows\CurrentVersion\Uninstall\*",
+            "HKCU:\Software\Microsoft\Windows\CurrentVersion\Uninstall\*"
+          )
+          $key = Get-ItemProperty $paths -ErrorAction SilentlyContinue |
+            Where-Object { $_.DisplayName -match "VoiceStudio|OmniVoice" } |
+            Select-Object -First 1
+          if (-not $key) {
+            Get-ItemProperty $paths -ErrorAction SilentlyContinue |
+              Where-Object DisplayName | ForEach-Object { Write-Host "  installed: $($_.DisplayName)" }
+            Write-Host "::error::MSI product not registered"; exit 1
+          }
+          Write-Host "✓ MSI product registered: $($key.DisplayName)"
+
+      # Source mode stays covered end-to-end behind -Source.
+      - name: Run installer — source (Windows)
+        if: runner.os == 'Windows'
+        env:
+          VOICESTUDIO_INSTALL_MODE: source
+        shell: pwsh
+        run: '& { $ErrorActionPreference = "Stop"; & "${{ github.workspace }}\scripts\install.ps1" }'
+
+      - name: Verify install — source (Windows)
+        if: runner.os == 'Windows'
+        shell: pwsh
+        run: |
+          if (-not (Test-Path .venv)) { Write-Host "::error::.venv missing"; exit 1 }
+          if (-not (Test-Path frontend\dist\index.html)) { Write-Host "::error::frontend build missing"; exit 1 }
+          Write-Host "✓ venv + frontend bundle present"
+
+      - name: Upload install log on failure
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: install-log-${{ matrix.os }}
+          path: |
+            /Users/runner/Library/Application Support/OmniVoice/*.log
+            /home/runner/.local/share/VoiceStudio/*.log
+            ${{ runner.temp }}/VoiceStudio/**/*.log
+          if-no-files-found: ignore
@@ -148,15 +148,20 @@ jobs:
  preview-gate:
    name: Preview gate
    runs-on: ubuntu-22.04
+    permissions:
+      contents: read
    outputs:
      is_preview: ${{ steps.decide.outputs.is_preview }}
      proceed: ${{ steps.decide.outputs.proceed }}
+      stable_tag: ${{ steps.decide.outputs.stable_tag }}
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 50
      - id: decide
        shell: bash
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          set -euo pipefail
          event="${{ github.event_name }}"
@@ -171,6 +176,13 @@ jobs:
              exit 1
            fi
            echo "is_preview=true" >> "$GITHUB_OUTPUT"
+            # Resolve once before the matrix starts so every platform stamps
+            # against the same immutable Stable-channel snapshot.
+            STABLE_TAG=$(gh release view --repo "$GITHUB_REPOSITORY" --json tagName --jq .tagName)
+            [[ "$STABLE_TAG" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] || {
+              echo "::error::latest stable release has an invalid tag"; exit 1;
+            }
+            echo "stable_tag=$STABLE_TAG" >> "$GITHUB_OUTPUT"
          else
            echo "is_preview=false" >> "$GITHUB_OUTPUT"
          fi
@@ -497,35 +509,22 @@ jobs:
            echo "APPLE_TEAM_ID=$TID"
          } >> "$GITHUB_ENV"

-      # Stamp each preview build with a unique, monotonically increasing semver
-      # PRERELEASE so the updater actually offers it (a rolling preview that
-      # always reported the static 0.3.0 never looked "newer", so no update was
-      # ever delivered). Ephemeral, CI-only — never committed. Tauri reads the
-      # bundle + updater version from tauri.conf.json, so rewriting it here
-      # stamps the artifacts + latest.json. Under the versioning hard rule
-      # (owner-set 2026-06-11) main is always last-release + 1, so BASE-N is a
-      # prerelease of the NEXT version and semver-sorts ABOVE the last stable
-      # (0.3.6-N > 0.3.5) — preview users naturally upgrade past stable, and
-      # the Windows MSI ProductVersion (which strips the prerelease → 0.3.6)
-      # is also correctly above the last stable.
+      # Stamp each preview with a numeric prerelease that is strictly above the
+      # latest stable release. Main may intentionally retain the released
+      # version while AUTO_VERSION_BUMP is disabled; in that case the helper
+      # advances the preview base by one patch so stable users can still opt in
+      # and receive it. The edit is ephemeral and never committed.
      - name: Stamp preview version
        if: needs.preview-gate.outputs.is_preview == 'true'
        shell: bash
+        env:
+          STABLE_TAG: ${{ needs.preview-gate.outputs.stable_tag }}
        run: |
          set -euo pipefail
-          # package.json is the single source of truth; tauri.conf.json reads its
-          # version from it ("version": "../package.json"), so stamping
-          # package.json restamps the whole bundle.
-          CONF=frontend/package.json
-          BASE=$(jq -r .version "$CONF")
-          # MSI/WiX requires the semver pre-release identifier to be numeric-only
-          # (and <= 65535). "preview.N" hard-fails the Windows bundler, so the
-          # preview stamp is BASE-N — still sorts below the stable BASE for the
-          # updater, still unique per run.
-          PREVIEW_VERSION="${BASE}-${{ github.run_number }}"
-          tmp=$(mktemp)
-          jq --arg v "$PREVIEW_VERSION" '.version = $v' "$CONF" > "$tmp"
-          mv "$tmp" "$CONF"
+          PREVIEW_VERSION=$(python scripts/stamp-preview-version.py \
+            --package-json frontend/package.json \
+            --stable-tag "$STABLE_TAG" \
+            --run-number "${{ github.run_number }}")
          echo "Stamped preview version: $PREVIEW_VERSION"

      # The rolling `preview` release is REUSED every night, and macOS updater
@@ -605,6 +604,21 @@ jobs:
            fi
          done < /tmp/stale.txt

+      # A retried job reuses its version and can collide with installers it
+      # uploaded before a later step failed. Keep other versions/arches intact;
+      # macOS versionless updater archives are scoped by release tag and arch.
+      - name: Clear this target's installer assets on retry
+        if: github.run_attempt > 1
+        shell: bash
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          RELEASE_TAG: ${{ (needs.preview-gate.outputs.is_preview == 'true') && 'preview' || github.ref_name }}
+          RELEASE_TARGET: ${{ matrix.rust_target }}
+        run: |
+          VERSION=$(python -c 'import json; print(json.load(open("frontend/package.json"))["version"])')
+          python scripts/clear-release-rerun-assets.py \
+            --tag "$RELEASE_TAG" --version "$VERSION" --target "$RELEASE_TARGET"
+
      - name: Build + release (Tauri)
        uses: tauri-apps/tauri-action@v0
        env:
@@ -649,6 +663,47 @@ jobs:
          updaterJsonPreferNsis: false
          includeUpdaterJson: true

+      - name: Build per-user Windows MSI
+        if: runner.os == 'Windows'
+        shell: bash
+        working-directory: frontend
+        env:
+          TAURI_SIGNING_PRIVATE_KEY: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY }}
+          TAURI_SIGNING_PRIVATE_KEY_PASSWORD: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY_PASSWORD }}
+        run: |
+          set -euo pipefail
+          python ../scripts/render-per-user-wix.py \
+            --source src-tauri/wix/main.wxs \
+            --system-wxs src-tauri/target/${{ matrix.rust_target }}/release/wix/x64/main.wxs \
+            --output src-tauri/target/wix-per-user/main.wxs
+          bunx tauri build --target ${{ matrix.rust_target }} --bundles msi \
+            --config src-tauri/tauri.per-user.conf.json
+          DIR="src-tauri/target/${{ matrix.rust_target }}/release/bundle/msi"
+          while IFS= read -r artifact; do
+            safe=${artifact// (Current User)/_Current_User}
+            [ "$safe" = "$artifact" ] || mv "$artifact" "$safe"
+          done < <(find "$DIR" -maxdepth 1 -type f -name '*Current*User*.msi*')
+
+      - name: Publish per-user Windows updater channel
+        if: runner.os == 'Windows'
+        shell: bash
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          RELEASE_TAG: ${{ (needs.preview-gate.outputs.is_preview == 'true') && 'preview' || github.ref_name }}
+        run: |
+          set -euo pipefail
+          DIR="frontend/src-tauri/target/${{ matrix.rust_target }}/release/bundle/msi"
+          MSI=$(find "$DIR" -name '*Current*User*.msi' -type f | head -1)
+          [ -n "$MSI" ] || { echo "per-user MSI missing"; find "$DIR" -type f; exit 1; }
+          [ -f "$MSI.sig" ] || { echo "per-user MSI signature missing"; exit 1; }
+          VERSION=$(jq -r .version frontend/package.json)
+          python scripts/build_windows_user_manifest.py \
+            --repo "$GITHUB_REPOSITORY" --tag "$RELEASE_TAG" --version "$VERSION" \
+            --asset "$(basename "$MSI")" --signature-file "$MSI.sig" \
+            --output latest-user.json
+          gh release upload "$RELEASE_TAG" "$MSI" "$MSI.sig" latest-user.json \
+            --clobber --repo "$GITHUB_REPOSITORY"
+
      # ── Installer smoke (Phase 0 GATE-03) ─────────────────────────────
      # Structural verification of the installed/extracted bundle. The thin
      # uv-venv installer ships NO frozen backend binary (the venv is built on
@@ -717,8 +772,9 @@ jobs:
        shell: bash
        run: |
          set -euo pipefail
-          MSI=$(find frontend/src-tauri/target/${{ matrix.rust_target }}/release/bundle/msi -name "*.msi" | head -1)
+          MSI=$(find frontend/src-tauri/target/${{ matrix.rust_target }}/release/bundle/msi -name "*.msi" ! -name '*Current*User*' | head -1)
          echo "Smoke-testing MSI: $MSI"
+          powershell.exe -NoProfile -ExecutionPolicy Bypass -File scripts/verify-windows-msi.ps1 -MsiPath "$(cygpath -w "$MSI")"
          # /quiet = no UI, /norestart = don't reboot the runner if a dep asks
          msiexec.exe //i "$(cygpath -w "$MSI")" //quiet //norestart
          INSTALL="/c/Program Files/VoiceStudio"
@@ -731,6 +787,16 @@ jobs:
          find "$INSTALL" -type f -path '*backend*main.py' | grep -q . || fail "backend source main.py missing"
          echo "OK — MSI installed shell + uv + backend resources"

+      - name: Per-user installer smoke (Windows, non-admin account)
+        if: runner.os == 'Windows'
+        timeout-minutes: 8
+        shell: bash
+        run: |
+          set -euo pipefail
+          MSI=$(find frontend/src-tauri/target/${{ matrix.rust_target }}/release/bundle/msi -name '*Current*User*.msi' | head -1)
+          powershell.exe -NoProfile -ExecutionPolicy Bypass \
+            -File scripts/smoke-per-user-msi.ps1 -MsiPath "$(cygpath -w "$MSI")"
+
      # linuxdeploy re-links .DirIcon as an ABSOLUTE symlink into the build
      # machine AFTER tauri's files-map has placed the real icon bytes — the
      # exact bug #1518 guarded against, resurfacing on the first real tag
@@ -159,7 +159,24 @@ tests/probe/reports/
 # reports). Working notes for whoever is driving a change, not a repo artifact.
 /remote/

+# OmniVoice GGUF runtime build artifacts (scripts/build-omnivoice-tts.sh).
+# Only 0-byte placeholders of omnivoice-tts-* are tracked; real binaries,
+# the checksums manifest and the copied libggml shared libs ship via CI.
+bin/libggml*
+bin/checksums.sha256
+bin/omnivoice-tts-linux-aarch64
+
 # Dubbing-demo intermediates. The .mp4/.srt/manifest.json in this directory ARE
 # committed (they ship with the app); the per-language source WAVs are just the
 # inputs scripts/render_dub_demo_audio.py hands to scripts/build_dub_demo.sh.
 backend/assets/samples/demo/dubbing/*.src.wav
+
+# Stray sqlite session artifacts (`<db-path>.ses`). An in-memory DB yields the
+# literal name `:memory:.ses`, and a path containing `:` cannot be checked out
+# on Windows at all — committing one fails every Windows CI job at the git
+# checkout step, before a single test runs. Guarded by
+# tests/test_no_windows_hostile_paths.py.
+*.ses
+
+# Generated Windows MSI diagnostic logs and installer payloads
+/wix-diagnostic-artifacts/
@@ -25,6 +25,8 @@ regexes = [
  '''^hf_QWERTYUIOPasdfghjklZXCVBNM0123456789xyzAB$''',
  # NLLB generation length argument, not the value of a credential.
  '''^max_length=400$''',
+  # Dubbing pane split-position localStorage key, not a credential.
+  '''^omnivoice\.dubSplit\.v1$''',
  # cryptography's Ed25519 private-key type name, not key material.
  '''^Ed25519PrivateKey$''',
 ]
@@ -10,20 +10,200 @@ the frozen-backend fallback mirror it for their toolchains.

 **Highlights**

- The backend now answers within a second of launch and narrates its startup step by step
- Reporting a bug from an outdated build now offers the latest release first
- The backend is only announced ready once it can actually serve, and crash-loop restarts now pace themselves
+- The desktop app builds and opens from a fresh clone again (#1818) — thanks @flutterkage2k!
+- GPUs with less VRAM than the engine needs no longer get half the compute-time budget a CPU gets (#1806) — thanks @VishvakR!
+- Gallery voice previews play again — the quality guard was rejecting good renders as silent (#1819) — thanks @flutterkage2k!
+- Tilde-separated number ranges are spoken clearly without running their endpoints together (#1821) — thanks @flutterkage2k!
+- Voice modes use themed tabs, with Synthesize and Convert pinned below their scrolling forms (#1823)
+- Fix current-user Windows installer validation and nested resource cleanup (#1873)
+- Keep generated frontend assets available while building the current-user Windows installer (#1873)
+
+- Voice cloning now starts with a clear upload-or-record choice, reveals recording and reference details only when needed, and keeps sampling controls under Production Overrides (#1817)
+- The first-run welcome line uses an instruction accepted by OmniVoice and VoiceDesign engines (#1861) — thanks @psiberfunk!

 ### Changed
+
+- Casting uses responsive SVG voice cards and searchable speaker menus that stay above surrounding panels (#1823)
+- Dubbing aligns output settings, brings review status forward, and simplifies transcript and glossary editing; Launchpad files and voices reflow into responsive grids (#1823)
+- Transcript segments use three readable rows for text, timing/status and voice controls, with heights that adapt to wrapping (#1823)
+- Dragging the waveform pans horizontally while a click still seeks, keeping the timed transcript aligned (#1823)
+- Bulk segment editing uses searchable voice and language menus, readable language names and a responsive selection toolbar (#1823)
+- Dubbing overlays playback controls on video, combines waveform and transcript in a compact timeline, and removes header/action background fills (#1823)
+- Dubbing uses compact casting, translation and output controls with responsive rows to leave more room for editing (#1823)
+- Export uses grouped format settings, themed track menus and switches, with a pinned filename summary and download action (#1823)
+- Dubbing output settings use icon-labelled switches, themed track and speaker menus, and clearer timing/transcript controls (#1823)
+- Casting voice menus use searchable themed options with SVG preset icons instead of native dropdowns (#1823)
+- Dubbing groups casting and translation controls with readable labels, SVG icons, searchable menus, and compact timeline spacing (#1823)
+- Production Overrides use readable icon-labelled controls and accessible Denoise/Postprocess switches (#1823)
+- Expanded navigation uses a theme-accent tint with subtle static wave gradients (#1823)
+- Convert groups source audio, target voice, and timing options into clearer controls; design choices include theme-matched SVG icons (#1823)
+- The expandable sidebar reveals workspace labels with restrained active states; language menus adapt to multiple columns on wider screens (#1823)
+- Voice design and recording use themed, keyboard-accessible selectors with clearer spacing and labels (#1823)
+- Voice tabs and upload/record controls have subtle SVG motion; Text adds clipboard paste and the upload area fills available height (#1823)
+- The title-bar label cycles through active speech, transcription, and LLM engines; bundled model labels correctly say OmniVoice (#1823)
+- The top-bar Engines panel groups Speech, Transcription, and LLM choices into tabs, with compact memory controls and no duplicate pickers (#1823)
+### Added
+
+### Docs
+
+### Fixed
+
+- Install documentation help now prints correctly on Windows consoles using legacy encodings (#1815) — thanks @dajiaohuang!
+- Saved transcriptions with missing or invalid timestamps now remain readable (#1799) — thanks @yunaremaia and @tvbht!
+- Copying a saved transcription now uses the shared clipboard helper and reports failed copies accurately (#1803) — thanks @tvbht!
+
+- Voice reference preparation reclaims allocator memory before one bounded retry, then reports persistent GPU out-of-memory failures (#1811)
+- `bun run desktop` now opens on a fresh clone: the Vite alias for `@tauri-apps/plugin-dialog` no longer assumes a nested `frontend/node_modules`, which bun's workspace hoisting leaves empty (#1818) — thanks @flutterkage2k!
+
+- Slow backend startups remain running with progress updates, and Retry interrupts startup without stale timeout failures (#1809)
+- Backend connection errors report crashes only when recorded evidence exists, and diagnostic waits honor cancellation (#1810)
+- A CUDA or ROCm GPU with less VRAM than the engine needs now gets the CPU compute-time budget instead of the shorter accelerated one, since it pages to system RAM and renders slower than the CPU would — applied to local generation, voice conversion, and remote worker deadlines alike (#1806) — thanks @VishvakR!
+- Gallery previews no longer fail with "the voice engine returned no audible audio" on perfectly good renders: the degenerate-buzz guard measured spectral flatness over the whole clip (so the value tracked clip length) against a threshold calibrated on a synthetic signal, and rejected real speech in every language tested (#1819) — thanks @flutterkage2k!
+- Speak tilde separators in integer, signed, and decimal ranges in English, Korean, Japanese, and Chinese (#1821) — thanks @flutterkage2k!
+
+- Keep recording and conversion work safe while switching methods, synchronize dubbing language controls, and localize timeline controls and timing warnings (#1841)
+
+- Dubbing playback starts before waveform decoding, automatic cast names are readable, and transcript timestamps have more room (#1823)
+- The title-bar engine button stays compact and stable while cycling labels, with engine names aligned right (#1823)
+- Long dubbing segment errors wrap in a bounded scrollable notice instead of widening the editor (#1823)
+- Voice dropdowns match their field width, use theme accents, and show recent voices only once (#1823)
+- Language menus no longer show a pale frame around their search header (#1823)
+- The notification count stays inside the title bar instead of clipping above the bell (#1823)
+- The workspace engine menu opens beside its button instead of at the opposite edge of the page (#1823)
+- Cloning reuses the dubbing language picker with flags, search, and single selection, opening above the pinned synthesis controls (#1823)
+- The first-run welcome line uses an instruction accepted by OmniVoice and VoiceDesign engines (#1861) — thanks @psiberfunk!
+- The header status dot now honors OS Reduce Motion instead of pulsing regardless (#1862) — thanks @psiberfunk!
+- Onboarding reads Hugging Face tokens locally, preserves Windows CLI logins, and requires successful discovery before replacing saved credentials (#1852) — thanks @psiberfunk!
+- The logs panel no longer reports “All clear” before log retrieval succeeds or while logs contain warnings or errors (#1870) — thanks @motodriver!
+- MOSS accelerator routing and status match runtime selection, with CPU fallback when device probing fails (#1830) — thanks @li-lizhe!
+- Confucius accelerator routing tolerates failed device probes, and dots.tts keeps safe default precision on non-CUDA hosts (#1831) — thanks @li-lizhe!
+- On macOS, the header status dot and kicker no longer render underneath the overlaid traffic lights (#1863) — thanks @psiberfunk!
+- The capture widget can hide after recording and recover from being left visible while idle (#1865) — thanks @psiberfunk!
+
+- macOS retains the shared desktop window sizing, resize limits, and file-drop behavior when native chrome is applied (#1865) — thanks @psiberfunk!
+
+- On macOS, the header no longer shows Windows-style minimize/maximize/close buttons alongside the native traffic lights (#1865) — thanks @psiberfunk!
+
+- Release retries replace their own partially uploaded installers without colliding with existing assets (#1871)
+- Timed-out voice engines finish process cleanup before retrying, and old timeout callbacks cannot kill replacement engines (#1872)
+
+
+- Fast macOS process exits no longer turn a completed shutdown into a permission error (#1809)
+
+## [0.5.2] — 2026-09-02
+
+**Highlights**
+
+- Show estimated and measured model, dependency, cache, and temporary disk costs in the engine catalogue (#1718)
+- Preview builds now stay newer than Stable even when automatic post-release version bumps are disabled (#1762)
+- CosyVoice setup guidance now separates downloaded model files from the runtime that makes the engine available (#1761)
+- MCP tools can now keep audio out of agent context by returning files and accepting base-path-confined file inputs (#1760) — thanks @agudmund!
+- Hear a dub line as you type it — an opt-in live preview streams TTS for the edited segment (#1769) — thanks @mvanhorn!
+- Studio gains a Convert method: re-say any clip in one of your saved voices, speech to speech, fully local (#1765) — thanks @mvanhorn!
+- Hardsub video export gains an opt-in karaoke word-highlight caption style (#1764) — thanks @mvanhorn!
+- The batch queue can now watch a folder: new videos dropped into it are dubbed automatically (#1768) — thanks @mvanhorn!
+- The audiobook player now shows the chapter text and highlights the word being narrated (#1766) — thanks @mvanhorn!
+- The dub editor gains a casting board: drag voice chips onto speakers, dropdowns stay in sync (#1767) — thanks @mvanhorn!
+
+### Changed
+
+- Voice Design simplified: the 12-row fine-grained block collapses to one summary line with a five-field editor, English accent and Chinese dialect merge into a single field, and the starting-point chips now show 5 with an overflow toggle (#1793)
+
+### Added
+
+- The audiobook result is now a synced-lyrics player: chapter text follows playback with the current word highlighted and click-to-seek, timed from the render's own chapter durations with a karaoke-style even split — no ASR pass, fully local (#1766) — thanks @mvanhorn!
+- The dub CAST strip expands into a project-level casting board: drag voice chips (clone profiles, design presets, Default) onto speaker rows — or pick from a keyboard listbox — writing the same per-speaker cast fields as the existing dropdowns (#1767) — thanks @mvanhorn!
+- Studio's new Convert method turns a dropped or recorded clip into an existing voice profile's voice, with optional source-duration matching (#1765) — thanks @mvanhorn!
+- Opt-in watch folder on the batch queue: pick a directory once and new videos are auto-enqueued with your last Add-to-queue settings, with pause/stop controls and copy-in-progress protection — files upload as bytes, paths never leave the app (#1768) — thanks @mvanhorn!
+- Hardsub export can now burn karaoke word-highlight captions: an opt-in Line | Karaoke control renders a word-timed ASS sweep from timings persisted at transcription, with an even-split fallback for older jobs and translated tracks, plus a `GET /dub/ass/{job_id}` sidecar (#1764) — thanks @mvanhorn!
+- Windows releases now include an independently updatable per-user MSI that installs and uninstalls without elevation (#1713)
+- Dub segments can now stream live TTS while you edit a translated line — opt-in toggle, existing `/ws/tts` socket, shared generation admission, exports still render at full quality (#1769) — thanks @mvanhorn!
+- Engine status and diagnostic bundles now record loaded execution provider, device, precision, fallback stage, accelerator identity, runtime versions, and parent-process memory visibility (#1717)
+
+### Docs
+
+- Local gigastt is now documented as a supported OpenAI-compatible ASR endpoint, with loopback privacy distinguished from remote servers (#1736) — thanks @ekhodzitsky!
+- The CosyVoice guide now states that packaged builds have no one-click runtime installer and records the exact readiness checks exposed by [Discussion 1631](https://github.com/debpalash/VoiceStudio/discussions/1631) (#1761)
+- A production private-API guide now covers pinned containers, root credentials, network isolation, streaming proxies, health checks, upgrades, and benchmark evidence (#1720)
+- RX 6700 XT/gfx1031 over WSL2 ROCDXG is now explicitly unverified until a published end-to-end GPU workload proves the mapped path (#1716)
+
+### Fixed
+
+
+- The generation compute-time budget is now a Settings control (Performance & Device) instead of an env-var-only setting the timeout error recommended with no UI path — the error copy points there too, and long CPU/MPS renders get an upfront heads-up before they start (#1787)
+- Windows: the backend can now start when the install path contains non-English characters (e.g. a CJK username) on a non-UTF-8 system code page — a new or broken Python environment now builds at an ASCII-safe path automatically (a healthy existing one is never relocated), and a specific error message names the cause and a working fix if the interpreter still crashes in `site` (#1783)
+- Exports and other native-picker actions no longer 403 with "Invalid or expired desktop authorization" when the desktop app and backend resolve different data directories, e.g. dev mode or a custom data folder (#1781)
+- Voice Design no longer lets you pick a Chinese dialect and an English accent together — the picker keeps them mutually exclusive instead of round-tripping a 400 (#1771)
+- The desktop app no longer attaches to an already-running backend on version string alone: it now verifies the backend's actual code fingerprint too, so an orphaned or manually started backend reporting the current version but running older code (e.g. a stale `destination_path` export 422) gets replaced instead of adopted (#1770)
+- Korean locale overhauled: 231 mistranslations corrected and all 493 missing keys translated (#1776) — thanks @j30231!
+- Japanese "Cleaning…" clone status now reads as denoising instead of housekeeping (#1775) — thanks @j30231!
+- The batch dubbing queue now has a UI entry point — a quiet link on the Dub landing (it was previously unreachable: the app switched on a mode nothing ever set) (#1768) — thanks @mvanhorn!
+- OpenAI-compatible ASR now requires HTTPS outside loopback and refuses redirects so audio stays on the configured origin (#1736)
+- Windows isolated engines now retain direct Job ownership without an extra Python supervisor process that can deadlock the child loader (#1734)
+- The setup splash now waits through the backend's full startup budget instead of reporting slow Windows CUDA initialization as stuck after two minutes (#1749)
+- Dubbing jobs can now reuse every source-language code produced by automatic ASR detection without a 400 error on the next upload (#1737)
+- Incomplete Sherpa-ONNX model snapshots now self-repair before recognizer startup instead of failing on a missing ONNX file (#1733)
+- OmniVoice subprocess startup now allows slow packaged Windows Python runtimes to signal readiness before termination (#1711)
+- SRT files selected during source analysis now wait for speaker cloning, then replace transcript text without losing voices (#1709)
+- Windows MSI deployments can now prohibit WebView2 bootstrap with `DISABLEWEBVIEW2BOOTSTRAP=1`, and `AUTOLAUNCHAPP=0` reliably suppresses first launch (#1714)
+- Subtitle rows now provide 100 ms timing steppers and flag adjacent overlaps without requiring precise timeline dragging (#1710)
+- Repair-sync failures now retain uv's final dependency error instead of reporting only an opaque exit status (#1705)
+- YouTube ingest now retries yt-dlp's transient “page needs to be reloaded” response (#1706)
+- Dictation model readiness now follows the live Hugging Face cache selected in Settings (#1707)
+- Dictation capture now queues native events whenever its webview listener unmounts or reloads instead of emitting them to nobody (#1707)
+- Desktop-contained backends now exit when their owning app disappears instead of surviving as stale port-3900 processes (#1707)
+
+## [0.5.1] — 2026-08-28
+
+**Highlights**
+
+- OmniVoice generation on Apple Silicon now runs in a crash-isolated child, so fatal MPS memory exits no longer take down the local backend (#1697, #1698) — thanks @ndntran14!
+- Model-load GPU exhaustion now returns a sanitized, actionable dubbing error, and readiness correctly attributes the shared model status to TTS (#1695)
+- Source-mode development now restarts an isolated backend crash without tearing down the UI, while repeated crash loops still stop loudly with diagnostics (#1690)
+- Dubbing playback now keeps an audible companion source when a WebView can render the preview picture but cannot decode its audio (#1692)
+- Model Catalogue engine rows now use the available desktop width and keep identity, runtime state, and actions from crowding one another (#1689)
+- VoiceStudio now acts as a local speech platform: other apps can trigger its native dictation or connect through versioned HTTP, WebSocket, JSON-RPC, CLI, and MCP transports (#1646)
+- A timed-out in-process dub transcription no longer starts a second WhisperX/CTranslate2 call over the abandoned native worker, preventing the overlapping access that preceded Windows `0xC0000005` exits (#1669)
+- Windows debugger termination code `0x40010004` is no longer misreported as a backend crash or charged against automatic restart recovery (#1663)
+- Studio now keeps one generation reservation across page changes, preventing a remount from stacking native jobs until the backend reports capacity busy or is killed under memory pressure (#1670)
+- Uploaded dubbing videos are normalized to browser-safe H.264/AAC before preview, preventing valid VP9, AV1, or Opus media from failing with “no supported sources” (#1644)
+- Dubbing now separates spoken and target languages, preserves translations through segment cleanup, and lets failed translations be retried or skipped without restarting the batch (#1654) — thanks @Number16BusShelter!
+- Importing replacement SRT subtitles now keeps each cue bound to the best-overlapping source speaker and clone instead of resetting every line to a random default voice (#1660) — thanks @invio-a11y!
+- Uploading a Dub preview no longer blocks every backend request while ffmpeg extracts its audio (#1667) — thanks @tfreyd!
+- Docker quick starts now require the administrator key needed through container NAT instead of starting a UI whose protected actions return 403 (#1651) — thanks @wd357dui!
+- WSL2 AMD containers now use the `/dev/dxg` ROCDXG bridge with actionable GPU diagnostics instead of silently falling back to CPU (#1655) — thanks @wd357dui!
+- Ad-hoc voice-clone references now stay alive until cancelled or timed-out GPU work actually stops reading them, so prompt caching can finish instead of failing on a deleted temp file (#1668) — thanks @tfreyd!
+- Dictation now stays bound to the app where it started and recovers locally from silent recognizer output (#1175)
+- The backend now answers within a second of launch and narrates its startup step by step (#1550)
+- Reporting a bug from an outdated build now offers the latest release first (#1547)
+- The backend is only announced ready once it can actually serve, and crash-loop restarts now pace themselves (#1548)
+- Invisible watermarking no longer stalls — or silently skips — the first take of a session (#1615)
+- Dub subtitles can be retimed, inserted, and merged in either direction from the segment table (#1612) — thanks @invio-a11y!
+
+### Changed
+- Model Catalogue now uses one breathable workspace canvas with simpler pane and engine-family navigation instead of nested cards and scroll regions (#1685)
+- Linux source launchers now catch missing libxdo and GStreamer audio plugins before they can cause a linker error or an aborted, blank WebKit renderer (#1680, #1682)
+- Dictation now carries one native output session from shortcut-down through final delivery, restores text, HTML, image, or file-list clipboards only when untouched, keeps Wayland copy-safe unless current-focus insertion is explicitly enabled, and retries silent Sherpa speech only through an already-installed local ASR model (#1175)
 - The backend binds its port immediately and reports startup progress live — `/health` answers 503-with-step and a new `/startup/progress` endpoint lists every step while PyTorch, API routes, and database migrations load in the background, so "starting at step X" is never mistakable for "dead"; the desktop splash narrates each step (#1550)

 ### Added
+- A bundled Rust loopback sidecar exposes dictation start/stop/toggle, focused-output sessions, discovery, and JSON-RPC; the backend adds versioned streaming events and a dependency-free CLI bridge for Herdr, coding agents, editors, desktop apps, and TUIs (#1646)
+- Headless NVIDIA and ROCm machines can now join as worker-only Docker Compose services with no published UI and durable protocol-v2 enrollment; update both machines together before reconnecting (#1638) — thanks @jkrogers9862!
+- Linux ARM64 (Asahi Apple Silicon) support for the OmniVoice GGUF engine — a `linux-aarch64` binary built with GGML Vulkan where the toolchain allows it, so Apple GPUs accelerate generation through the open-source Honeykrisp driver instead of falling back to CPU-only (#1641)
+- One-command install on every desktop OS: `curl -fsSL https://voicestudio.sh/install | sh` (macOS/Linux/WSL) or `irm https://voicestudio.sh/install | iex` (Windows) — the URL serves the right script per platform, and Windows gains a source installer (`scripts/install.ps1`) with a 3-OS CI smoke (#1626)
+- Per-line subtitle management in the dub table: a line's end time is editable alongside its start (typing a time and dragging its timeline edge now take the same path), lines merge with the previous row as well as the next (`Ctrl/Cmd+Shift+M`), and a new line can be inserted into the gap after any row (#1612) — thanks @invio-a11y!
+- CI now enforces performance regression budgets on the hot paths — operation-count tests pin streaming TTS to one synthesis per sentence and cached dub re-mixes to zero re-synthesis; fast-path guards cover zero re-decoding and ⌈N/W⌉ native batch calls when enabled (#1594)
+- Default-engine dubbing now synthesizes several segments per forward pass instead of one call per line — the width follows the host's device headroom (1 on CPU and low-VRAM cards, up to 8), `OMNIVOICE_DUB_BATCH_WIDTH` overrides it, and engines without native batching keep the single-segment path (#1594)
+- `/ws/tts` now reports real time-to-first-audio, and its RTF measures synthesis alone so a slow client can't inflate it (#1594)
+- The locally cached AudioSeal watermark generator warms on a background thread ~35s after boot (`OMNIVOICE_PRELOAD_WATERMARK=0` opts out; explicitly setting `=1` may download it), so the first synthesis no longer serializes the audioseal import + model load inline — measured at ~42s on a cold filesystem, 3s short of a 90s client timeout (#1576) — thanks @paoloantinori!
 - Voices you've cloned stay "warm" across restarts — encoded references now persist to disk (~10 KB each), so the first generation of a session skips the re-encode and any transcription pass; `OMNIVOICE_PROMPT_DISK_CACHE=0` opts out (#1565)
 - Optional FlashInfer acceleration for the default engine on CUDA (`OMNIVOICE_FLASHINFER=1`, ~2.2x measured) — needs the optional `flashinfer-python` package; missing package or kernel failure logs why and falls back to the standard path (#1565)
 - The bug reporter notices when you're on an outdated build and offers the latest release before filing — with a "File anyway" escape hatch — and stamps a `Build status` line into every report so up-to-date reports are tellable from stale ones (#1547)
 - Settings → Performance & Device gains a compute-device override (Auto / CUDA / ROCm / XPU / MPS / CPU, or `OMNIVOICE_DEVICE`) — pin the device when auto-detect picks wrong; only devices your machine actually has are offered (#1557)
+- Opt-in 24-layer PocketTTS checkpoints via `OMNIVOICE_POCKETTTS_24L` — better prosody for it/de/es/pt at roughly 2x render time (still faster than real-time); the fast 6-layer model stays the default (#1613) — thanks @paoloantinori!

 ### Docs
+- Supported-version and install guidance now identifies 0.5.1 as the stable desktop and container release (#1687)
 - The Docker Hub overview now shows the current engine-switching demo, Model Catalogue, and gallery voice workflow (#1593)
 - The Docker Hub overview and install guide now show the v0.5 tags and the built-in API-key/share-PIN security model instead of obsolete v0.4 and no-authentication guidance (#1592)
 - The READMEs now lead with download buttons and a three-step first-clone walkthrough, and a new benchmarks page anchors measured per-engine/per-device numbers on the in-repo harness (#1555)
@@ -31,6 +211,42 @@ the frozen-backend fallback mirror it for their toolchains.
 - The OmniVoice guide now covers combining style attributes with a reference clip (consistent instruct stabilizes cloning; the reference wins conflicts), inline pronunciation control (pinyin / CMU phonemes), and corrects the claim that the default engine can't do voice design — it can, from attributes (#1565)

 ### Fixed
+- Workspaces now measure their responsive width when the post-bootstrap shell actually mounts, so native UI scaling reflows Projects and History instead of crushing the Dubbing demo into unreadable columns (#1683)
+- Dubbing keeps the source-language selector visible after a local file is chosen, so ASR can be pinned before transcription starts (#1678) — thanks @Lonki-lomki-cloud!
+- First-run media-engine downloads become available to TTS immediately without a restart, and missing media-process failures now point to repair controls (#1677) — thanks @farhataligpt-dev!
+- Source installs on AMD GPUs honour `OMNIVOICE_TORCH_VARIANT=rocm`: `bun run desktop` now swaps in the ROCm torch wheel after `uv sync` and launches the backend without re-syncing, instead of silently reverting to the CPU-only CUDA build on every start (#1665) — thanks @uberclokr!
+- `bun run desktop` on a fresh clone no longer fails with "resource path `../../frontend/dist` doesn't exist" — the dev launcher creates the placeholder Tauri resource directory before compiling (#1664) — thanks @uberclokr!
+- macOS no longer loses TTS after the first request when Python lacks `os.waitid`; subprocess ownership now uses a safe `waitpid` fallback without risking reused process groups (#1656) — thanks @paoloantinori!
+- Desktop startup, Retry, reset, uninstall, shutdown, and crash recovery now share one backend lifecycle owner; quitting interrupts first-run installers and gracefully drains then force-cleans the full backend process tree, so overlaps cannot duplicate or orphan it (#1635) — thanks @Xohaibxobi!
+- Large Stories and Audiobook projects now persist in IndexedDB instead of overflowing the `omnivoice.app` localStorage envelope, with quota-safe migration and orderly exit/reload flushing (#1636) — thanks @leodzai!
+- OmniVoice and its crash-isolated subprocess now route to AMD ROCm GPUs instead of warning and falling back to CPU (#1629) — thanks @j4r3kb!
+- Dictation now cancels pending startup work, capture resources, sockets, and timers when the capture widget closes, preventing late work against a destroyed webview (#1645)
+- Streaming generation failures now show recognized recovery guidance and appear in Diagnostics instead of only returning a generic error (#1607)
+- The worker-capacity transport test no longer races its own setup: the 1-slot limit now goes through the enrollment handshake instead of mutating client config after connect, where the server's stream-open ConfigUpdate (carrying the registered capacity of 2) could overwrite it and fake an over-accept; failed CI twice on 2026-08-21 (#1630)
+- Moving words across a speaker boundary in a dub — merging two lines and splitting them again — no longer dubs the second half in the first speaker's voice; each half now keeps the speaker, voice, direction, gain, and language of whoever actually says it (#1612) — thanks @invio-a11y!
+- Dictation on a WebView that refuses a 16 kHz audio context (WKWebView) now low-passes before downsampling, so frequencies above 8 kHz stop folding into the speech the recognizer is fed (#1610)
+- A microphone context that cannot be resumed now reports a mic error instead of leaving the dictation pill on "Listening" while capturing nothing (#1610)
+- Dictation no longer retains a whole session's audio for silent-model recovery — an open mic grew that buffer by ~115 MB an hour; the recent two minutes are kept instead (#1610)
+- The clipboard-delivery status is now translated in all 21 languages, so Wayland users — where clipboard delivery is the default — no longer see an English string (#1610)
+- A native sherpa-onnx load failure of any exception type now degrades to "engine unavailable" instead of taking the dictation WebSocket down (#1610)
+- Dictation now ships Whisper Tiny as its one cross-platform default, avoiding Parakeet's measured empty decoding on Windows while keeping Parakeet selectable behind runtime fallback (#1175)
+- Re-mixing a dub no longer decodes, rewrites, and re-reads every cached segment — same-rate cached audio is reused directly (and rejected if truncated), switching timing modes can't reuse slot-truncated audio as natural-rate, and RVC respects natural-rate modes (#1594)
+- PocketTTS French works again — pocket-tts only ships a 24-layer French model and rejected the name the sidecar asked for, so every French request failed at model load; French now always loads `french_24l` (#1613) — thanks @paoloantinori!
+- Installing IndexTTS 2.5 no longer fails claiming an interrupted download — the weights repo ships `config.yaml` and VoiceStudio demanded a `config_v2_5.yaml` that exists in no upstream release; both names are accepted, so a hand-renamed checkout keeps working (#1611) — thanks @zuiaiyutu!
+- IndexTTS 2.5 no longer has long-text generation killed at 60 seconds — the sidecar now proves it is alive every 5 seconds while `infer()` runs, and its deadline rises to 900s (`OMNIVOICE_INDEXTTS_RECV_TIMEOUT_S`) (#1611) — thanks @zuiaiyutu!
+- The OpenAI-compatible `/v1/audio/speech` route now reuses the shared cached engine for explicit `model` ids instead of constructing a fresh engine — and its sidecar/model load, a ~28s floor per call for subprocess engines — on every request, with the same single-engine-resident discipline `/generate` applies (#1614) — thanks @paoloantinori!
+- The setup wizard's RAM check no longer blocks 8 GB machines whose OS reports ~7.8 GB usable — the thresholds now tolerate reserved memory, and `OMNIVOICE_RAM_PREFLIGHT=0` turns a genuine block into a warning for those who accept the OOM risk (#1618)
+- Invisible watermarking now runs eagerly instead of through `torch.compile` — AudioSeal's lazy compile sent the first embed of every session into Inductor's C++ codegen, which failed outright on macOS hosts whose toolchain couldn't serve it and shipped the audio unmarked after a 30-40s wait; first embed drops from 9.70s to 0.26s (#1615) — thanks @paoloantinori!
+- The macOS Accessibility blocker now rechecks while visible and closes as soon as the grant is enabled instead of keeping a stale permission prompt on screen (#1609)
+- The dubbing editor's video and transcript columns can now be resized by pointer or keyboard, and the chosen split persists across launches (#1571) — thanks @invio-a11y!
+- CPU-only synthesis now gets a bounded ten-minute execution budget, and a render that exhausts it is reported as a compute timeout instead of misleading "generation capacity is busy" queue pressure (#1588) — thanks @ChienNguyen1111!
+- Rapid Launchpad ↔ Dub navigation now replaces the workspace DOM owner cleanly, so late media/waveform cleanup cannot trigger React's `insertBefore` crash (#1590) — thanks @nicolas-jacques!
+- Watermark embedding failures now log the full traceback instead of just the exception message, so a silently-unmarked-audio incident (audio passes through unmarked by design) is diagnosable from the log alone (#1576) — thanks @paoloantinori!
+- Dubbing now recovers rapid two-speaker exchanges when diarization collapses them, defaults new projects to lip sync without overwriting saved timing choices, and keeps the editor usable on narrow screens (#1584) — thanks @victordonat0!
+- `OMNIVOICE_ASR_BACKEND=omnivoice` now selects the PyTorch-native Whisper path, so the documented ROCm escape hatch no longer fails as an unknown engine (#1582) — thanks @patmansk!
+- Network Sharing from Windows MSI/portable installs now serves the bundled web interface to LAN devices instead of redirecting them to their own `localhost` (#1589) — thanks @TWIISTED-STUDIOS!
+- Exported dubbed videos now mark the dubbed language as the default audio stream while keeping Original available as an explicit choice (#1575) — thanks @invio-a11y!
+- Cloning references can no longer exhaust system memory: transcript-free clips up to 75 seconds are searched in five bounded passages, longer clips ask to be trimmed, and supplied transcripts remain capped at 20 seconds to preserve alignment (#1578) — thanks @ACKAPOB!
 - Stored artifact subpaths now resolve after moving a data directory between Windows, macOS, Linux, and Docker, while traversal and symlink escapes remain blocked (#1559) — thanks @Eman-Yousaf!
 - A remote browser hitting an API-key-configured server's admin 403 now gets the API-key login form instead of endless console 403s, while desktop and PIN-only/no-key servers keep the plain loopback error so guests are never offered a login no key can satisfy (#1568) — thanks @paoloantinori!
 - The crash-isolated ASR sidecar and its download preflight now agree on which model to load — setting the shared faster-whisper model variable applies to both variants instead of the sidecar quietly using a different one (#1556)
@@ -38,6 +254,8 @@ the frozen-backend fallback mirror it for their toolchains.
 - Supervisor restarts after repeat crashes now back off (immediate, then 5s, then 15s) instead of respawning back-to-back, so a tight crash loop can't burn the whole restart budget in seconds (#1548)
 - The Linux desktop cleanup regression test now isolates build artifacts, so an existing developer build can no longer change its result (#1566)

+- Renaming, deleting, or revoking consent on a voice (and starring/clearing history, recording exports) now live-updates every open tab again — the sync routes' WebSocket events were silently dropped, which could look like "all my voices are gone" (#1561) — thanks @paoloantinori!
+
 ### CI
 - Project agents now share pinned Vite and FastAPI skills from skills.sh (#1594)
 - Weekly full-history secret scans no longer mistake the Ed25519 private-key type name for committed key material (#1591)
@@ -10,10 +10,10 @@ Copyright 2024-present Palash Debnath and VoiceStudio contributors.

 VoiceStudio is **free and open-source software, licensed under the GNU
 Affero General Public License, Version 3 (AGPL-3.0)**. You are free to use,
-copy, modify, and redistribute it — and that **includes commercial and internal
-business use**: run the app, use its outputs commercially, sell the audio you
-produce with it, provide professional/client services with it, and deploy it
-within your organization.
+copy, modify, and redistribute it. That **includes commercial and internal
+business use** of the application itself. Model weights, tokenizers, and other
+third-party assets retain their own terms; this application license does not
+grant or summarize rights under those separate terms.

 Because this is the **Affero** GPL, one additional obligation applies: if you
 modify VoiceStudio and make that modified version available to others over
@@ -41,6 +41,12 @@ is **separately licensed under Apache License 2.0** by its upstream authors and
 is not relicensed here. Apache License 2.0 is compatible with, and may be
 combined under, the GNU AGPL-3.0. See `pyproject.toml`.

+Downloaded model weights are not relicensed by VoiceStudio. The default
+`k2-fsa/OmniVoice` model card identifies its code as Apache-2.0 and pretrained
+weights as CC-BY-NC. Its `audio_tokenizer/LICENSE` contains separate Boson
+Higgs Audio 2 and Meta Llama community terms. A commercial license for
+VoiceStudio-owned code does not replace any of those terms.
+
 Third-party dependencies retain their own licenses. See `Cargo.lock`,
 `bun.lock`, and `uv.lock` for the resolved set.

@@ -1,24 +1,30 @@
 <div align="center">
-  <img src="docs/logo.png" alt="VoiceStudio logo" width="120" height="120" />
+  <p><img src="docs/logo.png" alt="VoiceStudio logo" width="120" height="120" /></p>
  <h1>VoiceStudio</h1>
+  <p>
+    <a href="https://trendshift.io/repositories/28176?utm_source=repository-badge&amp;utm_medium=badge&amp;utm_campaign=badge-repository-28176" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/repositories/28176" alt="VoiceStudio ranking on Trendshift" width="220" height="48" /></a>
+  </p>
  <p><sub>Previously OmniVoice-Studio</sub></p>
-  <h3>Local voice cloning, dubbing, dictation, and long-form audio.</h3>
-  <p>16 TTS engines · 11 ASR engines · 646-language catalogue · macOS, Windows, and Linux</p>
-  <p><strong>Local-first.</strong> No account, API key, subscription, or usage meter for the core workflow.</p>
+  <h3>Clone voices, dub video, dictate, and produce long-form audio on your own hardware.</h3>
+  <p>16 TTS engines · 11 ASR engines · 646-language catalogue · macOS, Windows, Linux, and Docker</p>
+  <p>No account, API key, subscription, or usage meter for the local workflow.</p>

  <p>
    <a href="#install">Install</a> ·
    <a href="#features">Features</a> ·
    <a href="#comparison">Compare</a> ·
    <a href="#requirements">Requirements</a> ·
+    <a href="#hardware-recommendations">Hardware</a> ·
    <a href="#engines">Engines</a> ·
    <a href="#architecture">Architecture</a> ·
    <a href="#api">API</a> ·
    <a href="#documentation">Docs</a> ·
+    <a href="#faq">FAQ</a> ·
    <a href="README_CN.md"><strong>简体中文</strong></a>
  </p>

  <p>
+    <a href="https://github.com/debpalash/VoiceStudio/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/debpalash/VoiceStudio/ci.yml?branch=main&style=flat-square&label=CI" alt="CI status" /></a>
    <a href="https://github.com/debpalash/VoiceStudio/stargazers"><img src="https://img.shields.io/github/stars/debpalash/VoiceStudio?style=flat-square&color=f59e0b" alt="GitHub stars" /></a>
    <a href="https://github.com/debpalash/VoiceStudio/releases"><img src="https://img.shields.io/github/downloads/debpalash/VoiceStudio/total?style=flat-square&color=8b5cf6&label=downloads" alt="Total downloads" /></a>
    <a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/github/v/release/debpalash/VoiceStudio?style=flat-square&color=10b981" alt="Latest release" /></a>
@@ -36,7 +42,7 @@
 </div>

 > [!WARNING]
-> **Active beta.** Use the [latest release](https://github.com/debpalash/VoiceStudio/releases/latest) for stable work or `main` for current fixes. Report problems through [GitHub Issues](https://github.com/debpalash/VoiceStudio/issues).
+> **Active beta.** Use the [latest release](https://github.com/debpalash/VoiceStudio/releases/latest) for stable work. `main` contains the newest fixes and may change between releases. Report problems through [GitHub Issues](https://github.com/debpalash/VoiceStudio/issues).

 ## At a glance

@@ -49,33 +55,63 @@
 | **Compute** | CUDA · Apple Silicon MPS/MLX · ROCm on Linux · CPU · optional remote workers |
 | **Interfaces** | Desktop app · local REST/SSE/WebSocket API · OpenAI-compatible audio API · MCP Server |
 | **Storage** | Voices, projects, settings, and outputs stay on the machine by default |
-| **License** | AGPL-3.0; optional engines keep their own model licenses |
+| **License** | AGPL-3.0 application; downloaded models keep their upstream terms |
+
+The Voice workspace starts with three tabs: **From audio** for cloning, **By design** for creating a voice, and **Convert** for speech-to-speech conversion. Each tab displays its own workflow, with Synthesize Audio or Convert pinned below the scrolling form. The top-bar **Engines** panel combines engine selection, loaded models, and unload/flush controls; <kbd>Ctrl</kbd>/<kbd>Cmd</kbd>+<kbd>E</kbd> opens it. The searchable language picker shares Dubbing’s flags and language list layout, selects one output language, and retains Auto and the full cloning catalogue. Language options flow into multiple columns when space allows. Expand **Workspaces** in the sidebar to reveal navigation labels; Escape collapses it.
+
+Dubbing places playback controls over the video with background blur and combines the waveform and timed transcript in one compact editing surface. Drag the zoomed waveform left or right to pan; click to seek. Translation language and ISO-code controls stay synchronized; Auto clears any previous language code and dialect. Transcript items group editable text, timing and status, and voice controls into three readable rows that wrap with the panel width. Output Options stays compact with the active settings shown in its summary; expand it to change output, timing, or voice matching. Transcript, glossary, and paste controls share a toolbar above the segment editor. Project details, workflow steps, and Generate/Verify/Export actions use an unfilled header.
+
+Output settings use aligned rows; review status appears before the collapsible transcript and glossary. Glossary terms have labelled entry fields and an explicit edit action. Launchpad arranges recent files and saved voices side by side when space allows, with responsive card grids and visible Open actions.
+
+The casting board shows icon-based voice cards and searchable selectors for each speaker. Drag a card onto a speaker or choose a voice from that speaker’s menu.

 <a id="install"></a>

 ## Install

+Download a package from the [latest release](https://github.com/debpalash/VoiceStudio/releases/latest), then follow the platform guide.
+
 | Platform | Package | Guide |
 |---|---|---|
-| macOS 13.3+ | DMG, Apple Silicon | [Install on macOS](docs/install/macos.md) |
-| Windows 10/11 | MSI, x64 | [Install on Windows](docs/install/windows.md) |
+| macOS 13.3+ | Apple Silicon DMG | [Install on macOS](docs/install/macos.md) |
+| Windows 10/11 | x64 MSI; choose the current-user build when listed to install without admin access | [Install on Windows](docs/install/windows.md#install-pre-built-msi) |
 | Linux | AppImage, x86_64 with glibc 2.39+ | [Install on Linux](docs/install/linux.md) |
-| Docker | CUDA, ROCm, or CPU | [Run with Docker](docs/install/docker.md) |
+| Docker | CUDA, ROCm, CPU, and worker-only GPU profiles | [Run with Docker](docs/install/docker.md) |

-Download packages from the [latest release](https://github.com/debpalash/VoiceStudio/releases/latest). First launch creates a managed Python environment and downloads the default model. Later launches reuse both.
+First launch creates a managed Python environment and downloads the default model. Later launches reuse both.

 > [!NOTE]
-> On macOS, first launch needs a one-time right-click → **Open** approval. Intel Macs cannot run the local Python backend; use a [remote backend](docs/install/macos.md) instead.
+> On macOS, first launch needs a one-time right-click, then **Open** approval. Intel Macs cannot run the local Python backend; use a [remote backend](docs/install/macos.md) instead.
+
+### Quick Docker run
+
+```bash
+docker run -d -p 127.0.0.1:3900:3900 -v omnivoice-data:/app/omnivoice_data --name voicestudio palashdeb/omnivoice-studio:stable
+```

 ### First voice

 1. Launch VoiceStudio and open **Voice Cloning**.
-2. Add a clean voice sample. Three seconds works; 5–15 seconds usually gives a better prompt.
+2. Add a clean voice sample. Three seconds works; 5 to 15 seconds usually gives a better prompt.
 3. Enter text, choose a language, then select **Generate**.

+> [!TIP]
+> **Try without installing:** Run VoiceStudio in the cloud via the [Google Colab notebook](https://colab.research.google.com/github/debpalash/VoiceStudio/blob/main/notebooks/OmniVoice_Studio_Colab.ipynb). Explore audio quality comparisons in [benchmarks](docs/benchmarks.md) and prompt design tips in [expressive speech](docs/expressive-speech.md).
+
+### Audio samples
+
+Listen to sample outputs produced locally with VoiceStudio:
+
+| Workflow | Prompt / Reference Audio | Generated Audio |
+|---|---|---|
+| **Voice Cloning** | [demo_voice.wav](backend/assets/samples/demo_voice.wav) | [demo_clone_output.wav](backend/assets/samples/demo_clone_output.wav) |
+| **Voice Design** (US News Anchor) | *"Clear, authoritative American broadcast tone"* | [demo_voice_design_us_news_anchor.wav](backend/assets/samples/voice_design/demo_voice_design_us_news_anchor.wav) |
+| **Voice Design** (UK Audiobook) | *"Warm, expressive British storytelling voice"* | [demo_voice_design_audiobook_uk_narrator.wav](backend/assets/samples/voice_design/demo_voice_design_audiobook_uk_narrator.wav) |
+| **Video Dubbing** (Multilingual) | [source.src.wav](backend/assets/samples/demo/dubbing/source.src.wav) | [Spanish](backend/assets/samples/demo/dubbing/dubbed_es.src.wav) · [French](backend/assets/samples/demo/dubbing/dubbed_fr.src.wav) · [Japanese](backend/assets/samples/demo/dubbing/dubbed_ja.src.wav) · [Chinese](backend/assets/samples/demo/dubbing/dubbed_zh.src.wav) |
+
 ### Run from source

-Install the [development prerequisites](.github/CONTRIBUTING.md#development-setup), then:
+Install the [development prerequisites](.github/CONTRIBUTING.md#development-setup) (Node 20+/Bun and Python 3.11+), then:

 ```bash
 git clone https://github.com/debpalash/VoiceStudio.git
@@ -84,7 +120,7 @@ bun install
 bun run desktop
 ```

-Use `bun run dev` for the browser UI. See [Contributing](.github/CONTRIBUTING.md) for services, tests, and platform packages.
+The desktop launcher configures Python dependencies on first run via `uv` automatically. Use `bun run dev` for the browser UI. See [Contributing](.github/CONTRIBUTING.md) for services, tests, and platform packages.

 ### If setup fails

@@ -99,22 +135,22 @@ Use `bun run dev` for the browser UI. See [Contributing](.github/CONTRIBUTING.md

 | Area | Included |
 |---|---|
-| **Voice Cloning** | Zero-shot synthesis from a short reference clip |
-| **Voice Design** | Create a voice from age, accent, pitch, style, and delivery instructions |
-| **Video Dubbing** | Transcribe, translate, preserve speakers, synthesize, and export video |
+| **Voice Cloning** | Zero-shot synthesis from a short reference clip ([guide](docs/engines/README.md)) |
+| **Voice Design** | Create a voice from age, accent, pitch, style, and delivery instructions ([expressive speech](docs/expressive-speech.md)) |
+| **Video Dubbing** | Transcribe, translate, preserve speakers, synthesize, and export video; compact translation settings include track selection, and completed dubs flag timing issues for review ([export guide](docs/dubbing/export.md)) |
 | **Stories and audiobooks** | Multi-voice scripts · EPUB/PDF import · chapter rendering · `.m4b` export |
-| **Dictation Widget** | System-wide shortcut, live transcription, optional local-LLM cleanup |
+| **[Dictation Widget](docs/features/dictation.md)** | System-wide shortcut, live transcription, optional local-LLM cleanup |
 | **Vocal Isolation** | Demucs speech/background separation |
-| **Speaker Diarization** | Pyannote and WhisperX speaker assignment |
-| **Batch Queue** | Queue large sets of audio and video jobs with per-job progress |
-| **Model Catalogue** | Install, remove, select, and route TTS, ASR, and LLM models |
-| **Remote Model Downloads** | Install models on enrolled remote workers with live progress |
-| **GPU Auto-Detect** | CUDA, MPS, ROCm, and CPU routing with per-engine checks |
+| **Speaker Diarization** | Pyannote and WhisperX speaker assignment ([guide](docs/features/diarization.md)) |
+| **Batch Queue** | Queue large sets of audio and video jobs with per-job progress, or watch a local folder for new videos |
+| **Model Catalogue** | Install, remove, select, and route TTS, ASR, and LLM models ([catalogue](docs/engines/README.md)) |
+| **Remote Model Downloads** | Install models on enrolled remote workers with live progress ([guide](docs/downloading-models.md)) |
+| **GPU Auto-Detect** | CUDA, MPS, ROCm, and CPU routing with per-engine checks ([performance](docs/performance.md)) |
 | **AI Watermark** | AudioSeal embedding and detection |
-| **MCP Server** | Synthesis and transcription tools for MCP clients |
-| **Diagnostics** | Self-checks, error journal, logs, and scrubbed support bundles |
+| **MCP Server** | Synthesis and transcription tools for MCP clients ([guide](docs/mcp.md)) |
+| **Diagnostics** | Self-checks, error journal, logs, and scrubbed support bundles ([troubleshooting](docs/install/troubleshooting.md)) |
 | **Local-first** | Core creation stays local; network-backed features are explicit opt-ins |
-| **Extensible** | Registry-based TTS, ASR, and plugin interfaces |
+| **Extensible** | Registry-based TTS, ASR, and plugin interfaces ([acceptance](docs/engine-acceptance.md)) |

 <table>
 <tr>
@@ -157,10 +193,20 @@ Requirements vary by engine. These values cover the default local workflow.
 | **Disk** | 10 GB free | 20 GB+ SSD |
 | **GPU** | Optional; CPU mode is supported | NVIDIA CUDA or Apple Silicon |
 | **VRAM** | 4 GB when using a GPU | 8 GB+; large optional engines need more |
-| **Python from source** | 3.11+ | 3.11–3.12 |
+| **Python from source** | 3.11+ | 3.11 or 3.12 |

 ROCm is Linux-only and opt-in. Windows AMD/Ryzen AI uses CPU. Systems with limited VRAM offload work to CPU when required. See [performance](docs/performance.md), [benchmarks](docs/benchmarks.md), and [engine disk usage](docs/engines/disk-usage.md).

+<a id="hardware-recommendations"></a>
+
+### Recommended stack by hardware
+
+| Hardware | Recommended TTS | Recommended ASR | Why |
+|---|---|---|---|
+| **Apple Silicon (M1–M4)** | [MLX-Audio](docs/engines/mlx-audio.md) · [OmniVoice](docs/engines/omnivoice.md) (MPS) | [MLX Whisper](docs/engines/mlx-whisper.md) · [Parakeet MLX](docs/engines/parakeet-mlx.md) | Native unified memory, lowest latency on macOS |
+| **NVIDIA GPU (8 GB+ VRAM)** | [OmniVoice](docs/engines/omnivoice.md) · [CosyVoice 3](docs/engines/cosyvoice.md) | [WhisperX](docs/engines/whisperx.md) | High-fidelity zero-shot cloning, word timestamps, diarization |
+| **Low VRAM / CPU-only** | [PocketTTS](docs/engines/pockettts.md) · [Sherpa-ONNX](docs/engines/sherpa-onnx.md) · [KittenTTS](docs/engines/kittentts.md) | [Moonshine](docs/engines/moonshine.md) · [Faster-Whisper](docs/engines/faster-whisper.md) (`int8`) | Low memory footprint, optimized CPU inference |
+
 <a id="engines"></a>

 ## Engines
@@ -173,22 +219,22 @@ Engine support is capability-specific. Check cloning, language, platform, memory

 | Engine | Languages | Clone | Instruct | Linux | macOS ARM | Windows | License |
 |---|:---:|:---:|:---:|:---:|:---:|:---:|---|
-| **VoiceStudio** (default, powered by k2-fsa/OmniVoice) | 600+ | Yes | Yes | CUDA/CPU | MPS | CUDA/CPU | [AGPL-3.0](LICENSE) app · [Apache-2.0](LICENSE-NOTICE.md) model |
-| **CosyVoice 3** | 9 + 18 dialects | Yes | Yes | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
-| **GPT-SoVITS** | 5 | Yes | — | CUDA/CPU | — | CUDA/CPU | MIT |
-| **VoxCPM2** | 30 | Yes | Yes | CUDA/CPU | MPS | CUDA/CPU | Apache-2.0 |
-| **MOSS-TTS-Nano** | 20 | Yes | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
-| **KittenTTS** | English | — | — | CPU | CPU | CPU | MIT |
-| **MLX-Audio** | Model-dependent | Varies | Varies | — | MLX | — | Varies |
-| **Sherpa-ONNX** | 20+ | — | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
-| **IndexTTS 2.5** ⚡ | ZH · EN · JA · ES · AR | Yes | — | CUDA/CPU | CPU | CUDA/CPU | Bilibili model license¹ |
-| **OmniVoice GGUF** ⚡ | 600+ | Yes | Yes | CUDA/CPU | MPS/CPU | CUDA/CPU | [AGPL-3.0](LICENSE) app · [Apache-2.0](LICENSE-NOTICE.md) model |
-| **OmniVoice (subprocess)** ⚡ | 600+ | Yes | Yes | CUDA/CPU | MPS | CUDA/CPU | [AGPL-3.0](LICENSE) app · [Apache-2.0](LICENSE-NOTICE.md) model |
-| **PocketTTS** ⚡ | EN · FR · DE · PT · IT · ES | Yes | — | CPU | CPU | CPU | CC-BY-4.0, gated² |
-| **Supertonic 3** ⚡ | 31 | — | — | CPU | CPU | CPU | OpenRAIL-M |
-| **MOSS-TTS-v1.5** ⚡ | 31 | Yes | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
-| **dots.tts** ⚡ | 24 | Yes | — | CUDA/CPU | CPU | — | Apache-2.0 |
-| **Confucius4-TTS** ⚡ | 14 | Yes | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
+| [**VoiceStudio** (default, powered by k2-fsa/OmniVoice)](docs/engines/omnivoice.md) | 600+ | Yes | Yes | CUDA/CPU | MPS | CUDA/CPU | [AGPL-3.0](LICENSE) app · [Apache-2.0 code, CC-BY-NC weights](https://huggingface.co/k2-fsa/OmniVoice#license)³ |
+| [**CosyVoice 3**](docs/engines/cosyvoice.md) | 9 + 18 dialects | Yes | Yes | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
+| [**GPT-SoVITS**](docs/engines/gpt-sovits.md) | 5 | Yes | No | CUDA/CPU | No | CUDA/CPU | MIT |
+| [**VoxCPM2**](docs/engines/voxcpm2.md) | 30 | Yes | Yes | CUDA/CPU | MPS | CUDA/CPU | Apache-2.0 |
+| [**MOSS-TTS-Nano**](docs/engines/moss-tts-nano.md) | 20 | Yes | No | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
+| [**KittenTTS**](docs/engines/kittentts.md) | English | No | No | CPU | CPU | CPU | MIT |
+| [**MLX-Audio**](docs/engines/mlx-audio.md) | Model-dependent | Varies | Varies | No | MLX | No | Varies |
+| [**Sherpa-ONNX**](docs/engines/sherpa-onnx.md) | 20+ | No | No | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
+| [**IndexTTS 2.5** ⚡](docs/engines/indextts.md) | ZH · EN · JA · ES · AR | Yes | No | CUDA/CPU | CPU | CUDA/CPU | Bilibili model license¹ |
+| [**OmniVoice GGUF** ⚡](docs/engines/omnivoice-gguf.md) | 600+ | Yes | Yes | CUDA/CPU | MPS/CPU | CUDA/CPU | [AGPL-3.0](LICENSE) app · [review the derivative model terms](https://huggingface.co/Serveurperso/OmniVoice-GGUF#license)³ |
+| [**OmniVoice (subprocess)** ⚡](docs/engines/omnivoice-subprocess.md) | 600+ | Yes | Yes | CUDA/CPU | MPS | CUDA/CPU | [AGPL-3.0](LICENSE) app · [Apache-2.0 code, CC-BY-NC weights](https://huggingface.co/k2-fsa/OmniVoice#license)³ |
+| [**PocketTTS** ⚡](docs/engines/pockettts.md) | EN · FR · DE · PT · IT · ES | Yes | No | CPU | CPU | CPU | CC-BY-4.0, gated² |
+| [**Supertonic 3** ⚡](docs/engines/supertonic3.md) | 31 | No | No | CPU | CPU | CPU | OpenRAIL-M |
+| [**MOSS-TTS-v1.5** ⚡](docs/engines/moss-tts-v15.md) | 31 | Yes | No | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
+| [**dots.tts** ⚡](docs/engines/dots-tts.md) | 24 | Yes | No | CUDA/CPU | CPU | No | Apache-2.0 |
+| [**Confucius4-TTS** ⚡](docs/engines/confucius4-tts.md) | 14 | Yes | No | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |

 ⚡ Installed or registered on demand.

@@ -196,6 +242,8 @@ Engine support is capability-specific. Check cloning, language, platform, memory

 ² PocketTTS shows its gated-access and CC-BY-4.0 terms before first use.

+³ The OmniVoice snapshot also includes an audio tokenizer under separate [Boson Higgs Audio 2 and Meta Llama community terms](https://huggingface.co/k2-fsa/OmniVoice/blob/main/audio_tokenizer/LICENSE). VoiceStudio's application license does not replace model or tokenizer terms.
+
 Clone-less engines cannot preserve a reference speaker in dubbing or pinned-voice batch jobs. VoiceStudio rejects those jobs instead of silently changing engines. Heavy engines have separate memory and platform limits; check their engine guide first.

 <a id="asr-engines"></a>
@@ -204,17 +252,17 @@ Clone-less engines cannot preserve a reference speaker in dubbing or pinned-voic

 | Engine | ID | Languages | Best fit |
 |---|---|:---:|---|
-| **WhisperX** (default) | `whisperx` | ~100 | Dubbing, subtitles, word-level timing |
-| **Faster-Whisper** | `faster-whisper` | ~100 | General cross-platform transcription |
-| **Faster-Whisper (isolated)** | `faster-whisper-isolated` | ~100 | Crash-isolated batch transcription |
-| **MLX Whisper** | `mlx-whisper` | ~100 | Apple Silicon |
-| **PyTorch Whisper** | `pytorch-whisper` | ~100 | CUDA, MPS, and CPU fallback |
-| **Parakeet TDT** | `nemo-parakeet` | English + 25 EU | Fast CPU/CUDA transcription |
-| **Parakeet TDT v3 (MLX)** | `parakeet-mlx` | 25 EU | Apple Silicon dictation and word timestamps |
-| **Moonshine** | `moonshine` | English | Low-power, low-latency ONNX |
-| **FunASR** | `funasr` | 50+ | VAD and inline diarization |
-| **sherpa-onnx** (live dictation) | `sherpa-onnx-asr` | Model-dependent | Streaming CPU dictation |
-| **OpenAI-compatible** ⚠️ remote | `openai-compat-asr` | Server-dependent | Qwen3-ASR or another compatible endpoint; audio leaves the machine |
+| [**WhisperX** (default)](docs/engines/whisperx.md) | `whisperx` | ~100 | Dubbing, subtitles, word-level timing |
+| [**Faster-Whisper**](docs/engines/faster-whisper.md) | `faster-whisper` | ~100 | General cross-platform transcription |
+| [**Faster-Whisper (isolated)**](docs/engines/faster-whisper-isolated.md) | `faster-whisper-isolated` | ~100 | Crash-isolated batch transcription |
+| [**MLX Whisper**](docs/engines/mlx-whisper.md) | `mlx-whisper` | ~100 | Apple Silicon |
+| [**PyTorch Whisper**](docs/engines/pytorch-whisper.md) | `pytorch-whisper` | ~100 | CUDA, MPS, and CPU fallback |
+| [**Parakeet TDT**](docs/engines/nemo-parakeet.md) | `nemo-parakeet` | English + 25 EU | Fast CPU/CUDA transcription |
+| [**Parakeet TDT v3 (MLX)**](docs/engines/parakeet-mlx.md) | `parakeet-mlx` | 25 EU | Apple Silicon dictation and word timestamps |
+| [**Moonshine**](docs/engines/moonshine.md) | `moonshine` | English | Low-power, low-latency ONNX |
+| [**FunASR**](docs/engines/funasr.md) | `funasr` | 50+ | VAD and inline diarization |
+| [**sherpa-onnx** (live dictation)](docs/engines/sherpa-onnx-asr.md) | `sherpa-onnx-asr` | Model-dependent | Streaming CPU dictation |
+| [**OpenAI-compatible** ⚠️ configured server](docs/engines/openai-compatible-asr.md) | `openai-compat-asr` | Server-dependent | Local gigastt/Qwen3-ASR or a remote endpoint; audio goes only to that server |

 WhisperX and Faster-Whisper retry with `int8` when efficient `float16` is unavailable. Pin `ASR_COMPUTE_TYPE=int8` or `float32` only if automatic selection still fails.

@@ -249,12 +297,12 @@ FastAPI backend

 - The desktop talks to a loopback-only backend on `localhost:3900`.
 - Loopback API calls need no server key. Remote access requires a share PIN or API key.
- Remote workers and OpenAI-compatible ASR are opt-in. The UI identifies when audio leaves the machine.
- Analytics is off until consent. If enabled, it sends allowlisted, content-free usage metadata—not text, audio, file names, or projects.
+- Remote workers and OpenAI-compatible ASR are opt-in. Loopback ASR may use HTTP and keeps audio on the machine; non-loopback endpoints require HTTPS, and redirects are not followed.
+- Analytics is off until consent. If enabled, it sends allowlisted, content-free usage metadata. It never sends text, audio, file names, or projects.

 <a id="api"></a>

-## OpenAI-compatible API
+## Local speech platform and OpenAI-compatible API

 Point an OpenAI-compatible audio client at the local backend:

@@ -267,6 +315,8 @@ Point an OpenAI-compatible audio client at the local backend:
 |---|---|
 | `POST /v1/audio/speech` | TTS to `mp3`, `opus`, `aac`, `flac`, `wav`, or `pcm`; select a profile with `voice` and an engine with `model` |
 | `POST /v1/audio/transcriptions` | STT to `json`, `text`, `verbose_json`, `srt`, or `vtt` |
+| `WS /v1/audio/transcriptions/stream` | Live PCM/WebM transcription with partial, utterance, and session-final events |
+| `GET /.well-known/voicestudio-speech` | Discover HTTP, WebSocket, MCP, and native dictation-control transports |
 | `GET /v1/audio/voices` | List local voice profiles and engines |

 ```python
@@ -283,19 +333,61 @@ with client.audio.speech.with_streaming_response.create(
    response.stream_to_file("speech.wav")
 ```

-The full API reference is in **Settings → OpenAPI Reference**. For LAN, Tailscale, or proxy access, read [API authentication](docs/api-auth.md) before exposing the backend.
+```bash
+# Quick test via cURL
+curl http://localhost:3900/v1/audio/speech \
+  -H "Content-Type: application/json" \
+  -d '{"model": "tts-1", "input": "Made on my own hardware.", "voice": "default", "response_format": "wav"}' \
+  --output speech.wav
+```
+
+The bundled Rust control sidecar lets Herdr, coding agents, VS Code, desktop apps,
+and TUIs trigger the system-wide dictation flow or reuse its native text
+insertion. See the [speech platform guide](docs/speech-platform.md). The full API
+reference is in **Settings → OpenAPI Reference**. For LAN, Tailscale, or proxy
+access, read [API authentication](docs/api-auth.md) before exposing the backend.

 ### Agent skills

 Install the VoiceStudio skills for Claude Code, Codex, Cursor, and other [skills.sh](https://skills.sh)-compatible agents:

 ```bash
-npx skills add debpalash/omnivoice-studio
+npx skills add debpalash/VoiceStudio
 ```

 - `omnivoice`: synthesize speech and transcribe audio through local VoiceStudio.
 - `oss-maintainer`: the repository's open-source maintenance workflow.

+### Model Context Protocol (MCP)
+
+VoiceStudio mounts an MCP server at `http://localhost:3900/mcp` for Claude Desktop, Cursor, and AI agents:
+
+```json
+{
+  "mcpServers": {
+    "voicestudio": {
+      "url": "http://localhost:3900/mcp"
+    }
+  }
+}
+```
+
+For clients requiring stdio transport, use the bundled local shim (`docs/mcp.json`):
+
+```json
+{
+  "mcpServers": {
+    "voicestudio": {
+      "command": "python",
+      "args": ["-m", "backend.mcp_shim"],
+      "cwd": "/path/to/VoiceStudio"
+    }
+  }
+}
+```
+
+See the [MCP guide](docs/mcp.md) for tools (`generate_speech`, `clone_voice`, `transcribe`), file streaming modes, and client bindings.
+
 ### Google Colab

 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/debpalash/VoiceStudio/blob/main/notebooks/OmniVoice_Studio_Colab.ipynb)
@@ -312,11 +404,13 @@ The [notebook](notebooks/OmniVoice_Studio_Colab.ipynb) runs the app and web UI o
 | Fix setup | [Troubleshooting](docs/install/troubleshooting.md) · [model downloads](docs/downloading-models.md) · [Hugging Face token](docs/setup/huggingface-token.md) |
 | Choose an engine | [Engine guides](docs/engines/README.md) · [benchmarks](docs/benchmarks.md) · [expressive speech](docs/expressive-speech.md) |
 | Tune hardware | [Performance](docs/performance.md) · [remote workers](docs/remote-workers.md) |
-| Build integrations | [API auth](docs/api-auth.md) · [MCP](docs/mcp.md) · [examples](examples/README.md) |
+| Build integrations | [Speech platform](docs/speech-platform.md) · [Private production API](docs/production-private-api.md) · [API auth](docs/api-auth.md) · [MCP](docs/mcp.md) · [examples](examples/README.md) |
 | Build VoiceStudio | [Contributing](.github/CONTRIBUTING.md) · [engine acceptance](docs/engine-acceptance.md) |
 | Track changes | [Changelog](CHANGELOG.md) · [roadmap](docs/ROADMAP.md) · [latest release](https://github.com/debpalash/VoiceStudio/releases/latest) |
 | Remove everything | [Uninstall guide](docs/install/uninstall.md) |

+<a id="faq"></a>
+
 ## FAQ

 <details>
@@ -328,19 +422,19 @@ Apple Silicon is supported with MPS and MLX options. Intel Macs cannot run the l
 <details>
 <summary><strong>How much VRAM do I need?</strong></summary>

-A GPU is optional. Use 4 GB VRAM as the minimum for accelerated work and 8 GB+ for the default multi-stage workflow. Large optional engines can require 12–16 GB or more. Check the [benchmarks](docs/benchmarks.md) and engine guide.
+A GPU is optional. Use 4 GB VRAM as the minimum for accelerated work and 8 GB+ for the default multi-stage workflow. Large optional engines can require 12 to 16 GB or more. Check the [benchmarks](docs/benchmarks.md) and engine guide.
 </details>

 <details>
 <summary><strong>Why does a longer reference clip not always improve the clone?</strong></summary>

-Cloning is zero-shot: the clip is a prompt, not training data. Use 5–15 seconds of one speaker, close to the microphone, without music, noise, or reverb. Match the tone and pace you want in the output. For training, see [data preparation](docs/data_preparation.md) and [training](docs/training.md).
+Cloning is zero-shot: the clip is a prompt, not training data. Use 5 to 15 seconds of one speaker, close to the microphone, without music, noise, or reverb. Match the tone and pace you want in the output. For training, see [data preparation](docs/data_preparation.md) and [training](docs/training.md).
 </details>

 <details>
 <summary><strong>Can I use generated audio commercially?</strong></summary>

-Yes under VoiceStudio's AGPL-3.0 terms. Optional engines and model weights may use different licenses; review the selected engine's license before commercial use.
+VoiceStudio's application license does not restrict generated audio, but it does not grant rights under a model's separate terms. The default OmniVoice repository labels its pretrained weights CC-BY-NC and includes a tokenizer under separate community terms. Review the selected model terms before commercial use.
 </details>

 <details>
@@ -362,17 +456,30 @@ Use `scripts/uninstall.sh` on macOS/Linux or `scripts\uninstall.ps1` on Windows.
 - [Good first issues](https://github.com/debpalash/VoiceStudio/labels/good%20first%20issue) for a scoped starting point.
 - [Contributing guide](.github/CONTRIBUTING.md) for setup, tests, and pull requests.

+<p align="center">
+  <a href="https://star-history.com/#debpalash/VoiceStudio&Date">
+    <img src="https://api.star-history.com/svg?repos=debpalash/VoiceStudio&type=Date" alt="Star History Chart" width="100%" />
+  </a>
+</p>
+
 ## Support development

 VoiceStudio is free and has no paid tier. Donations fund development and infrastructure.

 [Ko-fi](https://ko-fi.com/debpalash) · [PayPal](https://paypal.me/palashCoder) · [Sponsorship details](SPONSORS.md)

+## Responsible use and safety
+
+VoiceStudio enables zero-shot voice cloning and speech generation on personal hardware. Please use it responsibly:
+- **Consent:** Only clone or synthesize voices with explicit permission from the speaker.
+- **Audio provenance:** VoiceStudio integrates [AudioSeal](https://github.com/facebookresearch/audioseal) imperceptible watermarking by default to detect and identify synthetic speech without altering sound quality.
+- **Local privacy:** For the default local workflow, audio recordings, transcripts, voices, and projects remain strictly on your local disk; data leaves your device only when you explicitly configure remote workers or external ASR endpoints.
+
 ## License

-VoiceStudio is licensed under [AGPL-3.0](LICENSE). You may run it, modify it, use it internally, and sell generated audio. If you modify VoiceStudio and provide that modified version as a network service, AGPL requires you to offer the corresponding source under the same license. A commercial license is available for proprietary embedding; contact **VoiceStudio@palash.dev**. See [LICENSE-NOTICE.md](LICENSE-NOTICE.md) for the plain-language scope.
+VoiceStudio is licensed under [AGPL-3.0](LICENSE). You may run it, modify it, and use it internally. The application license itself does not restrict selling generated audio, but downloaded model and tokenizer terms may. If you modify VoiceStudio and provide that modified version as a network service, AGPL requires you to offer the corresponding source under the same license. A commercial license for VoiceStudio-owned code is available for proprietary embedding; it does not relicense third-party models. Contact **VoiceStudio@palash.dev**. See [LICENSE-NOTICE.md](LICENSE-NOTICE.md) for the plain-language scope.

-Optional engines and downloaded models retain their own licenses. The bundled `omnivoice/` model remains Apache-2.0 upstream.
+Optional engines and downloaded models retain their own licenses. The bundled `omnivoice/` Python code is Apache-2.0 upstream; the default downloaded weights and audio tokenizer use separate terms.

 ## Acknowledgments

@@ -20,6 +20,7 @@
  </p>

  <p>
+    <a href="https://github.com/debpalash/VoiceStudio/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/debpalash/VoiceStudio/ci.yml?branch=main&style=flat-square&label=CI" alt="CI 状态" /></a>
    <a href="https://github.com/debpalash/VoiceStudio/stargazers"><img src="https://img.shields.io/github/stars/debpalash/VoiceStudio?style=flat-square&color=f59e0b" alt="Star 数" /></a>
    <a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/github/v/release/debpalash/VoiceStudio?style=flat-square&color=10b981" alt="版本" /></a>
    <a href="LICENSE"><img src="https://img.shields.io/badge/license-AGPL--3.0-blue?style=flat-square" alt="许可证" /></a>
@@ -65,11 +66,27 @@
 - 🐧 **Linux** — [docs/install/linux.md](docs/install/linux.md)
 - 🐳 **Docker** — [docs/install/docker.md](docs/install/docker.md) · [Docker Hub: `palashdeb/omnivoice-studio`](https://hub.docker.com/r/palashdeb/omnivoice-studio)

+```bash
+# Docker 快速运行 (CPU / 本地环回模式)
+docker run -d -p 127.0.0.1:3900:3900 -v omnivoice-data:/app/omnivoice_data --name voicestudio palashdeb/omnivoice-studio:stable
+```
+
 **三步克隆出你的第一个声音：**

 1. **安装并启动。** 首次启动会自动搭建 Python 运行环境并下载模型权重——启动画面会逐步显示进度（仅首次，需要几分钟；之后即开即用）。
 2. 从启动台打开**语音克隆**，拖入任意声音的 **3 秒音频**。
-3. **输入一句话，点击生成。** 音频完全属于你——在你的设备上生成和保存，支持 646 种语言。
+3. **输入一句话，点击生成。** 音频在你的设备上生成并保存，支持 646 种语言（商业使用前请审阅所选模型与分词器的许可条款）。
+
+### 🎧 音频示例
+
+在线试听 VoiceStudio 本地生成的实际音频样例：
+
+| 工作流 | 提示词 / 参考音频 | 生成音频 |
+|---|---|---|
+| **声音克隆** | [demo_voice.wav](backend/assets/samples/demo_voice.wav) | [demo_clone_output.wav](backend/assets/samples/demo_clone_output.wav) |
+| **声音设计** (美语新闻主播) | *"清晰、权威的美国广播级音色"* | [demo_voice_design_us_news_anchor.wav](backend/assets/samples/voice_design/demo_voice_design_us_news_anchor.wav) |
+| **声音设计** (英式有声书) | *"温暖生动的英式故事讲述音色"* | [demo_voice_design_audiobook_uk_narrator.wav](backend/assets/samples/voice_design/demo_voice_design_audiobook_uk_narrator.wav) |
+| **视频配音** (多语种) | [source.src.wav](backend/assets/samples/demo/dubbing/source.src.wav) | [西班牙语](backend/assets/samples/demo/dubbing/dubbed_es.src.wav) · [法语](backend/assets/samples/demo/dubbing/dubbed_fr.src.wav) · [日语](backend/assets/samples/demo/dubbing/dubbed_ja.src.wav) · [中文](backend/assets/samples/demo/dubbing/dubbed_zh.src.wav) |

 觉得慢？[docs/performance.md](docs/performance.md) 讲清了生成时间到底花在哪里、有哪些调优开关，以及“它变慢了”的三个经典原因。各引擎/设备的实测数据见 [docs/benchmarks.md](docs/benchmarks.md)。

@@ -217,6 +234,16 @@ Hugging Face Token 的配置见
 > [!IMPORTANT]
 > **macOS Intel（x86_64）不支持本地后端：** 应用 UI 可以安装，但 Python 后端无法运行，因为 PyTorch 已不再发布 Intel Mac 轮子（[#889](https://github.com/debpalash/VoiceStudio/issues/889)）。Intel Mac 用户仍可让 UI 指向另一台机器上的远程后端——参见 [docs/install/macos.md](docs/install/macos.md)。

+<a id="hardware-recommendations"></a>
+
+### 💡 按硬件推荐引擎配置
+
+| 硬件配置 | 推荐 TTS 引擎 | 推荐 ASR 语音识别 | 优势 |
+|---|---|---|---|
+| **Apple Silicon (M1–M4)** | [MLX-Audio](docs/engines/mlx-audio.md) · [OmniVoice](docs/engines/omnivoice.md) (MPS) | [MLX Whisper](docs/engines/mlx-whisper.md) · [Parakeet MLX](docs/engines/parakeet-mlx.md) | 原生统一内存，macOS 上延迟最低、性能最强 |
+| **NVIDIA 显卡 (8 GB+ 显存)** | [OmniVoice](docs/engines/omnivoice.md) · [CosyVoice 3](docs/engines/cosyvoice.md) | [WhisperX](docs/engines/whisperx.md) | 极致零样本克隆品质、字级时间戳对齐与说话人分离 |
+| **低显存 / 仅 CPU 设备** | [PocketTTS](docs/engines/pockettts.md) · [Sherpa-ONNX](docs/engines/sherpa-onnx.md) · [KittenTTS](docs/engines/kittentts.md) | [Moonshine](docs/engines/moonshine.md) · [Faster-Whisper](docs/engines/faster-whisper.md) (`int8`) | 超低内存占用，针对 CPU 指令集深度优化 |
+
 <a id="tts-engines"></a>

 ### 🗣️ TTS 引擎
@@ -338,9 +365,9 @@ print(result.text)

 ### 📓 在 Google Colab 上运行

-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/debpalash/VoiceStudio/blob/main/notebooks/VoiceStudio_Studio_Colab.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/debpalash/VoiceStudio/blob/main/notebooks/OmniVoice_Studio_Colab.ipynb)

-没有本地 GPU？官方笔记本（[notebooks/VoiceStudio_Studio_Colab.ipynb](notebooks/VoiceStudio_Studio_Colab.ipynb)）可在免费的 Colab T4 上启动完整应用（包含 Web 界面）：在笔记本内直接构建前端，用 uv 安装后端（复用 Colab 预装的 CUDA PyTorch），并通过 Colab 内置端口代理打开界面。无需第三方隧道，也无需任何 API 密钥。随后还有一套覆盖全部主要功能的 API 导览，全部可在笔记本内直接播放：多语言 TTS、声音克隆与声音设计、已保存的声音档案、语音转写、AI 水印检测、OpenAI 兼容 API、多角色故事、带章节的 m4b 有声书，以及一个附带人声分离音轨的迷你视频配音。
+没有本地 GPU？官方笔记本（[notebooks/OmniVoice_Studio_Colab.ipynb](notebooks/OmniVoice_Studio_Colab.ipynb)）可在免费的 Colab T4 上启动完整应用（包含 Web 界面）：在笔记本内直接构建前端，用 uv 安装后端（复用 Colab 预装的 CUDA PyTorch），并通过 Colab 内置端口代理打开界面。无需第三方隧道，也无需任何 API 密钥。随后还有一套覆盖全部主要功能的 API 导览，全部可在笔记本内直接播放：多语言 TTS、声音克隆与声音设计、已保存的声音档案、语音转写、AI 水印检测、OpenAI 兼容 API、多角色故事、带章节的 m4b 有声书，以及一个附带人声分离音轨的迷你视频配音。

 ### 🤝 智能体技能（Agent Skills）

@@ -352,6 +379,36 @@ npx skills add debpalash/omnivoice-studio

 内含两个 [skills](https://skills.sh)：**`omnivoice`**——让任何智能体通过你的本地安装进行语音合成与转录（包括你克隆的声音），免费且离线；以及 **`oss-maintainer`**——本项目所遵循的维护者方法论，适合任何用智能体运营自己开源项目的人。

+### 🔌 模型上下文协议（MCP 服务器）
+
+VoiceStudio 在 `http://localhost:3900/mcp` 挂载了 MCP 服务，可供 Claude Desktop、Cursor 与自主智能体调用：
+
+```json
+{
+  "mcpServers": {
+    "voicestudio": {
+      "url": "http://localhost:3900/mcp"
+    }
+  }
+}
+```
+
+对于需要 stdio 管道传输的客户端，请使用内置的本地桥接脚本（`docs/mcp.json`）：
+
+```json
+{
+  "mcpServers": {
+    "voicestudio": {
+      "command": "python",
+      "args": ["-m", "backend.mcp_shim"],
+      "cwd": "/path/to/VoiceStudio"
+    }
+  }
+}
+```
+
+支持 `generate_speech`、`clone_voice`、`transcribe` 等工具与流式文件输出模式，详见 [docs/mcp.md](docs/mcp.md)。
+
 ---

 ## 🗺️ 路线图
@@ -542,6 +599,13 @@ VoiceStudio **免费**且采用 **AGPL-3.0** 许可——没有付费版，没
 VoiceStudio 完全本地运行——卸载就是删除应用及其写入的文件夹（模型缓存、Python 环境、你的声音/项目、配置）。运行 <code>scripts/uninstall.sh</code>（macOS/Linux）或 <code>scripts\uninstall.ps1</code>（Windows）——它会先以干跑方式列出每个文件夹及其大小，加 <code>--yes</code> 才会真正删除。完整的各平台路径列表和应用移除步骤见 <a href="docs/install/uninstall.md"><b>docs/install/uninstall.md</b></a>。
 </details>

+## 🛡️ 负责任使用与安全
+
+VoiceStudio 在个人硬件上提供零样本语音克隆与语音创作能力。我们提倡负责任的技术使用：
+- **明确授权：** 严禁在未经说话人本人知情并明确授权的情况下克隆其声音。
+- **AI 溯源：** VoiceStudio 默认集成 [AudioSeal](https://github.com/facebookresearch/audioseal) 不可见神经音频水印，在完全不影响听感音质的前提下精准标记合成语音。
+- **本地隐私：** 默认本地工作流下，所有音频、声音档案、项目与转录文本始终保存在你的本地设备上；仅当你主动配置远程工作节点或第三方 ASR 端点时，相应数据才会传输到对应服务。
+
 ---

 <a id="license"></a>
@@ -54,6 +54,15 @@ def _server_mode() -> bool:
    return os.environ.get("OMNIVOICE_SERVER_MODE", "").strip().lower() in _TRUTHY


+def validate_server_admin_key() -> None:
+    """Reject an explicitly blank key before a server-mode app starts."""
+    raw_key = os.environ.get("OMNIVOICE_API_KEY")
+    if _server_mode() and raw_key is not None and not raw_key.strip():
+        raise RuntimeError(
+            "OMNIVOICE_API_KEY is blank; configure a non-whitespace administrator key"
+        )
+
+
 def _configured_pin(request) -> str | None:
    """The active share PIN (``app.state.network_share.pin``) or None. Read via
    getattr so a bare Request stub (or a request that hit before lifespan set
@@ -49,6 +49,13 @@ def public_backends(entries: list[dict]) -> list[dict]:
            item["routing_reason"] = _public_routing_reason(
                item.get("routing_status"), item["routing_reason"]
            )
+        evidence = item.get("execution_evidence")
+        if isinstance(evidence, dict) and evidence.get("cpu_fallback_reason") is not None:
+            evidence = dict(evidence)
+            evidence["cpu_fallback_reason"] = _public_routing_reason(
+                "cpu_fallback", evidence["cpu_fallback_reason"]
+            )
+            item["execution_evidence"] = evidence
        safe.append(item)
    return safe

@@ -57,11 +57,12 @@ _PREVIEW_SEED = 42
 # 32 reliably converges to speech across the gallery's instruct/script space
 # at a one-time (cached) render cost.
 _PREVIEW_NUM_STEP = 32
-# Spectral-flatness floor below which a render is a degenerate tonal artifact
-# rather than speech. Real, mastered speech sits ~0.04–0.07; a tonal buzz
-# collapses to <0.005. 0.015 separates the two with wide margin and sits well
-# below even breathy/whisper voices (which are broadband → high flatness).
-_DEGENERATE_FLATNESS = 0.015
+# Reject near-pure tonal artifacts using mean framed spectral flatness.
+# Calibrated against the tracked speech demos exercised by
+# test_archetype_preview_quality.py: the quietest (Mandarin dubbing, 44.1 kHz)
+# measures ~7.7e-6, while the worst tested tonal buzz measures ~3.3e-9.
+# 1e-7 leaves >10x margin on both sides without rejecting low-flatness speech.
+_DEGENERATE_FLATNESS = 1e-7


 def _preview_key(a: dict) -> str:
@@ -248,24 +249,46 @@ def _is_blank_audio(audio_tensor) -> bool:
        return False


+_FLATNESS_FRAME = 1024
+_FLATNESS_HOP = 512
+#: Frames quieter than this fraction of the loudest frame's energy are the gaps
+#: between words, not speech; their spectrum is the noise floor and averaging it
+#: in drags the measurement toward the value of whatever silence sounds like.
+_FLATNESS_FRAME_FLOOR = 1e-4
+
+
 def _spectral_flatness(audio_tensor) -> Optional[float]:
-    """Geometric-mean / arithmetic-mean of the power spectrum.
+    """Mean per-frame geometric-mean / arithmetic-mean of the power spectrum.

    ~1.0 for broadband noise, →0 for a pure tone. The degenerate diffusion
-    renders this guards against are near-pure tonal buzzes (flatness <0.005),
-    distinct from both silence (caught by ``_is_blank_audio``) and real speech
-    (~0.04+). Returns ``None`` if it can't be computed so callers don't act on
-    a bad measurement.
+    renders this guards against are near-pure tonal buzzes, distinct from both
+    silence (caught by ``_is_blank_audio``) and real speech. Returns ``None``
+    if it can't be computed so callers don't act on a bad measurement.
+
+    Measured over short frames and averaged — the standard definition. A single
+    FFT of the whole clip (what this used to do) is not the same quantity: its
+    frequency resolution grows with clip length, so speech harmonics carve
+    ever-deeper nulls into the spectrum and the geometric mean collapses. That
+    made the result depend on how long the clip was rather than on what it
+    sounded like, and put real speech below the rejection threshold.
    """
    try:
        import torch

        t = audio_tensor if isinstance(audio_tensor, torch.Tensor) else torch.as_tensor(audio_tensor)
-        t = t.detach().to("cpu", dtype=torch.float32).flatten()
-        if t.numel() < 1024 or not torch.isfinite(t).all():
+        t = t.detach().to("cpu", dtype=torch.float32)
+        if t.ndim > 1:
+            t = t.mean(dim=0)
+        t = t.flatten()
+        if t.numel() < _FLATNESS_FRAME or not torch.isfinite(t).all():
            return None
-        spec = torch.fft.rfft(t * torch.hann_window(t.numel())).abs().pow(2) + 1e-12
-        return float(torch.exp(torch.mean(torch.log(spec))) / torch.mean(spec))
+        frames = t.unfold(0, _FLATNESS_FRAME, _FLATNESS_HOP)
+        spec = torch.fft.rfft(frames * torch.hann_window(_FLATNESS_FRAME)).abs().pow(2) + 1e-12
+        energy = spec.sum(dim=1)
+        spec = spec[energy > energy.max() * _FLATNESS_FRAME_FLOOR]
+        if spec.shape[0] == 0:
+            return None
+        return float((torch.exp(spec.log().mean(dim=1)) / spec.mean(dim=1)).mean())
    except Exception:  # never let the checker itself block a render
        return None

@@ -330,7 +353,7 @@ async def _render_archetype_wav(a: dict, out_path: Path) -> None:
    # GPU pool and brick the backend (#730 class). Budget comes from the shared
    # length-scaled helper (#1190) instead of the flat 300s default.
    from services.model_manager import generate_timeout_s
-    _budget = generate_timeout_s(text)
+    _budget = generate_timeout_s(text, engine=model)
    audio_tensor = await run_on_gpu_pool_guarded(
        lambda: _infer(_PREVIEW_SEED), what="Archetype preview generate",
        timeout=_budget)
@@ -357,15 +380,11 @@ async def _render_archetype_wav(a: dict, out_path: Path) -> None:
    # Runs on the dedicated watermark pool (#1190): AudioSeal embedding is CPU
    # work that holds no VRAM, so it must not occupy a GPU worker ahead of the
    # next generate on 1-worker hosts.
-    from services.watermark import mark_synthetic
-    from services.model_manager import get_watermark_pool
-    import functools
-    audio_tensor = await run_on_gpu_pool_guarded(
-        functools.partial(mark_synthetic, audio_tensor, model.sampling_rate,
-                          context="archetypes.render"),
-        what="Archetype watermark",
+    from services.watermark import mark_synthetic_async
+    audio_tensor = await mark_synthetic_async(
+        audio_tensor, model.sampling_rate,
+        context="archetypes.render",
        timeout=generate_timeout_s(""),
-        executor=get_watermark_pool(),
    )

    out_path.parent.mkdir(parents=True, exist_ok=True)
@@ -361,7 +361,7 @@ LONGFORM_NUM_STEP = 32
 LONGFORM_GUIDANCE_SCALE = 2.0


-def _seed_segment_rng(base_seed, text: str, nonce: int = 0) -> None:
+def _seed_segment_rng(base_seed, text: str, nonce: int = 0) -> int | None:
    """Apply a profile's pinned seed to this synth call (#1139).

    ``_resolve_voice`` has always fetched the profile ``seed`` — but only the
@@ -380,11 +380,13 @@ def _seed_segment_rng(base_seed, text: str, nonce: int = 0) -> None:
    must cover /generate and here together, not one path.
    """
    if base_seed is None:
-        return
+        return None
    import torch

    from services.audiobook import segment_seed
-    torch.manual_seed(segment_seed(base_seed, text, nonce))
+    seed = segment_seed(base_seed, text, nonce)
+    torch.manual_seed(seed)
+    return seed


 def _base_seed(opts: ExpressiveOptions, voice: dict):
@@ -508,16 +510,21 @@ def _build_synth(
                "get_model": get_model, "language": language, "opts": opts}

    backend = cls()
-    extra = _generic_extra_kwargs(opts)
+    native_proxy = bool(getattr(cls, "supports_native_omnivoice_controls", False))
+    extra = (_omnivoice_sampling_kwargs(opts) if native_proxy
+             else _generic_extra_kwargs(opts))
    next_nonce = _make_occ_counter(opts)

    def synth(text, voice_id, speed=None):
        v = resolve(voice_id)
-        _seed_segment_rng(_base_seed(opts, v), text, next_nonce())
+        seed = _seed_segment_rng(_base_seed(opts, v), text, next_nonce())
+        call_extra = dict(extra)
+        if native_proxy and seed is not None:
+            call_extra["seed"] = seed
        return backend.generate(
            text, language=language, ref_audio=v["ref_audio"],
            ref_text=v["ref_text"], instruct=v["instruct"], duration=None,
-            speed=float(speed) if speed else 1.0, **extra,
+            speed=float(speed) if speed else 1.0, **call_extra,
        )
    return {"mode": "generic", "resolve": resolve, "engine_id": engine_id,
            "synth": synth, "sample_rate": backend.sample_rate}
@@ -103,6 +103,93 @@ def _set_progress(job, stage, percent=0, **extra):
    job["progress"] = {"stage": stage, "percent": percent, **extra}


+#: Override for the native dub batch width. Set to 1 to disable batching.
+BATCH_WIDTH_ENV = "OMNIVOICE_DUB_BATCH_WIDTH"
+
+#: Hard ceiling on the override — a batch this wide is already amortizing
+#: almost all of the per-call setup, and beyond it the failure mode is an OOM
+#: that costs more than the saving.
+_MAX_BATCH_WIDTH = 16
+
+# Bound each allocation while persisting multipart uploads. Video inputs can
+# be many gigabytes; `await UploadFile.read()` with no size used to mirror the
+# entire file in process memory before writing it back out.
+_UPLOAD_CHUNK_BYTES = 1024 * 1024
+
+
+async def _save_upload(upload: UploadFile, destination: str) -> None:
+    try:
+        with open(destination, "wb") as output:
+            while chunk := await upload.read(_UPLOAD_CHUNK_BYTES):
+                output.write(chunk)
+    except BaseException:
+        try:
+            unlink_if_present(destination)
+        except FileCleanupError:
+            logger.warning("Could not remove incomplete batch upload", exc_info=True)
+        raise
+
+
+def _native_batch_width(backend) -> int:
+    """How many segments to render in one native batch on THIS host.
+
+    A native batch widens the forward pass, so the width cannot be a constant.
+    The default engine declares ``min_vram_gb = 6.0`` for a SINGLE job; an
+    unconditional 8-wide batch would OOM the 4-8 GB CUDA cards and the MPS
+    Macs where the per-segment path succeeds today — turning a throughput
+    optimization into a regression on exactly the hardware that already
+    struggles (#1616 is a 4 GB card reporting capacity failures). Default
+    behaviour must not get riskier on a host, so the width is derived from
+    measured headroom and falls back to 1 (no batching) when unknown.
+
+    CPU hosts get 1: batching there buys no kernel amortization and only
+    multiplies peak RAM.
+    """
+    override = os.environ.get(BATCH_WIDTH_ENV, "").strip()
+    if override:
+        try:
+            return max(1, min(_MAX_BATCH_WIDTH, int(override)))
+        except (TypeError, ValueError):
+            logger.warning(
+                "%s=%r is not an integer — deriving the batch width from the host instead.",
+                BATCH_WIDTH_ENV, override,
+            )
+    try:
+        from core.device_caps import detect_host_caps
+        caps = detect_host_caps()
+    except Exception:  # noqa: BLE001 — an unprobeable host takes the safe path
+        return 1
+    if caps.family == "cpu" or not caps.vram_gb:
+        return 1
+    headroom = caps.vram_gb - float(getattr(backend, "min_vram_gb", 0.0) or 0.0)
+    if headroom < 2.0:
+        return 1
+    if headroom < 6.0:
+        return 2
+    if headroom < 12.0:
+        return 4
+    return 8
+
+
+def _batch_timeout_s(texts: list[str], backend) -> float:
+    """Execution budget for one native batch.
+
+    Not the sum of the per-item budgets: ``generate_timeout_s`` returns a
+    floor (300s GPU / 600s CPU) plus per-length overage, so summing it across
+    eight items yields a ~2400s budget — and a wedged batch would hold a
+    GPU-pool worker for forty minutes before the reset this file depends on
+    (#730). One floor covers wedge detection for the whole call; only the
+    length-driven overage is genuinely additive.
+    """
+    from services.model_manager import generate_timeout_s
+
+    floor = generate_timeout_s("", engine=backend)
+    overage = sum(
+        max(0.0, generate_timeout_s(text, engine=backend) - floor) for text in texts
+    )
+    return floor + overage
+
+
 async def _run_batch_pipeline(job_id: str, job: dict):
    """Full batch dub pipeline: extract → transcribe → translate → generate → mix → export."""
    import subprocess
@@ -279,6 +366,111 @@ async def _run_batch_pipeline(job_id: str, job: dict):
        full_audio = torch.zeros(1, total_samples)
        total_segs = len(translated_segments)

+        # Native engines can amortize encoder/decoder setup across a small
+        # batch. Keep the adapter seam optional: engines without a real batch
+        # implementation inherit TTSBackend.generate_batch(), which preserves
+        # the established one-segment behavior below.
+        from services.tts_backend import TTSBackend
+        batched_audio: dict[int, torch.Tensor] = {}
+        has_native_batch = type(backend).generate_batch is not TTSBackend.generate_batch
+        if has_native_batch:
+            from services.text_normalization import normalize_for_tts
+
+            batch_ref_audio = None
+            batch_ref_text = None
+            if job.get("voice_id"):
+                from core.db import db_conn
+                from core.config import VOICES_DIR as _VD
+                with db_conn() as conn:
+                    row = conn.execute(
+                        "SELECT * FROM voice_profiles WHERE id=?",
+                        (job["voice_id"],),
+                    ).fetchone()
+                if row:
+                    if row["is_locked"] and row["locked_audio_path"]:
+                        batch_ref_audio = os.path.join(_VD, row["locked_audio_path"])
+                    elif row["ref_audio_path"]:
+                        batch_ref_audio = os.path.join(_VD, row["ref_audio_path"])
+                    batch_ref_text = row["ref_text"]
+
+            batch_width = _native_batch_width(backend)
+
+            async def _prefetch_batch(first_index: int) -> None:
+                """Render the batch beginning at ``first_index`` into
+                ``batched_audio``.
+
+                Rendered on demand rather than prerendering the whole track:
+                the tensors are popped as they are placed, so peak host memory
+                is one batch instead of every segment of the language — and
+                the progress bar tracks placement instead of running to the
+                end and restarting at segment 1.
+                """
+                if job["status"] == "cancelled":
+                    return
+                batch_rows = []
+                index = first_index
+                while index < total_segs and len(batch_rows) < batch_width:
+                    seg = translated_segments[index]
+                    if (seg.get("end", 0) - seg.get("start", 0) > 0.05
+                            and seg.get("text", "").strip()):
+                        batch_rows.append((index, seg))
+                    index += 1
+                if len(batch_rows) < 2:
+                    return  # nothing to amortize — the per-segment path is equal
+                batch_indices = [index for index, _ in batch_rows]
+                batch_texts = [
+                    normalize_for_tts(row.get("text", "").strip(), target_lang)
+                    for _, row in batch_rows
+                ]
+                batch_durations = [
+                    row.get("end", 0) - row.get("start", 0)
+                    for _, row in batch_rows
+                ]
+
+                def _render_native_batch():
+                    generated = backend.generate_batch(
+                        batch_texts,
+                        language=target_lang,
+                        ref_audio=batch_ref_audio,
+                        ref_text=batch_ref_text,
+                        duration=batch_durations,
+                        num_step=16,
+                        guidance_scale=2.0,
+                        speed=1.0,
+                        denoise=True,
+                        postprocess_output=True,
+                    )
+                    if len(generated) != len(batch_indices):
+                        raise RuntimeError(
+                            f"native batch returned {len(generated)} outputs for "
+                            f"{len(batch_indices)} segments"
+                        )
+                    rendered = []
+                    for audio_out in generated:
+                        if not getattr(backend, "applies_own_mastering", False):
+                            audio_out = apply_mastering(audio_out, sample_rate=sr)
+                        rendered.append(normalize_audio(audio_out, target_dBFS=-2.0))
+                    return rendered
+
+                try:
+                    rendered = await run_on_gpu_pool_guarded(
+                        _render_native_batch,
+                        what="Batch generate",
+                        timeout=_batch_timeout_s(batch_texts, backend),
+                    )
+                    batched_audio.update(zip(batch_indices, rendered))
+                except TimeoutError:
+                    # Do not immediately queue the same expensive work again:
+                    # the timed-out pool task may still be holding the device.
+                    raise
+                except Exception as e:
+                    logger.warning(
+                        "Native TTS batch failed for segments %s-%s; falling back per segment: %s",
+                        batch_indices[0] + 1,
+                        batch_indices[-1] + 1,
+                        e,
+                    )
+
        for i, seg in enumerate(translated_segments):
            if job["status"] == "cancelled":
                return
@@ -356,10 +548,15 @@ async def _run_batch_pipeline(job_id: str, job: dict):
                # Budget is the shared length-scaled one (#1190): a long segment
                # on CPU-class hardware no longer dies on the flat 300s.
                from services.model_manager import generate_timeout_s
-                audio_tensor = await run_on_gpu_pool_guarded(
-                    _gen, what="Batch generate",
-                    timeout=generate_timeout_s(seg_text),
-                )
+                if has_native_batch and i not in batched_audio:
+                    await _prefetch_batch(i)
+                if i in batched_audio:
+                    audio_tensor = batched_audio.pop(i)
+                else:
+                    audio_tensor = await run_on_gpu_pool_guarded(
+                        _gen, what="Batch generate",
+                        timeout=generate_timeout_s(seg_text, engine=backend),
+                    )

                # Fit to slot
                target_samples_seg = int(seg_duration * sr)
@@ -413,19 +610,15 @@ async def _run_batch_pipeline(job_id: str, job: dict):
        # unmarked while the interactive dub pipeline marked every segment.
        # One whole-track embed (chunked internally, #1045) is equivalent to
        # dub_generate's per-segment marks: the 16-bit message repeats
-        # throughout. Runs in the GPU pool like generate's finalize; never
-        # raises (degrades to unmarked on failure, same as every producer).
+        # throughout. Never raises (degrades to unmarked on failure, same as
+        # every producer).
        # Dispatched to the dedicated watermark pool, not the GPU pool (#1190):
        # AudioSeal embedding is CPU work that holds no VRAM, and a whole-track
        # embed is long enough that occupying a GPU worker with it stalled the
        # next language's segments on 1-worker hosts.
-        from services.watermark import mark_synthetic
-        from services.model_manager import get_watermark_pool
-        import functools
-        full_audio = await loop.run_in_executor(
-            get_watermark_pool(),
-            functools.partial(mark_synthetic, full_audio, sr,
-                              context="batch.dub_track"),
+        from services.watermark import mark_synthetic_async
+        full_audio = await mark_synthetic_async(
+            full_audio, sr, context="batch.dub_track",
        )

        # Same assembly pattern as dub_generate.py:390 — `full_audio` is a
@@ -515,9 +708,7 @@ async def enqueue_batch_job(
    ext = os.path.splitext(video.filename or "video.mp4")[1] or ".mp4"
    video_path = os.path.join(batch_dir, f"{job_id}{ext}")

-    with open(video_path, "wb") as f:
-        content = await video.read()
-        f.write(content)
+    await _save_upload(video, video_path)

    job = {
        "id": job_id,
@@ -27,6 +27,10 @@ Protocol:
         "detail": "..."}                                  — error ("detail"
                                                          kept for legacy)

+    Sherpa ``final`` frames additionally carry
+    ``"final_kind": "utterance"|"summary"``. Utterances are mid-session
+    commits; the summary is the authoritative whole-session result at EOF.
+
    Every ``final`` text is normalised by services.text_polish (leading
    capital for Latin scripts, terminal punctuation, single-spaced) so the
    pasted result reads like typed text. Partials are raw.
@@ -34,10 +38,14 @@ Protocol:
 from __future__ import annotations

 import asyncio
+import json
 import logging
+import math
 import os
 import tempfile
 import time
+import uuid
+from typing import Any

 from fastapi import APIRouter, WebSocket, WebSocketDisconnect

@@ -47,6 +55,9 @@ from services.text_polish import polish_text
 router = APIRouter()
 logger = logging.getLogger("omnivoice.capture_ws")

+SPEECH_PROTOCOL = "voicestudio.speech.v1"
+PLATFORM_STREAM_PATH = "/v1/audio/transcriptions/stream"
+
 # How often (seconds) to run transcription on the accumulated buffer.
 # Shorter = more responsive but more GPU load.
 PARTIAL_INTERVAL_S = float(os.environ.get("OMNIVOICE_STREAM_INTERVAL", "2.0"))
@@ -70,17 +81,79 @@ _AEC_NEAR = 0x00  # microphone frame (clean it, then buffer for ASR)
 _AEC_FAR = 0x01   # playback reference frame (feed the echo model only)


-def _requested_pcm_sample_rate(query_params) -> int | None:
-    """Return a bounded PCM rate for ``?pcm=1``/``?aec=1`` sessions."""
-    raw_pcm = query_params.get("pcm") in ("1", "true", "on")
-    aec = query_params.get("aec") in ("1", "true", "on")
-    if not raw_pcm and not aec:
-        return None
+# Client-supplied ``?sr=`` values outside the range real capture devices use
+# are replaced with 16 kHz. The rate sizes server-side state — RecoveryTail
+# multiplies it by RECOVERY_TAIL_SECONDS to compute its byte ceiling — so an
+# absurd rate must never be believed: it would re-open the unbounded-memory
+# path the recovery-tail cap closed.
+SR_MIN, SR_MAX = 8000, 96000
+
+
+def _is_end_control(text: str | None) -> bool:
+    """Accept the versioned JSON control frame and the legacy ``EOF`` frame."""
+    if text == "EOF":
+        return True
+    if not text:
+        return False
+    try:
+        message = json.loads(text)
+    except (TypeError, json.JSONDecodeError):
+        return False
+    return isinstance(message, dict) and message.get("type") == "input_audio.end"
+
+
+class _PlatformWebSocket:
+    """Add v1 session metadata without changing the legacy WebSocket contract."""
+
+    def __init__(self, websocket: WebSocket):
+        self._websocket = websocket
+        self.session_id = uuid.uuid4().hex
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._websocket, name)
+
+    async def send_json(self, data: Any, mode: str = "text") -> None:
+        if isinstance(data, dict):
+            data = dict(data)
+            data.setdefault("protocol", SPEECH_PROTOCOL)
+            data.setdefault("session_id", self.session_id)
+            if data.get("type") == "final":
+                data.setdefault("final_kind", "summary")
+        await self._websocket.send_json(data, mode=mode)
+
+
+def _bounded_sample_rate(query_params) -> int:
    try:
        sample_rate = int(query_params.get("sr", "16000"))
    except (TypeError, ValueError):
        return 16000
-    return sample_rate if 8000 <= sample_rate <= 96000 else 16000
+    return sample_rate if SR_MIN <= sample_rate <= SR_MAX else 16000
+
+
+def _requested_pcm_sample_rate(query_params) -> int | None:
+    """Return the bounded rate when the client transport is raw PCM.
+
+    Sherpa clients omit ``pcm=1`` because the selected model already defines
+    that transport. If the model is demoted or its runtime is unavailable, the
+    legacy recognizer fallback must still decode those same bytes as PCM.
+    """
+    raw_pcm = query_params.get("pcm") in ("1", "true", "on")
+    aec = query_params.get("aec") in ("1", "true", "on")
+    sherpa_pcm = False
+    requested_model = query_params.get("model")
+    if requested_model:
+        try:
+            from services.sherpa_dictation import is_sherpa_model
+            sherpa_pcm = is_sherpa_model(requested_model)
+        except Exception:  # noqa: BLE001
+            # A broken sherpa install must not decide the framing question —
+            # sherpa_pcm stays False and the session negotiates the
+            # MediaRecorder path; availability is re-probed (and reported)
+            # when the model is actually selected.
+            sherpa_pcm = False
+    if not raw_pcm and not aec and not sherpa_pcm:
+        return None
+    return _bounded_sample_rate(query_params)


 def _demux_aec_frame(data: bytes) -> tuple[str, bytes]:
@@ -137,21 +210,47 @@ def _select_sherpa_spec(websocket: WebSocket):
        from services import sherpa_dictation as sd
    except Exception:
        return None
+
+    def _usable_spec(model_id):
+        spec = sd.get_spec(model_id)
+        if spec is not None and sd.is_demoted(spec.id):
+            logger.warning(
+                "dictation model %s is demoted — using the capture ASR fallback",
+                spec.id,
+            )
+            return None
+        return spec
+
    requested = websocket.query_params.get("model")
    if requested:
-        return sd.get_spec(requested)  # explicit selection (may be None if bad)
+        return _usable_spec(requested)  # explicit selection (may be unavailable)
    # Fall back to the persisted dictation pref.
    try:
        from services.asr_backend import dictation_model_id
        mid = dictation_model_id()
    except Exception:
        mid = None
-    return sd.get_spec(mid) if mid else None
+    return _usable_spec(mid) if mid else None


+@router.websocket(PLATFORM_STREAM_PATH)
@router.websocket("/ws/transcribe")
 async def ws_transcribe(websocket: WebSocket):
    """Stream audio in, get partial + final transcription out."""
+    is_platform_stream = websocket.url.path == PLATFORM_STREAM_PATH
+    if is_platform_stream:
+        websocket = _PlatformWebSocket(websocket)
+    # A browser can reach localhost regardless of the page's own origin.
+    # Reject ambient cross-site WebSocket handshakes before the loopback-host
+    # shortcut or accept(), while keeping native clients (no Origin header)
+    # and configured/same-origin browser UIs working (#1646 review).
+    origin = websocket.headers.get("origin")
+    if origin:
+        from core.csrf import origin_allowed
+
+        if not origin_allowed(websocket):
+            await websocket.close(code=1008, reason="browser origin not allowed")
+            return
    # Loopback origin guard — refuse anything not from 127.0.0.1, ::1, or
    # localhost. Privileged HTTP routers use Depends(require_admin) at router
    # level; WebSocket dependency injection differs across FastAPI versions, so we
@@ -166,6 +265,16 @@ async def ws_transcribe(websocket: WebSocket):
        return

    await websocket.accept()
+    if is_platform_stream:
+        await websocket.send_json({
+            "type": "session.started",
+            "input_format": (
+                "audio/pcm;encoding=s16le;channels=1"
+                if _requested_pcm_sample_rate(websocket.query_params) is not None
+                else "audio/webm;codecs=opus"
+            ),
+            "sample_rate": _bounded_sample_rate(websocket.query_params),
+        })

    # Live-dictation engine selection. When a sherpa-onnx model is selected
    # (via ?model= or the dictation.model_id pref) AND sherpa is installed,
@@ -288,7 +397,7 @@ async def ws_transcribe(websocket: WebSocket):
                    total_bytes += len(data)
                    last_audio_time = time.monotonic()
                    continue
-                if msg.get("text") == "EOF":
+                if _is_end_control(msg.get("text")):
                    # Client signals end-of-audio but stays connected for `final`.
                    running = False
                    break
@@ -422,6 +531,64 @@ SHERPA_OFFLINE_SILENCE_S = float(os.environ.get("OMNIVOICE_SHERPA_OFFLINE_SILENC
 SHERPA_OFFLINE_RMS_FLOOR = float(os.environ.get("OMNIVOICE_SHERPA_OFFLINE_RMS", "0.01"))


+#: Seconds of audio retained for silent-model recovery. Recovery only needs
+#: enough speech to prove the model is broken and to re-transcribe what was
+#: said; retaining the whole session grew ~115 MB/hour at 16 kHz on an open
+#: mic, unbounded, and only ever got read when the fallback fired.
+RECOVERY_TAIL_DEFAULT_SECONDS = 120.0
+RECOVERY_TAIL_MAX_SECONDS = 300.0
+
+
+def _bounded_recovery_tail_seconds(value: str | None) -> float:
+    """Parse the recovery tail override without allowing unbounded buffers."""
+    try:
+        seconds = float(value) if value is not None else RECOVERY_TAIL_DEFAULT_SECONDS
+    except (TypeError, ValueError):
+        return RECOVERY_TAIL_DEFAULT_SECONDS
+    if not math.isfinite(seconds) or seconds <= 0:
+        return RECOVERY_TAIL_DEFAULT_SECONDS
+    return min(seconds, RECOVERY_TAIL_MAX_SECONDS)
+
+
+RECOVERY_TAIL_SECONDS = _bounded_recovery_tail_seconds(
+    os.environ.get("OMNIVOICE_DICTATION_RECOVERY_TAIL_S")
+)
+
+
+class RecoveryTail:
+    """The most recent ``RECOVERY_TAIL_SECONDS`` of session audio.
+
+    Keeps the *tail* rather than the head: a long dictation's useful speech is
+    what the user just said, and the silent-model check cares about how much
+    audio the session carried overall — which ``total_bytes`` still reports
+    truthfully after trimming.
+    """
+
+    __slots__ = ("_buf", "_max", "total_bytes")
+
+    def __init__(self, sample_rate: int, seconds: float = RECOVERY_TAIL_SECONDS):
+        # int16 mono → 2 bytes/sample. Floor of one frame so a nonsense rate
+        # or seconds value can't produce a zero-length buffer.
+        self._max = max(2, int(seconds * max(1, sample_rate)) * 2)
+        self._buf = bytearray()
+        self.total_bytes = 0
+
+    def extend(self, pcm: bytes) -> None:
+        self._buf.extend(pcm)
+        self.total_bytes += len(pcm)
+        excess = len(self._buf) - self._max
+        if excess > 0:
+            # int16 mono: trim whole samples only. A split frame can carry an
+            # odd byte count, and an odd trim would leave the tail starting
+            # mid-sample — every later sample byte-shifted, and the recovery
+            # transcription fed noise.
+            excess += excess % 2
+            del self._buf[:excess]
+
+    def tail(self) -> bytes:
+        return bytes(self._buf)
+
+
 def is_model_silent(text: str, heard_speech: bool, pcm_bytes: int) -> bool:
    """True when the dictation model produced NO text despite real speech.

@@ -448,19 +615,74 @@ def _pcm16_to_f32(pcm: bytes):
    return np.frombuffer(pcm, dtype=np.int16).astype(np.float32) / 32768.0


-async def _sherpa_session(websocket: WebSocket):
-    """Shared WS receive setup for the sherpa handlers.
+def _pcm16_rms(pcm: bytes) -> float:
+    samples = _pcm16_to_f32(pcm)
+    if not len(samples):
+        return 0.0
+    return float((samples * samples).mean() ** 0.5)

-    Returns ``(get_frame, state)`` where ``get_frame`` is an async callable
-    that yields the next near-end (mic) PCM bytes, ``b""`` for a keepalive/ref
-    frame, or ``None`` on EOF/disconnect. ``state`` carries sample rate, AEC,
-    and the disconnect flag for the caller's finaliser.
-    """
-    pcm_sr = 16000
+
+async def _recover_silent_sherpa(
+    spec, pcm: bytes, pcm_sr: int,
+) -> tuple[str, list[dict]]:
+    """Retry a token-silent Sherpa session through an installed local ASR."""
+    logger.warning(
+        "dictation model %s decoded NOTHING from %.1fs of speech-level audio "
+        "— falling back to the capture ASR engine for this session",
+        spec.id, len(pcm) / float(max(1, pcm_sr) * 2),
+    )
    try:
-        pcm_sr = int(websocket.query_params.get("sr", "16000"))
-    except (TypeError, ValueError):
-        pcm_sr = 16000
+        from services.asr_backend import asr_model_missing_error
+        fallback_missing = await asyncio.to_thread(
+            asr_model_missing_error,
+            purpose="dictation",
+            skip_sherpa=True,
+            require_installed=True,
+        )
+        if fallback_missing is not None:
+            logger.warning(
+                "dictation silent-model fallback is not installed (%s); "
+                "skipping recovery to avoid an automatic download",
+                fallback_missing.get("missing_repo_id", "unknown"),
+            )
+            return "", []
+
+        result = await _transcribe_buffer_full(
+            [pcm], pcm_sr=pcm_sr, skip_sherpa=True,
+        )
+        text = polish_text(_result_text(result))
+        if not text:
+            return "", []
+        # The RMS gate can fire on fan/keyboard noise. Only another recognizer
+        # producing words proves the audio held speech and makes persistent
+        # demotion safe.
+        try:
+            from services.sherpa_dictation import demote_model
+            if await asyncio.to_thread(demote_model, spec.id):
+                logger.error(
+                    "dictation model %s demoted on this machine — it will no longer be "
+                    "auto-selected. Pick it again in Settings to give it another chance.",
+                    spec.id,
+                )
+        except Exception:
+            logger.exception("silent-model demotion failed")
+        segments = (result or {}).get("segments") or [
+            {"start": 0.0, "end": None, "text": text}
+        ]
+        return text, segments
+    except Exception:
+        logger.exception("dictation silent-model fallback failed")
+        return "", []
+
+
+async def _sherpa_session(websocket: WebSocket):
+    """Shared WS setup for the sherpa handlers.
+
+    Returns ``(pcm_sr, aec)``: the bounded PCM sample rate for the session
+    and the echo canceller when ``?aec=1`` requested one (``None`` otherwise
+    or when AEC setup fails).
+    """
+    pcm_sr = _bounded_sample_rate(websocket.query_params)
    aec = None
    if websocket.query_params.get("aec") in ("1", "true", "on"):
        try:
@@ -498,7 +720,7 @@ async def _recv_pcm_frame(websocket: WebSocket, aec):
                return "skip", b""
            return "near", aec.process_near_end(payload)
        return "near", data
-    if msg.get("text") == "EOF":
+    if _is_end_control(msg.get("text")):
        return "eof", b""
    return "skip", b""

@@ -569,6 +791,8 @@ async def _run_sherpa_streaming(websocket: WebSocket, spec):

    last_partial = ""
    committed: list[str] = []     # finalized utterances this session
+    session_pcm = RecoveryTail(pcm_sr)   # bounded audio for silent-model recovery
+    heard_speech = False
    client_disconnected = False

    async def _send(payload) -> bool:
@@ -610,6 +834,9 @@ async def _run_sherpa_streaming(websocket: WebSocket, spec):
                break
            if kind == "skip":
                continue
+            session_pcm.extend(pcm)
+            if not heard_speech and _pcm16_rms(pcm) >= SHERPA_OFFLINE_RMS_FLOOR:
+                heard_speech = True
            text, endpoint = await asyncio.to_thread(_decode_after_feed, pcm)
            if endpoint:
                # Commit this utterance (polished — it gets pasted); reset
@@ -618,6 +845,7 @@ async def _run_sherpa_streaming(websocket: WebSocket, spec):
                if text:
                    committed.append(text)
                    await _send({"type": "final", "text": text,
+                                 "final_kind": "utterance",
                                 "segments": [{"start": 0.0, "end": None, "text": text}],
                                 "language": "auto", "engine": backend.id})
                rec.reset(stream)
@@ -644,7 +872,28 @@ async def _run_sherpa_streaming(websocket: WebSocket, spec):
    # Pieces are already polished; the join is too (polish is idempotent).
    full = " ".join(t for t in committed if t).strip()
    segments = [{"start": 0.0, "end": None, "text": t} for t in committed if t]
+
+    model_silent = is_model_silent(full, heard_speech, session_pcm.total_bytes)
+    if model_silent:
+        recovered, recovered_segments = await _recover_silent_sherpa(
+            spec, session_pcm.tail(), pcm_sr,
+        )
+        if recovered:
+            full = recovered
+            segments = recovered_segments
+
    if not client_disconnected:
+        payload = {"type": "final", "text": full, "final_kind": "summary",
+                   "segments": segments,
+                   "language": "auto", "engine": backend.id}
+        if model_silent:
+            payload["engine"] = "capture-asr-fallback" if full else backend.id
+            payload["model_silent"] = spec.id
+            payload["warning"] = (
+                f"The selected dictation model ({spec.id}) produced no text from your "
+                "speech. Switched to the fallback engine for this session — pick a "
+                "different model in Settings → Dictation."
+            )
        if full:
            # Hard-bounded refinement (~4s): never delays this summary `final`
            # beyond OMNIVOICE_REFINE_TIMEOUT_S even with a dead LLM endpoint.
@@ -653,14 +902,9 @@ async def _run_sherpa_streaming(websocket: WebSocket, spec):
                refined = await maybe_refine_async(full)
            except Exception:
                refined = None
-            payload = {"type": "final", "text": full, "segments": segments,
-                       "language": "auto", "engine": backend.id}
            if refined and refined != full:
                payload["refined_text"] = refined
-            await _send(payload)
-        else:
-            await _send({"type": "final", "text": "", "segments": [],
-                         "language": "auto", "engine": backend.id})
+        await _send(payload)
        try:
            await websocket.close()
        except Exception:
@@ -697,7 +941,7 @@ async def _run_sherpa_offline(websocket: WebSocket, spec):
    # whisper/zipformer transcribe the same bytes). Keep the whole session's
    # audio and whether any of it was speech-level, so the finaliser can tell
    # "user said nothing" (fine) from "model produced nothing" (broken).
-    session_pcm = bytearray()
+    session_pcm = RecoveryTail(pcm_sr)
    heard_speech = False
    running = True
    client_disconnected = False
@@ -716,12 +960,6 @@ async def _run_sherpa_offline(websocket: WebSocket, spec):
            client_disconnected = True
            return False

-    def _rms(pcm: bytes) -> float:
-        samples = _pcm16_to_f32(pcm)
-        if not len(samples):
-            return 0.0
-        return float((samples * samples).mean() ** 0.5)
-
    def _decode_window(pcm: bytes) -> str:
        samples = _pcm16_to_f32(pcm)
        if not len(samples):
@@ -740,7 +978,7 @@ async def _run_sherpa_offline(websocket: WebSocket, spec):
                    continue
                buf.extend(pcm)
                session_pcm.extend(pcm)
-                if not heard_speech and _rms(pcm) >= SHERPA_OFFLINE_RMS_FLOOR:
+                if not heard_speech and _pcm16_rms(pcm) >= SHERPA_OFFLINE_RMS_FLOOR:
                    heard_speech = True
                last_audio = time.monotonic()
        except WebSocketDisconnect:
@@ -766,6 +1004,7 @@ async def _run_sherpa_offline(websocket: WebSocket, spec):
        if text:
            committed.append(text)
            await _send({"type": "final", "text": text,
+                         "final_kind": "utterance",
                         "segments": [{"start": 0.0, "end": None, "text": text}],
                         "language": "auto", "engine": backend.id})

@@ -777,8 +1016,8 @@ async def _run_sherpa_offline(websocket: WebSocket, spec):
                continue
            snapshot = bytes(buf)
            if len(snapshot) > sil_bytes and \
-                    _rms(snapshot[-sil_bytes:]) < SHERPA_OFFLINE_RMS_FLOOR:
-                if _rms(snapshot[:-sil_bytes]) >= SHERPA_OFFLINE_RMS_FLOOR:
+                    _pcm16_rms(snapshot[-sil_bytes:]) < SHERPA_OFFLINE_RMS_FLOOR:
+                if _pcm16_rms(snapshot[:-sil_bytes]) >= SHERPA_OFFLINE_RMS_FLOOR:
                    await _commit(snapshot)
                else:
                    # Pure silence — drop it (keep the gate window for
@@ -824,39 +1063,18 @@ async def _run_sherpa_offline(websocket: WebSocket, spec):
    # quiet user — hand the session to the capture ASR backend so the user
    # still gets their words, and say which model let them down. Bounded to
    # this session; the pref is left alone so the user stays in control.
-    model_silent = is_model_silent(full, heard_speech, len(session_pcm))
+    model_silent = is_model_silent(full, heard_speech, session_pcm.total_bytes)
    if model_silent:
-        logger.warning(
-            "dictation model %s decoded NOTHING from %.1fs of speech-level audio "
-            "— falling back to the capture ASR engine for this session",
-            spec.id, len(session_pcm) / float(max(1, pcm_sr) * 2),
+        recovered, recovered_segments = await _recover_silent_sherpa(
+            spec, session_pcm.tail(), pcm_sr,
        )
-        # Demote it so the NEXT session doesn't repeat this round trip. The
-        # curated default can be broken on a platform we never tested (the
-        # NeMo-TDT decoder is, on Windows), and observing it beats guessing.
-        try:
-            from services.sherpa_dictation import demote_model
-            if demote_model(spec.id):
-                logger.error(
-                    "dictation model %s demoted on this machine — it will no longer be "
-                    "auto-selected. Pick it again in Settings to give it another chance.",
-                    spec.id,
-                )
-        except Exception:
-            logger.exception("silent-model demotion failed")
-        try:
-            result = await _transcribe_buffer_full([bytes(session_pcm)], pcm_sr=pcm_sr)
-            fb_text = polish_text((result or {}).get("text", "") or "")
-            if fb_text:
-                full = fb_text
-                segments = (result or {}).get("segments") or [
-                    {"start": 0.0, "end": None, "text": fb_text}
-                ]
-        except Exception:
-            logger.exception("dictation silent-model fallback failed")
+        if recovered:
+            full = recovered
+            segments = recovered_segments

    if not client_disconnected:
-        payload = {"type": "final", "text": full, "segments": segments,
+        payload = {"type": "final", "text": full, "final_kind": "summary",
+                   "segments": segments,
                   "language": "auto", "engine": backend.id}
        if model_silent:
            # The client surfaces this so a silently-broken model can't look
@@ -884,6 +1102,35 @@ async def _run_sherpa_offline(websocket: WebSocket, spec):
            pass


+def _result_text(result: dict | None) -> str:
+    """Normalize text from every ASR backend result shape.
+
+    Some backends return a top-level ``text`` value, while WhisperX, Faster
+    Whisper, Moonshine, and OpenAI-compatible ASR expose only ``segments`` and
+    ``chunks``. Dictation partials and finals must interpret both contracts the
+    same way.
+    """
+    if not isinstance(result, dict):
+        return ""
+
+    text = result.get("text")
+    if isinstance(text, str) and text.strip():
+        return text.strip()
+
+    for key in ("segments", "chunks"):
+        items = result.get(key)
+        if not isinstance(items, (list, tuple)):
+            continue
+        text = " ".join(
+            str(item.get("text", "")).strip()
+            for item in items
+            if isinstance(item, dict) and item.get("text")
+        ).strip()
+        if text:
+            return text
+    return ""
+
+
 async def _transcribe_buffer(chunks: list[bytes], *, pcm_sr: int | None = None) -> str:
    """Quick partial transcription of the current audio buffer."""

@@ -898,7 +1145,7 @@ async def _transcribe_buffer(chunks: list[bytes], *, pcm_sr: int | None = None)
        def _run():
            backend = get_capture_asr_backend()
            result = backend.transcribe(tmp, word_timestamps=False)
-            return result.get("text", "")
+            return _result_text(result)

        # Bound dictation transcribes (#730): a wedged whisperx/CTranslate2 call
        # must not hold its GPU-pool worker forever and starve TTS / other ASR
@@ -912,7 +1159,9 @@ async def _transcribe_buffer(chunks: list[bytes], *, pcm_sr: int | None = None)
            pass


-async def _transcribe_buffer_full(chunks: list[bytes], *, pcm_sr: int | None = None) -> dict:
+async def _transcribe_buffer_full(
+    chunks: list[bytes], *, pcm_sr: int | None = None, skip_sherpa: bool = False,
+) -> dict:
    """Full transcription with timing info for the final result."""
    tmp = _pcm16_to_wav(b"".join(chunks), pcm_sr) if pcm_sr else _chunks_to_wav(chunks)
    if tmp is None:
@@ -924,15 +1173,13 @@ async def _transcribe_buffer_full(chunks: list[bytes], *, pcm_sr: int | None = N
        from services.asr_backend import get_capture_asr_backend, run_transcribe_guarded

        def _run():
-            backend = get_capture_asr_backend()
+            backend = get_capture_asr_backend(skip_sherpa=skip_sherpa)
            t0 = time.perf_counter()
            result = backend.transcribe(tmp, word_timestamps=False)
            elapsed = round(time.perf_counter() - t0, 2)

            segments = result.get("segments", [])
-            full_text = result.get("text", "")
-            if not full_text and segments:
-                full_text = " ".join(s.get("text", "") for s in segments).strip()
+            full_text = _result_text(result)

            # Wave 1.1: strip Whisper hallucination loops from the final
            # text (the string that gets auto-pasted). Segments keep the
@@ -134,6 +134,82 @@ _save_job          = dub_pipeline.save_job
 # paste (or a mis-aimed binary) burn CPU in the parser.
 _MAX_SUBTITLE_PASTE_CHARS = 2_000_000

+_SRT_REPLACED_FIELDS = {
+    "id",
+    "start",
+    "end",
+    "text",
+    "text_original",
+    "translations",
+    "translate_error",
+    "translate_degraded",
+}
+
+
+def _best_overlapping_segment(cue: dict, existing: list[dict]) -> dict | None:
+    """Return the prior segment with the strongest temporal overlap."""
+    cue_start = float(cue.get("start") or 0.0)
+    cue_end = float(cue.get("end") or cue_start)
+    cue_mid = (cue_start + cue_end) / 2.0
+    best = None
+    best_key = None
+    for index, segment in enumerate(existing):
+        start = float(segment.get("start") or 0.0)
+        end = float(segment.get("end") or start)
+        overlap = min(cue_end, end) - max(cue_start, start)
+        if overlap <= 0:
+            continue
+        midpoint_distance = abs(cue_mid - ((start + end) / 2.0))
+        key = (overlap, -midpoint_distance, -index)
+        if best_key is None or key > best_key:
+            best = segment
+            best_key = key
+    return best
+
+
+def _carry_srt_voice_metadata(
+    cues: list[dict],
+    existing: list[dict],
+    segment_clones: dict | None,
+    speaker_clones: dict | None = None,
+) -> tuple[list[dict], dict]:
+    """Replace subtitle content while retaining the source cast assignment."""
+    source_clones = dict(segment_clones or {})
+    source_speaker_clones = dict(speaker_clones or {})
+    # Replacement cues get new positional ids. Starting from the old map would
+    # let an unmatched cue whose new id happens to equal an old id inherit an
+    # unrelated reference. Only explicitly overlap-matched references survive.
+    clones = {}
+    merged_segments = []
+    for new_id, cue in enumerate(cues):
+        prior = _best_overlapping_segment(cue, existing)
+        metadata = {
+            key: value
+            for key, value in (prior or {}).items()
+            if key not in _SRT_REPLACED_FIELDS
+        }
+        merged = {
+            **metadata,
+            "id": new_id,
+            "start": cue.get("start", 0.0),
+            "end": cue.get("end", 0.0),
+            "text": cue.get("text", ""),
+            "text_original": cue.get("text", ""),
+        }
+        if not merged.get("speaker_id"):
+            merged["speaker_id"] = cue.get("speaker_id") or "Speaker 1"
+        if prior is not None:
+            prior_id = str(prior.get("id", ""))
+            clone = source_clones.get(prior_id)
+            if clone is None:
+                clone = source_speaker_clones.get(prior.get("speaker_id"))
+            if clone is not None:
+                clones[str(new_id)] = clone
+                if merged.get("profile_id") == f"auto-seg:{prior_id}":
+                    merged["profile_id"] = f"auto-seg:{new_id}"
+        merged_segments.append(merged)
+    return merged_segments, clones
+

@router.post("/dub/parse-subtitle-text")
 def dub_parse_subtitle_text(req: ParseSubtitleTextRequest):
@@ -234,7 +310,32 @@ async def dub_import_srt(job_id: str, file: UploadFile = File(...)):
    else:
        segments = result.segments

+    prior_segments = [
+        segment for segment in (job.get("segments") or []) if isinstance(segment, dict)
+    ]
+    segments, segment_clones = _carry_srt_voice_metadata(
+        segments,
+        prior_segments,
+        job.get("segment_clones"),
+        job.get("speaker_clones"),
+    )
    job["segments"] = segments
+    job["segment_clones"] = segment_clones
+    # A pooled speaker clone is keyed only by a display label. Replacement
+    # cues can reuse that label without overlapping the original speaker, so
+    # retain matched pooled references as segment-specific clones above and
+    # drop the global map before rebuilding the cast.
+    job["speaker_clones"] = {}
+    if segment_clones:
+        from services.speaker_clone import build_cast_sources
+
+        job["cast_sources"] = build_cast_sources(
+            segments,
+            None,
+            segment_clones,
+        )
+    else:
+        job.pop("cast_sources", None)
    # `source_lang` stays whatever the user (or the upload step) set; we
    # don't try to language-detect off the cue text — that's noisy and the
    # user usually knows what their .srt is.
@@ -351,12 +452,13 @@ async def preview_upload(video: UploadFile = File(...)):
    safe_name = f"{uuid.uuid4().hex[:12]}"
    vid_path = os.path.join(PREVIEW_DIR, f"{safe_name}{ext}")
    wav_path = os.path.join(PREVIEW_DIR, f"{safe_name}.wav")
-    
-    with open(vid_path, "wb") as f:
-        f.write(await video.read())
-        
-    has_audio = False
-    if ext not in [".wav", ".mp3", ".m4a", ".aac"]:
+    payload = await video.read()
+
+    def _write_and_extract() -> bool:
+        with open(vid_path, "wb") as f:
+            f.write(payload)
+        if ext in {".wav", ".mp3", ".m4a", ".aac"}:
+            return False
        try:
            ffmpeg_cmd = [
                find_ffmpeg(), "-y", "-i", vid_path,
@@ -368,10 +470,16 @@ async def preview_upload(video: UploadFile = File(...)):
                stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
                timeout=300,
            )
-            has_audio = True
+            return True
        except Exception as e:
            logger.warning("FFmpeg extraction failed: %s", log_safe(e))
-            pass
+            return False
+
+    # File writes and ffmpeg are blocking operations. Keep them on the bounded
+    # CPU pool so a large preview cannot stall unrelated API requests (#1667).
+    has_audio = await asyncio.get_running_loop().run_in_executor(
+        _cpu_pool, _write_and_extract
+    )

    return {
        "url": f"/preview/{safe_name}{ext}",
@@ -410,12 +518,52 @@ _ingest_gen       = dub_pipeline.ingest_pipeline
 #: container so a mislabelled video can't slip past the video-skipping branch.
 _AUDIO_EXTS = {".wav", ".mp3", ".m4a", ".aac", ".flac", ".ogg", ".opus", ".wma"}

+# Source-language choices exposed by the first-party dub UI, plus every
+# language code Whisper can write back after auto-detection. A restored job
+# may reuse that detected value as the next upload's override, so rejecting our
+# own persisted codes strands otherwise valid dubbing sessions (#1737).
+# Keeping this an allow-list still rejects language names and private-use
+# BCP-47 tags. Values are normalized to lowercase below.
+_DUB_SOURCE_LANG_CODES = frozenset({
+    "af", "sq", "am", "ar", "hy", "az", "eu", "be", "bn", "bs", "bg",
+    "my", "ca", "cmn-hans", "cmn-hant", "hr", "cs", "da", "nl", "en",
+    "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "ha", "haw",
+    "he", "hi", "hu", "is", "id", "it", "ja", "jw", "kn", "kk", "km",
+    "ko", "ku", "ky", "lo", "la", "lv", "lt", "mk", "ms", "ml", "mt",
+    "mi", "mr", "mn", "ne", "no", "ps", "fa", "pl", "pt", "pa", "ro",
+    "ru", "sm", "gd", "sr", "sn", "sd", "si", "sk", "sl", "so", "es",
+    "su", "sw", "sv", "tg", "ta", "te", "th", "tr", "uk", "ur", "uz",
+    "vi", "cy", "xh", "yi", "yo", "zu",
+    "as", "ba", "bo", "br", "fo", "lb", "ln", "mg", "nn", "oc", "sa",
+    "tk", "tl", "tt", "yue", "zh",
+})
+
+
+def _source_lang_override(value: str | None) -> str | None:
+    """Normalize a user-selected source language; auto/und means detect."""
+    code = (value or "").strip().lower()
+    if code in {"", "auto", "und"}:
+        return None
+    if code not in _DUB_SOURCE_LANG_CODES:
+        raise HTTPException(status_code=400, detail="Invalid source language code")
+    return code
+
+
+def _detected_source_lang(value: str | None) -> str:
+    """Normalize an ASR language without truncating valid three-letter codes."""
+    code = (value or "en").split("_", 1)[0].strip().lower()
+    if code in _DUB_SOURCE_LANG_CODES:
+        return code
+    short = code[:2]
+    return short if short in _DUB_SOURCE_LANG_CODES else "en"
+

@router.post("/dub/upload")
 async def dub_upload(
    video: UploadFile = File(...),
    job_id: Optional[str] = Form(None),
    input_type: str = Form("video"),
+    source_lang: Optional[str] = Form(None),
 ):
    """Accept a media upload, write to disk, queue background prep task.

@@ -445,6 +593,7 @@ async def dub_upload(
            detail=f"Audio-only dubbing needs an audio file ({', '.join(sorted(_AUDIO_EXTS))}); got '{ext or 'no extension'}'.",
        )

+    source_lang_override = _source_lang_override(source_lang)
    os.makedirs(job_dir, exist_ok=True)

    video_path = os.path.join(job_dir, f"original{ext}")
@@ -456,7 +605,13 @@ async def dub_upload(
    await task_manager.add_task(
        task_id, "prep",
        _ingest_gen, job_id, job_dir,
-        {"kind": "file", "path": video_path, "input_type": input_type}, filename,
+        {
+            "kind": "file",
+            "path": video_path,
+            "input_type": input_type,
+            "source_lang": source_lang_override,
+        },
+        filename,
    )
    return JSONResponse(
        status_code=202,
@@ -478,6 +633,7 @@ async def dub_ingest_url(req: DubIngestUrlRequest, request: Request):
            status_code=400,
            detail="URL must start with http:// or https://. Paste a full video link (e.g. https://youtube.com/watch?v=…) or drop a local file instead.",
        )
+    source_lang_override = _source_lang_override(req.source_lang)

    try:
        import yt_dlp  # noqa: F401
@@ -513,6 +669,7 @@ async def dub_ingest_url(req: DubIngestUrlRequest, request: Request):
        "fetch_subs": bool(req.fetch_subs),
        "sub_langs": req.sub_langs or None,
        "cookie_file": cookie_path,
+        "source_lang": source_lang_override,
    }
    try:
        await task_manager.add_task(
@@ -577,6 +734,118 @@ def _clamp_num_speakers(value) -> Optional[int]:
    return value if 1 <= value <= 20 else None


+def _recover_from_phrase_embeddings(
+    diar_pipe,
+    diarized_segments: list[dict],
+    *,
+    phrases: list[dict],
+    requested_speakers: int | None,
+    audio_target: str,
+    segments: list[dict],
+    words: list,
+):
+    """Recover rapid turns when pyannote collapses a two-speaker exchange.
+
+    Uses ASR phrase boundaries and the embedding/audio components already
+    loaded by speaker-diarization-3.1. Weak or imbalanced clusters are rejected
+    so ordinary single-speaker recordings remain untouched. Returns
+    ``(segments, separation)`` or ``None``.
+    """
+    present = {
+        str(seg.get("speaker_id")) for seg in diarized_segments
+        if seg.get("speaker_id")
+    }
+    if len(present) > 1:
+        return None
+    usable_phrases = [
+        phrase for phrase in phrases
+        if phrase.get("text")
+        and float(phrase.get("end", 0.0)) - float(phrase.get("start", 0.0)) >= 0.75
+    ]
+    if len(usable_phrases) < 4:
+        return None
+    requested = int(requested_speakers) if requested_speakers else 2
+    if requested != 2:
+        return None
+    embedding = getattr(diar_pipe, "_embedding", None)
+    audio = getattr(diar_pipe, "_audio", None)
+    if embedding is None or audio is None:
+        return None
+    try:
+        import numpy as np
+        from pyannote.core import Segment as _PyannoteSegment
+        from sklearn.cluster import AgglomerativeClustering
+
+        vectors = []
+        durations = []
+        for phrase in usable_phrases:
+            start, end = float(phrase["start"]), float(phrase["end"])
+            duration = end - start
+            waveform, _ = audio.crop(
+                audio_target, _PyannoteSegment(start, end),
+                duration=duration, mode="pad",
+            )
+            vector = np.asarray(embedding(waveform[None])).reshape(-1)
+            if not np.isfinite(vector).all():
+                return None
+            vectors.append(vector)
+            durations.append(duration)
+        matrix = np.vstack(vectors)
+        labels = np.asarray(AgglomerativeClustering(
+            n_clusters=2, metric="cosine", linkage="average",
+        ).fit_predict(matrix))
+        if len(set(labels.tolist())) != 2:
+            return None
+
+        counts = [int(np.sum(labels == cluster)) for cluster in (0, 1)]
+        cluster_durations = [
+            float(sum(duration for duration, label in zip(durations, labels) if label == cluster))
+            for cluster in (0, 1)
+        ]
+        if min(counts) < 2 or min(cluster_durations) < 1.5:
+            return None
+
+        normalized = matrix / np.maximum(np.linalg.norm(matrix, axis=1, keepdims=True), 1e-8)
+        similarities = normalized @ normalized.T
+        within, cross = [], []
+        for left in range(len(labels)):
+            for right in range(left + 1, len(labels)):
+                target = within if labels[left] == labels[right] else cross
+                target.append(float(similarities[left, right]))
+        if not within or not cross:
+            return None
+        separation = float(np.mean(within) - np.mean(cross))
+        min_separation = 0.12 if requested_speakers == 2 else 0.18
+        if separation < min_separation:
+            logger.info(
+                "phrase-embedding speaker recovery rejected (separation=%.3f < %.3f)",
+                separation, min_separation,
+            )
+            return None
+
+        speaker_map = {}
+        turns = []
+        for phrase, label in zip(usable_phrases, labels.tolist()):
+            if label not in speaker_map:
+                speaker_map[label] = f"Speaker {len(speaker_map) + 1}"
+            turns.append({
+                "start": float(phrase["start"]),
+                "end": float(phrase["end"]),
+                "speaker": speaker_map[label],
+            })
+        # Assignment mutates segment dictionaries. Work on copies so a recovery
+        # rejected by the final two-speaker check cannot leak partial labels
+        # into the ordinary pyannote result.
+        assigned = assign_speakers_from_turns([dict(item) for item in segments], turns)
+        recovered = resplit_segments_by_turns(assigned, words, turns)
+        if len({item.get("speaker_id") for item in recovered if item.get("speaker_id")}) < 2:
+            return None
+        return recovered, separation
+    except Exception:
+        logger.exception("phrase-embedding speaker recovery failed")
+        return None
+
+
@router.get("/dub/transcribe-stream/{job_id}")
 async def dub_transcribe_stream(
    job_id: str,
@@ -912,6 +1181,12 @@ async def dub_transcribe_stream(
        # Words (global-timeline) retained so diarization can re-split a segment
        # that spans two speakers' turns at the word boundary (#486).
        all_words: list = []
+        # Preserve the ASR backend's natural phrase boundaries before
+        # segment_transcript merges short neighboring phrases. Pyannote 3.1
+        # occasionally collapses rapid exchanges into one dominant speaker; in
+        # that narrow case these phrase spans give its own WeSpeaker embedding
+        # model clean candidate utterances for a conservative recovery pass.
+        asr_phrase_segments: list[dict] = []
        detected_lang = None
        next_seg_id = 0
        chunk_errors: list[str] = []
@@ -981,21 +1256,13 @@ async def dub_transcribe_stream(
                        "error_code": failure["code"],
                    }

-            # Retry a failed/timed-out chunk once on a fresh pool before giving
-            # up. Otherwise a transient wedge on the FIRST chunk (whisperx often
-            # cold-loads its model there, the #730 hang) drops that whole window
-            # and the transcript is "missing the beginning, only middle+end".
-            # The retry reuses the same audio window, so a recovered chunk fills
-            # the hole instead of leaving silent gaps.
+            # Retry an ordinary completed failure once. A timed-out native call
+            # is different: its thread is still executing and must not overlap
+            # a retry against the same backend (#1669).
            part = None
+            timed_out = False
            for _attempt in range(1, _CHUNK_TRANSCRIBE_ATTEMPTS + 1):
-                # A wedged chunk gets the SAME guarded-timeout + pool-reset
-                # semantics as the whole-file paths (#730/#851):
-                # run_transcribe_guarded bounds the call, abandons the poisoned
-                # pool so the retry (and any concurrent TTS work) gets a fresh
-                # worker, and raises the actionable ASRTimeoutError. Run it as
-                # a task and poll so we can keep yielding pings — the
-                # EventSource connection drops without them.
+                # Run as a task and poll so pings keep the EventSource alive.
                task = asyncio.ensure_future(run_transcribe_guarded(
                    _gpu_pool, _transcribe_chunk,
                    what=f"Dub chunk {i + 1}/{chunks_n}",
@@ -1010,9 +1277,12 @@ async def dub_transcribe_stream(
                try:
                    part = task.result()
                except ASRTimeoutError:
-                    # The guard already reset the pool; keep the actionable
-                    # message (it names the durable fixes, and — after repeated
-                    # timeouts — the crash-isolated engine escape hatch).
+                    # Python cannot kill an in-process native transcribe. Do
+                    # not swap pools and retry over the still-running call:
+                    # concurrent whisperx/CTranslate2 access caused the native
+                    # Windows access violation in #1669. Stop this transcript;
+                    # the worker remains honestly occupied until it exits.
+                    timed_out = True
                    logger.error(
                        "Transcribe chunk %d/%d timed out after %.0fs (attempt %d/%d, job=%s)",
                        i + 1, chunks_n, transcribe_timeout_s, _attempt,
@@ -1031,23 +1301,36 @@ async def dub_transcribe_stream(
                # error-part; the timeout path already reset the pool).
                if part is not None and not part.get("error"):
                    break
-                if _attempt < _CHUNK_TRANSCRIBE_ATTEMPTS:
+                if timed_out:
+                    break
+                if not timed_out and _attempt < _CHUNK_TRANSCRIBE_ATTEMPTS:
                    logger.warning(
                        "Retrying transcribe chunk %d/%d after failure/timeout (next attempt %d/%d, job=%s)",
                        i + 1, chunks_n, _attempt + 1, _CHUNK_TRANSCRIBE_ATTEMPTS, log_safe(job_id),
                    )
-                    # A completed exception did not wedge the worker. Resetting
-                    # the pool here leaked a healthy executor on every ordinary
-                    # decode failure; run_transcribe_guarded already resets the
-                    # pool on the only case that needs it: a real timeout.
+                    # A completed exception did not leave native work behind,
+                    # so retrying this same audio window is safe.
            if part.get("error"):
                chunk_errors.append(part["error"])
                if part.get("error_code"):
                    chunk_error_codes.append(part["error_code"])
                logger.warning("Chunk %d/%d error: %s", i + 1, chunks_n, log_safe(part["error"]))
+            if timed_out:
+                break
            if detected_lang is None and part.get("language"):
                detected_lang = part["language"]
            asr_speaker_turns.extend(part.get("speaker_turns") or [])
+            for _phrase in part.get("chunks", []) or []:
+                _pts = _phrase.get("timestamp") or (None, None)
+                _ptext = (_phrase.get("text") or "").strip()
+                try:
+                    _ps, _pe = float(_pts[0]), float(_pts[1])
+                except (TypeError, ValueError, IndexError):
+                    continue
+                if _ptext and _pe > _ps:
+                    asr_phrase_segments.append({
+                        "start": _ps, "end": _pe, "text": _ptext,
+                    })
            chunk_segs = segment_transcript(part, duration=t1, scene_cuts=scene_cuts)
            # Same word source segment_transcript used (already global-timeline),
            # kept for the post-diarization speaker re-split (#486).
@@ -1313,7 +1596,25 @@ async def dub_transcribe_stream(
                assigned = assign_speakers_from_diarization(all_segments, diar)
                # #486: split any segment that spans two speakers' turns at the
                # word boundary (single-speaker segments pass through unchanged).
-                return resplit_segments_by_diarization(assigned, all_words, diar), None, "pyannote"
+                resplit = resplit_segments_by_diarization(assigned, all_words, diar)
+                recovered = _recover_from_phrase_embeddings(
+                    diar_pipe,
+                    resplit,
+                    phrases=asr_phrase_segments,
+                    requested_speakers=num_speakers,
+                    audio_target=asr_audio_target,
+                    segments=all_segments,
+                    words=all_words,
+                )
+                if recovered is not None:
+                    recovered_segments, separation = recovered
+                    logger.info(
+                        "Recovered rapid two-speaker exchange from ASR phrase embeddings "
+                        "(phrases=%d, separation=%.3f).",
+                        len(asr_phrase_segments), separation,
+                    )
+                    return recovered_segments, None, "phrase_embeddings"
+                return resplit, None, "pyannote"
            except Exception as e:
                logger.exception("Diarization failed")
                # Inline ASR turns beat the silence-gap heuristic as a crash
@@ -1522,7 +1823,9 @@ async def dub_transcribe_stream(
        except Exception as e:
            logger.warning("speaker_clone extraction skipped: %s", e)

-        job["source_lang"] = ((detected_lang or "en").split("_")[0][:2] or "en").lower()
+        job["source_lang"] = job.get("source_lang_override") or _detected_source_lang(
+            detected_lang
+        )
        job["full_transcript"] = " ".join(s.get("text", "") for s in final_segs)
        _save_job(job_id, job)

@@ -1719,7 +2022,9 @@ async def dub_transcribe(job_id: str, num_speakers: Optional[int] = None):
            except Exception as e:
                logger.warning("Failed to unload ASR backend: %s", e)

-        job["source_lang"] = (detected_lang or "en").split("_")[0][:2].lower()
+        job["source_lang"] = job.get("source_lang_override") or _detected_source_lang(
+            detected_lang
+        )

        scene_cuts = job.get("scene_cuts") or []
        segments = segment_transcript(result, duration=job.get("duration", 0.0), scene_cuts=scene_cuts)
@@ -1775,7 +2080,8 @@ async def dub_transcribe(job_id: str, num_speakers: Optional[int] = None):
            # Bound the whole-file transcribe (#730): a wedged whisperx/CTranslate2
            # call would otherwise hold its GPU-pool worker forever and starve
            # every other request into a "can't reach backend". run_transcribe_guarded
-            # also resets the pool on timeout so capacity is restored.
+            # leaves an unkillable native worker accounted for on timeout so a
+            # retry cannot overlap it (#1669).
            segments_result = await run_transcribe_guarded(_gpu_pool, _transcribe, what="Dub")
        except asyncio.CancelledError:
            job["aborted"] = True
@@ -23,6 +23,7 @@ from services.ffmpeg_utils import (
    find_ffmpeg,
    run_ffmpeg,
 )
+from services.karaoke_ass import build_ass, scale_words
 from services.video_retime import (
    DRIFT_TOLERANCE_S,
    RetimeError,
@@ -403,6 +404,27 @@ def _write_burn_srt(job: dict, exports_dir: str, stamp: str, dual: bool,
    return sub_path


+def _write_burn_ass(job: dict, exports_dir: str, stamp: str,
+                    fitted_segments: "list[dict] | None" = None,
+                    lang: "str | None" = None) -> str | None:
+    """Karaoke variant of ``_write_burn_srt``: word-timed ASS via ``build_ass``.
+
+    Same text/timing resolution (``_segments_for_lang`` + fitted-cue overlay,
+    which also scales per-word times onto the fitted timeline); the basename
+    is plain ASCII under exports_dir so it is ffmpeg-filter-safe. Returns
+    None if there are no segments to render.
+    """
+    segments = _segments_for_lang(job, lang)
+    if not segments:
+        return None
+    if fitted_segments:
+        segments = _apply_fitted_times(segments, fitted_segments)
+    sub_path = os.path.join(exports_dir, f"burn_subs_{stamp}.ass")
+    with open(sub_path, "w", encoding="utf-8") as f:
+        f.write(build_ass(segments))
+    return sub_path
+
+
 def _ffmpeg_filter_escape(path: str) -> str:
    """Escape a path for use inside an ffmpeg filter value (subtitles=...).

@@ -515,6 +537,20 @@ def _apply_fitted_times(segments: list[dict], fitted: list[dict]) -> list[dict]:
        patched = dict(seg)
        patched["start"] = float(cue["start"])
        patched["end"] = float(cue["end"])
+        # Karaoke burn-in: persisted word times live on the original timeline;
+        # scale them linearly onto the fitted cue span so the highlight sweep
+        # follows the retimed audio. Degenerate spans drop the words — export
+        # then falls back to an even split over the fitted span. Inert for
+        # SRT/VTT, which never read ``words``.
+        if isinstance(seg.get("words"), list) and seg.get("words"):
+            scaled = scale_words(
+                seg["words"], seg.get("start", 0.0), seg.get("end", 0.0),
+                patched["start"], patched["end"],
+            )
+            if scaled is not None:
+                patched["words"] = scaled
+            else:
+                patched.pop("words", None)
        out.append(patched)
    return out

@@ -572,11 +608,12 @@ def _build_audio_export_cmd(
 async def dub_download(
    job_id: str,
    preserve_bg: bool = Query(True, description="Mix background noise into dubbed tracks"),
-    default_track: str = Query("original"),
+    default_track: str = Query("", description="Default audio track; omitted selects the first dubbed track"),
    include_tracks: str = Query("", description="Comma-separated list of tracks to include (e.g. 'original,de,es'). Empty = include all."),
    save_authorization: str = Header("", alias="X-VoiceStudio-Path-Authorization"),
    burn_subs: bool = Query(False, description="Burn subtitles into the video stream (forces re-encode). Uses dual-subtitle layout when dual=1."),
    dual: bool = Query(False, description="When burn_subs=1, render translated on top of italicised original."),
+    karaoke: bool = Query(False, description="When burn_subs=1, burn a word-timed karaoke highlight (ASS) instead of line subtitles. Ignored when dual=1 (dual karaoke is unsupported — the line burn renders instead)."),
    out_format: str = Query("m4a", description="Audio-only jobs (#119): output container — wav, m4a, mp3, or flac. Ignored for video jobs."),
 ):
    # Strict allowlist on the path param BEFORE it reaches any filesystem
@@ -607,6 +644,18 @@ async def dub_download(
        for key, value in filtered_tracks.items()
    }

+    # A dub export should play the dub without requiring player-specific track
+    # selection. Keep ``original`` as an explicit opt-in, but when callers omit
+    # the preference choose the first generated dub consistently (#1575).
+    if (
+        filtered_tracks
+        and not (default_track == "original" and include_original)
+        and default_track not in filtered_tracks
+    ):
+        default_track = next(iter(filtered_tracks))
+    elif not filtered_tracks and include_original:
+        default_track = "original"
+
    if not filtered_tracks and not include_original:
        raise HTTPException(status_code=400, detail="No tracks selected for export")

@@ -631,12 +680,17 @@ async def dub_download(
        fmt = (out_format or "m4a").lower()
        if fmt not in _AUDIO_FORMAT_CODECS:
            fmt = "m4a"
-        # lang_code is already constrained to an existing track key, but
-        # allowlist-sanitize it before it reaches the output path so a path
-        # component can never carry separators/traversal (same pattern as
-        # safe_name below).
-        safe_lang = "".join(c for c in lang_code if c.isalnum() or c in "-_") or "track"
-        out_path = os.path.join(exports_dir, f"dubbed_audio_{safe_lang}_{stamp}.{fmt}")
+        # Keep route/job data out of the filesystem and logging trust boundary.
+        # The selected format reaches the path only through literal branches.
+        if fmt == "wav":
+            output_name = f"dubbed_audio_{stamp}.wav"
+        elif fmt == "mp3":
+            output_name = f"dubbed_audio_{stamp}.mp3"
+        elif fmt == "flac":
+            output_name = f"dubbed_audio_{stamp}.flac"
+        else:
+            output_name = f"dubbed_audio_{stamp}.m4a"
+        out_path = os.path.join(exports_dir, output_name)
        bg = _optional_dub_artifact(job.get("no_vocals_path"), job_id) if preserve_bg else None
        cmd = _build_audio_export_cmd(ffmpeg, track_info["path"], bg, out_path, fmt)
        try:
@@ -654,15 +708,28 @@ async def dub_download(
            )
        if not os.path.exists(out_path) or os.path.getsize(out_path) == 0:
            raise HTTPException(status_code=500, detail="ffmpeg audio export produced no output file")
-        logger.info("Dub audio export wrote %s (%d bytes)", out_path, os.path.getsize(out_path))
+        logger.info("Dub audio export completed (%d bytes)", os.path.getsize(out_path))

-        base_name = os.path.splitext(job.get("filename", "output"))[0]
-        safe_name = "".join(c for c in base_name if c.isalnum() or c in "-_ ").strip() or "output"
-        dl_name = f"dubbed_{safe_name}_{safe_lang}_{stamp}.{fmt}"
+        # Response metadata must not become a second path-like sink for job or
+        # request data. Keep the user-selected format through explicit literal
+        # branches; source names and language keys never enter the label.
+        if fmt == "wav":
+            dl_name = f"dubbed_audio_{stamp}.wav"
+        elif fmt == "mp3":
+            dl_name = f"dubbed_audio_{stamp}.mp3"
+        elif fmt == "flac":
+            dl_name = f"dubbed_audio_{stamp}.flac"
+        else:
+            dl_name = f"dubbed_audio_{stamp}.m4a"
        media_type = _MEDIA_TYPES.get(f".{fmt}", "audio/mp4")
        save_path = _consume_native_save(save_authorization)
        if save_path:
-            return _native_save(out_path, save_path, dl_name, media_type=media_type)
+            # Keep the request-derived download label out of the filesystem
+            # trust boundary. It is response metadata, not a source or
+            # destination path (CodeQL, #1575).
+            result = _native_save(out_path, save_path, "dubbed_audio", media_type=media_type)
+            result["display_name"] = dl_name
+            return result
        return FileResponse(
            out_path, media_type=media_type,
            headers={"Content-Disposition": content_disposition(dl_name)},
@@ -699,7 +766,18 @@ async def dub_download(
    fitted_segments = _fitted_segments_for(job, default_track) if default_track and default_track != "original" else None
    # Burn the DEFAULT track's text (P1.2) — it's the audio the viewer hears.
    _burn_lang = default_track if default_track and default_track != "original" else None
-    sub_path = _write_burn_srt(job, exports_dir, stamp, dual, fitted_segments=fitted_segments, lang=_burn_lang) if burn_subs else None
+    # Karaoke (word-highlight) burn writes an ASS instead of the line SRT.
+    # Dual layout keeps the line burn — dual karaoke is out of scope, matching
+    # the disabled control in the Export drawer. The default (karaoke off)
+    # takes exactly the legacy SRT path.
+    sub_path = None
+    sub_is_ass = False
+    if burn_subs:
+        if karaoke and not dual:
+            sub_path = _write_burn_ass(job, exports_dir, stamp, fitted_segments=fitted_segments, lang=_burn_lang)
+            sub_is_ass = sub_path is not None
+        if sub_path is None:
+            sub_path = _write_burn_srt(job, exports_dir, stamp, dual, fitted_segments=fitted_segments, lang=_burn_lang)

    # ── Smart Fit video retime (two-tier) ─────────────────────────────────
    # Tier 1 (≤48 chunks): single filter_complex graph inlined into the mux
@@ -799,14 +877,16 @@ async def dub_download(
        esc = _ffmpeg_filter_escape(sub_path)
        # Burn AFTER any retime so cues (already on the fitted timeline for
        # Smart Fit) land on the retimed video. Without retime this reduces
-        # to the legacy `[0:v]subtitles=…[vsub]` graph.
+        # to the legacy `[0:v]subtitles=…[vsub]` graph. Karaoke burns the
+        # word-timed ASS through the ass filter at the same graph position.
        if video_map.startswith("["):
            sub_src = video_map
        elif retimed_idx is not None:
            sub_src = f"[{retimed_idx}:v]"
        else:
            sub_src = "[0:v]"
-        filter_parts.append(f"{sub_src}subtitles='{esc}'[vsub]")
+        _sub_filter = "ass" if sub_is_ass else "subtitles"
+        filter_parts.append(f"{sub_src}{_sub_filter}='{esc}'[vsub]")
        video_map = "[vsub]"
    if stretch_entry:
        orig_dur = float(stretch_entry.get("orig_duration") or job.get("duration") or 0.0)
@@ -887,7 +967,10 @@ async def dub_download(
    if default_track == "original" and include_original:
        cmd += ["-disposition:a:0", "default"]
    else:
-        target_idx = 0
+        # A stale/missing language preference still means "play a dub", not
+        # "silently fall back to the source". The first processed dub is the
+        # deterministic fallback; ``original`` above remains explicit.
+        target_idx = tracks_to_process[0]["stream_idx"] if tracks_to_process else 0
        for t in tracks_to_process:
            if t['lang_code'] == default_track:
                target_idx = t["stream_idx"]
@@ -1566,7 +1649,10 @@ async def dub_download_audio(
        return _native_save(wav_path, save_path, dl_name, media_type="audio/wav")
    return FileResponse(
        wav_path, media_type="audio/wav",
-        headers={"Content-Disposition": content_disposition(dl_name)},
+        headers={
+            "Cache-Control": "no-store",
+            "Content-Disposition": content_disposition(dl_name),
+        },
    )


@@ -1708,6 +1794,50 @@ async def dub_export_vtt(
    )


+@router.get("/dub/ass/{job_id}")
+@router.get("/dub/ass/{job_id}/{filename}")
+async def dub_export_ass(
+    job_id: str,
+    lang: str = Query(None, description="Track language code. Same text/timing resolution as /dub/srt, rendered as a karaoke (word-highlight) ASS sidecar."),
+):
+    """Karaoke ASS sidecar — the same script the karaoke burn-in renders.
+
+    Raw text body like /dub/srt and /dub/vtt (the Tauri side writes the file
+    itself; no ?save_path= variant — see the comment above /dub/srt).
+    """
+    _job_dir_or_400(job_id)
+    lang = _safe_lang_or_400(lang)
+    job = _get_job(job_id)
+    if not job:
+        raise HTTPException(status_code=404, detail="Job not found")
+
+    segments = _segments_for_lang(job, lang)
+    if not segments:
+        raise HTTPException(status_code=400, detail="No transcript segments available")
+
+    # Same strategy-aware cue timing as /dub/srt. The fitted overlay also
+    # scales word times; the stretch_video cue path has no per-word record,
+    # so words are dropped and build_ass even-splits over the new spans.
+    fitted = _fitted_segments_for(job, lang)
+    if fitted:
+        segments = _apply_fitted_times(segments, fitted)
+    else:
+        cues = _fitted_cue_times(job, lang)
+        if cues:
+            segments = [
+                {**{k: v for k, v in seg.items() if k != "words"}, "start": s, "end": e}
+                for seg, (s, e) in zip(segments, cues)
+            ]
+
+    base_name = os.path.splitext(job.get('filename', 'video'))[0]
+    dl_name = f"subtitles_{base_name}_karaoke.ass"
+    return Response(
+        content=build_ass(segments),
+        media_type="text/plain",
+        headers={"Content-Disposition": content_disposition(dl_name)},
+    )
+
+
@router.get("/dub/export-segments/{job_id}")
 async def dub_export_segments_zip(job_id: str, lang: str = Query(None)):
    import zipfile
@@ -1,6 +1,7 @@
 import os
 import re
 import json
+import struct
 import logging
 import time
 import asyncio
@@ -80,6 +81,62 @@ def _prepare_oom_retry(error: Exception, *, execution_target: str) -> bool:
    return True


+def _cached_payload_intact(path: str, info) -> bool:
+    """Cheap truth check on a cached WAV whose header we are about to trust.
+
+    The natural-rate fast path hands the mixer a PATH instead of decoded
+    audio, so a cache whose header reads fine but whose payload is truncated
+    would only fail later, during assembly — after the timing plan (Smart Fit,
+    video stretch) had been computed from the header's frame count. The plan
+    would then describe audio that no longer exists and the segment would be
+    replaced by slot-length silence, leaving the persisted video plan and the
+    rendered track disagreeing.
+
+    Comparing the declared frame count against the physical ``data`` chunk
+    catches that without decoding: a truncated file cannot hold the samples
+    its header claims. Anything failing here falls through to the decoding path, which
+    already degrades to a warning plus silence. Formats with no fixed
+    bits-per-sample (compressed caches) are left to the decoder as before.
+    """
+    try:
+        bits = int(getattr(info, "bits_per_sample", 0) or 0)
+        frames = int(getattr(info, "num_frames", 0) or 0)
+        channels = int(getattr(info, "num_channels", 0) or 0)
+        if bits <= 0 or frames <= 0 or channels <= 0:
+            # Undecidable metadata fails CLOSED (review on #1620): these caches
+            # are PCM WAVs this module wrote itself, so anything else is
+            # unexpected — and the decode path this falls through to handles
+            # every format the fast path would have.
+            return False
+        payload = frames * channels * (bits // 8)
+        if payload <= 0:
+            return False
+
+        # A WAV may carry JUNK/LIST metadata before data, so its header is not
+        # necessarily 44 bytes. Locate the data chunk instead of counting
+        # metadata as audio; otherwise an extended header can mask truncation.
+        file_size = os.path.getsize(path)
+        with open(path, "rb") as wav:
+            header = wav.read(12)
+            if len(header) != 12 or header[:4] != b"RIFF" or header[8:12] != b"WAVE":
+                return False
+            offset = 12
+            while offset + 8 <= file_size:
+                wav.seek(offset)
+                chunk_id = wav.read(4)
+                chunk_size_raw = wav.read(4)
+                if len(chunk_id) != 4 or len(chunk_size_raw) != 4:
+                    return False
+                chunk_size = struct.unpack("<I", chunk_size_raw)[0]
+                data_offset = offset + 8
+                if chunk_id == b"data":
+                    return chunk_size >= payload and file_size >= data_offset + payload
+                offset = data_offset + chunk_size + (chunk_size % 2)
+        return False
+    except Exception:  # noqa: BLE001 — an unstattable cache is the decoder's problem
+        return False
+
+
 def _underrun_min_rate() -> float:
    """Floor for the underrun fill (audio slowed toward its slot, never below
    this rate). Default 0.85 stays natural-sounding; OMNIVOICE_UNDERRUN_MIN_RATE=1.0
@@ -446,6 +503,18 @@ async def dub_generate(job_id: str, req: DubRequest):
        backend = await resolve_generation_backend(require_cloning=True)
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        from core.failure import is_gpu_oom
+
+        if not is_gpu_oom(e):
+            raise
+        from core.public_errors import public_exception_response
+
+        payload = public_exception_response(
+            e,
+            fallback="The TTS model could not be loaded.",
+        )
+        raise HTTPException(status_code=503, detail=payload["detail"]) from e

    async def _stream(task_id):
        total = len(req.segments)
@@ -593,11 +662,11 @@ async def dub_generate(job_id: str, req: DubRequest):
        voice_match = (req.voice_match or "per_line").lower()
        _consistent_ref_memo: dict = {}
        remote_audio: dict[int, str] = {}
-        # Strategy-transition guard: smart_fit re-mixes the *natural-rate*
-        # per-segment WAVs from disk. If the previous run used strict_slot,
-        # the on-disk WAVs are slot-squeezed ("slotted") — reusing them would
-        # double-compress. Force one full regen; afterwards seg_wav_kind is
-        # "natural" and partial regen / fit-only re-mix (regen_only=[]) work.
+        # Strategy-transition guard: concise, stretch_video and smart_fit all
+        # re-mix *natural-rate* per-segment WAVs. If the previous run used
+        # strict_slot, the on-disk WAVs are slot-squeezed ("slotted") — the
+        # missing tails cannot be recovered by a re-mix. Force one full regen;
+        # afterwards partial regen / fit-only re-mix (regen_only=[]) is safe.
        # Jobs predating this field have unknown kind → also regen once.
        # P1.3: the kind is per-track now (each language renders under its own
        # strategy); the flat job["seg_wav_kind"] is only consulted for jobs
@@ -608,7 +677,7 @@ async def dub_generate(job_id: str, req: DubRequest):
        _wav_kind = (
            _kind_map.get(lang_code) if isinstance(_kind_map, dict) else job.get("seg_wav_kind")
        )
-        if strategy == "smart_fit" and regen_only is not None and _wav_kind != "natural":
+        if strategy != "strict_slot" and regen_only is not None and _wav_kind != "natural":
            regen_only = None
        # Manifest: stable segment id per current index. Per-segment WAVs are
        # named by stable id (dub_seg_path) so regen reuses the right audio after
@@ -759,15 +828,38 @@ async def dub_generate(job_id: str, req: DubRequest):
                if os.path.exists(seg_wav_path):
                    try:
                        _t_cache_0 = time.perf_counter()
+                        # Natural-rate caches are already the exact assembly
+                        # input.  Keep the durable path in the manifest so the
+                        # mixer decodes it once; the old path decoded here,
+                        # wrote an identical mix_<id> scratch WAV, then decoded
+                        # that copy again.  Header-only inspection preserves
+                        # the resample fallback for caches made by an engine
+                        # with a different sample rate.
+                        if strategy != "strict_slot":
+                            try:
+                                cached_info = torchaudio.info(seg_wav_path)
+                            except Exception:
+                                cached_info = None
+                            if (
+                                cached_info is not None
+                                and int(cached_info.sample_rate) == int(backend.sample_rate)
+                                and _cached_payload_intact(seg_wav_path, cached_info)
+                            ):
+                                all_segment_wavs.append(
+                                    (seg.start, seg.end, seg_wav_path, backend.sample_rate)
+                                )
+                                sync_scores.append(getattr(seg, 'sync_ratio', None) or 1.0)
+                                _t_cache += time.perf_counter() - _t_cache_0
+                                continue
+
                        cached_wav, cached_sr = torchaudio.load(seg_wav_path)
                        if cached_sr != backend.sample_rate:
                            import torchaudio.functional as AF
                            cached_wav = AF.resample(cached_wav, cached_sr, backend.sample_rate)
-                        # Pad/trim to slot — except smart_fit, whose mix
-                        # loop needs the natural-rate length to compute the
-                        # audio/video split (the seg_wav_kind guard above
-                        # guarantees these cached WAVs are natural-rate).
-                        if strategy != "smart_fit":
+                        # strict_slot persists slot-sized buffers.  Every other
+                        # strategy consumes natural-rate audio and lets the mix
+                        # loop fit it to the current timeline.
+                        if strategy == "strict_slot":
                            target_samples = int(seg_duration * backend.sample_rate)
                            current_samples = cached_wav.shape[-1]
                            if target_samples > current_samples:
@@ -1091,7 +1183,7 @@ async def dub_generate(job_id: str, req: DubRequest):
                            _num_step, req.guidance_scale, seg_speed, seg_profile, seg_effect_preset,
                        ),
                        what="Dub generate",
-                        timeout=generate_timeout_s(seg.text),
+                        timeout=generate_timeout_s(seg.text, engine=backend),
                    )
                _t_tts += time.perf_counter() - _t_tts_0

@@ -1164,12 +1256,15 @@ async def dub_generate(job_id: str, req: DubRequest):
                        if rvc_sr == backend.sample_rate:
                            audio_tensor = rvc_wav

-                            target_samples = int(seg_duration * backend.sample_rate)
-                            current_samples = audio_tensor.shape[-1]
-                            if target_samples > current_samples:
-                                audio_tensor = torch.nn.functional.pad(audio_tensor, (0, target_samples - current_samples))
-                            elif current_samples > target_samples:
-                                audio_tensor = audio_tensor[..., :target_samples]
+                            if strategy == "strict_slot":
+                                target_samples = int(seg_duration * backend.sample_rate)
+                                current_samples = audio_tensor.shape[-1]
+                                if target_samples > current_samples:
+                                    audio_tensor = torch.nn.functional.pad(
+                                        audio_tensor, (0, target_samples - current_samples)
+                                    )
+                                elif current_samples > target_samples:
+                                    audio_tensor = audio_tensor[..., :target_samples]
                    except Exception as e:
                        yield f"data: {json.dumps({'type': 'warning', 'segment': i, 'message': f'RVC skipped: {str(e)[:120]}'})}\n\n"

@@ -1208,7 +1303,15 @@ async def dub_generate(job_id: str, req: DubRequest):
                        pass
                    _release_audio_tensors()
            except Exception as e:
-                yield f"data: {json.dumps({'type': 'error', 'segment': i, 'error': str(e)})}\n\n"
+                # A task-stream error bypasses the global exception handler.
+                # Never publish engine exception text here: allocator errors
+                # carry process tables and arbitrary failures can carry paths,
+                # tokens, or source text. The shared helper enriches recognized
+                # classes using VoiceStudio-owned constants only.
+                from core.public_errors import stream_generation_failure
+
+                error_detail = stream_generation_failure(e)["detail"]
+                yield f"data: {json.dumps({'type': 'error', 'segment': i, 'error': error_detail})}\n\n"
                sr = backend.sample_rate
                all_segment_wavs.append(_store_mix_wav(seg.start, seg.end, torch.zeros(1, max(0, int(seg_duration * sr))), sr, f"mix_{seg_id}"))
                sync_scores.append(1.0)
@@ -1356,7 +1459,21 @@ async def dub_generate(job_id: str, req: DubRequest):
                seg_gain = getattr(seg_ref, "gain", None) if seg_ref is not None else None
                seg_gain = seg_gain if seg_gain is not None else 1.0
                seg_gain = max(0.0, min(2.0, seg_gain))
-                wav = _load_entry_wav((start, end, wav_path, sr), sr)
+                try:
+                    wav = _load_entry_wav((start, end, wav_path, sr), sr)
+                except Exception as e:
+                    # A WAV header can be readable while its payload is
+                    # truncated.  Direct cache reuse deliberately defers the
+                    # decode to assembly, so preserve the old recovery contract
+                    # here: warn and fill this slot with silence instead of
+                    # aborting the entire dub.
+                    warning = {
+                        "type": "warning",
+                        "segment": i,
+                        "message": f"cached seg lost, padding silence: {str(e)[:120]}",
+                    }
+                    yield f"data: {json.dumps(warning)}\n\n"
+                    wav = torch.zeros(1, max(0, int((end - start) * sr)))
                adjusted = wav * seg_gain
                if adjusted.ndim == 2 and adjusted.shape[0] > 1:
                    adjusted = adjusted.mean(dim=0, keepdim=True)
@@ -1798,7 +1915,7 @@ async def preview_segment(job_id: str, req: SegmentPreviewRequest):
    from services.model_manager import generate_timeout_s
    audio_tensor = await run_on_gpu_pool_guarded(
        _gen, what="Dub preview generate",
-        timeout=generate_timeout_s(req.text),
+        timeout=generate_timeout_s(req.text, engine=backend),
    )

    sr = backend.sample_rate
@@ -75,6 +75,21 @@ def list_tts_backends():
    return _family_payload("tts", tts_backend)


+@router.get(
+    "/engines/{engine_id}/disk-usage",
+    dependencies=[Depends(require_admin_action)],
+)
+def engine_disk_usage(engine_id: str):
+    """Measure owned engine bytes only when a catalogue row is opened."""
+    try:
+        tts_backend.get_backend_class(engine_id)
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Unknown TTS engine")
+    from services.engine_disk_usage import disk_usage_for
+
+    return disk_usage_for(engine_id)
+
+
@router.get("/engines/asr")
 def list_asr_backends():
    return _family_payload("asr", asr_backend)
@@ -8,6 +8,7 @@ import asyncio
 import tempfile
 import contextlib
 import logging
+import threading
 import traceback
 from typing import Optional
 from fastapi import APIRouter, File, Form, UploadFile, HTTPException
@@ -32,6 +33,83 @@ router = APIRouter()
 logger = logging.getLogger("omnivoice.generate")


+class _TempReferenceLease:
+    """Delete a request-owned reference once every abandoned reader drains."""
+
+    def __init__(self, path: str):
+        self.path = path
+        self._lock = threading.Lock()
+        self._active = 0
+        self._request_done = False
+        self._deleted = False
+
+    def acquire(self):
+        with self._lock:
+            if self._request_done:
+                raise RuntimeError("reference lease acquired after request cleanup")
+            self._active += 1
+        once_lock = threading.Lock()
+        released = False
+
+        def release() -> None:
+            nonlocal released
+            with once_lock:
+                if released:
+                    return
+                released = True
+            self._release()
+
+        return release
+
+    def _release(self) -> None:
+        delete = False
+        with self._lock:
+            self._active -= 1
+            if self._active < 0:
+                raise RuntimeError("reference lease released too many times")
+            if self._request_done and self._active == 0 and not self._deleted:
+                self._deleted = True
+                delete = True
+        if delete:
+            with contextlib.suppress(OSError):
+                os.remove(self.path)
+
+    def finish_request(self) -> None:
+        delete = False
+        with self._lock:
+            self._request_done = True
+            if self._active == 0 and not self._deleted:
+                self._deleted = True
+                delete = True
+        if delete:
+            with contextlib.suppress(OSError):
+                os.remove(self.path)
+
+
+async def _run_with_reference_lease(lease, factory):
+    """Hold an ad-hoc reference through one local GPU-pool dispatch."""
+    if lease is None:
+        return await factory(None)
+    release = lease.acquire()
+    abandoned = False
+    try:
+        return await factory(release)
+    except GpuPoolBusyError:
+        # Busy means no job started; release now. The callback may already have
+        # done so, and the lease token is deliberately idempotent.
+        release()
+        abandoned = True
+        raise
+    except (asyncio.CancelledError, GpuJobTimeoutError):
+        # The guard owns release now: immediately for a queued cancellation,
+        # or from the worker finalizer after an in-flight job drains.
+        abandoned = True
+        raise
+    finally:
+        if not abandoned:
+            release()
+
+
 def _profile_instruct(row):
    """Validator-safe instruct for a stored profile row.

@@ -47,6 +125,96 @@ def _profile_instruct(row):
    return heal_design_instruct(row["instruct"], vd)


+def _resolve_profile_conditioning(row, *, ref_text=None, instruct=None,
+                                  seed=None, language=None):
+    """Resolve a ``voice_profiles`` row into generation conditioning.
+
+    Extracted verbatim from /generate's inline profile-resolution block so
+    other synthesis routes (POST /convert) share the exact same semantics —
+    lock wins, ``kind`` is authoritative (0005), legacy pre-0004 rows fall
+    back to the is_locked/instruct inference, and #533's language fill.
+
+    Request-supplied values (``ref_text``/``instruct``/``seed``/``language``)
+    always win over the stored row; only gaps are filled. Returns a dict with
+    ``ref_audio_path`` / ``ref_text`` / ``instruct`` / ``seed`` / ``language``
+    / ``kind`` plus ``persist_ref_text`` — True when the caller should cache
+    an auto-transcribed reference transcript back onto the row (#1032).
+    """
+    out = {
+        "ref_audio_path": None, "ref_text": ref_text, "instruct": instruct,
+        "seed": seed, "language": language, "kind": None,
+        "persist_ref_text": False,
+    }
+    # `kind` is authoritative (0005): 'design' profiles condition on their
+    # deterministic rendered sample + instruct; 'clone' on the user's
+    # reference. Lock always wins (it pins a specific take). Rows from
+    # pre-0004 DBs mid-upgrade may lack the column → fall back to the legacy
+    # is_locked/instruct inference.
+    try:
+        profile_kind = row["kind"] or "clone"
+    except (KeyError, IndexError):
+        profile_kind = "design" if (
+            row["instruct"] and not row["is_locked"] and not row["ref_audio_path"]
+        ) else "clone"
+    out["kind"] = profile_kind
+    if row["is_locked"] and row["locked_audio_path"]:
+        out["ref_audio_path"] = os.path.join(VOICES_DIR, row["locked_audio_path"])
+        if not out["ref_text"]:
+            out["ref_text"] = row["ref_text"]
+        if not out["instruct"]:
+            out["instruct"] = _profile_instruct(row)
+        if out["seed"] is None and row["seed"] is not None:
+            out["seed"] = row["seed"]
+    elif profile_kind == "design":
+        # Rendered sample (if present) carries the voice identity; instruct
+        # alone is the fallback for legacy archetype rows.
+        out["ref_audio_path"] = (
+            os.path.join(VOICES_DIR, row["ref_audio_path"]) if row["ref_audio_path"] else None
+        )
+        if out["ref_audio_path"] and not out["ref_text"] and row["ref_text"]:
+            out["ref_text"] = row["ref_text"]
+        if not out["instruct"]:
+            out["instruct"] = _profile_instruct(row)
+        if out["seed"] is None and row["seed"] is not None:
+            out["seed"] = row["seed"]
+    elif row["instruct"] and not row["is_locked"] and not row["ref_audio_path"]:
+        # Legacy design-shaped row (pre-0004 archetype materialization failure
+        # path): instruct-only conditioning.
+        if not out["instruct"]:
+            out["instruct"] = _profile_instruct(row)
+        if out["seed"] is None and row["seed"] is not None:
+            out["seed"] = row["seed"]
+    else:
+        out["ref_audio_path"] = (
+            os.path.join(VOICES_DIR, row["ref_audio_path"]) if row["ref_audio_path"] else None
+        )
+        if not out["ref_text"] and row["ref_text"]:
+            out["ref_text"] = row["ref_text"]
+        elif out["ref_audio_path"] and not out["ref_text"]:
+            # Empty stored transcript → the caller's auto-transcribe will run;
+            # cache its result onto the profile so it runs ONCE, not on every
+            # generate (#1032 perf regression).
+            out["persist_ref_text"] = True
+        if not out["instruct"] and row["instruct"]:
+            out["instruct"] = row["instruct"]
+        if out["seed"] is None and row["seed"] is not None:
+            out["seed"] = row["seed"]
+    if out["language"] == "Auto":
+        out["language"] = None
+    # #533: a profile's stored language must drive generation when the request
+    # didn't pin one. An EXPLICIT non-Auto request language still wins; we
+    # only fill the gap. `row` is a sqlite3.Row, so guard the column lookup
+    # for pre-language DBs mid-upgrade.
+    if out["language"] is None:
+        try:
+            prof_lang = row["language"]
+        except (KeyError, IndexError):
+            prof_lang = None
+        if prof_lang and prof_lang != "Auto":
+            out["language"] = prof_lang
+    return out
+
+
 def _note_generate_progress() -> None:
    """Tell the pool guard this render just finished a unit of work (#1391).

@@ -381,6 +549,31 @@ def _is_timeout_failure(e) -> bool:
    return False


+def _is_media_process_launch_failure(exc: BaseException) -> bool:
+    """Identify an ffmpeg/ffprobe launch ENOENT without guessing from a file name."""
+    if not isinstance(exc, FileNotFoundError):
+        return False
+
+    # A regular missing reference/model file may itself be named "ffmpeg".
+    # Require the innermost raise site to be Python's process launcher so that
+    # basename collisions keep the normal missing-file diagnosis (#1677).
+    traceback_cursor = exc.__traceback__
+    if traceback_cursor is None:
+        return False
+    while traceback_cursor.tb_next is not None:
+        traceback_cursor = traceback_cursor.tb_next
+    origin_module = traceback_cursor.tb_frame.f_globals.get("__name__", "")
+    if origin_module != "subprocess" and not origin_module.startswith("asyncio."):
+        return False
+
+    filename = getattr(exc, "filename", None)
+    if not filename:
+        return "[winerror 2]" in str(exc).lower()
+    return os.path.basename(str(filename)).lower() in {
+        "ffmpeg", "ffmpeg.exe", "ffprobe", "ffprobe.exe",
+    }
+
+
 def _oom_friendly_reraise(e):
    """Best-effort cache flush + the user-facing OOM hint shared by both
    inference paths."""
@@ -405,6 +598,21 @@ def _oom_friendly_reraise(e):
    # that lost its +x bit) is NOT an OOM — don't send the user to the Flush
    # button; tell them what's actually wrong.
    es = str(e)
+    # #1677: Windows CreateProcess reports a missing executable as a bare
+    # ``FileNotFoundError: [WinError 2] ...`` with no filename, while POSIX
+    # includes the missing ffmpeg/ffprobe name. The bundled-media downloader
+    # now republishes PATH as soon as it finishes, but a failed/blocked
+    # download still needs an actionable recovery rather than the unknown-
+    # error dead end. Keep missing reference/model files on their own path.
+    for _exc in _exception_chain(e):
+        if _is_media_process_launch_failure(_exc):
+            raise RuntimeError(
+                "A required media program couldn't be launched. Open "
+                "Settings → Audio tools and use "
+                "Download/Repair for the media engine, then retry. If Audio "
+                "tools is already ready, repair the selected TTS engine and "
+                f"restart VoiceStudio. Underlying error: {_safe_exc_text(_exc)}"
+            ) from e
    if isinstance(e, PermissionError) or "Permission denied" in es or "Errno 13" in es:
        raise RuntimeError(
            f"A required engine binary couldn't be executed (permission denied). "
@@ -596,16 +804,23 @@ def _oom_friendly_reraise(e):
    ) from e


-def _generate_timeout_s(text: str) -> float:
+def _generate_timeout_s(text: str, *, execution_device=None, min_vram_gb=0.0) -> float:
    """Wall-clock budget for one generate, scaled to the request.

    Thin alias for the canonical helper, which moved to
    ``services.model_manager.generate_timeout_s`` (#1190) so /v1/audio/speech,
    batch, dub and archetype previews share it instead of each re-deriving (or,
    as they did, silently keeping the flat 300s).
+
+    ``min_vram_gb`` is the engine's declared VRAM floor. A GPU below it pages to
+    system RAM and renders slower than this machine's CPU, so it must not be
+    budgeted as fast hardware (#1804) — the same figure the dispatch already
+    hands the guard so a timeout message can name the card (#1226/#1222).
    """
    from services.model_manager import generate_timeout_s
-    return generate_timeout_s(text)
+    return generate_timeout_s(
+        text, execution_device=execution_device, min_vram_gb=min_vram_gb,
+    )


 def _run_inference(
@@ -695,15 +910,17 @@ def _run_backend_inference(
    backend, text, language, ref_audio_path, ref_text, instruct, duration,
    num_step, guidance_scale, speed, denoise, postprocess_output,
    used_seed, effect_preset="broadcast",
-    max_chunk_chars=None, crossfade_ms=None, *, dropped_sink=None,
+    max_chunk_chars=None, crossfade_ms=None, *, t_shift=None,
+    layer_penalty_factor=None, position_temperature=None,
+    class_temperature=None, dropped_sink=None,
 ):
    """Engine-aware twin of :func:`_run_inference` (issue #312).

    Runs the request through a pluggable ``TTSBackend`` adapter instead of the
-    VoiceStudio model directly. The adapter protocol is narrower than the
-    VoiceStudio-native surface — engine-specific extras (``t_shift``,
-    ``layer_penalty_factor``, …) only exist on the native path, which is why
-    VoiceStudio itself still goes through ``_run_inference``.
+    VoiceStudio model directly. A crash-isolated OmniVoice proxy advertises
+    ``supports_native_omnivoice_controls`` and receives the same advanced
+    controls and per-call seed as the native path; other adapters keep the
+    narrower protocol unchanged.
    """
    import torch
    try:
@@ -718,6 +935,18 @@ def _run_backend_inference(
            instruct=instruct, num_step=num_step, guidance_scale=guidance_scale,
            speed=speed, denoise=denoise, postprocess_output=postprocess_output,
        )
+        native_proxy = bool(
+            getattr(backend, "supports_native_omnivoice_controls", False)
+        )
+        if native_proxy:
+            gen_kwargs.update({
+                key: value for key, value in {
+                    "t_shift": t_shift,
+                    "layer_penalty_factor": layer_penalty_factor,
+                    "position_temperature": position_temperature,
+                    "class_temperature": class_temperature,
+                }.items() if value is not None
+            })
        sr = backend.sample_rate

        # Inline [pause Nms] markers (issue #276) work for every engine — the
@@ -727,10 +956,17 @@ def _run_backend_inference(
        has_pause = len(segments) > 1 or (segments and segments[0][1] > 0)

        if has_pause:
+            first_span = True
+
            def _gen_span(span_text):
+                nonlocal first_span
                # Per-span duration is left to the engine; an explicit overall
                # `duration` can't be meaningfully split across spans.
-                return backend.generate(span_text, duration=None, **gen_kwargs)
+                span_kwargs = dict(gen_kwargs)
+                if native_proxy and first_span and used_seed is not None:
+                    span_kwargs["seed"] = used_seed
+                first_span = False
+                return backend.generate(span_text, duration=None, **span_kwargs)
            audio_out = _render_with_pauses(_gen_span, segments, sr)
        else:
            # Wave 1.2: sentence-boundary chunking for long text (see
@@ -747,12 +983,19 @@ def _run_backend_inference(
                for i, chunk_text in enumerate(text_chunks):
                    if used_seed is not None:
                        torch.manual_seed(used_seed + i)
-                    parts.append(backend.generate(chunk_text, duration=None, **gen_kwargs))
+                    chunk_kwargs = dict(gen_kwargs)
+                    if native_proxy and used_seed is not None:
+                        chunk_kwargs["seed"] = used_seed + i
+                    parts.append(backend.generate(
+                        chunk_text, duration=None, **chunk_kwargs
+                    ))
                    _note_generate_progress()
                audio_out = concatenate_audio_chunks(parts, sr, _xfade_ms,
                                                     texts=text_chunks,
                                                     sink=dropped_sink)
            else:
+                if native_proxy and used_seed is not None:
+                    gen_kwargs["seed"] = used_seed
                audio_out = backend.generate(text, duration=duration, **gen_kwargs)

        return _apply_effect_chain(
@@ -870,7 +1113,6 @@ async def _finalize_generation(
    Returns ``(watermarked_tensor, meta)`` where ``meta`` carries
    ``id`` / ``filename`` / ``duration`` / ``gen_time``.
    """
-    loop = asyncio.get_running_loop()
    # Invisible AudioSeal provenance watermark on the final audio. Embedding
    # was previously only wired into the dub pipeline (dub_generate.py), so
    # plain TTS came out unmarked despite the setting being on — and the same
@@ -882,12 +1124,9 @@ async def _finalize_generation(
    # AudioSeal embedding is CPU work that holds no VRAM, so occupying a GPU
    # worker with it only delays the next generate on 1-worker hosts.
    if not already_marked:
-        from services.watermark import mark_synthetic
-        from services.model_manager import get_watermark_pool
-        audio_tensor = await loop.run_in_executor(
-            get_watermark_pool(),
-            functools.partial(mark_synthetic, audio_tensor, sample_rate,
-                              context="generate.finalize"),
+        from services.watermark import mark_synthetic_async
+        audio_tensor = await mark_synthetic_async(
+            audio_tensor, sample_rate, context="generate.finalize",
        )
    gen_time = round(time.time() - start_time, 2)

@@ -1198,6 +1437,10 @@ async def generate_speech(
    _backend = None
    _engine_min_vram_gb = getattr(backend_cls, "min_vram_gb", 0.0)
    _routing_notice = None
+    # Remote renders deliberately skip this host's capability gate. Keep the
+    # local fallback call's timeout device-neutral so the closure is valid
+    # without pretending the control plane describes the remote worker.
+    _routing = {"effective_device": None}

    if not _remote:
        # Single-active-engine memory discipline: hand back any OTHER resident
@@ -1297,6 +1540,7 @@ async def generate_speech(

    ref_audio_path = None
    cleanup_ref = False
+    ref_lease = None
    used_seed = seed
    resolved_profile_id = None
    history_mode = None  # profile.kind when a profile drives; else inferred at insert
@@ -1314,76 +1558,28 @@ async def generate_speech(
            row = conn.execute("SELECT * FROM voice_profiles WHERE id=?", (profile_id,)).fetchone()
        if row:
            resolved_profile_id = profile_id
-            # `kind` is authoritative (0005): 'design' profiles condition on
-            # their deterministic rendered sample + instruct; 'clone' on the
-            # user's reference. Lock always wins (it pins a specific take).
-            # Rows from pre-0004 DBs mid-upgrade may lack the column → fall
-            # back to the legacy is_locked/instruct inference.
-            try:
-                profile_kind = row["kind"] or "clone"
-            except (KeyError, IndexError):
-                profile_kind = "design" if (row["instruct"] and not row["is_locked"] and not row["ref_audio_path"]) else "clone"
-            history_mode = profile_kind
-            if row["is_locked"] and row["locked_audio_path"]:
-                ref_audio_path = os.path.join(VOICES_DIR, row["locked_audio_path"])
-                if not ref_text:
-                    ref_text = row["ref_text"]
-                if not instruct:
-                    instruct = _profile_instruct(row)
-                if used_seed is None and row["seed"] is not None:
-                    used_seed = row["seed"]
-            elif profile_kind == "design":
-                # Rendered sample (if present) carries the voice identity;
-                # instruct alone is the fallback for legacy archetype rows.
-                ref_audio_path = os.path.join(VOICES_DIR, row["ref_audio_path"]) if row["ref_audio_path"] else None
-                if ref_audio_path and not ref_text and row["ref_text"]:
-                    ref_text = row["ref_text"]
-                if not instruct:
-                    instruct = _profile_instruct(row)
-                if used_seed is None and row["seed"] is not None:
-                    used_seed = row["seed"]
-            elif row["instruct"] and not row["is_locked"] and not row["ref_audio_path"]:
-                # Legacy design-shaped row (pre-0004 archetype materialization
-                # failure path): instruct-only conditioning.
-                if not instruct:
-                    instruct = _profile_instruct(row)
-                if used_seed is None and row["seed"] is not None:
-                    used_seed = row["seed"]
-            else:
-                ref_audio_path = os.path.join(VOICES_DIR, row["ref_audio_path"]) if row["ref_audio_path"] else None
-                if not ref_text and row["ref_text"]:
-                    ref_text = row["ref_text"]
-                elif ref_audio_path and not ref_text:
-                    # Empty stored transcript → the auto-transcribe below will
-                    # run; cache its result onto the profile so it runs ONCE,
-                    # not on every generate (#1032 perf regression).
-                    persist_ref_text_profile_id = profile_id
-                if not instruct and row["instruct"]:
-                    instruct = row["instruct"]
-                if used_seed is None and row["seed"] is not None:
-                    used_seed = row["seed"]
-            if language == "Auto":
-                language = None
-            # #533: a profile's stored language must drive generation when the
-            # request didn't pin one. Without this the German (etc.) archetype
-            # generates with language=None and the model drifts to English —
-            # even though the archetype PREVIEW renders correctly (archetypes.py
-            # passes the language). An EXPLICIT non-Auto request language still
-            # wins; we only fill the gap. `row` is a sqlite3.Row, so guard the
-            # column lookup for pre-language DBs mid-upgrade.
-            if language is None:
-                try:
-                    prof_lang = row["language"]
-                except (KeyError, IndexError):
-                    prof_lang = None
-                if prof_lang and prof_lang != "Auto":
-                    language = prof_lang
+            # Shared with POST /convert — see _resolve_profile_conditioning
+            # for the resolution rules (kind-authoritative, lock wins, #533
+            # language fill, #1032 transcript-cache signal).
+            _cond = _resolve_profile_conditioning(
+                row, ref_text=ref_text, instruct=instruct, seed=used_seed,
+                language=language,
+            )
+            history_mode = _cond["kind"]
+            ref_audio_path = _cond["ref_audio_path"]
+            ref_text = _cond["ref_text"]
+            instruct = _cond["instruct"]
+            used_seed = _cond["seed"]
+            language = _cond["language"]
+            if _cond["persist_ref_text"]:
+                persist_ref_text_profile_id = profile_id
    elif ref_audio is not None:
        try:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
                f.write(await ref_audio.read())
                ref_audio_path = f.name
                cleanup_ref = True
+                ref_lease = _TempReferenceLease(ref_audio_path)
        except Exception as e:
            raise HTTPException(status_code=500, detail=str(e))

@@ -1400,13 +1596,19 @@ async def generate_speech(
        # built-in ASR fallback), so a timeout degrades to None rather than
        # failing the whole generate.
        try:
-            ref_text = await run_on_gpu_pool_guarded(
-                functools.partial(transcribe_reference, ref_audio_path),
-                what="Reference transcribe",
-                # Floor budget (#1190): a reference clip is seconds of audio,
-                # so the length-scaled bonus never applies — but the timeout is
-                # explicit here too, so no dispatch relies on a hidden default.
-                timeout=_generate_timeout_s(""),
+            ref_text = await _run_with_reference_lease(
+                ref_lease,
+                lambda release: run_on_gpu_pool_guarded(
+                    functools.partial(transcribe_reference, ref_audio_path),
+                    what="Reference transcribe",
+                    # Floor budget (#1190): a reference clip is seconds of audio,
+                    # so the length-scaled bonus never applies — but the timeout is
+                    # explicit here too, so no dispatch relies on a hidden default.
+                    timeout=_generate_timeout_s(
+                        "", execution_device=_routing["effective_device"]
+                    ),
+                    on_abandon=release,
+                )
            )
        # TimeoutError covers both the execution bound and pool saturation:
        # this path is best-effort either way.
@@ -1523,7 +1725,8 @@ async def generate_speech(
            local=gpu_gateway.LocalCall(
                _remote_only_local_call(_target_label),
                what="TTS generate",
-                timeout=_generate_timeout_s(text),
+                timeout=_generate_timeout_s(text, execution_device=_routing["effective_device"],
+                                            min_vram_gb=_engine_min_vram_gb),
                min_vram_gb=_engine_min_vram_gb,
            ),
            remote=_remote_call,
@@ -1662,19 +1865,30 @@ async def generate_speech(
                    "target_label": e.worker_label or _target_label,
                    "hint": e.hint,
                })
-            except Exception:
+            except Exception as exc:
                # Mid-job remote failure is NOT quietly redone here: the client
                # treats a retryable error as "surface it", so the user decides
-                # whether to spend the same minutes again on this machine.
-                logger.error("Remote generation failed", exc_info=True)
-                from core.public_errors import stream_failure
-                yield _line({"type": "error", **stream_failure("generation_failed")})
+                # whether to spend the same minutes again on this machine. Like
+                # the local streaming path, this in-band frame stands in for the
+                # global 500 handler, so it journals the scrubbed failure and
+                # names a recognized cause instead of the bare generic string
+                # (#1607).
+                logger.error(
+                    "Remote generation failed (class=%s)",
+                    type(exc).__name__,
+                )
+                from core.public_errors import stream_generation_failure
+                from core import error_journal
+
+                error_journal.record(
+                    exc, route="/generate", trace=traceback.format_exc()
+                )
+                yield _line({"type": "error", **stream_generation_failure(exc)})
            finally:
                if not render.done():
                    render.cancel()
-                if cleanup_ref and ref_audio_path:
-                    with contextlib.suppress(OSError):
-                        os.remove(ref_audio_path)
+                if cleanup_ref and ref_lease is not None:
+                    ref_lease.finish_request()

        return StreamingResponse(
            _remote_stream_events(),
@@ -1716,6 +1930,17 @@ async def generate_speech(
                        instruct=instruct, num_step=num_step,
                        guidance_scale=guidance_scale, speed=speed,
                        denoise=denoise, postprocess_output=postprocess_output,
+                        **({
+                            key: value for key, value in {
+                                "t_shift": t_shift,
+                                "layer_penalty_factor": layer_penalty_factor,
+                                "position_temperature": position_temperature,
+                                "class_temperature": class_temperature,
+                                "seed": used_seed + i if used_seed is not None else None,
+                            }.items() if value is not None
+                        } if getattr(
+                            _backend, "supports_native_omnivoice_controls", False
+                        ) else {}),
                    )
                    sr = _backend.sample_rate
                    skip = getattr(_backend, "applies_own_mastering", False)
@@ -1779,33 +2004,47 @@ async def generate_speech(
                if _has_pause or len(_text_chunks) <= 1:
                    # Single-shot pipeline, unchanged — streamed as one chunk.
                    if _backend is not None:
-                        audio_tensor = await run_on_gpu_pool_guarded(
-                            functools.partial(
-                                _run_backend_inference,
-                                _backend, text, language, ref_audio_path, ref_text,
-                                instruct, duration, num_step, guidance_scale, speed,
-                                denoise, postprocess_output, used_seed, effect_preset,
-                                max_chunk_chars, crossfade_ms, dropped_sink=_dropped_sink,
-                            ),
-                            what="TTS generate",
-                            min_vram_gb=_engine_min_vram_gb,
-                            timeout=_generate_timeout_s(text),
+                        audio_tensor = await _run_with_reference_lease(
+                            ref_lease,
+                            lambda release: run_on_gpu_pool_guarded(
+                                functools.partial(
+                                    _run_backend_inference,
+                                    _backend, text, language, ref_audio_path, ref_text,
+                                    instruct, duration, num_step, guidance_scale, speed,
+                                    denoise, postprocess_output, used_seed, effect_preset,
+                                    max_chunk_chars, crossfade_ms, t_shift=t_shift,
+                                    layer_penalty_factor=layer_penalty_factor,
+                                    position_temperature=position_temperature,
+                                    class_temperature=class_temperature,
+                                    dropped_sink=_dropped_sink,
+                                ),
+                                what="TTS generate",
+                                min_vram_gb=_engine_min_vram_gb,
+                                timeout=_generate_timeout_s(text, execution_device=_routing["effective_device"],
+                                                            min_vram_gb=_engine_min_vram_gb),
+                                on_abandon=release,
+                            )
                        )
                        sample_rate = _backend.sample_rate
                    else:
-                        audio_tensor = await run_on_gpu_pool_guarded(
-                            functools.partial(
-                                _run_inference,
-                                _model, text, language, ref_audio_path, ref_text,
-                                instruct, duration, num_step, guidance_scale, speed,
-                                t_shift, denoise, postprocess_output,
-                                layer_penalty_factor, position_temperature,
-                                class_temperature, used_seed, effect_preset,
-                                max_chunk_chars, crossfade_ms, dropped_sink=_dropped_sink,
-                            ),
-                            what="TTS generate",
-                            min_vram_gb=_engine_min_vram_gb,
-                            timeout=_generate_timeout_s(text),
+                        audio_tensor = await _run_with_reference_lease(
+                            ref_lease,
+                            lambda release: run_on_gpu_pool_guarded(
+                                functools.partial(
+                                    _run_inference,
+                                    _model, text, language, ref_audio_path, ref_text,
+                                    instruct, duration, num_step, guidance_scale, speed,
+                                    t_shift, denoise, postprocess_output,
+                                    layer_penalty_factor, position_temperature,
+                                    class_temperature, used_seed, effect_preset,
+                                    max_chunk_chars, crossfade_ms, dropped_sink=_dropped_sink,
+                                ),
+                                what="TTS generate",
+                                min_vram_gb=_engine_min_vram_gb,
+                                timeout=_generate_timeout_s(text, execution_device=_routing["effective_device"],
+                                                            min_vram_gb=_engine_min_vram_gb),
+                                on_abandon=release,
+                            )
                        )
                        sample_rate = _model.sampling_rate
                    yield _line({
@@ -1822,12 +2061,10 @@ async def generate_speech(
                    # (#1190): AudioSeal embedding is CPU work that owns no
                    # VRAM, and on a 1-worker host it used to serialize
                    # directly ahead of the next generate.
-                    from services.watermark import mark_synthetic
-                    from services.model_manager import get_watermark_pool
-                    _preview = await asyncio.get_running_loop().run_in_executor(
-                        get_watermark_pool(),
-                        functools.partial(mark_synthetic, audio_tensor, sample_rate,
-                                          context="generate.stream_preview"),
+                    from services.watermark import mark_synthetic_async
+                    _preview = await mark_synthetic_async(
+                        audio_tensor, sample_rate,
+                        context="generate.stream_preview",
                    )
                    yield _line({"type": "chunk", "seq": 0, "pcm": _pcm16_b64(_preview)})
                else:
@@ -1836,25 +2073,28 @@ async def generate_speech(
                    for i, chunk_text in enumerate(_text_chunks):
                        # Bounded per chunk + pool-reset on hang (#730 class);
                        # a timeout surfaces as an "error" event below.
-                        raw, preview, sample_rate = await run_on_gpu_pool_guarded(
-                            functools.partial(_render_stream_chunk, i, chunk_text),
-                            what="TTS generate",
-                            min_vram_gb=_engine_min_vram_gb,
-                            # Budget scaled to THIS chunk (#1190) — the flat
-                            # 300s here is what made long streamed renders fail
-                            # even after the v0.3.22 scaled budget shipped.
-                            timeout=_generate_timeout_s(chunk_text),
+                        raw, preview, sample_rate = await _run_with_reference_lease(
+                            ref_lease,
+                            lambda release: run_on_gpu_pool_guarded(
+                                functools.partial(_render_stream_chunk, i, chunk_text),
+                                what="TTS generate",
+                                min_vram_gb=_engine_min_vram_gb,
+                                # Budget scaled to THIS chunk (#1190) — the flat
+                                # 300s here is what made long streamed renders fail
+                                # even after the v0.3.22 scaled budget shipped.
+                                timeout=_generate_timeout_s(chunk_text, execution_device=_routing["effective_device"],
+                                                            min_vram_gb=_engine_min_vram_gb),
+                                on_abandon=release,
+                            )
                        )
                        parts.append(raw)
                        # Provenance-mark the streamed copy off the GPU pool
                        # (#1169 mark, #1190 placement): CPU-only AudioSeal
                        # work must not occupy a GPU worker between chunks.
-                        from services.watermark import mark_synthetic
-                        from services.model_manager import get_watermark_pool
-                        preview = await asyncio.get_running_loop().run_in_executor(
-                            get_watermark_pool(),
-                            functools.partial(mark_synthetic, preview, sample_rate,
-                                              context="generate.stream_preview"),
+                        from services.watermark import mark_synthetic_async
+                        preview = await mark_synthetic_async(
+                            preview, sample_rate,
+                            context="generate.stream_preview",
                        )
                        if i == 0:
                            # After the first render so lazy-loading engines
@@ -1869,7 +2109,7 @@ async def generate_speech(
                    audio_tensor = await run_on_gpu_pool_guarded(
                        functools.partial(_assemble_stream_chunks, parts, sample_rate),
                        what="TTS assemble",
-                        timeout=_generate_timeout_s(text),
+                        timeout=_generate_timeout_s(text, execution_device=_routing["effective_device"]),
                    )

                _, meta = await _finalize_generation(
@@ -1898,7 +2138,7 @@ async def generate_speech(
                # Client went away mid-stream — same semantics as aborting a
                # classic /generate mid-render: nothing is saved.
                raise
-            except (GpuJobTimeoutError, GpuPoolBusyError) as e:
+            except GpuPoolBusyError as e:
                # In-band error frame carries the machine-readable retryable
                # marker (#1190) — an NDJSON consumer can back off instead of
                # guessing from the prose.
@@ -1907,20 +2147,44 @@ async def generate_speech(
                failure = stream_failure("generation_busy")
                failure["retry_after"] = getattr(e, "retry_after", 30)
                yield _line({"type": "error", **failure})
+            except GpuJobTimeoutError:
+                # The worker started and spent its full execution budget. That
+                # is compute time, not queue pressure (#1588).
+                logger.error("Streaming generation exceeded its compute budget")
+                from core.public_errors import stream_failure
+                failure = stream_failure("generation_timeout")
+                failure["retry_after"] = 30
+                yield _line({"type": "error", **failure})
            except ValueError:
                logger.error("Streaming generation request rejected")
                from core.public_errors import stream_failure
                yield _line({"type": "error", **stream_failure("invalid_request")})
-            except Exception:
-                logger.error("Streaming generation failed unexpectedly")
-                from core.public_errors import stream_failure
-                yield _line({"type": "error", **stream_failure("generation_failed")})
+            except Exception as exc:
+                # A streaming request answers 200 and carries its failure as an
+                # in-band error frame, so it never reaches the global 500
+                # handler — which is where a classic /generate failure gets its
+                # scrubbed journal entry (Diagnostics / recent errors) AND its
+                # classified, actionable message. Both have to be reproduced
+                # here or a streaming generation failure is invisible in the
+                # diagnostic bundle and opaque to the user (#1607). The raw
+                # exception is NOT logged: it can carry a reference-clip path or
+                # a provider secret, and only the journal scrubs before storing.
+                logger.error(
+                    "Streaming generation failed unexpectedly (class=%s)",
+                    type(exc).__name__,
+                )
+                from core.public_errors import stream_generation_failure
+                from core import error_journal
+
+                error_journal.record(
+                    exc, route="/generate", trace=traceback.format_exc()
+                )
+                yield _line({"type": "error", **stream_generation_failure(exc)})
            finally:
                # Ownership of the temp reference clip moves to this generator
                # in stream mode (the route returns before rendering starts).
-                if cleanup_ref and ref_audio_path:
-                    with contextlib.suppress(OSError):
-                        os.remove(ref_audio_path)
+                if cleanup_ref and ref_lease is not None:
+                    ref_lease.finish_request()

        # Routing notice (#21): known before the stream starts, so it rides the
        # same headers the classic path uses — and now also carries "your
@@ -1958,7 +2222,11 @@ async def generate_speech(
                    _backend, text, language, ref_audio_path, ref_text, instruct,
                    duration, num_step, guidance_scale, speed, denoise,
                    postprocess_output, used_seed, effect_preset,
-                    max_chunk_chars, crossfade_ms, dropped_sink=_dropped_text,
+                    max_chunk_chars, crossfade_ms, t_shift=t_shift,
+                    layer_penalty_factor=layer_penalty_factor,
+                    position_temperature=position_temperature,
+                    class_temperature=class_temperature,
+                    dropped_sink=_dropped_text,
                )
            else:
                _local_render = functools.partial(
@@ -1969,14 +2237,19 @@ async def generate_speech(
                    class_temperature, used_seed, effect_preset,
                    max_chunk_chars, crossfade_ms, dropped_sink=_dropped_text,
                )
-            audio_tensor = await gpu_gateway.run(
-                _REMOTE_OP,
-                local=gpu_gateway.LocalCall(
-                    _local_render, what="TTS generate",
-                    timeout=_generate_timeout_s(text),
-                    min_vram_gb=_engine_min_vram_gb,
-                ),
-                decision=_decision,
+            audio_tensor = await _run_with_reference_lease(
+                ref_lease,
+                lambda release: gpu_gateway.run(
+                    _REMOTE_OP,
+                    local=gpu_gateway.LocalCall(
+                        _local_render, what="TTS generate",
+                        timeout=_generate_timeout_s(text, execution_device=_routing["effective_device"],
+                                                    min_vram_gb=_engine_min_vram_gb),
+                        min_vram_gb=_engine_min_vram_gb,
+                        on_abandon=release,
+                    ),
+                    decision=_decision,
+                )
            )
            # Read after generation: engines with lazy model loading report
            # their real rate only once weights are up.
@@ -2108,9 +2381,8 @@ async def generate_speech(
            ),
        )
    finally:
-        if cleanup_ref and ref_audio_path:
-            with contextlib.suppress(OSError):
-                os.remove(ref_audio_path)
+        if cleanup_ref and ref_lease is not None:
+            ref_lease.finish_request()

 def _safe_output_path(name):
    if not name:
@@ -160,7 +160,9 @@ _OPENAI_VOICE_ALIASES = {

 def _resolve_engine(model_id: str):
    """Map an OpenAI model name to a VoiceStudio backend."""
-    from services.tts_backend import get_backend_class, get_active_tts_backend
+    from services.tts_backend import (
+        get_backend_class, get_active_tts_backend, get_engine_instance_for,
+    )

    # Accept OpenAI model names as pass-through to the active engine.
    if model_id in ("tts-1", "tts-1-hd"):
@@ -177,8 +179,18 @@ def _resolve_engine(model_id: str):
            )
        from services.tts_backend import OmniVoiceBackend
        if cls is OmniVoiceBackend:
+            # OmniVoice only ever runs as the shared active engine — the
+            # explicit-omnivoice request is the active-engine request.
            return get_active_tts_backend()
-        return cls()
+        # Cached singleton, not a fresh cls(): SubprocessBackend engines would
+        # spawn a sidecar process and reload their model on EVERY request, and
+        # register a new atexit hook each time (get_engine_instance's contract).
+        # No router-local cache on top of it: the shared cache is keyed by
+        # CLASS precisely so id rebinds/evictions can't serve a stale instance,
+        # and cross-engine memory discipline is create_speech's
+        # evict_other_tts_engines call (the same seam /generate uses) — not a
+        # bespoke unload here.
+        return get_engine_instance_for(model_id)
    except ValueError:
        raise HTTPException(
            status_code=400,
@@ -388,6 +400,15 @@ async def create_speech(req: SpeechRequest):
    # VRAM eviction runs in get_model()'s warm-return path now, covering every
    # native TTS generate (this route, WS TTS, dub, batch, audiobook).

+    # Single-active-engine memory discipline (MM2-01), the same call /generate
+    # makes before its load: hand back every OTHER resident TTS engine's model
+    # before this one warms up, so switching `model` ids across requests —
+    # explicit id → explicit id, or explicit id → the tts-1/omnivoice aliases —
+    # can't stack multi-GB engines/sidecars. No-op when nothing else is
+    # resident; opt out with OMNIVOICE_SINGLE_ENGINE_RESIDENT=0.
+    from services.engine_memory import evict_other_tts_engines
+    await evict_other_tts_engines(backend.id)
+
    # ── #1033/#1037/#1014: warm the engine under the LOAD budget before the
    # generate clock starts. The T4 verification (#1014) measured a fresh
    # install's first /v1/audio/speech burning its whole 300s generate budget
@@ -454,7 +475,7 @@ async def create_speech(req: SpeechRequest):
        from services.model_manager import generate_timeout_s
        wav, sr = await run_on_gpu_pool_guarded(
            lambda: _run_tts(backend, text, kw), what="OpenAI TTS generate",
-            timeout=generate_timeout_s(text))
+            timeout=generate_timeout_s(text, engine=backend))
    except Exception as e:
        # #1172/#1173: typed failures get their real status + actionable
        # message (400 bad input / 503 broken engine binary) instead of a
@@ -35,11 +35,11 @@ class _HFTokenBody(BaseModel):
    token: str = Field(..., min_length=1, description="HuggingFace access token")


-def _state_response() -> dict:
+def _state_response(*, validate: bool = False) -> dict:
    """Return the same shape the React panel renders. Never includes raw token."""
    from services import token_resolver

-    s = token_resolver.state()
+    s = token_resolver.state(validate=validate)
    return {
        "active": s["active"],
        "sources": [asdict(row) for row in s["sources"]],
@@ -65,8 +65,7 @@ def save_hf_token(body: _HFTokenBody):

@router.delete("/hf-token")
 def clear_hf_token(also_clear_hf_cli: bool = Query(False)):
-    """Clear the App-source token. Optionally also call huggingface_hub.logout
-    to clear the canonical HF file. Returns the updated cascade state."""
+    """Clear the App token and optionally recognized local Hub token files."""
    from services import token_resolver
    try:
        token_resolver.clear_app_token(also_clear_hf_cli=also_clear_hf_cli)
@@ -82,13 +81,12 @@ def get_hf_token_state(fresh: bool = Query(False)):

    ``fresh=1`` drops the resolver's whoami validation cache first so the
    response re-runs whoami for every source — this is what the panel's
-    "Test now" button sends. Plain GETs (panel mounts) keep the 300s cache
-    so repeat Settings visits don't hammer the HF API.
+    "Test now" button sends. Plain GETs only inspect local token presence.
    """
    from services import token_resolver
    if fresh:
        token_resolver.invalidate_cache()
-    return _state_response()
+    return _state_response(validate=fresh)


 # ── Performance settings (INST-12) ────────────────────────────────────────
@@ -150,7 +148,7 @@ def _compute_device_state() -> dict:
    caps = device_caps.detect_host_caps()
    env_pin = (os.environ.get("OMNIVOICE_DEVICE") or "").strip().lower()
    auto_family = next(
-        (f for f in ("cuda", "rocm", "xpu", "mps") if f in caps.available_families),
+        (f for f in device_caps.ACCELERATOR_PRIORITY if f in caps.available_families),
        "cpu",
    )
    value = device_caps.requested_device_override()
@@ -1035,9 +1033,10 @@ def set_asr_openai_compat(body: _ASROpenAICompatBody):
    from services import asr_backend, settings_store

    if body.base_url is not None:
-        url = body.base_url.strip().rstrip("/")
-        if url and not url.startswith(("http://", "https://")):
-            raise HTTPException(status_code=400, detail="Base URL must start with http(s)://")
+        try:
+            url = asr_backend.normalize_openai_compat_asr_base_url(body.base_url)
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc)) from exc
        settings_store.set_text(asr_backend._ASR_OPENAI_COMPAT_BASE_URL_KEY, url)
    if body.model is not None:
        settings_store.set_text(
@@ -76,6 +76,7 @@ _cancelled: set[str] = set()
 _active_installs: set[str] = set()
 _active_installs_lock = threading.Lock()
 _install_tasks: set[asyncio.Task] = set()
+_install_tasks_by_repo: dict[str, asyncio.Task] = {}


 def _download_max_workers() -> int:
@@ -420,16 +421,11 @@ async def install_model(req: InstallModelRequest):
                f"Retry in {remaining}s or check your network."
            ),
        )
-    with _active_installs_lock:
-        if req.repo_id in _active_installs:
-            return {"status": "already_running", "repo_id": req.repo_id}
-        _active_installs.add(req.repo_id)
    loop = asyncio.get_running_loop()

    def _do():
        token = hf_progress.current_repo_id.set(req.repo_id)
        target_token = hf_progress.current_target.set("local")
-        _cancelled.discard(req.repo_id)  # clear any stale cancel from a prior run
        hf_progress.emit({
            "repo_id": req.repo_id,
            "filename": req.repo_id,
@@ -688,17 +684,53 @@ async def install_model(req: InstallModelRequest):
            with _active_installs_lock:
                _active_installs.discard(req.repo_id)

-    try:
-        task = loop.create_task(asyncio.to_thread(_do))
-        _install_tasks.add(task)
-        task.add_done_callback(_install_tasks.discard)
-    except Exception:
-        with _active_installs_lock:
+    with _active_installs_lock:
+        if req.repo_id in _active_installs:
+            return {"status": "already_running", "repo_id": req.repo_id}
+        _active_installs.add(req.repo_id)
+        # Admission and task publication are one atomic generation boundary:
+        # cancellation can never observe an admitted install without its task.
+        _cancelled.discard(req.repo_id)
+        try:
+            task = loop.create_task(asyncio.to_thread(_do))
+            _install_tasks.add(task)
+            _install_tasks_by_repo[req.repo_id] = task
+        except Exception:
            _active_installs.discard(req.repo_id)
-        raise
+            raise
+
+    def install_finished(completed: asyncio.Task) -> None:
+        with _active_installs_lock:
+            _install_tasks.discard(completed)
+            if _install_tasks_by_repo.get(req.repo_id) is completed:
+                _install_tasks_by_repo.pop(req.repo_id, None)
+
+    task.add_done_callback(install_finished)
    return {"status": "install_started", "repo_id": req.repo_id}


+async def cancel_install_and_wait(repo_id: str) -> None:
+    """Request cancellation and retain authority until its thread exits."""
+    from worker.async_utils import drain_task  # noqa: PLC0415
+
+    with _active_installs_lock:
+        _cancelled.add(repo_id)
+        _install_cooldowns.pop(repo_id, None)
+        task = _install_tasks_by_repo.get(repo_id)
+    if task is None:
+        return
+    try:
+        # asyncio.to_thread cannot stop snapshot_download mid-file. Cancelling
+        # its wrapper would only detach the thread, so wait until the blocking
+        # call observes the flag or naturally returns.
+        await drain_task(task)
+    finally:
+        with _active_installs_lock:
+            current = _install_tasks_by_repo.get(repo_id)
+            if current is None or current is task:
+                _cancelled.discard(repo_id)
+
+
@router.post("/models/install/cancel")
 async def cancel_install(req: InstallModelRequest):
    """Request cancellation of an in-flight install (FDL-11).
@@ -62,6 +62,11 @@ def setup_status():
 _MIN_NVIDIA_DRIVER = 555
 _RAM_FAIL_GB = 8
 _RAM_WARN_GB = 12
+# Installed DIMMs never fully reach the OS: firmware, integrated graphics and
+# kernel reservations shave off up to ~7% (an "8 GB" Windows laptop reports
+# ~7.8 GB usable). Thresholds are compared with this allowance applied so the
+# machines a threshold is meant to admit aren't blocked by that gap (#1618).
+_RAM_RESERVED_ALLOWANCE = 0.93


 def _run_cmd(args: list[str], timeout: float = 2.0) -> tuple[int, str]:
@@ -352,17 +357,28 @@ def preflight():

    # ── RAM
    ram = _ram_gb()
+    # Escape hatch (#1618): a preflight should inform, not brick setup —
+    # OMNIVOICE_RAM_PREFLIGHT=0 downgrades the hard block to a warning for
+    # users who accept the OOM risk. Same opt-out shape as
+    # OMNIVOICE_ASR_VRAM_PREFLIGHT.
+    ram_gate = os.environ.get(
+        "OMNIVOICE_RAM_PREFLIGHT", "1"
+    ).strip().lower() not in ("0", "false", "no")
    if ram == 0:
        ram_status, ram_detail, ram_fix = (
            "warn", "Could not detect system RAM.",
            "Install psutil in the backend environment or ignore this warning.",
        )
-    elif ram < _RAM_FAIL_GB:
+    elif ram < _RAM_FAIL_GB * _RAM_RESERVED_ALLOWANCE:
        ram_status, ram_detail, ram_fix = (
-            "fail", f"{ram:.1f} GB total (need ≥ {_RAM_FAIL_GB} GB)",
-            "The app will OOM on first dub. Close other apps or upgrade RAM.",
+            "fail" if ram_gate else "warn",
+            f"{ram:.1f} GB total (need ≥ {_RAM_FAIL_GB} GB)",
+            "The app will OOM on first dub. Close other apps or upgrade RAM."
+            if ram_gate else
+            "RAM check disabled via OMNIVOICE_RAM_PREFLIGHT=0 — dubbing may "
+            "OOM on this machine.",
        )
-    elif ram < _RAM_WARN_GB:
+    elif ram < _RAM_WARN_GB * _RAM_RESERVED_ALLOWANCE:
        ram_status, ram_detail, ram_fix = (
            "warn", f"{ram:.1f} GB total ({_RAM_WARN_GB}+ GB recommended)",
            "Long videos may hit swap. Keep other apps closed during dubbing.",
@@ -0,0 +1,160 @@
+"""Discovery contract for VoiceStudio's local speech platform.
+
+Interfaces should discover this document instead of hard-coding whichever
+dictation route the desktop happens to use.  Endpoint URLs are relative so the
+same response works on loopback, a tailnet GPU host, and a reverse proxy.
+"""
+from __future__ import annotations
+
+import os
+from typing import Literal
+
+from fastapi import APIRouter
+from pydantic import BaseModel, Field
+
+from core.version import APP_VERSION
+
+router = APIRouter(tags=["Speech Platform"])
+
+SPEECH_PROTOCOL = "voicestudio.speech.v1"
+STREAM_PATH = "/v1/audio/transcriptions/stream"
+
+
+class EndpointCapability(BaseModel):
+    path: str
+    transport: Literal["http", "websocket", "mcp-streamable-http", "mcp-stdio"]
+    method: str | None = None
+    protocol: str | None = None
+
+
+class StreamInputCapability(BaseModel):
+    framing: Literal["binary"] = "binary"
+    formats: list[str]
+    default_format: str
+    sample_rate_query: str = "sr"
+    end_control: dict[str, str]
+
+
+class StreamOutputCapability(BaseModel):
+    framing: Literal["json"] = "json"
+    events: list[str]
+    final_kinds: list[str]
+
+
+class SpeechFeatureCapabilities(BaseModel):
+    batch_transcription: bool = True
+    streaming_transcription: bool = True
+    partial_transcripts: bool = True
+    utterance_finals: bool = True
+    session_summary: bool = True
+    word_timestamps: bool = True
+    local_refinement: bool = True
+    acoustic_echo_cancellation: bool = True
+    native_dictation_control: bool = False
+
+
+class SpeechAuthCapabilities(BaseModel):
+    loopback: Literal["none"] = "none"
+    remote: Literal["bearer"] = "bearer"
+    header: str = "Authorization: Bearer <OMNIVOICE_API_KEY>"
+    browser_session_endpoint: str = "/api/auth/session"
+    websocket_ticket_endpoint: str = "/api/auth/ws-ticket"
+    websocket_ticket_query_parameter: Literal["ws_ticket"] = "ws_ticket"
+
+
+class SpeechCapabilities(BaseModel):
+    schema_: Literal["voicestudio.speech-capabilities"] = Field(
+        default="voicestudio.speech-capabilities",
+        serialization_alias="schema",
+    )
+    protocol: Literal["voicestudio.speech.v1"] = SPEECH_PROTOCOL
+    protocol_version: Literal["1.0"] = "1.0"
+    service: str = "VoiceStudio"
+    service_version: str = APP_VERSION
+    local_first: bool = True
+    endpoints: dict[str, EndpointCapability]
+    stream_input: StreamInputCapability
+    stream_output: StreamOutputCapability
+    features: SpeechFeatureCapabilities
+    authentication: SpeechAuthCapabilities
+
+
+def speech_capabilities() -> SpeechCapabilities:
+    """Return the stable, side-effect-free integration contract."""
+    endpoints = {
+        "capabilities": EndpointCapability(
+            path="/.well-known/voicestudio-speech",
+            transport="http",
+            method="GET",
+        ),
+        "batch_transcription": EndpointCapability(
+            path="/v1/audio/transcriptions",
+            transport="http",
+            method="POST",
+            protocol="openai.audio.transcriptions",
+        ),
+        "streaming_transcription": EndpointCapability(
+            path=STREAM_PATH,
+            transport="websocket",
+            protocol=SPEECH_PROTOCOL,
+        ),
+        "mcp": EndpointCapability(
+            path="/mcp",
+            transport="mcp-streamable-http",
+            method="POST",
+            protocol="mcp",
+        ),
+        "mcp_stdio": EndpointCapability(
+            path="python -m backend.mcp_shim",
+            transport="mcp-stdio",
+            protocol="mcp",
+        ),
+    }
+    native_control = False
+    try:
+        control_port = int(os.environ.get("VOICESTUDIO_SPEECH_CONTROL_PORT", ""))
+    except (TypeError, ValueError):
+        control_port = 0
+    if 0 < control_port <= 65535:
+        native_control = True
+        endpoints["native_dictation_control"] = EndpointCapability(
+            path=f"http://127.0.0.1:{control_port}/v1/capabilities",
+            transport="http",
+            method="GET",
+            protocol=SPEECH_PROTOCOL,
+        )
+
+    return SpeechCapabilities(
+        endpoints=endpoints,
+        stream_input=StreamInputCapability(
+            formats=[
+                "audio/pcm;encoding=s16le;channels=1",
+                "audio/webm;codecs=opus",
+            ],
+            default_format="audio/webm;codecs=opus",
+            end_control={"type": "input_audio.end"},
+        ),
+        stream_output=StreamOutputCapability(
+            events=["session.started", "status", "partial", "final", "error"],
+            final_kinds=["utterance", "summary"],
+        ),
+        features=SpeechFeatureCapabilities(
+            native_dictation_control=native_control,
+        ),
+        authentication=SpeechAuthCapabilities(),
+    )
+
+
+@router.get(
+    "/.well-known/voicestudio-speech",
+    response_model=SpeechCapabilities,
+    response_model_by_alias=True,
+)
+@router.get(
+    "/v1/audio/capabilities",
+    response_model=SpeechCapabilities,
+    response_model_by_alias=True,
+)
+async def get_speech_capabilities() -> SpeechCapabilities:
+    """Advertise batch, streaming, and agent-facing speech transports."""
+    return speech_capabilities()
@@ -203,8 +203,22 @@ def system_info():
    """
    try:
        _ffmpeg = find_ffmpeg()
+        from services import model_manager as _mm
+        from core import prefs as _prefs_mod
        return {
            "app_version": APP_VERSION,
+            "generate_timeout_s": _mm.GPU_JOB_TIMEOUT_S,
+            "cpu_generate_timeout_s": _mm.CPU_JOB_TIMEOUT_S,
+            # #1787 review fix: a saved prefs.json value for either key can be
+            # silently shadowed by an external env var (os.environ.setdefault
+            # in core.prefs.restore_env is a no-op when one is already
+            # present) — the Settings panel must say so rather than promise a
+            # restart will apply a value that never will.
+            "generate_timeout_shadowed": _prefs_mod.is_env_shadowed(
+                "OMNIVOICE_GENERATE_TIMEOUT_S"),
+            "cpu_generate_timeout_shadowed": _prefs_mod.is_env_shadowed(
+                "OMNIVOICE_CPU_GENERATE_TIMEOUT_S"),
+            "code_fingerprint": os.environ.get("OMNIVOICE_BUILD_FINGERPRINT", ""),
            "data_dir": DATA_DIR,
            "outputs_dir": OUTPUTS_DIR,
            "crash_log_path": CRASH_LOG_PATH,
@@ -240,6 +254,11 @@ def system_info():
        logger.exception("system_info failed — returning safe defaults")
        return {
            "app_version": APP_VERSION,
+            "generate_timeout_s": 300.0,
+            "cpu_generate_timeout_s": 600.0,
+            "generate_timeout_shadowed": False,
+            "cpu_generate_timeout_shadowed": False,
+            "code_fingerprint": os.environ.get("OMNIVOICE_BUILD_FINGERPRINT", ""),
            "data_dir": DATA_DIR,
            "outputs_dir": OUTPUTS_DIR,
            "crash_log_path": str(CRASH_LOG_PATH),
@@ -848,6 +867,14 @@ PERSISTENT_KEYS = {
    # the Rust sidecar reads OMNIVOICE_PORT at startup and the backend derives
    # the LAN-share/UI ports from the others.
    "OMNIVOICE_PORT", "OMNIVOICE_SHARE_PORT", "OMNIVOICE_UI_PORT",
+    # Per-job compute-time budgets (#1787). Both are captured at import time
+    # by services/model_manager.py (GPU_JOB_TIMEOUT_S / CPU_JOB_TIMEOUT_S), so
+    # a value saved here takes effect on the NEXT backend restart — same
+    # contract as OMNIVOICE_PORT above. Restored into os.environ during the
+    # "env_prefs" startup step (main.py), which runs before model_manager is
+    # first imported ("ml_imports"), so the restored value is what the module
+    # captures. The Settings UI must say so (RestartBadge).
+    "OMNIVOICE_GENERATE_TIMEOUT_S", "OMNIVOICE_CPU_GENERATE_TIMEOUT_S",
 }

 # Sidecar-engine install dirs (OMNIVOICE_INDEXTTS_DIR, …). The one-click
@@ -865,6 +892,16 @@ except Exception:  # pragma: no cover — defensive: env panel > installer wirin
 # being set so a bad value never reaches uvicorn / the share listener.
 _PORT_KEYS = {"OMNIVOICE_PORT", "OMNIVOICE_SHARE_PORT", "OMNIVOICE_UI_PORT"}

+# Keys whose value is a wall-clock compute-time budget in seconds (#1787).
+# Validated the same way as _PORT_KEYS: reject anything that isn't a
+# positive number before it reaches services/model_manager.py. Upper bound is
+# generous — long enough that a legitimate multi-hour, audiobook-length CPU
+# render is never blocked — but still bounded, so a fat-fingered extra digit
+# (300 -> 3000000) can't turn a wedged job into one that silently occupies a
+# worker for days before the guard ever fires.
+_TIMEOUT_KEYS = {"OMNIVOICE_GENERATE_TIMEOUT_S", "OMNIVOICE_CPU_GENERATE_TIMEOUT_S"}
+_MAX_GENERATE_TIMEOUT_S = 21600.0  # 6 hours
+

@router.post("/system/set-env")
 async def set_env_var(body: dict):
@@ -873,7 +910,7 @@ async def set_env_var(body: dict):
    Persistent keys (proxy, FFMPEG_PATH, translation provider keys, …) are
    saved to ``prefs.json`` so they survive backend restarts (restored at
    startup in ``main.py``). HF_TOKEN is persisted via
-    ``huggingface_hub.login()`` (and cleared via ``logout()``). Other keys
+    ``huggingface_hub.login()`` (and cleared with the shared token-file helper). Other keys
    are set on ``os.environ`` for the running process.

    The loopback-origin gate that previously lived inline here is now applied
@@ -908,6 +945,22 @@ async def set_env_var(body: dict):
                    status_code=400,
                    detail=f"Invalid port for {key}: must be between 1024 and 65535.",
                )
+        if key in _TIMEOUT_KEYS:
+            try:
+                timeout_n = float(value)
+            except (TypeError, ValueError):
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Invalid timeout for {key}: '{value}' is not a number.",
+                )
+            if not (0 < timeout_n <= _MAX_GENERATE_TIMEOUT_S):
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        f"Invalid timeout for {key}: must be greater than 0 "
+                        f"and at most {_MAX_GENERATE_TIMEOUT_S:.0f} seconds."
+                    ),
+                )
        os.environ[key] = value
        logger.info("Environment variable set (length=%d)", len(value))

@@ -932,14 +985,14 @@ async def set_env_var(body: dict):
        # Mirror the persistence on clear — wipe the saved token file too.
        if key == "HF_TOKEN":
            try:
-                from huggingface_hub import logout as _hf_logout
-                _hf_logout()
-                logger.info("HF token cleared from $HF_HOME/token via logout()")
-            except Exception as e:
-                logger.warning("Could not clear HF token file: %s", e)
+                from services.token_resolver import clear_hf_cli_tokens
+                clear_hf_cli_tokens()
+                logger.info("Local Hugging Face token files cleared")
+            except Exception:
+                raise HTTPException(status_code=500, detail="Could not clear local Hugging Face token files") from None

    # HF_TOKEN persistence is handled above via huggingface_hub.login()/
-    # logout() — it never touches prefs.json. Everything else in
+    # clear_hf_cli_tokens() — it never touches prefs.json. Everything else in
    # PERSISTENT_KEYS (proxy, FFMPEG_PATH, translation provider keys, …) is
    # saved to prefs.json so it survives backend restarts (restored at
    # startup in main.py). Non-persistent keys stay process-local.
@@ -950,7 +1003,13 @@ async def set_env_var(body: dict):
        else:
            prefs_delete(prefs_key)

-    return {"key": key, "set": bool(value)}
+    # #1787 review fix: tell the caller up front when the value just saved is
+    # being shadowed by an external env var — set at THIS process's startup,
+    # before our own prefs restore ran, so it predicts the next restart too.
+    # A response that just said {"set": True} let the Settings panel promise
+    # a restart would apply a value that never will.
+    from core.prefs import is_env_shadowed
+    return {"key": key, "set": bool(value), "shadowed": is_env_shadowed(key)}


@router.post("/clean-audio")
@@ -1041,7 +1100,7 @@ def asr_backends():
 def hf_token_state():
    """Return the 3-source HF token cascade state for the Settings UI
    (Wave 2 React panel consumes this). Never returns the raw token —
-    only a masked preview, whoami username, and per-source validity.
+    only a masked preview and local presence; no outbound validation.
    """
    from dataclasses import asdict
    from services import token_resolver
@@ -10,7 +10,8 @@ as they're generated. This unlocks:
 Protocol:
    → Client sends JSON: {"text": "...", "voice": "profile_id", ...}
    ← Server sends binary audio chunks (PCM16 @ 24kHz mono) as generated
-    ← Server sends JSON: {"type": "done", "duration_s": 4.2, "gen_time_s": 1.1}
+    ← Server sends JSON: {"type": "done", "duration_s": 4.2,
+      "gen_time_s": 1.1, "ttfa_ms": 180.0, "rtf": 0.262}
    ← Server sends JSON: {"type": "error", "detail": "..."}

 The chunked delivery targets <100ms time-to-first-audio (TTFA) on warm models.
@@ -33,6 +34,30 @@ logger = logging.getLogger("omnivoice.tts_stream")
 # Smaller chunks = lower latency but more WebSocket overhead.
 CHUNK_SAMPLES = int(os.environ.get("OMNIVOICE_STREAM_CHUNK", "4800"))

+# Module seam for deterministic latency-contract tests.  Keep every timing
+# sample on the same monotonic clock.
+_perf_counter = time.perf_counter
+
+
+async def _resolve_stream_backend(engine_id: str | None):
+    """Resolve the live-stream engine without bypassing host isolation."""
+    from services.tts_backend import (
+        OmniVoiceBackend,
+        active_backend_id,
+        get_active_tts_backend,
+        get_backend_class,
+    )
+
+    if engine_id:
+        return get_backend_class(engine_id)()
+
+    cls = get_backend_class(active_backend_id())
+    if cls is OmniVoiceBackend:
+        from services.model_manager import get_model
+
+        return get_active_tts_backend(model=await get_model())
+    return get_active_tts_backend()
+

 class StreamTTSRequest(BaseModel):
    """Client request for streaming TTS."""
@@ -85,7 +110,7 @@ async def ws_tts(websocket: WebSocket):
                })
                continue

-            t0 = time.perf_counter()
+            t0 = _perf_counter()
            text = data["text"]

            # Remote GPU: this socket stays on this machine, and says so.
@@ -127,10 +152,6 @@ async def ws_tts(websocket: WebSocket):

            try:
                # Resolve engine
-                from services.tts_backend import (
-                    get_active_tts_backend,
-                    get_backend_class,
-                )
                engine_id = data.get("engine")
                # #1224: leave a breadcrumb when memory is already tight before
                # a heavy load. /generate has done this since the 16 GB-Mac
@@ -146,13 +167,7 @@ async def ws_tts(websocket: WebSocket):
                    log_if_low(f"TTS stream load ({engine_id or 'active engine'})")
                except Exception:
                    pass
-                if engine_id:
-                    cls = get_backend_class(engine_id)
-                    backend = cls()
-                else:
-                    from services.model_manager import get_model
-                    model = await get_model()
-                    backend = get_active_tts_backend(model=model)
+                backend = await _resolve_stream_backend(engine_id)

                # ── Routing gate (#21 — no silent CPU fallback). WebSockets have
                # no response headers, so this uses frames: an error frame +
@@ -258,6 +273,11 @@ async def ws_tts(websocket: WebSocket):
                from services.model_manager import run_on_gpu_pool_guarded

                def _generate(sentence_text):
+                    # Timed INSIDE the pool worker: the guarded dispatch below
+                    # can queue behind other jobs, and queue wait is not
+                    # synthesis (review on #1620) — under contention it would
+                    # inflate rtf without the engine slowing at all.
+                    _synth_t0 = _perf_counter()
                    from services.audio_dsp import apply_mastering, normalize_audio
                    from services.watermark import mark_synthetic
                    wav = backend.generate(sentence_text, **kw)
@@ -279,12 +299,19 @@ async def ws_tts(websocket: WebSocket):
                    # watermark._iter_chunks), which is inherent to marking
                    # ultra-short clips, not a coverage gap.
                    wav = mark_synthetic(wav, sr_actual, context="tts_stream.sentence")
-                    return wav, sr_actual
+                    return wav, sr_actual, _perf_counter() - _synth_t0

                import torch
                total_samples = 0
                sr = backend.sample_rate
                started = False
+                first_audio_at: float | None = None
+                # Synthesis time only. The wall clock below also carries socket
+                # delivery and the per-chunk event-loop yields, so deriving RTF
+                # from it reports "how slow was the client" as if it were engine
+                # throughput — on a slow consumer that inflates RTF without the
+                # engine having changed at all.
+                synth_time = 0.0

                for sentence in sentences:
                    # Bounded + pool-reset on hang so a wedged generate can't
@@ -294,11 +321,12 @@ async def ws_tts(websocket: WebSocket):
                    # Length-scaled budget per sentence (#1190) — the flat 300s
                    # default is gone from every dispatch.
                    from services.model_manager import generate_timeout_s
-                    wav_tensor, sr = await run_on_gpu_pool_guarded(
+                    wav_tensor, sr, sentence_synth_s = await run_on_gpu_pool_guarded(
                        functools.partial(_generate, sentence),
                        what="TTS generate",
-                        timeout=generate_timeout_s(sentence),
+                        timeout=generate_timeout_s(sentence, engine=backend),
                    )
+                    synth_time += sentence_synth_s

                    if not started:
                        # Send metadata after the first generation so
@@ -325,25 +353,49 @@ async def ws_tts(websocket: WebSocket):
                        end = min(sent_samples + CHUNK_SAMPLES, n_samples)
                        chunk = pcm_bytes[sent_samples * 2: end * 2]
                        await websocket.send_bytes(chunk)
+                        if first_audio_at is None:
+                            # TTFA ends when the first audio bytes have been
+                            # handed to the socket.  The previous log used the
+                            # whole-render duration and called it TTFA.
+                            first_audio_at = _perf_counter()
                        sent_samples = end
                        # Yield to event loop between chunks for responsiveness
                        await asyncio.sleep(0)
                    total_samples += n_samples

-                gen_time = round(time.perf_counter() - t0, 3)
+                finished_at = _perf_counter()
+                wall_time_raw = max(0.0, finished_at - t0)
+                synth_time_raw = max(0.0, synth_time)
+                gen_time = round(wall_time_raw, 3)
                duration = round(total_samples / sr, 3)
+                ttfa_ms = (
+                    round(max(0.0, first_audio_at - t0) * 1000.0, 1)
+                    if first_audio_at is not None
+                    else None
+                )
+                # RTF is a render metric: synthesis seconds per audio second.
+                rtf = (
+                    round(synth_time_raw / (total_samples / sr), 3)
+                    if total_samples > 0
+                    else None
+                )

                await websocket.send_json({
                    "type": "done",
                    "duration_s": duration,
                    "gen_time_s": gen_time,
+                    "ttfa_ms": ttfa_ms,
+                    "rtf": rtf,
                    "samples": total_samples,
                    "sample_rate": sr,
                    "engine": backend.id,
                })
                logger.info(
-                    "TTS stream: %.1fs audio in %.1fs (TTFA=%.0fms)",
-                    duration, gen_time, gen_time * 1000,
+                    "TTS stream: %.1fs audio in %.1fs (TTFA=%s, RTF=%s)",
+                    duration,
+                    gen_time,
+                    f"{ttfa_ms:.0f}ms" if ttfa_ms is not None else "n/a",
+                    f"{rtf:.3f}" if rtf is not None else "n/a",
                )

            except Exception as e:
@@ -0,0 +1,380 @@
+"""Speech-to-speech voice changer — Studio's Convert method (POST /convert).
+
+The user drops (or records) a source clip, picks an existing voice profile,
+and gets the same words back in that profile's voice: the active ASR backend
+transcribes the clip (no word timestamps — the text is all we need), the
+active TTS engine re-synthesizes it conditioned on the profile's reference
+audio, and — by default — the take is pitch-preservingly time-stretched
+(ffmpeg atempo, clamped to one well-behaved 0.5–2.0 stage) so it lands near
+the source clip's duration.
+
+Deliberately reuses the /generate choke points instead of re-deriving them:
+
+* profile row → conditioning via ``generation._resolve_profile_conditioning``
+  (lock wins, ``kind`` authoritative, #533 language fill),
+* engine resolution via ``services.tts_backend.resolve_generation_backend``
+  (never a silent OmniVoice fallback; ``require_cloning=True`` refuses
+  clone-less engines with the actionable switch-engine message),
+* synthesis via ``generation._run_backend_inference`` on the guarded GPU
+  pool (#730 bound + reset; busy/timeout → retryable 503),
+* provenance + persistence via ``services.watermark.mark_synthetic_async``
+  and ``generation._finalize_generation`` (watermark → WAV in OUTPUTS_DIR →
+  history row → retention prune), marked AFTER the stretch so the take users
+  keep carries exactly one whole-take mark.
+
+Local-first: no network calls; ASR-model-less installs get the same typed
+409 download CTA as /transcribe; a backend mid-shutdown surfaces the global
+503 ``[shutting_down]`` (ModelLoadInterruptedByShutdown → main.py handler).
+Reachability matches /generate: loopback bind by default, with the shared
+network-share PIN / API-key middleware gating any non-loopback exposure.
+"""
+from __future__ import annotations
+
+import asyncio
+import functools
+import logging
+import os
+import tempfile
+import time
+
+from fastapi import APIRouter, File, Form, HTTPException, UploadFile
+
+router = APIRouter()
+logger = logging.getLogger("omnivoice.convert")
+
+#: ffmpeg's atempo filter is well-behaved in [0.5, 2.0] per stage. Convert
+#: clamps to ONE stage by design: needing more than 2× either way means the
+#: synthesized speech differs so much from the source that "matching" it
+#: would produce chipmunk/slow-motion artifacts worse than the mismatch.
+ATEMPO_MIN = 0.5
+ATEMPO_MAX = 2.0
+
+#: Within this relative tolerance the durations already match — stretching
+#: would resample the whole take for an inaudible gain.
+_MATCH_TOLERANCE = 0.02
+
+#: Convert clips are short conversational inputs, not long-form media. Stream
+#: them to disk in bounded chunks so a network-share client cannot make the
+#: backend materialize an arbitrarily large multipart upload in memory.
+_MAX_SOURCE_AUDIO_BYTES = 64 * 1024 * 1024
+_UPLOAD_CHUNK_BYTES = 1024 * 1024
+
+
+async def _copy_source_upload(audio: UploadFile, destination) -> int:
+    """Stream ``audio`` into ``destination`` with the Convert upload cap."""
+    total = 0
+    while True:
+        chunk = await audio.read(_UPLOAD_CHUNK_BYTES)
+        if not chunk:
+            return total
+        total += len(chunk)
+        if total > _MAX_SOURCE_AUDIO_BYTES:
+            raise HTTPException(
+                status_code=413,
+                detail="Source audio is too large (maximum 64 MB).",
+            )
+        destination.write(chunk)
+
+
+def _clamped_tempo_ratio(tts_duration_s: float, source_duration_s: float) -> "float | None":
+    """The atempo ratio that fits the take into the source duration, or None.
+
+    ratio > 1 speeds the take up (it came out longer than the source),
+    ratio < 1 slows it down. Clamped to a single atempo stage's [0.5, 2.0];
+    None when either duration is unusable or they already match.
+    """
+    if not source_duration_s or source_duration_s <= 0:
+        return None
+    if not tts_duration_s or tts_duration_s <= 0:
+        return None
+    ratio = tts_duration_s / source_duration_s
+    if abs(ratio - 1.0) <= _MATCH_TOLERANCE:
+        return None
+    return min(ATEMPO_MAX, max(ATEMPO_MIN, ratio))
+
+
+async def _match_source_duration(audio_tensor, sample_rate: int, source_duration_s: float):
+    """Best-effort pitch-preserving stretch of the take toward the source
+    clip's duration. Returns the input unchanged when no stretch is needed
+    or ffmpeg fails — a duration mismatch is better than a failed convert."""
+    n_samples = int(audio_tensor.shape[-1])
+    ratio = _clamped_tempo_ratio(n_samples / sample_rate, source_duration_s)
+    if ratio is None:
+        return audio_tensor
+    target_samples = max(1, int(round(n_samples / ratio)))
+    from services.ffmpeg_utils import _pitch_preserving_stretch
+    try:
+        return await _pitch_preserving_stretch(audio_tensor, target_samples, sample_rate)
+    except Exception as e:  # noqa: BLE001 — stretch is opt-in polish, never fatal
+        logger.warning("duration match skipped — atempo stretch failed: %s", e)
+        return audio_tensor
+
+
+async def _transcribe_source(tmp_path: str, *, source_lease=None) -> dict:
+    """Active-ASR transcription of the uploaded clip (no word timestamps).
+
+    Mirrors POST /transcribe: typed 409 + download CTA before any backend
+    is constructed (never a silent multi-GB auto-download), the guarded GPU
+    pool dispatch (#730), 504 on timeout, and the same 409 when the loader
+    degrades onto an engine with no weights on disk (#1185).
+    """
+    from services.asr_backend import (
+        ASRModelMissingError,
+        ASRTimeoutError,
+        asr_model_missing_detail,
+        asr_model_missing_error,
+        run_transcribe_guarded,
+    )
+
+    missing = await asyncio.to_thread(asr_model_missing_error, purpose="transcribe")
+    if missing is not None:
+        raise HTTPException(
+            status_code=409,
+            detail={**missing, "message": asr_model_missing_detail(missing)},
+        )
+
+    def _run():
+        # `load_*`, not `get_*`: the loader runs ensure_loaded() and degrades
+        # past an engine whose deep import chain is broken (#1185).
+        from services.asr_backend import load_active_asr_backend
+        backend = load_active_asr_backend()
+        return backend.transcribe(tmp_path, word_timestamps=False)
+
+    from services.model_manager import _gpu_pool
+    release = source_lease.acquire() if source_lease is not None else None
+    abandoned = False
+    try:
+        return await run_transcribe_guarded(
+            _gpu_pool,
+            _run,
+            what="Voice convert",
+            on_abandon=release,
+        )
+    except asyncio.CancelledError:
+        # The guard now owns the lease token until the native worker drains.
+        abandoned = True
+        raise
+    except ASRTimeoutError as e:
+        abandoned = True
+        logger.warning("Convert transcription timed out: %s", e)
+        raise HTTPException(status_code=504, detail=str(e))
+    except ASRModelMissingError as e:
+        raise HTTPException(
+            status_code=409,
+            detail={**e.payload, "message": asr_model_missing_detail(e.payload)},
+        )
+    finally:
+        if release is not None and not abandoned:
+            release()
+
+
+@router.post("/convert")
+async def convert_speech(
+    audio: UploadFile = File(...),
+    profile_id: str = Form(...),
+    match_duration: bool = Form(True),
+):
+    """Convert a spoken clip into an existing voice profile's voice.
+
+    Multipart form: ``audio`` (the source clip), ``profile_id`` (an existing
+    voice profile), optional ``match_duration`` (default on — atempo the take
+    toward the source clip's length, clamped to 0.5–2.0×).
+
+    Returns JSON ``{audio_url, text, duration_s, id}`` — the take is saved to
+    OUTPUTS_DIR and served from the ``/audio`` mount like every other take.
+    """
+    from core.db import db_conn
+    from api.routers.generation import _resolve_profile_conditioning, _TempReferenceLease
+
+    # ── Profile first: strict 404, unlike /generate's silent skip — Convert
+    # has no meaning without a target voice.
+    with db_conn() as conn:
+        row = conn.execute(
+            "SELECT * FROM voice_profiles WHERE id=?", (profile_id,)
+        ).fetchone()
+    if not row:
+        raise HTTPException(
+            status_code=404,
+            detail="That voice profile doesn't exist. It may have been deleted from another tab.",
+        )
+    cond = _resolve_profile_conditioning(row)
+
+    # ── Save the upload before loading an engine. Every ASR backend (and
+    # ffprobe) needs a file path; the bounded streaming copy rejects oversized
+    # network-share requests without materializing them in process memory or
+    # starting heavyweight model work.
+    ext = os.path.splitext(audio.filename or "audio.wav")[1] or ".wav"
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=ext)
+    source_lease = None
+    try:
+        try:
+            await _copy_source_upload(audio, tmp)
+        finally:
+            tmp.close()
+        source_lease = _TempReferenceLease(tmp.name)
+
+        # ── Engine gate before ASR/TTS work: the shared resolver refuses a
+        # clone-less engine with the actionable switch-engine message (→ 400),
+        # and a backend mid-shutdown raises ModelLoadInterruptedByShutdown out
+        # of the model load → the global 503 [shutting_down] handler.
+        from services.tts_backend import resolve_generation_backend
+        try:
+            backend = await resolve_generation_backend(
+                require_cloning=True, cloning_purpose="voice conversion",
+            )
+        except ValueError as e:
+            raise HTTPException(status_code=400, detail=str(e))
+
+        result = await _transcribe_source(tmp.name, source_lease=source_lease)
+
+        segments = result.get("segments", [])
+        text = result.get("text", "")
+        if not text and segments:
+            text = " ".join(s.get("text", "") for s in segments).strip()
+        # Same final-text hygiene as /transcribe: strip Whisper hallucination
+        # loops, then deterministic polish (leading capital + terminal
+        # punctuation) so the TTS input reads as typed text.
+        from services.refinement import collapse_repetitive_artifacts
+        from services.text_polish import polish_text
+        text = polish_text(collapse_repetitive_artifacts(text))
+        if not text or not text.strip():
+            raise HTTPException(
+                status_code=422,
+                detail=(
+                    "No speech was recognized in the source clip, so there is "
+                    "nothing to convert. Record or drop a clip with clear, "
+                    "audible speech and try again."
+                ),
+            )
+
+        # #308/#1032 parity with /generate: a clone profile saved without a
+        # transcript conditions better when its reference clip is transcribed,
+        # and that transcript is cached onto the row so it happens ONCE, not
+        # per convert. Best-effort exactly like /generate — a timeout/failure
+        # degrades to ref_text=None and the engine's own fallback. The ASR
+        # model is already warm here (the source transcribe above just used it).
+        if cond["ref_audio_path"] and not cond["ref_text"]:
+            from api.routers.generation import (
+                _generate_timeout_s,
+                _persist_profile_ref_text,
+            )
+            from services.asr_backend import transcribe_reference
+            from services.model_manager import run_on_gpu_pool_guarded
+            try:
+                cond["ref_text"] = await run_on_gpu_pool_guarded(
+                    functools.partial(transcribe_reference, cond["ref_audio_path"]),
+                    what="Reference transcribe",
+                    timeout=_generate_timeout_s(""),
+                )
+            except TimeoutError as e:
+                logger.warning(
+                    "reference transcribe hung (%s); using engine ASR fallback", e,
+                )
+                cond["ref_text"] = None
+            if cond["ref_text"] and cond["persist_ref_text"]:
+                _persist_profile_ref_text(profile_id, cond["ref_text"])
+
+        # Source duration for the optional match: the container's own length
+        # (ffprobe), falling back to the last ASR segment end. Best-effort —
+        # None just skips the stretch.
+        source_duration_s = None
+        if match_duration:
+            from services.ffmpeg_utils import probe_duration
+            source_duration_s = await probe_duration(
+                tmp.name, allowed_root=os.path.dirname(tmp.name),
+            )
+            if not source_duration_s and segments:
+                source_duration_s = max((s.get("end", 0) or 0) for s in segments) or None
+
+        # ── Same text choke point as /generate: engine-agnostic normalization
+        # (numbers→words, junk strip) on the fully resolved language.
+        from services.text_normalization import normalize_for_tts
+        language = cond["language"]
+        text = normalize_for_tts(text, language)
+
+        used_seed = cond["seed"]
+        if used_seed is None:
+            import random
+            used_seed = random.randint(0, 2**31 - 1)
+
+        from api.routers.generation import (
+            _finalize_generation,
+            _generate_timeout_s,
+            _run_backend_inference,
+        )
+        from services.model_manager import (
+            GpuJobTimeoutError,
+            GpuPoolBusyError,
+            run_on_gpu_pool_guarded,
+        )
+
+        start_time = time.time()
+        _render = functools.partial(
+            _run_backend_inference,
+            backend, text, language, cond["ref_audio_path"], cond["ref_text"],
+            cond["instruct"],
+            None,        # duration — the model picks; match_duration owns pacing
+            16, 2.0,     # num_step / guidance_scale (the /generate defaults)
+            1.0,         # speed
+            True, True,  # denoise / postprocess_output
+            used_seed,
+        )
+        try:
+            audio_tensor = await run_on_gpu_pool_guarded(
+                _render,
+                what="Voice convert",
+                timeout=_generate_timeout_s(
+                    text, min_vram_gb=getattr(type(backend), "min_vram_gb", 0.0),
+                ),
+                min_vram_gb=getattr(type(backend), "min_vram_gb", 0.0),
+            )
+        except GpuPoolBusyError as e:
+            raise HTTPException(
+                status_code=503, detail=str(e),
+                headers={"Retry-After": str(e.retry_after),
+                         "X-OmniVoice-Retryable": "true"},
+            ) from e
+        except GpuJobTimeoutError as e:
+            raise HTTPException(
+                status_code=503, detail=str(e),
+                headers={"Retry-After": "30", "X-OmniVoice-Retryable": "true"},
+            ) from e
+        except ValueError as e:
+            raise HTTPException(status_code=400, detail=str(e)) from e
+        sample_rate = backend.sample_rate
+
+        if match_duration and source_duration_s:
+            audio_tensor = await _match_source_duration(
+                audio_tensor, sample_rate, source_duration_s,
+            )
+
+        # Provenance mark AFTER the stretch (one whole-take mark on the audio
+        # the user actually keeps), then the shared finalize tail — WAV in
+        # OUTPUTS_DIR, self-healing history row, retention prune, event emit.
+        from services.watermark import mark_synthetic_async
+        audio_tensor = await mark_synthetic_async(
+            audio_tensor, sample_rate, context="convert.finalize",
+        )
+        _, meta = await _finalize_generation(
+            audio_tensor, sample_rate, text=text, history_mode="convert",
+            ref_audio_path=cond["ref_audio_path"], language=language,
+            instruct=cond["instruct"], resolved_profile_id=profile_id,
+            used_seed=used_seed, start_time=start_time,
+            already_marked=True,
+        )
+
+        return {
+            "id": meta["id"],
+            "audio_url": f"/audio/{meta['filename']}",
+            "text": text,
+            "duration_s": meta["duration"],
+            "gen_time_s": meta["gen_time"],
+        }
+    finally:
+        if source_lease is not None:
+            source_lease.finish_request()
+        else:
+            try:
+                os.unlink(tmp.name)
+            except OSError:
+                pass
@@ -23,13 +23,16 @@ appears and is replaced by the GPU gateway.
 from __future__ import annotations

 import asyncio
+import contextlib
 import logging

 from fastapi import APIRouter, Depends, HTTPException, Request
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field

 from api.dependencies import require_admin
 from worker import registry, routing, service
+from worker.async_utils import drain_task, to_thread_and_defer_cancellation

 logger = logging.getLogger("omnivoice.worker")

@@ -158,6 +161,19 @@ def agent_status() -> dict:
    return worker_agent.agent.status()


+@router.get("/agent/readiness", include_in_schema=False, response_model=None)
+def agent_readiness() -> JSONResponse:
+    """Container readiness: 200 only after this process registered as a worker."""
+    from worker import agent as worker_agent  # noqa: PLC0415
+
+    readiness = worker_agent.agent.readiness()
+    return JSONResponse(
+        status_code=200 if readiness["ready"] else 503,
+        content=readiness,
+        headers={} if readiness["ready"] else {"Retry-After": "2"},
+    )
+
+
 def _refuse_when_env_pinned(worker_agent) -> None:
    """OMNIVOICE_WORKER_MODE wins over the setting everywhere else.

@@ -176,6 +192,63 @@ def _refuse_when_env_pinned(worker_agent) -> None:
        )


+async def _finish_cleanup(awaitable):
+    """Run rollback to completion even if its HTTP task was cancelled."""
+    task = asyncio.create_task(awaitable)
+    await drain_task(task)
+    return task.result()
+
+
+async def _set_worker_mode(worker_agent, enabled: bool) -> None:
+    _result, cancelled = await to_thread_and_defer_cancellation(
+        worker_agent.set_worker_mode_enabled, enabled
+    )
+    if cancelled:
+        raise asyncio.CancelledError
+
+
+async def _restore_agent_transaction(
+    worker_agent, previous: dict, *, was_running: bool
+) -> None:
+    """Restore durable enrollment/settings and the exact prior live state."""
+    try:
+        await _finish_cleanup(worker_agent.agent.stop())
+        await _finish_cleanup(worker_agent.restore_enrollment(previous))
+        if was_running and not worker_agent.agent.running:
+            await _finish_cleanup(worker_agent.agent.start())
+        elif not was_running and worker_agent.agent.running:
+            await _finish_cleanup(worker_agent.agent.stop())
+    except worker_agent.EnrollmentRollbackError:
+        raise
+    except BaseException as exc:
+        message = (
+            "The previous worker state could not be restored safely. "
+            "Worker mode remains stopped; fix its enrollment/settings storage, then retry."
+        )
+        with contextlib.suppress(BaseException):
+            await _finish_cleanup(worker_agent.agent.stop())
+        worker_agent.agent.last_error = message
+        raise worker_agent.EnrollmentRollbackError(message) from exc
+
+
+def _raise_agent_transaction_failure(
+    worker_agent, operation: BaseException, rollback: BaseException | None
+) -> None:
+    if isinstance(operation, asyncio.CancelledError):
+        if rollback is not None:
+            logger.error(
+                "Worker rollback failed during request cancellation",
+                exc_info=(type(rollback), rollback, rollback.__traceback__),
+            )
+        raise operation
+    if rollback is not None:
+        raise HTTPException(status_code=409, detail=str(rollback)) from rollback
+    if isinstance(operation, Exception):
+        worker_agent.agent.last_error = str(operation)
+        raise HTTPException(status_code=409, detail=str(operation)) from operation
+    raise operation
+
+
@router.post("/agent/join")
 async def join_control_plane(request: JoinRequest) -> dict:
    """Redeem a join code and start working for that control plane.
@@ -200,26 +273,37 @@ async def join_control_plane(request: JoinRequest) -> dict:
    # says it joined and never lends anything (CodeRabbit).
    _refuse_when_env_pinned(worker_agent)
    async with worker_agent.agent.lifecycle:
-        # A rejoin replaces a working enrollment. Keep enough to put it back:
-        # pinning the new certificate overwrites the old one on disk, so a
-        # failed rejoin would otherwise leave the machine unable to reconnect
-        # to the control plane it was already serving.
-        previous = worker_agent.snapshot_enrollment()
-        await worker_agent.agent.stop()
        try:
+            previous, cancelled = await to_thread_and_defer_cancellation(
+                worker_agent.snapshot_enrollment
+            )
+        except worker_agent.EnrollmentStateError as exc:
+            worker_agent.agent.last_error = str(exc)
+            raise HTTPException(status_code=409, detail=str(exc)) from exc
+        if cancelled:
+            raise asyncio.CancelledError
+        was_running = worker_agent.agent.running
+
+        # A rejoin stops a working agent before the replacement is accepted.
+        # Stop, acceptance and the durable setting are one transaction: every
+        # failure, including cancellation, restores both trust and live state.
+        try:
+            await worker_agent.agent.stop()
            await worker_agent.agent.start(token_text=token)
            # Success is the control plane ACCEPTING this worker, not the
            # connection being scheduled — see wait_until_registered.
            await worker_agent.agent.wait_until_registered()
-        except Exception as exc:
-            worker_agent.agent.last_error = str(exc)
-            await worker_agent.agent.stop()
-            await worker_agent.restore_enrollment(previous)
-            raise HTTPException(status_code=409, detail=str(exc)) from exc
+            await _set_worker_mode(worker_agent, True)
+        except BaseException as exc:
+            rollback_exc = None
+            try:
+                await _restore_agent_transaction(
+                    worker_agent, previous, was_running=was_running
+                )
+            except BaseException as rollback_error:
+                rollback_exc = rollback_error
+            _raise_agent_transaction_failure(worker_agent, exc, rollback_exc)
        worker_agent.agent.last_error = ""
-        # Persisted only after the join actually worked: a machine that failed
-        # to enrol must not come back up trying again forever.
-        worker_agent.set_worker_mode_enabled(True)
        return worker_agent.agent.status()


@@ -235,19 +319,35 @@ async def set_agent_enabled(request: EnableRequest) -> dict:

    _refuse_when_env_pinned(worker_agent)
    async with worker_agent.agent.lifecycle:
-        if request.enabled:
-            try:
+        try:
+            previous, cancelled = await to_thread_and_defer_cancellation(
+                worker_agent.snapshot_enrollment
+            )
+        except worker_agent.EnrollmentStateError as exc:
+            worker_agent.agent.last_error = str(exc)
+            raise HTTPException(status_code=409, detail=str(exc)) from exc
+        if cancelled:
+            raise asyncio.CancelledError
+        was_running = worker_agent.agent.running
+
+        try:
+            if request.enabled:
                await worker_agent.agent.start()
                await worker_agent.agent.wait_until_registered()
-            except Exception as exc:
-                worker_agent.agent.last_error = str(exc)
+                await _set_worker_mode(worker_agent, True)
+            else:
                await worker_agent.agent.stop()
-                raise HTTPException(status_code=409, detail=str(exc)) from exc
-            worker_agent.agent.last_error = ""
-            worker_agent.set_worker_mode_enabled(True)
-        else:
-            await worker_agent.agent.stop()
-            worker_agent.set_worker_mode_enabled(False)
+                await _set_worker_mode(worker_agent, False)
+        except BaseException as exc:
+            rollback_exc = None
+            try:
+                await _restore_agent_transaction(
+                    worker_agent, previous, was_running=was_running
+                )
+            except BaseException as rollback_error:
+                rollback_exc = rollback_error
+            _raise_agent_transaction_failure(worker_agent, exc, rollback_exc)
+        worker_agent.agent.last_error = ""
        return worker_agent.agent.status()


@@ -263,9 +363,14 @@ def create_enrollment(request: EnrollRequest) -> dict:
            status_code=409,
            detail="Remote workers are turned off. Enable them in Settings → System → Remote workers first.",
        )
-    token = service.control_plane.create_enrollment(
-        endpoint=request.endpoint, label=request.label, ttl_seconds=request.ttl_seconds
-    )
+    try:
+        token = service.control_plane.create_enrollment(
+            endpoint=request.endpoint,
+            label=request.label,
+            ttl_seconds=request.ttl_seconds,
+        )
+    except service.EndpointCertificateError as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
    return {
        "token": token.encode(),
        "endpoint": token.endpoint,
@@ -275,23 +380,55 @@ def create_enrollment(request: EnrollRequest) -> dict:
    }


+def _persist_worker_update(
+    worker_id: str, request: WorkerUpdate
+):
+    """Write policy on a worker thread; live publication stays loop-owned."""
+    return registry.update_policy(
+        worker_id,
+        name=request.name,
+        enabled=request.enabled,
+        priority=request.priority,
+    )
+
+
@router.patch("/{worker_id}")
-def update_worker(worker_id: str, request: WorkerUpdate) -> dict:
-    worker = registry.get(worker_id)
-    if worker is None:
+async def update_worker(worker_id: str, request: WorkerUpdate) -> dict:
+    pool = service.control_plane.pool if service.control_plane.running else None
+    live = None
+    was_pending = False
+    if pool is not None:
+        # Quiesce dispatch before releasing authority for the SQLite write.
+        # The publication after the await restores the exact prior state, so a
+        # concurrent registration handoff remains quiesced for its own reason.
+        with registry.authority_guard():
+            live = pool.get(worker_id)
+            if live is not None:
+                was_pending = live.registration_pending
+                live.registration_pending = True
+    updated = None
+    cancelled = False
+    try:
+        updated, cancelled = await to_thread_and_defer_cancellation(
+            _persist_worker_update, worker_id, request
+        )
+    finally:
+        if pool is not None:
+            with registry.authority_guard():
+                if updated is not None:
+                    # Pool state, including the cached record the scheduler
+                    # reads, belongs to the app's event loop.
+                    pool.refresh_record(updated)
+                current = pool.get(worker_id)
+                if current is live:
+                    current.registration_pending = was_pending
+    if updated is None:
+        if cancelled:
+            raise asyncio.CancelledError
        raise HTTPException(status_code=404, detail="No such worker.")
-    if request.name is not None:
-        registry.rename(worker_id, request.name)
-    if request.enabled is not None:
-        registry.set_enabled(worker_id, request.enabled)
-    if request.priority is not None:
-        registry.set_priority(worker_id, request.priority)
-    updated = registry.get(worker_id)
-    # Keep the live copy in step, so the scheduler and its logs do not go on
-    # using the name or priority this worker had when it connected.
-    if updated is not None and service.control_plane.running:
-        service.control_plane.pool.refresh_record(updated)
-    return updated.to_dict() if updated else {}
+    if cancelled:
+        raise asyncio.CancelledError
+    return updated.to_dict()


@router.post("/{worker_id}/consent")
@@ -305,7 +442,7 @@ def grant_consent(worker_id: str) -> dict:


@router.post("/{worker_id}/resume")
-def clear_breaker(worker_id: str) -> dict:
+async def clear_breaker(worker_id: str) -> dict:
    """Clear a paused worker's circuit breakers.

    The user fixed the machine and knows it — a breaker with no manual clear is
@@ -320,18 +457,53 @@ def clear_breaker(worker_id: str) -> dict:


@router.delete("/{worker_id}")
-def revoke_worker(worker_id: str) -> dict:
+async def revoke_worker(worker_id: str) -> dict:
    """Remove a worker — which means revoke its key, not hide the row.

    Its in-flight work is released so it can be retried elsewhere rather than
    waiting out a lease on a machine that will never answer again.
    """
-    if registry.get(worker_id) is None:
+    pool = service.control_plane.pool if service.control_plane.running else None
+    live = None
+    was_pending = False
+    if pool is not None:
+        with registry.authority_guard():
+            live = pool.get(worker_id)
+            if live is not None:
+                was_pending = live.registration_pending
+                live.registration_pending = True
+    try:
+        revoked, cancelled = await to_thread_and_defer_cancellation(
+            registry.revoke, worker_id
+        )
+    except BaseException:
+        if pool is not None:
+            with registry.authority_guard():
+                current = pool.get(worker_id)
+                if current is live:
+                    current.registration_pending = was_pending
+        raise
+    if not revoked:
+        if pool is not None:
+            with registry.authority_guard():
+                current = pool.get(worker_id)
+                if current is live:
+                    current.registration_pending = was_pending
+        if cancelled:
+            raise asyncio.CancelledError
        raise HTTPException(status_code=404, detail="No such worker.")
-    registry.revoke(worker_id)
-    if service.control_plane.running:
-        service.control_plane.scheduler.on_disconnected(worker_id)
-        service.control_plane.pool.breakers.forget_worker(worker_id)
+
+    # The tombstone committed before any egress/session mutation. Everything
+    # below is loop-owned and published under the same scheduler authority read
+    # used by next_assignment(), so no task can bind in the handoff window.
+    with registry.authority_guard():
+        if service.control_plane.running:
+            if service.control_plane.servicer is not None:
+                service.control_plane.servicer.revoke_worker_sessions(worker_id)
+            service.control_plane.scheduler.on_disconnected(worker_id)
+            service.control_plane.pool.breakers.forget_worker(worker_id)
+    if cancelled:
+        raise asyncio.CancelledError
    return {"ok": True, "revoked": worker_id}


@@ -375,7 +547,9 @@ async def submit_task(request: Request, body: SubmitTaskRequest) -> dict:

    scheduler = service.control_plane.scheduler
    try:
-        task = scheduler.submit(
+        submit = getattr(scheduler, "submit_async", None)
+        submit = submit if callable(submit) else scheduler.submit
+        submitted = submit(
            operation=body.operation,
            engine=body.engine,
            model_id=body.model_id,
@@ -384,6 +558,7 @@ async def submit_task(request: Request, body: SubmitTaskRequest) -> dict:
            deadline_seconds=body.deadline_seconds,
            pinned_worker_id=routing.decide().worker_id or None,
        )
+        task = await submitted if asyncio.iscoroutine(submitted) else submitted
    except QueueFull as exc:
        raise HTTPException(status_code=429, detail=str(exc)) from exc

@@ -505,8 +680,33 @@ async def set_inbound_enabled(request: InboundEnableRequest) -> dict:
                "machine. Change that environment setting and restart VoiceStudio."
            ),
        )
+
+    requested_bind = (
+        inbound_service.normalise_bind_host(request.bind)
+        if request.bind
+        else inbound_service.bind_host()
+    )
+    requested_port = request.port or inbound_service.bind_port()
+    if (
+        request.enabled
+        and inbound_service.node.running
+        and (
+            requested_bind != inbound_service.bind_host()
+            or requested_port != inbound_service.node.port
+        )
+    ):
+        # start() is intentionally idempotent while a listener owns its
+        # socket. Persisting a new endpoint here would make the UI report a
+        # narrower/different bind while the original socket stayed live.
+        raise HTTPException(
+            status_code=409,
+            detail=(
+                "Turn off Accept connections before changing its bind address "
+                "or port."
+            ),
+        )
    if request.bind:
-        inbound_service.set_bind_host(request.bind)
+        inbound_service.set_bind_host(requested_bind)
    if request.port:
        inbound_service.set_bind_port(request.port)
    inbound_service.set_enabled(request.enabled)
@@ -535,6 +735,7 @@ def issue_inbound_key(request: IssueKeyRequest) -> dict:
    is stored, so it cannot be shown again, only replaced.
    """
    from worker.inbound import service as inbound_service  # noqa: PLC0415
+    from worker.inbound.keys import KeyLimitExceeded  # noqa: PLC0415

    if not inbound_service.node.running:
        raise HTTPException(
@@ -544,7 +745,10 @@ def issue_inbound_key(request: IssueKeyRequest) -> dict:
                "Settings → System → Remote workers → Accept connections first."
            ),
        )
-    issued = inbound_service.node.keys.issue(request.label)
+    try:
+        issued = inbound_service.node.keys.issue(request.label)
+    except KeyLimitExceeded as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
    return {
        "key_id": issued.key.key_id,
        "label": issued.key.label,
@@ -555,12 +759,12 @@ def issue_inbound_key(request: IssueKeyRequest) -> dict:


@router.delete("/inbound/keys/{key_id}")
-def revoke_inbound_key(key_id: str) -> dict:
+async def revoke_inbound_key(key_id: str) -> dict:
    """Revoke one panel. Everyone else stays connected — the whole reason keys
    are per panel rather than one shared node key."""
    from worker.inbound import service as inbound_service  # noqa: PLC0415

-    if not inbound_service.node.keys.revoke(key_id):
+    if not await inbound_service.node.revoke_key(key_id):
        raise HTTPException(status_code=404, detail="No such key.")
    return inbound_service.node.snapshot()

@@ -579,6 +783,7 @@ async def add_inbound_connection(request: ConnectRequest) -> dict:
    """Paste a connection string from a GPU machine and dial it."""
    from worker.inbound import service as inbound_service  # noqa: PLC0415
    from worker.inbound.connection_string import InvalidConnectionString  # noqa: PLC0415
+    from worker.inbound.connector import InboundConnectionError  # noqa: PLC0415

    if not service.control_plane.running:
        raise HTTPException(
@@ -597,12 +802,18 @@ async def add_inbound_connection(request: ConnectRequest) -> dict:
        # surfaces as "cannot connect", which is what a firewall, a wrong port
        # and a dead node all say too.
        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    except InboundConnectionError as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
    return {"endpoint": connection.endpoint, "connections": inbound_service.outbound.snapshot()}


@router.delete("/inbound/connections/{endpoint}")
 async def remove_inbound_connection(endpoint: str) -> dict:
    from worker.inbound import service as inbound_service  # noqa: PLC0415
+    from worker.inbound.connector import InboundConnectionError  # noqa: PLC0415

-    await inbound_service.outbound.remove(endpoint)
+    try:
+        await inbound_service.outbound.remove(endpoint)
+    except InboundConnectionError as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
    return {"connections": inbound_service.outbound.snapshot()}
@@ -26,6 +26,21 @@ class SystemInfoResponse(BaseModel):
    model_config = ConfigDict(extra="allow")

    app_version: str = ""
+    # Effective compute-time budgets (seconds) for one synthesis job — the
+    # values services/model_manager.py's GPU_JOB_TIMEOUT_S / CPU_JOB_TIMEOUT_S
+    # captured at backend import time (#1787). A value just saved via
+    # /system/set-env is NOT reflected here until the next restart.
+    generate_timeout_s: float = 300.0
+    cpu_generate_timeout_s: float = 600.0
+    # True when an external env var (shell, `.env`, Docker, …) is currently
+    # shadowing a prefs.json save for this key — see core.prefs.is_env_shadowed.
+    generate_timeout_shadowed: bool = False
+    cpu_generate_timeout_shadowed: bool = False
+    # #1770: the desktop attach handshake's code fingerprint — whatever
+    # Tauri set OMNIVOICE_BUILD_FINGERPRINT to when it spawned this process,
+    # echoed back verbatim. Blank when unset (dev mode, a manually started
+    # backend). See frontend/src-tauri/src/backend.rs::code_fingerprint_is_current.
+    code_fingerprint: str = ""
    data_dir: str
    outputs_dir: str
    crash_log_path: str
@@ -159,17 +159,16 @@ models:
  - repo_id: "csukuangfj/sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8"
    label: "Parakeet TDT v3 (sherpa-onnx — dictation, 25 EU langs)"
    role: ASR
-    size_gb: 0.18
+    size_gb: 0.67
    engine: sherpa-onnx
    dictation_id: sherpa-parakeet-tdt-v3
    tag: offline
-    curated_on: [all]
-    note: "Recommended live-dictation default. CPU, int8 ONNX. Requires sherpa-onnx."
+    note: "Multilingual European-language dictation. CPU, int8 ONNX. Requires sherpa-onnx."

  - repo_id: "csukuangfj/sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8"
    label: "Parakeet TDT v2 (sherpa-onnx — dictation, English)"
    role: ASR
-    size_gb: 0.17
+    size_gb: 0.66
    engine: sherpa-onnx
    dictation_id: sherpa-parakeet-tdt-v2
    tag: offline
@@ -178,7 +177,7 @@ models:
  - repo_id: "csukuangfj/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20"
    label: "Zipformer Bilingual (sherpa-onnx — streaming, zh+en)"
    role: ASR
-    size_gb: 0.13
+    size_gb: 0.2
    engine: sherpa-onnx
    dictation_id: sherpa-zipformer-bilingual-zh-en
    tag: streaming
@@ -187,7 +186,7 @@ models:
  - repo_id: "csukuangfj/sherpa-onnx-streaming-paraformer-bilingual-zh-en"
    label: "Paraformer Bilingual (sherpa-onnx — streaming, zh+en)"
    role: ASR
-    size_gb: 0.115
+    size_gb: 0.24
    engine: sherpa-onnx
    dictation_id: sherpa-paraformer-bilingual-zh-en
    tag: streaming
@@ -196,7 +195,7 @@ models:
  - repo_id: "csukuangfj/sherpa-onnx-streaming-zipformer-en-20M-2023-02-17"
    label: "Zipformer Streaming EN 20M (sherpa-onnx — streaming, English)"
    role: ASR
-    size_gb: 0.128
+    size_gb: 0.044
    engine: sherpa-onnx
    dictation_id: sherpa-zipformer-en-20m
    tag: streaming
@@ -205,7 +204,7 @@ models:
  - repo_id: "csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23"
    label: "Zipformer Streaming ZH 14M (sherpa-onnx — streaming, Chinese)"
    role: ASR
-    size_gb: 0.074
+    size_gb: 0.025
    engine: sherpa-onnx
    dictation_id: sherpa-zipformer-zh-14m
    tag: streaming
@@ -214,11 +213,12 @@ models:
  - repo_id: "csukuangfj/sherpa-onnx-whisper-tiny"
    label: "Whisper Tiny (sherpa-onnx — dictation, 90+ langs)"
    role: ASR
-    size_gb: 0.116
+    size_gb: 0.104
    engine: sherpa-onnx
    dictation_id: sherpa-whisper-tiny
    tag: offline
-    note: "Multilingual offline dictation (auto-detect). CPU, int8 ONNX. Requires sherpa-onnx."
+    curated_on: [all]
+    note: "Recommended cross-platform dictation default (auto-detect). CPU, int8 ONNX. Requires sherpa-onnx."

  # ── Diarisation ───────────────────────────────────────────────────────

@@ -15,6 +15,18 @@ def get_app_data_dir():
        return os.path.expanduser("~/.omnivoice")


+def _configured_hf_token_path():
+    """Match Hub's token location without importing or refreshing credentials."""
+    default_cache = os.path.join(os.path.expanduser("~"), ".cache")
+    hf_home = os.environ.get("HF_HOME", os.path.join(os.environ.get("XDG_CACHE_HOME", default_cache), "huggingface"))
+    return os.path.expandvars(os.path.expanduser(os.environ.get("HF_TOKEN_PATH", os.path.join(hf_home, "token"))))
+
+
+# Snapshot recognized locations before automatic model-cache redirection.
+# Explicit cache/token overrides restrict clearing to their selected location.
+HF_CLI_TOKEN_PATHS = (_configured_hf_token_path(),)
+
+
 def _ensure_short_hf_cache_on_windows():
    """Redirect HuggingFace cache to a short path on Windows.

@@ -38,6 +50,14 @@ def _ensure_short_hf_cache_on_windows():
        return
    short_cache = os.path.join(local_app, "OmniVoice", "hf_cache")
    os.makedirs(short_cache, exist_ok=True)
+    if "HF_TOKEN_PATH" not in os.environ:
+        global HF_CLI_TOKEN_PATHS
+        canonical = HF_CLI_TOKEN_PATHS[0]
+        legacy = os.path.join(short_cache, "token")
+        HF_CLI_TOKEN_PATHS = tuple(dict.fromkeys((canonical, legacy)))
+        # Keep existing app-written logins usable without copying credentials.
+        selected = canonical if os.path.exists(canonical) or not os.path.exists(legacy) else legacy
+        os.environ.setdefault("HF_TOKEN_PATH", selected)
    os.environ["HF_HOME"] = short_cache
    os.environ["HF_HUB_CACHE"] = short_cache

@@ -0,0 +1,716 @@
+"""Nested subprocess ownership for desktop-managed backend operations.
+
+The desktop owns the backend with an OS process group/Job.  Engine and
+installer operations also need an independently terminable subtree: killing
+only their direct child on a timeout leaves uv/git/model workers holding pipes
+and mutating files.
+
+On POSIX a small supervisor is the unreaped leader of a nested process group.
+A control-pipe EOF (including kernel EOF when the backend dies) kills that
+group; the parent also drains the group before reaping its stable leader. On
+Windows the backend retains a nested kill-on-close Job directly and assigns
+the suspended operation before resuming it. The outer desktop Job remains the
+terminal fallback.
+
+Standalone/server launches use the same nested owner, preserving their
+independently terminable subtree without relying on ``taskkill`` or discovery.
+"""
+from __future__ import annotations
+
+import os
+import signal
+import struct
+import subprocess
+import sys
+import threading
+import time
+from pathlib import Path
+from typing import Any, Optional
+
+
+_RESULT = struct.Struct("!i")
+_DESKTOP_MARKER = "OMNIVOICE_DESKTOP_CONTAINED"
+_DRAIN_FD_ENV = "OMNIVOICE_DESKTOP_DRAIN_FD"
+
+
+def backend_drain_fd(*, required: bool = False) -> Optional[int]:
+    """Validated Rust-owned drain writer inherited by the desktop backend."""
+    if os.name != "posix" or os.environ.get(_DESKTOP_MARKER) != "1":
+        return None
+    try:
+        fd = int(os.environ[_DRAIN_FD_ENV])
+        os.fstat(fd)
+    except (KeyError, ValueError, OSError) as exc:
+        if required:
+            raise RuntimeError(
+                "desktop backend is missing its live nested-operation drain descriptor"
+            ) from exc
+        return None
+    return fd
+
+
+def secure_backend_drain_fd() -> None:
+    """Restore CLOEXEC after Rust's one intentional backend inheritance."""
+    fd = backend_drain_fd(required=True)
+    if fd is not None:
+        os.set_inheritable(fd, False)
+
+
+class OwnedPopen:
+    """Popen-compatible handle for a desktop-owned nested operation."""
+
+    def __init__(
+        self,
+        proc: subprocess.Popen,
+        control_fd: int,
+        result_fd: int,
+    ) -> None:
+        self._proc = proc
+        self._control_fd: Optional[int] = control_fd
+        self._result_fd: Optional[int] = result_fd
+        self._returncode: Optional[int] = None
+        self._lock = threading.RLock()
+
+        # Popen callers use these directly (protocol pipes and log drains).
+        self.stdin = proc.stdin
+        self.stdout = proc.stdout
+        self.stderr = proc.stderr
+
+    @property
+    def pid(self) -> int:
+        return self._proc.pid
+
+    @property
+    def args(self) -> Any:
+        return self._proc.args
+
+    @property
+    def returncode(self) -> Optional[int]:
+        return self._returncode
+
+    def _close_control(self) -> None:
+        fd, self._control_fd = self._control_fd, None
+        if fd is not None:
+            try:
+                os.close(fd)
+            except OSError:
+                # Cleanup is idempotent; another teardown path already closed it.
+                pass
+
+    def _read_result(self, fallback: int) -> int:
+        fd, self._result_fd = self._result_fd, None
+        if fd is None:
+            return fallback
+        try:
+            payload = b""
+            while len(payload) < _RESULT.size:
+                chunk = os.read(fd, _RESULT.size - len(payload))
+                if not chunk:
+                    break
+                payload += chunk
+            return _RESULT.unpack(payload)[0] if len(payload) == _RESULT.size else fallback
+        except OSError:
+            return fallback
+        finally:
+            try:
+                os.close(fd)
+            except OSError:
+                # The descriptor may have been closed by cancellation cleanup.
+                pass
+
+    def _posix_exited_unreaped(self) -> bool:
+        flags = os.WEXITED | os.WNOHANG | os.WNOWAIT
+        info = os.waitid(os.P_PID, self.pid, flags)
+        return info is not None and info.si_pid != 0
+
+    def _posix_exited_reaping(self) -> Optional[int]:
+        """macOS fallback for :meth:`_posix_exited_unreaped` (#1656).
+
+        CPython on macOS does not expose ``os.waitid`` (HAVE_WAITID is not set
+        in its build), so the WNOWAIT probe is unavailable there. This
+        fallback *reaps* the wrapper with ``waitpid(WNOHANG)``: it returns
+        the wrapper's exit code once it has exited, None while it is still
+        running, and raises ``ChildProcessError`` when another owner already
+        reaped it (the same refusal the waitid probe gives).
+
+        Reaping earlier than the WNOWAIT dance loses the pre-reap group kill
+        in :meth:`poll`; that is safe because the supervisor's control-pipe
+        EOF already terminates the whole nested group (#1635 design).
+        """
+        pid, status = os.waitpid(self.pid, os.WNOHANG)
+        if pid != self.pid:
+            return None
+        rc = os.waitstatus_to_exitcode(status)
+        # Publish on the underlying Popen so its own wait()/poll() no-op.
+        self._proc.returncode = rc
+        return rc
+
+    def _posix_exit_state_reaping(self) -> Optional[int]:
+        """:meth:`_posix_exited_reaping` plus one concession: if the leader
+        was already reaped through *this* Popen (``_proc.returncode`` known),
+        report that code rather than refusing — reaping by our own handle is
+        not the foreign reaper the ECHILD refusal exists for."""
+        try:
+            return self._posix_exited_reaping()
+        except ChildProcessError:
+            return self._proc.returncode
+
+    def _signal_owned_group(self, sig: int) -> None:
+        # The numeric group is safe only while its direct-child leader remains
+        # ours and unreaped.  ECHILD therefore refuses rather than guessing.
+        try:
+            os.waitid(os.P_PID, self.pid, os.WEXITED | os.WNOHANG | os.WNOWAIT)
+        except ChildProcessError:
+            return
+        except AttributeError:
+            # macOS CPython has no os.waitid (#1656). waitpid still proves
+            # that this exact numeric pid is our live child: ECHILD refuses a
+            # foreign-reaped/reused pid, while pid == self.pid records an exit
+            # without ever signalling the now-unowned process-group number.
+            try:
+                pid, status = os.waitpid(self.pid, os.WNOHANG)
+            except ChildProcessError:
+                return
+            if pid == self.pid:
+                self._proc.returncode = os.waitstatus_to_exitcode(status)
+                return
+        try:
+            os.killpg(self.pid, sig)
+        except ProcessLookupError:
+            # The owned group exited between the waitid probe and the signal.
+            pass
+
+    def poll(self) -> Optional[int]:
+        with self._lock:
+            if self._returncode is not None:
+                return self._returncode
+            if os.name == "posix":
+                try:
+                    if hasattr(os, "waitid"):
+                        if not self._posix_exited_unreaped():
+                            return None
+                        self._signal_owned_group(signal.SIGKILL)
+                        wrapper_rc = self._proc.wait()
+                    else:
+                        # macOS CPython: no os.waitid (#1656) — the reaping
+                        # probe already terminated/killed nothing; the group
+                        # is torn down by the control-pipe EOF in _close_control.
+                        wrapper_rc = self._posix_exit_state_reaping()
+                        if wrapper_rc is None:
+                            return None
+                except ChildProcessError:
+                    # Never signal a potentially reused group after another
+                    # owner reaped the stable leader.
+                    return None
+            else:
+                wrapper_rc = self._proc.poll()
+                if wrapper_rc is None:
+                    return None
+            self._close_control()
+            self._returncode = self._read_result(wrapper_rc)
+            return self._returncode
+
+    def wait(self, timeout: Optional[float] = None) -> int:
+        deadline = None if timeout is None else time.monotonic() + timeout
+        while True:
+            rc = self.poll()
+            if rc is not None:
+                return rc
+            if deadline is not None and time.monotonic() >= deadline:
+                raise subprocess.TimeoutExpired(self.args, timeout)
+            time.sleep(0.01)
+
+    def terminate(self) -> None:
+        with self._lock:
+            if self._returncode is not None:
+                return
+            self._close_control()
+            if os.name == "posix":
+                self._signal_owned_group(signal.SIGTERM)
+            else:
+                # Closing the control pipe asks the supervisor to terminate
+                # its nested Job.  The stable wrapper handle is a fallback.
+                try:
+                    self._proc.terminate()
+                except OSError:
+                    # The wrapper exited after the return-code check.
+                    pass
+
+    def kill(self) -> None:
+        with self._lock:
+            if self._returncode is not None:
+                return
+            self._close_control()
+            if os.name == "posix":
+                self._signal_owned_group(signal.SIGKILL)
+            else:
+                try:
+                    self._proc.kill()
+                except OSError:
+                    # The wrapper exited after the return-code check.
+                    pass
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._proc, name)
+
+    def __del__(self) -> None:
+        self._close_control()
+        fd, self._result_fd = self._result_fd, None
+        if fd is not None:
+            try:
+                os.close(fd)
+            except OSError:
+                # Finalization may race explicit wait or cancellation cleanup.
+                pass
+
+
+class WindowsJobPopen:
+    """Popen-compatible handle whose child tree lives in a retained Job.
+
+    Windows Job handles already provide the stable ownership that POSIX needs
+    a supervisor process group for. Keeping the handle in the backend means an
+    abrupt backend exit closes it in the kernel and kills the whole operation
+    tree, without inserting a second Python process in the sidecar loader path
+    (#1734).
+    """
+
+    def __init__(self, proc: subprocess.Popen, job: Any, kernel32: Any) -> None:
+        self._proc = proc
+        self._job = job
+        self._kernel32 = kernel32
+        self._lock = threading.RLock()
+        self.stdin = proc.stdin
+        self.stdout = proc.stdout
+        self.stderr = proc.stderr
+
+    @property
+    def pid(self) -> int:
+        return self._proc.pid
+
+    @property
+    def args(self) -> Any:
+        return self._proc.args
+
+    @property
+    def returncode(self) -> Optional[int]:
+        return self._proc.returncode
+
+    def _close_job(self, *, terminate: bool) -> None:
+        job, self._job = self._job, None
+        if job is None:
+            return
+        try:
+            if terminate:
+                self._kernel32.TerminateJobObject(job, 1)
+        finally:
+            self._kernel32.CloseHandle(job)
+
+    def poll(self) -> Optional[int]:
+        with self._lock:
+            rc = self._proc.poll()
+            if rc is None:
+                return None
+            # A successful direct child may leave helpers behind. Match the
+            # supervisor contract by draining the retained Job before return.
+            self._close_job(terminate=True)
+            return rc
+
+    def wait(self, timeout: Optional[float] = None) -> int:
+        try:
+            rc = self._proc.wait(timeout=timeout)
+        except subprocess.TimeoutExpired:
+            raise
+        with self._lock:
+            self._close_job(terminate=True)
+        return rc
+
+    def terminate(self) -> None:
+        with self._lock:
+            self._close_job(terminate=True)
+
+    def kill(self) -> None:
+        self.terminate()
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._proc, name)
+
+    def __del__(self) -> None:
+        try:
+            self._close_job(terminate=True)
+        except Exception:
+            pass  # interpreter shutdown; closing the OS handle is best-effort
+
+
+def _spawn_windows_owned(argv: list[str], kwargs: dict[str, Any]) -> WindowsJobPopen:
+    """Start *argv* suspended, assign its tree to a Job, then resume it."""
+    import ctypes
+
+    job, kernel32, wintypes = _windows_job()
+    child: Optional[subprocess.Popen] = None
+    popen_kwargs = dict(kwargs)
+    supplied_env = popen_kwargs.get("env")
+    operation_env = dict(os.environ if supplied_env is None else supplied_env)
+    operation_env.pop(_DRAIN_FD_ENV, None)
+    operation_env.pop(_DESKTOP_MARKER, None)
+    popen_kwargs["env"] = operation_env
+    supplied_flags = int(popen_kwargs.pop("creationflags", 0))
+    popen_kwargs["creationflags"] = supplied_flags | 0x08000000 | 0x00000004
+    try:
+        child = subprocess.Popen(argv, **popen_kwargs)
+        assign = kernel32.AssignProcessToJobObject
+        assign.argtypes = (wintypes.HANDLE, wintypes.HANDLE)
+        assign.restype = wintypes.BOOL
+        if not assign(job, wintypes.HANDLE(child._handle)):
+            raise OSError(ctypes.get_last_error(), "AssignProcessToJobObject")
+        _resume_windows_process(kernel32, wintypes, child.pid)
+        return WindowsJobPopen(child, job, kernel32)
+    except BaseException:
+        kernel32.TerminateJobObject(job, 1)
+        if child is not None:
+            try:
+                child.kill()
+            except OSError:
+                pass  # the suspended child may already have exited
+            try:
+                child.wait(timeout=5)
+            except (OSError, subprocess.TimeoutExpired):
+                pass  # Job termination remains the authoritative cleanup
+        kernel32.CloseHandle(job)
+        raise
+
+
+def spawn_owned(
+    argv: list[str], **kwargs: Any
+) -> "subprocess.Popen | OwnedPopen | WindowsJobPopen":
+    """Spawn an operation with a stable, independently terminable owner."""
+
+    if os.name == "nt":
+        return _spawn_windows_owned(argv, kwargs)
+
+    drain_fd = backend_drain_fd(required=True)
+    control_read, control_write = os.pipe()
+    result_read, result_write = os.pipe()
+    wrapper_argv = _supervisor_argv(
+        control_read,
+        result_write,
+        argv,
+    )
+    wrapper_kwargs = dict(kwargs)
+    wrapper_kwargs["start_new_session"] = True
+    pass_fds = [control_read, result_write]
+    if drain_fd is not None:
+        pass_fds.append(drain_fd)
+        if wrapper_kwargs.get("env") is not None:
+            wrapper_env = dict(wrapper_kwargs["env"])
+            wrapper_env[_DESKTOP_MARKER] = "1"
+            wrapper_env[_DRAIN_FD_ENV] = str(drain_fd)
+            wrapper_kwargs["env"] = wrapper_env
+    wrapper_kwargs["pass_fds"] = tuple(pass_fds)
+    try:
+        proc = subprocess.Popen(wrapper_argv, **wrapper_kwargs)
+    except BaseException:
+        # The finally block exclusively owns the child-side endpoints. Closing
+        # them here as well risks closing a reused descriptor in another thread.
+        for fd in (control_write, result_read):
+            try:
+                os.close(fd)
+            except OSError:
+                # A partial spawn may already have closed a parent-side endpoint.
+                pass
+        raise
+    finally:
+        for fd in (control_read, result_write):
+            try:
+                os.close(fd)
+            except OSError:
+                # Popen may have consumed an inherited child-side endpoint.
+                pass
+    return OwnedPopen(proc, control_write, result_read)
+
+
+def _supervisor_argv(
+    control_token: int,
+    result_token: int,
+    argv: list[str],
+) -> list[str]:
+    prefix = [sys.executable]
+    if not getattr(sys, "frozen", False):
+        prefix.append(str(Path(__file__).resolve().parents[1] / "main.py"))
+    return [
+        *prefix,
+        "--supervise",
+        str(control_token),
+        str(result_token),
+        "--",
+        *map(str, argv),
+    ]
+
+
+def _write_result(fd: int, returncode: int) -> None:
+    try:
+        os.write(fd, _RESULT.pack(int(returncode)))
+    except OSError:
+        # The caller may have cancelled and closed its result reader.
+        pass
+    finally:
+        try:
+            os.close(fd)
+        except OSError:
+            # Writing or cancellation may already have closed the descriptor.
+            pass
+
+
+def _operation_env() -> dict[str, str]:
+    env = os.environ.copy()
+    # The operation intentionally does not own the Rust drain writer. Avoid
+    # exposing a stale numeric token which nested code could mistake as valid.
+    env.pop(_DRAIN_FD_ENV, None)
+    env.pop(_DESKTOP_MARKER, None)
+    return env
+
+
+def _supervise_posix(control_fd: int, result_fd: int, argv: list[str]) -> int:
+    def cancel_on_eof() -> None:
+        try:
+            while os.read(control_fd, 1):
+                pass
+        except OSError:
+            # Closing the control descriptor is itself a cancellation signal.
+            pass
+        os.killpg(os.getpgrp(), signal.SIGKILL)
+
+    threading.Thread(target=cancel_on_eof, daemon=True).start()
+    try:
+        child = subprocess.Popen(argv, close_fds=True, env=_operation_env())
+        rc = child.wait()
+    except OSError:
+        rc = 127
+    _write_result(result_fd, rc)
+    # Drain children which outlived the operation before the stable group
+    # leader exits.  SIGKILL intentionally includes this supervisor.
+    os.killpg(os.getpgrp(), signal.SIGKILL)
+    return rc  # unreachable
+
+
+def _windows_job() -> tuple[Any, Any, Any]:
+    import ctypes
+    import ctypes.wintypes as wintypes
+
+    kernel32 = ctypes.WinDLL("kernel32", use_last_error=True)
+    kernel32.CloseHandle.argtypes = (wintypes.HANDLE,)
+    kernel32.CloseHandle.restype = wintypes.BOOL
+    kernel32.TerminateJobObject.argtypes = (wintypes.HANDLE, wintypes.UINT)
+    kernel32.TerminateJobObject.restype = wintypes.BOOL
+    kernel32.ReadFile.argtypes = (
+        wintypes.HANDLE,
+        ctypes.c_void_p,
+        wintypes.DWORD,
+        ctypes.POINTER(wintypes.DWORD),
+        ctypes.c_void_p,
+    )
+    kernel32.ReadFile.restype = wintypes.BOOL
+    kernel32.WriteFile.argtypes = (
+        wintypes.HANDLE,
+        ctypes.c_void_p,
+        wintypes.DWORD,
+        ctypes.POINTER(wintypes.DWORD),
+        ctypes.c_void_p,
+    )
+    kernel32.WriteFile.restype = wintypes.BOOL
+    create = kernel32.CreateJobObjectW
+    create.argtypes = (ctypes.c_void_p, wintypes.LPCWSTR)
+    create.restype = wintypes.HANDLE
+    job = create(None, None)
+    if not job:
+        raise OSError(ctypes.get_last_error(), "CreateJobObjectW")
+
+    class BasicLimits(ctypes.Structure):
+        _fields_ = [
+            ("PerProcessUserTimeLimit", ctypes.c_longlong),
+            ("PerJobUserTimeLimit", ctypes.c_longlong),
+            ("LimitFlags", wintypes.DWORD),
+            ("MinimumWorkingSetSize", ctypes.c_size_t),
+            ("MaximumWorkingSetSize", ctypes.c_size_t),
+            ("ActiveProcessLimit", wintypes.DWORD),
+            ("Affinity", ctypes.c_size_t),
+            ("PriorityClass", wintypes.DWORD),
+            ("SchedulingClass", wintypes.DWORD),
+        ]
+
+    class IoCounters(ctypes.Structure):
+        _fields_ = [(name, ctypes.c_ulonglong) for name in (
+            "ReadOperationCount", "WriteOperationCount", "OtherOperationCount",
+            "ReadTransferCount", "WriteTransferCount", "OtherTransferCount",
+        )]
+
+    class ExtendedLimits(ctypes.Structure):
+        _fields_ = [
+            ("BasicLimitInformation", BasicLimits),
+            ("IoInfo", IoCounters),
+            ("ProcessMemoryLimit", ctypes.c_size_t),
+            ("JobMemoryLimit", ctypes.c_size_t),
+            ("PeakProcessMemoryUsed", ctypes.c_size_t),
+            ("PeakJobMemoryUsed", ctypes.c_size_t),
+        ]
+
+    info = ExtendedLimits()
+    info.BasicLimitInformation.LimitFlags = 0x00002000  # KILL_ON_JOB_CLOSE
+    set_info = kernel32.SetInformationJobObject
+    set_info.argtypes = (wintypes.HANDLE, ctypes.c_int, ctypes.c_void_p, wintypes.DWORD)
+    set_info.restype = wintypes.BOOL
+    if not set_info(job, 9, ctypes.byref(info), ctypes.sizeof(info)):
+        error = ctypes.get_last_error()
+        kernel32.CloseHandle(job)
+        raise OSError(error, "SetInformationJobObject")
+    return job, kernel32, wintypes
+
+
+def _resume_windows_process(kernel32: Any, wintypes: Any, pid: int) -> None:
+    import ctypes
+
+    class ThreadEntry(ctypes.Structure):
+        _fields_ = [
+            ("dwSize", wintypes.DWORD),
+            ("cntUsage", wintypes.DWORD),
+            ("th32ThreadID", wintypes.DWORD),
+            ("th32OwnerProcessID", wintypes.DWORD),
+            ("tpBasePri", wintypes.LONG),
+            ("tpDeltaPri", wintypes.LONG),
+            ("dwFlags", wintypes.DWORD),
+        ]
+
+    kernel32.CreateToolhelp32Snapshot.argtypes = (wintypes.DWORD, wintypes.DWORD)
+    kernel32.CreateToolhelp32Snapshot.restype = wintypes.HANDLE
+    kernel32.Thread32First.argtypes = (wintypes.HANDLE, ctypes.POINTER(ThreadEntry))
+    kernel32.Thread32First.restype = wintypes.BOOL
+    kernel32.Thread32Next.argtypes = (wintypes.HANDLE, ctypes.POINTER(ThreadEntry))
+    kernel32.Thread32Next.restype = wintypes.BOOL
+    kernel32.OpenThread.argtypes = (wintypes.DWORD, wintypes.BOOL, wintypes.DWORD)
+    kernel32.OpenThread.restype = wintypes.HANDLE
+    kernel32.ResumeThread.argtypes = (wintypes.HANDLE,)
+    kernel32.ResumeThread.restype = wintypes.DWORD
+
+    snapshot = kernel32.CreateToolhelp32Snapshot(0x00000004, 0)
+    invalid = ctypes.c_void_p(-1).value
+    if snapshot == invalid:
+        raise OSError(ctypes.get_last_error(), "CreateToolhelp32Snapshot")
+    try:
+        entry = ThreadEntry(dwSize=ctypes.sizeof(ThreadEntry))
+        found = kernel32.Thread32First(snapshot, ctypes.byref(entry))
+        while found:
+            if entry.th32OwnerProcessID == pid:
+                thread = kernel32.OpenThread(0x0002, False, entry.th32ThreadID)
+                if not thread:
+                    raise OSError(ctypes.get_last_error(), "OpenThread")
+                try:
+                    if kernel32.ResumeThread(thread) == 0xFFFFFFFF:
+                        raise OSError(ctypes.get_last_error(), "ResumeThread")
+                    return
+                finally:
+                    kernel32.CloseHandle(thread)
+            found = kernel32.Thread32Next(snapshot, ctypes.byref(entry))
+    finally:
+        kernel32.CloseHandle(snapshot)
+    raise OSError("suspended operation thread was not found")
+
+
+def _supervise_windows(control_fd: int, result_fd: int, argv: list[str]) -> int:
+    import ctypes
+
+    job, kernel32, wintypes = _windows_job()
+    cancelled = threading.Event()
+    job_lock = threading.Lock()
+    job_open = True
+
+    def terminate_job() -> None:
+        with job_lock:
+            if job_open:
+                kernel32.TerminateJobObject(job, 1)
+
+    def cancel_on_eof() -> None:
+        byte = ctypes.create_string_buffer(1)
+        count = wintypes.DWORD()
+        while kernel32.ReadFile(
+            wintypes.HANDLE(control_fd), byte, 1, ctypes.byref(count), None
+        ) and count.value:
+            pass
+        kernel32.CloseHandle(wintypes.HANDLE(control_fd))
+        cancelled.set()
+        terminate_job()
+
+    threading.Thread(target=cancel_on_eof, daemon=True).start()
+    child: Optional[subprocess.Popen] = None
+    rc = 127
+    try:
+        child = subprocess.Popen(
+            argv,
+            close_fds=True,
+            env=_operation_env(),
+            creationflags=0x08000000 | 0x00000004,  # NO_WINDOW | SUSPENDED
+        )
+        assign = kernel32.AssignProcessToJobObject
+        assign.argtypes = (wintypes.HANDLE, wintypes.HANDLE)
+        assign.restype = wintypes.BOOL
+        if not assign(job, wintypes.HANDLE(child._handle)):
+            raise OSError(ctypes.get_last_error(), "AssignProcessToJobObject")
+        if cancelled.is_set():
+            terminate_job()
+        else:
+            _resume_windows_process(kernel32, wintypes, child.pid)
+        rc = child.wait()
+        # A successful direct child may leave helpers behind; terminate the
+        # nested stable Job before reporting completion.
+        terminate_job()
+    except OSError:
+        terminate_job()
+        if child is not None:
+            try:
+                # Assignment itself may have failed, leaving this suspended
+                # process outside the nested Job.  Terminate it through its
+                # stable process handle before waiting; never strand an
+                # unassigned operation or rely on the outer desktop Job.
+                child.kill()
+            except OSError:
+                # The suspended child may have exited during Job teardown.
+                pass
+            try:
+                child.wait(timeout=5)
+            except (OSError, subprocess.TimeoutExpired):
+                # The outer desktop Job remains the terminal containment fallback.
+                pass
+    finally:
+        payload = _RESULT.pack(int(rc))
+        payload_buffer = ctypes.create_string_buffer(payload)
+        written = wintypes.DWORD()
+        kernel32.WriteFile(
+            wintypes.HANDLE(result_fd),
+            payload_buffer,
+            len(payload),
+            ctypes.byref(written),
+            None,
+        )
+        kernel32.CloseHandle(wintypes.HANDLE(result_fd))
+        with job_lock:
+            job_open = False
+            kernel32.CloseHandle(job)
+    return rc
+
+
+def supervisor_main(args: list[str]) -> int:
+    if len(args) < 5 or args[0] != "--supervise" or args[3] != "--":
+        return 2
+    control_fd = int(args[1])
+    result_fd = int(args[2])
+    argv = args[4:]
+    secure_backend_drain_fd()
+    if os.name == "posix":
+        return _supervise_posix(control_fd, result_fd, argv)
+    return _supervise_windows(control_fd, result_fd, argv)
+
+
+def _main() -> int:
+    return supervisor_main(sys.argv[1:])
+
+
+if __name__ == "__main__":
+    raise SystemExit(_main())
@@ -274,7 +274,8 @@ _BASE_SCHEMA = """
        started_at REAL,
        finished_at REAL,
        lease_expires_at REAL,
-        grace_expires_at REAL
+        grace_expires_at REAL,
+        deadlines_json TEXT
    );
    CREATE INDEX IF NOT EXISTS idx_remote_attempts_task ON remote_task_attempts(task_id);
    CREATE INDEX IF NOT EXISTS idx_remote_attempts_worker ON remote_task_attempts(worker_id, state);
@@ -35,7 +35,8 @@ import sys
 from dataclasses import dataclass
 from typing import Literal

-DeviceFamily = Literal["cuda", "rocm", "mps", "xpu", "cpu"]
+DeviceFamily = Literal["cuda", "rocm", "mps", "xpu", "npu", "cpu"]
+ACCELERATOR_PRIORITY = ("cuda", "rocm", "xpu", "npu", "mps")

 # Stable substring stamped onto notes that represent a real kernel-launch risk
 # (arch/driver mismatch) — as opposed to advisory notes (multi-GPU, VRAM query
@@ -177,6 +178,23 @@ def gfx_for_hsa_override(value: str) -> str | None:
 #: The ROCm kernel driver interface. Its absence, or its presence without
 #: permission, are the two commonest reasons a ROCm host silently runs on CPU.
 _KFD_DEVICE = "/dev/kfd"
+_DXG_DEVICE = "/dev/dxg"
+_DXG_RUNTIME_PATHS = (
+    "/usr/lib/libdxcore.so",
+    "/usr/lib/librocdxg.so",
+    "/usr/share/rocdxg/dids.conf",
+)
+
+
+def _rocm_requires_dxg_detection(version: object) -> bool:
+    """Whether WSL's ROCDXG bridge still needs its explicit opt-in."""
+    try:
+        parts = str(version).split(".")
+        return (int(parts[0]), int(parts[1])) < (7, 13)
+    except (IndexError, TypeError, ValueError):
+        # Unknown versions get the conservative advice. The variable is
+        # harmless on newer runtimes and necessary on every older one.
+        return True


 def why_no_gpu(torch) -> tuple[str, ...]:
@@ -230,6 +248,40 @@ def why_no_gpu(torch) -> tuple[str, ...]:
        # /dev/kfd only exists on Linux; on any other platform its absence
        # says nothing, so don't invent a reason.
        if sys.platform.startswith("linux"):
+            if not os.path.exists(_KFD_DEVICE) and os.path.exists(_DXG_DEVICE):
+                if not os.access(_DXG_DEVICE, os.R_OK | os.W_OK):
+                    return (
+                        f"ROCm {hip} is installed and {_DXG_DEVICE} exists, "
+                        "but this process cannot open it — pass "
+                        "--device /dev/dxg to the WSL container",
+                    )
+                dxg_detection = os.environ.get("HSA_ENABLE_DXG_DETECTION", "").strip()
+                if dxg_detection == "0":
+                    return (
+                        f"ROCm {hip} is installed and {_DXG_DEVICE} is reachable, "
+                        "but HSA_ENABLE_DXG_DETECTION=0 explicitly disables the "
+                        "WSL GPU bridge; remove it or set it to 1",
+                    )
+                if _rocm_requires_dxg_detection(hip) and dxg_detection != "1":
+                    return (
+                        f"ROCm {hip} is installed and {_DXG_DEVICE} is "
+                        "reachable, but this pre-7.13 runtime requires "
+                        "HSA_ENABLE_DXG_DETECTION=1 inside WSL containers",
+                    )
+                missing = [
+                    path for path in _DXG_RUNTIME_PATHS if not os.path.exists(path)
+                ]
+                if missing:
+                    return (
+                        f"ROCm {hip} can reach {_DXG_DEVICE}, but the WSL "
+                        "ROCDXG runtime mounts are incomplete; missing: "
+                        f"{', '.join(missing)}",
+                    )
+                return (
+                    f"ROCm {hip} and the WSL ROCDXG bridge are reachable, "
+                    "but no GPU was enumerated — verify the AMD Windows "
+                    "driver, librocdxg/ROCm compatibility, and host `rocminfo`",
+                )
            if not os.path.exists(_KFD_DEVICE):
                return (
                    f"ROCm {hip} is installed but {_KFD_DEVICE} is not "
@@ -482,9 +534,13 @@ def _probe() -> HostCaps:
        # is the whole truth in that case (CodeRabbit, #1425).
        notes.extend(why_no_gpu(torch))

-    # ── Intel XPU via IPEX ───────────────────────────────────────────────
+    # Older builds register XPU through IPEX; modern torch exposes it directly.
    try:
        import intel_extension_for_pytorch  # noqa: F401
+    except Exception:
+        # Optional IPEX may be absent or incompatible; still probe native torch XPU.
+        pass
+    try:
        if hasattr(torch, "xpu") and torch.xpu.is_available():
            detected.append("xpu")
            if not device_name:
@@ -495,7 +551,23 @@ def _probe() -> HostCaps:
                    pass
            notes.append("XPU VRAM not queried (unreliable across IPEX versions)")
    except Exception:
-        # IPEX absent or XPU probe failed — no XPU on this host.
+        # XPU probe failed — no usable XPU on this host.
+        pass
+
+    # Vendor extensions may register an NPU with torch. Probe only an already
+    # registered backend; never install or import an optional vendor package.
+    try:
+        if hasattr(torch, "npu") and torch.npu.is_available():
+            detected.append("npu")
+            if not device_name:
+                try:
+                    device_name = torch.npu.get_device_name(0)
+                except Exception:
+                    # An unavailable display name does not invalidate a usable NPU.
+                    pass
+            notes.append("NPU VRAM not queried")
+    except Exception:
+        # Missing or broken vendor backends mean no usable NPU; continue probing.
        pass

    # ── Apple Silicon MPS ────────────────────────────────────────────────
@@ -528,7 +600,7 @@ def _probe() -> HostCaps:

    # Preferred family by priority; cpu when nothing accelerated was detected.
    family: DeviceFamily = "cpu"
-    for pref in ("cuda", "rocm", "xpu", "mps"):
+    for pref in ACCELERATOR_PRIORITY:
        if pref in detected:
            family = pref  # type: ignore[assignment]
            break
@@ -21,6 +21,7 @@ Check shape:
 """
 from __future__ import annotations

+import importlib
 import os
 import platform
 import shutil
@@ -367,10 +368,44 @@ def run_diagnostics(include_network: bool = True, deep: bool = False) -> dict:
    counts = {OK: 0, WARN: 0, FAIL: 0}
    for c in checks:
        counts[c["status"]] += 1
+    engine_execution = []
+    for family in ("tts", "asr"):
+        active = "unknown"
+        try:
+            module = importlib.import_module(f"services.{family}_backend")
+            active = module.active_backend_id()
+            row = next((item for item in module.list_backends() if item.get("id") == active), None)
+            if row is not None:
+                engine_execution.append({
+                    "family": family,
+                    "engine_id": active,
+                    **row["execution_evidence"],
+                })
+        except Exception:  # noqa: BLE001 - evidence must not break diagnostics
+            # Preserve the other family's successful evidence and make this
+            # collection failure explicit without exposing exception text.
+            engine_execution.append({
+                "family": family,
+                "engine_id": active,
+                "implementation_variant": None,
+                "declared_device_families": [],
+                "evidence_state": "collection_failed",
+                "actual_execution_provider": None,
+                "actual_execution_device": None,
+                "gpu_name": None,
+                "gpu_architecture": None,
+                "precision_or_quantization": None,
+                "cpu_fallback_reason": None,
+                "cpu_fallback_stage": None,
+                "parent_memory_observable": None,
+                "runtime_versions": {},
+            })
+
    return {
        "app_version": APP_VERSION,
        "platform": scrub_text(platform.platform()),
        "checks": checks,
+        "engine_execution": engine_execution,
        "summary": {
            "ok": counts[FAIL] == 0,
            "passed": counts[OK],
@@ -395,6 +430,35 @@ def format_text(report: dict) -> str:
        lines.append(f"{tag[c['status']]} {c['label']}: {c['detail']}")
        if c.get("hint"):
            lines.append(f"       hint: {c['hint']}")
+    if report.get("engine_execution"):
+        lines.append("")
+        lines.append("Engine execution evidence:")
+        for item in report["engine_execution"]:
+            if item.get("actual_execution_provider"):
+                provider = item["actual_execution_provider"]
+            elif item.get("evidence_state") == "subprocess_loaded_provider_unreported":
+                provider = "loaded child; provider not reported"
+            else:
+                provider = "not loaded"
+            precision = item.get("precision_or_quantization") or "unknown"
+            device = item.get("actual_execution_device") or "unknown"
+            gpu = item.get("gpu_name") or "none"
+            architecture = item.get("gpu_architecture") or "unknown"
+            fallback_stage = item.get("cpu_fallback_stage") or "none"
+            fallback_reason = item.get("cpu_fallback_reason") or "none"
+            versions = ",".join(
+                f"{name}={version}"
+                for name, version in sorted(item.get("runtime_versions", {}).items())
+            ) or "none"
+            visible = "yes" if item.get("parent_memory_observable") else "no"
+            lines.append(
+                f"  {item['family']}:{item['engine_id']} provider={provider}; "
+                f"device={device}; gpu={gpu}; architecture={architecture}; "
+                f"precision={precision}; fallback-stage={fallback_stage}; "
+                f"fallback-reason={fallback_reason}; runtimes={versions}; "
+                f"evidence-state={item.get('evidence_state', 'unknown')}; "
+                f"parent-memory-visible={visible}"
+            )
    s = report["summary"]
    lines.append("")
    lines.append(
@@ -23,9 +23,17 @@ logger = logging.getLogger("omnivoice.events")
 _listeners: list[asyncio.Queue] = []
 _lock = asyncio.Lock()

+# The loop that serves /ws/events, captured on first use. Sync FastAPI
+# endpoints (rename/delete profile, revoke consent) run in threadpool workers
+# where `asyncio.get_running_loop()` raises, which used to silently drop their
+# events — the UI then never refetched the voice list (#1158 class).
+_serving_loop: asyncio.AbstractEventLoop | None = None
+

 async def subscribe() -> asyncio.Queue:
    """Register a new listener. Returns a Queue that receives event dicts."""
+    global _serving_loop
+    _serving_loop = asyncio.get_running_loop()
    q: asyncio.Queue = asyncio.Queue(maxsize=64)
    async with _lock:
        _listeners.append(q)
@@ -57,11 +65,29 @@ def emit(kind: str, payload: dict[str, Any] | None = None) -> None:
    }
    event_str = json.dumps(event)
    try:
-        loop = asyncio.get_running_loop()
-        loop.create_task(_broadcast(event_str))
+        caller_loop = asyncio.get_running_loop()
    except RuntimeError:
-        # No event loop running (unlikely in FastAPI context but safe)
+        caller_loop = None
+    target_loop = _serving_loop or caller_loop
+    if target_loop is None:
+        # No serving loop yet — nobody to notify; dropping is correct.
        logger.debug("No event loop — event dropped: %s", kind)
+        return
+    try:
+        if caller_loop is target_loop:
+            target_loop.create_task(_broadcast(event_str))
+        else:
+            # Sync endpoints and async producers on a foreign loop must both
+            # hand off: the lock and listener queues belong to serving_loop.
+            target_loop.call_soon_threadsafe(_schedule_broadcast, event_str)
+    except RuntimeError:
+        # The serving loop closed between capture and use (app shutdown).
+        logger.debug("Event loop closed — event dropped: %s", kind)
+
+
+def _schedule_broadcast(event_str: str) -> None:
+    """Run `_broadcast` on the serving loop; called via call_soon_threadsafe."""
+    asyncio.get_running_loop().create_task(_broadcast(event_str))


 async def _broadcast(event_str: str) -> None:
@@ -73,11 +99,11 @@ async def _broadcast(event_str: str) -> None:
                q.put_nowait(event_str)
            except asyncio.QueueFull:
                # Slow consumer — drop oldest, then push. Not a race (#1163):
-                # every queue op runs on the single event loop, and there is
-                # no await between the QueueFull and this get_nowait/put_nowait
-                # pair — no consumer can interleave, so get_nowait cannot raise
-                # QueueEmpty here. emit() from a foreign thread drops the event
-                # before ever touching a queue (see the RuntimeError branch).
+                # every queue op runs on the single event loop (a foreign
+                # thread's emit() hands off via call_soon_threadsafe first),
+                # and there is no await between the QueueFull and this
+                # get_nowait/put_nowait pair — no consumer can interleave, so
+                # get_nowait cannot raise QueueEmpty here.
                try:
                    q.get_nowait()
                    q.put_nowait(event_str)
@@ -52,6 +52,7 @@ _REDACTED_VALUE = "***REDACTED***"
 # One-line "what to do" per docs-taxonomy key. Keys mirror error_docs_map's
 # taxonomy; the docs URL itself stays owned by error_docs_map.
 _HINTS: dict[str, str] = {
+    "GPU_OOM": "Close other GPU-heavy apps or unload models, then retry. You can also choose CPU in Settings → Performance & Device or select a smaller TTS engine.",
    "WORKER_AT_CAPACITY": "Wait for a running job on that worker to finish, or choose another available worker and retry.",
    "MODEL_NOT_INSTALLED": "Install or enable this engine on the worker machine, then refresh its capabilities and retry.",
    "MODEL_NOT_DOWNLOADED": "Open Models, install this model on the selected worker, then retry when the download completes.",
@@ -290,6 +291,9 @@ def append_hf_mirror_hint(text: str) -> str:
 # must NOT be added: its bare "timed out" trigger would stamp a "video server"
 # hint on a model-load timeout that leaks through the 500 handler.
 _CONTEXT_FREE_HINT_CLASSES = frozenset({
+    # Device allocator signatures are specific enough to attach the shared
+    # recovery without exposing CUDA's process table or filesystem paths.
+    "GPU_OOM",
    "SOCKS_PROXY_SUPPORT_MISSING",
    "SSL_HANDSHAKE_FAILURE",
    # Its trigger is an exact OpenSSL string, so it cannot be confused with
@@ -323,6 +327,38 @@ def append_hint(text: str) -> str:
    return f"{text} — {hint}" if hint else text


+_GPU_OOM_SIGNATURES = (
+    "cuda out of memory",
+    "cuda error: out of memory",
+    "cuda_error_out_of_memory",
+    "mps backend out of memory",
+    "hip out of memory",
+    "out of memory on device",
+)
+
+
+def is_gpu_oom(error: BaseException | str) -> bool:
+    """Recognize device OOMs through wrappers without importing torch."""
+    pending: list[BaseException] = [error] if isinstance(error, BaseException) else []
+    seen: set[int] = set()
+    while pending:
+        current = pending.pop()
+        if id(current) in seen:
+            continue
+        seen.add(id(current))
+        if type(current).__name__ == "OutOfMemoryError":
+            return True
+        if any(signature in str(current).lower() for signature in _GPU_OOM_SIGNATURES):
+            return True
+        if current.__cause__ is not None:
+            pending.append(current.__cause__)
+        if current.__context__ is not None:
+            pending.append(current.__context__)
+    if isinstance(error, str):
+        return any(signature in error.lower() for signature in _GPU_OOM_SIGNATURES)
+    return False
+
+
 def classify(reason: str) -> str:
    """Map a failure reason to a docs-taxonomy key, or "" when unknown.

@@ -330,6 +366,8 @@ def classify(reason: str) -> str:
    backend log / diagnostic names the same class the UI deeplink will use.
    """
    low = (reason or "").lower()
+    if is_gpu_oom(low):
+        return "GPU_OOM"
    if "pkg_resources" in low:
        return "PKG_RESOURCES_MISSING"
    if "quarantine" in low or "is damaged" in low or "gatekeeper" in low:
@@ -519,6 +557,7 @@ def classify(reason: str) -> str:
        or "unable to download video" in low
        or "remote end closed" in low
        or "timed out" in low
+        or "the page needs to be reloaded" in low
    ):
        return "VIDEO_DOWNLOAD_NETWORK"
    # #1227: Windows Smart App Control / WDAC / AppLocker refused to load a
@@ -0,0 +1,34 @@
+"""Terminate a desktop-contained backend when its owning shell disappears."""
+from __future__ import annotations
+
+import os
+import sys
+import threading
+from typing import BinaryIO, Callable
+
+
+def _watch_parent_pipe(reader: BinaryIO, exit_process: Callable[[int], None]) -> None:
+    """Block until the desktop-owned stdin pipe closes, then exit immediately."""
+    try:
+        while reader.read(1):
+            pass
+    except (OSError, ValueError):
+        # A broken or already-closed parent-owned pipe is equivalent to EOF.
+        pass
+    exit_process(0)
+
+
+def arm_desktop_parent_watchdog() -> bool:
+    """Use stdin EOF as an unforgeable parent-liveness signal for desktop runs."""
+    if os.environ.get("OMNIVOICE_DESKTOP_CONTAINED") != "1":
+        return False
+    reader = getattr(sys.stdin, "buffer", None)
+    if reader is None:
+        return False
+    threading.Thread(
+        target=_watch_parent_pipe,
+        args=(reader, os._exit),
+        name="desktop-parent-watchdog",
+        daemon=True,
+    ).start()
+    return True
@@ -7,6 +7,7 @@ only the unguessable capability token crosses loopback HTTP.
 from __future__ import annotations

 import json
+import logging
 import os
 import re
 import secrets
@@ -14,6 +15,8 @@ import stat

 from core.config import DATA_DIR

+logger = logging.getLogger("omnivoice.path_authorization")
+
 _TOKEN_RE = re.compile(r"[0-9a-f]{64}\Z")
 _KINDS = {
    "models_dir",
@@ -40,25 +43,49 @@ def consume(token: str, expected_kind: str) -> str:
    if expected_kind not in _KINDS or not _TOKEN_RE.fullmatch(token or ""):
        raise PathAuthorizationError("Invalid or expired desktop authorization")
    root = _AUTH_DIR
+    # Distinguish "the store exists but this token isn't in it" (expired /
+    # already consumed / never issued — normal, no server-side signal) from
+    # "the store doesn't exist at all" (the desktop app and this backend are
+    # very likely pointed at different data directories, e.g. a dev backend
+    # started without OMNIVOICE_DATA_DIR, or a stale custom data folder — see
+    # #1781). The client-facing message is byte-identical either way (never
+    # leak local filesystem paths, or even which case occurred, over HTTP —
+    # CWE-200); the mismatch case additionally gets a server log line so it's
+    # diagnosable instead of a silent 403. That log line is deliberately
+    # path-free too (CWE-532: per-user filesystem paths, e.g. a home
+    # directory username, are sensitive and don't belong in application
+    # logs) — it names the failure mode, not the directory.
+    try:
+        entries = os.scandir(root)
+    except FileNotFoundError as exc:
+        logger.warning(
+            "path authorization store does not exist; the desktop app and "
+            "this backend likely resolved different data directories "
+            "(see #1781)"
+        )
+        raise PathAuthorizationError("Invalid or expired desktop authorization") from exc
+    except OSError as exc:
+        raise PathAuthorizationError("Invalid or expired desktop authorization") from exc
    candidate = None
    try:
-        for entry in os.scandir(root):
-            if not _TOKEN_RE.fullmatch(entry.name.removesuffix(".json")):
-                continue
-            if not entry.is_file(follow_symlinks=False):
-                continue
-            try:
-                with open(entry.path, "r", encoding="utf-8") as handle:
-                    probe = json.load(handle)
-            except (OSError, UnicodeError, json.JSONDecodeError):
-                continue  # Ignore corrupt/stale capabilities; they authorize nothing.
-            if isinstance(probe, dict) and secrets.compare_digest(
-                str(probe.get("token", "")), token
-            ):
-                candidate = entry.path
-                break
+        with entries:
+            for entry in entries:
+                if not _TOKEN_RE.fullmatch(entry.name.removesuffix(".json")):
+                    continue
+                if not entry.is_file(follow_symlinks=False):
+                    continue
+                try:
+                    with open(entry.path, "r", encoding="utf-8") as handle:
+                        probe = json.load(handle)
+                except (OSError, UnicodeError, json.JSONDecodeError):
+                    continue  # Ignore corrupt/stale capabilities; they authorize nothing.
+                if isinstance(probe, dict) and secrets.compare_digest(
+                    str(probe.get("token", "")), token
+                ):
+                    candidate = entry.path
+                    break
        if candidate is None:
-            raise OSError("capability not found")
+            raise PathAuthorizationError("Invalid or expired desktop authorization")
        claimed = os.path.join(root, f".consuming-{os.getpid()}-{secrets.token_hex(16)}")
        os.replace(candidate, claimed)
    except OSError as exc:
@@ -91,3 +91,53 @@ def resolve(key: str, *, env: Optional[str] = None, default: Any = None) -> Any:
        if v:
            return v
    return get(key, default)
+
+
+# ── external-override detection (#1787 review fix) ──────────────────────────
+# restore_env() below uses os.environ.setdefault(), so a value already present
+# in the process's environment (shell profile, `.env`, Docker `-e`, systemd
+# unit, …) silently wins over anything saved in prefs.json — the setdefault
+# call is a no-op. That is the right behavior (env stays authoritative,
+# matching resolve()'s contract above), but a Settings control that persists a
+# value to prefs.json must not tell the user it "took effect after restart"
+# when an external source will keep shadowing it on every future restart too.
+#
+# _EXTERNALLY_PROVIDED records, once per process start, every bare key that
+# was ALREADY present in os.environ the moment restore_env() ran — i.e.
+# before our own setdefault() calls could have put it there, and before any
+# value our Settings UI ever wrote (Settings only ever writes prefs.json plus
+# the CURRENT process's os.environ; it never touches a shell profile or `.env`
+# file). Snapshotting unconditionally — not only for keys prefs.json already
+# has an entry for — means is_env_shadowed() also answers correctly for a key
+# a user is about to save for the FIRST time. Membership is stable for the
+# life of the process (nothing removes an inherited env var), and since a
+# plain restart re-inherits the same shell / container environment, it is
+# also a reliable predictor for the NEXT start: if the external source is
+# still exporting the key, the next restart will be shadowed again the same
+# way.
+_EXTERNALLY_PROVIDED: frozenset[str] = frozenset()
+
+
+def restore_env(data: dict) -> None:
+    """Restore ``env.*`` prefs into ``os.environ`` (startup only).
+
+    Called once from main.py's ``env_prefs`` step, before any user code reads
+    ``os.environ``. Snapshots which keys were already externally provided —
+    see :func:`is_env_shadowed` — then applies every saved ``env.*`` pref via
+    ``setdefault`` (never overriding an explicitly-set env var).
+    """
+    global _EXTERNALLY_PROVIDED
+    _EXTERNALLY_PROVIDED = frozenset(os.environ.keys())
+    for k, v in data.items():
+        if not k.startswith("env.") or not v:
+            continue
+        os.environ.setdefault(k[len("env."):], str(v))
+
+
+def is_env_shadowed(key: str) -> bool:
+    """Whether *key* was already present in the environment from a source
+    other than our own prefs restore, as of the last time :func:`restore_env`
+    ran. If prefs.json holds (or will hold) a saved value for *key*, that
+    value is being silently ignored — and will be again on the next restart —
+    unless the external source is removed."""
+    return key in _EXTERNALLY_PROVIDED
@@ -28,6 +28,15 @@ def stream_failure(code: str) -> dict[str, object]:
            "detail": "Generation capacity is busy. Try again shortly.",
            "retryable": True,
        },
+        "generation_timeout": {
+            "code": "generation_timeout",
+            "detail": (
+                "Generation exceeded the compute-time limit. The backend is "
+                "still running; try a shorter passage, or raise the "
+                "compute-time budget in Settings → Performance & Device."
+            ),
+            "retryable": True,
+        },
        "invalid_request": {
            "code": "invalid_request",
            "detail": "The generation request could not be processed.",
@@ -65,6 +74,48 @@ def stream_failure(code: str) -> dict[str, object]:
    return dict(failures.get(code, failures["generation_failed"]))


+def stream_generation_failure(error: BaseException | object) -> dict[str, object]:
+    """``generation_failed`` stream metadata, enriched with the actual cause.
+
+    The bare "Generation failed. Check the selected engine and try again." is
+    the floor for an *unrecognized* failure. When the private exception DOES
+    classify to a known failure class — a corrupt model cache, an unreachable
+    Hugging Face mirror, a missing ffmpeg/ffprobe, a Windows paging-file limit,
+    a SOCKS/TLS proxy problem, … — the stable VoiceStudio-owned remediation for
+    that class is appended so the user can self-diagnose instead of guessing
+    which engine or which failure. This is the same enrichment the classic
+    (non-streaming) ``/generate`` 500 already gets via
+    :func:`public_exception_response`; the in-band streaming error frame
+    replaces the global 500 handler for a streaming request and used to bypass
+    it entirely (#1607).
+
+    Only VoiceStudio-owned constants are copied — never a substring of
+    ``error`` (Constitution I). Never raises: a diagnosis failure must not
+    replace the failure being diagnosed.
+    """
+    payload = stream_failure("generation_failed")
+    try:
+        enriched = public_exception_response(error, fallback=str(payload["detail"]))
+    except Exception:
+        return payload
+    hint = enriched.get("hint")
+    if hint:
+        payload["detail"] = enriched["detail"]
+        payload["hint"] = hint
+        topic = enriched.get("docs_topic")
+        if topic:
+            payload["docs_topic"] = topic
+            try:
+                from core import error_docs_map
+
+                url = error_docs_map.ERROR_DOCS.get(topic, "")
+            except Exception:
+                url = ""
+            if url:
+                payload["docs_url"] = url
+    return payload
+
+
 def public_failure(
    logger: logging.Logger,
    log_message: str,
@@ -24,7 +24,7 @@ from pathlib import Path
 # tests/test_app_version.py::test_all_version_files_in_lockstep and bumped by
 # release.yml's version-bump job, so it stays equal to
 # pyproject/tauri.conf/Cargo/package.json.
-_FALLBACK_VERSION = "0.5.0"
+_FALLBACK_VERSION = "0.5.2"


 def _fallback_version() -> str:
@@ -56,15 +56,15 @@ class Confucius4Backend(SubprocessBackend):

    id = "confucius4-tts"
    display_name = (
-        "Confucius4-TTS (LLM, 14 langs, cross-lingual zero-shot clone, CUDA/CPU, Apache-2.0)"
+        "Confucius4-TTS (LLM, 14 langs, cross-lingual zero-shot clone, Apache-2.0)"
    )
    supports_voice_design = False  # timbre comes from a reference clip
    # Upstream vocoder rate (config target_sample_rate) — confirmed 22 050 Hz by
    # a live run (2026-07-02); still re-read from the sidecar's ready/audio frames.
    _DEFAULT_SAMPLE_RATE = 22050
-    # CUDA fast path + CPU fallback, both exercised (CPU end-to-end validated).
-    # No MPS claim — upstream has no Metal path.
-    gpu_compat = ("cuda", "cpu")
+    # Match device propagation into upstream .to(device). XPU/NPU routing is
+    # contract-tested, not a claim of physical-hardware synthesis validation.
+    gpu_compat = ("cuda", "rocm", "xpu", "npu", "cpu")

    @classmethod
    def is_available(cls) -> tuple[bool, str]:
@@ -104,7 +104,7 @@ def _ensure_clone_on_sys_path() -> None:


 def _load_model(stdout):
-    """Cold-construct the Confucius4 model (CUDA, else CPU — both validated)."""
+    """Cold-construct using an available torch accelerator, with CPU fallback."""
    global _model
    if _model is not None:
        return _model
@@ -115,7 +115,18 @@ def _load_model(stdout):
    import torch
    from confuciustts.cli.inference import ConfuciusTTS  # type: ignore[import-not-found]

-    device = "cuda" if torch.cuda.is_available() else "cpu"
+    try:
+        # Existing manually provisioned venvs may predate torch.accelerator.
+        current_accelerator = getattr(getattr(torch, "accelerator", None), "current_accelerator", None)
+        if current_accelerator is None:
+            device = torch.device("cuda") if torch.cuda.is_available() else None
+        else:
+            device = current_accelerator(check_available=True)
+        device = device.type if device is not None else "cpu"  # 'cuda', 'npu', 'mps', 'xpu', 'cpu'
+    except Exception:
+        device = "cpu"  # Broken accelerator drivers must not block CPU loading.
+    if device == "mps":
+        device = "cpu"  # MPS was slower than CPU in the existing validation run
    _send(stdout, {"op": "progress", "stage": "loading_model", "percent": 50})

    _model = ConfuciusTTS(config_path=_config_path(), device=device)
@@ -115,7 +115,12 @@ def _load_runtime(stdout):
    from dots_tts.runtime import DotsTtsRuntime  # type: ignore[import-not-found]

    repo = os.environ.get("OMNIVOICE_DOTS_TTS_MODEL", _DEFAULT_REPO)
-    default_precision = "bfloat16" if torch.cuda.is_available() else "float32"
+    # Match DotsTtsRuntime's own CUDA/CPU selection. Its _check_torch_env
+    # rejects half precision without CUDA, even when an XPU/NPU is available.
+    try:
+        default_precision = "bfloat16" if torch.cuda.is_available() else "float32"
+    except Exception:
+        default_precision = "float32"  # Probe failure must not force half precision.
    precision = os.environ.get("OMNIVOICE_DOTS_TTS_PRECISION", default_precision)
    optimize = os.environ.get("OMNIVOICE_DOTS_TTS_OPTIMIZE", "0") == "1"

@@ -28,6 +28,7 @@ packages. The parent only ever spawns it as a subprocess.
 from __future__ import annotations

 import logging
+import math
 import os
 import re
 from typing import TYPE_CHECKING
@@ -164,6 +165,23 @@ class IndexTTS2Backend(SubprocessBackend):
        from engines.indextts.bootstrap import resolve_indextts_venv
        return resolve_indextts_venv()

+    @property
+    def recv_timeout_s(self) -> float:
+        # IndexTTS was the only sidecar left on the 60s class default while
+        # pockettts and omnivoice-subprocess both raised theirs. infer() is one
+        # blocking upstream call, so a long passage legitimately outruns 60s and
+        # the parent's watchdog killed a healthy synthesis (#1611). main.py also
+        # heartbeats during infer(), which is what actually proves liveness —
+        # this deadline is the ceiling for a sidecar that has gone genuinely
+        # silent. OMNIVOICE_INDEXTTS_RECV_TIMEOUT_S tunes it.
+        try:
+            v = float(os.environ.get("OMNIVOICE_INDEXTTS_RECV_TIMEOUT_S", "900"))
+        except (ValueError, TypeError):
+            return 900.0
+        if not math.isfinite(v):  # reject inf/nan so the deadline can't be disabled
+            return 900.0
+        return max(30.0, v)
+
    @classmethod
    def sidecar_script(cls):
        from engines.indextts.bootstrap import INDEXTTS_SIDECAR_SCRIPT
@@ -63,11 +63,13 @@ Restrictions:
 from __future__ import annotations

 import base64
+import contextlib
 import json
 import os
 import struct
 import sys
 import tempfile
+import threading
 import traceback


@@ -117,11 +119,59 @@ EMOTION_KWARGS_ALLOWLIST = frozenset({
 # ── wire protocol ─────────────────────────────────────────────────────────


+#: Seconds between keep-alive progress frames during a long blocking call.
+_HEARTBEAT_S = 5.0
+
+#: Serializes _send across threads (the heartbeat below + the main loop) so
+#: concurrent length+body writes can't interleave and corrupt the framing.
+_send_lock = threading.Lock()
+
+
 def _send(stream, obj: dict) -> None:
    body = json.dumps(obj, separators=(",", ":")).encode("utf-8")
-    stream.write(struct.pack("!I", len(body)))
-    stream.write(body)
-    stream.flush()
+    with _send_lock:
+        stream.write(struct.pack("!I", len(body)))
+        stream.write(body)
+        stream.flush()
+
+
+@contextlib.contextmanager
+def _heartbeat(stdout, stage: str):
+    """Emit a progress frame every ~5s for the duration of the block.
+
+    IndexTTS spends the whole of a cold load and the whole of ``infer()``
+    inside one blocking upstream call, saying nothing on the wire. The parent
+    reads that silence two ways, and BOTH kill a perfectly healthy synthesis
+    of a long passage (#1611):
+
+      * ``SubprocessBackend.generate`` re-arms its recv watchdog on every
+        frame, so with no frames it hard-kills the sidecar at recv_timeout_s;
+      * each frame also reports activity to the GPU pool's execution clock
+        (#1367), so with no frames the outer generate budget expires and
+        blames the hardware.
+
+    Raising the deadline alone therefore does not fix long-text generation —
+    the sidecar has to prove it is alive. Percent climbs 1..99 because the
+    upstream call exposes no real progress; it is a liveness signal, not a
+    measurement.
+    """
+    stop = threading.Event()
+
+    def _beat() -> None:
+        pct = 1
+        while not stop.wait(_HEARTBEAT_S):
+            pct = min(pct + 1, 99)
+            try:
+                _send(stdout, {"op": "progress", "stage": stage, "percent": pct})
+            except Exception:
+                return  # pipe gone — the main loop will surface it
+    hb = threading.Thread(target=_beat, name=f"indextts-{stage}-heartbeat", daemon=True)
+    hb.start()
+    try:
+        yield
+    finally:
+        stop.set()
+        hb.join(timeout=_HEARTBEAT_S + 1)


 def _recv(stream):
@@ -160,14 +210,40 @@ def _torch_bf16_supported() -> bool:
        return False


+#: Model-config filenames to look for, most-preferred first, per version.
+#: IndexTeam/IndexTTS-2.5 ships ``config.yaml``; VoiceStudio used to demand
+#: ``config_v2_5.yaml``, a name that exists in no upstream revision, so the
+#: install failed until the user hand-renamed the file (#1611). Both names are
+#: accepted now — the hand-renamed installs must keep working untouched — and
+#: the renamed one wins, because a user who created it did so deliberately.
+_CFG_NAMES = {
+    "2.5": ("config_v2_5.yaml", "config.yaml"),
+    "2": ("config.yaml",),
+}
+
+
+def _resolve_cfg_path(model_dir: str, *, version: str) -> str:
+    """First accepted config that exists in ``model_dir``.
+
+    Falls back to the last candidate when none exist, so the failure surfaces
+    as upstream's own "no such file" naming a real expected path rather than
+    a name no upstream release has ever shipped.
+    """
+    names = _CFG_NAMES.get(version, _CFG_NAMES["2"])
+    for name in names:
+        candidate = os.path.join(model_dir, name)
+        if os.path.isfile(candidate):
+            return candidate
+    return os.path.join(model_dir, names[-1])
+
+
 def _model_init_kwargs(
    repo_dir: str, *, version: str, reduced_precision: bool,
 ) -> dict:
    """Build version-specific constructor arguments for IndexTTS 2.5 or 2."""
    model_dir = os.path.join(repo_dir, "checkpoints")
-    cfg_name = "config_v2_5.yaml" if version == "2.5" else "config.yaml"
    kwargs = {
-        "cfg_path": os.path.join(model_dir, cfg_name),
+        "cfg_path": _resolve_cfg_path(model_dir, version=version),
        "model_dir": model_dir,
        "use_cuda_kernel": False,
        "use_deepspeed": False,
@@ -216,7 +292,8 @@ def _load_model(stdout) -> object:
    model_kw = _model_init_kwargs(
        repo_dir, version=_model_version, reduced_precision=reduced_precision,
    )
-    _model = IndexTTS2(**model_kw)
+    with _heartbeat(stdout, "loading_model"):
+        _model = IndexTTS2(**model_kw)

    _send(stdout, {"op": "progress", "stage": "loading_model", "percent": 100})
    return _model
@@ -276,7 +353,10 @@ def _handle_synthesize(msg: dict, stdout) -> None:
        tmp_path = tmp.name
    try:
        infer_kw["output_path"] = tmp_path
-        model.infer(**infer_kw)
+        # A long passage keeps infer() busy for minutes with nothing on the
+        # wire; without this the parent kills the sidecar mid-synthesis (#1611).
+        with _heartbeat(stdout, "synthesizing"):
+            model.infer(**infer_kw)
        pcm_b64, sr, n_samples = _wav_to_pcm_b64(tmp_path)
    finally:
        try:
@@ -29,15 +29,11 @@ Do NOT import ``main.py`` from the parent process — it runs under a
 different venv (``transformers==5.0.0``) and importing it in-process would
 re-introduce the exact conflict this isolation exists to avoid.

-Hardware honesty (cross-platform rule): MOSS-TTS-v1.5's upstream documents
-only CUDA and CPU. There is **no documented or tested MPS path** — the
-custom ``trust_remote_code`` modelling code and the separate audio
-tokenizer are unverified on Apple Silicon. We therefore advertise
-``gpu_compat = ("cuda", "cpu")`` and the sidecar selects ``cuda`` when
-present else ``cpu`` — it never silently routes to MPS where it might
-crash. On Apple Silicon the engine honestly resolves to CPU (slow but
-correct), and the engine is opt-in regardless, so it never becomes a
-broken default on any platform.
+Hardware routing follows the sidecar's runtime-available PyTorch accelerator:
+CUDA/ROCm, XPU, or a registered NPU. MPS remains excluded; CPU is the fallback.
+XPU/NPU routing is covered with mocked device contracts, not physical-hardware
+synthesis certification; users need a compatible torch/vendor runtime in the
+isolated engine venv.
 """
 from __future__ import annotations

@@ -85,13 +81,13 @@ class MossTTSV15Backend(SubprocessBackend):

    id = "moss-tts-v15"
    display_name = (
-        "MOSS-TTS-v1.5 (8B, 31 langs, zero-shot clone, CUDA/CPU, Apache-2.0)"
+        "MOSS-TTS-v1.5 (8B, 31 langs, zero-shot clone, Apache-2.0)"
    )
    supports_voice_design = False  # requires ref audio for timbre cloning
    _DEFAULT_SAMPLE_RATE = 24000
-    # Honest hardware surface: upstream documents CUDA + CPU only. MPS is
-    # undocumented / untested, so we do NOT claim it (cross-platform rule).
-    gpu_compat = ("cuda", "cpu")
+    # Accelerator routing requires its matching runtime in the isolated venv.
+    # MPS remains untested and is deliberately excluded.
+    gpu_compat = ("cuda", "rocm", "xpu", "npu", "cpu")

    # ── availability ───────────────────────────────────────────────────────

@@ -111,7 +107,7 @@ class MossTTSV15Backend(SubprocessBackend):
            return False, (
                "MOSS-TTS-v1.5 venv not found. Set OMNIVOICE_MOSS_TTS_V15_DIR "
                "to your MOSS-TTS clone (the directory containing pyproject.toml) "
-                "and restart VoiceStudio. CUDA or CPU only (no MPS). See "
+                "and restart VoiceStudio. Install the matching PyTorch runtime. See "
                "docs/engines/moss-tts-v15.md for the full install walk-through."
            )
        if not MOSS_TTS_V15_SIDECAR_SCRIPT.exists():
@@ -119,7 +115,7 @@ class MossTTSV15Backend(SubprocessBackend):
                "MOSS-TTS-v1.5 sidecar script missing at "
                f"{MOSS_TTS_V15_SIDECAR_SCRIPT} — reinstall VoiceStudio."
            )
-        return True, "ok (CUDA when present, else CPU)"
+        return True, "ok (runtime-available accelerator or CPU; no MPS)"

    @classmethod
    def venv_python(cls):
@@ -138,11 +138,12 @@ _state = None
 def _load_model(stdout):
    """Cold-construct the MOSS-TTS-v1.5 processor + model.

-    Device selection is CUDA-or-CPU only — MOSS's upstream documents no MPS
-    path and the custom ``trust_remote_code`` modelling code is untested on
-    Apple Silicon, so we never route to MPS where it might crash. dtype is
-    bf16 on CUDA, fp32 on CPU (bf16 CPU ops are spotty). Emits progress
-    frames so the parent can surface the multi-GB cold-load latency.
+    Device selection uses the torch.accelerator API to support any backend
+    (CUDA, NPU, XPU, etc.) automatically. MPS is excluded — MOSS's upstream
+    ``trust_remote_code`` modelling code is untested on Apple Silicon. dtype is
+    bf16 on GPU-class accelerators, fp32 on CPU (bf16 CPU ops are spotty).
+    Emits progress frames so the parent can surface the multi-GB cold-load
+    latency.
    """
    global _state
    if _state is not None:
@@ -154,8 +155,22 @@ def _load_model(stdout):
    from transformers import AutoModel, AutoProcessor

    repo, revision = _model_source()
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    dtype = torch.bfloat16 if device == "cuda" else torch.float32
+    # current_accelerator() returns None on CPU-only builds (no accelerator
+    # compiled in) or when no accelerator is available; fall back to "cpu".
+    # Existing manually provisioned venvs may predate torch.accelerator.
+    current_accelerator = getattr(getattr(torch, "accelerator", None), "current_accelerator", None)
+    try:
+        if current_accelerator is None:
+            accel = torch.device("cuda") if torch.cuda.is_available() else None
+        else:
+            accel = current_accelerator(check_available=True)
+    except Exception:
+        # Optional drivers can fail during probing; CPU loading remains usable.
+        accel = None
+    device = accel.type if accel is not None else "cpu"  # 'cuda', 'npu', 'mps', 'xpu', 'cpu'
+    if device == "mps":
+        device = "cpu"  # MOSS is untested on MPS; fall back to CPU for safety
+    dtype = torch.bfloat16 if device != "cpu" else torch.float32
    # "sdpa" works on CUDA + CPU and needs no extra dep. flash_attention_2
    # (Ampere+ CUDA, optional flash-attn) is opt-in via env.
    attn = os.environ.get("OMNIVOICE_MOSS_TTS_V15_ATTN", "sdpa")
@@ -98,6 +98,7 @@ def _platform_slug() -> str:
        darwin-x86_64
        windows-x86_64
        linux-x86_64
+        linux-aarch64
    """
    system = platform.system().lower()
    machine = platform.machine().lower()
@@ -107,6 +108,8 @@ def _platform_slug() -> str:
        return "darwin-x86_64"
    if system == "windows":
        return "windows-x86_64"
+    if system == "linux" and machine in ("arm64", "aarch64"):
+        return "linux-aarch64"
    # Linux + everything else falls into the linux slug.
    return "linux-x86_64"

@@ -1,7 +1,9 @@
 """omnivoice-subprocess: the resident OmniVoice TTS engine in a crash-isolated
 sidecar process (#730/#1190).

-The default ``omnivoice`` engine runs in-process on the GPU ``ThreadPoolExecutor``.
+The ``omnivoice`` engine runs in-process on CUDA, ROCm, and CPU. On MPS it is
+resolved to :class:`OmniVoiceMPSSubprocessBackend` so a fatal native allocator
+exit cannot take down the local API process.
 When a generate or load there exceeds its execution budget the pool is "reset"
 but the abandoned worker *thread* cannot be killed (Python cannot interrupt a
 native torch/MPS call), so it holds the MPS device until it finishes on its
@@ -13,16 +15,11 @@ timeout the parent's watchdog calls ``proc.kill()``, reclaiming the child's
 VRAM/device, and the next request transparently respawns a fresh sidecar. That
 is the one thing the in-process engine structurally cannot do.

-OPT-IN (Settings -> Engines, or ``OMNIVOICE_TTS_BACKEND=omnivoice-subprocess``);
-the in-process ``omnivoice`` stays the default so existing users see no change.
+The explicit ``omnivoice-subprocess`` id remains available on every host for
+operators who want the same containment elsewhere.

-Tradeoff vs the in-process engine: identical model and quality, a little extra
-per-call overhead (one stdio round-trip), and it does not carry the native
-advanced-parameter surface (``t_shift`` / ``layer_penalty_factor`` /
-``position_temperature`` / ``class_temperature``) or parent-side seed
-determinism, because the generic ``backend.generate`` path does not forward
-those. Acceptable for unattended / reaction-triggered use where reliability
-matters more than those controls.
+Tradeoff vs the in-process engine: identical model, controls, seed behavior,
+and quality, with a little extra per-call overhead (one stdio round-trip).

 Unlike IndexTTS / dots.tts / Supertonic-3, this sidecar runs under the PARENT
 interpreter (``venv_python() -> sys.executable``): the goal here is crash
@@ -51,10 +48,15 @@ class OmniVoiceSubprocessBackend(SubprocessBackend):
    id = "omnivoice-subprocess"
    display_name = "OmniVoice (subprocess-isolated, killable on timeout)"
    _DEFAULT_SAMPLE_RATE = 24000
-    gpu_compat = ("cuda", "mps", "cpu")
+    gpu_compat = ("cuda", "rocm", "mps", "cpu")
    # Match OmniVoiceBackend: the measured floor below which a render that
    # should take seconds runs for minutes (the #1226/#1222 4 GB reports).
    min_vram_gb = 6.0
+    # Packaged Windows hosts can spend more than the base 30 seconds starting
+    # the shared Python runtime before this stdlib-only sidecar emits ready.
+    # Keep the bound below the 300-second generation budget while avoiding the
+    # repeated false kill captured in #1711.
+    spawn_ready_timeout_s = 120.0

    @classmethod
    def is_available(cls) -> tuple[bool, str]:
@@ -102,4 +104,34 @@ class OmniVoiceSubprocessBackend(SubprocessBackend):
        return ["multi"]


-__all__ = ["OmniVoiceSubprocessBackend"]
+class OmniVoiceMPSSubprocessBackend(OmniVoiceSubprocessBackend):
+    """Effective ``omnivoice`` implementation on MPS.
+
+    Native torch/MPS allocator failures can terminate the process without a
+    catchable Python exception. Keeping the same engine id and model surface in
+    a child makes that failure recoverable while Settings, APIs, and saved
+    projects continue to refer to ``omnivoice``.
+    """
+
+    id = "omnivoice"
+    display_name = "VoiceStudio (k2-fsa/OmniVoice, 600+ languages)"
+    supports_native_omnivoice_controls = True
+
+    def generate(self, text: str, **kw):
+        from services.model_manager import make_room_before_generate
+
+        make_room_before_generate()
+        try:
+            return super().generate(text, **kw)
+        except RuntimeError as exc:
+            if "sidecar closed pipe mid-generate" not in str(exc):
+                raise
+            raise RuntimeError(
+                "The isolated OmniVoice engine stopped during generation, "
+                "usually because macOS reclaimed it under memory pressure. "
+                "The VoiceStudio backend is still running. Close memory-heavy "
+                "apps or select a smaller TTS engine, then retry."
+            ) from exc
+
+
+__all__ = ["OmniVoiceMPSSubprocessBackend", "OmniVoiceSubprocessBackend"]
@@ -50,6 +50,8 @@ OMNIVOICE_SAMPLE_RATE = 24000
 _GEN_KW_ALLOWLIST = (
    "language", "instruct", "duration", "num_step", "guidance_scale",
    "speed", "denoise", "postprocess_output", "preprocess_prompt",
+    "t_shift", "layer_penalty_factor", "position_temperature",
+    "class_temperature", "audio_chunk_duration", "audio_chunk_threshold",
 )

 _model = None
@@ -183,6 +185,12 @@ def _handle_synthesize(msg: dict, stdout) -> None:
    ref_text = msg.get("ref_text") or None
    gen_kw = {k: msg[k] for k in _GEN_KW_ALLOWLIST if k in msg}

+    seed = msg.get("seed")
+    if seed is not None:
+        import torch
+
+        torch.manual_seed(int(seed))
+
    audios = model.generate(
        text=text, ref_audio=ref_audio, ref_text=ref_text, **gen_kw
    )
@@ -151,6 +151,40 @@ def _pocket_language(raw) -> str:
    )


+_TRUTHY = {"1", "true", "yes", "on"}
+
+
+def _has_24l_config(language: str) -> bool:
+    """Whether the installed pocket-tts ships a 24-layer checkpoint for
+    ``language`` (it/de/es/pt/fr in 2.1.0; english has none)."""
+    try:
+        from pocket_tts.models.tts_model import CONFIGS_DIR  # type: ignore[import-not-found]  # noqa: PLC0415
+    except Exception as exc:  # noqa: BLE001 — absence of the package is not fatal here
+        # Log it, though: if a future pocket-tts moves CONFIGS_DIR, the 24L
+        # opt-in would otherwise go silently inert.
+        print(f"pockettts sidecar: 24l config probe failed: {exc!r}", file=sys.stderr)
+        return False
+    from pathlib import Path  # noqa: PLC0415
+
+    return (Path(CONFIGS_DIR) / f"{language}_24l.yaml").is_file()
+
+
+def _model_config_name(language: str) -> str:
+    """Pocket-tts config name to load: the 6-layer default, or the 24-layer
+    checkpoint when OMNIVOICE_POCKETTTS_24L is set and one exists for the
+    language. Opt-in only — defaults keep the fast model; the 24-layer variant
+    trades roughly 4x transformer compute for better prosody.
+
+    French is the exception: pocket-tts 2.1.0 only ships a 24-layer French
+    model and load_model(language="french") raises, so French always maps to
+    french_24l regardless of the env var."""
+    if language == "french":
+        return "french_24l"
+    if os.environ.get("OMNIVOICE_POCKETTTS_24L", "").strip().lower() not in _TRUTHY:
+        return language
+    return f"{language}_24l" if _has_24l_config(language) else language
+
+
 def _load_model(stdout, language: str):
    """Cold-construct the PocketTTS model for ``language`` (cached per language).
    Emits progress frames for the parent watchdog. Raises on failure (e.g.
@@ -178,7 +212,7 @@ def _load_model(stdout, language: str):
    try:
        from pocket_tts import TTSModel  # type: ignore[import-not-found]  # noqa: PLC0415

-        model = TTSModel.load_model(language=language)
+        model = TTSModel.load_model(language=_model_config_name(language))
        _MODELS[language] = model
    finally:
        stop.set()
@@ -9,6 +9,24 @@ _backend_dir = os.path.dirname(os.path.abspath(__file__))
 if _backend_dir not in sys.path:
    sys.path.insert(0, _backend_dir)

+# PyInstaller re-executes this entry module when the frozen backend binary is
+# launched. Nested operation supervisors therefore dispatch here, before math,
+# logging, FastAPI, torch, or any application initialization. Source launches
+# use this same entry contract so frozen/source behavior cannot drift.
+if __name__ == "__main__" and len(sys.argv) > 1 and sys.argv[1] == "--supervise":
+    from core.contained_subprocess import supervisor_main
+
+    raise SystemExit(supervisor_main(sys.argv[1:]))
+
+# Rust clears CLOEXEC only for the backend exec. Re-arm PEP 446 immediately:
+# nested supervisors receive this descriptor solely through explicit pass_fds,
+# so a third-party close_fds=False child cannot hold the desktop drain barrier.
+from core.contained_subprocess import secure_backend_drain_fd  # noqa: E402
+
+secure_backend_drain_fd()
+
+import math  # noqa: E402
+
 # Windows: run every child process (ffmpeg, engine sidecars, yt-dlp, demucs, …)
 # WITHOUT popping a console window. The backend itself is spawned console-less by
 # the Tauri shell, so on Windows each console subprocess it launches would
@@ -59,6 +77,7 @@ os.environ.setdefault("FOR_DISABLE_CONSOLE_CTRL_HANDLER", "1")
 # (utils.hf_progress.SafeFileWrapper — same wrapper the patched hub tqdm
 # already uses for its own fp.)
 from utils.hf_progress import SafeFileWrapper as _SafeStdio  # noqa: E402
+from core.parent_liveness import arm_desktop_parent_watchdog  # noqa: E402

 # Force UTF-8 stdio before wrapping (#1155): on Windows the spawned backend's
 # stdout defaults to cp1252, and any library that prints user text (kittentts
@@ -71,6 +90,11 @@ for _stream in (sys.stdout, sys.stderr):
    except Exception:  # noqa: BLE001 — pythonw/frozen builds may lack reconfigure
        pass

+# The desktop keeps the backend's stdin pipe open for its own lifetime. EOF is
+# therefore a stable ownership signal that survives PID reuse and lets a child
+# terminate even when the shell crashes before its normal process-tree teardown.
+arm_desktop_parent_watchdog()
+
 if not getattr(sys.stdout, "_is_safe_wrapper", False):
    sys.stdout = _SafeStdio(sys.stdout)
 if not getattr(sys.stderr, "_is_safe_wrapper", False):
@@ -369,19 +393,36 @@ def _env_flag(name: str, default: bool = False) -> bool:
 _EAGER = _env_flag("OMNIVOICE_EAGER_INIT", default=("pytest" in sys.modules))


+def _env_float(name: str, default: float) -> float:
+    """Parse a float env override, rejecting negative and non-finite values.
+
+    Shared by the preload-delay / timeout knobs: NaN would silently never
+    fire, a negative would fire during startup I/O, so both fall back to the
+    default instead (the bug class CodeRabbit flagged on the watermark knob
+    in PR #1577 — latent in the older copies too, closed here for all)."""
+    raw = os.environ.get(name, "")
+    try:
+        value = float(raw) if raw.strip() else default
+    except ValueError:
+        return default
+    return value if math.isfinite(value) and value >= 0 else default
+
+
 def _capture_preload_delay_s() -> float:
    """Seconds after boot before the dictation (capture ASR) model warms.

    Late enough that it never competes with startup I/O or the TTS preload;
    overridable via OMNIVOICE_CAPTURE_PRELOAD_DELAY (mostly for tests)."""
-    raw = os.environ.get("OMNIVOICE_CAPTURE_PRELOAD_DELAY", "")
-    try:
-        v = float(raw)
-        if v >= 0:
-            return v
-    except (TypeError, ValueError):
-        pass
-    return 30.0
+    return _env_float("OMNIVOICE_CAPTURE_PRELOAD_DELAY", 30.0)
+
+def _watermark_preload_delay_s() -> float:
+    """Seconds after boot before the AudioSeal generator warm-up fires.
+
+    Own knob, NOT ``_capture_preload_delay_s`` + offset: a capture-specific
+    env override must not retime the watermark warm too, and the two cold
+    imports shouldn't fire on the same tick (CodeRabbit, PR #1577). Default
+    35s sits ~5s past the capture-ASR warm for the same reason."""
+    return _env_float("OMNIVOICE_PRELOAD_WATERMARK_DELAY", 35.0)


 def _capture_preload_ram_ok(min_free_bytes: int = 4 * 1024**3) -> bool:
@@ -398,14 +439,7 @@ def _capture_preload_ram_ok(min_free_bytes: int = 4 * 1024**3) -> bool:
 def _mcp_start_timeout_s() -> float:
    """Seconds to wait for the MCP session manager to start before giving up
    and serving without it (#632). Overridable via OMNIVOICE_MCP_START_TIMEOUT_S."""
-    raw = os.environ.get("OMNIVOICE_MCP_START_TIMEOUT_S", "")
-    try:
-        v = float(raw)
-        if v > 0:
-            return v
-    except (TypeError, ValueError):
-        pass
-    return 30.0
+    return max(_env_float("OMNIVOICE_MCP_START_TIMEOUT_S", 30.0), 0.001)


 async def _serve_mcp(session_manager, ready: "asyncio.Event", stop: "asyncio.Event") -> None:
@@ -551,13 +585,14 @@ def _phase_a_build_inner() -> None:
        pass  # never block startup on the migration; it retries next launch
    # Restore persisted env vars from prefs.json (Settings UI writes them
    # there so they survive backend restarts) — before any user code reads
-    # os.environ, and never overriding an explicitly-set env var.
+    # os.environ, and never overriding an explicitly-set env var. Also
+    # snapshots which keys an external source (shell, `.env`, Docker, …)
+    # already provided, so a Settings control can tell the user their saved
+    # value is being shadowed instead of silently promising it will apply
+    # (core.prefs.is_env_shadowed — #1787 review fix).
    try:
-        from core.prefs import _load as _load_all_prefs
-        _prefs = _load_all_prefs()
-        for _k, _v in _prefs.items():
-            if _k.startswith("env.") and _v:
-                os.environ.setdefault(_k[len("env."):], str(_v))
+        from core.prefs import _load as _load_all_prefs, restore_env
+        restore_env(_load_all_prefs())
    except Exception:
        pass  # prefs.json missing or broken — fine on first run
    # yt-dlp user-update overlay: must run before anything imports yt_dlp so
@@ -638,6 +673,7 @@ def _phase_a_build_inner() -> None:
        events,
        capture,
        capture_ws,
+        speech_platform,
        dictation,
        openai_compat,
        tts_stream,
@@ -650,14 +686,15 @@ def _phase_a_build_inner() -> None:
        settings as settings_router,  # Phase 1 AUTH-03: HF token save/clear/state
        media_tools as media_tools_router,  # Audio tools: ffmpeg/ffprobe/yt-dlp
        auth as auth_router,
+        voice_convert,  # Studio Convert: speech-to-speech via ASR → TTS
    )
    from api.routers import mcp_bindings as _mcp_bindings_router  # noqa: E402
    from api.routers import workers as workers_router  # noqa: E402
    _router_modules.extend([
-        system, profiles, exports, generation, dub_core, dub_generate,
+        system, profiles, exports, generation, voice_convert, dub_core, dub_generate,
        dub_export, dub_translate, projects, glossary, engines, tools,
        stories, setup, gallery, archetypes, describe_voice, community,
-        batch, watermark, events, capture, capture_ws, dictation,
+        batch, watermark, events, capture, capture_ws, speech_platform, dictation,
        openai_compat, tts_stream, marketplace, personas, sonitranslate,
        audiobook, longform_jobs, pronunciation, settings_router,
        media_tools_router, auth_router, _mcp_bindings_router, workers_router,
@@ -852,6 +889,8 @@ async def _phase_b(app: FastAPI) -> None:
    # #1174: arm model loads for THIS run — an in-process relaunch may carry a
    # stale shutting-down flag from a previous lifespan.
    model_loads_reset_shutdown()
+    from services.model_manager import begin_watermark_pool_lifecycle
+    begin_watermark_pool_lifecycle()
    app.state.idle_task = asyncio.create_task(idle_worker())
    app.state.worker_task = asyncio.create_task(task_manager.worker())
    # Warm the TTS model in the background so first /generate is instant.
@@ -905,6 +944,50 @@ async def _phase_b(app: FastAPI) -> None:
    else:
        logger.info("Capture ASR preload disabled; dictation ASR will load on first use.")

+    # Watermark: warm the AudioSeal generator in the background so the first
+    # mark_synthetic doesn't serialize the audioseal import + model load
+    # inside the first synthesis (measured ~42 s inline on a cold filesystem,
+    # 2026-08-17 macOS report — 3 s short of the client's 90 s timeout).
+    # Small model on CPU; deferred a few seconds past the capture-ASR warm so
+    # the two cold imports don't contend for the same disk, and no RAM guard
+    # is needed. Runs on the watermark pool — where the model is used — not
+    # the shared default executor.
+    if _env_flag("OMNIVOICE_PRELOAD_WATERMARK", default=True):
+        async def _preload_watermark():
+            await asyncio.sleep(_watermark_preload_delay_s())
+            loop = asyncio.get_running_loop()
+            from services import watermark as _watermark
+
+            # Gate BEFORE touching get_watermark_pool(): the pool is lazy so
+            # hosts with watermarking disabled never spawn its thread, and
+            # creating it unconditionally would break that invariant. The
+            # race with a first embed is benign — pool creation is itself
+            # lock-guarded.
+            if not _watermark.will_mark():
+                logger.debug("Watermark preload skipped (disabled or audioseal absent)")
+                return
+            from services.model_manager import get_watermark_pool
+
+            # Default startup may warm an existing local checkpoint but may
+            # not fetch one. Only an explicit user opt-in permits a download.
+            raw_preload = os.environ.get("OMNIVOICE_PRELOAD_WATERMARK", "")
+            allow_download = raw_preload.strip().lower() in {"1", "true", "yes", "on"}
+
+            try:
+                await loop.run_in_executor(
+                    get_watermark_pool(),
+                    lambda: _watermark.prefetch_generator(
+                        allow_download=allow_download
+                    ),
+                )
+            except Exception:
+                # prefetch_generator swallows its own errors; this guards the
+                # setup half (imports, pool construction) so a broken warm-up
+                # is visible now, not as an unretrieved exception at shutdown.
+                logger.warning("Watermark preload task failed", exc_info=True)
+
+        app.state.watermark_preload_task = asyncio.create_task(_preload_watermark())
+
    # ── MCP session manager (Wave 2.2) ────────────────────────────────────
    # Run it in its OWN task owning the full enter→exit lifecycle (anyio
    # task-affinity, see _serve_mcp); only wait, with a timeout, for ready —
@@ -934,6 +1017,9 @@ async def _phase_b(app: FastAPI) -> None:

@asynccontextmanager
 async def lifespan(app: FastAPI):
+    from api.dependencies import validate_server_admin_key
+
+    validate_server_admin_key()
    # Startup watchdog (#632): a silent hang during startup (e.g. a model-load /
    # MCP deadlock on some platforms) means "Application startup complete" never
    # logs and the app sits forever with no error. If startup hasn't finished
@@ -1080,8 +1166,20 @@ async def lifespan(app: FastAPI):
        getattr(app.state, "worker_task", None),
        getattr(app.state, "preload_task", None),
        getattr(app.state, "capture_preload_task", None),
+        getattr(app.state, "watermark_preload_task", None),
        timeout=20.0,
    )
+    # The watermark warm-up runs on its dedicated 1-worker pool. Cancellation
+    # detaches the asyncio future but cannot kill a thread inside AudioSeal,
+    # so drain it fully before lifespan teardown reports completion.
+    try:
+        from services.model_manager import shutdown_watermark_pool as _wm_drain
+
+        _wm_drain()
+    except Exception:
+        # Best-effort drain: a failure here must not abort the remaining
+        # shutdown steps (model unload, MCP teardown) below.
+        logger.warning("Watermark pool drain failed at shutdown", exc_info=True)
    # Unload the model and free GPU memory
    try:
        import services.model_manager as mm
@@ -7,8 +7,8 @@ Run standalone:

 Tools exposed:
    generate_speech   — text → WAV audio (voice clone or design)
-    clone_voice       — base64 reference audio → new voice profile
-    transcribe        — base64 audio → text
+    clone_voice       — reference audio (base64, or a file path) → new voice profile
+    transcribe        — audio (base64, or a file path) → text
    list_voices       — enumerate saved voice profiles
    list_languages    — available TTS languages
    list_personalities — voice personality presets
@@ -17,6 +17,18 @@ Tools exposed:
 Resources exposed:
    voice://{profile_id}  — voice profile metadata
    history://recent      — last 20 generated audio items
+
+Output mode (OMNIVOICE_MCP_OUTPUT_MODE):
+    resources — generate_speech returns the WAV as base64 inline (the original
+                contract; default)
+    files     — it returns a URL to the render (and, with a base path, a WAV
+                written there); nothing large ever enters the agent's context
+    both      — both of the above
+
+File inputs (OMNIVOICE_MCP_BASE_PATH):
+    One directory that agents may read audio from (transcribe / clone_voice
+    `*_path` arguments) and receive files in (files mode). It is the security
+    boundary: with no base path configured, path-shaped inputs are refused.
 """
 from __future__ import annotations

@@ -25,6 +37,8 @@ import base64
 import json
 import logging
 import os
+import re
+import stat
 import sys

 logger = logging.getLogger("omnivoice.mcp")
@@ -69,6 +83,244 @@ def _sniff_audio_ext(raw: bytes) -> str:
    return ".wav"


+# ── Output mode + the base path boundary ─────────────────────────────────
+# An LLM agent that receives a WAV as base64 pays for every byte in context:
+# a 1.4 s clip already brushes per-result token caps, and a paragraph of
+# narration blows them outright. The ElevenLabs MCP settled this with an
+# OUTPUT_MODE (files / resources / both) and a BASE_PATH that doubles as the
+# security boundary for file-shaped inputs; the same two knobs here, named in
+# the OMNIVOICE_* family the rest of the server reads.
+
+_OUTPUT_MODES = ("resources", "files", "both")
+_MAX_INPUT_BYTES = 200 * 1024 * 1024
+_SAFE_AUDIO_ID = re.compile(r"^[A-Za-z0-9_-]{1,64}$")
+
+
+def _output_mode() -> str:
+    """How generate_speech hands audio back (OMNIVOICE_MCP_OUTPUT_MODE).
+
+    'resources' is the original base64-inline contract and stays the default
+    so existing integrations see no change; 'files' returns a URL to the
+    render (plus a WAV under the base path when one is configured); 'both'
+    returns everything. Anything unrecognized falls back to 'resources' with
+    a warning rather than failing the tool."""
+    mode = os.environ.get("OMNIVOICE_MCP_OUTPUT_MODE", "resources").strip().lower()
+    if mode not in _OUTPUT_MODES:
+        logger.warning(
+            "OMNIVOICE_MCP_OUTPUT_MODE=%r is not one of %s; using 'resources'",
+            mode, _OUTPUT_MODES,
+        )
+        return "resources"
+    return mode
+
+
+def _base_path() -> "str | None":
+    """The one directory agents may read audio from and receive files in
+    (OMNIVOICE_MCP_BASE_PATH), realpath'd; None when unset."""
+    raw = os.environ.get("OMNIVOICE_MCP_BASE_PATH", "").strip()
+    if not raw:
+        return None
+    return os.path.realpath(os.path.expanduser(raw))
+
+
+def _resolve_under_base(path: str) -> str:
+    """Absolute realpath of ``path`` when it lies inside the base path.
+
+    Relative paths resolve against the base; absolute paths must already be
+    inside it. Both sides are realpath'd, so a symlink pointing outward cannot
+    smuggle a read in. Raises ValueError with an agent-legible reason when no
+    base path is configured or the path escapes it."""
+    base = _base_path()
+    if base is None:
+        raise ValueError(
+            "OMNIVOICE_MCP_BASE_PATH is not set; file paths are refused until it "
+            "names a directory"
+        )
+    candidate = os.path.realpath(os.path.join(base, os.path.expanduser(path)))
+    if not _path_is_under_base(base, candidate):
+        raise ValueError(f"{path!r} resolves outside OMNIVOICE_MCP_BASE_PATH")
+    return candidate
+
+
+def _opened_file_is_confined(fd: int, resolved: str, base: str) -> bool:
+    """Verify that an opened descriptor still names a file under ``base``."""
+    proc_fd = f"/proc/self/fd/{fd}"
+    if os.path.exists(proc_fd):
+        return _path_is_under_base(base, os.path.realpath(proc_fd))
+    try:
+        current = os.path.realpath(resolved)
+        return _path_is_under_base(base, current) and os.path.samestat(
+            os.fstat(fd), os.stat(current, follow_symlinks=False)
+        )
+    except OSError:
+        return False
+
+
+def _path_is_under_base(base: str, candidate: str) -> bool:
+    try:
+        common = os.path.commonpath([base, candidate])
+    except ValueError:  # different drives on Windows
+        return False
+    return os.path.normcase(common) == os.path.normcase(base)
+
+
+def _open_under_base(path: str, flags: int, *, mode: int = 0o600) -> tuple[int, str]:
+    """Open ``path`` without following a component replaced after validation."""
+    base = _base_path()
+    if base is None:
+        raise ValueError(
+            "OMNIVOICE_MCP_BASE_PATH is not set; file paths are refused until it "
+            "names a directory"
+        )
+    resolved = _resolve_under_base(path)
+    relative = os.path.relpath(resolved, base)
+    parts = [part for part in relative.split(os.sep) if part not in ("", ".")]
+    if not parts or parts[0] == os.pardir:
+        raise ValueError(f"{path!r} resolves outside OMNIVOICE_MCP_BASE_PATH")
+
+    no_follow = getattr(os, "O_NOFOLLOW", 0)
+    close_on_exec = getattr(os, "O_CLOEXEC", 0)
+    binary = getattr(os, "O_BINARY", 0)
+    file_flags = flags | no_follow | close_on_exec | binary
+    supports_dir_fd = os.open in getattr(os, "supports_dir_fd", ())
+    directory_flag = getattr(os, "O_DIRECTORY", 0)
+
+    if supports_dir_fd and directory_flag:
+        directory_flags = os.O_RDONLY | directory_flag | no_follow | close_on_exec
+        directory_fd = os.open(base, directory_flags)
+        try:
+            for component in parts[:-1]:
+                next_fd = os.open(component, directory_flags, dir_fd=directory_fd)
+                os.close(directory_fd)
+                directory_fd = next_fd
+            fd = os.open(parts[-1], file_flags, mode, dir_fd=directory_fd)
+        finally:
+            os.close(directory_fd)
+    else:
+        fd = os.open(resolved, file_flags, mode)
+
+    if not _opened_file_is_confined(fd, resolved, base):
+        os.close(fd)
+        raise ValueError(f"{path!r} resolves outside OMNIVOICE_MCP_BASE_PATH")
+    return fd, resolved
+
+
+def _read_input_audio(
+    audio_base64: "str | None",
+    audio_path: "str | None",
+    *,
+    label: str = "audio_base64",
+    too_big: str = "audio exceeds 200 MB limit",
+) -> "tuple[bytes | None, str | None]":
+    """Audio bytes from exactly one of the two input lanes, or (None, error).
+
+    The base64 lane keeps its data-URI tolerance and 200 MB cap; the path lane
+    is honored only inside the base path (the security boundary) and applies
+    the same cap to the file's size before reading it."""
+    if bool(audio_base64) == bool(audio_path):
+        return None, f"pass exactly one of {label} or the matching *_path argument"
+    if audio_path:
+        try:
+            fd, _resolved = _open_under_base(audio_path, os.O_RDONLY)
+        except ValueError as e:
+            return None, str(e)
+        except FileNotFoundError:
+            return None, f"no such file under OMNIVOICE_MCP_BASE_PATH: {audio_path!r}"
+        except OSError as e:
+            return None, f"could not safely read {audio_path!r}: {e}"
+        with os.fdopen(fd, "rb") as handle:
+            info = os.fstat(handle.fileno())
+            if not stat.S_ISREG(info.st_mode):
+                return None, f"{audio_path!r} is not a regular file"
+            if info.st_size > _MAX_INPUT_BYTES:
+                return None, too_big
+            raw = handle.read(_MAX_INPUT_BYTES + 1)
+        if len(raw) > _MAX_INPUT_BYTES:
+            return None, too_big
+        if not raw:
+            return None, f"{label} is empty"
+        return raw, None
+    encoded = (
+        audio_base64.split(",", 1)[-1]
+        if audio_base64.startswith("data:")
+        else audio_base64
+    )
+    max_encoded_bytes = 4 * ((_MAX_INPUT_BYTES + 2) // 3)
+    if len(encoded) > max_encoded_bytes:
+        return None, too_big
+    raw = _decode_ref_audio(audio_base64)
+    if raw is None:
+        return None, f"{label} is not valid base64"
+    if not raw:
+        return None, f"{label} is empty"
+    if len(raw) > _MAX_INPUT_BYTES:
+        return None, too_big
+    return raw, None
+
+
+def _write_output(audio_id: str, raw: bytes) -> str:
+    """Land a render under the base path as ``<audio_id>.wav``; returns the path."""
+    if not _SAFE_AUDIO_ID.fullmatch(audio_id):
+        raise ValueError("backend returned an invalid X-Audio-Id header")
+    base = _base_path()
+    os.makedirs(base, exist_ok=True)
+    filename = f"{audio_id}.wav"
+    fd, path = _open_under_base(filename, os.O_WRONLY | os.O_CREAT | os.O_EXCL)
+    with os.fdopen(fd, "wb") as handle:
+        handle.write(raw)
+    return path
+
+
+def _post_timeout_s() -> float:
+    """Seconds the tools wait on a backend POST (OMNIVOICE_MCP_TIMEOUT_S,
+    default 120). A CPU host renders a paragraph in minutes and serializes
+    generations, so an agent behind another render used to hit the fixed
+    budget with an empty-message timeout; the knob follows the backend's own
+    OMNIVOICE_GENERATE_TIMEOUT_S when a deployment raises that."""
+    raw = os.environ.get("OMNIVOICE_MCP_TIMEOUT_S", "").strip()
+    try:
+        value = float(raw) if raw else 120.0
+    except ValueError:
+        logger.warning("OMNIVOICE_MCP_TIMEOUT_S=%r is not a number; using 120", raw)
+        return 120.0
+    return value if value > 0 else 120.0
+
+
+def _maybe_number(value):
+    """A response-header number as a number, or the raw text (e.g. '?')."""
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return value
+
+
+def _speech_result(audio_id: str, gen_time, duration, raw: bytes, api_base: str) -> dict:
+    """The generate_speech reply shaped by the output mode.
+
+    The backend already keeps every render on disk and serves it at
+    ``/audio/<audio_id>.wav``, so files mode costs nothing but a URL - plus one
+    write when a base path invites the WAV into the agent's own directory."""
+    if not _SAFE_AUDIO_ID.fullmatch(audio_id):
+        raise ValueError("backend returned an invalid X-Audio-Id header")
+    mode = _output_mode()
+    out = {
+        "audio_id": audio_id,
+        "generation_time_s": gen_time,
+        "audio_duration_s": duration,
+        "format": "wav",
+        "output_mode": mode,
+    }
+    if mode in ("files", "both"):
+        out["audio_url"] = f"{api_base.rstrip('/')}/audio/{audio_id}.wav"
+        if _base_path() is not None:
+            out["output_path"] = _write_output(audio_id, raw)
+        else:
+            out["note"] = "set OMNIVOICE_MCP_BASE_PATH to also receive the WAV as a file"
+    if mode in ("resources", "both"):
+        out["wav_base64"] = base64.b64encode(raw).decode("ascii")
+    return out
+
+
 # ── Lazy imports — keeps startup fast when not using MCP ────────────────


@@ -147,7 +399,7 @@ def create_mcp_server():

    async def _api_post_form(path: str, data: dict, files: dict | None = None):
        import httpx
-        async with httpx.AsyncClient(base_url=_api_base(), timeout=120) as c:
+        async with httpx.AsyncClient(base_url=_api_base(), timeout=_post_timeout_s()) as c:
            r = await c.post(path, data=data, files=files or {})
            r.raise_for_status()
            return r
@@ -190,8 +442,12 @@ def create_mcp_server():
            steps: Diffusion steps (8=fast/draft, 16=balanced, 32=quality).

        Returns:
-            JSON with audio_id, generation_time, audio_duration, and
-            base64-encoded WAV data.
+            JSON with audio_id, generation_time_s, audio_duration_s and the
+            audio itself shaped by OMNIVOICE_MCP_OUTPUT_MODE: base64 WAV data
+            ('resources', the default), a URL to the render plus a WAV under
+            OMNIVOICE_MCP_BASE_PATH when one is set ('files'), or all of the
+            above ('both'). Prefer 'files' for LLM agents: nothing large
+            enters the context.
        """
        # Per-agent voice binding (Wave 2.2): explicit arg wins; otherwise
        # resolve this client's bound profile, then the global default.
@@ -218,18 +474,10 @@ def create_mcp_server():
        r = await _api_post_form("/generate", data=form)

        audio_id = r.headers.get("X-Audio-Id", "unknown")
-        gen_time = r.headers.get("X-Gen-Time", "?")
-        duration = r.headers.get("X-Audio-Duration", "?")
+        gen_time = _maybe_number(r.headers.get("X-Gen-Time", "?"))
+        duration = _maybe_number(r.headers.get("X-Audio-Duration", "?"))

-        wav_b64 = base64.b64encode(r.content).decode("ascii")
-
-        return (
-            f'{{"audio_id":"{audio_id}",'
-            f'"generation_time_s":{gen_time},'
-            f'"audio_duration_s":{duration},'
-            f'"format":"wav",'
-            f'"wav_base64":"{wav_b64}"}}'
-        )
+        return json.dumps(_speech_result(audio_id, gen_time, duration, r.content, _api_base()))

    @mcp.tool()
    async def list_voices() -> str:
@@ -266,30 +514,39 @@ def create_mcp_server():
        )

    @mcp.tool()
-    async def transcribe(audio_base64: str, language: str | None = None) -> str:
+    async def transcribe(
+        audio_base64: str | None = None,
+        audio_path: str | None = None,
+        language: str | None = None,
+    ) -> str:
        """Transcribe spoken audio to text.

+        Pass exactly one of audio_base64 or audio_path.
+
        Args:
            audio_base64: Base64-encoded audio bytes (wav/mp3/webm/m4a).
+            audio_path: Path to an audio file under OMNIVOICE_MCP_BASE_PATH
+                (relative to it, or absolute inside it). The base path is the
+                security boundary: with none configured, paths are refused.
+                Prefer this lane for LLM agents - the audio never enters the
+                agent's context.
            language: Optional language hint; omit for auto-detect.

        Returns:
            JSON with the recognized text, language, and duration.
        """
-        try:
-            raw = base64.b64decode(audio_base64, validate=True)
-        except Exception:
-            return '{"error":"audio_base64 is not valid base64"}'
-        # 200 MB cap — same spirit as voicebox's transcribe gate. Keeps a
-        # buggy/hostile agent from posting an unbounded blob.
-        if len(raw) > 200 * 1024 * 1024:
-            return '{"error":"audio exceeds 200 MB limit"}'
+        # 200 MB cap on both lanes — same spirit as voicebox's transcribe
+        # gate. Keeps a buggy/hostile agent from posting an unbounded blob.
+        raw, err = _read_input_audio(audio_base64, audio_path)
+        if err:
+            return json.dumps({"error": err})
        data = {}
        if language:
            data["language"] = language
        r = await _api_post_form(
            "/transcribe", data=data,
-            files={"audio": ("audio.wav", raw, "application/octet-stream")},
+            files={"audio": (f"audio{_sniff_audio_ext(raw)}", raw,
+                             "application/octet-stream")},
        )
        return str(r.json())

@@ -319,15 +576,17 @@ def create_mcp_server():
    @mcp.tool()
    async def clone_voice(
        name: str,
-        ref_audio_base64: str,
+        ref_audio_base64: str | None = None,
        ref_text: str = "",
        instruct: str = "",
        language: str = "Auto",
+        ref_audio_path: str | None = None,
    ) -> str:
        """Clone a new voice profile from a reference audio sample.

        The new voice is immediately available for use with generate_speech
-        (pass the returned profile_id as the profile_id argument).
+        (pass the returned profile_id as the profile_id argument). Pass
+        exactly one of ref_audio_base64 or ref_audio_path.

        Args:
            name: A human-friendly name for the cloned voice.
@@ -338,19 +597,20 @@ def create_mcp_server():
                quality for some engines).
            instruct: Optional style instruction (e.g. 'whisper', 'excited').
            language: Language of the reference audio (ISO code or 'Auto').
+            ref_audio_path: Path to the reference audio under
+                OMNIVOICE_MCP_BASE_PATH (relative to it, or absolute inside
+                it); refused when no base path is configured. Prefer this
+                lane for LLM agents - the clip never enters the context.

        Returns:
            JSON with the new profile's id, name, and kind.
        """
-        # Reject oversized inputs before decoding (base64 is always larger
-        # than raw, so this is a safe lower bound on the decoded size).
-        if len(ref_audio_base64) > 200 * 1024 * 1024:
-            return '{"error":"reference audio exceeds 200 MB limit"}'
-        raw = _decode_ref_audio(ref_audio_base64)
-        if raw is None:
-            return '{"error":"ref_audio_base64 is not valid base64"}'
-        if not raw:
-            return '{"error":"ref_audio_base64 is empty"}'
+        raw, err = _read_input_audio(
+            ref_audio_base64, ref_audio_path,
+            label="ref_audio_base64", too_big="reference audio exceeds 200 MB limit",
+        )
+        if err:
+            return json.dumps({"error": err})
        import httpx
        try:
            r = await _api_post_form(
@@ -0,0 +1,18 @@
+"""Retain the dispatch-time deadline policy across worker/control-plane loss."""
+from alembic import op
+import sqlalchemy as sa
+
+revision = "0011_remote_attempt_deadlines"
+down_revision = "0010_remote_worker_schema"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    columns = op.get_bind().execute(sa.text("PRAGMA table_info(remote_task_attempts)"))
+    if not any(row[1] == "deadlines_json" for row in columns):
+        op.add_column("remote_task_attempts", sa.Column("deadlines_json", sa.Text(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column("remote_task_attempts", "deadlines_json")
@@ -190,6 +190,7 @@ class ParseSubtitleTextRequest(BaseModel):
 class DubIngestUrlRequest(BaseModel):
    url: str
    job_id: Optional[str] = None
+    source_lang: Optional[str] = None
    # When true and the URL is a caption-bearing host (YouTube, Vimeo, TED…),
    # ask yt-dlp to also download the original-language + any additional
    # sub_langs as VTT. The UI uses this to seed a transcript without running
@@ -33,7 +33,12 @@ WS_TICKET_PREFIX = "ovs_ws_ticket_"
 _TOKEN_BYTES = 32
 _ENCODED_TOKEN_LENGTH = 43
 _TOKEN_BODY_RE = re.compile(rf"^[A-Za-z0-9_-]{{{_ENCODED_TOKEN_LENGTH}}}$")
-_ALLOWED_WS_PATHS = frozenset({"/ws/events", "/ws/transcribe"})
+# Every ticketed WebSocket route. The first-party mirror is ``ALLOWED_WS_PATHS``
+# in frontend/src/api/authSession.ts — a route missing here mints a 422 and the
+# UI consumer fails silently (#1769 added /ws/tts for the live dub preview).
+_ALLOWED_WS_PATHS = frozenset(
+    {"/ws/events", "/ws/transcribe", "/ws/tts", "/v1/audio/transcriptions/stream"}
+)
 _ADMIN_CAPABILITIES = frozenset({"consume", "admin"})
 _KEY_GENERATION_INFO = b"omnivoice-admin-key-generation-v1"

@@ -24,12 +24,15 @@ faster-whisper because it's available on every platform we ship to).
 from __future__ import annotations

 import asyncio
+import ipaddress
 import logging
 import os
 import re
 import contextlib
 import threading
 import time
+import weakref
+from urllib.parse import urlsplit
 from utils.containment import contain_system_exit

 from abc import ABC, abstractmethod
@@ -88,10 +91,9 @@ def reset_pool_after_wedge(executor, *, what: str = "ASR") -> bool:


 # ── Consecutive-timeout streak → recommend the crash-isolated engine ────────
-# A pool reset restores *capacity*, but the wedged CTranslate2/whisperx thread
-# keeps its VRAM until the process exits. When guarded transcribes keep timing
-# out back-to-back in one session, resets clearly aren't recovering the
-# underlying hang — the durable fix is the crash-isolated sidecar engine
+# A timed-out CTranslate2/whisperx thread keeps its worker and VRAM until the
+# native call exits. When guarded transcribes keep timing out back-to-back in
+# one session, the durable fix is the crash-isolated sidecar engine
 # (services.subprocess_asr, #393), whose child process CAN be hard-killed to
 # reclaim the hung call and its VRAM. We only *recommend* it (log + error
 # message); we never switch engines automatically (owner rule: no silent
@@ -146,41 +148,89 @@ def _isolated_engine_hint(streak: int) -> str:

 async def run_transcribe_guarded(executor, fn, *, what: str = "ASR",
                                 timeout: float = ASR_TRANSCRIBE_TIMEOUT_S,
-                                 timeout_env: str = "OMNIVOICE_ASR_TRANSCRIBE_TIMEOUT_S"):
+                                 timeout_env: str = "OMNIVOICE_ASR_TRANSCRIBE_TIMEOUT_S",
+                                 reset_on_timeout: bool = False,
+                                 on_abandon=None):
    """Run a blocking transcribe ``fn`` in ``executor`` with a hard wall-clock
    bound. On timeout, raise :class:`ASRTimeoutError` with guidance instead of
    letting the request hang forever.

-    ``run_in_executor`` cannot cancel the underlying thread, so a wedged
-    transcribe (a CTranslate2 / whisperx / VAD hang seen on some Windows + CUDA
-    setups, #730) keeps occupying its GPU-pool worker. With a 1–2 worker pool
-    that starves every *other* request — including TTS generate — and the next
-    thing the user does surfaces as "Can't reach the local backend" even though
-    the process is alive. So on timeout we also ``reset()`` the pool when it
-    supports it (``_ResilientGpuPool``): the wedged thread is abandoned and the
-    next submit gets a fresh worker, restoring capacity without an app restart.
-    The orphaned thread still holds its VRAM until the process exits, which is
-    why the message still recommends a smaller ASR model / Flush as the durable
-    fix. Executors without ``reset`` (a plain ThreadPoolExecutor in tests) just
-    get the bound + actionable error.
+    A future cannot cancel the underlying thread, so a timed-out
+    in-process CTranslate2/whisperx call still owns its model and device. The
+    default deliberately leaves that worker accounted for: swapping in a fresh
+    pool and immediately retrying the same backend overlaps two native calls,
+    which produced the Windows access violation in #1669. A caller backed by a
+    genuinely killable process may opt into ``reset_on_timeout``.
+
+    ``on_abandon`` is called once after a timed-out or cancelled worker can no
+    longer access its inputs. Queued work cancelled before it starts calls it
+    immediately; running work calls it from the worker finalizer. Normal
+    completion leaves cleanup with the caller.
    """
    loop = asyncio.get_running_loop()
    # Same SystemExit containment as the TTS pool (#1133 class): an ASR
    # dependency written as a CLI must not be able to shut the backend down.
-    fut = loop.run_in_executor(executor, contain_system_exit(fn, what))
+    inner = contain_system_exit(fn, what)
+    abandon_lock = threading.Lock()
+    abandon_state = {
+        "requested": False,
+        "finished": False,
+        "callback_called": False,
+    }
+
+    def _fire_abandon_callback() -> None:
+        if on_abandon is None:
+            return
+        with abandon_lock:
+            if abandon_state["callback_called"]:
+                return
+            abandon_state["callback_called"] = True
+        try:
+            on_abandon()
+        except Exception:  # noqa: BLE001 — cleanup cannot hide the ASR result
+            logger.exception("%s abandon cleanup failed", what)
+
+    def _job():
+        try:
+            return inner()
+        finally:
+            with abandon_lock:
+                abandon_state["finished"] = True
+                abandoned = abandon_state["requested"]
+            if abandoned:
+                _fire_abandon_callback()
+
+    concurrent_fut = executor.submit(_job)
+    fut = asyncio.wrap_future(concurrent_fut, loop=loop)
+
+    def _abandon() -> None:
+        cancelled_before_start = concurrent_fut.cancel()
+        with abandon_lock:
+            abandon_state["requested"] = True
+            finished = abandon_state["finished"]
+        fut.cancel()
+        if cancelled_before_start or finished:
+            _fire_abandon_callback()
+
    try:
-        result = await asyncio.wait_for(fut, timeout=timeout)
+        # Shield the wrapper so timeout does not discard our ability to tell a
+        # queued cancellation from a native thread that is still running.
+        result = await asyncio.wait_for(asyncio.shield(fut), timeout=timeout)
+    except asyncio.CancelledError:
+        _abandon()
+        raise
    except asyncio.TimeoutError:
-        # Free the poisoned pool so a hung transcribe can't keep starving TTS /
-        # other ASR work (the "can't reach backend" symptom, #730).
-        reset_pool_after_wedge(executor, what=what)
+        _abandon()
+        if reset_on_timeout:
+            reset_pool_after_wedge(executor, what=what)
        streak = _note_transcribe_timeout()
        msg = (
            f"{what} transcription exceeded {timeout:.0f}s and was abandoned — "
            "the backend is running, but the ASR model is too heavy for the "
            "available compute. Most often the GPU is VRAM-starved: the resident "
            "TTS model and a large ASR model (large-v3) contend for memory. "
-            "Capacity was restored automatically, but for a durable fix Flush the "
+            "The native call cannot be killed safely, so its capacity remains "
+            "reserved until it exits. For a durable fix Flush the "
            "TTS model to free VRAM, pick a smaller ASR model in "
            f"Model Catalogue → Models, or set ASR to CPU. (Raise {timeout_env} "
            "for very long transcribes.)"
@@ -310,6 +360,16 @@ class ASRBackend(ABC):
    # broken GPU path, strictly worse than the honest `cpu_fallback`.)
    gpu_compat: tuple[str, ...] = ("cpu",)

+    def execution_evidence_loaded(self) -> bool:
+        """Whether this instance has live model state worth reporting."""
+        if getattr(self, "runs_out_of_process", False):
+            proc = getattr(self, "_proc", None)
+            return proc is not None and proc.poll() is None
+        return any(
+            getattr(self, attr, None) is not None
+            for attr in ("_model", "_asr", "_pipeline", "_pipe", "_transcriber", "_rec")
+        )
+
    @classmethod
    @abstractmethod
    def is_available(cls) -> tuple[bool, str]:
@@ -956,6 +1016,8 @@ class FasterWhisperBackend(ASRBackend):
        # (after the #551 compute_type / #255 OOM→CPU fallback chain).
        self._device: str | None = None
        self._compute_type: str | None = None
+        self._fallback_reason: str | None = None
+        self._fallback_stage: str | None = None

    @classmethod
    def is_available(cls) -> tuple[bool, str]:
@@ -1033,6 +1095,8 @@ class FasterWhisperBackend(ASRBackend):
                except Exception:  # noqa: BLE001 — cache clear is best-effort
                    pass
                device = "cpu"
+                self._fallback_reason = "CUDA memory was exhausted while loading the engine"
+                self._fallback_stage = "model_load"
                candidates = _compute_type_candidates(device)
                compute_type = candidates[0]
                continue
@@ -1991,6 +2055,42 @@ _ASR_OPENAI_COMPAT_MODEL_KEY = "asr.openai_compat.model"
 _ASR_OPENAI_COMPAT_SECRET_NAME = "asr_openai_compat_key"


+def normalize_openai_compat_asr_base_url(value: str) -> str:
+    """Normalize a safe ASR endpoint, allowing plain HTTP only on loopback."""
+    base = (value or "").strip().rstrip("/")
+    if not base:
+        return ""
+    try:
+        parsed = urlsplit(base)
+        _ = parsed.port
+    except (TypeError, ValueError) as exc:
+        raise ValueError("Invalid OpenAI-compatible ASR base URL") from exc
+    scheme = parsed.scheme.lower()
+    if (
+        scheme not in {"http", "https"}
+        or not parsed.hostname
+        or parsed.username is not None
+        or parsed.password is not None
+        or parsed.query
+        or parsed.fragment
+    ):
+        raise ValueError(
+            "OpenAI-compatible ASR base URL must be a credential-free HTTP(S) URL"
+        )
+    host = parsed.hostname.lower()
+    loopback = host == "localhost"
+    if not loopback:
+        try:
+            address = ipaddress.ip_address(host)
+            address = getattr(address, "ipv4_mapped", None) or address
+            loopback = address.is_loopback
+        except ValueError:
+            loopback = False
+    if scheme == "http" and not loopback:
+        raise ValueError("Non-loopback OpenAI-compatible ASR endpoints require HTTPS")
+    return base
+
+
 def resolve_openai_compat_asr_base_url() -> str:
    from services import settings_store
    return (
@@ -2054,7 +2154,7 @@ def probe_openai_compat_server(
    maps to a translated message:

      not_configured   no base URL anywhere
-      invalid_url      base URL without an http(s):// scheme
+      invalid_url      malformed URL or non-loopback HTTP endpoint
      ok               2xx — ``model_found`` says whether the configured
                       model appears in the server's list (None = unknown)
      ok_no_models     404/405/501 — reachable, but no /models endpoint
@@ -2069,7 +2169,7 @@ def probe_openai_compat_server(

    from core.scrub import scrub_text

-    base = (base_url if base_url is not None else resolve_openai_compat_asr_base_url()).strip().rstrip("/")
+    configured_base = base_url if base_url is not None else resolve_openai_compat_asr_base_url()
    mdl = (model if model is not None else resolve_openai_compat_asr_model()).strip()
    if api_key is None:
        key = resolve_openai_compat_asr_api_key()
@@ -2085,9 +2185,11 @@ def probe_openai_compat_server(
        "model_found": None,
        "detail": None,
    }
-    if not base:
+    if not configured_base.strip():
        return out
-    if not base.startswith(("http://", "https://")):
+    try:
+        base = normalize_openai_compat_asr_base_url(configured_base)
+    except ValueError:
        out["status"] = "invalid_url"
        return out

@@ -2098,7 +2200,7 @@ def probe_openai_compat_server(
    try:
        with httpx.Client(
            timeout=httpx.Timeout(timeout_s, connect=min(5.0, timeout_s)),
-            follow_redirects=True,
+            follow_redirects=False,
        ) as client:
            resp = client.get(f"{base}/models", headers=headers)
    except httpx.TimeoutException as exc:
@@ -2161,13 +2263,20 @@ class OpenAICompatASRBackend(ASRBackend):
    gpu_compat = ("cpu",)  # network client only — no local compute

    def __init__(self):
-        self._base_url = resolve_openai_compat_asr_base_url()
+        self._base_url = normalize_openai_compat_asr_base_url(
+            resolve_openai_compat_asr_base_url()
+        )
        self._model = resolve_openai_compat_asr_model()

    @classmethod
    def is_available(cls) -> tuple[bool, str]:
-        if not resolve_openai_compat_asr_base_url():
+        base_url = resolve_openai_compat_asr_base_url()
+        if not base_url:
            return False, "Configure a server endpoint in Model Catalogue → Engines"
+        try:
+            normalize_openai_compat_asr_base_url(base_url)
+        except ValueError as exc:
+            return False, str(exc)
        try:
            import openai  # noqa: F401
        except ImportError:
@@ -2175,13 +2284,18 @@ class OpenAICompatASRBackend(ASRBackend):
        return True, "ready"

    def _client(self):
-        from openai import OpenAI
+        from openai import DefaultHttpxClient, OpenAI
        api_key = resolve_openai_compat_asr_api_key() or "not-needed"
        # max_retries=0: mirrors llm_skills.resolve_skill_client — a
        # rate-limited/slow server retrying inside the SDK would blow past
        # whatever bounded timeout the caller (dub transcribe, dictation)
        # expects from a single call.
-        return OpenAI(base_url=self._base_url, api_key=api_key, max_retries=0)
+        return OpenAI(
+            base_url=self._base_url,
+            api_key=api_key,
+            max_retries=0,
+            http_client=DefaultHttpxClient(follow_redirects=False),
+        )

    def transcribe(self, audio_path: str, *, word_timestamps: bool = True) -> dict:
        logger.info(
@@ -2360,6 +2474,8 @@ _LAST_ERRORS: dict[str, str] = {}
 # failing ASR wholesale. Per-process by design: repairing the env requires a
 # reinstall / ``uv sync --reinstall`` and an app restart anyway.
 _DEEP_IMPORT_BROKEN: dict[str, str] = {}
+_RUNTIME_EVIDENCE: dict[str, dict] = {}
+_RUNTIME_INSTANCES: weakref.WeakValueDictionary[str, "ASRBackend"] = weakref.WeakValueDictionary()


 def _deep_import_reason(cls: type["ASRBackend"], exc: ImportError) -> str:
@@ -2390,6 +2506,7 @@ def list_backends() -> list[dict]:
    """
    from core.device_caps import detect_host_caps
    from core.scrub import scrub_text
+    from services.engine_evidence import snapshot as execution_snapshot
    from services.engine_routing import routing_fields
    caps = detect_host_caps()

@@ -2414,6 +2531,24 @@ def list_backends() -> list[dict]:
            _LAST_ERRORS[bid] = scrub_text(msg)
        isolation = "subprocess" if getattr(cls, "_is_subprocess_isolated", False) else "in-process"
        gpu_compat = getattr(cls, "gpu_compat", ("cpu",))
+        routing = routing_fields(gpu_compat, caps)
+        # Cached load-time facts are valid only while their exact backend still
+        # owns live model state. Recompute from that instance so unload/reaping
+        # cannot leave ghost GPU/provider evidence in diagnostics.
+        instance = (
+            _ISOLATED_INSTANCES.get(bid)
+            if isolation == "subprocess"
+            else _RUNTIME_INSTANCES.get(bid)
+        )
+        execution_evidence = execution_snapshot(
+            engine_id=bid,
+            engine_cls=cls,
+            instance=instance,
+            routing=routing,
+            caps=caps,
+        )
+        if execution_evidence["evidence_state"] == "not_loaded":
+            _RUNTIME_EVIDENCE.pop(bid, None)
        out.append({
            "id": bid,
            "display_name": cls.display_name,
@@ -2425,7 +2560,14 @@ def list_backends() -> list[dict]:
            "last_error": _LAST_ERRORS.get(bid),
            "isolation_mode": isolation,
            "gpu_compat": list(gpu_compat),
-            **routing_fields(gpu_compat, caps),
+            **routing,
+            "execution_evidence": execution_evidence or execution_snapshot(
+                engine_id=bid,
+                engine_cls=cls,
+                instance=None,
+                routing=routing,
+                caps=caps,
+            ),
        })
    return out

@@ -2564,7 +2706,10 @@ def _auto_detect() -> str:
 def active_backend_id() -> str:
    explicit = os.environ.get("OMNIVOICE_ASR_BACKEND")
    if explicit:
-        return explicit
+        # #1582's public spelling predates the registry name. Keep it as a
+        # compatibility alias for the PyTorch-native Whisper implementation
+        # that can use ROCm/HIP; every ASR consumer resolves through here.
+        return "pytorch-whisper" if explicit == "omnivoice" else explicit
    from core import prefs
    picked = prefs.get("asr_backend")
    if picked:
@@ -2663,6 +2808,21 @@ def load_active_asr_backend(*, asr_pipe=None) -> ASRBackend:
                raise ASRModelMissingError(missing)
        try:
            backend.ensure_loaded()
+            from core.device_caps import detect_host_caps
+            from services.engine_evidence import snapshot as execution_snapshot
+            from services.engine_routing import routing_fields
+
+            cls = type(backend)
+            caps = detect_host_caps()
+            routing = routing_fields(getattr(cls, "gpu_compat", ("cpu",)), caps)
+            _RUNTIME_EVIDENCE[bid] = execution_snapshot(
+                engine_id=bid,
+                engine_cls=cls,
+                instance=backend,
+                routing=routing,
+                caps=caps,
+            )
+            _RUNTIME_INSTANCES[bid] = backend
            return backend
        except ImportError as e:
            # ModuleNotFoundError and its ImportError parent ("cannot import
@@ -2992,7 +3152,7 @@ def _capture_prefers_parakeet() -> bool:
    return _parakeet_mlx_installed()


-def get_capture_asr_backend() -> ASRBackend:
+def get_capture_asr_backend(*, skip_sherpa: bool = False) -> ASRBackend:
    """Pick the fastest ASR engine for capture / dictation.

    Selection order:
@@ -3017,6 +3177,9 @@ def get_capture_asr_backend() -> ASRBackend:

    Returns a cached singleton so the model stays warm between calls; the
    singleton is rebuilt if the selected sherpa model changes.
+
+    ``skip_sherpa`` is used only to validate a token-silent Sherpa result with
+    the installed capture fallback before persisting model demotion.
    """
    global _capture_backend, _capture_backend_key

@@ -3025,7 +3188,7 @@ def get_capture_asr_backend() -> ASRBackend:
    # call get_sherpa_dictation_backend concurrently) can't both build a model.
    with _capture_backend_lock:
        # 0. Honor an explicit sherpa dictation model selection.
-        sherpa_id = dictation_model_id()
+        sherpa_id = None if skip_sherpa else dictation_model_id()
        if sherpa_id:
            ok, _ = SherpaDictationBackend.is_available()
            if ok:
@@ -3192,7 +3355,10 @@ def _capture_whisper_repo() -> str | None:
    return os.environ.get("OMNIVOICE_PYTORCH_ASR_MODEL", _PYTORCH_ASR_DEFAULT)


-def _recommended_asr_model(purpose: str, missing_repo: str | None) -> dict | None:
+def _recommended_asr_model(
+    purpose: str, missing_repo: str | None, *, prefer_sherpa: bool = True,
+    excluded_sherpa_model_id: str | None = None,
+) -> dict | None:
    """The catalog entry to offer in the download CTA.

    Offline: the missing repo itself when it's in the catalog (guarantees
@@ -3212,20 +3378,38 @@ def _recommended_asr_model(purpose: str, missing_repo: str | None) -> dict | Non

    by_id = {m["repo_id"]: m for m in KNOWN_MODELS}
    exact = by_id.get(missing_repo) if missing_repo else None
-    want_sherpa = False
-    if purpose == "dictation":
-        if exact is not None and exact.get("engine") == "sherpa-onnx":
+
+    def _eligible(m: dict, *, sherpa: bool) -> bool:
+        if (m.get("engine") == "sherpa-onnx") != sherpa:
+            return False
+        if sherpa and m.get("dictation_id") == excluded_sherpa_model_id:
+            return False
+        return _model_supported(m)
+
+    if purpose != "dictation":
+        if exact is not None and _model_supported(exact):
            return _shape(exact)
+        prefer_sherpa = False
+
+    if purpose == "dictation" and prefer_sherpa:
        ok, _ = SherpaDictationBackend.is_available()
-        want_sherpa = ok
-    if not want_sherpa and exact is not None and _model_supported(exact):
+        if ok:
+            if exact is not None and _eligible(exact, sherpa=True):
+                return _shape(exact)
+            for m in KNOWN_MODELS:
+                if (m.get("role") == "ASR" and _eligible(m, sherpa=True)
+                        and _model_curated(m)):
+                    return _shape(m)
+
+    # No usable Sherpa recommendation remains (runtime unavailable, explicit
+    # fallback probe, or the sole curated entry is the demoted model). Offer
+    # the exact capture fallback so download → retry cannot loop.
+    if exact is not None and _eligible(exact, sherpa=False):
        return _shape(exact)
    for m in KNOWN_MODELS:
        if m.get("role") != "ASR":
            continue
-        if (m.get("engine") == "sherpa-onnx") != want_sherpa:
-            continue
-        if _model_curated(m) and _model_supported(m):
+        if _eligible(m, sherpa=False) and _model_curated(m):
            return _shape(m)
    return None

@@ -3256,7 +3440,9 @@ def _repo_installed(repo: str) -> bool:

 def asr_model_missing_error(*, purpose: str = "transcribe",
                            sherpa_model_id: str | None = None,
-                            backend_id: str | None = None) -> dict | None:
+                            backend_id: str | None = None,
+                            skip_sherpa: bool = False,
+                            require_installed: bool = False) -> dict | None:
    """None when the active ASR selection can transcribe without downloading
    anything; otherwise the typed ``{"error": "asr_model_missing", ...}``
    payload for a 409 / SSE / WS error with a download CTA.
@@ -3268,6 +3454,11 @@ def asr_model_missing_error(*, purpose: str = "transcribe",
    ``?model=`` override. Installed state comes from the same HF-cache helpers
    the model store uses (see :func:`_repo_installed`), so the answer matches
    the Model Catalogue → Models install badges.
+    ``skip_sherpa`` probes only the non-Sherpa capture fallback; silent-model
+    recovery uses it before deciding whether persistent demotion is warranted.
+    ``require_installed`` makes unknown/custom selections fail closed for that
+    recovery path so it can never turn the normal fail-open policy into an
+    implicit model download.

    FAIL-OPEN rule: a repo the model catalog doesn't know (a custom
    ``ASR_MODEL_*`` pin, pytorch-whisper's default repo, an unrecognized
@@ -3277,27 +3468,55 @@ def asr_model_missing_error(*, purpose: str = "transcribe",
    a broken preflight must degrade to the old behaviour, not block ASR.
    """
    try:
+        prefer_sherpa_recommendation = not skip_sherpa
+        excluded_sherpa_model_id = None
        if purpose == "dictation":
-            sid = sherpa_model_id or dictation_model_id()
+            sid = None if skip_sherpa else (sherpa_model_id or dictation_model_id())
            if sid:
                ok, _ = SherpaDictationBackend.is_available()
                if ok:
                    from services import sherpa_dictation as _sd
                    spec = _sd.get_spec(sid)
+                    # A recognizer observed returning silence must follow the
+                    # same capture fallback as execution, even when the
+                    # frontend keeps sending its persisted `?model=` value.
                    if spec is not None:
-                        if _sd.is_installed(spec):
-                            return None
-                        return {
-                            "error": ASR_MODEL_MISSING,
-                            "missing_repo_id": spec.repo_id,
-                            "recommended": _recommended_asr_model(purpose, spec.repo_id),
-                        }
+                        if _sd.is_demoted(spec.id):
+                            excluded_sherpa_model_id = spec.id
+                        else:
+                            if _sd.is_installed(spec):
+                                return None
+                            return {
+                                "error": ASR_MODEL_MISSING,
+                                "missing_repo_id": spec.repo_id,
+                                "recommended": _recommended_asr_model(
+                                    purpose, spec.repo_id,
+                                ),
+                            }
            repo = _capture_whisper_repo()
        else:
            repo = _offline_asr_repo(backend_id)
        if repo is None:
+            if require_installed:
+                return {
+                    "error": ASR_MODEL_MISSING,
+                    "missing_repo_id": "unresolved-capture-fallback",
+                    "recommended": None,
+                }
            return None  # explicit opt-in engine — can't (and shouldn't) preflight
        from api.routers.setup.models import get_model_catalog
+        if require_installed:
+            if _repo_installed(repo):
+                return None
+            return {
+                "error": ASR_MODEL_MISSING,
+                "missing_repo_id": repo,
+                "recommended": _recommended_asr_model(
+                    purpose, repo,
+                    prefer_sherpa=prefer_sherpa_recommendation,
+                    excluded_sherpa_model_id=excluded_sherpa_model_id,
+                ),
+            }
        if get_model_catalog().get(repo) is None:
            return None  # not installable from the CTA — fail open (see docstring)
        if _repo_installed(repo):
@@ -3305,7 +3524,11 @@ def asr_model_missing_error(*, purpose: str = "transcribe",
        return {
            "error": ASR_MODEL_MISSING,
            "missing_repo_id": repo,
-            "recommended": _recommended_asr_model(purpose, repo),
+            "recommended": _recommended_asr_model(
+                purpose, repo,
+                prefer_sherpa=prefer_sherpa_recommendation,
+                excluded_sherpa_model_id=excluded_sherpa_model_id,
+            ),
        }
    except Exception:  # noqa: BLE001 — preflight is best-effort, never a blocker
        logger.warning("ASR install preflight failed — proceeding without it",
@@ -742,6 +742,74 @@ def _ensure_browser_playable_mp4(video_path: str) -> str:
    return video_path


+async def _ensure_browser_playable_mp4_for_job(job_id: str, video_path: str) -> str:
+    """Normalize an upload through the job's cancellable process registry."""
+    is_mp4 = video_path.lower().endswith(".mp4")
+    vcodec, acodec = await asyncio.to_thread(_probe_codecs, video_path)
+    if is_mp4 and vcodec in _BROWSER_VIDEO_CODECS and acodec in _BROWSER_AUDIO_CODECS:
+        return video_path
+
+    target = os.path.splitext(video_path)[0] + ".mp4"
+    if target == video_path:
+        target = os.path.splitext(video_path)[0] + ".browser.mp4"
+    run_proc = run_proc_factory(job_id)
+    ffmpeg_bin = find_ffmpeg()
+
+    async def attempt(cmd: list[str]) -> int:
+        try:
+            proc, _stdout, _stderr = await run_proc(cmd, timeout=1800.0)
+            return proc.returncode
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:
+            logger.warning(
+                "Browser-media normalization process failed for %s: %s",
+                log_safe(video_path),
+                log_safe(exc),
+            )
+            return 1
+
+    rc = 1
+    if not is_mp4:
+        rc = await attempt(
+            [
+                ffmpeg_bin, "-y", "-i", video_path,
+                "-c:v", "copy", "-c:a", "copy",
+                "-movflags", "+faststart", target,
+            ]
+        )
+        if rc == 0 and os.path.exists(target):
+            target_vcodec, target_acodec = await asyncio.to_thread(_probe_codecs, target)
+            if (
+                target_vcodec not in _BROWSER_VIDEO_CODECS
+                or target_acodec not in _BROWSER_AUDIO_CODECS
+            ):
+                rc = 1
+        else:
+            rc = 1
+    if rc != 0:
+        rc = await attempt(
+            [
+                ffmpeg_bin, "-y", "-i", video_path,
+                "-c:v", "libx264", "-preset", "veryfast", "-crf", "23",
+                "-pix_fmt", "yuv420p", "-c:a", "aac", "-b:a", "192k",
+                "-movflags", "+faststart", target,
+            ]
+        )
+    if rc == 0 and os.path.exists(target) and target != video_path:
+        try:
+            os.remove(video_path)
+        except OSError:
+            pass  # Best effort: the normalized target is already complete.
+        return target
+    logger.warning(
+        "Could not transcode %s to browser-playable mp4 — the in-app "
+        "video player may render this file as a black box.",
+        log_safe(video_path),
+    )
+    return video_path
+
+
 # Bounded retry for transient download failures (#579/#598). yt-dlp's own
 # `retries`/`fragment_retries` cover per-fragment HTTP flakes, but a broken
 # pipe ([Errno 32]) raised while the write side of a pipe closes mid-stream
@@ -1257,6 +1325,13 @@ async def ingest_pipeline(
        except Exception:
            dur = 0.0

+        # URL downloads already pass through this guard in yt_download_sync.
+        # Uploaded videos did not, so a valid VP9/AV1/Opus upload could be
+        # processed successfully but remain undecodable by the in-app WebView.
+        # Codec probing/transcoding is blocking; keep it off the event loop.
+        if source.get("kind") != "url" and input_type != "audio":
+            video_path = await _ensure_browser_playable_mp4_for_job(job_id, video_path)
+
        # Content-hash cache: reuse artifacts from previous matching jobs.
        content_hash = await asyncio.to_thread(compute_file_hash, audio_path)
        cached = find_cached_job(content_hash, job_id)
@@ -1295,6 +1370,7 @@ async def ingest_pipeline(
                "scene_cuts": scene_cuts,
                "youtube_subs": youtube_subs_by_lang or None,
                "input_type": input_type,
+                "source_lang_override": source.get("source_lang"),
            }
            if not put_and_save_job(
                job_id, full_job, filename=filename, duration=dur, content_hash=content_hash,
@@ -1323,6 +1399,7 @@ async def ingest_pipeline(
                "scene_cuts": [],
                "youtube_subs": youtube_subs_by_lang or None,
                "input_type": input_type,
+                "source_lang_override": source.get("source_lang"),
            }
            if not put_and_save_job(
                job_id, partial, filename=filename, duration=dur, content_hash=content_hash,
@@ -0,0 +1,230 @@
+"""Structured pre-install and measured disk costs for TTS engines."""
+from __future__ import annotations
+
+import os
+import threading
+import time
+from functools import lru_cache
+from pathlib import Path
+
+_GIB = 1024**3
+_CACHE_TTL_SECONDS = 10.0
+_measurement_cache: dict[str, tuple[float, dict]] = {}
+_measurement_lock = threading.Lock()
+
+# Catalogue/build estimates. ``None`` is deliberate: unknown costs must stay
+# visible instead of being silently treated as zero.
+_ESTIMATES: dict[str, dict] = {
+    "omnivoice": {
+        "package_download_bytes": None,
+        "unique_installed_bytes": None,
+        "potentially_shared_bytes": None,
+        "temporary_free_bytes": None,
+        "confidence": "estimated",
+        "destination": "hf_model_cache",
+        "deduplication": None,
+    },
+    "kittentts": {
+        "package_download_bytes": None,
+        "unique_installed_bytes": None,
+        "potentially_shared_bytes": None,
+        "temporary_free_bytes": None,
+        "confidence": "estimated",
+        "destination": "hf_model_cache",
+        "deduplication": None,
+    },
+}
+_MODEL_REPOS = {
+    "omnivoice": "k2-fsa/OmniVoice",
+    "kittentts": "KittenML/kitten-tts-mini-0.8",
+}
+
+
+def _volume_root(path: Path) -> str:
+    """Mount point/drive containing a possibly not-yet-created destination."""
+    try:
+        current = path.expanduser().resolve()
+        while not current.exists() and current.parent != current:
+            current = current.parent
+        device = current.stat().st_dev
+        while current.parent != current and current.parent.stat().st_dev == device:
+            current = current.parent
+        return str(current)
+    except OSError:
+        return "unknown"
+
+
+def _hf_cache_path() -> Path:
+    configured = (
+        os.environ.get("HF_HUB_CACHE")
+        or os.environ.get("HUGGINGFACE_HUB_CACHE")
+        or os.environ.get("HF_HOME")
+    )
+    return Path(configured) if configured else Path.home() / ".cache" / "huggingface"
+
+
+@lru_cache(maxsize=None)
+def _catalog_model_bytes(engine_id: str) -> int | None:
+    """Resolve the weight estimate from config/models.yaml, its source of truth."""
+    repo_id = _MODEL_REPOS.get(engine_id)
+    if repo_id is None:
+        return None
+    try:
+        import yaml
+
+        catalog_path = Path(__file__).resolve().parents[1] / "config" / "models.yaml"
+        entries = yaml.safe_load(catalog_path.read_text(encoding="utf-8"))["models"]
+        model = next(item for item in entries if item["repo_id"] == repo_id)
+        return round(float(model["size_gb"]) * _GIB)
+    except (OSError, KeyError, StopIteration, TypeError, ValueError):
+        return None
+
+
+def _dir_size(path: Path) -> int:
+    total = 0
+    try:
+        for root, _dirs, files in os.walk(path):
+            for filename in files:
+                try:
+                    total += os.path.getsize(os.path.join(root, filename))
+                except OSError:
+                    continue
+    except OSError:
+        return 0
+    return total
+
+
+def _sidecar_estimate(engine_id: str) -> dict | None:
+    try:
+        from services.sidecar_install import get_spec, managed_root
+
+        spec = get_spec(engine_id)
+    except Exception:
+        return None
+    if spec is None:
+        return None
+    model_bytes = spec.weights_bytes
+    dependency_bytes = spec.dependency_bytes
+    return {
+        "model_download_bytes": model_bytes,
+        "package_download_bytes": dependency_bytes,
+        "unique_installed_bytes": spec.required_bytes,
+        "potentially_shared_bytes": spec.potentially_shared_bytes,
+        "temporary_free_bytes": spec.temporary_free_bytes,
+        "confidence": spec.disk_confidence,
+        "destination": "engine_data",
+        "destination_volume": _volume_root(managed_root(spec)),
+        "deduplication": "uv_same_volume",
+    }
+
+
+def estimate_for(engine_id: str) -> dict:
+    estimate = _sidecar_estimate(engine_id) or _ESTIMATES.get(engine_id)
+    if estimate is not None:
+        return {
+            "model_download_bytes": _catalog_model_bytes(engine_id),
+            "destination_volume": _volume_root(_hf_cache_path()),
+            **estimate,
+        }
+    return {
+        "model_download_bytes": None,
+        "package_download_bytes": None,
+        "unique_installed_bytes": None,
+        "potentially_shared_bytes": None,
+        "temporary_free_bytes": None,
+        "confidence": "unknown",
+        "destination": "unknown",
+        "destination_volume": "unknown",
+        "deduplication": None,
+    }
+
+
+def _measure_sidecar(engine_id: str) -> dict | None:
+    try:
+        from services.sidecar_install import get_spec, managed_checkout, managed_root
+
+        spec = get_spec(engine_id)
+    except Exception:
+        return None
+    if spec is None:
+        return None
+    checkout = managed_checkout(spec)
+    if not checkout.is_dir():
+        return None
+    model = _dir_size(checkout / spec.weights_subdir)
+    environment = _dir_size(checkout / ".venv")
+    total = _dir_size(managed_root(spec))
+    shared_cache = _dir_size(managed_root(spec).parent / ".uv-cache")
+    return {
+        "model_bytes": model,
+        "environment_bytes": environment,
+        "cache_bytes": shared_cache,
+        "total_owned_bytes": total,
+        "confidence": "measured",
+    }
+
+
+def _measure_model_cache(engine_id: str) -> dict | None:
+    repo_id = _MODEL_REPOS.get(engine_id)
+    if repo_id is None:
+        return None
+    try:
+        from huggingface_hub import scan_cache_dir
+
+        repo = next((item for item in scan_cache_dir().repos if item.repo_id == repo_id), None)
+    except Exception:
+        return None
+    if repo is None or repo.size_on_disk <= 0:
+        return None
+    size = int(repo.size_on_disk)
+    return {
+        "model_bytes": size,
+        # The model lives in this cache; cache overhead is not separately
+        # attributable without double-counting the same hardlinked blobs.
+        "environment_bytes": None,
+        "cache_bytes": 0,
+        "total_owned_bytes": size,
+        "confidence": "measured",
+    }
+
+
+def actual_for(engine_id: str) -> dict:
+    now = time.monotonic()
+    cached = _measurement_cache.get(engine_id)
+    if cached and now - cached[0] < _CACHE_TTL_SECONDS:
+        return dict(cached[1])
+    # A cache miss can recursively walk a sidecar and the shared uv cache.
+    # Coalesce concurrent requests so callers cannot multiply that work.
+    with _measurement_lock:
+        now = time.monotonic()
+        cached = _measurement_cache.get(engine_id)
+        if cached and now - cached[0] < _CACHE_TTL_SECONDS:
+            return dict(cached[1])
+        actual = _measure_sidecar(engine_id) or _measure_model_cache(engine_id) or {
+            "model_bytes": None,
+            "environment_bytes": None,
+            "cache_bytes": None,
+            "total_owned_bytes": None,
+            "confidence": "unknown",
+        }
+        _measurement_cache[engine_id] = (now, actual)
+        return dict(actual)
+
+
+def disk_usage_for(engine_id: str) -> dict:
+    """Stable API shape consumed by the engine catalogue."""
+    return {"estimate": estimate_for(engine_id), "actual": actual_for(engine_id)}
+
+
+def disk_summary_for(engine_id: str) -> dict:
+    """Cheap list payload; measurement is deferred until the row is opened."""
+    return {
+        "estimate": estimate_for(engine_id),
+        "actual": {
+            "model_bytes": None,
+            "environment_bytes": None,
+            "cache_bytes": None,
+            "total_owned_bytes": None,
+            "confidence": "unknown",
+        },
+    }
@@ -0,0 +1,119 @@
+"""Sanitized, reproducible execution evidence for TTS and ASR engines."""
+from __future__ import annotations
+
+import importlib.metadata
+import platform
+from typing import Any
+
+
+def _version(distribution: str) -> str | None:
+    try:
+        return importlib.metadata.version(distribution)
+    except importlib.metadata.PackageNotFoundError:
+        return None
+
+
+def _value(instance: object, *names: str) -> str | None:
+    for name in names:
+        try:
+            value = getattr(instance, name, None)
+            if value is not None and not callable(value):
+                text = str(value).strip()
+                if text and len(text) <= 80 and "/" not in text and "\\" not in text:
+                    return text
+        except Exception:
+            continue
+    return None
+
+
+def runtime_versions(engine_id: str) -> dict[str, str]:
+    """Relevant installed library versions, never paths or environment values."""
+    names = {"python": platform.python_version()}
+    candidates = ["torch"]
+    low = engine_id.lower()
+    if "faster" in low or "whisperx" in low:
+        candidates.extend(["ctranslate2", "faster-whisper"])
+    if "sherpa" in low or "moonshine" in low:
+        candidates.append("onnxruntime")
+    if "mlx" in low:
+        candidates.append("mlx")
+    for name in candidates:
+        if (version := _version(name)) is not None:
+            names[name] = version
+    return names
+
+
+def snapshot(
+    *,
+    engine_id: str,
+    engine_cls: type,
+    instance: object | None,
+    routing: dict[str, Any],
+    caps: object,
+) -> dict[str, Any]:
+    """Return fixed-shape evidence; actual fields stay null until an instance loads."""
+    isolated = bool(
+        getattr(engine_cls, "_is_subprocess_isolated", False)
+        or getattr(engine_cls, "runs_out_of_process", False)
+    )
+    loaded = False
+    probe_failed = False
+    if instance is not None:
+        try:
+            contract = getattr(instance, "execution_evidence_loaded", False)
+            loaded = bool(contract() if callable(contract) else contract)
+        except Exception:  # noqa: BLE001 - third-party lifecycle descriptors may raise
+            probe_failed = True
+
+    actual_device = None
+    provider = None
+    precision = None
+    if loaded:
+        actual_device = _value(instance, "_device", "device", "execution_device")
+        provider = _value(instance, "_provider", "provider", "execution_provider")
+        precision = _value(
+            instance, "_compute_type", "compute_type", "_dtype", "dtype", "quantization"
+        )
+        if provider is None and actual_device is not None:
+            provider = actual_device
+
+    runtime_fallback_reason = _value(instance, "_fallback_reason", "fallback_reason") if loaded else None
+    runtime_fallback_stage = _value(instance, "_fallback_stage", "fallback_stage") if loaded else None
+    status = routing.get("routing_status")
+    fallback = status == "cpu_fallback" or runtime_fallback_reason is not None
+    evidence_state = "not_loaded"
+    if probe_failed:
+        evidence_state = "probe_error"
+    elif loaded:
+        evidence_state = "loaded"
+        if isolated and provider is None and actual_device is None:
+            evidence_state = "subprocess_loaded_provider_unreported"
+    return {
+        "implementation_variant": f"{engine_cls.__module__}.{engine_cls.__name__}",
+        "declared_device_families": list(getattr(engine_cls, "gpu_compat", ("cpu",))),
+        "evidence_state": evidence_state,
+        "actual_execution_provider": provider,
+        "actual_execution_device": actual_device,
+        "gpu_name": getattr(caps, "device_name", "") or None,
+        "gpu_architecture": _gpu_architecture(getattr(caps, "family", "cpu")),
+        "precision_or_quantization": precision,
+        "cpu_fallback_reason": runtime_fallback_reason or (routing.get("routing_reason") if fallback else None),
+        "cpu_fallback_stage": runtime_fallback_stage or ("routing_preflight" if fallback else None),
+        "parent_memory_observable": not isolated,
+        "runtime_versions": runtime_versions(engine_id),
+    }
+
+
+def _gpu_architecture(family: str) -> str | None:
+    if family not in {"cuda", "rocm"}:
+        return "apple-silicon" if family == "mps" else None
+    try:
+        import torch
+
+        if family == "rocm":
+            props = torch.cuda.get_device_properties(0)
+            return str(getattr(props, "gcnArchName", "") or "") or None
+        major, minor = torch.cuda.get_device_capability(0)
+        return f"sm_{major}{minor}"
+    except Exception:
+        return None
@@ -31,6 +31,30 @@ class RoutingResult(TypedDict):
    routing_reason: str | None     # raw, pre-scrub


+def under_provisioned_vram(caps: HostCaps, min_vram_gb: float = 0.0) -> bool:
+    """Is this host's DEDICATED VRAM below the engine's declared floor?
+
+    The one definition of "under-provisioned", shared by everything that acts
+    on the verdict: the routing caveat below, the timeout guidance, and — since
+    #1804 — the compute-time budget itself (``model_manager
+    .generate_timeout_s``). It was written out inline in each of them, which is
+    how the budget came to disagree with the warning printed next to it.
+
+    Dedicated-VRAM families ONLY. On MPS, ``HostCaps.vram_gb`` is a heuristic
+    (system RAM / 2, see device_caps) for a UNIFIED memory pool; comparing it
+    against a floor measured on discrete CUDA hardware would tell every 8 GB Mac
+    its 4 GB "VRAM" is too small for an engine that runs fine there. A VRAM
+    figure of 0 means the probe failed — don't guess from it. A floor of 0 means
+    the engine declares none, and inventing one is worse than staying quiet.
+    """
+    if not min_vram_gb or min_vram_gb <= 0:
+        return False
+    if getattr(caps, "family", None) not in ("cuda", "rocm"):
+        return False
+    vram_gb = float(getattr(caps, "vram_gb", 0.0) or 0.0)
+    return 0 < vram_gb < float(min_vram_gb)
+
+
 def _caveat(caps: HostCaps, min_vram_gb: float = 0.0) -> str | None:
    """A caveat string for an otherwise-accelerated host, or None.

@@ -51,16 +75,7 @@ def _caveat(caps: HostCaps, min_vram_gb: float = 0.0) -> str | None:
    for note in caps.notes:
        if KERNEL_RISK_MARKER in note:
            return f"{caps.family.upper()} selected, but: {note}"
-    # Dedicated-VRAM families ONLY. On MPS, HostCaps.vram_gb is a heuristic
-    # (system RAM / 2, see device_caps) for a UNIFIED memory pool — comparing
-    # it against a floor measured on discrete CUDA hardware would tell every
-    # 8 GB Mac its 4 GB "VRAM" is too small for an engine that runs fine there.
-    # Different memory model, different (unmeasured) floor; don't guess.
-    if (
-        caps.family in ("cuda", "rocm")
-        and min_vram_gb > 0
-        and 0 < caps.vram_gb < min_vram_gb
-    ):
+    if under_provisioned_vram(caps, min_vram_gb):
        device = caps.device_name or caps.family.upper()
        return (
            f"{device} has {caps.vram_gb:.1f} GB VRAM; this engine wants about "
@@ -208,5 +223,5 @@ def routing_fields(

 __all__ = [
    "RoutingStatus", "RoutingResult", "resolve_routing", "routing_fields",
-    "routing_notice", "header_safe_reason",
+    "routing_notice", "header_safe_reason", "under_provisioned_vram",
 ]
@@ -177,6 +177,9 @@ class LocalCall:
    queue_timeout: Optional[float] = None
    # The engine's declared VRAM floor; only shapes the timeout message.
    min_vram_gb: float = 0.0
+    # Called once a local worker abandoned by its waiter can no longer touch
+    # request-owned inputs. Normal completion does not call it (#1668).
+    on_abandon: Optional[Callable[[], None]] = None
    # Some remote-first callers cannot construct the local callable without
    # loading the very model they are trying to offload.  Prepare it only when
    # routing/fallback actually selects this machine.
@@ -465,6 +468,7 @@ async def _run_local(call: LocalCall, *, admit: bool = False, executor=None) ->
        queue_timeout=call.queue_timeout,
        min_vram_gb=call.min_vram_gb,
        executor=executor,
+        on_abandon=call.on_abandon,
    )


@@ -499,7 +503,9 @@ async def _run_remote(
        deadline = _default_deadline(call.operation, params.get("text"))

    try:
-        task = scheduler.submit(
+        submit = getattr(scheduler, "submit_async", None)
+        submit = submit if callable(submit) else scheduler.submit
+        submitted = submit(
            operation=call.operation,
            engine=call.engine,
            model_id=call.model_id,
@@ -508,6 +514,7 @@ async def _run_remote(
            deadline_seconds=deadline,
            pinned_worker_id=decision.worker_id,
        )
+        task = await submitted if asyncio.iscoroutine(submitted) else submitted
    except QueueFull as exc:
        raise _NotDispatched(str(exc)) from exc

@@ -0,0 +1,220 @@
+"""Karaoke (word-highlight) ASS builder for dub hardsub export.
+
+Pure text-in/text-out: no ffmpeg, no models, no filesystem. ``build_ass``
+turns subtitle cues into an ASS script whose lines carry ``\\k``/``\\kf``
+karaoke tags, so ffmpeg's ``ass=`` filter burns a word-by-word highlight
+sweep instead of the static line the SRT path renders.
+
+Word timing sources, in order:
+
+1. ``cue["words"]`` — per-word ``{text, start, end}`` persisted at
+   transcribe time (services.segmentation). Used only when the words still
+   spell the cue's display text: after translation the persisted ASR words
+   are source-language tokens, so re-using their timing would burn the
+   wrong language. The display text is always authoritative.
+2. Even split — the cue text's whitespace tokens spread uniformly across
+   ``[start, end]``. This is the compatibility path for jobs transcribed
+   before word persistence and for translated tracks.
+
+Dual-layout karaoke is intentionally unsupported (out of scope): callers
+must fall back to the line (SRT) burn when the dual layout is requested.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Optional, Sequence
+
+_WS = re.compile(r"\s+")
+
+#: Default ASS canvas. libass scales the script to the real video size, so
+#: one reference resolution keeps font/margin proportions stable everywhere.
+DEFAULT_PLAY_RES = (1920, 1080)
+
+_HEADER_TEMPLATE = """[Script Info]
+; Generated by VoiceStudio karaoke burn-in
+ScriptType: v4.00+
+PlayResX: {res_x}
+PlayResY: {res_y}
+WrapStyle: 0
+ScaledBorderAndShadow: yes
+
+[V4+ Styles]
+Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
+Style: Default,Arial,64,&H0000E7FF,&H00FFFFFF,&H00101010,&H7F000000,0,0,0,0,100,100,0,0,1,3,1,2,96,96,48,1
+
+[Events]
+Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
+"""
+
+
+def _norm(text: object) -> str:
+    return _WS.sub(" ", str(text or "").strip())
+
+
+def _ass_time(seconds: float) -> str:
+    """``H:MM:SS.CC`` (centiseconds) — the ASS event timestamp format."""
+    cs = max(0, int(round(float(seconds) * 100)))
+    h, rem = divmod(cs, 360000)
+    m, rem = divmod(rem, 6000)
+    s, c = divmod(rem, 100)
+    return f"{h}:{m:02d}:{s:02d}.{c:02d}"
+
+
+def _ass_escape(text: str) -> str:
+    """Escape a display token for an ASS Dialogue text field.
+
+    Braces would open an override block (user text like ``{\\b1}`` must render
+    literally, never execute); newlines become ASS hard line breaks.
+    """
+    return (
+        str(text)
+        .replace("{", "\\{")
+        .replace("}", "\\}")
+        .replace("\r\n", "\\N")
+        .replace("\n", "\\N")
+        .replace("\r", "\\N")
+    )
+
+
+def _cs(seconds: float) -> int:
+    """Karaoke tag duration in centiseconds; ≥1 so a tag never renders as 0."""
+    return max(1, int(round(float(seconds) * 100)))
+
+
+def even_split_words(text: str, start: float, end: float) -> list[dict]:
+    """Uniformly distribute the cue text's whitespace tokens over [start, end].
+
+    The export fallback for jobs transcribed before per-word persistence and
+    for translated tracks (whose persisted words are source-language tokens).
+    """
+    tokens = [tok for tok in _WS.split(str(text or "").strip()) if tok]
+    if not tokens:
+        return []
+    start = float(start)
+    dur = max(0.0, float(end) - start) / len(tokens)
+    return [
+        {"text": tok, "start": start + i * dur, "end": start + (i + 1) * dur}
+        for i, tok in enumerate(tokens)
+    ]
+
+
+def scale_words(
+    words: Sequence[dict],
+    orig_start: float,
+    orig_end: float,
+    new_start: float,
+    new_end: float,
+) -> Optional[list[dict]]:
+    """Map word times linearly from [orig_start, orig_end] → [new_start, new_end].
+
+    Used when Smart Fit moves a cue onto the fitted timeline: the persisted
+    word times live on the original timeline and must ride along. Returns
+    ``None`` when either span is degenerate (caller should drop the words so
+    export falls back to an even split over the new span).
+    """
+    orig_span = float(orig_end) - float(orig_start)
+    new_span = float(new_end) - float(new_start)
+    if orig_span <= 0 or new_span <= 0:
+        return None
+    ratio = new_span / orig_span
+    out: list[dict] = []
+    for w in words:
+        try:
+            ws = float(w["start"])
+            we = float(w["end"])
+        except (KeyError, TypeError, ValueError):
+            return None
+        out.append({
+            **w,
+            "start": round(float(new_start) + (ws - float(orig_start)) * ratio, 3),
+            "end": round(float(new_start) + (we - float(orig_start)) * ratio, 3),
+        })
+    return out
+
+
+def _usable_words(cue: dict, text: str) -> Optional[list[tuple[str, float, float]]]:
+    """Persisted words, iff well-formed AND they spell the cue's display text."""
+    words = cue.get("words")
+    if not isinstance(words, list) or not words:
+        return None
+    clean: list[tuple[str, float, float]] = []
+    for w in words:
+        if not isinstance(w, dict):
+            return None
+        wtext = _norm(w.get("text"))
+        try:
+            ws = float(w["start"])
+            we = float(w["end"])
+        except (KeyError, TypeError, ValueError):
+            return None
+        if wtext:
+            clean.append((wtext, ws, we))
+    if not clean:
+        return None
+    if _norm(" ".join(t for t, _, _ in clean)) != text:
+        return None
+    return clean
+
+
+def _karaoke_text(cue: dict, text: str, start: float, end: float) -> str:
+    """One Dialogue text field: ``{\\k…}`` lead-in + per-word ``{\\kf…}`` tags.
+
+    Each word's sweep runs until the next word starts (the classic karaoke
+    layout — inter-word gaps finish the previous word's fill), and the last
+    word sweeps out to the cue end.
+    """
+    words = _usable_words(cue, text) or [
+        (w["text"], w["start"], w["end"]) for w in even_split_words(text, start, end)
+    ]
+    # Clamp into the cue span and enforce monotonic starts so malformed
+    # persisted data can only mistime the sweep, never corrupt the script.
+    clamped: list[tuple[str, float]] = []
+    prev = start
+    for wtext, ws, _ in words:
+        ws = min(max(ws, prev), end)
+        clamped.append((wtext, ws))
+        prev = ws
+    parts: list[str] = []
+    lead = clamped[0][1] - start
+    if lead > 0.005:
+        parts.append(f"{{\\k{_cs(lead)}}}")
+    for i, (wtext, ws) in enumerate(clamped):
+        nxt = clamped[i + 1][1] if i + 1 < len(clamped) else end
+        sep = " " if i + 1 < len(clamped) else ""
+        parts.append(f"{{\\kf{_cs(max(nxt, ws) - ws)}}}{_ass_escape(wtext)}{sep}")
+    return "".join(parts)
+
+
+def build_ass(
+    cues: Sequence[dict],
+    *,
+    dual: bool = False,
+    play_res: tuple[int, int] = DEFAULT_PLAY_RES,
+) -> str:
+    """Build a karaoke ASS script from subtitle cues ({text, start, end, words?}).
+
+    One ``Default`` style; one Dialogue event per cue. ``dual`` exists for
+    signature parity with the line burn but dual-layout karaoke is out of
+    scope — callers must keep the SRT line burn for dual, so requesting it
+    here is a contract violation, not a rendering mode.
+    """
+    if dual:
+        raise ValueError(
+            "dual-layout karaoke is not supported; use the line (SRT) burn for dual subtitles"
+        )
+    res_x, res_y = play_res
+    lines = [_HEADER_TEMPLATE.format(res_x=int(res_x), res_y=int(res_y))]
+    for cue in cues or []:
+        text = _norm(cue.get("text"))
+        if not text:
+            continue
+        start = float(cue["start"])
+        end = float(cue["end"])
+        if end <= start:
+            end = start + 0.1
+        lines.append(
+            f"Dialogue: 0,{_ass_time(start)},{_ass_time(end)},Default,,0,0,0,,"
+            f"{_karaoke_text(cue, text, start, end)}"
+        )
+    return "\n".join(lines) + "\n"
@@ -154,6 +154,17 @@ def bundled_dir() -> str:
    return os.path.join(media_tools_dir(), f"ffbin-{_FFBIN_COMMIT[:12]}", _platform_key())


+def _publish_bundled_on_path() -> None:
+    """Make a newly validated bundle visible to bare-name subprocess calls."""
+    directory = os.path.abspath(bundled_dir())
+    current = os.environ.get("PATH", "")
+    entries = current.split(os.pathsep) if current else []
+    if os.path.normcase(directory) in {os.path.normcase(entry) for entry in entries if entry}:
+        return
+    os.environ["PATH"] = os.pathsep.join([directory, *entries])
+    logger.info("Published the acquired media-tool directory on PATH")
+
+
 def _exe(name: str) -> str:
    return f"{name}.exe" if sys.platform == "win32" else name

@@ -231,12 +242,21 @@ def acquire_bundled(wait: bool = False) -> dict:
        _ops["acquire"].update(state="running", progress=0.0, error=None)

    if all(bundled_tool_path(t) and _binary_runs(bundled_tool_path(t)) for t in TOOLS):
+        # The bundle may have arrived after startup's one-time PATH publish
+        # (first-run acquisition is asynchronous). Make it visible to pydub
+        # and other dependencies that launch ffmpeg/ffprobe by bare name now,
+        # without requiring a backend restart (#1677).
+        _publish_bundled_on_path()
        _set_op("acquire", state="done", progress=1.0)
        return _op_snapshot()["acquire"]

    def _worker():
        try:
            _do_acquire()
+            # Startup cannot publish binaries which do not exist yet. The
+            # background worker must complete that second half atomically with
+            # installation so the very next synthesis can use the tools.
+            _publish_bundled_on_path()
            _set_op("acquire", state="done", progress=1.0, error=None)
            logger.info("media-tools: bundled ffmpeg/ffprobe installed at %s", bundled_dir())
        except Exception as e:
@@ -4,8 +4,9 @@ import sys
 import time
 import asyncio
 import logging
+import queue
 import threading
-from concurrent.futures import ThreadPoolExecutor, Executor
+from concurrent.futures import Executor, Future, ThreadPoolExecutor

 from utils.containment import contain_system_exit

@@ -397,7 +398,29 @@ def __getattr__(name: str):
 # (generation.py, tts_stream.py) were the last unguarded dispatch — and the
 # residual on-main reports all fail on generate:start (audio). This is the same
 # guard generalised so every GPU dispatch shares one recovery path.
+_GENERATE_TIMEOUT_EXPLICIT = "OMNIVOICE_GENERATE_TIMEOUT_S" in os.environ
 GPU_JOB_TIMEOUT_S = float(os.environ.get("OMNIVOICE_GENERATE_TIMEOUT_S", "300.0"))
+_CONFIGURED_GPU_JOB_TIMEOUT_S = GPU_JOB_TIMEOUT_S
+# CPU synthesis is healthy but substantially slower than accelerated inference.
+# Keep a separate, bounded floor so a short render on CPU is not abandoned at
+# the GPU-oriented five-minute deadline (#1588).
+#
+# #1787 review fix: an explicit OMNIVOICE_CPU_GENERATE_TIMEOUT_S must ALWAYS
+# govern CPU dispatches, even when OMNIVOICE_GENERATE_TIMEOUT_S is ALSO
+# explicit. Before this flag existed, `universal_override` below treated any
+# explicit GENERATE_TIMEOUT_S as authoritative for CPU too, so the Settings
+# panel's "CPU budget" row could be saved and silently never apply whenever
+# the "Accelerated" row was also set — the exact defect (a control that looks
+# like it works and doesn't) issue #1787 exists to remove. Setting ONLY
+# OMNIVOICE_GENERATE_TIMEOUT_S keeps its historical "universal" behavior
+# unchanged (test_explicit_universal_generate_timeout_wins_on_cpu) — nobody
+# who already relies on that single-var override loses it. The only case that
+# changes is the previously-undocumented, previously-broken combination of
+# setting BOTH: the more specific (CPU) value now wins for CPU jobs, matching
+# what a user who filled in both Settings rows was told would happen.
+_CPU_GENERATE_TIMEOUT_EXPLICIT = "OMNIVOICE_CPU_GENERATE_TIMEOUT_S" in os.environ
+CPU_JOB_TIMEOUT_S = float(os.environ.get("OMNIVOICE_CPU_GENERATE_TIMEOUT_S", "600.0"))
+_CONFIGURED_CPU_JOB_TIMEOUT_S = CPU_JOB_TIMEOUT_S

 # Queue-wait budget — a SEPARATE, deliberately generous clock (#1190/#1202).
 # The execution bound above must never be spent waiting in line: a job queued
@@ -500,7 +523,10 @@ class GpuPoolBusyError(TimeoutError):
        self.retry_after = max(1, int(round(retry_after)))


-def generate_timeout_s(text: "str | None") -> float:
+def generate_timeout_s(
+    text: "str | None", *, engine: object = None, execution_device: "str | None" = None,
+    min_vram_gb: float = 0.0,
+) -> float:
    """THE wall-clock execution budget for one synthesis job, scaled to input.

    Single source of truth for every TTS dispatch (#1190/#1202). The
@@ -511,15 +537,67 @@ def generate_timeout_s(text: "str | None") -> float:
    on long inputs. Lives here (not in a router) so every router shares it
    without importing generation.py.

-    Policy: floor at the configured OMNIVOICE_GENERATE_TIMEOUT_S, plus 1s per
-    40 characters past a 1200-character free allowance — generous enough for
+    Policy: floor at the configured OMNIVOICE_GENERATE_TIMEOUT_S (accelerated
+    hosts) or OMNIVOICE_CPU_GENERATE_TIMEOUT_S (CPU hosts — the latter wins
+    for CPU whenever it is itself explicit, even if the former also is; see
+    the #1787 comment on the module-level constants), plus 1s per 40
+    characters past a 1200-character free allowance — generous enough for
    CPU-class hardware, still bounded (a wedged job is caught in minutes, not
    hours).
+
+    #1804: "accelerated" is not one performance class. A card with less VRAM
+    than the engine declares it needs pages to system RAM over PCIe and renders
+    SLOWER than the same machine's CPU would — yet, judged by device family
+    alone, it was handed HALF the CPU budget. That inversion is what three 4 GB
+    reporters hit (#1226 GTX 1650 Ti, #1222 Quadro P2000, #1804 GTX 1650), all
+    on the engine that declares a 6 GB floor. Every layer already knew: routing
+    raises a caveat, the preflight toast warns, and the timeout message names
+    the card. Only the budget ignored it. So an under-provisioned accelerator
+    now floors at the CPU budget — the class of hardware it actually performs
+    like. ``min_vram_gb`` is the engine's declared floor; callers that pass
+    ``engine`` get it read off the engine automatically.
    """
-    return max(
-        GPU_JOB_TIMEOUT_S,
-        GPU_JOB_TIMEOUT_S + (max(0, len(text or "") - 1200) / 40.0),
-    )
+    base = GPU_JOB_TIMEOUT_S
+    try:
+        from core.device_caps import detect_host_caps
+        caps = detect_host_caps()
+        family = execution_device or caps.family
+        if not min_vram_gb and engine is not None:
+            min_vram_gb = float(getattr(engine, "min_vram_gb", 0.0) or 0.0)
+        if execution_device is None and engine is not None:
+            from services.engine_routing import resolve_routing
+            compat = getattr(engine, "gpu_compat", None)
+            if compat is None:
+                compat = getattr(type(engine), "gpu_compat", (family, "cpu"))
+            if tuple(compat) == ("cpu",):
+                family = "cpu"
+            else:
+                family = resolve_routing(compat, caps, min_vram_gb)["effective_device"]
+        universal_override = (
+            _GENERATE_TIMEOUT_EXPLICIT
+            or GPU_JOB_TIMEOUT_S != _CONFIGURED_GPU_JOB_TIMEOUT_S
+        )
+        # An explicit (env-set, or runtime-changed the same way tests do)
+        # CPU budget is more specific than the universal override and always
+        # wins for CPU dispatches — see the #1787 comment above.
+        cpu_explicit = (
+            _CPU_GENERATE_TIMEOUT_EXPLICIT
+            or CPU_JOB_TIMEOUT_S != _CONFIGURED_CPU_JOB_TIMEOUT_S
+        )
+        if family == "cpu" and (cpu_explicit or not universal_override):
+            base = CPU_JOB_TIMEOUT_S
+        elif not universal_override and family in ("cuda", "rocm"):
+            from services.engine_routing import under_provisioned_vram
+
+            if under_provisioned_vram(caps, min_vram_gb):
+                # `max`, never a plain assignment: an operator who raised the
+                # accelerated budget above the CPU one must not have it cut.
+                base = max(base, CPU_JOB_TIMEOUT_S)
+    except Exception:
+        # Device probing is advisory here; the configured universal bound is
+        # still safe when a platform probe is unavailable during startup.
+        pass
+    return base + (max(0, len(text or "") - 1200) / 40.0)


 def _retry_after_estimate(stats: dict) -> float:
@@ -599,7 +677,8 @@ async def run_on_gpu_pool_guarded(fn, *, what: str = "GPU job",
                                  timeout: "float | None" = None,
                                  executor=None,
                                  queue_timeout: "float | None" = None,
-                                  min_vram_gb: float = 0.0):
+                                  min_vram_gb: float = 0.0,
+                                  on_abandon=None):
    """Run blocking ``fn`` on the GPU pool, bounding **execution** — not the
    wait for a free worker.

@@ -627,6 +706,12 @@ async def run_on_gpu_pool_guarded(fn, *, what: str = "GPU job",
    at 0 — the default, and correct for every non-TTS job on this pool
    (reference transcribe, watermarking, dub steps) — the under-provisioned-GPU
    wording is never used, because nothing measured says it applies (#1226).
+
+    ``on_abandon`` is called once, after a job whose caller stopped waiting can
+    no longer access its inputs. A queued job that is cancelled before it
+    starts calls it immediately; a running thread calls it from ``_job``'s
+    finalizer. Normal completion never calls it. This lets request-owned temp
+    files outlive abandoned workers without delaying ordinary requests (#1668).
    """
    loop = asyncio.get_running_loop()
    ex = executor if executor is not None else _get_gpu_pool()
@@ -641,6 +726,24 @@ async def run_on_gpu_pool_guarded(fn, *, what: str = "GPU job",
    # job's model-load heartbeats (#1367). A dict, not a nonlocal: the closure
    # runs on a pool thread while the waiter reads from the event loop.
    _ident_box: dict = {}
+    _abandon_lock = threading.Lock()
+    _abandon_state = {
+        "requested": False,
+        "finished": False,
+        "callback_called": False,
+    }
+
+    def _fire_abandon_callback() -> None:
+        if on_abandon is None:
+            return
+        with _abandon_lock:
+            if _abandon_state["callback_called"]:
+                return
+            _abandon_state["callback_called"] = True
+        try:
+            on_abandon()
+        except Exception:  # noqa: BLE001 — cleanup cannot hide the pool result
+            logger.exception("%s abandon cleanup failed", _log_safe(what))

    def _job():
        # First thing the worker does: tell the awaiting coroutine the
@@ -657,8 +760,26 @@ async def run_on_gpu_pool_guarded(fn, *, what: str = "GPU job",
            # Idents are reused by the OS; a stale heartbeat under this ident
            # must not vouch for some future job on the same thread.
            _MODEL_LOAD_ACTIVITY.pop(threading.get_ident(), None)
+            with _abandon_lock:
+                _abandon_state["finished"] = True
+                abandoned = _abandon_state["requested"]
+            if abandoned:
+                _fire_abandon_callback()
+
+    concurrent_fut = ex.submit(_job)
+    fut = asyncio.wrap_future(concurrent_fut, loop=loop)
+
+    def _abandon() -> None:
+        # Keep the concurrent future so we can distinguish a job cancelled out
+        # of the queue from a thread that Python cannot stop once it has begun.
+        cancelled_before_start = concurrent_fut.cancel()
+        with _abandon_lock:
+            _abandon_state["requested"] = True
+            finished = _abandon_state["finished"]
+        fut.cancel()
+        if cancelled_before_start or finished:
+            _fire_abandon_callback()

-    fut = loop.run_in_executor(ex, _job)
    waiter = asyncio.ensure_future(started.wait())
    try:
        # Phase 1 — queue wait. Watch the future too, so a job that fails or is
@@ -671,6 +792,7 @@ async def run_on_gpu_pool_guarded(fn, *, what: str = "GPU job",
        # Caller went away (client disconnect). We stop awaiting the job, so
        # make sure its eventual result/exception is consumed rather than
        # logged as "Future exception was never retrieved".
+        _abandon()
        fut.add_done_callback(_swallow_abandoned)
        raise
    finally:
@@ -680,7 +802,7 @@ async def run_on_gpu_pool_guarded(fn, *, what: str = "GPU job",
        # Never picked up: cancel it out of the queue (a not-yet-started
        # concurrent future cancels cleanly) and report saturation, NOT a
        # too-heavy job.
-        fut.cancel()
+        _abandon()
        fut.add_done_callback(_swallow_abandoned)
        stats = gpu_pool_stats(ex)
        logger.warning(
@@ -751,14 +873,14 @@ async def run_on_gpu_pool_guarded(fn, *, what: str = "GPU job",
        # Caller went away mid-execution. The old wait_for cancelled the
        # wrapper itself; asyncio.wait does not, so do both halves here or the
        # eventual result is logged as "Future exception was never retrieved".
-        fut.cancel()
+        _abandon()
        fut.add_done_callback(_swallow_abandoned)
        raise
    except asyncio.TimeoutError as timeout_exc:
        # Parity with the old wait_for semantics: cancel the asyncio wrapper;
        # the worker thread keeps going regardless. Consume whatever it
        # eventually produces.
-        fut.cancel()
+        _abandon()
        fut.add_done_callback(_swallow_abandoned)
        # Capture the stacks BEFORE reset(): reset() replaces the executor, and
        # once the wedged thread is no longer a pool worker we can no longer
@@ -974,6 +1096,7 @@ def _timeout_guidance(
    """
    family = "cuda"  # conservative default: GPU wording if the probe fails
    device_name, vram_gb = "", 0.0
+    _caps = None  # a failed probe stays None; under_provisioned_vram() reads it safely
    try:
        from core.device_caps import detect_host_caps
        _caps = detect_host_caps()
@@ -1021,11 +1144,9 @@ def _timeout_guidance(
    # a threshold applied without knowing whose job it is would confidently
    # misdiagnose most of them. And on MPS `vram_gb` is a unified-memory
    # heuristic (RAM/2), not a dedicated pool to compare against.
-    if (
-        min_vram_gb > 0
-        and family in ("cuda", "rocm")
-        and 0 < vram_gb < min_vram_gb
-    ):
+    from services.engine_routing import under_provisioned_vram
+
+    if under_provisioned_vram(_caps, min_vram_gb):
        return common + (
            f"{device_name or 'this GPU'} has {vram_gb:.1f} GB of VRAM and "
            f"this engine wants about {min_vram_gb:.0f} GB — generations here "
@@ -1057,21 +1178,166 @@ def _timeout_guidance(
 # doubling the effective queue depth of a streamed multi-chunk render.
 # Giving it its own tiny pool removes that head-of-line blocking with no VRAM
 # risk, because the work was never on the device to begin with.
-_watermark_pool_singleton: "ThreadPoolExecutor | None" = None
-_watermark_pool_lock = threading.Lock()
+_WATERMARK_STOP = object()


-def get_watermark_pool() -> ThreadPoolExecutor:
-    """Dedicated 1-worker pool for provenance marking. Built lazily so hosts
-    with watermarking disabled never spawn the thread."""
-    global _watermark_pool_singleton
-    if _watermark_pool_singleton is None:
-        with _watermark_pool_lock:
-            if _watermark_pool_singleton is None:
-                _watermark_pool_singleton = ThreadPoolExecutor(
-                    max_workers=1, thread_name_prefix="watermark",
+class _WatermarkExecutor(Executor):
+    """Single daemon worker with a bounded shutdown contract.
+
+    ``ThreadPoolExecutor`` uses non-daemon workers that Python joins at exit,
+    so ``wait=False`` still delays process exit while ``wait=True`` can hang
+    lifespan teardown forever. AudioSeal loading is not cooperatively
+    cancellable; a daemon worker plus a bounded join is the only thread-based
+    contract that both preserves in-process model warm-up and guarantees exit.
+    """
+
+    def __init__(self) -> None:
+        self._items: queue.Queue = queue.Queue()
+        self._lock = threading.Lock()
+        self._shutdown = False
+        self._thread: threading.Thread | None = None
+
+    def submit(self, fn, /, *args, **kwargs) -> Future:
+        future: Future = Future()
+        with self._lock:
+            if self._shutdown:
+                raise RuntimeError("cannot schedule new futures after shutdown")
+            if self._thread is None:
+                self._thread = threading.Thread(
+                    target=self._run,
+                    name="watermark_0",
+                    daemon=True,
                )
-    return _watermark_pool_singleton
+                self._thread.start()
+            self._items.put((future, fn, args, kwargs))
+        return future
+
+    def _run(self) -> None:
+        while True:
+            item = self._items.get()
+            if item is _WATERMARK_STOP:
+                return
+            future, fn, args, kwargs = item
+            if not future.set_running_or_notify_cancel():
+                continue
+            try:
+                future.set_result(fn(*args, **kwargs))
+            except (Exception, SystemExit, KeyboardInterrupt) as exc:
+                future.set_exception(exc)
+
+    def is_stopped(self) -> bool:
+        """Whether shutdown has completed and this executor can be replaced."""
+        with self._lock:
+            return self._shutdown and (
+                self._thread is None or not self._thread.is_alive()
+            )
+
+    def is_shutdown(self) -> bool:
+        with self._lock:
+            return self._shutdown
+
+    def shutdown(
+        self,
+        wait: bool = True,
+        *,
+        cancel_futures: bool = False,
+        timeout: float | None = None,
+    ) -> bool:
+        with self._lock:
+            self._shutdown = True
+            thread = self._thread
+            if cancel_futures:
+                while True:
+                    try:
+                        item = self._items.get_nowait()
+                    except queue.Empty:
+                        break
+                    if item is not _WATERMARK_STOP:
+                        item[0].cancel()
+            self._items.put(_WATERMARK_STOP)
+        if wait and thread is not None:
+            thread.join(timeout=timeout)
+        return thread is None or not thread.is_alive()
+
+
+_watermark_pool_singleton: "_WatermarkExecutor | None" = None
+_watermark_pool_lock = threading.Lock()
+_watermark_pool_accepting = True
+
+
+def begin_watermark_pool_lifecycle() -> None:
+    """Open watermark submissions for a newly-started app lifespan."""
+    global _watermark_pool_accepting, _watermark_pool_singleton
+    with _watermark_pool_lock:
+        if (
+            _watermark_pool_singleton is not None
+            and _watermark_pool_singleton.is_stopped()
+        ):
+            _watermark_pool_singleton = None
+        _watermark_pool_accepting = (
+            _watermark_pool_singleton is None
+            or not _watermark_pool_singleton.is_shutdown()
+        )
+
+
+def get_watermark_pool() -> _WatermarkExecutor:
+    """Dedicated 1-worker pool for provenance marking. Built lazily so hosts
+    with watermarking disabled never spawn the thread.
+
+    The executor is captured and returned UNDER the lock: reading the global
+    again after an unlocked null-check could race shutdown_watermark_pool's
+    reset and hand out None (CodeRabbit, PR #1577)."""
+    global _watermark_pool_accepting, _watermark_pool_singleton
+    with _watermark_pool_lock:
+        if not _watermark_pool_accepting:
+            if (
+                _watermark_pool_singleton is not None
+                and _watermark_pool_singleton.is_stopped()
+            ):
+                _watermark_pool_singleton = None
+                _watermark_pool_accepting = True
+            else:
+                raise RuntimeError("watermark executor is shutting down")
+        if (
+            _watermark_pool_singleton is not None
+            and _watermark_pool_singleton.is_stopped()
+        ):
+            _watermark_pool_singleton = None
+        if _watermark_pool_singleton is None:
+            _watermark_pool_singleton = _WatermarkExecutor()
+        return _watermark_pool_singleton
+
+
+def shutdown_watermark_pool(*, timeout: float = 20.0) -> None:
+    """Drain the watermark pool at app shutdown (PR #1577).
+
+    Refuse queued work and wait for the active operation: Python cannot kill
+    a thread inside AudioSeal loading, so returning early would let model
+    initialization continue during interpreter teardown. The draining pool
+    remains published until its worker stops, preventing concurrent producers
+    from creating a replacement that escapes this shutdown. A process that
+    keeps running after lifespan shutdown (the test suite does exactly this)
+    gets a fresh pool once the old worker has actually stopped."""
+    global _watermark_pool_accepting, _watermark_pool_singleton
+    with _watermark_pool_lock:
+        _watermark_pool_accepting = False
+        pool = _watermark_pool_singleton
+    if pool is not None:
+        stopped = pool.shutdown(
+            wait=True,
+            cancel_futures=True,
+            timeout=max(0.0, float(timeout)),
+        )
+        if stopped:
+            with _watermark_pool_lock:
+                if _watermark_pool_singleton is pool:
+                    _watermark_pool_singleton = None
+        else:
+            logger.warning(
+                "Watermark worker exceeded the %.1fs shutdown deadline; "
+                "abandoning its daemon thread",
+                timeout,
+            )


 model = None  # type: ignore
@@ -1259,14 +1525,17 @@ def get_best_device():
    # ── DirectML — universal Windows GPU (probe reports this as "cpu") ─
    # Reached only when no torch family was detected (family == "cpu"), which is
    # exactly the DirectML case — the probe classifies DirectML hosts as cpu.
-    try:
-        import torch_directml
-        if torch_directml.device_count() > 0:
-            logger.info("Using DirectML device (GPU %d)", 0)
-            return str(torch_directml.device(0))
-    except ImportError:
-        pass
+    if family == "cpu":
+        try:
+            import torch_directml
+            if torch_directml.device_count() > 0:
+                logger.info("Using DirectML device (GPU %d)", 0)
+                return str(torch_directml.device(0))
+        except ImportError:
+            # DirectML is optional; an absent package leaves CPU available.
+            pass

+    # Other families need an explicitly compatible loader (e.g. NPU sidecars).
    return "cpu"

 _COMPILE_ERR_MODULE_PREFIXES = ("torch._dynamo", "torch._inductor", "torch.fx", "triton")
@@ -2628,6 +2897,21 @@ async def preload_model():
    if model is not None:
        return  # already loaded

+    # On MPS the configured ``omnivoice`` id resolves to a crash-isolated
+    # sidecar. Warming the native singleton here would put the same fatal MPS
+    # allocator risk back into the API process before the isolated engine is
+    # ever asked to synthesize.
+    try:
+        from core.device_caps import detect_host_caps
+
+        if detect_host_caps().family == "mps":
+            logger.info(
+                "Native TTS preload skipped: OmniVoice uses crash isolation on this host."
+            )
+            return
+    except Exception:  # noqa: BLE001 -- preload selection must stay best-effort
+        logger.debug("effective TTS preload selection failed", exc_info=True)
+
    # A machine lending its GPU has no local user to warm the model FOR. This
    # preload exists to make the first /generate feel instant for the person
    # sitting in front of the app; on a headless node there is nobody sitting
@@ -2717,7 +3001,10 @@ async def preload_model():
                "The TTS model could not be loaded. Settings → Logs → Backend "
                "has the full error."
            )
-        _set_loading("failed", detail, error=detail)
+        # `sub_stage` is a public API enum and the frontend keys failure state
+        # off `error`. Keep the human-readable word "failed" in the detail,
+        # not in the state machine (#1695).
+        _set_loading("error", detail, error=detail)

 def get_model_status():
    is_loaded = model is not None
@@ -79,6 +79,7 @@ class Segment:
    text: str
    speaker_id: str = "Speaker 1"
    id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
+    extra: dict = field(default_factory=dict)

    @property
    def duration(self) -> float:
@@ -90,6 +91,7 @@ class Segment:

    def to_dict(self) -> dict:
        return {
+            **self.extra,
            "id": self.id,
            "start": round(self.start, 2),
            "end": round(self.end, 2),
@@ -98,6 +100,69 @@ class Segment:
        }


+def _serialize_words(words: Sequence[Word]) -> list[dict]:
+    """Word objects → the ``{text, start, end}`` dicts persisted on segments.
+
+    Per-word timing is kept on each segment (``Segment.extra["words"]``, so
+    ``to_dict`` carries it onto the job) to drive the karaoke hardsub export.
+    """
+    return [
+        {"text": w.text, "start": round(w.start, 3), "end": round(w.end, 3)}
+        for w in words
+    ]
+
+
+def _merge_segment_extra(target: Segment, incoming: Segment, *, prepend: bool) -> None:
+    """Preserve editor metadata when cleanup folds ``incoming`` into ``target``."""
+    # Word lists must CONCATENATE in text order (the setdefault below would
+    # otherwise adopt the incoming list wholesale when the target has none,
+    # then double it). Capture both sides before setdefault runs.
+    raw_target_words = target.extra.get("words")
+    raw_incoming_words = incoming.extra.get("words")
+    for key, value in incoming.extra.items():
+        target.extra.setdefault(key, value)
+    target_words = raw_target_words if isinstance(raw_target_words, list) else []
+    incoming_words = raw_incoming_words if isinstance(raw_incoming_words, list) else []
+    if target_words or incoming_words:
+        target.extra["words"] = (
+            incoming_words + target_words if prepend else target_words + incoming_words
+        )
+
+    def joined(left: object, right: object) -> str:
+        return _clean(f"{left or ''} {right or ''}")
+
+    target_original = target.extra.get("text_original")
+    incoming_original = incoming.extra.get("text_original")
+    if target_original is not None or incoming_original is not None:
+        target.extra["text_original"] = (
+            joined(incoming_original, target_original)
+            if prepend
+            else joined(target_original, incoming_original)
+        )
+
+    raw_target_translations = target.extra.get("translations")
+    raw_incoming_translations = incoming.extra.get("translations")
+    target_translations = raw_target_translations if isinstance(raw_target_translations, dict) else {}
+    incoming_translations = (
+        raw_incoming_translations if isinstance(raw_incoming_translations, dict) else {}
+    )
+    if target_translations or incoming_translations:
+        merged = {}
+        languages = {
+            *target_translations.keys(),
+            *incoming_translations.keys(),
+        }
+        for language in languages:
+            target_text = target_translations.get(language)
+            incoming_text = incoming_translations.get(language)
+            merged[language] = (
+                joined(incoming_text, target_text)
+                if prepend
+                else joined(target_text, incoming_text)
+            )
+        target.extra["translations"] = merged
+
+
 def _clean(text: str) -> str:
    return _WS.sub(" ", (text or "").strip())

@@ -191,7 +256,10 @@ def _build_segments_from_words(words: Sequence[Word]) -> List[Segment]:
        if not text:
            buf = []
            return
-        segments.append(Segment(start=buf_start, end=buf[-1].end, text=text))
+        segments.append(Segment(
+            start=buf_start, end=buf[-1].end, text=text,
+            extra={"words": _serialize_words(buf)},
+        ))
        buf = []
        if not force:
            buf_start = 0.0
@@ -249,6 +317,7 @@ def _build_segments_from_words(words: Sequence[Word]) -> List[Segment]:
                    start=buf_start,
                    end=left_buf[-1].end,
                    text=_clean(" ".join(x.text for x in left_buf)),
+                    extra={"words": _serialize_words(left_buf)},
                ))
                buf = list(right_buf)
                buf_start = right_buf[0].start
@@ -317,12 +386,14 @@ def _merge_short(segments: List[Segment]) -> List[Segment]:
                i += 1
                continue
            if target is prev:
+                _merge_segment_extra(prev, s, prepend=False)
                prev.text = _clean(prev.text + " " + s.text)
                prev.end = max(prev.end, s.end)
                segments.pop(i)
                did_merge = True
                continue
            if target is nxt:
+                _merge_segment_extra(nxt, s, prepend=True)
                nxt.text = _clean(s.text + " " + nxt.text)
                nxt.start = min(nxt.start, s.start)
                segments.pop(i)
@@ -360,6 +431,7 @@ def _stitch_adjacent_shorts(segments: List[Segment]) -> List[Segment]:
                and b.duration <= STITCH_DUR
                and combined_dur <= MAX_DUR
            ):
+                _merge_segment_extra(a, b, prepend=False)
                a.text = _clean(a.text + " " + b.text)
                a.end = b.end
                segments.pop(i + 1)
@@ -386,6 +458,11 @@ def clean_up_segments(segments: List[dict]) -> List[dict]:
                text=_clean(str(s.get("text", ""))),
                speaker_id=str(s.get("speaker_id") or "Speaker 1"),
                id=str(s.get("id") or uuid.uuid4().hex[:8]),
+                extra={
+                    key: value
+                    for key, value in s.items()
+                    if key not in {"id", "start", "end", "text", "speaker_id"}
+                },
            ))
        except (TypeError, ValueError):
            continue
@@ -429,11 +506,23 @@ def _apply_scene_cuts(segments: List[Segment], scene_cuts: Iterable[float]) -> L
                or (remaining.end - cut) < MIN_DUR
            ):
                continue
+            # Segment text is the joined word texts, so a whitespace-boundary
+            # text split maps exactly onto a word-count split of the list.
+            words = remaining.extra.get("words")
+            left_extra: dict = {}
+            right_extra: dict = {}
+            if isinstance(words, list) and words:
+                n_left = len(left_text.split())
+                if n_left and len(words) > n_left:
+                    left_extra = {"words": words[:n_left]}
+                    right_extra = {"words": words[n_left:]}
            out.append(Segment(
                start=remaining.start, end=cut, text=left_text, speaker_id=remaining.speaker_id,
+                extra=left_extra,
            ))
            remaining = Segment(
                start=cut, end=remaining.end, text=right_text, speaker_id=remaining.speaker_id,
+                extra=right_extra,
            )
        out.append(remaining)
    return out
@@ -665,6 +754,10 @@ def _resplit_core(
            piece["text"] = text
            piece["start"] = s0 if k == 0 else ws[0].start
            piece["end"] = s1 if k == n_runs - 1 else ws[-1].end
+            # dict(seg) copied the WHOLE segment's word list into every piece;
+            # each piece keeps only its own run's words (karaoke burn-in).
+            if "words" in piece:
+                piece["words"] = _serialize_words(ws)
            if label:
                piece["speaker_id"] = label
            if piece_no > 0:
@@ -254,6 +254,31 @@ def get_text(key: str, default: Optional[str] = None) -> Optional[str]:
        return default


+def get_text_state(key: str) -> tuple[bool, str]:
+    """Return ``(is_present, value)`` without hiding storage failures.
+
+    Rollback snapshots must distinguish a missing row from an unreadable
+    database.  ``get_text`` deliberately collapses those cases for ordinary
+    preference reads, so transactional callers use this strict variant.
+    """
+    if key == _TOKEN_KEY or key.startswith(_SECRET_PREFIX):
+        raise ValueError(
+            "get_text_state refuses to read an encrypted secret row; "
+            "use get_hf_token()/get_secret() for secrets"
+        )
+    from core.db import db_conn
+
+    with db_conn() as conn:
+        row = conn.execute(
+            "SELECT value FROM settings WHERE key = ?", (key,)
+        ).fetchone()
+    if row is None:
+        return False, ""
+    if row[0] is None:
+        return True, ""
+    return True, str(row[0])
+
+
 def set_text(key: str, value: str) -> None:
    """Persist a non-encrypted text value into the settings table.

@@ -274,6 +299,19 @@ def set_text(key: str, value: str) -> None:
        )


+def clear_text(key: str) -> None:
+    """Remove a non-encrypted text setting, preserving a missing-row default."""
+    if key == _TOKEN_KEY or key.startswith(_SECRET_PREFIX):
+        raise ValueError(
+            "clear_text refuses to delete an encrypted secret row; "
+            "use clear_hf_token()/clear_secret() for secrets"
+        )
+    from core.db import db_conn
+
+    with db_conn() as conn:
+        conn.execute("DELETE FROM settings WHERE key = ?", (key,))
+
+
 # ── Phase 4 Plan 04-01 (GGUF-04): per-engine quant override ────────────────
 #
 # Settings row "gguf_quant_override" holds either:
@@ -110,7 +110,7 @@ class SherpaModelSpec:
 #    the same HF tree API on 2026-08-07 — not estimated. Every one of the seven
 #    was wrong before, and in both directions, which is worse than uniformly
 #    optimistic: the two Parakeets under-reported by ~3.8x (0.18 -> 0.67 GB),
-#    so the recommended default quietly downloaded four times what the picker
+#    so installing v3 quietly downloaded four times what the picker
 #    promised on a metered or small-disk machine; but the two low-RAM
 #    zipformers OVER-reported by ~3x (0.128 -> 0.044), making the fallback
 #    models look bulkier than the heavyweights they exist to rescue users
@@ -129,7 +129,6 @@ _MODELS: dict[str, SherpaModelSpec] = {
        kind="offline-transducer",
        size_gb=0.67,
        languages="25 European languages",
-        recommended=True,
        heavy=True,
        model_type="nemo_transducer",
        files={
@@ -223,6 +222,7 @@ _MODELS: dict[str, SherpaModelSpec] = {
        kind="offline-whisper",
        size_gb=0.104,
        languages="90+ languages (auto-detect)",
+        recommended=True,
        files={
            "encoder": "tiny-encoder.int8.onnx",
            "decoder": "tiny-decoder.int8.onnx",
@@ -231,7 +231,7 @@ _MODELS: dict[str, SherpaModelSpec] = {
    ),
 }

-DEFAULT_MODEL_ID = "sherpa-parakeet-tdt-v3"
+DEFAULT_MODEL_ID = "sherpa-whisper-tiny"

 # repo_id → model id, so the model-store list (keyed by repo_id) can be
 # enriched with the dictation metadata, and so capture can map either key.
@@ -261,6 +261,23 @@ def sherpa_available() -> tuple[bool, str]:
        return True, "ready"
    except ImportError as e:
        return False, f"sherpa-onnx not installed: {e}. Install with: uv add sherpa-onnx"
+    except Exception as e:  # noqa: BLE001 — an availability probe must fail closed
+        # Native wheel failures surface as OSError/RuntimeError rather than
+        # ImportError (missing DLL/dylib/so, loader or runtime init failure) —
+        # but the set is open-ended: an extension module is free to raise
+        # anything at init. This is an availability question, so ANY failure to
+        # import means "not available", never an exception escaping to the
+        # caller. SherpaDictationBackend.is_available() calls this directly and
+        # capture_ws.ws_transcribe calls that without a guard, so an unexpected
+        # type here took the WebSocket down instead of falling back (#1610).
+        return False, f"sherpa-onnx unavailable ({type(e).__name__}): {e}"
+
+
+def _usable_model_file(path: str) -> bool:
+    try:
+        return os.path.isfile(path) and os.path.getsize(path) > 0
+    except OSError:
+        return False


 def _resolve_model_dir(spec: SherpaModelSpec, *, download: bool = True) -> str:
@@ -271,41 +288,114 @@ def _resolve_model_dir(spec: SherpaModelSpec, *, download: bool = True) -> str:
    Restricts the fetch to the exact int8 assets we pin via ``allow_patterns``
    so we never pull the bundled fp32 weights or test wavs.
    """
-    from huggingface_hub import constants as hf_constants
    from huggingface_hub import snapshot_download
-    from services.hf_revisions import installed_revision, revision_for
+    from services.hf_revisions import revision_for

    wanted = list(spec.files.values())
+    cache_dir = _live_hub_cache_dir()
    # Probe the revision an existing installation actually resolved.  Older
    # releases followed ``main`` and may therefore have a different snapshot;
    # retaining it preserves offline upgrades.  Any network fetch still uses
    # the reviewed immutable pin.
-    installed = installed_revision(spec.repo_id, hf_constants.HF_HUB_CACHE)
-    try:
-        return snapshot_download(
-            repo_id=spec.repo_id,
-            revision=installed,
-            local_files_only=True,
-            allow_patterns=wanted,
-        )
-    except Exception:
-        if not download:
-            raise
+    installed = _installed_snapshot(spec)
+    if installed:
+        return installed
+    if not download:
+        raise FileNotFoundError(f"No complete cached snapshot for {spec.repo_id}")
+
+    # A Windows cache can retain a snapshot entry whose target blob vanished,
+    # or a zero-byte ONNX placeholder left by an interrupted download.  Hub may
+    # then treat that entry as already materialized and return the same broken
+    # snapshot.  Repair those entries before asking for another download so the
+    # recognizer never receives a path to a file that does not resolve (#1733).
+    from services.hf_cache_repair import (
+        find_dangling_entries,
+        repair_repo_cache,
+        repo_cache_dir,
+    )
+
+    if find_dangling_entries(repo_cache_dir(spec.repo_id, cache_dir)):
+        repair = repair_repo_cache(spec.repo_id, cache_dir)
+        installed = _installed_snapshot(spec)
+        if installed:
+            return installed
+        if not repair.get("ok"):
+            logger.warning(
+                "sherpa dictation: cache repair for %s failed: %s",
+                spec.repo_id,
+                repair.get("error") or repair.get("outcome") or "unknown error",
+            )
+
    logger.info("sherpa dictation: downloading %s on first use", spec.repo_id)
-    return snapshot_download(
+    snapshot = snapshot_download(
        repo_id=spec.repo_id,
        revision=revision_for(spec.repo_id),
        allow_patterns=wanted,
+        cache_dir=cache_dir,
+    )
+    missing = [
+        name for name in wanted
+        if not _usable_model_file(os.path.join(snapshot, name))
+    ]
+    if not missing:
+        return snapshot
+
+    # Verify after the Hub reports success.  This catches hosts where a broken
+    # snapshot entry short-circuits snapshot_download.  The generic repair
+    # removes only broken entries, preserves blobs, and retries the immutable
+    # installed revision.
+    repair = repair_repo_cache(spec.repo_id, cache_dir)
+    installed = _installed_snapshot(spec)
+    if installed:
+        return installed
+    detail = repair.get("error") or repair.get("outcome") or "repair did not restore them"
+    raise FileNotFoundError(
+        f"Sherpa model cache is incomplete for {spec.repo_id}; missing "
+        f"{', '.join(missing)}. Cache repair failed: {detail}. Reinstall this "
+        "model from Model Catalogue."
    )


+def _live_hub_cache_dir() -> str:
+    """The effective hub root, evaluated after Settings restores the env."""
+    direct = os.environ.get("HF_HUB_CACHE") or os.environ.get("HUGGINGFACE_HUB_CACHE")
+    if direct:
+        return os.path.expanduser(direct)
+    home = os.environ.get("HF_HOME") or os.path.expanduser("~/.cache/huggingface")
+    return os.path.join(os.path.expanduser(home), "hub")
+
+
+def _installed_snapshot(spec: SherpaModelSpec) -> str | None:
+    """Complete snapshot for the recorded revision in the live cache."""
+    from services.hf_revisions import installed_revision
+
+    cache_dir = _live_hub_cache_dir()
+    revision = installed_revision(spec.repo_id, cache_dir)
+    snapshot = os.path.join(
+        cache_dir,
+        "models--" + spec.repo_id.replace("/", "--"),
+        "snapshots",
+        revision,
+    )
+    if all(
+        _usable_model_file(os.path.join(snapshot, filename))
+        for filename in spec.files.values()
+    ):
+        return snapshot
+    return None
+
+
 def is_installed(spec: SherpaModelSpec) -> bool:
-    """True if every pinned asset is already present in the HF cache."""
-    try:
-        d = _resolve_model_dir(spec, download=False)
-    except Exception:
-        return False
-    return all(os.path.isfile(os.path.join(d, f)) for f in spec.files.values())
+    """True if the recorded cached snapshot contains every pinned asset.
+
+    Do not use ``snapshot_download(local_files_only=True)`` for this probe.
+    ``huggingface_hub.constants.HF_HUB_CACHE`` is fixed when that module is
+    first imported, while VoiceStudio can restore its cache directory later
+    from the durable user settings. Resolve the live root and the recorded
+    revision ourselves so readiness and loading cannot disagree after a cache
+    move, desktop relaunch, or stale snapshot (#1707).
+    """
+    return _installed_snapshot(spec) is not None


 # ── Recognizers ──────────────────────────────────────────────────────────────
@@ -397,13 +487,10 @@ def build_online_recognizer(spec: SherpaModelSpec, *, download: bool = True):
 # transcribe the same bytes. It is a defect inside sherpa-onnx that the app
 # cannot fix by configuration.
 #
-# The curated default therefore cannot be trusted to WORK just because it is
-# installed — and which platforms are affected is not knowable up front, so
-# hard-coding a different default per OS would only be a guess. Instead the app
-# learns from what it observes: when a session hears real speech and the model
-# returns nothing, that model is demoted on THIS machine and stops being
-# selected. Self-correcting wherever the breakage actually is, and a no-op
-# everywhere it isn't.
+# Installation alone therefore cannot prove that a recognizer works. When a
+# session hears real speech and the model returns nothing, that model is
+# demoted on this machine and stops being selected. This self-corrects wherever
+# the decoder defect appears and is a no-op everywhere it does not.

 #: prefs key holding the list of model ids demoted on this machine.
 PREF_SILENT_MODELS = "dictation.silent_models"
@@ -60,6 +60,7 @@ from pathlib import Path
 from typing import Callable, Optional

 from core.config import DATA_DIR
+from core.contained_subprocess import OwnedPopen, WindowsJobPopen, spawn_owned

 logger = logging.getLogger("omnivoice.sidecar_install")

@@ -108,9 +109,18 @@ class SidecarSpec:
    weights_repo_id: Optional[str] = None   # HF repo downloaded into <checkout>/<weights_subdir>
    weights_revision: Optional[str] = None  # reviewed HF commit
    weights_subdir: str = "checkpoints"
-    weights_config_name: str = "config.yaml"  # required model config inside weights_subdir
+    # Model-config filenames accepted inside weights_subdir. A tuple, not a
+    # single name: IndexTTS 2.5's weights repo ships config.yaml, but installs
+    # predating #1611 were only usable after hand-renaming it to
+    # config_v2_5.yaml, and those must keep working without a reinstall.
+    weights_config_names: tuple[str, ...] = ("config.yaml",)
    docs_path: str = "docs/engines"         # where the manual-install fallback lives
    required_bytes: int = 12 * _GIB    # conservative source+venv+weights estimate for preflight
+    weights_bytes: Optional[int] = None
+    dependency_bytes: Optional[int] = None
+    potentially_shared_bytes: Optional[int] = None
+    temporary_free_bytes: Optional[int] = None
+    disk_confidence: str = "unknown"
    # Called after a successful install/uninstall so the engine's memoised
    # venv resolution re-probes (import inside the lambda — never at module load).
    invalidate: Callable[[], None] = field(default=lambda: None)
@@ -146,12 +156,17 @@ SPECS: dict[str, SidecarSpec] = {
        weights_repo_id="IndexTeam/IndexTTS-2.5",
        weights_revision="d0aa86e75bb6f3437f3831e95056fa72842d89ef",
        weights_subdir="checkpoints",
-        weights_config_name="config_v2_5.yaml",
+        weights_config_names=("config.yaml", "config_v2_5.yaml"),
        docs_path="docs/engines/indextts.md",
        # ~0.1 GB source + up to ~6 GB venv (torch + transformers<5) +
        # ~6 GB weights. Deliberately conservative; the preflight subtracts
        # whatever a partial install already put on disk.
        required_bytes=12 * _GIB,
+        weights_bytes=6 * _GIB,
+        dependency_bytes=6 * _GIB,
+        potentially_shared_bytes=None,
+        temporary_free_bytes=12 * _GIB,
+        disk_confidence="estimated",
        invalidate=_indextts_invalidate,
        installed_probe=_indextts_installed,
    ),
@@ -307,6 +322,25 @@ def _dir_size_bytes(path: Path) -> int:
    return total


+def _preserved_install_bytes(spec: SidecarSpec, checkout: Path) -> tuple[int, int]:
+    """Return bytes preserved for the final install and dependency peak.
+
+    Resumable weights reduce the final download requirement, but they do not
+    reduce uv's separate environment-build peak. Only source and a usable
+    existing venv count against that peak.
+    """
+    if not _source_present(spec, checkout):
+        return 0, 0
+
+    weights_dir = checkout / spec.weights_subdir
+    weights = _dir_size_bytes(weights_dir) if spec.weights_repo_id else 0
+    venv_dir = checkout / ".venv"
+    venv = _dir_size_bytes(venv_dir)
+    source = max(0, _dir_size_bytes(checkout) - weights - venv)
+    usable_venv = venv if _venv_python(venv_dir).is_file() else 0
+    return source + usable_venv + weights, source + usable_venv
+
+
 def disk_free_bytes(path: Path) -> int:
    """Free bytes on the volume backing *path* (nearest existing ancestor).
    Never raises; 0 when the volume can't be probed."""
@@ -329,8 +363,17 @@ def disk_space_error(spec: SidecarSpec) -> Optional[str]:
    root = managed_root(spec)
    # A preserved predecessor is not a partial copy of the new install: the
    # upgrade needs its full space until the new sidecar is verified.
-    already = _dir_size_bytes(managed_checkout(spec))
-    remaining = max(0, spec.required_bytes - already)
+    checkout = managed_checkout(spec)
+    # Credit only bytes the later steps preserve. An invalid layout or revision
+    # marker makes _step_fetch_source delete the whole checkout.
+    preserved, dependency_peak_credit = _preserved_install_bytes(spec, checkout)
+    remaining = max(0, spec.required_bytes - preserved)
+    if spec.temporary_free_bytes is not None:
+        # Resumable model weights are unrelated to uv's dependency-build peak.
+        remaining = max(
+            remaining,
+            max(0, spec.temporary_free_bytes - dependency_peak_credit),
+        )
    free = disk_free_bytes(root)
    if free <= 0:
        return None  # can't probe → never block on missing information
@@ -879,11 +922,11 @@ def _weights_present(spec: SidecarSpec) -> bool:
    actual = marker[:2] if len(marker) >= 2 else marker + [""]
    if actual != expected:
        return False
-    return _weights_floor_ok(wdir, config_name=spec.weights_config_name)
+    return _weights_floor_ok(wdir, config_names=spec.weights_config_names)


-def _weights_floor_ok(wdir: Path, *, config_name: str = "config.yaml") -> bool:
-    if not (wdir / config_name).is_file():
+def _weights_floor_ok(wdir: Path, *, config_names: tuple[str, ...] = ("config.yaml",)) -> bool:
+    if not any((wdir / name).is_file() for name in config_names):
        return False
    floor = 5 * 1024 * 1024
    try:
@@ -969,7 +1012,7 @@ def _step_fetch_weights(spec: SidecarSpec, job: dict) -> None:
        hf_progress.unregister_listener(listener_id)
        hf_progress.current_repo_id.reset(repo_token)

-    if not _weights_floor_ok(wdir, config_name=spec.weights_config_name):
+    if not _weights_floor_ok(wdir, config_names=spec.weights_config_names):
        raise _StepError(
            "Weight download finished but no plausible weight files were found — "
            "the download was likely interrupted.",
@@ -994,6 +1037,11 @@ def _step_persist(spec: SidecarSpec, job: dict) -> None:
 # ── Subprocess runner with live log capture ────────────────────────────────


+def _install_containment_kwargs() -> dict:
+    """Nested process-group/Job ownership is supplied by ``spawn_owned``."""
+    return {}
+
+
 def _run_logged(job: dict, argv: list[str], *, timeout: float,
                env: "dict[str, str] | None" = None) -> int:
    """Run *argv*, streaming combined stdout+stderr lines into the job log.
@@ -1008,13 +1056,12 @@ def _run_logged(job: dict, argv: list[str], *, timeout: float,
    killed child — a blocking ``for line in proc.stdout`` on this thread
    would hang past the timeout waiting for pipe EOF.
    """
-    popen_kwargs: dict = {}
-    if os.name == "posix":
-        # New session → we can kill the whole process group on timeout
-        # instead of only the direct child.
-        popen_kwargs["start_new_session"] = True
+    # ``spawn_owned`` creates the local timeout group/Job before the operation
+    # starts. POSIX links it to backend death through a control pipe; Windows
+    # retains a kill-on-close Job handle in this backend process.
+    popen_kwargs = _install_containment_kwargs()
    try:
-        proc = subprocess.Popen(
+        proc = spawn_owned(
            argv,
            stdout=subprocess.PIPE,
            stderr=subprocess.STDOUT,
@@ -1049,29 +1096,18 @@ def _run_logged(job: dict, argv: list[str], *, timeout: float,


 def _kill_tree(proc: "subprocess.Popen") -> None:
-    """Kill the child and its whole process tree, on every platform.
-
-    POSIX: the child was started in its own session, so SIGKILL the group.
-    Windows: ``proc.kill()`` only terminates the direct child — a git/uv
-    helper it spawned would keep running (and writing into the checkout)
-    past our timeout — so use ``taskkill /T`` to fell the tree.
-    """
-    if os.name == "posix":
-        import signal
+    """Kill an operation through its stable nested group/Job owner."""
+    if isinstance(proc, (OwnedPopen, WindowsJobPopen)):
+        # The retained supervisor/process-group or nested Job is the stable
+        # per-operation owner.  Do not fall back to a direct PID kill.
+        proc.kill()
        try:
-            os.killpg(proc.pid, signal.SIGKILL)
+            proc.wait(timeout=5)
+        except subprocess.TimeoutExpired:
            return
-        except (ProcessLookupError, PermissionError, OSError):
-            pass  # group already gone / not ours — fall through to plain kill
-    else:  # Windows
-        try:
-            subprocess.run(
-                ["taskkill", "/F", "/T", "/PID", str(proc.pid)],
-                capture_output=True, timeout=15,
-            )
-            return
-        except (OSError, subprocess.SubprocessError):
-            pass  # taskkill unavailable/failed — fall through to plain kill
+        return
+    # A test double or a legacy caller without the nested owner can only be
+    # stopped through its stable direct-process handle.
    try:
        proc.kill()
    except OSError:
@@ -32,9 +32,9 @@ Threat-model summary (see Plan 02-01 frontmatter):
              AUTH-05 installed (``HFTokenRedactor``) on the root logger.
    T-02-04 — compromised sidecar emitting unexpected ops: parent allowlist
              ``PARENT_INBOUND_OPS`` rejects everything else.
-    T-02-05 — Tauri group-kill scope: ``start_new_session=True`` on Unix
-              and ``CREATE_NEW_PROCESS_GROUP`` on Windows isolate the
-              sidecar's process group.
+    T-02-05 — nested containment: a retained POSIX supervisor process group or
+              Windows Job owns each engine operation, while still permitting
+              independent timeout teardown and cleanup on backend death.
 """
 from __future__ import annotations

@@ -56,6 +56,7 @@ from typing import Optional
 import numpy as np
 import torch

+from core.contained_subprocess import spawn_owned
 from services.tts_backend import TTSBackend

 logger = logging.getLogger("omnivoice.subprocess_backend")
@@ -362,6 +363,7 @@ class SubprocessBackend(TTSBackend):
    # be a different class object from the one the subclass closed over.
    # A duck-typed marker survives that.
    _is_subprocess_isolated: bool = True
+    spawn_ready_timeout_s: float = SPAWN_READY_TIMEOUT_S

    # Generation happens in the sidecar: parent-side accelerator counters
    # can't see its allocations (see TTSBackend.runs_out_of_process).
@@ -383,6 +385,10 @@ class SubprocessBackend(TTSBackend):

    def __init__(self) -> None:
        self._proc: Optional[subprocess.Popen] = None
+        # A failed bounded reap must retain ownership and forbid reuse. This
+        # lock is separate from _lock: the receive owner joins its watchdog.
+        self._timeout_quarantine: list[subprocess.Popen] = []
+        self._timeout_quarantine_lock = threading.Lock()
        # Single lock serialises spawn + every send/recv pair so two threads
        # can't interleave half-frames on the same pipe.
        self._lock = threading.Lock()
@@ -449,6 +455,10 @@ class SubprocessBackend(TTSBackend):
    def _spawn(self) -> None:
        """Launch the sidecar if not already running. Blocks on the ready
        handshake. Caller must hold self._lock."""
+        if not self._retry_timeout_cleanup():
+            raise RuntimeError(
+                f"{self.id} sidecar is still stopping after a timeout; retry once it exits"
+            )
        if self._proc is not None and self._proc.poll() is None:
            return  # already up

@@ -470,13 +480,6 @@ class SubprocessBackend(TTSBackend):
            "env": env,
            "bufsize": 0,  # unbuffered binary pipes
        }
-        # Process-group isolation so the Tauri lib.rs group-kill in shutdown
-        # doesn't escape into other children. See T-02-05.
-        if sys.platform == "win32":
-            kwargs["creationflags"] = subprocess.CREATE_NEW_PROCESS_GROUP
-        else:
-            kwargs["start_new_session"] = True
-
        # `venv_python()` resolves the engine's interpreter, and on a cold
        # first run that is not cheap: it spawns each candidate to import the
        # engine (bounded, but tens of seconds on a slow disk), and if none is
@@ -509,7 +512,7 @@ class SubprocessBackend(TTSBackend):
            self.id, Path(python_path).name, Path(script_path).name,
        )
        try:
-            self._proc = subprocess.Popen([python_path, script_path], **kwargs)
+            self._proc = spawn_owned([python_path, script_path], **kwargs)
        except OSError as exc:
            raise InvalidBinaryError(
                python_path,
@@ -530,7 +533,7 @@ class SubprocessBackend(TTSBackend):
        # Block on the ready handshake. A sidecar that fails to emit ready
        # within SPAWN_READY_TIMEOUT_S is killed and the failure is raised.
        try:
-            frame = self._recv_with_timeout(SPAWN_READY_TIMEOUT_S)
+            frame = self._recv_with_timeout(self.spawn_ready_timeout_s)
        except Exception:
            self._force_kill()
            raise
@@ -547,6 +550,7 @@ class SubprocessBackend(TTSBackend):
        """Idempotent. Sends {op:shutdown}; falls back to terminate/kill."""
        proc = self._proc
        if proc is None:
+            self._retry_timeout_cleanup()
            return
        try:
            try:
@@ -582,6 +586,7 @@ class SubprocessBackend(TTSBackend):
                        pass
        finally:
            self._proc = None
+            self._retry_timeout_cleanup()

    def _force_kill(self) -> None:
        """Internal: kill a sidecar that never reached the ready state."""
@@ -782,38 +787,59 @@ class SubprocessBackend(TTSBackend):
        return msg

    def _recv_with_timeout(self, timeout_s: float) -> Optional[dict]:
-        """Recv that aborts if the sidecar goes silent.
+        """Read one frame, finishing timeout cleanup before the caller can retry.

-        Implemented by polling the proc for liveness with a deadline. We
-        don't block on a `select` of the pipe because Windows can't select
-        on subprocess pipes — keeping the implementation cross-platform
-        means a simpler polling loop here.
+        A watchdog closes the pipe on timeout; Windows cannot select on pipes.
+        EOF alone does not prove the owned process/supervisor has exited.
        """
        # On Unix we could use selectors; on Windows the pipe is not
        # selectable. Use a watchdog thread that kills the sidecar on
        # timeout — that triggers EOF on stdout, so _recv returns None
        # and the caller raises.
-        watchdog = threading.Timer(timeout_s, self._timeout_kill)
+        proc = self._proc
+        watchdog = threading.Timer(timeout_s, self._timeout_kill, args=(proc,))
        watchdog.daemon = True
        watchdog.start()
        try:
            return self._recv()
        finally:
            watchdog.cancel()
+            # cancel() cannot stop an already-running callback. Finish its
+            # bounded reap before another receive or generation starts.
+            watchdog.join()
            self._touch()  # any reply (or attempt) counts as recent activity

-    def _timeout_kill(self) -> None:
-        proc = self._proc
+    def _timeout_kill(self, proc: Optional[subprocess.Popen]) -> None:
+        """Kill only the child this receive captured, then reap its owner."""
        if proc is None:
            return
+        logger.error("[%s] sidecar exceeded recv timeout; killing", self.id)
        try:
-            logger.error(
-                "[%s] sidecar exceeded recv timeout; killing",
-                self.id,
-            )
            proc.kill()
        except Exception:
+            # A raced exit can make kill fail, but its owner still needs reaping.
            pass
+        try:
+            proc.wait(timeout=2)
+        except Exception:
+            # Do not discard a possibly live owner, or replace a newer _proc.
+            with self._timeout_quarantine_lock:
+                if not any(item is proc for item in self._timeout_quarantine):
+                    self._timeout_quarantine.append(proc)
+        else:
+            with self._timeout_quarantine_lock:
+                self._timeout_quarantine = [
+                    item for item in self._timeout_quarantine if item is not proc
+                ]
+
+    def _retry_timeout_cleanup(self) -> bool:
+        """Retry bounded cleanup, retaining every owner that could still be live."""
+        with self._timeout_quarantine_lock:
+            pending = tuple(self._timeout_quarantine)
+        for proc in pending:
+            self._timeout_kill(proc)
+        with self._timeout_quarantine_lock:
+            return not self._timeout_quarantine

    # ── stderr drain ───────────────────────────────────────────────────────

@@ -112,6 +112,13 @@ _FULL_NAME_TO_CODE = {
    "vietnamese": "vi",
    "kazakh": "kz",
    "standard arabic": "ar",
+    # Below: inert for num2words (absent from _NUM2WORDS_LANGS, which reads
+    # digits natively for these scripts), present so _plain_lang_code can
+    # resolve them for the digit-range rule.
+    "korean": "ko",
+    "japanese": "ja",
+    "chinese": "zh",
+    "mandarin chinese": "zh",
 }

 # ISO codes whose num2words locale name differs.
@@ -178,6 +185,82 @@ def _num2words_lang(language: Optional[str]) -> Optional[str]:
    return None


+def _plain_lang_code(language: Optional[str]) -> Optional[str]:
+    """Resolve a request language to a bare ISO code, with no num2words gate.
+
+    :func:`_num2words_lang` answers "may I call num2words for this?" and so
+    returns ``None`` for ko/ja/zh/th/vi. Rules that are not num2words-backed
+    need the code itself, which is what this returns.
+    """
+    if not language:
+        return None
+    s = str(language).strip().lower()
+    if not s or s == "auto":
+        return None
+    code = _FULL_NAME_TO_CODE.get(s)
+    if code:
+        return code
+    m = _ISO_CODE_RE.match(s)
+    if m:
+        return _ISO_ALIASES.get(m.group(1), m.group(1))
+    return None
+
+
+# ── Digit ranges ─────────────────────────────────────────────────────────────
+# "20~30" loses its separator at the engine and reads as ONE number: OmniVoice
+# says "이십삼" (23) for "20~30초". Speak the separator instead. Verified by
+# rendering each form and transcribing it back (ko, OmniVoice):
+#     "20~30초"        → heard "23초"          ✗
+#     "20에서 30초"     → heard "20에서 30초"    ✓
+# Only the tilde family is rewritten — those are unambiguously range marks
+# between digits. An ASCII hyphen is left alone on purpose: it also spells
+# dates, phone numbers and product codes, where "to" would be wrong.
+#: Spacing is part of the form, not decoration: a Korean postposition binds to
+#: the numeral ("20에서 30"), Japanese and Chinese set no spaces at all, and
+#: English needs them on both sides.
+_RANGE_FORM = {
+    "ko": "{a}에서 {b}",
+    "ja": "{a}から{b}",
+    "zh": "{a}到{b}",
+    "en": "{a} to {b}",
+}
+
+#: ASCII tilde, wave dash, fullwidth tilde — Japanese and Korean IMEs emit the
+#: latter two, so all three have to match.
+#:
+#: Match complete signed/decimal endpoints; reject partial numbers and product
+#: codes while allowing adjacent CJK units. Guard all tilde forms so malformed
+#: chains cannot be partially rewritten, including when their separators have
+#: whitespace around them.
+_RANGE_MARKS = "~\u301c\uff5e"
+_RANGE_ENDPOINT = r"[+-]?(?:\d{1,6}(?:\.\d{1,6})?|\.\d{1,6})"
+_NUM_RANGE_RE = re.compile(
+    rf"(?<![\d.,A-Za-z+{_RANGE_MARKS}-])({_RANGE_ENDPOINT})"
+    rf"\s*[{_RANGE_MARKS}]\s*({_RANGE_ENDPOINT})"
+    rf"(?![\d.,A-Za-z+{_RANGE_MARKS}-])"
+)
+
+
+def _speak_number_ranges(text: str, lang: str) -> str:
+    """Speak complete tilde ranges only for languages with a verified form."""
+    form = _RANGE_FORM.get(lang)
+    if not form:
+        return text
+
+    def replace(match: re.Match) -> str:
+        before, after = match.start() - 1, match.end()
+        while before >= 0 and text[before].isspace():
+            before -= 1
+        while after < len(text) and text[after].isspace():
+            after += 1
+        if ((before >= 0 and text[before] in _RANGE_MARKS)
+                or (after < len(text) and text[after] in _RANGE_MARKS)):
+            return match.group(0)
+        return form.format(a=match.group(1), b=match.group(2))
+
+    return _NUM_RANGE_RE.sub(replace, text)
+
+
 # ── Universal safety filters (all languages) ─────────────────────────────────

 # Zero-width & bidi controls, C0/C1 controls (except \t \n \r), BOM, U+FFFD.
@@ -487,6 +570,11 @@ def normalize_text(text: str, language: Optional[str] = None) -> str:
    if not text:
        return text or ""
    out = _safety_filters(text)
+    # Runs outside the num2words gate below: ko/ja/zh keep their digits (that
+    # gate returns None for them) but still need the range mark spoken.
+    plain = _plain_lang_code(language)
+    if plain:
+        out = _outside_brackets(out, lambda t: _speak_number_ranges(t, plain))
    lang = _num2words_lang(language)
    if lang:
        if lang in _ABBREV_COMPILED:
@@ -4,7 +4,7 @@ Resolution priority (highest → lowest):

  1. app    — `settings_store.get_hf_token()` (encrypted in SQLite)
  2. env    — `HF_TOKEN` or the legacy `HUGGING_FACE_HUB_TOKEN` env var
-  3. hf-cli — `huggingface_hub.get_token()` (canonical ~/.cache/huggingface/token)
+  3. hf-cli — the selected local Hub token file (`HF_TOKEN_PATH`)

 For each candidate, the resolver calls `huggingface_hub.whoami(token=...)`
 to verify the token is live; any HTTP error (401, 403, network) skips to
@@ -20,6 +20,7 @@ from __future__ import annotations
 import hashlib
 import logging
 import os
+from pathlib import Path
 import threading
 import time
 from dataclasses import dataclass
@@ -46,7 +47,7 @@ class SourceState:
    set: bool
    masked: Optional[str]
    whoami_user: Optional[str]
-    whoami_ok: bool
+    whoami_ok: Optional[bool]


 # ── module-level cache ────────────────────────────────────────────────────
@@ -79,19 +80,26 @@ def _read_app() -> Optional[str]:
        return None


+def _clean_token(value: Optional[str]) -> Optional[str]:
+    if not value:
+        return None
+    return value.replace("\r", "").replace("\n", "").strip() or None
+
+
 def _read_env() -> Optional[str]:
    # HF docs explicitly accept either name; user may have either exported.
    val = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")
-    return val or None
+    return _clean_token(val)


 def _read_hf_cli() -> Optional[str]:
    try:
-        import huggingface_hub
-        tok = huggingface_hub.get_token()
-        return tok or None
+        from huggingface_hub import constants
+        return _clean_token(Path(constants.HF_TOKEN_PATH).read_text(encoding="utf-8"))
+    except FileNotFoundError:
+        return None
    except Exception:
-        logger.exception("huggingface_hub.get_token failed")
+        logger.warning("Could not read the local Hugging Face token file")
        return None


@@ -184,17 +192,17 @@ def on_401(active_source: Source) -> Optional[ResolvedToken]:
    return resolve(skip=frozenset({active_source}))


-def state() -> dict:
+def state(*, validate: bool = False) -> dict:
    """Return one SourceState per priority position so the Settings UI can
    render the cascade table. Includes a masked token + whoami result;
-    never includes the raw token."""
+    never includes the raw token. Reads are local unless validation is explicitly requested."""
    rows: list[SourceState] = []
    active: Optional[Source] = None
    for source in _PRIORITY:
        token = _READERS[source]()
        if token:
-            username = _validate(source, token)
-            ok = username is not None
+            username = _validate(source, token) if validate else None
+            ok = (username is not None) if validate else None
            rows.append(SourceState(
                source=source,
                set=True,
@@ -249,15 +257,30 @@ def save_app_token(token: str) -> None:
    invalidate_cache()


+def clear_hf_cli_tokens() -> None:
+    """Remove recognized Hub token files without refreshing or revoking tokens."""
+    from core.config import HF_CLI_TOKEN_PATHS
+    from huggingface_hub import constants
+
+    # Hub's active path remains authoritative if imported before app config.
+    paths = set(HF_CLI_TOKEN_PATHS) | {constants.HF_TOKEN_PATH}
+    failed = False
+    for token_path in paths:
+        path = Path(token_path)
+        for target in (path, path.parent / "stored_tokens"):
+            try:
+                target.unlink(missing_ok=True)
+            except OSError:
+                failed = True
+    invalidate_cache()
+    if failed:
+        raise OSError("Could not clear all local Hugging Face token files")
+
+
 def clear_app_token(also_clear_hf_cli: bool = False) -> None:
-    """Remove from the encrypted settings store; optionally also call
-    `huggingface_hub.logout()` to clear the canonical HF file."""
+    """Clear the encrypted app token, optionally recognized local Hub files."""
    from services import settings_store
    settings_store.clear_hf_token()
    if also_clear_hf_cli:
-        try:
-            import huggingface_hub
-            huggingface_hub.logout()
-        except Exception:
-            logger.exception("huggingface_hub.logout failed (non-fatal)")
+        clear_hf_cli_tokens()
    invalidate_cache()
@@ -341,6 +341,44 @@ class TTSBackend(ABC):
        Engines that don't support this will ignore the parameter.
        """

+    def generate_batch(
+        self,
+        texts: list[str],
+        *,
+        ref_audio=None,
+        ref_text=None,
+        instruct=None,
+        language=None,
+        duration=None,
+        speed=1.0,
+        **extras,
+    ) -> list[torch.Tensor]:
+        """Synthesize several utterances, preserving the single-item contract.
+
+        Engines with a native batch forward pass override this method.  The
+        default keeps every existing adapter correct while giving callers one
+        stable seam and per-item keyword handling.
+        """
+        if not texts:
+            return []
+
+        def _item(value, index):
+            return value[index] if isinstance(value, list) else value
+
+        return [
+            self.generate(
+                text,
+                ref_audio=_item(ref_audio, index),
+                ref_text=_item(ref_text, index),
+                instruct=_item(instruct, index),
+                language=_item(language, index),
+                duration=_item(duration, index),
+                speed=_item(speed, index),
+                **extras,
+            )
+            for index, text in enumerate(texts)
+        ]
+
    # ── Lifecycle (Phase 2 will enforce per-engine overrides) ──────────────
    #
    # Today every backend lazily loads its weights on first `generate()` and
@@ -369,6 +407,13 @@ class TTSBackend(ABC):
    # entirely (it drives the shared model_manager singleton).
    _MODEL_ATTRS: tuple[str, ...] = ("_model", "_tts")

+    def execution_evidence_loaded(self) -> bool:
+        """Whether this instance has live model state worth reporting."""
+        if self.runs_out_of_process:
+            proc = getattr(self, "_proc", None)
+            return proc is not None and proc.poll() is None
+        return any(getattr(self, attr, None) is not None for attr in self._MODEL_ATTRS)
+
    def unload(self) -> None:
        """Release the heavy model this backend holds, and free device caches.

@@ -521,7 +566,12 @@ def _get_clone_prompt(
 ):
    """Return a cached/precomputed ``VoiceClonePrompt`` for
    (ref_audio, ref_text, preprocess_prompt), or ``None`` to fall back to the
-    inline ref path. Never raises.
+    inline ref path.
+
+    Raises only on a device OOM that survives a cache-drop retry (#1790): the
+    inline path is the same allocation on the same device, so falling back to
+    it after an OOM cannot succeed and has been observed taking the whole
+    process down instead. Every other failure still falls back silently.

    ``store=False`` still *reads* the cache (a hit is free) but never inserts:
    it exists for single-use references — a dub's per-segment ref clips are each
@@ -551,10 +601,43 @@ def _get_clone_prompt(
                ref_audio, ref_text=ref_text, preprocess_prompt=preprocess_prompt
            )
        except Exception as e:  # noqa: BLE001 — fall back, never break synthesis
-            logger.warning(
-                "voice-clone prompt precompute failed; using inline ref: %s", e
-            )
-            return None
+            # #1790/#1777: a GPU OOM is the one failure this fallback cannot
+            # absorb. `generate()`'s inline ref path runs the SAME encode on the
+            # SAME device — the docstring above says so, because producing
+            # identical output is the point — so returning None after an OOM
+            # guarantees a second OOM moments later, on a device with even less
+            # headroom than the first attempt found. Both reporters' backends
+            # then died with a Windows access violation (exit code
+            # -1073741819) seconds after this exact log line, mid-generation on
+            # a GPU that had just refused an 86 MiB allocation.
+            #
+            # An OOM here is also the most recoverable kind: the allocator is
+            # typically holding reserved-but-unallocated blocks (#1790's own
+            # log reports 90 MiB reserved against an 86 MiB request). Drop them
+            # and try once more. If it still will not fit, raise — the failure
+            # layer turns a device OOM into the actionable GPU_OOM message
+            # ("close other GPU-heavy apps or unload models…"), which is a far
+            # better answer than walking into a native fault.
+            from core.failure import is_gpu_oom
+
+            if is_gpu_oom(e):
+                logger.warning(
+                    "voice-clone prompt precompute hit a device OOM (%s) — "
+                    "releasing allocator caches and retrying once", e,
+                )
+                try:
+                    from services.model_manager import free_vram
+                    free_vram()
+                except Exception:  # noqa: BLE001 — reclaim is best-effort
+                    logger.debug("VRAM reclaim before OOM retry failed", exc_info=True)
+                prompt = model.create_voice_clone_prompt(
+                    ref_audio, ref_text=ref_text, preprocess_prompt=preprocess_prompt
+                )
+            else:
+                logger.warning(
+                    "voice-clone prompt precompute failed; using inline ref: %s", e
+                )
+                return None
        if store:
            _prompt_disk_save(key, prompt)
    if not store:
@@ -631,7 +714,7 @@ class OmniVoiceBackend(TTSBackend):

    id = "omnivoice"
    display_name = "VoiceStudio (k2-fsa/OmniVoice, 600+ languages)"
-    gpu_compat = ("cuda", "mps", "cpu")
+    gpu_compat = ("cuda", "rocm", "mps", "cpu")
    # Derived from the pool's own per-job budget (_GPU_VRAM_PER_JOB_GB = 5.0 in
    # model_manager, itself measured from the ~1.6 GB forward + autoregressive
    # decode and the co-loaded WhisperX on the clone path), plus room for the
@@ -717,6 +800,73 @@ class OmniVoiceBackend(TTSBackend):
        )
        return audios[0]

+    def generate_batch(self, texts: list[str], **kw) -> list[torch.Tensor]:
+        """Use OmniVoice's native variable-length batch generation.
+
+        Batch callers pass per-item language, duration, speed and reference
+        lists.  Reusable clone prompts are prepared once and handed to the
+        model together; an incomplete prompt batch falls back to the proven
+        single-item path instead of changing synthesis semantics.
+        """
+        self._ensure_loaded()
+        if not texts:
+            return []
+
+        def _items(value):
+            if isinstance(value, list):
+                return value
+            return [value] * len(texts)
+
+        def _item_kwargs(index):
+            return {
+                key: value[index] if isinstance(value, list) else value
+                for key, value in kw.items()
+            }
+
+        ref_audios = _items(kw.get("ref_audio"))
+        ref_texts = _items(kw.get("ref_text"))
+        cache_ref = bool(kw.get("cache_ref", True))
+        preprocess_prompt = bool(kw.get("preprocess_prompt", True))
+        prompts = []
+        if any(ref_audios):
+            for ref_audio, ref_text in zip(ref_audios, ref_texts):
+                if not ref_audio:
+                    prompts = []
+                    break
+                prompt = _get_clone_prompt(
+                    self._model,
+                    ref_audio,
+                    ref_text,
+                    preprocess_prompt,
+                    store=cache_ref,
+                )
+                if prompt is None:
+                    prompts = []
+                    break
+                prompts.append(prompt)
+
+        if any(ref_audios) and len(prompts) != len(texts):
+            return [self.generate(text, **_item_kwargs(i))
+                    for i, text in enumerate(texts)]
+
+        gen_kw = dict(
+            language=kw.get("language"),
+            instruct=kw.get("instruct"),
+            duration=kw.get("duration"),
+            speed=kw.get("speed", 1.0),
+            denoise=kw.get("denoise", True),
+            postprocess_output=kw.get("postprocess_output", True),
+            num_step=kw.get("num_step", 16),
+            guidance_scale=kw.get("guidance_scale", 2.0),
+            preprocess_prompt=preprocess_prompt,
+        )
+        if prompts:
+            gen_kw["voice_clone_prompt"] = prompts
+        else:
+            gen_kw["ref_audio"] = None
+            gen_kw["ref_text"] = None
+        return self._model.generate(text=texts, **gen_kw)
+
    def unload(self) -> None:
        """Release the OmniVoice model (MM2-02). OmniVoice shares the singleton
        owned by ``model_manager``, so dropping our local ref isn't enough — we
@@ -2186,9 +2336,9 @@ _INSTALL_HINTS: dict[str, str] = {
    "omnivoice-gguf":"Bundled — runs the C++ omnivoice-tts binary in bin/. Quants download lazily from Serveurperso/OmniVoice-GGUF on first generate.",
    "supertonic3":   "uv sync --extra supertonic  (CPU-only ONNX, 31 langs, ~400 MB model on first use; OpenRAIL-M model license)",
    "pockettts":     "uv sync --extra pockettts  (Kyutai, CPU-only, ~100 MB model on first use; MIT code + CC-BY-4.0 weights; HF-gated, review terms and set HF_TOKEN)",
-    "moss-tts-v15":  "git clone OpenMOSS/MOSS-TTS + set OMNIVOICE_MOSS_TTS_V15_DIR  (own venv, transformers==5.0; 8B, ~16 GB weights; CUDA/CPU, no MPS; Apache-2.0)",
+    "moss-tts-v15":  "git clone OpenMOSS/MOSS-TTS + set OMNIVOICE_MOSS_TTS_V15_DIR  (own venv, transformers==5.0; 8B, ~16 GB weights; CUDA/ROCm/XPU/NPU/CPU, no MPS; Apache-2.0)",
    "dots-tts":      "git clone rednote-hilab/dots.tts + set OMNIVOICE_DOTS_TTS_DIR  (own venv, transformers==4.57; 2B, ~9 GB weights; CUDA/CPU, Linux/macOS only — no Windows; Apache-2.0)",
-    "confucius4-tts":"git clone netease-youdao/Confucius4-TTS + set OMNIVOICE_CONFUCIUS4_TTS_DIR  (own Python 3.10 venv; 14-lang cross-lingual zero-shot clone; ~5 GB weights auto-download; CUDA/CPU, no MPS; Apache-2.0)",
+    "confucius4-tts":"git clone netease-youdao/Confucius4-TTS + set OMNIVOICE_CONFUCIUS4_TTS_DIR  (own Python 3.10 venv; 14-lang cross-lingual zero-shot clone; ~5 GB weights auto-download; CUDA/ROCm/XPU/NPU/CPU, no MPS; Apache-2.0)",
 }


@@ -2259,7 +2409,7 @@ def list_backends() -> list[dict]:
          "one_click_install": bool,                # services.sidecar_install can provision it in-app
          "last_error":     Optional[str],          # cached most-recent failure
          "isolation_mode": "in-process" | "subprocess",
-          "gpu_compat":     list[str],              # subset of {cuda, rocm, mps, xpu, cpu}
+          "gpu_compat":     list[str],              # subset of {cuda, rocm, mps, xpu, npu, cpu}
          "supports_cloning": Optional[bool],       # True/False from the class attr; None when
                                                    #   model-dependent (property, e.g. mlx-audio)
          "effective_device": str,                  # device this engine uses on THIS host
@@ -2287,12 +2437,15 @@ def list_backends() -> list[dict]:
    # Routing is host-aware but the host caps are constant per process, so probe
    # ONCE here and resolve each engine's effective device against the same caps.
    from core.device_caps import detect_host_caps
+    from services.engine_disk_usage import disk_summary_for
+    from services.engine_evidence import snapshot as execution_snapshot
    from services.engine_routing import routing_fields
    caps = detect_host_caps()
    installable = _sidecar_installable_ids()

    out: list[dict] = []
    for bid, cls in _REGISTRY.items():
+        cls = _effective_backend_class(bid, cls, caps.family)
        try:
            ok, msg = cls.is_available()
        except Exception:
@@ -2319,6 +2472,12 @@ def list_backends() -> list[dict]:
        # descriptor, not a bool, so report None (= model-dependent) there
        # instead of an always-truthy false positive.
        _clone = getattr(cls, "supports_cloning", True)
+        routing = routing_fields(gpu_compat, caps, getattr(cls, "min_vram_gb", 0.0))
+        loaded_instance = None
+        if _active_instance_id == bid:
+            loaded_instance = _active_instance
+        if loaded_instance is None:
+            loaded_instance = _ENGINE_INSTANCES.get(cls)
        out.append({
            "id": bid,
            "display_name": cls.display_name,
@@ -2338,6 +2497,7 @@ def list_backends() -> list[dict]:
            # in-app (Settings renders an Install button instead of leading
            # with the manual setup snippet).
            "one_click_install": bid in installable,
+            "disk_usage": disk_summary_for(bid),
            "last_error": _LAST_ERRORS.get(bid),
            "isolation_mode": isolation,
            "gpu_compat": list(gpu_compat),
@@ -2345,7 +2505,14 @@ def list_backends() -> list[dict]:
            "min_vram_gb": getattr(cls, "min_vram_gb", 0.0) or None,
            # effective_device / routing_status / routing_reason (scrubbed);
            # the reason now also carries the under-provisioned-GPU caveat.
-            **routing_fields(gpu_compat, caps, getattr(cls, "min_vram_gb", 0.0)),
+            **routing,
+            "execution_evidence": execution_snapshot(
+                engine_id=bid,
+                engine_cls=cls,
+                instance=loaded_instance,
+                routing=routing,
+                caps=caps,
+            ),
        })
        # #981: mlx-audio multiplexes 7+ curated models behind one backend id
        # — surface the roster + the currently-active pick so Settings can
@@ -2366,10 +2533,29 @@ def list_backends() -> list[dict]:
    return out


+def _effective_backend_class(
+    backend_id: str,
+    backend_cls: type[TTSBackend],
+    host_family: str | None = None,
+) -> type[TTSBackend]:
+    """Resolve host-specific containment without changing the configured id."""
+    if backend_id != "omnivoice":
+        return backend_cls
+    if host_family is None:
+        from core.device_caps import detect_host_caps
+
+        host_family = detect_host_caps().family
+    if host_family != "mps":
+        return backend_cls
+    from engines.omnivoice_subprocess import OmniVoiceMPSSubprocessBackend
+
+    return OmniVoiceMPSSubprocessBackend
+
+
 def get_backend_class(backend_id: str) -> type[TTSBackend]:
    if backend_id not in _REGISTRY:
        raise ValueError(f"Unknown TTS backend: {backend_id!r}. Known: {list(_REGISTRY)}")
-    return _REGISTRY[backend_id]
+    return _effective_backend_class(backend_id, _REGISTRY[backend_id])


 def cloning_capable_engine_ids() -> list[str]:
@@ -20,12 +20,17 @@ Usage:

 from __future__ import annotations

+import contextlib
 import logging
 import math
+import os
+import threading
 import time
-import torch
+from pathlib import Path
 from typing import Optional

+import torch
+
 from core.prefs import resolve

 logger = logging.getLogger("omnivoice.watermark")
@@ -37,6 +42,20 @@ _detector = None
 _audioseal_available: Optional[bool] = None
 # Monotonic stamp of the last embed/detect, for the idle release below.
 _last_used = 0.0
+# Per-model locks for the lazy builds below: the startup prefetch thread
+# races the first embed, and both must share ONE build (a double load doubles
+# the cold-start cost the prefetch exists to hide). One lock PER MODEL — a
+# single shared lock made the ~42s generator prefetch block unrelated detector
+# loads and the idle reaper behind it. release_idle_models acquires both, in
+# this fixed order (nothing else nests them, so no cycle is possible).
+_generator_lock = threading.Lock()
+_detector_lock = threading.Lock()
+
+# True when the generator exists ONLY because the startup prefetch built it
+# and no embed/detect has used it since. The idle reaper grants one extra
+# idle window before dropping such a model, so a first synthesis at minute
+# 20 still finds it warm (code-review finding 2 on the prefetch PR).
+_prefetched_unused = False

 # 16-bit message: "OM" in ASCII = 0x4F 0x4D = 0100_1111 0100_1101
 # This is our signature — every VoiceStudio-generated audio carries it.
@@ -50,6 +69,86 @@ OMNI_MESSAGE = [0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1]
 _CHUNK_SECONDS = 30


+# AudioSeal vendors moshi's ``@torch_compile_lazy`` on SEANetEncoder.forward,
+# so the first EMBED — not the model load, which prefetch already warms —
+# calls torch.compile and drops into Inductor's C++ codegen. On hosts whose
+# C++ toolchain can't serve Inductor that compile raises CppCompileError, the
+# embed fail-opens, and audio ships unmarked: a macOS arm64 deployment lost
+# provenance marking on 10/10 takes while paying 30-40 s for the first failed
+# compile and 5-8 s for each later one (#1615).
+#
+# The compile is pure cost even where it succeeds. Measured on an M3 (5 s of
+# 24 kHz audio, three consecutive embeds): compiled 9.70 / 0.26 / 0.23 s vs
+# eager 0.30 / 0.28 / 0.27 s — a ~10 s first-embed tax to save ~0.03 s per
+# later embed, on CPU work that is already bounded by the 30 s chunk loop.
+# So watermarking runs eager on every platform.
+def _moshi_compile_module():
+    """AudioSeal's vendored moshi compile switch module, or None.
+
+    Resolved per call rather than at import: ``_check_available()`` is what
+    guarantees audioseal is importable, and it runs later than this module.
+    """
+    try:
+        from audioseal.libs.moshi.utils import compile as moshi_compile
+    except Exception:  # noqa: BLE001 — any import shape change degrades, not crashes
+        return None
+    return moshi_compile
+
+
+_eager_lock = threading.Lock()
+#: Depth of nested/concurrent eager scopes, and the switch value to put back
+#: when the last one exits. One dict rather than two module scalars: the
+#: fields are only meaningful together, and only under _eager_lock.
+_eager_state: dict = {"depth": 0, "saved": None}
+_eager_guard_warned = False
+
+
+def _warn_missing_eager_guard() -> None:
+    global _eager_guard_warned
+    _eager_guard_warned = True
+    logger.info(
+        "audioseal's no_compile switch is unavailable — watermarking may run "
+        "through torch.compile and pay (or fail) an Inductor C++ compile (#1615)."
+    )
+
+
+@contextlib.contextmanager
+def _eager_audioseal():
+    """Run the AudioSeal model eagerly, restoring the switch on the way out.
+
+    Upstream's own ``no_compile()`` saves and restores ``_compile_disabled``
+    per call, which is not safe when two watermark calls overlap: the first to
+    exit restores False while the second is still mid-embed, handing it back
+    the compile this whole fix exists to avoid. So the flag is reference
+    counted here — it goes True on the outermost entry and only comes back on
+    the outermost exit — rather than serializing embeds behind a lock, which
+    would cost real throughput on concurrent generations.
+
+    Degrades to a plain call if a future audioseal drops the helper
+    (``tests/test_watermark_no_torch_compile_1615.py`` fails loudly on that
+    upgrade rather than letting the compile creep back in).
+    """
+    moshi = _moshi_compile_module()
+    if moshi is None:
+        if not _eager_guard_warned:
+            _warn_missing_eager_guard()
+        yield
+        return
+    with _eager_lock:
+        if _eager_state["depth"] == 0:
+            _eager_state["saved"] = moshi._compile_disabled
+        _eager_state["depth"] += 1
+        moshi._compile_disabled = True
+    try:
+        yield
+    finally:
+        with _eager_lock:
+            _eager_state["depth"] -= 1
+            if _eager_state["depth"] == 0:
+                moshi._compile_disabled = _eager_state["saved"]
+                _eager_state["saved"] = None
+
+
 def _iter_chunks(audio: torch.Tensor, sample_rate: int):
    """Yield ≤ ~_CHUNK_SECONDS slices of (batch, channels, samples) audio
    along the time axis. A sub-second tail is folded into the previous chunk
@@ -77,28 +176,82 @@ def _check_available() -> bool:
    return _audioseal_available


-def _get_generator():
-    """Lazy-load the AudioSeal generator model."""
-    global _generator, _last_used
-    _last_used = time.monotonic()
-    if _generator is None:
-        from audioseal import AudioSeal
-        _generator = AudioSeal.load_generator("audioseal_wm_16bits")
-        _generator.eval()
-        logger.info("AudioSeal generator loaded (16-bit message mode)")
-    return _generator
+def _get_generator(mark_prefetched: bool = False):
+    """Lazy-load the AudioSeal generator model.
+
+    Owns the idle-reaper grace in ONE critical section: the startup prefetch
+    claims it (``mark_prefetched=True``) only when THIS call builds the model,
+    and every other call (a real embed) consumes it — no call-site blocks, no
+    window between two lock scopes where the claim could land on an
+    already-used model.
+    """
+    global _generator, _last_used, _prefetched_unused
+    with _generator_lock:
+        _last_used = time.monotonic()
+        if _generator is None:
+            from audioseal import AudioSeal
+            _generator = AudioSeal.load_generator("audioseal_wm_16bits")
+            _generator.eval()
+            logger.info("AudioSeal generator loaded (16-bit message mode)")
+            _prefetched_unused = mark_prefetched
+        elif not mark_prefetched:
+            _prefetched_unused = False
+        return _generator


 def _get_detector():
    """Lazy-load the AudioSeal detector model."""
    global _detector, _last_used
-    _last_used = time.monotonic()
-    if _detector is None:
-        from audioseal import AudioSeal
-        _detector = AudioSeal.load_detector("audioseal_detector_16bits")
-        _detector.eval()
-        logger.info("AudioSeal detector loaded (16-bit message mode)")
-    return _detector
+    with _detector_lock:
+        _last_used = time.monotonic()
+        if _detector is None:
+            from audioseal import AudioSeal
+            _detector = AudioSeal.load_detector("audioseal_detector_16bits")
+            _detector.eval()
+            logger.info("AudioSeal detector loaded (16-bit message mode)")
+        return _detector
+
+
+def _generator_checkpoint_cached() -> bool:
+    """Return whether AudioSeal can warm without contacting Hugging Face.
+
+    AudioSeal 0.2 stores the checkpoint in ``<cache>/audioseal`` even though
+    it uses huggingface_hub to fetch it. Keep startup local-first: an ordinary
+    boot may consume that file, but must never turn prefetch into a download.
+    """
+    cache_root = os.environ.get("AUDIOSEAL_CACHE_DIR") or os.environ.get(
+        "XDG_CACHE_HOME"
+    )
+    root = Path(cache_root).expanduser() if cache_root else Path.home() / ".cache"
+    return (root / "audioseal" / "generator_base.pth").is_file()
+
+
+def prefetch_generator(*, allow_download: bool = False) -> None:
+    """Warm the AudioSeal generator eagerly (startup background thread).
+
+    The first ``mark_synthetic`` otherwise pays the audioseal import plus the
+    generator load inline — measured at ~42 s on a cold filesystem (2026-08-17
+    macOS deployment), serialized inside the first synthesis and 3 s short of
+    a 90 s client timeout. Warming here overlaps that span with the TTS model
+    load. No-op when watermarking is off or audioseal is absent; a failure
+    logs and leaves the lazy path to retry on first embed. Default startup is
+    also cache-only; a download is allowed only when the user explicitly set
+    ``OMNIVOICE_PRELOAD_WATERMARK=1``.
+    """
+    try:
+        if not will_mark():
+            logger.debug("Watermark prefetch skipped (disabled or audioseal absent)")
+            return
+        if not allow_download and not _generator_checkpoint_cached():
+            logger.info("Watermark prefetch skipped: AudioSeal checkpoint is not cached")
+            return
+        _get_generator(mark_prefetched=True)
+        logger.info("AudioSeal generator prefetched in the background")
+    except Exception:
+        logger.warning(
+            "Watermark prefetch failed; the first embed will retry inline",
+            exc_info=True,
+        )


 def release_idle_models(idle_seconds: float, *, now: Optional[float] = None) -> bool:
@@ -114,14 +267,28 @@ def release_idle_models(idle_seconds: float, *, now: Optional[float] = None) ->
    Returns True if anything was released. Never raises: this runs from the
    idle reaper, which must survive it.
    """
-    global _generator, _detector
-    if _generator is None and _detector is None:
-        return False
-    stamp = time.monotonic() if now is None else float(now)
-    if stamp - _last_used < idle_seconds:
-        return False
-    _generator = None
-    _detector = None
+    global _generator, _detector, _prefetched_unused
+    with _generator_lock, _detector_lock:
+        if _generator is None and _detector is None:
+            return False
+        stamp = time.monotonic() if now is None else float(now)
+        if stamp - _last_used < idle_seconds:
+            return False
+        if _prefetched_unused:
+            # The startup prefetch built the generator and nothing has used
+            # it yet. Drop the grace (one extra idle window only) instead of
+            # the model, so a first synthesis shortly after boot still finds
+            # it warm — the exact scenario the prefetch exists for.
+            _prefetched_unused = False
+            logger.info(
+                "Idle watermark models are prefetch-warmed but unused; "
+                "granting one more idle window before releasing."
+            )
+            return False
+        # Under the locks so a release racing the prefetch or a first embed
+        # can't wipe a model the lazy path just built.
+        _generator = None
+        _detector = None
    logger.info("Idle timeout reached. Released the AudioSeal watermark models.")
    return True

@@ -200,6 +367,62 @@ def mark_synthetic(
    return marked


+async def mark_synthetic_async(
+    waveform: torch.Tensor,
+    sample_rate: int,
+    *,
+    context: str,
+    force: bool = False,
+    timeout: float | None = None,
+) -> torch.Tensor:
+    """Dispatch marking without letting a draining pool lose finished audio."""
+    import asyncio
+    import functools
+
+    from services.model_manager import (
+        GpuJobTimeoutError,
+        GpuPoolBusyError,
+        get_watermark_pool,
+        run_on_gpu_pool_guarded,
+    )
+
+    try:
+        pool = get_watermark_pool()
+    except RuntimeError:
+        logger.warning("Watermark skipped while the prior worker is shutting down")
+        return waveform
+
+    job = functools.partial(
+        mark_synthetic, waveform, sample_rate, context=context, force=force
+    )
+    try:
+        if timeout is not None:
+            return await run_on_gpu_pool_guarded(
+                job, what="Audio watermark", timeout=timeout, executor=pool
+            )
+        return await asyncio.get_running_loop().run_in_executor(pool, job)
+    except (GpuJobTimeoutError, GpuPoolBusyError):
+        # Watermarking is provenance best-effort: a typed execution overrun or
+        # queue saturation must not discard synthesis that already completed.
+        logger.warning("Watermark skipped after its bounded dispatch expired")
+        return waveform
+    except asyncio.CancelledError:
+        # A queued future is cancelled during pool teardown. Caller-driven
+        # cancellation while the pool is live must retain normal semantics.
+        if not pool.is_shutdown():
+            raise
+        logger.warning("Watermark skipped while the pool is shutting down")
+        return waveform
+    except RuntimeError:
+        # Shutdown may begin after admission but before Executor.submit().
+        # Preserve unrelated worker failures; only lifecycle rejection is
+        # fail-open because finished synthesis must not be lost to teardown.
+        if not pool.is_shutdown():
+            raise
+        logger.warning("Watermark skipped while the pool is shutting down")
+        return waveform
+
+
@torch.no_grad()
 def embed_watermark(
    waveform: torch.Tensor,
@@ -243,13 +466,14 @@ def embed_watermark(

        # AudioSeal operates at 16kHz internally; it handles resampling, but
        # we need to inform it of the source rate for correct embedding.
-        watermarked = torch.cat(
-            [
-                generator(seg, sample_rate=sample_rate, message=msg)
-                for seg in _iter_chunks(audio, sample_rate)
-            ],
-            dim=-1,
-        )
+        with _eager_audioseal():
+            watermarked = torch.cat(
+                [
+                    generator(seg, sample_rate=sample_rate, message=msg)
+                    for seg in _iter_chunks(audio, sample_rate)
+                ],
+                dim=-1,
+            )

        # Restore original shape
        if len(original_shape) == 2:
@@ -260,7 +484,7 @@ def embed_watermark(
        return watermarked

    except Exception as e:
-        logger.warning("Watermark embedding failed (passing through original): %s", e)
+        logger.warning("Watermark embedding failed (passing through original): %s", e, exc_info=True)
        return waveform


@@ -307,12 +531,13 @@ def detect_watermark(
        # embedding does, and a splice where only part of the file is
        # VoiceStudio audio still registers (a whole-file average would dilute it).
        best_conf, decoded_msg = -1.0, None
-        for seg in _iter_chunks(audio, sample_rate):
-            result = detector.detect_watermark(seg, sample_rate=sample_rate, message_threshold=0.5)
-            seg_conf = float(result[0]) if isinstance(result, tuple) else 0.0
-            if seg_conf > best_conf:
-                best_conf = seg_conf
-                decoded_msg = result[1] if isinstance(result, tuple) and len(result) > 1 else None
+        with _eager_audioseal():
+            for seg in _iter_chunks(audio, sample_rate):
+                result = detector.detect_watermark(seg, sample_rate=sample_rate, message_threshold=0.5)
+                seg_conf = float(result[0]) if isinstance(result, tuple) else 0.0
+                if seg_conf > best_conf:
+                    best_conf = seg_conf
+                    decoded_msg = result[1] if isinstance(result, tuple) and len(result) > 1 else None
        confidence = max(best_conf, 0.0)

        # Decode message bits
@@ -337,7 +562,7 @@ def detect_watermark(
        }

    except Exception as e:
-        logger.warning("Watermark detection failed: %s", e)
+        logger.warning("Watermark detection failed: %s", e, exc_info=True)
        return {
            "is_watermarked": False,
            "confidence": 0.0,
@@ -0,0 +1 @@
+"""Dependency-free client for VoiceStudio's local speech platform."""
@@ -0,0 +1,278 @@
+"""CLI/module bridge for terminals, editor extensions, and agent hooks.
+
+The desktop app must be running for native dictation control. Batch
+transcription can also target a standalone or remote VoiceStudio backend.
+"""
+from __future__ import annotations
+
+import argparse
+import ipaddress
+import json
+import mimetypes
+import os
+from pathlib import Path
+import secrets
+import sys
+from typing import Any
+from urllib import error, request
+from urllib.parse import urlsplit
+
+DEFAULT_CONTROL_URL = "http://127.0.0.1:3902"
+DEFAULT_ENGINE_URL = "http://127.0.0.1:3900"
+
+
+class SpeechClientError(RuntimeError):
+    pass
+
+
+class _RejectCredentialRedirect(request.HTTPRedirectHandler):
+    def redirect_request(self, req, fp, code, msg, headers, newurl):  # noqa: ARG002
+        raise SpeechClientError("VoiceStudio refused a credentialed redirect")
+
+
+def _join_url(base_url: str, path: str) -> str:
+    return f"{base_url.rstrip('/')}/{path.lstrip('/')}"
+
+
+def _decode_error(exc: error.HTTPError) -> str:
+    try:
+        body = exc.read().decode("utf-8", errors="replace")
+    except Exception:
+        body = ""
+    try:
+        detail = json.loads(body)
+    except (TypeError, json.JSONDecodeError):
+        detail = body.strip()
+    return f"HTTP {exc.code}: {detail or exc.reason}"
+
+
+def _is_loopback_host(host: str | None) -> bool:
+    if not host:
+        return False
+    if host.lower() == "localhost":
+        return True
+    try:
+        return ipaddress.ip_address(host).is_loopback
+    except ValueError:
+        return False
+
+
+def _open(req: request.Request, timeout: float = 300.0) -> tuple[bytes, str]:
+    target = urlsplit(req.full_url)
+    scheme = target.scheme.lower()
+    if scheme not in {"http", "https"}:
+        raise SpeechClientError("VoiceStudio URLs must use http:// or https://")
+    credentialed = bool(req.get_header("Authorization"))
+    if credentialed and scheme != "https" and not _is_loopback_host(target.hostname):
+        raise SpeechClientError("Remote VoiceStudio credentials require https://")
+    try:
+        opener = (
+            request.build_opener(_RejectCredentialRedirect())
+            if credentialed
+            else request.build_opener()
+        )
+        with opener.open(req, timeout=timeout) as response:  # noqa: S310
+            return response.read(), response.headers.get("Content-Type", "")
+    except error.HTTPError as exc:
+        raise SpeechClientError(_decode_error(exc)) from exc
+    except error.URLError as exc:
+        raise SpeechClientError(f"VoiceStudio is unavailable: {exc.reason}") from exc
+
+
+def _json_request(method: str, url: str, payload: Any | None = None) -> Any:
+    data = None if payload is None else json.dumps(payload).encode("utf-8")
+    headers = {"Accept": "application/json"}
+    if data is not None:
+        headers["Content-Type"] = "application/json"
+    body, _ = _open(request.Request(url, data=data, headers=headers, method=method), timeout=10.0)
+    try:
+        return json.loads(body)
+    except json.JSONDecodeError as exc:
+        raise SpeechClientError("VoiceStudio returned invalid JSON") from exc
+
+
+def _encode_multipart(
+    *,
+    filename: str,
+    audio: bytes,
+    fields: dict[str, str],
+    boundary: str | None = None,
+) -> tuple[bytes, str]:
+    boundary = boundary or f"voicestudio-{secrets.token_hex(16)}"
+    marker = boundary.encode("ascii")
+    parts: list[bytes] = []
+    for name, value in fields.items():
+        parts.extend(
+            [
+                b"--" + marker + b"\r\n",
+                f'Content-Disposition: form-data; name="{name}"\r\n\r\n'.encode(),
+                value.encode("utf-8"),
+                b"\r\n",
+            ]
+        )
+    safe_filename = Path(filename).name.replace('"', "") or "audio.wav"
+    content_type = mimetypes.guess_type(safe_filename)[0] or "application/octet-stream"
+    if Path(safe_filename).suffix.lower() in {".wav", ".wave"}:
+        content_type = "audio/wav"
+    parts.extend(
+        [
+            b"--" + marker + b"\r\n",
+            (
+                'Content-Disposition: form-data; name="file"; '
+                f'filename="{safe_filename}"\r\n'
+            ).encode(),
+            f"Content-Type: {content_type}\r\n\r\n".encode(),
+            audio,
+            b"\r\n--" + marker + b"--\r\n",
+        ]
+    )
+    return b"".join(parts), f"multipart/form-data; boundary={boundary}"
+
+
+def _control(args: argparse.Namespace, action: str) -> int:
+    method = "GET" if action in {"status", "capabilities"} else "POST"
+    path = {
+        "status": "/v1/status",
+        "capabilities": "/v1/capabilities",
+        "start": "/v1/dictation/start",
+        "stop": "/v1/dictation/stop",
+        "toggle": "/v1/dictation/toggle",
+    }[action]
+    result = _json_request(method, _join_url(args.control_url, path))
+    print(json.dumps(result, ensure_ascii=False, indent=2))
+    return 0
+
+
+def _read_audio(path: str, stdin_filename: str) -> tuple[bytes, str]:
+    if path == "-":
+        return sys.stdin.buffer.read(), stdin_filename
+    audio_path = Path(path)
+    try:
+        return audio_path.read_bytes(), audio_path.name
+    except OSError as exc:
+        display_name = path.replace("\\", "/").rsplit("/", 1)[-1] or "audio input"
+        reason = exc.strerror or type(exc).__name__
+        raise SpeechClientError(f"could not read '{display_name}': {reason}") from exc
+
+
+def _response_text(body: bytes, content_type: str) -> str:
+    decoded = body.decode("utf-8", errors="replace")
+    if "json" not in content_type.lower():
+        return decoded
+    try:
+        payload = json.loads(decoded)
+    except json.JSONDecodeError:
+        return decoded
+    if isinstance(payload, dict) and isinstance(payload.get("text"), str):
+        return payload["text"]
+    return decoded
+
+
+def _transcribe(args: argparse.Namespace) -> int:
+    audio, filename = _read_audio(args.audio, args.stdin_filename)
+    fields = {
+        "model": args.model,
+        "response_format": args.response_format,
+    }
+    if args.language:
+        fields["language"] = args.language
+    body, content_type = _encode_multipart(filename=filename, audio=audio, fields=fields)
+    headers = {"Content-Type": content_type, "Accept": "application/json, text/plain"}
+    api_key = os.environ.get("OMNIVOICE_API_KEY", "").strip()
+    if api_key:
+        headers["Authorization"] = f"Bearer {api_key}"
+
+    output_session_id = None
+    if args.insert:
+        session = _json_request(
+            "POST", _join_url(args.control_url, "/v1/output/sessions")
+        )
+        output_session_id = session["session_id"]
+
+    session_needs_cleanup = output_session_id is not None
+    try:
+        response_body, response_type = _open(
+            request.Request(
+                _join_url(args.engine_url, "/v1/audio/transcriptions"),
+                data=body,
+                headers=headers,
+                method="POST",
+            )
+        )
+        if output_session_id is not None:
+            _json_request(
+                "POST",
+                _join_url(
+                    args.control_url,
+                    f"/v1/output/sessions/{output_session_id}/insert",
+                ),
+                {"text": _response_text(response_body, response_type)},
+            )
+            session_needs_cleanup = False
+    finally:
+        if session_needs_cleanup:
+            try:
+                _json_request(
+                    "DELETE",
+                    _join_url(args.control_url, f"/v1/output/sessions/{output_session_id}"),
+                )
+            except Exception:  # noqa: BLE001
+                # Best-effort cleanup must not replace the original failure or
+                # KeyboardInterrupt that brought control into this finally.
+                pass
+
+    sys.stdout.buffer.write(response_body)
+    if response_body and not response_body.endswith(b"\n"):
+        sys.stdout.buffer.write(b"\n")
+    return 0
+
+
+def _parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        prog="voicestudio-speech",
+        description="Control and consume VoiceStudio's local speech platform.",
+    )
+    parser.add_argument(
+        "--control-url",
+        default=os.environ.get("VOICESTUDIO_SPEECH_URL", DEFAULT_CONTROL_URL),
+    )
+    parser.add_argument(
+        "--engine-url",
+        default=os.environ.get("VOICESTUDIO_URL", DEFAULT_ENGINE_URL),
+    )
+    subparsers = parser.add_subparsers(dest="command", required=True)
+    for command in ("status", "capabilities", "start", "stop", "toggle"):
+        subparsers.add_parser(command)
+
+    transcribe = subparsers.add_parser("transcribe")
+    transcribe.add_argument("audio", help="audio file, or - for stdin")
+    transcribe.add_argument("--stdin-filename", default="audio.wav")
+    transcribe.add_argument("--model", default="whisper-1")
+    transcribe.add_argument("--language")
+    transcribe.add_argument(
+        "--format",
+        dest="response_format",
+        choices=("json", "text", "verbose_json", "srt", "vtt"),
+        default="text",
+    )
+    transcribe.add_argument(
+        "--insert",
+        action="store_true",
+        help="insert the result into the app focused when this command starts",
+    )
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = _parser().parse_args(argv)
+    try:
+        if args.command == "transcribe":
+            return _transcribe(args)
+        return _control(args, args.command)
+    except (SpeechClientError, KeyError) as exc:
+        print(f"voicestudio-speech: {exc}", file=sys.stderr)
+        return 2
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
@@ -7,13 +7,15 @@ old silence-only guard missed it (the buzz is loud, not silent) so the garbage
 was cached and served.

 These tests cover the fix *without the 5 GB model / a GPU*: they drive the pure
-``_spectral_flatness`` / ``_is_unusable_audio`` helpers with synthetic signals,
-and assert the render constants didn't regress. The real end-to-end render is
-verified manually (spectral flatness back in the speech range + Whisper ASR).
+``_spectral_flatness`` / ``_is_unusable_audio`` helpers with synthetic tones and
+tracked speech demo renders, and assert the render constants did not regress.
 """
 from __future__ import annotations

 import math
+from pathlib import Path
+
+import soundfile as sf

 import pytest

@@ -39,6 +41,13 @@ def _white_noise() -> "torch.Tensor":
    return 0.5 * (torch.rand(N, generator=g) * 2 - 1)


+def _two_tone_buzz() -> "torch.Tensor":
+    """Two inharmonic partials — the other shape a collapsed render takes."""
+    t = torch.arange(N, dtype=torch.float32) / SR
+    s = torch.sin(2 * math.pi * 180.0 * t) + 0.6 * torch.sin(2 * math.pi * 361.0 * t)
+    return 0.8 * s / s.abs().max()
+
+
 def _speech_like() -> "torch.Tensor":
    """Broadband + harmonic + amplitude-modulated — a coarse stand-in for voiced
    speech: several harmonics (formant-ish), additive noise (consonants), and a
@@ -85,6 +94,71 @@ def test_speech_like_is_usable():
    assert arch._is_unusable_audio(_speech_like()) is False


+# ── Threshold stays between the two things it has to separate ───────────────
+# Synthetic broadband speech has much higher flatness than real voiced audio.
+# Measure both sides of the threshold against actual inputs, including the
+# existing demo renders that the old thresholds rejected.
+
+
+def test_tonal_ceiling_is_measured_not_assumed():
+    """Derive the tonal side of the margin instead of trusting a literal.
+
+    A bare constant would keep passing if `_spectral_flatness` stopped scoring
+    tones near zero, so measure the degenerate signals here and require the
+    threshold to clear the worst of them tenfold.
+    """
+    tones = [
+        arch._spectral_flatness(_pure_tone(80.0)),
+        arch._spectral_flatness(_pure_tone(220.0)),
+        arch._spectral_flatness(_two_tone_buzz()),
+    ]
+    assert all(t is not None for t in tones)
+    assert max(tones) * 10 < arch._DEGENERATE_FLATNESS
+
+
+_SAMPLES = Path(__file__).resolve().parents[1] / "assets" / "samples"
+_SPEECH_FIXTURES = [
+    "demo_voice.wav",
+    "demo_clone_output.wav",
+    *[f"voice_design/demo_voice_design_{name}.wav" for name in (
+        "audiobook_uk_narrator", "aussie_podcaster", "bedtime_storyteller",
+        "gravelly_villain", "indian_support_agent", "mandarin_sichuan", "us_news_anchor",
+    )],
+    *[f"dictation/{name}.wav" for name in (
+        "en_conversational", "en_technical", "fr_reservation",
+    )],
+    *[f"demo/dubbing/{name}.src.wav" for name in (
+        "source", "dubbed_es", "dubbed_fr", "dubbed_ja", "dubbed_zh",
+    )],
+]
+
+
+@pytest.mark.parametrize("fixture", _SPEECH_FIXTURES)
+def test_real_shipped_speech_clears_quality_floor(fixture):
+    # These are existing tracked demo renders, not synthesized stand-ins or
+    # asserted measurements. Loading PCM needs neither a model nor a network.
+    audio, _sample_rate = sf.read(_SAMPLES / fixture, dtype="float32", always_2d=True)
+    speech = torch.from_numpy(audio.T)
+    flatness = arch._spectral_flatness(speech)
+    assert flatness is not None
+    assert flatness > arch._DEGENERATE_FLATNESS * 10
+    assert arch._is_unusable_audio(speech) is False
+
+
+def test_flatness_is_not_clip_length_dependent():
+    """Repeating a signal must not change what it measures.
+
+    The whole-clip FFT this replaced failed exactly here: its frequency
+    resolution grew with duration, so the same audio measured 0.0229 at 3 s and
+    ~0 at 12 s (100% drift). Framed, the drift is under 0.1%.
+    """
+    short = _speech_like()
+    long = torch.cat([short] * 4)
+    a, b = arch._spectral_flatness(short), arch._spectral_flatness(long)
+    assert a is not None and b is not None
+    assert abs(a - b) / a < 0.02
+
+
 # ── Constants didn't regress ────────────────────────────────────────────────
 def test_preview_render_constants():
    # 16 steps under-converged on the social script; the fix bumped it.
@@ -11,6 +11,7 @@ that the error message tells the user what to do.
 import asyncio
 import os
 import sys
+import threading
 import time

 import pytest
@@ -75,17 +76,71 @@ def test_fast_transcribe_passes_through():
    pool.shutdown(wait=True)


+def test_timeout_defers_abandon_cleanup_until_running_worker_finishes():
+    """A timed-out native worker may still be reading request-owned inputs."""
+    pool = ThreadPoolExecutor(max_workers=1)
+    started = threading.Event()
+    finish = threading.Event()
+    cleaned = threading.Event()
+
+    def _slow():
+        started.set()
+        finish.wait(timeout=5)
+        assert not cleaned.is_set()
+        return "done"
+
+    async def _go():
+        with pytest.raises(ASRTimeoutError):
+            await run_transcribe_guarded(
+                pool,
+                _slow,
+                what="Convert",
+                timeout=0.05,
+                on_abandon=cleaned.set,
+            )
+        assert started.is_set()
+        assert not cleaned.is_set()
+        finish.set()
+        await asyncio.to_thread(cleaned.wait, 2)
+        assert cleaned.is_set()
+
+    try:
+        asyncio.run(_go())
+    finally:
+        finish.set()
+        pool.shutdown(wait=True)
+
+
+def test_normal_completion_keeps_abandon_cleanup_with_caller():
+    pool = ThreadPoolExecutor(max_workers=1)
+    cleaned = threading.Event()
+
+    async def _go():
+        result = await run_transcribe_guarded(
+            pool,
+            lambda: "done",
+            what="Convert",
+            timeout=5,
+            on_abandon=cleaned.set,
+        )
+        assert result == "done"
+        assert not cleaned.is_set()
+
+    try:
+        asyncio.run(_go())
+    finally:
+        pool.shutdown(wait=True)
+
+
 def test_timeout_error_is_a_timeouterror_subclass():
    # Routers that catch broad TimeoutError (openai_compat) must also catch ours.
    assert issubclass(ASRTimeoutError, TimeoutError)


-def test_timeout_resets_a_resilient_pool_to_restore_capacity():
-    # #730: a wedged transcribe holds its GPU-pool worker forever; with a 1-2
-    # worker pool that starves TTS generate and surfaces as "can't reach
-    # backend". On timeout, run_transcribe_guarded must reset() a pool that
-    # supports it (the real _ResilientGpuPool) so the next submit gets a fresh
-    # worker — capacity restored without an app restart.
+def test_timeout_does_not_overlap_an_in_process_native_worker():
+    # #1669: reset() cannot kill the old native thread. A fresh pool let the
+    # retry enter the same whisperx/CTranslate2 model concurrently and the
+    # process died with 0xC0000005. Keep the old worker accounted for instead.
    class _FakePool(ThreadPoolExecutor):
        def __init__(self):
            super().__init__(max_workers=1)
@@ -105,7 +160,7 @@ def test_timeout_resets_a_resilient_pool_to_restore_capacity():
            await run_transcribe_guarded(pool, _hang, what="Dub", timeout=0.2)

    asyncio.run(_go())
-    assert pool.reset_calls == 1
+    assert pool.reset_calls == 0
    pool.shutdown(wait=False)


@@ -112,6 +112,42 @@ class TestEnqueue:
        job = client.get(f"/batch/jobs/{job_id}").json()
        assert job["filename"] == "test.mp4"

+    @pytest.mark.asyncio
+    async def test_upload_is_persisted_in_bounded_chunks(self, batch, tmp_path):
+        class RecordingUpload:
+            def __init__(self):
+                self.read_sizes = []
+                self.remaining = b"video"
+
+            async def read(self, size):
+                self.read_sizes.append(size)
+                chunk, self.remaining = self.remaining[:size], self.remaining[size:]
+                return chunk
+
+        upload = RecordingUpload()
+        destination = tmp_path / "video.mp4"
+        await batch._save_upload(upload, str(destination))
+
+        assert destination.read_bytes() == b"video"
+        assert upload.read_sizes == [batch._UPLOAD_CHUNK_BYTES, batch._UPLOAD_CHUNK_BYTES]
+
+    @pytest.mark.asyncio
+    async def test_failed_upload_removes_partial_file(self, batch, tmp_path):
+        class FailingUpload:
+            calls = 0
+
+            async def read(self, _size):
+                self.calls += 1
+                if self.calls == 1:
+                    return b"partial"
+                raise OSError("upload interrupted")
+
+        destination = tmp_path / "video.mp4"
+        with pytest.raises(OSError, match="upload interrupted"):
+            await batch._save_upload(FailingUpload(), str(destination))
+
+        assert not destination.exists()
+

 class TestListJobs:
    def test_empty(self, client):
@@ -0,0 +1,346 @@
+"""Stable nested operation ownership (model-free, cross-platform seams)."""
+import ctypes
+import builtins
+import os
+import runpy
+import subprocess
+import sys
+import threading
+import time
+import types
+from ctypes import wintypes
+from pathlib import Path
+
+import pytest
+
+from core import contained_subprocess as owned
+
+
+class _Call:
+    def __init__(self, fn):
+        self.fn = fn
+
+    def __call__(self, *args):
+        return self.fn(*args)
+
+
+def test_supervisor_argv_uses_entry_module_for_source_and_frozen_binary(monkeypatch):
+    monkeypatch.delattr(owned.sys, "frozen", raising=False)
+    source = owned._supervisor_argv(3, 4, ["operation"])
+    assert source[:2] == [sys.executable, str(Path(owned.__file__).parents[1] / "main.py")]
+    assert source[2:] == ["--supervise", "3", "4", "--", "operation"]
+
+    monkeypatch.setattr(owned.sys, "frozen", True, raising=False)
+    frozen = owned._supervisor_argv(3, 4, ["operation"])
+    assert frozen == [sys.executable, "--supervise", "3", "4", "--", "operation"]
+
+
+def test_source_main_dispatches_supervisor_before_heavy_imports(monkeypatch):
+    calls = []
+    fake = types.ModuleType("core.contained_subprocess")
+    fake.supervisor_main = lambda args: calls.append(args) or 23
+    monkeypatch.setitem(sys.modules, "core.contained_subprocess", fake)
+    main_path = Path(owned.__file__).parents[1] / "main.py"
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        [str(main_path), "--supervise", "3", "4", "--", "operation"],
+    )
+    original_import = builtins.__import__
+
+    def guard_heavy_import(name, *args, **kwargs):
+        if name == "math":
+            raise AssertionError("supervisor dispatch reached application imports")
+        return original_import(name, *args, **kwargs)
+
+    monkeypatch.setattr(builtins, "__import__", guard_heavy_import)
+    with pytest.raises(SystemExit, match="23"):
+        runpy.run_path(str(main_path), run_name="__main__")
+    assert calls == [["--supervise", "3", "4", "--", "operation"]]
+
+
+@pytest.mark.skipif(os.name != "posix", reason="Unix drain pipe contract")
+def test_drain_fd_is_explicitly_inherited_by_wrapper_but_not_operation(monkeypatch):
+    drain_read, drain_write = os.pipe()
+    monkeypatch.setenv("OMNIVOICE_DESKTOP_CONTAINED", "1")
+    monkeypatch.setenv("OMNIVOICE_DESKTOP_DRAIN_FD", str(drain_write))
+    owned.secure_backend_drain_fd()
+    assert not os.get_inheritable(drain_write)
+    implicit_probe = subprocess.check_output(
+        [
+            sys.executable,
+            "-c",
+            "import os; "
+            "fd=int(os.environ['OMNIVOICE_DESKTOP_DRAIN_FD']); "
+            "\ntry: os.fstat(fd); print('leaked')"
+            "\nexcept OSError: print('closed')",
+        ],
+        close_fds=False,
+        text=True,
+    )
+    assert implicit_probe.strip() == "closed"
+    script = (
+        "import os,time; token=os.environ.get('OMNIVOICE_DESKTOP_DRAIN_FD'); "
+        "marker=os.environ.get('OMNIVOICE_DESKTOP_CONTAINED'); "
+        "\nif token is None and marker is None: state='stripped'"
+        "\nelse:"
+        "\n try: os.fstat(int(token)); state='leaked'"
+        "\n except OSError: state='closed'"
+        "\nprint(state, flush=True); time.sleep(60)"
+    )
+    proc = owned.spawn_owned(
+        [sys.executable, "-c", script],
+        stdout=subprocess.PIPE,
+        text=True,
+    )
+    try:
+        assert proc.stdout.readline().strip() == "stripped"
+        os.close(drain_write)
+        drain_write = -1
+        os.set_blocking(drain_read, False)
+        with pytest.raises(BlockingIOError):
+            os.read(drain_read, 1)  # wrapper still holds the only writer
+        proc.kill()
+        proc.wait(timeout=5)
+        deadline = time.monotonic() + 2
+        while time.monotonic() < deadline:
+            try:
+                if os.read(drain_read, 1) == b"":
+                    break
+            except BlockingIOError:
+                time.sleep(0.01)
+        else:
+            pytest.fail("wrapper exit did not close the desktop drain writer")
+    finally:
+        if drain_write >= 0:
+            os.close(drain_write)
+        os.close(drain_read)
+        if proc.poll() is None:
+            proc.kill()
+            proc.wait(timeout=5)
+
+
+def test_invalid_or_missing_desktop_drain_fd_fails_safe(monkeypatch):
+    monkeypatch.setenv("OMNIVOICE_DESKTOP_CONTAINED", "1")
+    monkeypatch.setenv("OMNIVOICE_DESKTOP_DRAIN_FD", "not-an-fd")
+    with pytest.raises(RuntimeError, match="missing its live.*drain descriptor"):
+        owned.spawn_owned([sys.executable, "-c", "print('unsafe')"])
+
+    monkeypatch.delenv("OMNIVOICE_DESKTOP_DRAIN_FD")
+    with pytest.raises(RuntimeError, match="missing its live.*drain descriptor"):
+        owned.secure_backend_drain_fd()
+
+    monkeypatch.delenv("OMNIVOICE_DESKTOP_CONTAINED")
+    assert owned.backend_drain_fd(required=True) is None
+    proc = owned.spawn_owned(
+        [sys.executable, "-c", "print('standalone')"],
+        stdout=subprocess.PIPE,
+        text=True,
+    )
+    assert proc.stdout.readline().strip() == "standalone"
+    assert proc.wait(timeout=5) == 0
+
+
+def test_windows_operation_is_in_kill_on_close_job_before_resume(monkeypatch):
+    """The child gets no instruction before stable nested Job assignment."""
+    events = []
+    job_closed = threading.Event()
+    job = 99
+
+    def close_handle(handle):
+        value = getattr(handle, "value", handle)
+        events.append(("close", value))
+        if value == job:
+            job_closed.set()
+        return True
+
+    kernel = type("Kernel", (), {})()
+    kernel.AssignProcessToJobObject = _Call(
+        lambda assigned_job, process: events.append(("assign", assigned_job, process)) or True
+    )
+    kernel.TerminateJobObject = _Call(
+        lambda assigned_job, code: events.append(("terminate", assigned_job, code)) or True
+    )
+    kernel.WriteFile = _Call(
+        lambda handle, payload, size, written, overlap: events.append(("write", size)) or True
+    )
+    kernel.CloseHandle = _Call(close_handle)
+
+    def read_control(*_args):
+        job_closed.wait(2)
+        return False
+
+    kernel.ReadFile = _Call(read_control)
+    monkeypatch.setattr(owned, "_windows_job", lambda: (job, kernel, wintypes))
+    monkeypatch.setattr(
+        owned,
+        "_resume_windows_process",
+        lambda _kernel, _types, pid: events.append(("resume", pid)),
+    )
+
+    class Child:
+        _handle = 77
+        pid = 123
+
+        def wait(self, timeout=None):
+            events.append(("wait", timeout))
+            return 0
+
+    monkeypatch.setattr(
+        owned.subprocess,
+        "Popen",
+        lambda *args, **kwargs: events.append(("spawn", kwargs["creationflags"])) or Child(),
+    )
+
+    assert owned._supervise_windows(11, 12, ["operation.exe"]) == 0
+    assert job_closed.wait(1)
+
+    names = [event[0] for event in events]
+    assert names.index("assign") < names.index("resume") < names.index("wait")
+    assert names.index("wait") < names.index("terminate") < names.index("write")
+
+
+def test_windows_assignment_failure_kills_suspended_unowned_child(monkeypatch):
+    """A child outside the nested Job must be killed through its stable handle."""
+    events = []
+    job_closed = threading.Event()
+    job = 99
+
+    def close_handle(handle):
+        value = getattr(handle, "value", handle)
+        events.append(("close", value))
+        if value == job:
+            job_closed.set()
+        return True
+
+    kernel = type("Kernel", (), {})()
+    kernel.AssignProcessToJobObject = _Call(
+        lambda assigned_job, process: events.append(("assign", assigned_job, process))
+        or False
+    )
+    kernel.TerminateJobObject = _Call(
+        lambda assigned_job, code: events.append(("terminate", assigned_job, code)) or True
+    )
+    kernel.WriteFile = _Call(
+        lambda handle, payload, size, written, overlap: events.append(("write", size)) or True
+    )
+    kernel.CloseHandle = _Call(close_handle)
+
+    def read_control(*_args):
+        job_closed.wait(2)
+        return False
+
+    kernel.ReadFile = _Call(read_control)
+    monkeypatch.setattr(owned, "_windows_job", lambda: (job, kernel, wintypes))
+    monkeypatch.setattr(ctypes, "get_last_error", lambda: 5, raising=False)
+
+    class Child:
+        _handle = 77
+        pid = 123
+
+        def kill(self):
+            events.append(("kill",))
+
+        def wait(self, timeout=None):
+            events.append(("wait", timeout))
+            return 1
+
+    monkeypatch.setattr(
+        owned.subprocess,
+        "Popen",
+        lambda *args, **kwargs: events.append(("spawn", kwargs["creationflags"])) or Child(),
+    )
+
+    assert owned._supervise_windows(11, 12, ["operation.exe"]) == 127
+    assert job_closed.wait(1)
+
+    names = [event[0] for event in events]
+    assert names.index("assign") < names.index("terminate") < names.index("kill")
+    assert names.index("kill") < names.index("wait") < names.index("write")
+
+
+def test_windows_direct_job_owner_assigns_before_resume(monkeypatch):
+    """Windows skips the extra Python wrapper but retains pre-start Job ownership."""
+    events = []
+    job = 99
+
+    kernel = type("Kernel", (), {})()
+    kernel.AssignProcessToJobObject = _Call(
+        lambda assigned_job, process: events.append(("assign", assigned_job, process)) or True
+    )
+    kernel.TerminateJobObject = _Call(
+        lambda assigned_job, code: events.append(("terminate", assigned_job, code)) or True
+    )
+    kernel.CloseHandle = _Call(
+        lambda handle: events.append(("close", getattr(handle, "value", handle))) or True
+    )
+    monkeypatch.setattr(owned, "_windows_job", lambda: (job, kernel, wintypes))
+    monkeypatch.setattr(
+        owned,
+        "_resume_windows_process",
+        lambda _kernel, _types, pid: events.append(("resume", pid)),
+    )
+
+    class Child:
+        _handle = 77
+        pid = 123
+        args = ["operation.exe"]
+        stdin = None
+        stdout = object()
+        stderr = object()
+        returncode = None
+
+        def poll(self):
+            return self.returncode
+
+        def wait(self, timeout=None):
+            events.append(("wait", timeout))
+            return self.returncode
+
+        def kill(self):
+            events.append(("kill",))
+
+    child = Child()
+
+    def fake_popen(argv, **kwargs):
+        events.append(("spawn", argv, kwargs))
+        return child
+
+    monkeypatch.setattr(owned.subprocess, "Popen", fake_popen)
+    proc = owned._spawn_windows_owned(
+        ["operation.exe"],
+        {
+            "env": {
+                "KEEP": "yes",
+                "OMNIVOICE_DESKTOP_CONTAINED": "1",
+                "OMNIVOICE_DESKTOP_DRAIN_FD": "42",
+            },
+            "creationflags": 0x00000200,
+        },
+    )
+
+    names = [event[0] for event in events]
+    assert names[:3] == ["spawn", "assign", "resume"]
+    spawn_argv, spawn_kwargs = events[0][1:]
+    assert spawn_argv == ["operation.exe"]
+    assert spawn_kwargs["creationflags"] == 0x08000204
+    assert spawn_kwargs["env"] == {"KEEP": "yes"}
+    assert proc.stdout is child.stdout
+
+    child.returncode = 0
+    assert proc.poll() == 0
+    assert [event[0] for event in events][-2:] == ["terminate", "close"]
+
+
+def test_spawn_owned_selects_direct_windows_job_path(monkeypatch):
+    sentinel = object()
+    calls = []
+    monkeypatch.setattr(owned.os, "name", "nt")
+    monkeypatch.setattr(
+        owned,
+        "_spawn_windows_owned",
+        lambda argv, kwargs: calls.append((argv, kwargs)) or sentinel,
+    )
+
+    assert owned.spawn_owned(["sidecar.exe"], text=True) is sentinel
+    assert calls == [(["sidecar.exe"], {"text": True})]
@@ -0,0 +1,126 @@
+"""macOS fallback for the os.waitid probe (#1656).
+
+CPython on macOS does not expose os.waitid, so OwnedPopen's WNOWAIT dance
+crashed with AttributeError on every poll after the first spawn. These tests
+simulate that platform (monkeypatch os.waitid away) and pin the fallback:
+poll/wait/kill must work, exit codes must be real, and an already-reaped
+leader must be refused (ChildProcessError path), never signalled blind.
+"""
+import os
+import subprocess
+import sys
+import time
+
+import pytest
+
+from core import contained_subprocess as owned
+
+
+def _make_owned(argv):
+    cr, cw = os.pipe()
+    rr, rw = os.pipe()
+    proc = subprocess.Popen(argv, start_new_session=True)
+    os.close(cw)
+    os.close(rw)  # result writer gone: _read_result falls back to wrapper rc
+    return owned.OwnedPopen(proc, cr, rr), proc
+
+
+@pytest.fixture()
+def no_waitid(monkeypatch):
+    monkeypatch.delattr(os, "waitid", raising=False)
+
+
+def test_poll_running_then_exited_without_waitid(no_waitid):
+    h, _ = _make_owned([sys.executable, "-c", "import time; time.sleep(1.5)"])
+    try:
+        assert h.poll() is None, "running child must poll None"
+        h._proc.wait()
+        deadline = time.monotonic() + 5
+        rc = None
+        while rc is None and time.monotonic() < deadline:
+            rc = h.poll()
+            time.sleep(0.05)
+        assert rc == 0
+        assert h.poll() == 0
+    finally:
+        h._close_control()
+        if h._result_fd is not None:
+            os.close(h._result_fd)
+
+
+def test_poll_reports_real_exit_code_without_waitid(no_waitid):
+    h, _ = _make_owned([sys.executable, "-c", "raise SystemExit(3)"])
+    try:
+        deadline = time.monotonic() + 5
+        while h.poll() is None and time.monotonic() < deadline:
+            time.sleep(0.05)
+        assert h.poll() == 3
+    finally:
+        h._close_control()
+        if h._result_fd is not None:
+            os.close(h._result_fd)
+
+
+def test_wait_returns_after_kill_without_waitid(no_waitid):
+    h, _ = _make_owned([sys.executable, "-c", "import time; time.sleep(30)"])
+    try:
+        h.kill()
+        rc = h.wait(timeout=5)
+        assert rc != 0
+    finally:
+        h._close_control()
+        if h._result_fd is not None:
+            os.close(h._result_fd)
+
+
+def test_reaped_by_own_popen_reports_code_without_waitid(no_waitid):
+    h, proc = _make_owned([sys.executable, "-c", "pass"])
+    try:
+        proc.wait()  # reaped through OUR handle: known code, not a refusal
+        assert h.poll() == 0
+    finally:
+        h._close_control()
+        if h._result_fd is not None:
+            os.close(h._result_fd)
+
+
+def test_foreign_reaped_leader_is_refused_without_waitid(no_waitid):
+    h, proc = _make_owned([sys.executable, "-c", "pass"])
+    try:
+        # Reap OUTSIDE this handle: Popen never learns the code, so poll must
+        # refuse (None) rather than guess or signal a maybe-reused group.
+        while True:
+            pid, _ = os.waitpid(proc.pid, os.WNOHANG)
+            if pid == proc.pid:
+                break
+            time.sleep(0.05)
+        assert h.poll() is None
+    finally:
+        h._close_control()
+        if h._result_fd is not None:
+            os.close(h._result_fd)
+
+
+def test_kill_after_pid_reuse_does_not_signal_without_waitid(no_waitid, monkeypatch):
+    """A foreign-reaped leader's reused numeric pid must not authorize killpg."""
+    import signal as _signal
+
+    h, proc = _make_owned([sys.executable, "-c", "pass"])
+    try:
+        while True:
+            pid, _ = os.waitpid(proc.pid, os.WNOHANG)
+            if pid == proc.pid:
+                break
+            time.sleep(0.05)
+        # Model the numeric pid being reused: kill(pid, 0) would succeed even
+        # though waitpid still reports that the original child is no longer
+        # ours. The old guard therefore reached killpg and fails this test.
+        monkeypatch.setattr(os, "kill", lambda _pid, _sig: None)
+        signalled = []
+        monkeypatch.setattr(os, "killpg", lambda pid, sig: signalled.append((pid, sig)))
+        h._signal_owned_group(_signal.SIGKILL)
+        assert signalled == []
+    finally:
+        h._close_control()
+        if h._result_fd is not None:
+            os.close(h._result_fd)
@@ -1,16 +1,15 @@
 """A dictation model that decodes nothing gets demoted, not re-selected forever.

-`sherpa-parakeet-tdt-v3` is the curated default, and on Windows it installs
-cleanly, loads without error, and returns an empty token list for clear speech
+On Windows, `sherpa-parakeet-tdt-v3` installs cleanly, loads without error,
+and returns an empty token list for clear speech
 (both quantisations, both decoding methods, sherpa-onnx 1.13.3 and 1.13.4)
 while whisper and zipformer transcribe the same bytes. The defect is inside
 sherpa-onnx's NeMo-TDT decoder — unfixable from here by configuration.

-Hard-coding a different default per OS would be a guess: we have evidence for
-one platform only. So the app observes instead. When a session hears real
-speech and the model returns nothing, that model is demoted ON THIS MACHINE and
-stops being auto-selected, which self-corrects wherever the breakage actually
-is and is a no-op everywhere it isn't.
+Whisper Tiny is now the cross-platform default, while Parakeet remains
+selectable. Runtime demotion still protects users who select a recognizer that
+loads successfully but decodes nothing: it is demoted on this machine and the
+next session follows the capture fallback.

 These tests pin the demotion round trip and, critically, that the user can
 always take back control by re-picking the model.
@@ -1,13 +1,13 @@
 """A dictation model that decodes NOTHING must fall back, not fail silently.

-Found on Windows with the curated default `sherpa-parakeet-tdt-v3`: the model
+Found on Windows with `sherpa-parakeet-tdt-v3`: the model
 downloads, loads with zero errors, and is correctly detected as a TDT model
 (`num_durations: 5`) — then returns an empty token list for clear speech.
 Measured against the same 18.9s WAV, on the same machine, same sherpa-onnx:

    sherpa-whisper-tiny     -> "Alright, here we are. I hope that's all..."
    sherpa-zipformer-en-20m -> "ANTS BOTH IN WHAT DISGUISED THIS THAT..."
-    parakeet-tdt-v3 (int8)  -> ''      <-- the curated default
+    parakeet-tdt-v3 (int8)  -> ''
    parakeet-tdt-v3 (fp32)  -> ''
    parakeet-tdt-v2 (int8)  -> ''

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+import asyncio
+import io
+import threading
+
+import pytest
+from fastapi import UploadFile
+
+
+@pytest.mark.asyncio
+async def test_preview_ffmpeg_does_not_block_event_loop(monkeypatch, tmp_path):
+    from api.routers import dub_core
+
+    loop = asyncio.get_running_loop()
+    started = asyncio.Event()
+    release = threading.Event()
+
+    def slow_ffmpeg(*_args, **_kwargs):
+        loop.call_soon_threadsafe(started.set)
+        assert release.wait(timeout=2)
+
+    monkeypatch.setattr(dub_core, "PREVIEW_DIR", str(tmp_path))
+    monkeypatch.setattr(dub_core, "find_ffmpeg", lambda: "ffmpeg")
+    monkeypatch.setattr(dub_core.subprocess, "run", slow_ffmpeg)
+    upload = UploadFile(filename="preview.mp4", file=io.BytesIO(b"video"))
+
+    before = loop.time()
+    task = asyncio.create_task(dub_core.preview_upload(upload))
+    try:
+        await asyncio.wait_for(started.wait(), timeout=1)
+        assert loop.time() - before < 0.5
+    finally:
+        release.set()
+
+    result = await task
+    assert result["audioUrl"].endswith(".wav")
@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+import asyncio
+import threading
+from concurrent.futures import ThreadPoolExecutor
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_abandoned_reader_keeps_adhoc_reference_until_worker_finishes(tmp_path):
+    from api.routers.generation import (
+        _TempReferenceLease,
+        _run_with_reference_lease,
+    )
+    from services.model_manager import run_on_gpu_pool_guarded
+
+    reference = tmp_path / "reference.wav"
+    reference.write_bytes(b"voice")
+    lease = _TempReferenceLease(str(reference))
+    started = threading.Event()
+    release_worker = threading.Event()
+    worker_read = threading.Event()
+
+    def read_reference():
+        started.set()
+        assert release_worker.wait(timeout=2)
+        assert reference.read_bytes() == b"voice"
+        worker_read.set()
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        task = asyncio.create_task(
+            _run_with_reference_lease(
+                lease,
+                lambda on_abandon: run_on_gpu_pool_guarded(
+                    read_reference,
+                    executor=executor,
+                    timeout=1,
+                    on_abandon=on_abandon,
+                ),
+            )
+        )
+        assert await asyncio.to_thread(started.wait, 1)
+        task.cancel()
+        cancelled = await asyncio.gather(task, return_exceptions=True)
+        assert isinstance(cancelled[0], asyncio.CancelledError)
+
+        lease.finish_request()
+        assert reference.exists()
+        release_worker.set()
+        assert await asyncio.to_thread(worker_read.wait, 1)
+
+    for _ in range(100):
+        if not reference.exists():
+            break
+        await asyncio.sleep(0.01)
+    assert not reference.exists()
+
+
+def test_normal_request_deletes_adhoc_reference_immediately(tmp_path):
+    from api.routers.generation import _TempReferenceLease
+
+    reference = tmp_path / "reference.wav"
+    reference.write_bytes(b"voice")
+    lease = _TempReferenceLease(str(reference))
+
+    release = lease.acquire()
+    release()
+    lease.finish_request()
+
+    assert not reference.exists()
@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import asyncio
+import threading
+from concurrent.futures import ThreadPoolExecutor
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_abandon_callback_waits_for_running_worker_to_finish():
+    from services.model_manager import run_on_gpu_pool_guarded
+
+    started = threading.Event()
+    release = threading.Event()
+    cleaned = threading.Event()
+
+    def job():
+        started.set()
+        assert release.wait(timeout=2)
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        task = asyncio.create_task(
+            run_on_gpu_pool_guarded(
+                job,
+                executor=executor,
+                timeout=1,
+                on_abandon=cleaned.set,
+            )
+        )
+        assert await asyncio.to_thread(started.wait, 1)
+        task.cancel()
+        cancelled = await asyncio.gather(task, return_exceptions=True)
+        assert isinstance(cancelled[0], asyncio.CancelledError)
+
+        assert not cleaned.is_set()
+        release.set()
+        assert await asyncio.to_thread(cleaned.wait, 1)
+
+
+@pytest.mark.asyncio
+async def test_queued_cancellation_releases_without_running_job():
+    from services.model_manager import GpuPoolBusyError, run_on_gpu_pool_guarded
+
+    hog_started = threading.Event()
+    release_hog = threading.Event()
+    cleaned = threading.Event()
+    queued_job_ran = threading.Event()
+
+    def hog():
+        hog_started.set()
+        assert release_hog.wait(timeout=2)
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        hog_future = executor.submit(hog)
+        assert hog_started.wait(timeout=1)
+        try:
+            with pytest.raises(GpuPoolBusyError):
+                await run_on_gpu_pool_guarded(
+                    queued_job_ran.set,
+                    executor=executor,
+                    timeout=1,
+                    queue_timeout=0.05,
+                    on_abandon=cleaned.set,
+                )
+            assert cleaned.is_set()
+            assert not queued_job_ran.is_set()
+        finally:
+            release_hog.set()
+            hog_future.result(timeout=1)
@@ -17,18 +17,31 @@ import json
 import math
 import array
 import base64
+import io
+import os
+import subprocess
+import sys
+import time
+import asyncio
+from pathlib import Path

 import pytest

-from services.subprocess_backend import SubprocessBackend, RECV_TIMEOUT_S
-from services.tts_backend import get_backend_class
-from engines.omnivoice_subprocess import OmniVoiceSubprocessBackend
+from services.subprocess_backend import (
+    RECV_TIMEOUT_S,
+    SubprocessBackend,
+)
+from services.tts_backend import OmniVoiceBackend, get_backend_class, list_backends
+from engines.omnivoice_subprocess import (
+    OmniVoiceMPSSubprocessBackend,
+    OmniVoiceSubprocessBackend,
+)


 # ── stub sidecar (model-free) ──────────────────────────────────────────────

 STUB_SIDECAR = r'''
-import sys, json, struct, time, math, array, base64
+import sys, os, json, struct, time, math, array, base64, subprocess

 def _send(o):
    b = json.dumps(o, separators=(",", ":")).encode()
@@ -60,9 +73,20 @@ while True:
        sys.exit(0)
    elif op == "synthesize":
        t = m.get("text", "")
+        if t == "CRASH":
+            os._exit(137)
        if t == "HANG":
            while True:  # wedge forever; the parent must hard-kill us
                time.sleep(1)
+        if t == "HANG_CHILD":
+            subprocess.Popen([
+                sys.executable,
+                "-c",
+                "import os,time; time.sleep(1); "
+                "open(os.environ['OMNIVOICE_TIMEOUT_MARKER'], 'w').write('bad')",
+            ])
+            while True:
+                time.sleep(1)
        # Emit progress frames before the audio when asked, to exercise the
        # parent's progress-consuming recv loop (the cold-load fix).
        if t.startswith("PROG:"):
@@ -98,6 +122,80 @@ def test_registry_resolves_to_subprocess_backend():
    assert get_backend_class("omnivoice-subprocess") is OmniVoiceSubprocessBackend


+@pytest.mark.parametrize(
+    ("family", "expected_name"),
+    [("mps", "OmniVoiceMPSSubprocessBackend"), ("cuda", "OmniVoiceBackend"),
+     ("cpu", "OmniVoiceBackend")],
+)
+def test_omnivoice_is_crash_isolated_only_on_mps(monkeypatch, family, expected_name):
+    from core.device_caps import HostCaps
+
+    available = (family, "cpu") if family != "cpu" else ("cpu",)
+    monkeypatch.setattr(
+        "core.device_caps.detect_host_caps",
+        lambda: HostCaps(family=family, available_families=available),
+    )
+
+    resolved = get_backend_class("omnivoice")
+    assert resolved.__name__ == expected_name
+    if family != "mps":
+        assert resolved is OmniVoiceBackend
+
+
+def test_engine_catalogue_reports_effective_mps_isolation(monkeypatch):
+    from core.device_caps import HostCaps
+    from services import tts_backend
+
+    monkeypatch.setattr(tts_backend, "_REGISTRY", {"omnivoice": OmniVoiceBackend})
+    monkeypatch.setattr(
+        "core.device_caps.detect_host_caps",
+        lambda: HostCaps(family="mps", available_families=("mps", "cpu")),
+    )
+    monkeypatch.setattr(
+        "engines.omnivoice_subprocess.OmniVoiceSubprocessBackend.is_available",
+        classmethod(lambda cls: (True, "ready")),
+    )
+
+    row = next(item for item in list_backends() if item["id"] == "omnivoice")
+    assert row["isolation_mode"] == "subprocess"
+
+
+def test_mps_startup_does_not_preload_native_model(monkeypatch):
+    from core.device_caps import HostCaps
+    from services import model_manager
+
+    monkeypatch.setattr(
+        "core.device_caps.detect_host_caps",
+        lambda: HostCaps(family="mps", available_families=("mps", "cpu")),
+    )
+    monkeypatch.setenv("OMNIVOICE_TTS_BACKEND", "omnivoice")
+    monkeypatch.setattr(model_manager, "model", None)
+
+    async def fail_load():
+        raise AssertionError("native OmniVoice must not load in the API process on MPS")
+
+    monkeypatch.setattr(model_manager, "_load_model_with_timeout", fail_load)
+    asyncio.run(model_manager.preload_model())
+
+
+def test_streaming_mps_path_does_not_load_native_model(monkeypatch):
+    from api.routers.tts_stream import _resolve_stream_backend
+    from services import model_manager, tts_backend
+
+    sentinel = object()
+    monkeypatch.setattr(tts_backend, "active_backend_id", lambda: "omnivoice")
+    monkeypatch.setattr(
+        tts_backend, "get_backend_class", lambda _id: OmniVoiceMPSSubprocessBackend,
+    )
+    monkeypatch.setattr(tts_backend, "get_active_tts_backend", lambda: sentinel)
+
+    async def fail_load():
+        raise AssertionError("streaming must not load native OmniVoice on MPS")
+
+    monkeypatch.setattr(model_manager, "get_model", fail_load)
+    assert asyncio.run(_resolve_stream_backend(None)) is sentinel
+
+
 def test_is_marked_subprocess_isolated():
    # list_backends() detects isolation via this duck-typed marker, not issubclass.
    assert getattr(OmniVoiceSubprocessBackend, "_is_subprocess_isolated", False) is True
@@ -136,11 +234,81 @@ def test_base_default_recv_timeout_is_60s():
    assert _PlainBackend().recv_timeout_s == 60.0


+def test_sidecar_spawn_delegates_all_containment_to_nested_owner(monkeypatch, tmp_path):
+    from services import subprocess_backend as backend_module
+
+    captured = {}
+
+    class StubProcess:
+        stderr = io.BytesIO()
+
+        @staticmethod
+        def poll():
+            return None
+
+    def fake_spawn(argv, **kwargs):
+        captured.update(kwargs)
+        return StubProcess()
+
+    monkeypatch.setattr(_PlainBackend, "venv_python", classmethod(lambda cls: Path(sys.executable)))
+    monkeypatch.setattr(
+        _PlainBackend,
+        "sidecar_script",
+        classmethod(lambda cls: tmp_path / "stub.py"),
+    )
+    monkeypatch.setattr(backend_module, "spawn_owned", fake_spawn)
+    monkeypatch.setattr(backend_module, "_ensure_reaper_running", lambda: None)
+    backend = _PlainBackend()
+    monkeypatch.setattr(backend, "_recv_with_timeout", lambda _timeout: {"op": "ready"})
+
+    try:
+        backend._spawn()
+        assert not ({"start_new_session", "creationflags", "preexec_fn"} & captured.keys())
+    finally:
+        backend._proc = None
+
+
 def test_omnivoice_subprocess_recv_timeout_overrides_default():
    b = OmniVoiceSubprocessBackend()
    assert b.recv_timeout_s == 300.0  # aligns with the generate budget


+def test_omnivoice_subprocess_has_longer_spawn_budget_than_other_sidecars():
+    assert _PlainBackend.spawn_ready_timeout_s == 30.0
+    assert OmniVoiceSubprocessBackend.spawn_ready_timeout_s == 120.0
+
+
+def test_spawn_uses_backend_specific_ready_timeout(monkeypatch, tmp_path):
+    _use_stub(monkeypatch, tmp_path / "unused.py")
+    backend = OmniVoiceSubprocessBackend()
+    observed = []
+
+    class StubProcess:
+        stderr = io.BytesIO()
+
+        @staticmethod
+        def poll():
+            return None
+
+    monkeypatch.setattr(
+        "services.subprocess_backend.spawn_owned",
+        lambda *_args, **_kwargs: StubProcess(),
+    )
+    monkeypatch.setattr(
+        backend,
+        "_recv_with_timeout",
+        lambda timeout: observed.append(timeout) or {"op": "ready"},
+    )
+    monkeypatch.setattr("services.subprocess_backend._ensure_reaper_running", lambda: None)
+
+    try:
+        backend._spawn()
+    finally:
+        backend._proc = None
+
+    assert observed == [120.0]
+
+
 def test_omnivoice_subprocess_recv_timeout_env_override(monkeypatch):
    monkeypatch.setenv("OMNIVOICE_SIDECAR_RECV_TIMEOUT_S", "120")
    assert OmniVoiceSubprocessBackend().recv_timeout_s == 120.0
@@ -205,6 +373,48 @@ def test_wedged_sidecar_is_hard_killed_and_recovers(stub_sidecar, monkeypatch):
        b.shutdown()


+def test_mps_proxy_survives_fatal_child_exit_and_recovers(stub_sidecar, monkeypatch):
+    _use_stub(monkeypatch, stub_sidecar)
+    monkeypatch.setattr(
+        "services.model_manager.make_room_before_generate", lambda: None,
+    )
+    b = OmniVoiceMPSSubprocessBackend()
+    try:
+        with pytest.raises(RuntimeError, match="backend is still running"):
+            b.generate("CRASH")
+        assert b._proc is not None and b._proc.poll() is not None
+        assert b.generate("ok").shape[1] == 24000
+    finally:
+        b.shutdown()
+
+
+def test_desktop_timeout_kills_engine_subtree_before_late_mutation(
+    stub_sidecar, monkeypatch, tmp_path
+):
+    marker = tmp_path / "late-engine-mutation"
+    monkeypatch.setenv("OMNIVOICE_DESKTOP_CONTAINED", "1")
+    drain_read, drain_write = os.pipe()
+    monkeypatch.setenv("OMNIVOICE_DESKTOP_DRAIN_FD", str(drain_write))
+    monkeypatch.setenv("OMNIVOICE_TIMEOUT_MARKER", str(marker))
+    _use_stub(monkeypatch, stub_sidecar)
+    monkeypatch.setattr(
+        OmniVoiceSubprocessBackend,
+        "recv_timeout_s",
+        property(lambda self: 0.3),
+    )
+    b = OmniVoiceSubprocessBackend()
+    try:
+        with pytest.raises(RuntimeError):
+            b.generate("HANG_CHILD")
+        time.sleep(1.2)
+        assert not marker.exists()
+        assert b.generate("ok").shape[1] == 24000
+    finally:
+        b.shutdown()
+        os.close(drain_write)
+        os.close(drain_read)
+
+
 def test_generate_does_not_deadlock_when_called_on_gpu_pool_worker(stub_sidecar, monkeypatch):
    # Regression: /v1/audio/speech and /generate dispatch backend.generate() via
    # run_on_gpu_pool_guarded, i.e. ON a gpu-pool worker. generate() must NOT
@@ -222,3 +432,254 @@ def test_generate_does_not_deadlock_when_called_on_gpu_pool_worker(stub_sidecar,
        assert tensor.shape[1] == 24000
    finally:
        b.shutdown()
+
+
+def test_sidecar_forwards_native_controls_and_applies_seed(monkeypatch):
+    import torch
+    from engines.omnivoice_subprocess import main as sidecar
+
+    calls = []
+    seeds = []
+    frames = []
+
+    class FakeModel:
+        sampling_rate = 24000
+
+        def generate(self, **kwargs):
+            calls.append(kwargs)
+            return [torch.zeros(1, 16)]
+
+    monkeypatch.setattr(sidecar, "_load_model", lambda _stdout: FakeModel())
+    monkeypatch.setattr(sidecar, "_send", lambda _stdout, frame: frames.append(frame))
+    real_manual_seed = torch.manual_seed
+    monkeypatch.setattr(
+        torch, "manual_seed", lambda seed: (seeds.append(seed), real_manual_seed(seed))[1],
+    )
+
+    sidecar._handle_synthesize({
+        "text": "hello",
+        "seed": 123,
+        "t_shift": 0.4,
+        "layer_penalty_factor": 0.2,
+        "position_temperature": 0.7,
+        "class_temperature": 0.8,
+        "audio_chunk_duration": 10,
+        "audio_chunk_threshold": 0.6,
+    }, object())
+
+    assert seeds == [123]
+    assert calls == [{
+        "text": "hello",
+        "ref_audio": None,
+        "ref_text": None,
+        "t_shift": 0.4,
+        "layer_penalty_factor": 0.2,
+        "position_temperature": 0.7,
+        "class_temperature": 0.8,
+        "audio_chunk_duration": 10,
+        "audio_chunk_threshold": 0.6,
+    }]
+    assert frames[-1]["op"] == "audio"
+
+
+def test_generation_proxy_forwards_native_controls_and_seed():
+    import torch
+    from api.routers.generation import _run_backend_inference
+
+    calls = []
+
+    class Proxy:
+        id = "omnivoice"
+        display_name = "OmniVoice"
+        sample_rate = 24000
+        applies_own_mastering = True
+        supports_native_omnivoice_controls = True
+
+        def generate(self, text, **kwargs):
+            calls.append((text, kwargs))
+            return torch.zeros(1, 240)
+
+    _run_backend_inference(
+        Proxy(), "hello", "en", None, None, None, None,
+        16, 2.0, 1.0, False, False, 321,
+        t_shift=0.4, layer_penalty_factor=0.2,
+        position_temperature=0.7, class_temperature=0.8,
+    )
+
+    assert calls == [("hello", {
+        "duration": None,
+        "language": "en",
+        "ref_audio": None,
+        "ref_text": None,
+        "instruct": None,
+        "num_step": 16,
+        "guidance_scale": 2.0,
+        "speed": 1.0,
+        "denoise": False,
+        "postprocess_output": False,
+        "t_shift": 0.4,
+        "layer_penalty_factor": 0.2,
+        "position_temperature": 0.7,
+        "class_temperature": 0.8,
+        "seed": 321,
+    })]
+
+
+def test_timeout_reaps_captured_process_before_recv_returns(monkeypatch):
+    import threading
+
+    class Process:
+        def __init__(self):
+            self.killed = threading.Event()
+            self.reaped = False
+            self.wait_entered = threading.Event()
+            self.release_wait = threading.Event()
+
+        def kill(self):
+            self.killed.set()  # EOF may arrive before the process is reaped.
+
+        def wait(self, timeout):
+            assert timeout is not None
+            self.wait_entered.set()
+            assert self.release_wait.wait(2)
+            self.reaped = True
+            return -9
+
+    proc = Process()
+    backend = OmniVoiceSubprocessBackend()
+    backend._proc = proc
+
+    def recv():
+        assert proc.killed.wait(2)
+        return None
+
+    monkeypatch.setattr(backend, '_recv', recv)
+    returned = threading.Event()
+    results = []
+
+    def receive():
+        results.append(backend._recv_with_timeout(0.01))
+        returned.set()
+
+    reader = threading.Thread(target=receive)
+    reader.start()
+    try:
+        assert proc.wait_entered.wait(2)
+        assert not returned.wait(0.05), "EOF must not release the caller before process cleanup"
+    finally:
+        proc.release_wait.set()
+        reader.join(2)
+        backend._proc = None
+    assert not reader.is_alive()
+    assert returned.is_set()
+    assert results == [None]
+    assert proc.reaped
+
+
+def test_timeout_never_kills_a_replacement_process(monkeypatch):
+    from unittest.mock import Mock
+    import services.subprocess_backend as module
+
+    class ManualTimer:
+        def __init__(self, _timeout, callback, args=()):
+            self.callback = lambda: callback(*args)
+            self.daemon = False
+
+        def start(self):
+            pass
+
+        def cancel(self):
+            pass
+
+        def join(self):
+            pass
+
+    timers = []
+    def timer(*args, **kwargs):
+        result = ManualTimer(*args, **kwargs)
+        timers.append(result)
+        return result
+
+    monkeypatch.setattr(module.threading, 'Timer', timer)
+    backend = OmniVoiceSubprocessBackend()
+    original, replacement = Mock(), Mock()
+    backend._proc = original
+
+    def recv():
+        backend._proc = replacement
+        timers[0].callback()
+        return None
+
+    monkeypatch.setattr(backend, '_recv', recv)
+    try:
+        backend._recv_with_timeout(1)
+        original.kill.assert_called_once()
+        replacement.kill.assert_not_called()
+    finally:
+        backend._proc = None
+
+
+@pytest.mark.parametrize("failure", ["wait", "kill"])
+def test_timeout_quarantine_blocks_reuse_and_retains_cleanup_handle(failure):
+    class StuckProcess:
+        stdin = None
+        def __init__(self):
+            self.exited = False
+            self.kill_calls = 0
+        def poll(self):
+            return 0 if self.exited else None
+        def kill(self):
+            self.kill_calls += 1
+            if failure == "kill" and not self.exited:
+                raise PermissionError("kill failed")
+        def terminate(self):
+            pass
+        def wait(self, timeout):
+            if not self.exited:
+                raise subprocess.TimeoutExpired("stuck-sidecar", timeout)
+            return 0
+
+    backend = OmniVoiceSubprocessBackend()
+    proc = StuckProcess()
+    backend._proc = proc
+    try:
+        backend._timeout_kill(proc)
+        with pytest.raises(RuntimeError, match="still stopping"):
+            backend._spawn()
+        backend.shutdown()
+        # Even after shutdown clears the current slot, ownership survives;
+        # retry must not silently start a second process next to this one.
+        before = proc.kill_calls
+        with pytest.raises(RuntimeError, match="still stopping"):
+            backend._spawn()
+        assert proc.kill_calls > before
+    finally:
+        proc.exited = True
+        backend.shutdown()
+
+
+def test_timeout_quarantine_does_not_clear_or_kill_replacement():
+    from unittest.mock import Mock
+    backend = OmniVoiceSubprocessBackend()
+    original = Mock()
+    original.wait.side_effect = subprocess.TimeoutExpired("old-sidecar", 2)
+    replacement = Mock()
+    replacement.poll.return_value = None
+    backend._proc = replacement
+    try:
+        backend._timeout_kill(original)
+        with pytest.raises(RuntimeError, match="still stopping"):
+            backend._spawn()
+        assert backend._proc is replacement
+        replacement.kill.assert_not_called()
+        # Once the captured owner is reaped, reuse of the healthy replacement
+        # is allowed without starting or terminating another process.
+        original.wait.side_effect = None
+        original.wait.return_value = 0
+        backend._spawn()
+        assert backend._proc is replacement
+        replacement.kill.assert_not_called()
+    finally:
+        original.wait.side_effect = None
+        backend._proc = None
+        backend.shutdown()
@@ -0,0 +1,76 @@
+"""#1618 — RAM preflight must not hard-block the machines it means to admit.
+
+An "8 GB" machine reports ~7.8 GB usable (firmware/iGPU/kernel reservations),
+so comparing reported RAM against the marketing-size threshold blocked exactly
+the boundary hardware the ≥8 GB rule intends to allow. The check now applies
+``_RAM_RESERVED_ALLOWANCE`` to both thresholds, and
+``OMNIVOICE_RAM_PREFLIGHT=0`` downgrades a genuine fail to a warning.
+"""
+
+import pytest
+
+from api.routers.setup import wizard
+
+
+def _ram_check(monkeypatch, ram_gb: float, env: str | None = None) -> dict:
+    # Keep the preflight hermetic: stub the probes that hit the network or
+    # auto-acquire media tools, so each RAM assertion stays fast and offline.
+    monkeypatch.setattr(wizard, "_network_check", lambda: {
+        "id": "network", "label": "Network", "status": "pass",
+        "detail": "stubbed", "fix": None, "mirror_reachable": True,
+    })
+    import services.media_tools as media_tools
+    monkeypatch.setattr(media_tools, "summary", lambda auto_acquire=True: None)
+    monkeypatch.setattr(wizard, "_ram_gb", lambda: ram_gb)
+    if env is None:
+        monkeypatch.delenv("OMNIVOICE_RAM_PREFLIGHT", raising=False)
+    else:
+        monkeypatch.setenv("OMNIVOICE_RAM_PREFLIGHT", env)
+    resp = wizard.preflight()
+    checks = resp["checks"] if isinstance(resp, dict) else resp.checks
+    for c in checks:
+        c = c if isinstance(c, dict) else c.model_dump()
+        if c["id"] == "ram":
+            return c
+    raise AssertionError("no ram check in preflight response")
+
+
+def test_8gb_installed_reporting_7_84_usable_is_not_blocked(monkeypatch):
+    """The #1618 report: 7.84 GB usable on an 8 GB laptop was a hard fail."""
+    check = _ram_check(monkeypatch, 7.84)
+    assert check["status"] != "fail"
+
+
+def test_boundary_at_allowance_passes_the_fail_gate(monkeypatch):
+    check = _ram_check(
+        monkeypatch, wizard._RAM_FAIL_GB * wizard._RAM_RESERVED_ALLOWANCE
+    )
+    assert check["status"] != "fail"
+
+
+def test_genuinely_low_ram_still_fails(monkeypatch):
+    check = _ram_check(monkeypatch, 6.0)
+    assert check["status"] == "fail"
+
+
+@pytest.mark.parametrize("env", ["0", "false", "no"])
+def test_escape_hatch_downgrades_fail_to_warn(monkeypatch, env):
+    check = _ram_check(monkeypatch, 6.0, env=env)
+    assert check["status"] == "warn"
+    assert "OMNIVOICE_RAM_PREFLIGHT" in (check["fix"] or "")
+
+
+def test_escape_hatch_not_triggered_by_other_values(monkeypatch):
+    check = _ram_check(monkeypatch, 6.0, env="1")
+    assert check["status"] == "fail"
+
+
+def test_12gb_installed_reporting_11_8_usable_passes_clean(monkeypatch):
+    """Same reservation gap at the warn threshold: 12 GB installed ≈ 11.8."""
+    check = _ram_check(monkeypatch, 11.8)
+    assert check["status"] == "pass"
+
+
+def test_warn_band_between_thresholds(monkeypatch):
+    check = _ram_check(monkeypatch, 9.0)
+    assert check["status"] == "warn"
@@ -76,6 +76,35 @@ class TestUnloadOnABC:
        )


+def test_omnivoice_native_batch_preserves_per_item_controls():
+    """The adapter forwards variable-length batch controls to OmniVoice."""
+    import torch
+
+    tts = _load_tts_backend_module()
+    calls = []
+
+    class _Model:
+        sampling_rate = 24000
+
+        def generate(self, **kwargs):
+            calls.append(kwargs)
+            return [torch.zeros(1, 12000), torch.zeros(1, 24000)]
+
+    backend = tts.OmniVoiceBackend(model=_Model())
+    outputs = backend.generate_batch(
+        ["short", "long"],
+        language=["en", "es"],
+        duration=[0.5, 1.0],
+        speed=[1.0, 0.8],
+    )
+
+    assert [output.shape[-1] for output in outputs] == [12000, 24000]
+    assert calls[0]["text"] == ["short", "long"]
+    assert calls[0]["language"] == ["en", "es"]
+    assert calls[0]["duration"] == [0.5, 1.0]
+    assert calls[0]["speed"] == [1.0, 0.8]
+
+
 class TestUnloadDefaultBehavior:
    """The default no-op must actually be safe to call."""

@@ -154,4 +183,4 @@ class TestExistingSubclassesInherit:
            assert callable(getattr(cls, "unload", None)), (
                f"{cls.__name__} has no callable unload() — even via the "
                "ABC inheritance. Did someone shadow it?"
-            )
+            )
@@ -0,0 +1,61 @@
+"""Cancellation helpers for work that cannot be stopped mid-call."""
+
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Callable
+from typing import Any, TypeVar
+
+_Result = TypeVar("_Result")
+
+
+async def drain_task(task: asyncio.Task[Any]) -> None:
+    """Wait for ``task`` even if the waiter is cancelled again."""
+    while not task.done():
+        try:
+            await asyncio.shield(task)
+        except asyncio.CancelledError:
+            continue
+        except BaseException:
+            break
+    if task.done():
+        try:
+            task.result()
+        except BaseException:
+            pass
+
+
+async def to_thread_and_drain_on_cancel(
+    function: Callable[..., _Result], /, *args: Any
+) -> _Result:
+    """Run a blocking call without detaching it when its waiter is cancelled."""
+    thread_task = asyncio.create_task(asyncio.to_thread(function, *args))
+    try:
+        return await asyncio.shield(thread_task)
+    except asyncio.CancelledError:
+        await drain_task(thread_task)
+        raise
+
+
+async def to_thread_and_defer_cancellation(
+    function: Callable[..., _Result], /, *args: Any
+) -> tuple[_Result, bool]:
+    """Finish a durable call and report cancellation after its result is known.
+
+    Authority writes need their event-loop publication even when the HTTP
+    caller disappears while SQLite is committing. Returning the cancellation
+    flag lets the caller publish that result first, then propagate cancellation.
+    """
+    thread_task = asyncio.create_task(asyncio.to_thread(function, *args))
+    try:
+        return await asyncio.shield(thread_task), False
+    except asyncio.CancelledError:
+        await drain_task(thread_task)
+        return thread_task.result(), True
+
+
+__all__ = [
+    "drain_task",
+    "to_thread_and_defer_cancellation",
+    "to_thread_and_drain_on_cancel",
+]
@@ -59,10 +59,18 @@ _VRAM_PER_JOB_BYTES = 5 * 1024**3
 #   cpu     — oversubscription just thrashes
 _ALWAYS_SERIAL = frozenset({"mps", "mlx", "cpu", ""})

-# Absolute ceiling regardless of how much memory a card reports. Beyond this
-# the bottleneck stops being VRAM and starts being scheduler overhead and
-# host-side I/O contention.
-_MAX_DERIVED = 4
+# Absolute protocol ceiling regardless of how much memory a peer reports.
+# Beyond this the bottleneck stops being VRAM and starts being scheduler
+# overhead and host-side I/O contention.  It is public because every wire
+# boundary must clamp to the same number; a UINT32_MAX heartbeat must not grow
+# a scheduler queue that local derivation would never create.
+MAX_CONCURRENT_TASKS = 4
+
+
+def clamp_concurrency(value: int, *, allow_zero: bool = False) -> int:
+    """Bound an advertised concurrency value to the server's safe range."""
+    minimum = 0 if allow_zero else 1
+    return max(minimum, min(MAX_CONCURRENT_TASKS, int(value)))

 # Bounds on how long a parked slot is held. The caller passes the timed-out
 # job's own execution budget — the longest its thread can still legitimately be
@@ -104,7 +112,7 @@ def derive_concurrency(
    budget = max(min_model_bytes, _VRAM_PER_JOB_BYTES)
    if budget <= 0:
        return 1
-    return max(1, min(_MAX_DERIVED, int(free_memory_bytes // budget)))
+    return clamp_concurrency(int(free_memory_bytes // budget))


@dataclass
@@ -149,6 +157,9 @@ class WorkerCapacity:
    resident_models: set[str] = field(default_factory=set)
    slots: dict[str, ModelSlot] = field(default_factory=dict)

+    def __post_init__(self) -> None:
+        self.max_concurrent_tasks = clamp_concurrency(self.max_concurrent_tasks)
+
    @staticmethod
    def slot_key(engine: str, model_id: str) -> str:
        return f"{engine}:{model_id}"
@@ -198,6 +209,21 @@ class WorkerCapacity:
            )
        slot.active += 1

+    def reserve_unknown(self) -> None:
+        """Consume worker-wide capacity for claimed work we cannot classify.
+
+        Reconciliation will tell the peer to cancel a terminal or unknown
+        attempt, but until that cancellation lands it is still using the GPU.
+        """
+        self.active_tasks += 1
+
+    def release_unknown(self) -> bool:
+        """Release one exact reconciled claim with no model-slot identity."""
+        if self.active_tasks <= 0:
+            return False
+        self.active_tasks -= 1
+        return True
+
    def release(
        self,
        engine: str,
@@ -267,8 +293,12 @@ class WorkerCapacity:
    ) -> None:
        """Adopt a heartbeat snapshot. The worker is the source of truth for
        what it is actually running."""
-        self.active_tasks = max(0, active_tasks)
-        reported_ceiling = self.active_tasks + max(0, available_slots)
+        self.active_tasks = clamp_concurrency(active_tasks, allow_zero=True)
+        bounded_available = clamp_concurrency(available_slots, allow_zero=True)
+        bounded_available = min(
+            bounded_available, MAX_CONCURRENT_TASKS - self.active_tasks
+        )
+        reported_ceiling = self.active_tasks + bounded_available
        if reported_ceiling > 0:
            # Adopted, not merely grown. The worker computes this as its own
            # ``max_concurrent_tasks``, so a ceiling we refuse to lower is one
@@ -305,4 +335,10 @@ class WorkerCapacity:
        }


-__all__ = ["ModelSlot", "WorkerCapacity", "derive_concurrency"]
+__all__ = [
+    "MAX_CONCURRENT_TASKS",
+    "ModelSlot",
+    "WorkerCapacity",
+    "clamp_concurrency",
+    "derive_concurrency",
+]
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				`"""Dependency-free client for VoiceStudio's local speech platform."""`