Compare commits

...
Author SHA1 Message Date
debpalash 2d7df6a75c fix(desktop): retain live shell during backup cleanup 2026-08-20 08:52:30 +05:30
debpalash a2577e46ea fix(desktop): recover interrupted frontend swap 2026-08-20 08:44:03 +05:30
debpalash eed841a8ca fix(bootstrap): replace packaged frontend safely 2026-08-20 08:09:15 +05:30
debpalash df4d016a7d Merge remote-tracking branch 'origin/main' into fix/1589-packaged-lan-ui 2026-08-20 08:03:24 +05:30
Palash Debnath ca7fb9c68d Merge pull request #1594 from debpalash/chore/project-agent-skills
chore(agents): install project development skills
2026-08-20 02:32:26 +00:00
debpalash fd6d21401b Merge remote-tracking branch 'origin/main' into fix/1589-packaged-lan-ui
# Conflicts:
#	CHANGELOG.md
2026-08-20 07:51:49 +05:30
debpalash c9adcb2647 fix(sharing): bundle LAN frontend in desktop installs 2026-08-20 07:50:50 +05:30
debpalash 51163cf260 Merge remote-tracking branch 'origin/main' into chore/project-agent-skills 2026-08-20 07:46:31 +05:30
Palash Debnath 4ce4f05c06 Merge pull request #1559 from Eman-Yousaf/fix/path-security-separator-parity
fix(paths): treat / as a separator on Windows so stored sub-paths resolve
2026-08-20 02:11:45 +00:00
debpalash e77feae817 chore(agents): install project development skills 2026-08-20 07:38:35 +05:30
debpalash fdc02b398e Merge remote-tracking branch 'origin/main' into fix/path-security-separator-parity 2026-08-20 06:40:46 +05:30
Palash Debnath 6e1bb44e0d docs(docker): add product media to Docker Hub overview (#1593)
Add the current v0.5 engine-switching GIF plus Model Catalogue and gallery-save screenshots to the canonical Docker Hub overview using absolute raw GitHub asset URLs. Includes a changelog entry.
2026-08-20 01:10:21 +00:00
Palash Debnath 4dc90a7f4f docs(docker): refresh Docker Hub overview for v0.5 authentication (#1592)
Refresh current v0.5.0/0.5 tag examples, document API-key and share-PIN behavior, and require encrypted private-overlay access for remote deployments. Keeps the Docker install guide and changelog synchronized.
2026-08-20 00:40:18 +00:00
debpalash 3b64d317ae fix(paths): accept persisted separators on every host 2026-08-20 06:08:28 +05:30
debpalash ee7202b1eb Merge remote-tracking branch 'origin/main' into codex/pr1559 2026-08-20 06:07:23 +05:30
Paolo Antinori 871d68a6ff fix(auth): offer API-key login on server-mode admin 403s (#1569)
Fix server-mode admin authentication recovery without trapping PIN-only deployments, and prevent stale 403 responses from clearing or superseding newly issued sessions. Includes backend/frontend regression coverage, docs, and changelog credit for @paoloantinori.
2026-08-20 00:03:37 +00:00
Palash Debnath b37466b2e5 fix(ci): allowlist Ed25519 type-name false positive (#1591)
Restore weekly full-history gitleaks scans by allowlisting only the exact cryptography type name Ed25519PrivateKey, with an exact-value regression guard and changelog entry.
2026-08-19 22:16:40 +00:00
Palash DebnathandClaude Fable 5 2d5f2e800e feat(omnivoice): voice prompts that survive restarts + opt-in FlashInfer (~2.2x) (#1565)
* feat(omnivoice): port upstream VoiceClonePrompt persistence + FlashInfer opt-in

Upstream k2-fsa teardown ports, verified with generated voice samples:

- VoiceClonePrompt.save()/.load() (upstream format v1, weights_only-safe)
  on the vendored model, and a disk layer under the in-memory prompt LRU
  (DATA_DIR/prompt_cache, keyed by ref path+mtime+ref_text+preprocess,
  32 newest kept, OMNIVOICE_PROMPT_DISK_CACHE=0 opts out). First generation
  of a session with a known voice skips the reference re-encode and any
  auto-transcription pass — verified across two real processes (encodes=1
  then encodes=0, same voice).
- omnivoice_flashinfer.py ported (packed CFG attention, fused kernels,
  optional CUDA graphs), schedule adapted to our num_step+1 divergence.
  Opt-in via OMNIVOICE_FLASHINFER=1|graph, CUDA-only, replaces
  torch.compile for the session; missing package / apply failure / runtime
  failure all degrade with a named reason (same #278 contract as compile:
  classify → unapply → retry once, session latch). Measured 2.20x at
  batch=1 on an RTX 4090 with byte-identical text and clean ASR round-trip.
- Docs: OmniVoice guide gains instruct+reference combination semantics
  (consistent instruct stabilizes cloning, reference wins conflicts),
  inline pronunciation control (pinyin / CMU), prompt persistence, and
  corrects the 'no voice design' claim; performance.md documents both new
  env knobs.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* chore: point changelog entries at the real PR number (#1565)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(pr): harden FlashInfer lifecycle + prompt-cache writes per review

Bot harvest round 1 (#1565): unapply on apply-failure (half-patched model
could crash the next render); pin eager-mode FlashInfer inference to one
thread too — the attention plan and packed position ids are per-generation
module state, so interleaved _gpu_pool workers would corrupt each other;
restore the CAPTURED pre-apply attention impl (could be flash_attention_2)
instead of assuming sdpa; unique tmp name per prompt-cache write; correct
the _forward_logits layout docstring; resolve VoiceClonePrompt at test
runtime; docs — Known limits keeps only the limitation, performance.md
states the VRAM cost and scopes the fallback claim to classified kernel
failures.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(pr): round-2 review — publish only a fully restored model, redact latch reason, tighten CPU-persistence test

Greptile: the runtime fallback now unapplies BEFORE swapping generate, so
a concurrent render keeps queuing behind the thread-affinity wrapper while
teardown mutates modules. CodeRabbit: FlashInfer failure reasons pass
through core.failure.sanitize before latching/logging (wheel paths embed
the user's home); the save-portability test now creates the tokens on CUDA
when available and asserts the persisted payload itself is CPU-resident.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(pr): fail-closed latch reason when the sanitizer itself breaks

CodeQL empty-except + CodeRabbit round 3: if core.failure.sanitize raises,
the raw reason (home paths, wheel paths) was latched anyway. Now only the
exception class survives with a fixed redaction note; two regression tests
(normal redaction + sanitizer failure).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-16 16:25:29 +00:00
Palash DebnathandClaude Fable 5 4db02d0c97 fix(test): watermark producer scan must match code, not prose (#1564)
* fix(test): watermark producer scan must match code, not prose

ee35d238 broke main's CI by adding a comment that *mentions*
backend.generate() to worker/transport/server.py — the watermark coverage
guard greps raw source, so the comment made the module a 'producer' that
never marks. Blank COMMENT/STRING token spans before matching (layout
preserved, unparseable files fall back to a raw scan) and apply the same
rule to the allowlist staleness check; a new self-test pins the class.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(test): keep f-string code scannable; require code-level mark_synthetic

Greptile P1 + CodeRabbit on #1564: on Python <=3.11 an entire f-string is
one STRING token, so blanking it would let a synthesis call inside a
replacement field evade the producer scan — f-prefixed strings now stay
raw there (fail closed), while 3.12+ blanks only literal FSTRING_MIDDLE
text. The 'module references mark_synthetic' certification is now also
code-only, so a comment can't satisfy it. Self-test extended with both.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-16 15:44:58 +00:00
velixio ee35d2389e fix: require remote voice render parity 2026-08-15 18:49:08 +05:30
velixio 1fda5bdf96 fix: preserve voice identity on remote workers 2026-08-15 18:49:08 +05:30
Palash Debnath 2477dde688 docs: streamline README structure and specifications (#1560)
* docs: streamline README structure and specifications

* docs: address README review findings
2026-08-15 13:06:44 +00:00
Palash DebnathandClaude Fable 5 48c9a3b1f8 feat(settings): compute-device override (auto / CUDA / ROCm / XPU / MPS / CPU) (#1557)
* feat(settings): compute-device override — auto | CUDA | ROCm | XPU | MPS | CPU

Auto-detect stays the default; the override kills the 'auto-detect picked
wrong' issue class. Applied at the single choke point (_probe()'s family
selection) so routing, get_best_device(), and every badge inherit it.
Resolution: OMNIVOICE_DEVICE env > Settings pick (prefs.json) > auto (#981
pattern). An override can steer, never invent hardware: a family the host
lacks is noted and ignored; cpu is always honorable. Applies at next
backend start (host caps are immutable per process — same restart contract
as the rest of the Performance tab, RestartBadge shown).

GET/PUT /api/settings/compute-device (admin-gated) reports resolved vs
applied so the panel shows restart-required truthfully and disables itself
under an env pin instead of pretending.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(changelog): entry for the compute-device override (#1557)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(device-override): harvest — the override reaches CT2 ASR, full i18n, honest edge states

- _ctranslate2_cuda_ok() and the ASR sidecar now gate on the probe's family,
  so a cpu pin (or ROCm host) can never hand CTranslate2 a CUDA device —
  the override reaches every CT2 loader through one shared gate
- override_ignored exposed by the API and shown by the panel (env pin naming
  a device this machine lacks: auto is in effect, restart won't change it)
- all 8 panel strings + 5 device-family labels translated into all 21
  locales; failed saves keep their error visible through the re-sync
- test isolation: cleanup drops OMNIVOICE_DEVICE before re-probing so no
  overridden caps leak into later tests; panel tests wait for loaded state
- xpu/intel search keywords; oxfmt formatting

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(device-override): round 2 — fail-safe probe fallbacks, complete i18n, combined pin state

- a broken capability probe now means CPU everywhere (CT2 gate + ASR
  sidecar) — never a torch-derived guess that would bypass a cpu pin or
  re-open #1529 on ROCm; regression test added
- env-pinned AND not-detected shows both facts in one subtitle
- device_load_failed/perf_save_failed translated into all 21 locales;
  CJK/th/vi/ar strings no longer say literal 'Auto'
- test_ctranslate2_never_gets_cuda_on_a_rocm_build pins the probe family
  (it was order-dependent on the lru_cache before)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(test): pin the probe family in the faster-whisper OOM-fallback test

Same class as the rocm-build test: it mocked torch but not the probe the
new override gate consults first, so on a cpu-family CI host the CUDA
fallback chain under test was unreachable.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-15 05:08:51 +00:00
Palash DebnathandClaude Fable 5 030d5ea01f docs(engines): a guide for every engine + index; fix two engine-metadata bugs (#1556)
* docs(engines): a guide for every engine + index; fix two engine-metadata bugs

21 new pages under docs/engines/ (10 TTS, 10 ASR, index README) — every
registered engine now has one: what it's for, platform support, model env
vars, quirks with issue refs. Linked from both READMEs' engine sections.

Code fixes found while verifying facts against the registries:
- KittenTTS docstring claimed default voice 'Jasper'; the code default is
  expr-voice-2-f
- the isolated-ASR sidecar read only ASR_MODEL_FW while the download
  preflight read ASR_MODEL_FASTER — set one and the other quietly used a
  different model; both now resolve ASR_MODEL_FW-override → ASR_MODEL_FASTER
- moonshine's install hint named 'useful-moonshine', a package the backend
  never imports; now moonshine-onnx / moonshine-voice

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(changelog): entries for the engine guides + sidecar model fix (#1556)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(engines): second-harvest fixes — TLS guidance, matrix/code alignment, CN counts

- README matrix aligned to gpu_compat (the code is the source of truth):
  CosyVoice macOS is CPU not MPS, IndexTTS and GGUF gain their real
  CUDA/CPU/MPS cells
- gpt-sovits guide: prefer https/tunnel for non-loopback servers, plaintext
  warning; first-use download guidance on both OmniVoice pages
- preflight empty-env fallback matches the sidecar (ASR_MODEL_FASTER='' no
  longer resolves a different repo)
- nano installs via uv pip; kitten log level wording; index links install
  guides incl. the Gatekeeper step; README_CN engine counts 16/11

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(readme-cn): the all-engines-local claim now excludes the remote client

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-15 03:55:59 +00:00
Palash DebnathandClaude Fable 5 b79ba9bd3b docs(readme): lead with download + first clone; seed benchmarks page (#1555)
* docs(readme): lead with download + first clone; seed benchmarks page

Quickstart (installers, install guides, a three-step first-clone walkthrough)
moves above What's-new/Features in both READMEs — visitors get the action
before the pitch. New docs/benchmarks.md anchors measured per-engine/device
numbers on the bench_pipeline.py harness, community-contributed, no estimates.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(changelog): entry for the README conversion restructure (#1555)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(bench): emit RTF + CUDA peak VRAM; guard NaN RAM; define the benchmarks schema

Bot harvest on #1555: the tts stage now prints RTF per warm measurement and
CUDA peak VRAM (None elsewhere — no made-up zeros), the stage floor refuses
unmeasurable RAM instead of sailing past a NaN comparison (FLOOR_GB=0
overrides), docs/benchmarks.md columns map 1:1 to what the harness prints,
and the download badges say they open the release page.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(readme): link palash.dev from the maker section

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(bench): name the resolved engine, track VRAM from resolution, comment the guards

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(readme): the quick-switch gif is the hero image

The hero shows motion now; the Launchpad screenshot moves into the 0.5.0
What's-new slot so nothing appears twice.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(bench): peak VRAM is reserved memory; adapter engines name their model

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(bench): subprocess-isolated engines report VRAM n/a, not a parent-side zero

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(bench): out-of-process detection is declarative; sherpa rows name their model

'runs_out_of_process' is now a TTSBackend attribute set by SubprocessBackend
AND omnivoice-gguf (which inherits TTSBackend directly but spawns a binary
per generate — the isinstance check missed it). Duck-typed for the same
module-purge reason as _is_subprocess_isolated. Sherpa-onnx identity comes
from _model_dir's basename when _model_id is absent.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(bench): backends self-report model identity via TTSBackend.model_identity()

Greptile enumerated the adapter engines one at a time (mlx _model_id,
sherpa _model_dir, cosyvoice env-only) — the attribute sniffing rots per
engine. The hook fixes the class: each multi-model backend reports its
own identity, the profiler just asks.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-15 02:41:40 +00:00
Palash DebnathandClaude Fable 5 3d0c9605df test(shell): backend-lifecycle fault-injection harness (#1551)
* test(shell): backend-lifecycle fault-injection harness

Runs spawn_backend_and_wait/supervise_backend against REAL dying child
processes and asserts the user receives the correct NAMED diagnosis —
not merely that recovery happens. Wrong/missing explanation was 61% of
the historical "can't reach the backend" class; this rig is the
permanent regression harness for every future lifecycle fix.

Seam: OMNIVOICE_BACKEND_CMD (JSON argv or whitespace form) runs any
command as "the backend" — venv bootstrap and ffmpeg resolution are
skipped, everything else (err-log run offsets, drainer threads, env
pinning, real OS pipes, spawn-failure diagnostics) stays real. Plus
OMNIVOICE_LOG_DIR (per-test log+marker dirs, also a support tool) and
harness-only timing overrides OMNIVOICE_STARTUP_BUDGET_S /
OMNIVOICE_SUPERVISOR_POLL_MS whose production defaults are pinned by
unit tests. Lifecycle fns genericized over tauri::Runtime for the
MockRuntime app; behavior-neutral with the env unset (unit-pinned).

Scenarios (tests/backend_lifecycle.rs, scenario children = this test
binary re-invoking itself; serial by mutex + CI --test-threads=1):
- port conflict (exit 78) → the detectHints-matchable port phrasing
- generic chained traceback → root cause survives into the diagnosis
  and the crash marker
- spawn failure → spawn diagnostic reaches the user, NO bogus marker
- slow start past budget → timeout names the budget + last stderr
- post-Ready crash loop → 3 restarts announced, markers before restarts,
  "kept crashing" diagnosis naming the last exit
- SIGKILL (unix) → named as signal 9
- deliberate kill → supervisor yields silently, no marker, never Failed
- deferred-startup FATAL → the named step reaches the user, forensics,
  and the splash narration

CI: harness added to the 3-OS tauri-cross-platform matrix.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* chore(ci): temporary Windows loader bisect probe for the harness binary

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(shell): embed Common-Controls v6 manifest into Windows test binaries

Bisected on #1551: EVERY integration-test binary of this crate died at
load on Windows with STATUS_ENTRYPOINT_NOT_FOUND (0xc0000139) — cargo
gives test binaries no manifest, so the loader resolves comctl32 v5,
which lacks the TaskDialogIndirect entry point tauri's dialog/tray stack
imports. build.rs now embeds tests/windows-test.manifest via
rustc-link-arg-tests on Windows targets. Bisect probe removed.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(test): scenario gate is PID-valued — the parent can't self-inject

CodeRabbit on #1551: in a parallel local `cargo test`, the parent's own
scenario_child test could observe the armed env and start playing the
backend in-process (binding the port, idling 600s). The gate value is
now the arming process's PID; a matching PID stays inert, so only the
spawned child — a different process — runs the scenario.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-14 18:45:27 +00:00
Palash DebnathandClaude Fable 5 bb813ff676 feat(startup): bind the socket in ~1s and narrate startup step by step (#1550)
* feat(startup): bind the socket in ~1s and narrate startup step by step

The structural fix for the "can't reach the local backend" class (~1 in 5
of every issue ever filed): uvicorn served nothing until torch import
(10-20s cold), the 30-router fan-out, an import-time DB migration, the
cuDNN preload, and alembic all finished — every slow or fragile step
rendered as an unexplained dead backend.

main.py now keeps module scope fast and defers the heavy work:
- _phase_a_build (executor thread): prefs/env restore + #963 migration,
  yt-dlp overlay, cuDNN preload, torchaudio, model_manager, router
  imports — order preserved, literal imports so PyInstaller still traces.
- _phase_a_finalize (event loop, no awaits → atomic wrt requests):
  include_router, mounts, MCP, SPA, openapi bust.
- _phase_b: the old lifespan startup body; handles on app.state so
  shutdown survives a startup that never finished.
- Eager mode (pytest / OMNIVOICE_EAGER_INIT=1) runs everything at import
  — byte-equivalent behavior for the ~100 lifespan-less TestClient sites
  and for embedders (dump_api_routes, probe boot runner opt in).

While starting: /health answers 503 with the current step, new
/startup/progress serves the full ledger (always 200), and
StartupGateMiddleware 503s everything else with the [starting] marker
(same skip-the-Report-button convention as [shutting_down]). A deferred
failure keeps import-crash semantics: traceback to stderr → shell crash
forensics, run sentinel stays uncleared, exit 1 names the failed step.

Shell: startup_progress() probe (marker-header-gated so a foreign
responder can't narrate the splash) feeds per-step log lines into the
launch poll and the supervisor's reconnect wait. --health-check absorbs
the deferred init (60→180s); --diagnose runs Phase A up front so it
still sees restored prefs. Docker HEALTHCHECK semantics unchanged
(curl -f fails on 503 exactly as it did on connection-refused).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(startup): join the Phase A thread on shutdown; async fail-path sleep

Bot-review harvest on #1550: cancelling the deferred-startup task cannot
stop the executor thread inside Phase A's blocking imports — shutdown now
waits (bounded, only when a build started and hasn't finished) on a
thread-completion event so interpreter teardown can't race a mid-import
(#1000 class). The failure path's last-poll beat is now awaited, not
time.sleep — a blocking sleep froze the very loop that beat exists to let
serve. Also: dump_api_routes forces eager (assignment, not setdefault),
and the integration test's child gets DEVNULL instead of an undrained
pipe that could wedge a cold boot.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(startup): close the Phase A submission race; CodeQL nits

Review finds on #1550: shutdown could sample _phase_a_started unset
while the executor callable was queued-but-not-running, skipping the
thread join. started is now set BEFORE submission, the submission is
shielded so a cancel can't strand a queued callable that would never set
_phase_a_finished, and the wrapper sets finished on every exit including
the already-built early return. Contract pinned by
test_phase_a_thread_join_contract. Plus explanatory comments on the new
bare excepts and a consistent return in the gate's websocket branch.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-14 15:23:02 +00:00
Palash DebnathandClaude Fable 5 bc6acec5a3 fix(shell): gate Ready on the deep health probe; pace crash-loop restarts (#1548)
* fix(shell): gate Ready on the deep health probe; pace crash-loop restarts

Two supervisor hardenings from the backend-reliability root-cause pass:

Ready now requires backend_ready() — the identity probe (/system/info
string-sniff) AND the deep probe (/profiles must 200) — at both Ready
transitions (startup poll, supervisor respawn wait). The shallow probe
alone announced a backend whose install/DB had broken underneath as up;
the UI looked alive while every real request 500'd or dead-ended on
"can't reach the backend". Death detection stays process-exit-only, so a
busy-but-alive backend is still never killed.

Supervisor respawns now back off: first respawn immediate (a one-off
crash self-heals fast), then 5s, then 15s, capped — the budget check
ends a hopeless loop, not an unbounded sleep. The pause runs behind the
already-visible "reconnecting" banner and yields within 500ms to app
quit or a deliberate retry-flow replace.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(shell): yield backoff to a tracked replacement child, not just the flag

Greptile P1 on #1548: a completed Retry/Clean&Retry sets the deliberate-
kill flag and track_backend_child clears it — possibly both between two
500ms backoff samples, so the flag alone can be missed and the old
supervisor would free_port() the retry's healthy replacement. The dead
child we observed can never read as alive again, so a live tracked child
during backoff can only be a replacement — yield to it promptly so the
retry's spawn_backend_and_wait can claim the supervisor slot at Ready.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(shell): backoff yields on spawn-generation change, not liveness

Second Greptile pass on #1548: a replacement child that itself exits
before the old supervisor's next 500ms sample read as "still dead" under
the liveness check, so ownership transfer was missed. The spawn
generation (bumped by every track_backend_child, never un-bumped) is
observable regardless of the replacement's fate — snapshot it at death
detection, yield the moment it changes.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(shell): snapshot spawn generation before observing the exit

Third-pass review find: sampled after try_wait, a replacement tracked in
the gap bakes its own generation into the snapshot and the ownership
transfer is missed. Snapshot first, and re-check once more before
touching the port so the zero-backoff first respawn is covered too.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-14 14:41:21 +00:00
Palash DebnathandClaude Fable 5 94ba362ef2 feat(triage): crash-class recurrence report — the reliability metric (#1549)
* feat(triage): crash-class recurrence report — the reliability metric

scripts/crash_class_report.py measures the "backend died / never came
up" class (the project's #1 lifetime failure, ~1 in 5 of all issues)
filtered to reports from the current version — the definition of done
for the reliability cycle. Buckets by the bug reporter's Build-status
stamp (#1547): current / outdated / unknown (pre-deflection builds), so
deflection-miss noise never pollutes the number the work is judged on.

tests/scripts/test_crash_class_report.py pins the title→sub-class
mapping against the real historical title shapes and locks the stamp
literals to frontend/src/utils/bugReport.js so a reworded marker fails
in CI instead of silently zeroing the metric.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(triage): --version is authoritative; loud fetch-cap warning

Bot-review harvest on #1549: with --version, the Environment Version
line now decides the bucket (extracted to pure classify_build + tests) —
a report stamped "current at filing time" during another version's
window no longer counts toward this version's recurrence. Hitting the
500-issue fetch cap now warns loudly instead of silently understating.
The stamp lockstep test asserts the full Build-status prefix.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-14 14:20:35 +00:00
Palash DebnathandClaude Fable 5 aabe5783f3 fix(report): offer the latest release before filing from an outdated build (#1547)
* fix(report): offer the latest release before filing from an outdated build

6 in 10 sampled "can't reach the backend" reports came from builds that
were already obsolete when filed, and were closed with "please update" —
pure triage noise. Every Report-bug affordance now funnels through
openBugReport(): on an outdated build it offers the latest release first
(with a "File anyway" escape hatch), and the report body carries a
triage-greppable "**Build status:**" line either way, so current-version
recurrence — the reliability metric — is countable separately from
stale-build reports.

Freshness sources per deployment (behavior identical, implementation per
mode): desktop reads the Rust updater's channel-aware verdict from the
store (no new network path, no CSP widening); browser/dev/Docker make one
bounded latest-release GET, only once the user has initiated the report
flow whose destination is github.com. An 'unknown' dev build stays
silent entirely — never nudged, never claimed current.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(report): anchor version parsing; zh-TW reportBug.title in Traditional

Bot-review harvest on #1547: parseVersionTriple now rejects trailing
non-semver data (1.2.3.4, 1.2.3garbage) instead of silently reading the
leading triple into an outdated/current verdict; the pre-existing zh-TW
reportBug.title was Simplified-script — now properly Traditional.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-08-14 13:44:24 +00:00
Palash DebnathandGius 854b4852ed perf(frontend): coalesce persistence writes off input paths (#1546)
Defer and coalesce omnivoice.app and omni_ui persistence behind a 250 ms
quiet window with a 1,000 ms hard maximum, preserving storage schemas,
synchronous pending reads, legacy formats, Factory Reset semantics,
widget read-only ownership, and lifecycle (pagehide/visibilitychange)
durability. Adds scheduler, restore, reset, StrictMode, concurrent-render,
role-ownership, and migration regression tests plus an opt-in
production-bundle responsiveness harness.

Lands #1541 by @bultodepapas (maintainer landing branch; the out-of-scope
attribution-policy commit was dropped).

Co-authored-by: Gius <bultodepapas@gmail.com>
2026-08-14 13:25:36 +00:00
Eman-Yousaf 579f2e0a2e fix(paths): treat / as a separator on Windows so stored sub-paths resolve
resolve_within split candidate paths on os.sep alone. Windows accepts /
as a real separator but os.sep is \ there, so a persisted sub-path such
as "job_123/out.mp4" stayed a single component, failed the
basename-equality check, and raised UnsafePath — while the identical
value split cleanly and resolved on POSIX. A data directory written on
Linux or by the Docker deployment and then opened by the Windows desktop
app hit exactly that.

Split on both separator families instead, which is what the comment
above the split already states the code intends. This is not a
loosening: every component still goes through the same basename / "." /
".." / empty rejection, and the commonpath containment check and symlink
resolution below are unchanged. POSIX behaviour is unchanged too — a
backslash is already rejected there as a foreign separator before the
split runs.

This also restores real coverage of the symlink-escape guard on Windows.
test_resolve_within_rejects_symlink_escape asserts through
"link/secret.wav", which previously raised at component validation
before reaching the containment check it exists to cover, so it passed
for the wrong reason. It now matches on the reason.
2026-08-14 17:39:09 +05:00
144 changed files with 11614 additions and 1174 deletions
+60
View File
@@ -0,0 +1,60 @@
---
name: fastapi-python
description: Expert in FastAPI Python development with best practices for APIs and async operations
---
# FastAPI Python
You are an expert in FastAPI and Python backend development.
## Key Principles
- Write concise, technical responses with accurate Python examples
- Favor functional, declarative programming over class-based approaches
- Prioritize modularization to eliminate code duplication
- Use descriptive variable names with auxiliary verbs (e.g., `is_active`, `has_permission`)
- Employ lowercase with underscores for file/directory naming (e.g., `routers/user_routes.py`)
- Export routes and utilities explicitly
- Follow the RORO (Receive an Object, Return an Object) pattern
## Python/FastAPI Standards
- Use `def` for pure functions, `async def` for asynchronous operations
- Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries
- Structure: exported router, sub-routes, utilities, static content, types (models, schemas)
- Use ordinary Python control flow; prefer readability over compressed one-line conditionals
## Error Handling
- Handle edge cases at function entry points
- Employ early returns for error conditions
- Place happy path logic last
- Avoid unnecessary else statements; use if-return patterns
- Implement guard clauses for preconditions
- Provide proper error logging and user-friendly messaging
## FastAPI-Specific Guidelines
- Use functional components (plain functions) and Pydantic models for input validation
- Declare routes with clear return type annotations
- Prefer lifespan context managers for managing startup and shutdown events
- Leverage middleware for logging, error monitoring, and optimization
- Use HTTPException for expected errors and model them as specific HTTP responses
- Apply Pydantic's BaseModel consistently for validation
## Performance Optimization
- Minimize blocking I/O. In `async def` handlers, use awaitable database/API clients; put synchronous SQLite or other blocking work in synchronous routes or explicitly offload it
- Implement caching with Redis or in-memory stores
- Optimize Pydantic serialization/deserialization
- Use lazy loading for large datasets
## Key Conventions
1. Rely on FastAPI's dependency injection system
2. Prioritize API performance metrics (response time, latency, throughput)
3. Structure routes and dependencies for readability and maintainability
## Dependencies
FastAPI, Pydantic v2, asyncpg/aiomysql, SQLAlchemy 2.0
+357
View File
@@ -0,0 +1,357 @@
---
name: vite
description: Expert guidance for Vite development with modern build tooling, HMR, framework integrations, and performance optimization
---
# Vite Development
You are an expert in Vite, modern JavaScript/TypeScript build tooling, and frontend development.
## Key Principles
- Leverage native ES modules for fast development
- Use Vite's opinionated defaults when possible
- Configure only what needs customization
- Understand the dev/build differences
- Optimize for both development speed and production performance
## Project Setup
### Basic Configuration
```typescript
// vite.config.ts
import { defineConfig } from 'vite';
import react from '@vitejs/plugin-react';
export default defineConfig({
plugins: [react()],
server: {
port: 3000,
open: true,
},
build: {
outDir: 'dist',
sourcemap: true,
},
});
```
### Path Aliases
```typescript
import { defineConfig } from 'vite';
export default defineConfig({
resolve: {
alias: {
'@': new URL('./src', import.meta.url).pathname,
'@components': new URL('./src/components', import.meta.url).pathname,
'@utils': new URL('./src/utils', import.meta.url).pathname,
},
},
});
```
## Environment Variables
### Usage
```typescript
// .env
VITE_API_URL=https://api.example.com
VITE_APP_TITLE=My App
// In code
const apiUrl = import.meta.env.VITE_API_URL;
const isDev = import.meta.env.DEV;
const isProd = import.meta.env.PROD;
const mode = import.meta.env.MODE;
```
### Type Definitions
```typescript
// src/vite-env.d.ts
/// <reference types="vite/client" />
interface ImportMetaEnv {
readonly VITE_API_URL: string;
readonly VITE_APP_TITLE: string;
}
interface ImportMeta {
readonly env: ImportMetaEnv;
}
```
## Hot Module Replacement
### Manual HMR
```typescript
// For libraries without HMR support
if (import.meta.hot) {
import.meta.hot.accept('./module.ts', (newModule) => {
// Handle the updated module
console.log('Module updated:', newModule);
});
import.meta.hot.dispose(() => {
// Cleanup before module is replaced
});
}
```
## Asset Handling
### Static Assets
```typescript
// Import as URL
import imageUrl from './image.png';
// <img src={imageUrl} />
// Import as string (raw)
import shaderCode from './shader.glsl?raw';
// Import as worker
import Worker from './worker.ts?worker';
const worker = new Worker();
```
### Public Directory
```
public/
├── favicon.ico # Served at /favicon.ico
├── robots.txt # Served at /robots.txt
└── images/ # Served at /images/
```
## Framework Integrations
### React
```typescript
import { defineConfig } from 'vite';
import react from '@vitejs/plugin-react';
export default defineConfig({
plugins: [
react({
// Babel plugins
babel: {
plugins: ['@emotion/babel-plugin'],
},
}),
],
});
```
### Vue
```typescript
import { defineConfig } from 'vite';
import vue from '@vitejs/plugin-vue';
export default defineConfig({
plugins: [vue()],
});
```
### Svelte
```typescript
import { defineConfig } from 'vite';
import { svelte } from '@sveltejs/vite-plugin-svelte';
export default defineConfig({
plugins: [svelte()],
});
```
## Build Optimization
### Code Splitting
```typescript
// Dynamic imports create separate chunks
const AdminPanel = lazy(() => import('./AdminPanel'));
// Manual chunks
export default defineConfig({
build: {
rollupOptions: {
output: {
manualChunks: {
vendor: ['react', 'react-dom'],
utils: ['lodash', 'date-fns'],
},
},
},
},
});
```
### Chunk Size Optimization
```typescript
export default defineConfig({
build: {
chunkSizeWarningLimit: 500,
rollupOptions: {
output: {
manualChunks(id) {
if (id.includes('node_modules')) {
return id.split('node_modules/')[1].split('/')[0];
}
},
},
},
},
});
```
## CSS Handling
### CSS Modules
```typescript
// styles.module.css is auto-detected
import styles from './styles.module.css';
// <div className={styles.container}>
```
### PostCSS
```javascript
// postcss.config.js
export default {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
};
```
### Preprocessors
```typescript
// Automatically handled with package installed
// npm install -D sass
import './styles.scss';
```
## Proxy Configuration
```typescript
export default defineConfig({
server: {
proxy: {
'/api': {
target: 'http://localhost:4000',
changeOrigin: true,
rewrite: (path) => path.replace(/^\/api/, ''),
},
'/socket.io': {
target: 'ws://localhost:4000',
ws: true,
},
},
},
});
```
## Plugin Development
```typescript
// my-vite-plugin.ts
import type { Plugin } from 'vite';
export function myPlugin(): Plugin {
return {
name: 'my-plugin',
// Hook: modify config
config(config, { mode }) {
return {
define: {
__BUILD_TIME__: JSON.stringify(new Date().toISOString()),
},
};
},
// Hook: transform code
transform(code, id) {
if (id.endsWith('.md')) {
return {
code: `export default ${JSON.stringify(code)}`,
map: null,
};
}
},
// Hook: configure dev server
configureServer(server) {
server.middlewares.use((req, res, next) => {
// Custom middleware
next();
});
},
};
}
```
## Testing with Vitest
```typescript
// vitest.config.ts
import { defineConfig } from 'vitest/config';
export default defineConfig({
test: {
globals: true,
environment: 'jsdom',
setupFiles: './src/test/setup.ts',
coverage: {
provider: 'v8',
reporter: ['text', 'json', 'html'],
},
},
});
```
## SSR Configuration
```typescript
export default defineConfig({
build: {
ssr: true,
rollupOptions: {
input: './src/entry-server.ts',
},
},
ssr: {
external: ['express'],
noExternal: ['my-ui-library'],
},
});
```
## Library Mode
```typescript
export default defineConfig({
build: {
lib: {
entry: './src/index.ts',
name: 'MyLib',
fileName: (format) => `my-lib.${format}.js`,
},
rollupOptions: {
external: ['react', 'react-dom'],
output: {
globals: {
react: 'React',
'react-dom': 'ReactDOM',
},
},
},
},
});
```
## Best Practices
- Use `vite preview` to test production builds locally
- Keep dependencies that support ESM in regular deps
- Use `optimizeDeps.include` for CommonJS dependencies
- Enable `build.sourcemap` for debugging production
- Use `server.warmup` for faster dev server starts
+11
View File
@@ -271,6 +271,17 @@ jobs:
working-directory: frontend/src-tauri
run: cargo test --lib --target ${{ matrix.rust_target }} --message-format=short
# Backend-lifecycle fault-injection harness: real child processes die
# scripted deaths through the OMNIVOICE_BACKEND_CMD seam, and each
# scenario asserts the user-visible diagnosis names the actual cause
# (port conflict / traceback root cause / spawn failure / timeout /
# crash-loop exhaustion / signal 9 / deliberate replace / deferred-
# startup step). Serial: the scenarios share process-global state
# (env vars, crash store, kill-intended flag) by design.
- name: Cargo test (backend lifecycle harness)
working-directory: frontend/src-tauri
run: cargo test --test backend_lifecycle --target ${{ matrix.rust_target }} --message-format=short -- --test-threads=1
# ── Cross-platform Python runtime smoke (Phase 0 GATE-02) ───────────────
# Loads the frozen tests/fixtures/omnivoice_data/ fixture and boots the
# FastAPI app in-process via TestClient on macOS/Windows/Linux. Catches
+2
View File
@@ -25,4 +25,6 @@ regexes = [
'''^hf_QWERTYUIOPasdfghjklZXCVBNM0123456789xyzAB$''',
# NLLB generation length argument, not the value of a credential.
'''^max_length=400$''',
# cryptography's Ed25519 private-key type name, not key material.
'''^Ed25519PrivateKey$''',
]
+4
View File
@@ -35,6 +35,10 @@ Binding for every AI agent (Claude, Codex, Cursor, review bots, …). CLAUDE.md
## Agent skills
Project development skills are pinned in `skills-lock.json` and installed under
`.agents/skills/`: Vite and FastAPI.
Repository rules and tracker mappings override generic skill guidance.
### Issue tracker
GitHub Issues on `debpalash/VoiceStudio`, via the `gh` CLI. See `docs/agents/issue-tracker.md`.
+37
View File
@@ -6,6 +6,42 @@ The format is loosely based on [Keep a Changelog](https://keepachangelog.com/).
`frontend/package.json` is the app-version source of truth; Cargo, Python, and
the frozen-backend fallback mirror it for their toolchains.
## [Unreleased]
**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
### Changed
- 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
- 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)
### Docs
- 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)
- Every engine now has its own guide — 21 new pages under docs/engines plus an index covering all 16 TTS and 11 ASR engines, linked from both READMEs (#1556)
- 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
- 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!
- 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)
- "Ready" now requires the deep health probe (a working database-backed route), not just the identity probe — a backend whose install broke underneath can no longer be announced up while every real request fails (#1548)
- 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)
### 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)
## [0.5.0] — 2026-08-13
**Highlights**
@@ -31,6 +67,7 @@ the frozen-backend fallback mirror it for their toolchains.
### Changed
- Gallery personas now preview through the local backend, retain their complete voice-design recipe, and open directly in Voice, Stories, or Audiobook. (#1542)
- Typing and large workspace edits no longer serialize and rewrite persisted documents on every input; writes are coalesced off the interaction path — thanks @bultodepapas! (#1541)
- Support amount choices now use every theme's shared card, accent and focus tokens. (#1530)
- Sponsoring, commercial licensing and getting in touch are one page now. They answered the same question between them and each used to live somewhere else, so they are three sections on a single scroll — the footer heart, the commercial-licence links and Contact all land on it, at the section you asked for. (#1522)
- Model Catalogue switches panes with tabs instead of a two-state toggle, and the Engine Compatibility Matrix's TTS / ASR / LLM switcher is now tabs too — arrow-key navigable, and each tab still shows the engine it would use. (#1522)
+4 -1
View File
@@ -66,7 +66,10 @@ Architecture not yet mapped. Follow existing patterns found in the codebase.
<!-- GSD:skills-start source:skills/ -->
## Project Skills
No project skills found. Add skills to any of: `.claude/skills/`, `.agents/skills/`, `.cursor/skills/`, `.github/skills/`, or `.codex/skills/` with a `SKILL.md` index file.
- `vite` — Vite configuration, assets, HMR, builds, and Vitest guidance.
- `fastapi-python` — FastAPI and Pydantic implementation patterns.
Canonical copies live under `.agents/skills/`; `skills-lock.json` pins their sources and hashes. Claude should follow these paths directly, avoiding cross-platform symlinks.
<!-- GSD:skills-end -->
<!-- GSD:workflow-start source:GSD defaults -->
+281 -447
View File
@@ -1,551 +1,385 @@
<div align="center">
<img src="docs/logo.png" alt="VoiceStudio Logo" width="120" height="120" />
<img src="docs/logo.png" alt="VoiceStudio logo" width="120" height="120" />
<h1>VoiceStudio</h1>
<p><sub><em>previously OmniVoice-Studio</em></sub></p>
<h3>Make voices. Tell stories. Keep the files. ♡</h3>
<p>Clone, design, dub, dictate, and build audiobooks in one open-source desktop studio.<br/><b>Local-first by default.</b> No subscription or usage meter. Optional online services stay opt-in.</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>
<p>
<a href="#quickstart">Quickstart</a> ·
<a href="#install">Install</a> ·
<a href="#features">Features</a> ·
<a href="#why-voicestudio">Why VoiceStudio</a> ·
<a href="#tts-engines">Engines</a> ·
<a href="#openai-api">API</a> ·
<a href="#sponsor--donate">Donate</a> ·
<a href="#contributing">Contributing</a> ·
<a href="https://voicestudio.sh">Website</a> ·
<a href="https://voicestudio.sh/docs">Docs</a> ·
<a href="https://status.voicestudio.sh">Status</a> ·
<a href="https://discord.gg/bzQavDfVV9">Discord</a> ·
<a href="https://x.com/idebpalash">X</a> ·
<a href="#comparison">Compare</a> ·
<a href="#requirements">Requirements</a> ·
<a href="#engines">Engines</a> ·
<a href="#architecture">Architecture</a> ·
<a href="#api">API</a> ·
<a href="#documentation">Docs</a> ·
<a href="README_CN.md"><strong>简体中文</strong></a>
</p>
<p>
<a href="https://github.com/debpalash/VoiceStudio/stargazers"><img src="https://img.shields.io/github/stars/debpalash/VoiceStudio?style=flat-square&color=f59e0b" alt="Stars" /></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="Release" /></a>
<a href="LICENSE"><img src="https://img.shields.io/badge/license-AGPL--3.0-blue?style=flat-square" alt="License" /></a>
<a href="https://github.com/debpalash/VoiceStudio/issues"><img src="https://img.shields.io/github/issues/debpalash/VoiceStudio?style=flat-square&color=ef4444" alt="Issues" /></a>
<a href="https://discord.gg/bzQavDfVV9"><img src="https://img.shields.io/badge/Discord-Join_Community-5865F2?style=flat-square&logo=discord&logoColor=white" alt="Discord" /></a>
<a href="https://x.com/idebpalash"><img src="https://img.shields.io/badge/X-Follow_for_updates-000000?style=flat-square&logo=x&logoColor=white" alt="Follow on X" /></a>
<a href="https://ko-fi.com/debpalash"><img src="https://img.shields.io/badge/Ko--fi-Support_Us-FF5E5B?style=flat-square&logo=ko-fi&logoColor=white" alt="Ko-fi" /></a>
<a href="https://paypal.me/palashCoder"><img src="https://img.shields.io/badge/PayPal-Donate-00457C?style=flat-square&logo=paypal&logoColor=white" alt="PayPal" /></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>
<a href="LICENSE"><img src="https://img.shields.io/badge/license-AGPL--3.0-blue?style=flat-square" alt="AGPL-3.0 license" /></a>
<a href="https://discord.gg/bzQavDfVV9"><img src="https://img.shields.io/badge/Discord-Community-5865F2?style=flat-square&logo=discord&logoColor=white" alt="Discord community" /></a>
</p>
<p>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/⬇_Download-macOS_·_Windows_·_Linux-10b981?style=for-the-badge" alt="Download the latest release" /></a>
</p>
<p>
<a href="https://trendshift.io/repositories/28176?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-28176" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/28176/daily?language=Python" alt="debpalash%2FVoiceStudio | Trendshift" width="250" height="55"/></a>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/Download-macOS_·_Windows_·_Linux-10b981?style=for-the-badge" alt="Download VoiceStudio" /></a>
</p>
</div>
<br/>
<div align="center">
<img src="docs/screenshot-launchpad.png" alt="VoiceStudio — Launchpad" width="100%"/>
<img src="docs/media/0.5.0/quick-switch.gif" alt="Switching TTS engines from the VoiceStudio status bar" width="100%" />
</div>
> **Your voice is personal. Your studio should feel personal too.** VoiceStudio keeps its core workflow on your hardware: clone, design, dub, dictate, and publish in 646 languages without a subscription or usage meter. Network-backed engines and services are optional, visible choices—not hidden requirements.
> [!WARNING]
> **Active beta.** Things may break between releases — for the newest fixes, run from source. Bug reports and PRs are very welcome: [open an issue](https://github.com/debpalash/VoiceStudio/issues) or [join Discord](https://discord.gg/bzQavDfVV9).
> **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).
<a id="whats-new"></a>
## At a glance
## 🆕 What's new in 0.5.0
| | VoiceStudio |
|---|---|
| **Workflows** | Voice cloning and design, video dubbing, dictation, stories, audiobooks, batch generation |
| **Language catalogue** | 646 TTS languages; actual coverage and quality depend on the selected engine |
| **Engines** | 16 TTS · 11 ASR · switch in Model Catalogue or with <kbd>Ctrl</kbd>/<kbd>Cmd</kbd>+<kbd>E</kbd> |
| **Platforms** | macOS 13.3+ on Apple Silicon · Windows 10/11 x64 · Linux x86_64 with glibc 2.39+ |
| **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 |
The rename release — full notes: [v0.5.0 release](https://github.com/debpalash/VoiceStudio/releases/tag/v0.5.0) · [CHANGELOG](CHANGELOG.md).
<a id="install"></a>
- 🏷️ **A new name** — VoiceStudio (previously OmniVoice-Studio): one waveform-and-spark identity across app, docs, and installers. Your data folder, settings, and Docker image paths stay put.
- 📚 **Model Catalogue** — engines and models in one workspace: every TTS, ASR, and LLM engine with its device routing and install state; pick defaults, install or remove weights.
- ⚡ **Engine quick-switch** — change TTS/ASR/LLM engines from the status bar or anywhere with <kbd>Ctrl</kbd>/<kbd>Cmd</kbd>+<kbd>E</kbd> — ready-only choices, memory status, environment-pin protection.
- 🖧 **Remote GPU workers** — lend another machine's GPU with a join code and a QR scan; a **Compute** control picks where jobs run, and several people can share one GPU box over revocable, certificate-pinned connections.
- 🔐 **Hardened server mode** — admin actions require an API key, exchanged for short-lived scoped sessions that never sit in browser storage or WebSocket URLs.
- 💾 **Gallery voices → local profiles** — save any gallery voice as a profile of your own and use it in every picker.
- 🎤 **Dictation on Wayland** — the portal shortcut actually fires now, and the recording pill is back on every desktop.
## Install
<div align="center">
<img src="docs/media/0.5.0/quick-switch.gif" alt="Switching engines from the status bar" width="640"/>
<br/><sub>Engine quick-switch from the status bar — <kbd>Ctrl</kbd>/<kbd>Cmd</kbd>+<kbd>E</kbd> from any workspace</sub>
</div>
| 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) |
| 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) |
<br/>
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.
<table>
<tr>
<td width="50%"><img src="docs/media/0.5.0/catalogue.png" alt="Model Catalogue — engines pane" width="100%"/></td>
<td width="50%"><img src="docs/media/0.5.0/gallery-save.png" alt="Saving a gallery voice as a profile" width="100%"/></td>
</tr>
<tr>
<td align="center"><sub><b>Model Catalogue</b> — every engine, its routing and install state</sub></td>
<td align="center"><sub><b>Gallery → profile</b> — keep a gallery voice as your own</sub></td>
</tr>
</table>
> [!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.
### First voice
1. Launch VoiceStudio and open **Voice Cloning**.
2. Add a clean voice sample. Three seconds works; 515 seconds usually gives a better prompt.
3. Enter text, choose a language, then select **Generate**.
### Run from source
Install the [development prerequisites](.github/CONTRIBUTING.md#development-setup), then:
```bash
git clone https://github.com/debpalash/VoiceStudio.git
cd VoiceStudio
bun install
bun run desktop
```
Use `bun run dev` for the browser UI. See [Contributing](.github/CONTRIBUTING.md) for services, tests, and platform packages.
### If setup fails
- Run **Settings → About → Run self-check** or `uv run python backend/main.py --diagnose --deep`.
- Check [install troubleshooting](docs/install/troubleshooting.md).
- Save a scrubbed diagnostic bundle from the app when opening an issue.
- For slow generation, compare [measured benchmarks](docs/benchmarks.md) and [performance settings](docs/performance.md).
<a id="features"></a>
## Features
## Features
Three flagships, five more headliners, and a dozen under the fold.
| 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 |
| **Stories and audiobooks** | Multi-voice scripts · EPUB/PDF import · chapter rendering · `.m4b` export |
| **Dictation Widget** | 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 |
| **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 |
| **Local-first** | Core creation stays local; network-backed features are explicit opt-ins |
| **Extensible** | Registry-based TTS, ASR, and plugin interfaces |
<table>
<tr>
<td width="33%"><img src="docs/features/clone.png" alt="Voice Cloning" width="100%"/></td>
<td width="33%"><img src="docs/features/design.png" alt="Voice Design" width="100%"/></td>
<td width="33%"><img src="docs/features/dub.png" alt="Video Dubbing" width="100%"/></td>
<td width="50%"><img src="docs/media/0.5.0/catalogue.png" alt="VoiceStudio Model Catalogue" width="100%" /></td>
<td width="50%"><img src="docs/media/0.5.0/gallery-save.png" alt="Saving a gallery voice as a local profile" width="100%" /></td>
</tr>
<tr>
<td align="center">🎙️ <b>Voice Cloning</b><br/><sub>3-sec clip → any voice · 646 languages · zero-shot</sub></td>
<td align="center">🎨 <b>Voice Design</b><br/><sub>Describe it — gender, age, accent, emotion</sub></td>
<td align="center">🎬 <b>Video Dubbing</b><br/><sub>Transcribe → translate → re-voice → MP4</sub></td>
<td align="center"><sub>Model Catalogue: engine, device, and install state</sub></td>
<td align="center"><sub>Gallery: save a shared voice as a local profile</sub></td>
</tr>
</table>
<table>
<tr>
<td align="center" width="20%">📖<br/><b>Audiobook</b><br/><sub>EPUB/PDF → .m4b, multi-voice cast</sub></td>
<td align="center" width="20%">🎭<br/><b>Stories</b><br/><sub>Multi-voice script editor</sub></td>
<td align="center" width="20%">⌨️<br/><b>Dictation Widget</b><br/><sub><kbd>⌘⇧Space</kbd> in any app</sub></td>
<td align="center" width="20%">🔐<br/><b>Local-first</b><br/><sub>Core creation stays on your machine</sub></td>
<td align="center" width="20%">🤖<br/><b>MCP Server</b><br/><sub>Use from Claude, Cursor, …</sub></td>
</tr>
</table>
<a id="comparison"></a>
<details>
<summary><b>…and 12 more</b> — catalogue, remote GPUs, isolation, diarization, batch, watermarking, and friends</summary>
## Comparison
<br/>
VoiceStudio trades managed cloud compute for local control. This is the practical difference:
- 📚 **Model Catalogue** — one workspace for every TTS/ASR/LLM engine and model: defaults, device routing, install or remove weights — and quick-switch engines from anywhere with <kbd>Ctrl</kbd>/<kbd>Cmd</kbd>+<kbd>E</kbd>.
- 🖧 **Remote GPU workers** — send jobs to GPUs on your other machines: join code + QR enrolment, Remote Model Downloads with per-worker live progress, chapter-by-chapter audiobook rendering with local fallback. Off by default; see [docs/remote-workers.md](docs/remote-workers.md).
- 🔊 **Vocal Isolation** — Demucs-powered: splits speech from music and keeps the background bed.
- 👥 **Speaker Diarization** — Pyannote + WhisperX auto-identify who said what.
- 📦 **Batch Queue** — drop 50 videos, walk away; per-job progress bars.
- 🛡️ **AI Watermark** — AudioSeal (Meta): invisible, survives compression.
- 🔬 **Diagnostics** — self-check suite, error journal, scrubbed diagnostic bundles.
- ⚡ **GPU Auto-Detect & Routing** — CUDA · MPS · ROCm (Linux, opt-in) · CPU; ≤8 GB VRAM auto-offloads; per-engine GPU preflight, no silent CPU fallback.
- 🧩 **Extensible** — subclass `TTSBackend`, add any engine in ~50 lines.
- 🎒 **Portable personas** — export voices as `.ovsvoice` bundles: identity + watermark.
- ♾️ **Unlimited TTS** — sentence-chunked generation, no length cap, streaming via WebSocket.
- 🧠 **Dictation + LLM** — local-LLM cleanup of transcripts, optional echo cancellation.
</details>
---
<a id="quickstart"></a>
## ⚡ Quickstart
<div align="center">
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/macOS-DMG_(Apple_Silicon)-000?style=for-the-badge&logo=apple&logoColor=white" alt="Download macOS DMG" /></a>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/Windows-MSI_(x64)-0078D4?style=for-the-badge&logo=windows&logoColor=white" alt="Download Windows MSI" /></a>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/Linux-AppImage_(x64)-FCC624?style=for-the-badge&logo=linux&logoColor=black" alt="Download Linux AppImage" /></a>
<br/>
<sub><b>macOS:</b> first launch needs a one-time approval — right-click → <b>Open</b> (or System Settings → Privacy &amp; Security → <b>"Open Anyway"</b> on macOS 15). No Terminal needed. <a href="docs/install/macos.md#gatekeeper-quarantine">Why?</a> · <b>Intel Macs:</b> local backend unsupported (<a href="https://github.com/debpalash/VoiceStudio/issues/889">#889</a>) — <a href="docs/install/macos.md">details</a>.</sub>
</div>
**Install guide:** [🍎 macOS](docs/install/macos.md) · [🪟 Windows](docs/install/windows.md) · [🐧 Linux](docs/install/linux.md) · [🐳 Docker](docs/install/docker.md)
<details>
<summary><b>🧰 Troubleshooting · slow generation · HF tokens · restricted networks</b></summary>
<br/>
- **Something broke?** Run the self-check — **Settings → About → "Run self-check"** (or `uv run python backend/main.py --diagnose --deep`) — then the [top 10 install errors](docs/install/troubleshooting.md). **"Save diagnostic bundle"** packages scrubbed logs for a bug report.
- **Feels slow?** [docs/performance.md](docs/performance.md) — where the time goes and how to tune it.
- **Want breaths, laughter, emotion?** [docs/expressive-speech.md](docs/expressive-speech.md) — what each engine can do today.
- **HF tokens · diarization · download speed / mirrors:** [tokens](docs/setup/huggingface-token.md) · [diarization](docs/features/diarization.md) · [downloads](docs/downloading-models.md).
- **Coming from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning)?** [Migration guide](docs/migration/real-time-voice-cloning.md).
</details>
---
<a id="why-voicestudio"></a>
## ⚖️ Why VoiceStudio
Cloud voice tools are convenient, but they put your workflow behind an account, a meter, and somebody else's infrastructure. VoiceStudio gives you a capable studio that runs on your hardware, with optional integrations when you choose them.
| | **ElevenLabs** | **VoiceStudio** |
| | **VoiceStudio** | **Typical hosted voice service** |
|---|---|---|
| **Pricing** | Subscription and usage limits | Free & open-source (AGPL-3.0) · [Commercial license](#license) for proprietary use |
| **Voice Cloning** | ✅ 3s clip | ✅ 3s clip, zero-shot |
| **Voice Design** | ✅ Gender, age | ✅ Gender, age, accent, pitch, style, dialect |
| **Audiobook / Stories** | ❌ | ✅ Full audiobook editor + multi-voice stories (EPUB/PDF import, .m4b export) |
| **Languages** | Plan/model dependent | **646** |
| **Video Dubbing** | ✅ Cloud-only | ✅ Fully local |
| **Data Privacy** | Audio is processed remotely | Core workflow runs locally; online services are explicit opt-ins |
| **API Keys** | Account required | Not needed for the local workflow |
| **GPU Support** | N/A (cloud) | CUDA · Apple Silicon · ROCm (Linux) · CPU — plus your other machines' GPUs as [remote workers](docs/remote-workers.md) |
| **Desktop App** | ❌ | ✅ macOS · Windows · Linux |
| **TTS Engines** | 1 | **16** — [full matrix](#tts-engines) |
| **ASR Engines** | 1 | **11** — [full lineup](#asr-engines) |
| **MCP Server** | ❌ | ✅ Use from Claude, Cursor, any MCP client |
| **Self-check** | ❌ | ✅ Diagnostics suite, error journal, scrubbed debug bundles |
| **Customizable** | ❌ Closed | ✅ Fork it, extend it, ship it |
| **Best fit** | Private, offline, self-hosted, or high-volume work | Fast setup without local model management |
| **Data path** | Local by default; remote features are opt-in | Audio and text are processed by the provider |
| **Cost model** | Free software; you supply the hardware | Subscription, credits, or metered API use |
| **Setup** | Install the app and model weights | Create an account and use the web app or API |
| **Performance** | Depends on your engine and hardware | Provider manages compute and scaling |
| **Offline use** | Yes, after required models are installed | Usually requires a network connection |
| **Customization** | Source, engines, models, API, and routing are open | Limited to provider options |
| **Maintenance** | You manage updates, disk, and compute | Provider manages infrastructure |
Professional-grade voice AI, minus the subscription and the cloud. Convinced? [Come build with us.](https://discord.gg/bzQavDfVV9)
<a id="requirements"></a>
---
## Requirements
## 🖥️ System Requirements
Requirements vary by engine. These values cover the default local workflow.
| | **Minimum** | **Recommended** |
|---|---|---|
| **OS** | Windows 10, macOS 13.3+ (Apple Silicon), Ubuntu 24.04+ (glibc 2.39+) | Any modern 64-bit OS |
| **OS** | Windows 10 x64 · macOS 13.3 Apple Silicon · Linux x86_64 with glibc 2.39+ | Current supported OS release |
| **RAM** | 8 GB | 16 GB+ |
| **VRAM (GPU)** | 4 GB (auto-offloads TTS to CPU) | 8 GB+ (NVIDIA RTX 3060+) |
| **Disk** | 10 GB free (models + cache) | 20 GB+ SSD |
| **Python** | 3.10+ (managed by `uv`) | 3.113.12 |
| **GPU** | Optional — CPU works | NVIDIA CUDA · Apple Silicon MPS · AMD ROCm (Linux only) |
| **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+ | 3.113.12 |
> [!NOTE]
> **A GPU is optional** — the whole pipeline runs on CPU (just slower), and on ≤8 GB VRAM, TTS auto-offloads to CPU. Caveats: **AMD ROCm** is Linux-only + opt-in ([Linux](docs/install/linux.md#amd-gpu-rocm)) — Windows AMD/Ryzen AI is CPU-only ([Windows](docs/install/windows.md#gpu-support)); **macOS Intel** can't run the local backend, so point it at a remote one ([#889](https://github.com/debpalash/VoiceStudio/issues/889) · [macOS](docs/install/macos.md)).
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="engines"></a>
## Engines
Engine support is capability-specific. Check cloning, language, platform, memory, and license before choosing one. Full setup guides: [docs/engines](docs/engines/README.md).
<a id="tts-engines"></a>
### 🗣️ TTS Engines
**16 engines, one picker.** VoiceStudio (default, 600+ languages) is always available; seven more are opt-in and auto-detected (CosyVoice 3, GPT-SoVITS, VoxCPM2, MOSS-TTS-Nano, KittenTTS, MLX-Audio, Sherpa-ONNX), plus eight lazy-installed opt-ins (IndexTTS 2.5, OmniVoice GGUF, OmniVoice subprocess, PocketTTS, Supertonic 3, MOSS-TTS-v1.5, dots.tts, Confucius4-TTS). Switch in **Model Catalogue → Engines** — or from anywhere with <kbd>Ctrl</kbd>/<kbd>Cmd</kbd>+<kbd>E</kbd>; the choice applies everywhere synthesis happens.
<details>
<summary><b>📊 The full matrix</b> — 16 engines × platform × clone/instruct × license</summary>
<br/>
### Text to speech
| Engine | Languages | Clone | Instruct | Linux | macOS ARM | Windows | License |
|--------|:---------:|:-----:|:--------:|:-----:|:---------:|:-------:|:-------:|
| **VoiceStudio** (default, powered by k2-fsa/OmniVoice) | 600+ | ✅ | ✅ | ✅ CUDA/CPU | MPS | CUDA/CPU | Built-in |
| **CosyVoice 3** | 9 + 18 dialects | ✅ | ✅ | CUDA/CPU | ✅ MPS | ✅ CUDA/CPU | Apache-2.0 |
| **GPT-SoVITS** | 5 | | — | CUDA/CPU | — | CUDA/CPU | MIT |
| **VoxCPM2** | 30 | ✅ | ✅ | ✅ CUDA/CPU | MPS | CUDA/CPU | Apache-2.0 |
| **MOSS-TTS-Nano** | 20 | | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
| **KittenTTS** | English | — | — | CPU | CPU | CPU | MIT |
| **MLX-Audio** (Kokoro, Qwen3-TTS, CSM, Dia, …) | Multi | Varies | Varies | | ✅ Native | | Varies |
| **Sherpa-ONNX** | 20+ | — | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
| **IndexTTS 2.5** ⚡ | ZH · EN · JA · ES · AR | | — | CUDA | — | CUDA | Bilibili model license¹ |
| **OmniVoice GGUF** ⚡ | 600+ | ✅ | ✅ | ✅ CPU | CPU | CPU | Built-in |
| **OmniVoice (subprocess)**² | 600+ | ✅ | ✅ | ✅ CUDA/CPU | MPS | CUDA/CPU | Built-in |
| **PocketTTS** (Kyutai) | EN · FR · DE · PT · IT · ES | | — | CPU | CPU | CPU | CC-BY-4.0 (gated |
| **Supertonic 3** ⚡ | 31 | — | — | CPU | CPU | CPU | OpenRAIL-M |
| **MOSS-TTS-v1.5** (8B) | 31 | | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
| **dots.tts** (2B) | 24 | | — | CUDA/CPU | CPU | | Apache-2.0 |
| **Confucius4-TTS** ⚡ | 14 | | — | CUDA/CPU | CPU | CUDA/CPU | Apache-2.0 |
|---|:---:|:---:|:---:|:---:|:---:|:---:|---|
| **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 |
¹ IndexTTS 2.5 requires a separate written Bilibili license above 100 million
monthly active users or RMB 1 billion in annual revenue. Review its
[model license](https://huggingface.co/IndexTeam/IndexTTS-2.5/blob/main/LICENSE)
before enabling the optional sidecar.
Installed or registered on demand.
² **OmniVoice (subprocess)** is the same resident model as the default engine, run
in a crash-isolated child process: a wedged generation can be hard-killed and its
VRAM reclaimed. Opt-in for unattended synthesis and VRAM-tight MPS hosts —
[docs/engines/omnivoice-subprocess.md](docs/engines/omnivoice-subprocess.md).
¹ IndexTTS 2.5 requires a separate written Bilibili license above 100 million monthly active users or RMB 1 billion annual revenue. Review the [model license](https://huggingface.co/IndexTeam/IndexTTS-2.5/blob/main/LICENSE).
³ **PocketTTS** (Kyutai) is a fast, low-latency CPU engine with zero-shot cloning;
its gated model access and CC-BY-4.0 conditions are shown for review in-app before
first use.
² PocketTTS shows its gated-access and CC-BY-4.0 terms before first use.
GPT-SoVITS connects to `http://127.0.0.1:9880` by default. To use a server on
another machine, set `OMNIVOICE_GPTSOVITS_URL` to its credential-free
`http://` or `https://` origin and add that machine's CIDR to
`OMNIVOICE_TRUSTED_NETWORKS`; redirects and untrusted destinations are rejected.
> **CUDA** = GPU-accelerated · **MPS** = Apple Silicon Metal · **CPU** = runs everywhere, slower for large models · KittenTTS, MOSS-TTS-Nano, and PocketTTS run realtime on CPU · MLX-Audio is Apple Silicon only · ⚡ = lazy-registered (installed on first use)
>
> **Clone** matters beyond single-clip generation: Video Dubbing (and any Batch job with a pinned voice) needs reference-audio cloning to preserve speaker identity, so picking a Clone-less engine (KittenTTS, Sherpa-ONNX, Supertonic 3) as the active engine fails those jobs up front with an actionable message instead of silently falling back to VoiceStudio.
>
> **MOSS-TTS-v1.5** (8B, ~16 GB), **dots.tts** (2B, ~9 GB), and **Confucius4-TTS** are heavyweight opt-ins that run in their own isolated venv from a local clone. None claims Apple-Silicon MPS (CPU on Macs); dots.tts has no Windows path; Confucius4 wants CUDA (CPU works, ~17× realtime). Details: [MOSS-TTS-v1.5](docs/engines/moss-tts-v15.md) · [dots.tts](docs/engines/dots-tts.md) · [Confucius4-TTS](docs/engines/confucius4-tts.md).
</details>
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>
### 🎧 ASR Engines
### Speech to text
**11 engines** — they power dictation, video dubbing, and subtitles. **WhisperX** is the cross-platform default (~100 languages, word-level timing); the rest are opt-in and auto-detected. Switch in **Model Catalogue → Engines**. Ten run fully on-device; the eleventh (OpenAI-compatible) is an optional remote client for Qwen3-ASR or any compatible server.
| 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 |
<details>
<summary><b>📊 The full lineup</b> — 11 engines, what each is best at, and compute-type notes</summary>
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.
<br/>
<a id="architecture"></a>
| Engine | `OMNIVOICE_ASR_BACKEND` | Languages | Best for |
|--------|-------------------------|:---------:|----------|
| **WhisperX** (default) | `whisperx` | ~100 | Dubbing & subtitles — word-level timing via wav2vec2 forced alignment |
| **Faster-Whisper** | `faster-whisper` | ~100 | Fast transcription on Linux / macOS / Windows (CTranslate2) |
| **Faster-Whisper (isolated)** | `faster-whisper-isolated` | ~100 | Same as Faster-Whisper but crash-isolated in a subprocess — an ASR crash won't take down the app |
| **MLX Whisper** | `mlx-whisper` | ~100 | Native Apple Silicon speed (Apple MLX / Metal) |
| **PyTorch Whisper** | `pytorch-whisper` | ~100 | CUDA / CPU fallback via 🤗 Transformers (no cuDNN 8 needed) |
| **Parakeet TDT** | `nemo-parakeet` | English + 25 EU | SOTA accuracy at ~10× realtime even on CPU, auto language detection (NVIDIA NeMo, CUDA/CPU) |
| **Parakeet TDT v3 (MLX)** | `parakeet-mlx` | 25 EU | The Parakeet tier for Apple Silicon — word timestamps, ~2 GB unified memory, dictation-grade speed via MLX. Dictation prefers it automatically for its 25 European languages; other languages keep multilingual Whisper. |
| **Moonshine** | `moonshine` | English | Edge / low-latency, ONNX |
| **FunASR** | `funasr` | 50+ | All-in-one multilingual — built-in VAD + inline speaker diarization (SenseVoice) |
| **sherpa-onnx** (live dictation) | `sherpa-onnx-asr` | 25 EU + 90+ | Live, faster-than-real-time dictation — small streaming/offline ONNX models, CPU, identical on macOS / Windows / Linux. Picked per-model in **Settings → Voice**. |
| **OpenAI-compatible** ⚠️ remote | `openai-compat-asr` | Server-dependent | A path to **Qwen3-ASR** today (self-hosted server), any OpenAI-compatible transcription endpoint, or OpenAI's own API — configure + test in **Model Catalogue → Engines** (ASR tab). Audio leaves your machine to whatever server you point it at; see [docs/engines/openai-compatible-asr.md](docs/engines/openai-compatible-asr.md). |
## Architecture
> If Dubbing needs an ASR model that is not installed yet, it offers the recommended download in place, shows its progress, and retries transcription on the same job when the model is ready.
>
> **GPU without efficient float16?** On older NVIDIA GPUs (Maxwell/Pascal, GTX 16xx) or after a CTranslate2/cuDNN mismatch, the CTranslate2 ASR engines (WhisperX, Faster-Whisper) can't run `float16` and VoiceStudio automatically retries on `int8` — no config needed. If transcription still fails, pin the compute type with `ASR_COMPUTE_TYPE=int8` (or `float32` for CPU) and restart the backend.
</details>
---
## 🏗️ Architecture
A **Tauri v2** desktop shell (Rust) wraps a **React** UI and a bundled **Python/FastAPI** backend that runs as a local sidecar on `localhost:3900`. Every layer runs on your machine by default; the only network paths are the ones you opt into (remote GPU workers, a remote backend, or an OpenAI-compatible ASR endpoint).
```
┌────────────────────────────────────────────────────────────────────┐
│ Tauri v2 shell — Rust │
│ window state · global dictation hotkey · system tray · │
│ signed auto-updater (stable/preview) · single-instance · │
│ first-run bootstrap (installs uv + Python venv) · blank guard │
├────────────────────────────────────────────────────────────────────┤
│ Frontend — React + Vite │
│ Studio · Dub · Stories · Audiobook · Gallery · Catalogue · │
│ Dictation · Batch · Diagnostics — Zustand store · WS bus │
│ ▲ IPC / HTTP + WS │
├──────────────────────────┼─────────────────────────────────────────┤
│ Backend — FastAPI sidecar @ localhost:3900 │
│ 100+ REST endpoints · SSE + WebSocket streaming · │
│ SQLite + Alembic (omnivoice_data/) · OpenAI-compatible API │
├───────────┬───────────┬───────────┬───────────┬────────────────────┤
│ TTS ×16 │ ASR ×11 │ Demucs │ Pyannote │ AudioSeal │
│ clone / │ WhisperX │ vocal │ speaker │ watermark │
│ design │ +10 more │ isolation│ diariz. │ embed / detect │
├───────────┴───────────┴───────────┴───────────┴────────────────────┤
│ Engine routing — per-engine GPU preflight, no silent CPU fallback │
│ Hardware: CUDA · MPS · ROCm (Linux) · CPU (auto-detected) │
│ + optional remote GPU workers on your other machines │
└────────────────────────────────────────────────────────────────────┘
```text
Tauri v2 desktop shell (Rust)
│ IPC
React + Vite UI
│ HTTP · SSE · WebSocket on localhost:3900
FastAPI backend
├── TTS / ASR engine registries
├── dubbing / audio / long-form pipelines
├── OpenAI-compatible API and MCP server
└── SQLite + Alembic → omnivoice_data/
```
<a id="openai-api"></a>
| Layer | Path | Responsibility |
|---|---|---|
| Desktop shell | `frontend/src-tauri/` | Window lifecycle, tray, shortcuts, updater, sidecar bootstrap |
| Frontend | `frontend/src/` | React UI, Zustand state, API and event clients, i18n |
| API | `backend/api/` | REST routes, schemas, auth boundaries, streaming |
| Core services | `backend/services/` | Generation, dubbing, audio processing, persistence |
| Engines | `backend/engines/` | Isolated and optional engine adapters |
| Worker system | `backend/worker/` | Authenticated remote compute and job transport |
| Data | `omnivoice_data/` | Projects, voices, settings, logs, and SQLite state |
| Delivery | `scripts/`, `deploy/`, `.github/workflows/` | Development, packaging, containers, releases, CI |
## 🔌 OpenAI-compatible API
### Network boundary
<div align="center">
- 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.
**Drop-in replacement for OpenAI / ElevenLabs audio.** One line — no key, no code changes:
<a id="api"></a>
## OpenAI-compatible API
Point an OpenAI-compatible audio client at the local backend:
```diff
- base_url="https://api.openai.com/v1"
+ base_url="http://localhost:3900/v1"
```
</div>
Your existing scripts, agents, and OpenAI/ElevenLabs SDK calls now run **locally** on whatever engine you have active. What the cloud can't do: `voice` takes **your own cloned-voice profile IDs**, and `model` can pin a **specific engine** per request.
| Endpoint | What it does |
| Endpoint | Purpose |
|---|---|
| `POST /v1/audio/speech` | TTS — text in; `mp3` / `opus` / `aac` / `flac` / `wav` / `pcm` out. `model`: `tts-1`/`tts-1-hd` (active engine) or a specific one (`voxcpm2`, `cosyvoice`, …). `voice`: a cloned profile ID, `default`, or an OpenAI name (`alloy`, …). `speed` supported. |
| `POST /v1/audio/transcriptions` | STT — audio file in; `json` / `text` / `verbose_json` / `srt` / `vtt` out (`verbose_json` adds word-level timings). `whisper-1` maps to your active ASR engine. |
| `GET /v1/audio/voices` | VoiceStudio extension — lists every voice profile and engine, so clients can discover your clones. |
**Speak with your own cloned voice:**
| `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` |
| `GET /v1/audio/voices` | List local voice profiles and engines |
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:3900/v1", api_key="none") # any string — nothing checks it
# Find your cloned voices: GET /v1/audio/voices lists profile IDs
client = OpenAI(base_url="http://localhost:3900/v1", api_key="local")
with client.audio.speech.with_streaming_response.create(
model="tts-1", voice="<profile-id>", input="Made on my own hardware.") as r:
r.stream_to_file("speech.wav")
# STT
print(client.audio.transcriptions.create(model="whisper-1", file=open("clip.wav", "rb")).text)
model="tts-1",
voice="<profile-id>",
input="Made on my own hardware.",
response_format="wav",
) as response:
response.stream_to_file("speech.wav")
```
Want the whole surface (100+ endpoints)? The full REST API reference is embedded in the app — **Settings → OpenAPI Reference** (Scalar-powered), or the `{}` button in the footer.
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.
Calling the backend from **another machine** (LAN, Tailscale, behind a proxy)? It's loopback-only and unauthenticated by default; to reach it remotely you set a share PIN or an API key, and admin actions require the key — exchanged for short-lived scoped sessions. [docs/api-auth.md](docs/api-auth.md) covers the exact headers, query params, `401`/`403`/`429` meanings, and the `OMNIVOICE_TRUSTED_NETWORKS` exemption.
### Agent skills
### 📓 Run on Google Colab
Install the VoiceStudio skills for Claude Code, Codex, Cursor, and other [skills.sh](https://skills.sh)-compatible agents:
[![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)
No local GPU? The [official notebook](notebooks/OmniVoice_Studio_Colab.ipynb) boots the full app — web UI included — on a free Colab T4, then walks the whole feature surface as a guided tour with inline playback. No tunnels, no API keys.
### 🤝 Agent Skills
Teach your coding agent to speak and listen through your local VoiceStudio — one command, works with **Claude Code, Codex, Cursor, Grok, Kimi, opencode**, and any [skills.sh](https://skills.sh)-compatible agent:
```sh
```bash
npx skills add debpalash/omnivoice-studio
```
Ships two skills: **`omnivoice`** — generate speech (including your cloned voices) and transcribe audio from any agent, free and fully offline — and **`oss-maintainer`** — the maintainer methodology this project is run with.
- `omnivoice`: synthesize speech and transcribe audio through local VoiceStudio.
- `oss-maintainer`: the repository's open-source maintenance workflow.
---
### Google Colab
<a id="roadmap"></a>
[![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)
## 🗺️ Roadmap
The [notebook](notebooks/OmniVoice_Studio_Colab.ipynb) runs the app and web UI on a Colab GPU. Colab is remote compute, so uploaded audio and project data do not remain local to your machine.
What's up next (lip-sync v2, hosted demo, plugin marketplace, real-time voice changer) and the full history of everything shipped so far live in **[docs/ROADMAP.md](docs/ROADMAP.md)**.
<a id="documentation"></a>
---
## Documentation
<a id="sponsor--donate"></a>
| Need | Read |
|---|---|
| Install | [macOS](docs/install/macos.md) · [Windows](docs/install/windows.md) · [Linux](docs/install/linux.md) · [Docker](docs/install/docker.md) |
| 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 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) |
## 💜 Sponsor / Donate
## FAQ
One developer, real AI-agent bills. If VoiceStudio is useful to you, chipping in keeps development full-time — every dollar goes straight to the bills.
<details>
<summary><strong>Does it work on Apple Silicon and Intel Macs?</strong></summary>
Apple Silicon is supported with MPS and MLX options. Intel Macs cannot run the local backend because current PyTorch wheels are unavailable; they can connect to a remote backend. See [macOS installation](docs/install/macos.md).
</details>
<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 1216 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 515 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.
</details>
<details>
<summary><strong>Does VoiceStudio collect data?</strong></summary>
Not unless you opt in. Analytics is off by default and skipping consent keeps it off. When enabled, the app sends allowlisted, content-free usage metadata. Text, audio, file names, voices, and projects are excluded. Change this at **Settings → Privacy**.
</details>
<details>
<summary><strong>How do I remove VoiceStudio and its data?</strong></summary>
Use `scripts/uninstall.sh` on macOS/Linux or `scripts\uninstall.ps1` on Windows. Both show a dry run before deletion. See the [uninstall guide](docs/install/uninstall.md) for every path.
</details>
## Community and contributing
- [GitHub Issues](https://github.com/debpalash/VoiceStudio/issues) for reproducible bugs and feature requests.
- [Discord](https://discord.gg/bzQavDfVV9) for setup help and project discussion.
- [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.
## 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)
## 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.
Optional engines and downloaded models retain their own licenses. The bundled `omnivoice/` model remains Apache-2.0 upstream.
## Acknowledgments
VoiceStudio builds on [OmniVoice](https://github.com/k2-fsa/OmniVoice), [WhisperX](https://github.com/m-bain/whisperX), [Demucs](https://github.com/facebookresearch/demucs), [Pyannote](https://github.com/pyannote/pyannote-audio), [CTranslate2](https://github.com/OpenNMT/CTranslate2), [AudioSeal](https://github.com/facebookresearch/audioseal), [Tauri](https://tauri.app), [Supertonic](https://huggingface.co/Supertone/supertonic-3), [Sherpa-ONNX](https://github.com/k2-fsa/sherpa-onnx), [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS), and [PocketTTS](https://kyutai.org).
<div align="center">
<img src="https://img.shields.io/badge/raised_%2410_of_%24200-5%25-EAB308?style=for-the-badge" alt="This month's agent-bill fund: $10 / $200" />
<br/><br/>
<a href="https://ko-fi.com/debpalash"><img src="https://img.shields.io/badge/Ko--fi-Support_❤️-FF5E5B?style=for-the-badge&logo=ko-fi&logoColor=white" alt="Ko-fi" /></a>
&nbsp;&nbsp;
<a href="https://paypal.me/palashCoder"><img src="https://img.shields.io/badge/PayPal-Donate-00457C?style=for-the-badge&logo=paypal&logoColor=white" alt="PayPal" /></a>
</div>
<a id="sponsors"></a>
### 🌟 Sponsors
VoiceStudio is **free** and **AGPL-3.0** — no paid tier, no SaaS revenue. Sponsors keep development going, and in return get a logo slot here, in the app, and (for top tiers) on the project website. It's a thank-you, never a paywall. **[See tiers & become a sponsor →](SPONSORS.md)**
<div align="center">
<!-- SPONSORS:START — logo slots are filled here as sponsors come aboard; see SPONSORS.md -->
**Your logo here** — [become a sponsor](SPONSORS.md)
<!-- SPONSORS:END -->
</div>
---
## 💬 Community
<div align="center">
<a href="https://discord.gg/bzQavDfVV9"><img src="https://img.shields.io/badge/💬_Discord-Join_Community-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Join Discord" /></a>
<a href="https://x.com/idebpalash"><img src="https://img.shields.io/badge/𝕏_Follow-for_updates-000000?style=for-the-badge&logo=x&logoColor=white" alt="Follow on X" /></a>
<br/>
<sub>Release news, setup help, GPU troubleshooting, feature votes, and showing off your dubs. We respond to setup questions within hours, not days.</sub>
</div>
---
<a id="contributing"></a>
## 🤝 Contributing
Yes please — bug fixes, new TTS engine adapters, UI improvements, docs, translations. All of it. Start with the **[Contributing Guide](.github/CONTRIBUTING.md)** (setup, code style, PR workflow), browse [good first issues](https://github.com/debpalash/VoiceStudio/labels/good%20first%20issue), or ask in [Discord](https://discord.gg/bzQavDfVV9).
---
## ❓ FAQ
<details>
<summary><b>Does it work on Apple Silicon (M1/M2/M3/M4)?</b></summary>
<br/>
Yes. MPS acceleration is auto-detected. MLX-optimized Whisper models are available for faster transcription on Apple hardware. <b>Intel Macs are not supported</b>: the app UI installs, but the local Python backend cannot run because PyTorch no longer ships Intel-Mac wheels (<a href="https://github.com/debpalash/VoiceStudio/issues/889">#889</a>) — an Intel Mac can only be used with a remote backend.
</details>
<details>
<summary><b>How much VRAM do I need?</b></summary>
<br/>
<b>4 GB minimum.</b> With ≤8 GB, the TTS model is automatically offloaded to CPU during transcription. With 8+ GB, everything runs on GPU simultaneously. No GPU at all? CPU mode works — just slower (~3× for TTS). You can also lend a GPU from another machine you own via <a href="docs/remote-workers.md">remote workers</a>.
</details>
<details>
<summary><b>What languages are supported?</b></summary>
<br/>
646 languages for TTS via the VoiceStudio model. Transcription (WhisperX) supports 99 languages. Translation coverage depends on the target language pair.
</details>
<details>
<summary><b>Why doesn't a longer reference clip sound more like me?</b></summary>
<br/>
Because VoiceStudio's cloning is <b>zero-shot</b>: your clip is a <i>prompt</i> the model conditions on — it is never trained on, and past a short window extra audio is simply unused (the dubbing pipeline targets ~8 s and hard-caps at 15 s). <b>What moves clone quality is the clip, not its length</b>: record 515 seconds of continuous natural speech, close to the mic, in a quiet room with no reverb or music, one speaker, delivered in the tone and pace you want — the clone copies your delivery, not just your timbre. Want trained-on-your-voice fidelity? That's offline fine-tuning, not an in-app button: <a href="docs/data_preparation.md">docs/data_preparation.md</a> + <a href="docs/training.md">docs/training.md</a>.
</details>
<details>
<summary><b>Can I use this commercially?</b></summary>
<br/>
<b>Yes — commercial use is free</b> under the <a href="https://www.gnu.org/licenses/agpl-3.0.html">AGPL-3.0</a>: run it, sell the audio you make, dub client videos, deploy it across your team. One obligation: if you <b>modify</b> VoiceStudio and offer the modified version to others over a network, you must share that modified source under the same terms. Embedding it in a closed-source product instead? A commercial license is available — see <a href="#license">License</a>.
</details>
<details>
<summary><b>Can I add my own TTS engine?</b></summary>
<br/>
Yes. Subclass <code>TTSBackend</code> in <code>backend/services/tts_backend.py</code> and add it to the <code>_REGISTRY</code> dictionary — ~50 lines. The sixteen built-in engines all work this way; see <a href="#tts-engines">TTS Engines</a> and <a href="docs/engine-acceptance.md">docs/engine-acceptance.md</a>.
</details>
<details>
<summary><b>Does VoiceStudio collect any data about me?</b></summary>
<br/>
<b>Not unless you explicitly say yes.</b> On first run the app <i>asks</i> — one screen, two equal-weight buttons, no pre-ticked box — and until you answer yes, VoiceStudio sends nothing: no analytics, no telemetry, no accounts, no phone-home. Skipping the question means no. Your text, audio, voices, and projects never leave your machine either way.
If you do opt in (also togglable anytime under <b>Settings → Privacy → "Help improve VoiceStudio"</b>), what's sent is anonymous, content-free usage stats: generations (engine, language, generation time, character <i>count</i>, error <i>type</i>), plus app lifecycle — an install ping, updates (version-to-version), crashes (error class and a <i>bucketed</i> uptime, never logs), error <i>types</i> (capped, deduplicated), and a single uninstall ping if you remove it. Never your text, audio, file names, or anything identifying — enforced in code by a property allowlist (<code>backend/core/analytics.py</code>), not just a promise. Every build — installer, Docker, or built from source — asks the same first-run question and stays off unless you say yes. Your own numbers live in <b>Settings → Usage</b>, computed locally, sent nowhere.
</details>
<details>
<summary><b>How do I uninstall it / remove all its data?</b></summary>
<br/>
VoiceStudio is fully local — uninstalling is just deleting the app plus the folders it wrote (model cache, Python env, your voices/projects, config). Run <code>scripts/uninstall.sh</code> (macOS/Linux) or <code>scripts\uninstall.ps1</code> (Windows) — it prints every folder with its size as a dry-run first, then deletes on <code>--yes</code>. The full per-platform path list and app-removal steps are in <a href="docs/install/uninstall.md"><b>docs/install/uninstall.md</b></a>.
</details>
---
<a id="license"></a>
## 📜 License
VoiceStudio is free and open-source software under the [**GNU Affero General Public License v3.0 (AGPL-3.0)**](https://www.gnu.org/licenses/agpl-3.0.html).
**Free for any use — including commercial and internal business use.** Run it, sell the audio you produce with it, dub your own or clients' videos, roll it out across your team — all free, no license needed. As a **network copyleft** license, AGPL adds one obligation: if you **modify** VoiceStudio and offer that modified version to others over a network, you must make the complete corresponding source of your modified version available to them under the same AGPL-3.0 terms.
A **commercial license** is available for organizations that want to embed VoiceStudio in a **closed-source or proprietary** product or service without the AGPL-3.0 copyleft obligations. **Pricing tiers coming soon.** Inquiries: **VoiceStudio@palash.dev**.
The bundled `omnivoice/` TTS model by Han Zhu remains Apache-2.0 upstream. See [`LICENSE`](LICENSE) for the full, binding terms, and [`LICENSE-NOTICE.md`](LICENSE-NOTICE.md) for the plain-language summary and scope.
---
## 🙏 Acknowledgments
VoiceStudio stands on exceptional open-source work: [OmniVoice (k2-fsa)](https://github.com/k2-fsa/OmniVoice) — the core zero-shot TTS model · [WhisperX](https://github.com/m-bain/whisperX) · [Demucs](https://github.com/facebookresearch/demucs) · [Pyannote](https://github.com/pyannote/pyannote-audio) · [CTranslate2](https://github.com/OpenNMT/CTranslate2) · [AudioSeal](https://github.com/facebookresearch/audioseal) · [Tauri](https://tauri.app) · [Supertonic](https://huggingface.co/Supertone/supertonic-3) · [Sherpa-ONNX](https://github.com/k2-fsa/sherpa-onnx) · [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) · [Kyutai PocketTTS](https://kyutai.org) — thank you.
<a id="more-from-the-maker"></a>
### 🧰 More local open-source from the maker
[**Opal** 💠](https://github.com/debpalash/Opal) — play everything: the media player for the AI era · [**memxt** 🧠](https://github.com/debpalash/memxt) — local long-term memory for coding agents. Same rule: **your data stays on your machine.**
---
<div align="center">
<br/>
If you read this far, you're our kind of person.<br/>
**[⭐ Star this repo](https://github.com/debpalash/VoiceStudio)** so others can find it too.<br/>
**[💬 Join the Discord](https://discord.gg/bzQavDfVV9)** to share what you build.<br/>
**[❤️ Support development](https://ko-fi.com/debpalash)** — fund the AI agent bills that keep VoiceStudio shipping.
<br/>
<a href="https://star-history.com/#debpalash/VoiceStudio&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=debpalash/VoiceStudio&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=debpalash/VoiceStudio&type=Date" />
<img alt="Star History" src="https://api.star-history.com/svg?repos=debpalash/VoiceStudio&type=Date&theme=dark" width="600" />
</picture>
</a>
<strong><a href="https://github.com/debpalash/VoiceStudio/releases/latest">Download VoiceStudio</a></strong> ·
<a href="https://github.com/debpalash/VoiceStudio">Star the project</a> ·
<a href="https://discord.gg/bzQavDfVV9">Join Discord</a>
</div>
+59 -52
View File
@@ -37,7 +37,7 @@
<br/>
<div align="center">
<img src="docs/screenshot-launchpad.png" alt="VoiceStudio — 启动台" width="100%"/>
<img src="docs/media/0.5.0/quick-switch.gif" alt="VoiceStudio — 从状态栏快速切换 TTS 引擎" width="100%"/>
</div>
> **声音很私人,创作空间也应该真正属于你。** VoiceStudio 的核心流程运行在你的硬件上:克隆、设计、配音、听写,并以 646 种语言创作,不需要订阅,也没有用量计费。联网引擎和服务始终是清晰可见的可选项,而不是隐藏依赖。
@@ -45,6 +45,56 @@
> [!WARNING]
> **活跃 Beta 阶段。** 各版本之间可能出现故障——如需最新修复,请从源码运行。非常欢迎 Bug 报告和 PR:[提交 Issue](https://github.com/debpalash/VoiceStudio/issues) 或 [加入 Discord](https://discord.gg/bzQavDfVV9)。
<a id="quickstart"></a>
## ⚡ 快速开始
<div align="center">
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/macOS-DMG_(Apple_Silicon)-000?style=for-the-badge&logo=apple&logoColor=white" alt="下载 macOS DMG" /></a>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/Windows-MSI_(x64)-0078D4?style=for-the-badge&logo=windows&logoColor=white" alt="下载 Windows MSI" /></a>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/Linux-AppImage_(x64)-FCC624?style=for-the-badge&logo=linux&logoColor=black" alt="下载 Linux AppImage" /></a>
<br/>
<sub>三个按钮都会打开最新发布页——在资源列表中下载对应你系统的安装包。</sub><br/>
<sub><b>macOS</b>首次启动需要一次性批准——右键点击 → <b>打开</b>macOS 15 上为 系统设置 → 隐私与安全性 → <b>“仍要打开”</b>)。无需终端。<a href="docs/install/macos.md#gatekeeper-quarantine">为什么?</a> · <b>Intel Mac</b>不支持本地后端(<a href="https://github.com/debpalash/VoiceStudio/issues/889">#889</a>)——<a href="docs/install/macos.md">详情</a>。</sub>
</div>
选择你的操作系统,按指南从头到尾操作:
- 🍎 **macOS** — [docs/install/macos.md](docs/install/macos.md)
- 🪟 **Windows** — [docs/install/windows.md](docs/install/windows.md)
- 🐧 **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)
**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。
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)。
> 正在从 **[CorentinJ/Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning)**(现已归档)迁移过来?我们有专门的迁移指南:[docs/migration/real-time-voice-cloning.md](docs/migration/real-time-voice-cloning.md)。
<details>
<summary><b>🧰 卡住了?自检、Token 与受限网络</b></summary>
<br/>
先运行内置自检——在应用中打开 **设置 → 关于 → “运行自检”**,或在源码检出目录中执行
`uv run python backend/main.py --diagnose`(加 `--deep` 还会实际加载当前引擎进行测试)。然后查看
[docs/install/troubleshooting.md](docs/install/troubleshooting.md) 中排名前
10 的安装错误。运行时出错时,应用内的错误界面会直接深链到对应条目;**设置 → 关于 →
“保存诊断包”** 会把脱敏日志与自检报告打包,方便附在 Bug 报告里。
Hugging Face Token 的配置见
[docs/setup/huggingface-token.md](docs/setup/huggingface-token.md)。说话人分离相关的模型访问门槛见
[docs/features/diarization.md](docs/features/diarization.md)。下载速度、⚡ 快速下载(Xet)状态,以及受限网络 / 镜像选项见
[docs/downloading-models.md](docs/downloading-models.md)。
</details>
---
<a id="features"></a>
## ✨ 功能
@@ -112,49 +162,6 @@
---
<a id="quickstart"></a>
## ⚡ 快速开始
<div align="center">
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/macOS-DMG_(Apple_Silicon)-000?style=for-the-badge&logo=apple&logoColor=white" alt="下载 macOS DMG" /></a>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/Windows-MSI_(x64)-0078D4?style=for-the-badge&logo=windows&logoColor=white" alt="下载 Windows MSI" /></a>
<a href="https://github.com/debpalash/VoiceStudio/releases/latest"><img src="https://img.shields.io/badge/Linux-AppImage_(x64)-FCC624?style=for-the-badge&logo=linux&logoColor=black" alt="下载 Linux AppImage" /></a>
<br/>
<sub><b>macOS</b>首次启动需要一次性批准——右键点击 → <b>打开</b>macOS 15 上为 系统设置 → 隐私与安全性 → <b>“仍要打开”</b>)。无需终端。<a href="docs/install/macos.md#gatekeeper-quarantine">为什么?</a> · <b>Intel Mac</b>不支持本地后端(<a href="https://github.com/debpalash/VoiceStudio/issues/889">#889</a>)——<a href="docs/install/macos.md">详情</a>。</sub>
</div>
选择你的操作系统,按指南从头到尾操作:
- 🍎 **macOS** — [docs/install/macos.md](docs/install/macos.md)
- 🪟 **Windows** — [docs/install/windows.md](docs/install/windows.md)
- 🐧 **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)
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。
> 正在从 **[CorentinJ/Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning)**(现已归档)迁移过来?我们有专门的迁移指南:[docs/migration/real-time-voice-cloning.md](docs/migration/real-time-voice-cloning.md)。
<details>
<summary><b>🧰 卡住了?自检、Token 与受限网络</b></summary>
<br/>
先运行内置自检——在应用中打开 **设置 → 关于 → “运行自检”**,或在源码检出目录中执行
`uv run python backend/main.py --diagnose`(加 `--deep` 还会实际加载当前引擎进行测试)。然后查看
[docs/install/troubleshooting.md](docs/install/troubleshooting.md) 中排名前
10 的安装错误。运行时出错时,应用内的错误界面会直接深链到对应条目;**设置 → 关于 →
“保存诊断包”** 会把脱敏日志与自检报告打包,方便附在 Bug 报告里。
Hugging Face Token 的配置见
[docs/setup/huggingface-token.md](docs/setup/huggingface-token.md)。说话人分离相关的模型访问门槛见
[docs/features/diarization.md](docs/features/diarization.md)。下载速度、⚡ 快速下载(Xet)状态,以及受限网络 / 镜像选项见
[docs/downloading-models.md](docs/downloading-models.md)。
</details>
---
<a id="why-voicestudio"></a>
## 💡 为什么选择 VoiceStudio
@@ -173,8 +180,8 @@ Hugging Face Token 的配置见
| **API 密钥** | 需要账号 | 本地流程不需要 |
| **GPU 支持** | 不适用(云端) | CUDA · Apple Silicon · ROCmLinux)· CPU |
| **桌面应用** | ❌ | ✅ macOS · Windows · Linux |
| **TTS 引擎** | 1 | **14** — [完整矩阵](#tts-engines) |
| **ASR 引擎** | 1 | **10** — [完整阵容](#asr-engines) |
| **TTS 引擎** | 1 | **16** — [完整矩阵](#tts-engines) |
| **ASR 引擎** | 1 | **11** — [完整阵容](#asr-engines) |
| **MCP 服务器** | ❌ | ✅ 可从 Claude、Cursor 及任何 MCP 客户端使用 |
| **自检** | ❌ | ✅ 诊断套件、错误日志、脱敏调试包 |
| **可定制** | ❌ 闭源 | ✅ 随你 Fork、扩展、发布 |
@@ -214,10 +221,10 @@ Hugging Face Token 的配置见
### 🗣️ TTS 引擎
**14 个引擎,一个选择器。** VoiceStudio(默认,支持 600+ 语言)始终可用;另有七个引擎可选装并自动检测(CosyVoice 3、GPT-SoVITS、VoxCPM2、MOSS-TTS-Nano、KittenTTS、MLX-Audio、Sherpa-ONNX),外加个按需延迟安装的重量级引擎(IndexTTS 2.5、OmniVoice GGUF、Supertonic 3、MOSS-TTS-v1.5、dots.tts、Confucius4-TTS)。在 **设置 → TTS 引擎** 中切换;所选引擎将应用于所有语音合成场景。
**16 个引擎,一个选择器。** VoiceStudio(默认,支持 600+ 语言)始终可用;另有七个引擎可选装并自动检测(CosyVoice 3、GPT-SoVITS、VoxCPM2、MOSS-TTS-Nano、KittenTTS、MLX-Audio、Sherpa-ONNX),外加个按需延迟安装的引擎(IndexTTS 2.5、OmniVoice GGUF、OmniVoice 子进程版、PocketTTS、Supertonic 3、MOSS-TTS-v1.5、dots.tts、Confucius4-TTS)。在 **设置 → TTS 引擎** 中切换;所选引擎将应用于所有语音合成场景。**每个引擎都有独立指南:[docs/engines](docs/engines/README.md)(英文)。**
<details>
<summary><b>📊 完整矩阵</b>——14 个引擎 × 平台 × 克隆/指令 × 许可证</summary>
<summary><b>📊 完整矩阵</b>——16 个引擎 × 平台 × 克隆/指令 × 许可证</summary>
<br/>
@@ -254,10 +261,10 @@ Hugging Face Token 的配置见
### 🎧 ASR 引擎
**10 个引擎**——它们驱动听写、视频配音和字幕。**WhisperX** 是跨平台的默认引擎(约 100 种语言,词级时间对齐);其余引擎均为可选装并自动检测。在 **设置 → 引擎** 中切换。个完全在本地设备上运行;第十个(OpenAI 兼容)是可选的远程客户端,可用于 Qwen3-ASR 或任何兼容的服务器。
**11 个引擎**——它们驱动听写、视频配音和字幕。**WhisperX** 是跨平台的默认引擎(约 100 种语言,词级时间对齐);其余引擎均为可选装并自动检测。在 **设置 → 引擎** 中切换。个完全在本地设备上运行;第十个(OpenAI 兼容)是可选的远程客户端,可用于 Qwen3-ASR 或任何兼容的服务器。
<details>
<summary><b>📊 完整阵容</b>——10 个引擎、各自的强项与计算类型说明</summary>
<summary><b>📊 完整阵容</b>——11 个引擎、各自的强项与计算类型说明</summary>
<br/>
@@ -274,7 +281,7 @@ Hugging Face Token 的配置见
| **sherpa-onnx**(实时听写) | `sherpa-onnx-asr` | 25 种欧洲语言 + 90+ | 实时、快于实时的听写——小体积流式/离线 ONNX 模型(Parakeet TDT v3/v2、流式 Zipformer 与 Paraformer、Whisper Tiny),CPU 运行,macOS / Windows / Linux 表现完全一致。在 **设置 → 语音** 中按模型选择。 |
| **OpenAI 兼容** ⚠️ 远程 | `openai-compat-asr` | 取决于服务器 | 当下通往 **Qwen3-ASR** 的路径(自托管服务器,无需等 transformers 支持)、任何 OpenAI 兼容的转录端点,或 OpenAI 官方 API——无需安装,在 **设置 → 引擎**(ASR 标签页)中配置并测试连接。音频会离开你的设备,发送到你指定的任何服务器;参见 [docs/engines/openai-compatible-asr.md](docs/engines/openai-compatible-asr.md)。 |
> Whisper 系列引擎覆盖约 100 种语言;**FunASR / SenseVoice** 额外提供一条多语言一体化路径,内置语音活动检测与行内说话人分离。**sherpa-onnx** 驱动实时听写的模型选择器——你边说,文字边出现。每个引擎都在本地设备上运行——无需 API 密钥,无需云端。
> Whisper 系列引擎覆盖约 100 种语言;**FunASR / SenseVoice** 额外提供一条多语言一体化路径,内置语音活动检测与行内说话人分离。**sherpa-onnx** 驱动实时听写的模型选择器——你边说,文字边出现。除可选的 OpenAI 兼容远程客户端外,所有引擎都在本地设备上运行——无需 API 密钥,无需云端。
> **GPU 不支持高效 float16** 在较老的 NVIDIA GPUMaxwell/Pascal、GTX 16xx)上,或在 CTranslate2/cuDNN 版本不匹配之后,CTranslate2 系 ASR 引擎(WhisperX、Faster-Whisper)无法运行 `float16`VoiceStudio 会自动改用 `int8` 重试——无需配置。如果转录仍然失败,可用 `ASR_COMPUTE_TYPE` 环境变量固定计算类型(逃生舱口):`ASR_COMPUTE_TYPE=int8`CPU 用 `float32`)。将其设为 `int8` 并重启后端。
@@ -574,7 +581,7 @@ VoiceStudio 站在这些杰出开源工作的肩膀上:
## 🧰 来自同一作者的更多本地开源项目
喜欢这种本地优先的理念?它是一脉相承的——同一位作者,同一条准则:**你的数据只留在你的设备上。**
喜欢这种本地优先的理念?它是一脉相承的——同一位作者,同一条准则:**你的数据只留在你的设备上。** 全部项目见 [palash.dev](https://palash.dev)。
<table>
<tr>
+27 -2
View File
@@ -157,6 +157,31 @@ def require_loopback(request: Request) -> None:
raise HTTPException(status_code=403, detail="loopback origin required")
def _admin_gate_403() -> None:
"""Raise the admin-gate 403 with a detail that states what would ACTUALLY
satisfy the gate. The bundled UI routes any 403 whose detail mentions
"admin api key" to the API-key login form (frontend ``client.ts``; the
literal contract is locked by ``tests/test_auth_gate_detail_lockstep.py``),
so the wording must not name a key where presenting one cannot help.
The detail names the key only when the gate would accept one: server mode
WITH an API key configured. Every other rejection desktop mode (the
credential checks in the callers only run under server mode) and a
server-mode deployment with only a share PIN or nothing configured keeps
the plain loopback detail, because only loopback can use admin there.
Naming the key in those cases would trap a LAN-share guest in a login
form that can never succeed (#1213, #1525; PR #1569 review).
"""
raise HTTPException(
status_code=403,
detail=(
"loopback origin or admin API key required"
if _server_mode() and remote_api_key()
else "loopback origin required"
),
)
def require_admin(request: Request) -> None:
"""Gate RCE/filesystem-capable admin routers.
@@ -180,7 +205,7 @@ def require_admin(request: Request) -> None:
return
if _request_presents_admin_credential(request):
return
raise HTTPException(status_code=403, detail="loopback origin or admin API key required")
_admin_gate_403()
def require_admin_action(request: Request) -> None:
@@ -198,7 +223,7 @@ def require_admin_action(request: Request) -> None:
side_effectful_get=True,
):
return
raise HTTPException(status_code=403, detail="loopback origin or admin API key required")
_admin_gate_403()
def require_desktop(request: Request) -> None:
+77
View File
@@ -133,6 +133,83 @@ def set_torch_compile_disabled(body: _TorchCompileBody):
return _torch_compile_state()
# ── Compute-device override (Settings → Performance) ──────────────────────
class _ComputeDeviceBody(BaseModel):
value: str = Field(..., description="auto | cuda | rocm | xpu | mps | cpu")
def _compute_device_state() -> dict:
"""Everything the Performance panel needs to render the device control:
the resolved pick (env > prefs > auto), what this process actually applied
at probe time (differs after a change until restart caps are immutable
per process), what auto would pick, and which families exist here."""
from core import device_caps
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),
"cpu",
)
value = device_caps.requested_device_override()
return {
"value": value,
"applied": caps.requested_family,
"restart_required": value != caps.requested_family,
# The running process asked for a family it doesn't have (env pin on
# the wrong machine, hardware removed): auto is in effect, and a
# restart would not change that — the panel says so instead of
# pretending the pick took.
"override_ignored": (
caps.requested_family not in ("auto", caps.family)
),
"effective_family": caps.family,
"auto_family": auto_family,
"available_families": list(caps.available_families),
"env_pinned": env_pin in device_caps.DEVICE_OVERRIDE_CHOICES and env_pin != "",
"choices": list(device_caps.DEVICE_OVERRIDE_CHOICES),
}
@router.get("/compute-device")
def get_compute_device():
"""Current compute-device override state (Settings → Performance)."""
return _compute_device_state()
@router.put("/compute-device")
def set_compute_device(body: _ComputeDeviceBody):
"""Persist the compute-device pick. Applied by the capability probe at
the next backend start (host caps are immutable per process same
restart contract as the rest of the Performance tab). ``OMNIVOICE_DEVICE``
always wins over this pick; the UI shows the pin instead of pretending."""
from core import device_caps, prefs
value = (body.value or "").strip().lower()
if value not in device_caps.DEVICE_OVERRIDE_CHOICES:
raise HTTPException(
status_code=400,
detail=f"Unknown device '{value}'. Valid: {', '.join(device_caps.DEVICE_OVERRIDE_CHOICES)}",
)
caps = device_caps.detect_host_caps()
if value not in ("auto", "cpu") and value not in caps.available_families:
raise HTTPException(
status_code=400,
detail=(
f"'{value}' is not available on this host "
f"(have: {', '.join(caps.available_families)})"
),
)
try:
prefs.set_("compute_device", value)
except Exception:
logger.exception("set_compute_device failed")
raise HTTPException(status_code=500, detail="Failed to persist setting")
return _compute_device_state()
# ── Generation-history retention (Studio takes rail) ──────────────────────
+49
View File
@@ -374,6 +374,33 @@ class HostCaps:
probe_ok: bool = True
"""``False`` only when torch could not be imported (degraded CPU-only)."""
requested_family: str = "auto"
"""The user's compute-device override as requested — ``"auto"`` when none.
``family`` reflects what was actually honored: an override that names a
family this host doesn't have is noted and ignored, never obeyed blindly."""
#: Every value the compute-device override accepts. "auto" = today's
#: priority pick; "cpu" is always honorable (invariant: cpu is always
#: available); accelerator names are honored only when detected.
DEVICE_OVERRIDE_CHOICES: tuple[str, ...] = ("auto", "cuda", "rocm", "xpu", "mps", "cpu")
def requested_device_override() -> str:
"""The user's compute-device pick: ``OMNIVOICE_DEVICE`` env > the Settings
choice (``compute_device`` in prefs.json) > ``"auto"``. Env wins so
power-users can pin a device without the UI silently undoing it (same
resolution order as engine selection, #981). Unknown values normalize to
``"auto"`` the probe must never raise."""
try:
from core import prefs
raw = prefs.resolve("compute_device", env="OMNIVOICE_DEVICE", default="auto")
except Exception:
raw = os.environ.get("OMNIVOICE_DEVICE", "auto")
val = str(raw or "auto").strip().lower()
return val if val in DEVICE_OVERRIDE_CHOICES else "auto"
def _probe() -> HostCaps:
"""Run the probe once. Enumerates every failure branch from the spec's
@@ -386,6 +413,7 @@ def _probe() -> HostCaps:
available_families=("cpu",),
notes=("torch not importable; treating host as CPU-only",),
probe_ok=False,
requested_family=requested_device_override(),
)
notes: list[str] = []
@@ -507,6 +535,26 @@ def _probe() -> HostCaps:
# available_families: every detected accelerator + cpu, deduped, cpu last.
available: tuple[DeviceFamily, ...] = tuple(dict.fromkeys([*detected, "cpu"]))
# User override (Settings → Performance, or OMNIVOICE_DEVICE): honored
# only when the named family actually exists on this host — an override
# can steer, it cannot invent hardware. Applied here, at the single
# choke point, so routing, model loads (get_best_device delegates its
# family decision here), and every badge inherit it for free.
requested = requested_device_override()
if requested != "auto":
if requested in available:
if requested != family:
notes.append(
f"compute device pinned to '{requested}' by user override "
f"(auto would pick '{family}')"
)
family = requested # type: ignore[assignment]
else:
notes.append(
f"requested compute device '{requested}' is not available on "
f"this host (have: {', '.join(available)}) — using '{family}'"
)
return HostCaps(
family=family,
available_families=available,
@@ -515,6 +563,7 @@ def _probe() -> HostCaps:
driver=driver,
notes=tuple(notes),
probe_ok=True,
requested_family=requested,
)
+12 -6
View File
@@ -17,6 +17,13 @@ _WINDOWS_RESERVED_NAMES = frozenset({"CON", "PRN", "AUX", "NUL"}) | frozenset(
f"{prefix}{number}" for prefix in ("COM", "LPT") for number in range(1, 10)
)
# Both separator families, so a stored sub-path splits into the same components
# on every host. Windows accepts ``/`` as a real separator, so splitting on
# ``os.sep`` alone left ``"job/out.mp4"`` as a single component there while the
# identical value split cleanly on POSIX. POSIX input never reaches this with a
# backslash — it is rejected as a foreign separator before the split.
_PATH_SEPARATORS = re.compile(r"[\\/]")
class UnsafePath(ValueError):
"""Raised when a path crosses its allowed filesystem boundary."""
@@ -52,11 +59,10 @@ def resolve_within(root: os.PathLike[str] | str, value: os.PathLike[str] | str)
raw = os.fspath(value) if value is not None else ""
if not isinstance(raw, str) or not raw:
raise UnsafePath("path is empty")
# Treat both separator families as structural on every host. Otherwise a
# Windows traversal string is an innocent-looking filename when validated
# on Linux (and can become dangerous after persisted data is moved).
if os.sep != "\\" and ("\\" in raw or bool(ntpath.splitdrive(raw)[0])):
raise UnsafePath("path uses a foreign separator or drive")
# Treat both separator families as structural on every host while still
# rejecting Windows drive paths before rebuilding relative components.
if os.sep != "\\" and bool(ntpath.splitdrive(raw)[0]):
raise UnsafePath("path uses a drive")
root_path = Path(root).expanduser().resolve(strict=False)
root_text = str(root_path)
if os.path.isabs(raw):
@@ -69,7 +75,7 @@ def resolve_within(root: os.PathLike[str] | str, value: os.PathLike[str] | str)
# containment proof explicit to static analysis, this rejects empty,
# dot, parent, drive, and separator-bearing components before Path sees
# any persisted/request-derived string.
parts = raw.split(os.sep)
parts = _PATH_SEPARATORS.split(raw)
clean_parts: list[str] = []
for part in parts:
clean = os.path.basename(part)
+125
View File
@@ -0,0 +1,125 @@
"""Startup progress ledger — what the backend is doing before it can serve.
Why this exists: the project's #1 lifetime failure class is "can't reach the
local backend", and a large slice of it was never a dead backend at all —
just one that couldn't say "I'm starting, currently loading PyTorch" because
nothing listened until every heavy import and migration finished. main.py now
binds the socket early and defers the heavy work; this module is the shared
state the early `/health` + `/startup/progress` endpoints report from while
that work runs.
Thread-safety: the deferred init runs Phase A in an executor thread while the
event loop serves probes, so every mutation and snapshot takes the lock.
"""
from __future__ import annotations
import threading
import time
# Execution order matters only for display; the ledger records whatever order
# steps actually begin in. Keep ids stable — the desktop shell field-sniffs
# them and tests pin them.
STEPS: "dict[str, str]" = {
"env_prefs": "Restoring settings…",
"native_preload": "Preparing GPU libraries…",
"ml_imports": "Loading ML runtime (PyTorch)…",
"api_routes": "Loading API routes…",
"db_migrate": "Preparing database…",
"services_start": "Starting background services…",
}
_lock = threading.Lock()
_t0 = time.monotonic()
_current: "str | None" = None
_done: "list[tuple[str, float]]" = [] # (step_id, seconds it took)
_started_at: float = 0.0
_ready = False
_error: "dict | None" = None
def begin_step(step_id: str) -> None:
global _current, _started_at
with _lock:
_finish_current_locked()
_current = step_id
_started_at = time.monotonic()
def _finish_current_locked() -> None:
global _current
if _current is not None:
_done.append((_current, round(time.monotonic() - _started_at, 2)))
_current = None
def mark_ready() -> None:
global _ready
with _lock:
_finish_current_locked()
_ready = True
def fail(message: str) -> None:
"""Record a startup failure against the step that was running."""
global _error
with _lock:
_error = {"step": _current, "message": str(message)[:500]}
def is_ready() -> bool:
with _lock:
return _ready
def current_step() -> "tuple[str | None, str | None]":
"""(step_id, human label) of the active step, or (None, None)."""
with _lock:
if _current is None:
return None, None
return _current, STEPS.get(_current, _current)
def snapshot() -> dict:
"""The `/startup/progress` body. Always safe to call, never raises."""
with _lock:
if _error is not None:
status = "failed"
elif _ready:
status = "ready"
else:
status = "starting"
states = {sid: "pending" for sid in STEPS}
for sid, _t in _done:
states[sid] = "done"
if _current is not None:
states[_current] = "active"
if _error is not None and _error.get("step"):
states[_error["step"]] = "failed"
durations = dict(_done)
return {
"status": status,
"step": _current,
"label": STEPS.get(_current, _current) if _current else None,
"steps": [
{
"id": sid,
"label": label,
"state": states.get(sid, "pending"),
**({"t": durations[sid]} if sid in durations else {}),
}
for sid, label in STEPS.items()
],
"elapsed_s": round(time.monotonic() - _t0, 2),
"error": _error,
}
def _reset_for_tests() -> None:
global _current, _ready, _error, _started_at
with _lock:
_current = None
_done.clear()
_ready = False
_error = None
_started_at = 0.0
+19 -3
View File
@@ -85,11 +85,27 @@ def _get_model():
global _model
if _model is None:
from faster_whisper import WhisperModel
name = os.environ.get("ASR_MODEL_FW", "large-v3")
# Same weights as in-process faster-whisper: ASR_MODEL_FASTER selects
# for BOTH variants, ASR_MODEL_FW stays as a sidecar-only override.
# Before this, the sidecar read only ASR_MODEL_FW while the download
# preflight read ASR_MODEL_FASTER — set one and the other variant (or
# the preflight) quietly used a different model.
name = (
os.environ.get("ASR_MODEL_FW")
or os.environ.get("ASR_MODEL_FASTER")
or "large-v3"
)
try:
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
# The probe honors the user compute-device override and the
# ROCm/CT2 incompatibility (#1529) — the child must agree with
# the parent's device decision, not re-derive its own.
from core.device_caps import detect_host_caps
device = "cuda" if detect_host_caps().family == "cuda" else "cpu"
except Exception:
# Fail SAFE: guessing "cuda" from torch here would bypass a cpu
# override and hand CTranslate2 HIP-flavoured cuda on ROCm
# (#1529). CPU always works; say why in the sidecar log.
print("asr-sidecar: device probe failed — using cpu", file=sys.stderr, flush=True)
device = "cpu"
# Degrade fp16 → int8 rather than crash on GPUs without efficient fp16
# (older Maxwell/Pascal, GTX 16xx, CTranslate2/cuDNN mismatch) (#551).
@@ -353,6 +353,9 @@ def _make_backend_class():
display_name = "OmniVoice (GGUF, hardware-adaptive)"
gpu_compat = ("cuda", "mps", "cpu")
supports_voice_design = False
# Every generate() spawns the external binary — allocations live in
# that process, invisible to parent-side accelerator counters.
runs_out_of_process = True
# 24 kHz mono Higgs Audio v2 — same as the in-process OmniVoice.
_SAMPLE_RATE = 24_000
+626 -401
View File
File diff suppressed because it is too large Load Diff
+28 -4
View File
@@ -2325,7 +2325,7 @@ _INSTALL_HINTS: dict[str, str] = {
"mac-ARM source installs since 0.3.22. Parakeet TDT v3 on the GPU via "
"MLX: 25 European languages, word timestamps, ~2 GB unified memory.)"
),
"moonshine": "pip install useful-moonshine (edge/CPU-optimized ASR)",
"moonshine": "uv pip install moonshine-onnx (or moonshine-voice; edge/CPU-optimized ASR)",
"funasr": "pip install funasr (SenseVoiceSmall + FSMN-VAD; CUDA or CPU)",
"sherpa-onnx-asr": "uv add sherpa-onnx (ONNX live dictation; CPU, cross-platform)",
"openai-compat-asr": (
@@ -2495,7 +2495,23 @@ def _ctranslate2_cuda_ok() -> bool:
CUDA runtime version" — the #1529 report, an AMD RX 7900 XTX in the
:rocm Docker image. Real CUDA only; ROCm hosts take the CPU path here
(auto-detect prefers pytorch-whisper there, which does use HIP).
Also honors the user compute-device override (Settings Performance /
``OMNIVOICE_DEVICE``): a host pinned to cpu (or any non-cuda family)
must not hand CTranslate2 a CUDA device the probe applies the
override, so gating on its family covers every CT2 loader at once.
"""
try:
from core.device_caps import detect_host_caps
if detect_host_caps().family != "cuda":
return False
except Exception: # noqa: BLE001 — fail SAFE, not fast
# Without a working probe we can't know whether an override or a
# ROCm build is in play — guessing "cuda" from torch here is exactly
# the #1529 crash. CPU always works.
logger.warning("device probe failed — CTranslate2 taking the CPU path", exc_info=True)
return False
return _cuda_reported_available() and not _rocm_torch()
@@ -3120,10 +3136,18 @@ def _offline_asr_repo(backend_id: str | None = None) -> str | None:
bid = backend_id or active_backend_id()
if bid == "whisperx":
return _fw_repo(os.environ.get("ASR_MODEL_WHISPERX", "large-v3"))
if bid in ("faster-whisper", "faster-whisper-isolated"):
# The crash-isolated sidecar loads the SAME CT2 weights as in-process
# faster-whisper (it reuses the ASR_MODEL_FASTER selection).
if bid == "faster-whisper":
return _fw_repo(os.environ.get("ASR_MODEL_FASTER", _FASTER_WHISPER_DEFAULT))
if bid == "faster-whisper-isolated":
# Mirror the sidecar's own resolution (_asr_sidecar/main.py):
# ASR_MODEL_FW is a sidecar-only override, otherwise the shared
# ASR_MODEL_FASTER selection applies — so the preflight can never
# download a different repo than the sidecar will load.
return _fw_repo(
os.environ.get("ASR_MODEL_FW")
or os.environ.get("ASR_MODEL_FASTER")
or _FASTER_WHISPER_DEFAULT
)
if bid == "mlx-whisper":
return os.environ.get("ASR_MODEL", _MLX_MODEL_DEFAULT)
if bid == "parakeet-mlx":
+93
View File
@@ -57,6 +57,99 @@ def _force_compile_requested() -> bool:
return value.strip().lower() in {"1", "true", "yes", "on"}
# ── FlashInfer opt-in (upstream k2-fsa port) ────────────────────────────────
# Explicit power-user opt-in, CUDA-only: OMNIVOICE_FLASHINFER=1 patches the
# OmniVoice model with flashinfer packed attention (~2x per upstream's
# benchmarks); =graph additionally captures CUDA graphs (best at batch=1).
# Off by default — `flashinfer` is not a shipped dependency, and an
# optimization must never be a point of failure. Session-sticky failure
# latch mirrors torch.compile's (#278).
_FLASHINFER_ENV = "OMNIVOICE_FLASHINFER"
_flashinfer_runtime_failure: Optional[str] = None
def flashinfer_mode() -> str:
"""The user's ``OMNIVOICE_FLASHINFER`` request: 'off' | 'on' | 'graph'.
Unknown values normalize to 'off' with a log line naming the env var, so
a typo degrades to the default path instead of half-applying.
"""
value = os.environ.get(_FLASHINFER_ENV, "").strip().lower()
if value in {"", "0", "false", "no", "off"}:
return "off"
if value in {"1", "true", "yes", "on"}:
return "on"
if value == "graph":
return "graph"
logger.warning(
"%s=%r not recognized (valid: 0, 1, graph) — FlashInfer stays off.",
_FLASHINFER_ENV, value,
)
return "off"
def should_flashinfer(device: str) -> str:
"""Resolve the FlashInfer request against this host: 'off' | 'on' | 'graph'.
Requires all of: the ``OMNIVOICE_FLASHINFER`` opt-in, device == "cuda"
(flashinfer is CUDA-only), the ``flashinfer`` package importable, and no
earlier runtime failure this session. Every refusal is logged with the
reason and the knob's name — the user asked for it, so silence would read
as "the setting doesn't work".
"""
mode = flashinfer_mode()
if mode == "off":
return "off"
if device != "cuda":
logger.warning(
"%s requested but the compute device is %r — FlashInfer is "
"CUDA-only, continuing without it.", _FLASHINFER_ENV, device,
)
return "off"
if importlib.util.find_spec("flashinfer") is None:
logger.warning(
"%s requested but the `flashinfer` package is not installed — "
"continuing without it. Install with: uv pip install "
"flashinfer-python flashinfer-jit-cache "
"--extra-index-url https://flashinfer.ai/whl/cu128/ "
"(pick the index matching your CUDA build).", _FLASHINFER_ENV,
)
return "off"
if _flashinfer_runtime_failure is not None:
logger.info(
"FlashInfer skipped: failed earlier this session (%s) — using the "
"standard path.", _flashinfer_runtime_failure,
)
return "off"
return mode
def mark_flashinfer_runtime_failure(reason: str) -> None:
"""Latch a FlashInfer apply/runtime failure for the rest of the process,
same contract as ``mark_compile_runtime_failure``."""
global _flashinfer_runtime_failure
try:
# Import/kernel errors embed absolute paths (wheels under the user's
# home) — redact before latching, since the reason is logged here and
# re-logged on every later skip.
from core.failure import sanitize
reason = sanitize(reason)
except Exception:
# Fail closed: if the redactor itself breaks, latching the raw text
# would defeat the redaction. Keep only the exception class (the part
# before ':' in our "Type: message" reasons) and drop the message.
reason = (
f"{(reason or '').split(':', 1)[0][:80]} "
"(details redacted: sanitizer unavailable)"
).strip()
_flashinfer_runtime_failure = reason or "unknown FlashInfer runtime failure"
logger.warning(
"FlashInfer disabled for this session after a runtime failure: %s",
_flashinfer_runtime_failure,
)
def _cuda_arch_supported_for_compile() -> "tuple[bool, str]":
"""Check the GPU's architecture against this torch build's arch list.
+168 -1
View File
@@ -1376,6 +1376,122 @@ def _install_compile_fallback(_model) -> None:
_model.generate = _generate_with_compile_fallback
# ── FlashInfer runtime fallback (upstream k2-fsa port) ──────────────────────
def _is_flashinfer_runtime_failure(exc: BaseException) -> bool:
"""True when an exception originates in the FlashInfer fast path (the
flashinfer package, our omnivoice_flashinfer patch module, or CUDA-graph
capture/replay) rather than in the model or the request itself. Same
chain/traceback walk as ``_is_compile_runtime_failure``."""
import traceback as _tb
tb_markers = ("/flashinfer/", "omnivoice_flashinfer")
msg_markers = ("flashinfer", "cuda graph", "cudagraph")
seen: set[int] = set()
cur: BaseException | None = exc
while cur is not None and id(cur) not in seen:
seen.add(id(cur))
mod = type(cur).__module__ or ""
if mod.startswith("flashinfer"):
return True
msg = str(cur).lower()
if any(marker in msg for marker in msg_markers):
return True
try:
for frame in _tb.extract_tb(cur.__traceback__):
filename = (frame.filename or "").replace("\\", "/")
if any(marker in filename for marker in tb_markers):
return True
except Exception:
pass
if cur.__cause__ is not None:
cur = cur.__cause__
elif not cur.__suppress_context__:
cur = cur.__context__
else:
cur = None
return False
def _unapply_flashinfer(_model) -> None:
"""Restore the standard execution path on a FlashInfer-patched model.
``apply_flashinfer`` works entirely through *instance-level* state
MethodType-bound ``forward``/``_generate_iterative`` overrides and
``_fi_*`` attributes so deleting those attributes restores the class
implementations exactly. The attention implementation is restored to the
one captured before apply (``_fi_orig_attn_impl`` could be
flash_attention_2, not just sdpa), and use_cache is re-enabled."""
llm = getattr(_model, "llm", None)
orig_attn = getattr(_model, "_fi_orig_attn_impl", None) or "sdpa"
if llm is not None:
for module in llm.modules():
if "forward" in vars(module):
del module.forward
for attr in ("_fi_w_qkv", "_fi_qkv_split", "_fi_rope_theta", "_fi_w_gate_up"):
if attr in vars(module):
delattr(module, attr)
try:
llm.set_attn_implementation(orig_attn)
except Exception:
logger.exception(
"failed to restore %s attention after FlashInfer", orig_attn
)
llm.config.use_cache = True
for attr in (
"_fi_orig_attn_impl",
"_generate_iterative",
"_fi_runner",
"_fi_graph_cache",
"_fi_enable_cuda_graph",
"_fi_graph_buckets",
"_fi_overhead_budget",
):
if attr in vars(_model):
delattr(_model, attr)
def _install_flashinfer_fallback(_model) -> None:
"""Wrap ``model.generate`` so a FlashInfer failure at inference time falls
back to the standard path instead of failing the generation the same
contract as ``_install_compile_fallback`` (#278): an optimization must
never turn a working generation into an error."""
orig_generate = _model.generate
def _generate_with_flashinfer_fallback(*args, **kwargs):
try:
return orig_generate(*args, **kwargs)
except Exception as exc:
if not _is_flashinfer_runtime_failure(exc):
raise
logger.warning(
"FlashInfer runtime failure during generation (%s: %s) — "
"restoring the standard path and disabling FlashInfer for "
"this session. Generation is being retried without it.",
type(exc).__name__, exc,
)
from services import engine_env
engine_env.mark_flashinfer_runtime_failure(
f"{type(exc).__name__}: {exc}"
)
# Unapply BEFORE exposing the eager path: while the teardown
# mutates modules, _model.generate still routes through the
# thread-affinity wrapper, so a concurrent render queues behind
# this call instead of racing the half-restored model (Greptile,
# #1565 round 2). Only a fully restored model is published.
_unapply_flashinfer(_model)
_model.generate = orig_generate
try:
return orig_generate(*args, **kwargs)
except Exception as plain_exc:
# `from None`: a genuine standard-path failure must not be
# chained to — and misread as — the FlashInfer error.
raise plain_exc from None
_model.generate = _generate_with_flashinfer_fallback
# ── #315: thread affinity for cudagraph-compiled models ─────────────────────
# `torch.compile(mode="reduce-overhead")` captures CUDA graphs, and captured
# graph state is **thread-local** (torch/_inductor/cudagraph_trees keys its
@@ -2117,6 +2233,57 @@ def _load_model_sync():
"to stop preloading it alongside TTS."
) from asr_exc
# FlashInfer opt-in (upstream k2-fsa port): packed CFG attention +
# fused kernels, ~2x on upstream's benchmarks. Applied INSTEAD of
# torch.compile — both rewrite the llm's execution and they do not
# compose. Best-effort: any apply failure latches the session off and
# the standard path continues untouched.
flashinfer_applied = False
try:
from services.engine_env import (
mark_flashinfer_runtime_failure,
should_flashinfer,
)
fi_mode = should_flashinfer(device)
if fi_mode != "off":
_set_loading("compiling", "Applying FlashInfer kernels…")
try:
from omnivoice.models.omnivoice_flashinfer import apply_flashinfer
# Captured BEFORE apply so unapply (either the failure
# branch below or the generate-time fallback) restores
# the true prior implementation.
_model._fi_orig_attn_impl = getattr(
_model.llm.config, "_attn_implementation", "sdpa"
)
apply_flashinfer(_model, enable_cuda_graph=(fi_mode == "graph"))
except Exception as fi_exc: # noqa: BLE001 — perf opt, never fatal
mark_flashinfer_runtime_failure(
f"{type(fi_exc).__name__}: {fi_exc}"
)
# apply_flashinfer mutates the model as it goes — a
# failure partway leaves half-patched modules that would
# crash the next render (Greptile, #1565). Restore fully.
_unapply_flashinfer(_model)
else:
flashinfer_applied = True
_install_flashinfer_fallback(_model)
# BOTH modes pin inference to one thread. Graph mode for
# the #315 reason (captured CUDA-graph state is
# thread-local); eager mode because the FlashInfer
# attention wrapper and packed position ids are planned
# per generation in module state — two _gpu_pool workers
# interleaving plan() and run() would corrupt each
# other's layout (CodeRabbit/Greptile, #1565).
_install_compile_thread_affinity(_model)
logger.info(
"FlashInfer applied (mode=%s) — torch.compile skipped "
"for this load.", fi_mode,
)
except Exception:
logger.exception("FlashInfer opt-in check failed; continuing without")
try:
# plan-02 (#65): gate on Triton availability (+ user setting), not
# just device==cuda. Triton has no Windows wheel, so the old
@@ -2124,7 +2291,7 @@ def _load_model_sync():
# falls back to eager there.
from services.engine_env import should_torch_compile
if should_torch_compile(device):
if not flashinfer_applied and should_torch_compile(device):
_set_loading("compiling", "Compiling model (torch.compile)…")
try:
_model.llm = torch.compile(_model.llm, mode=_TORCH_COMPILE_MODE)
+4
View File
@@ -363,6 +363,10 @@ class SubprocessBackend(TTSBackend):
# A duck-typed marker survives that.
_is_subprocess_isolated: bool = True
# Generation happens in the sidecar: parent-side accelerator counters
# can't see its allocations (see TTSBackend.runs_out_of_process).
runs_out_of_process: bool = True
# Default sample rate; subclasses override.
_DEFAULT_SAMPLE_RATE = 24000
+146 -14
View File
@@ -300,6 +300,23 @@ class TTSBackend(ABC):
#: 0 means "no meaningful floor" (CPU-class engines) and never warns.
min_vram_gb: float = 0.0
#: True when generation allocates in ANOTHER process — a dedicated-venv
#: sidecar (SubprocessBackend) or a spawned binary (omnivoice-gguf).
#: Parent-process accelerator counters cannot see those allocations, so
#: profilers/diagnostics must not attribute the parent's VRAM numbers to
#: the engine. Duck-typed (attribute, not issubclass) for the same
#: module-purge reason as `_is_subprocess_isolated`.
runs_out_of_process: bool = False
def model_identity(self) -> Optional[str]:
"""Which concrete model this backend would run, for adapter engines
that host several very different models behind one backend id
(mlx-audio, sherpa-onnx, cosyvoice). None means the engine id
already names the model. Profilers and diagnostics use this to
label results without it, Kokoro-under-mlx and Dia-under-mlx
rows are indistinguishable."""
return None
@abstractmethod
def generate(
self,
@@ -395,6 +412,95 @@ _PROMPT_CACHE_MAX = 8
_prompt_cache: "OrderedDict[tuple, object]" = OrderedDict()
_prompt_cache_lock = threading.Lock()
# Disk layer under the in-memory LRU (upstream k2-fsa VoiceClonePrompt.save/
# load format). The in-memory cache dies with the process, so the first
# generation of every session re-encodes each voice (~0.4 s + an ASR pass when
# ref_text is missing). Encoded prompts are tiny (a (8, T) int token tensor +
# transcript), so we persist them and reload across restarts. Keyed by the
# same tuple as the memory cache — the ref file's mtime is inside the key, so
# an edited reference never matches a stale file; stale files age out via the
# mtime prune. Best-effort like the memory cache: any failure means "no disk
# hit / no disk write", never a failed generation. OMNIVOICE_PROMPT_DISK_CACHE=0
# disables the layer entirely.
_PROMPT_DISK_CACHE_MAX = 32
def _prompt_disk_dir():
"""Return the prompt-cache directory (created on first use), or None when
the layer is disabled or the directory can't be created."""
if os.environ.get("OMNIVOICE_PROMPT_DISK_CACHE", "1") == "0":
return None
try:
from core.config import DATA_DIR
path = os.path.join(str(DATA_DIR), "prompt_cache")
os.makedirs(path, exist_ok=True)
return path
except Exception as e: # noqa: BLE001 — cache layer must never break synthesis
logger.debug("prompt disk cache unavailable: %s", e)
return None
def _prompt_disk_path(cache_dir: str, key: tuple) -> str:
import hashlib
digest = hashlib.sha256(repr(key).encode("utf-8")).hexdigest()[:32]
return os.path.join(cache_dir, f"{digest}.pt")
def _prompt_disk_load(key: tuple):
"""Load a persisted prompt for ``key``, or None. Never raises."""
cache_dir = _prompt_disk_dir()
if cache_dir is None:
return None
path = _prompt_disk_path(cache_dir, key)
if not os.path.exists(path):
return None
try:
from omnivoice.models.omnivoice import VoiceClonePrompt
prompt = VoiceClonePrompt.load(path)
# Freshen so the LRU prune (by mtime) keeps actively used voices.
os.utime(path, None)
return prompt
except Exception as e: # noqa: BLE001
logger.warning("failed to load cached voice prompt %s: %s", path, e)
try:
os.remove(path) # corrupt/incompatible file — don't retry it forever
except OSError:
pass
return None
def _prompt_disk_save(key: tuple, prompt) -> None:
"""Persist ``prompt`` under ``key`` and prune old entries. Never raises."""
cache_dir = _prompt_disk_dir()
if cache_dir is None:
return
path = _prompt_disk_path(cache_dir, key)
try:
# Unique per write: two GPU-pool threads missing the same key must not
# interleave writes into one tmp file (os.replace stays atomic).
import uuid
tmp = f"{path}.tmp.{os.getpid()}.{uuid.uuid4().hex[:8]}"
prompt.save(tmp)
os.replace(tmp, path)
except Exception as e: # noqa: BLE001
logger.warning("failed to persist voice prompt to %s: %s", path, e)
return
try:
entries = [
os.path.join(cache_dir, f)
for f in os.listdir(cache_dir)
if f.endswith(".pt")
]
entries.sort(key=lambda p: os.path.getmtime(p), reverse=True)
for old in entries[_PROMPT_DISK_CACHE_MAX:]:
os.remove(old)
except OSError as e:
logger.debug("prompt disk cache prune skipped: %s", e)
def _clone_prompt_key(ref_audio: str, ref_text, preprocess_prompt: bool = True):
try:
@@ -433,15 +539,24 @@ def _get_clone_prompt(
if hit is not None:
_prompt_cache.move_to_end(key)
return hit
try:
# Encode outside the lock (slow). Mirrors exactly what generate() would
# do inline for this ref (omnivoice.py:964-978), so output is identical.
prompt = model.create_voice_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
# Memory miss → disk (survives restarts). A disk hit skips the encode AND
# the ASR transcription pass a ref_text-less reference would trigger.
prompt = _prompt_disk_load(key)
if prompt is None:
try:
# Encode outside the lock (slow). Mirrors exactly what generate()
# would do inline for this ref (omnivoice.py:964-978), so output is
# identical.
prompt = model.create_voice_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
if store:
_prompt_disk_save(key, prompt)
if not store:
return prompt
with _prompt_cache_lock:
@@ -1075,7 +1190,8 @@ class KittenTTSBackend(TTSBackend):
- English only
- Much faster + much smaller install
Preset voice is chosen via `extras["voice"]` (defaults to "Jasper"). Any
Preset voice is chosen via `extras["voice"]` (defaults to DEFAULT_VOICE,
"expr-voice-2-f"). Any
`ref_audio` / `instruct` / `language` arg is ignored with a log line so
the common call-site doesn't need to know which engine it's talking to.
"""
@@ -1384,6 +1500,9 @@ class MLXAudioBackend(TTSBackend):
def sample_rate(self) -> int:
return self._sr
def model_identity(self) -> Optional[str]:
return self._model_id
@property
def supported_languages(self) -> list[str]:
# Per-model; Kokoro supports 8, Qwen3 ~4, Kugel 24. Return "multi"
@@ -1571,6 +1690,18 @@ class CosyVoiceBackend(TTSBackend):
def supported_languages(self) -> list[str]:
return ["zh", "en", "ja", "ko", "yue", "de", "es", "fr", "it", "ru"]
@staticmethod
def _resolved_model_dir() -> str:
return os.environ.get(
"OMNIVOICE_COSYVOICE_MODEL",
"pretrained_models/Fun-CosyVoice3-0.5B",
)
def model_identity(self) -> Optional[str]:
# v1/v2/v3 all live behind the one "cosyvoice" id — the directory
# basename is the only thing that tells the models apart.
return os.path.basename(os.path.normpath(self._resolved_model_dir()))
def _ensure_loaded(self):
if self._model is not None:
return
@@ -1578,10 +1709,7 @@ class CosyVoiceBackend(TTSBackend):
if not ok:
raise RuntimeError(f"CosyVoice unavailable: {msg}")
from cosyvoice.cli.cosyvoice import AutoModel # type: ignore[import-not-found]
model_dir = os.environ.get(
"OMNIVOICE_COSYVOICE_MODEL",
"pretrained_models/Fun-CosyVoice3-0.5B",
)
model_dir = self._resolved_model_dir()
logger.info("Loading CosyVoice from %s", model_dir)
self._model = AutoModel(model_dir=model_dir)
@@ -1814,6 +1942,10 @@ class SherpaOnnxBackend(TTSBackend):
self._tts = None
self._model_dir = os.environ.get("OMNIVOICE_SHERPA_MODEL", "")
def model_identity(self) -> Optional[str]:
model_dir = (self._model_dir or "").strip()
return os.path.basename(os.path.normpath(model_dir)) if model_dir else None
@classmethod
def is_available(cls) -> tuple[bool, str]:
try:
+10
View File
@@ -125,6 +125,16 @@ def test_faster_whisper_float16_unsupported_falls_back_to_int8(monkeypatch):
)
monkeypatch.setitem(sys.modules, "torch", fake_torch)
# The compute-device override gate consults the capability probe before
# the torch mock above — pin it to a CUDA family so the fallback chain
# under test is reachable on a cpu-only CI host.
from core.device_caps import HostCaps
monkeypatch.setattr(
"core.device_caps.detect_host_caps",
lambda: HostCaps(family="cuda", available_families=("cuda", "cpu")),
)
be = FasterWhisperBackend()
be._ensure_model()
+40 -15
View File
@@ -460,30 +460,55 @@ class TaskExecutor:
@staticmethod
def _synthesize(backend, text: str, params: dict):
"""Call the engine through the same serial GPU gate local jobs use.
"""Render through the same seeded pipeline as local ``/generate``.
Held against the idle sweep for the duration: a long generation touches
the instance cache once, at the start, so on elapsed time alone it is
indistinguishable from a model nobody wants any more.
Do not reduce this to ``backend.generate()``. The control plane sends
a complete render contract (pinned gallery seed, synthetic reference,
quality controls, chunking, effects); calling the adapter directly
silently turns a selected gallery voice into a fresh random take.
"""
from services import tts_backend # noqa: PLC0415
from api.routers.generation import _run_backend_inference, _run_inference # noqa: PLC0415
kwargs = {
key: params[key]
for key in (
"ref_audio",
"ref_text",
"instruct",
"language",
"duration",
"description",
"speed",
)
if params.get(key) is not None
}
language = params.get("language")
ref_audio = params.get("ref_audio")
ref_text = params.get("ref_text")
instruct = params.get("instruct")
duration = params.get("duration")
num_step = params.get("num_step", 16)
guidance_scale = params.get("guidance_scale", 2.0)
speed = params.get("speed", 1.0)
denoise = params.get("denoise", True)
postprocess_output = params.get("postprocess_output", True)
used_seed = params.get("seed")
effect_preset = params.get("effect_preset", "broadcast")
max_chunk_chars = params.get("max_chunk_chars")
crossfade_ms = params.get("crossfade_ms")
try:
with tts_backend.engine_in_use(backend):
return backend.generate(text, **kwargs)
if isinstance(backend, tts_backend.OmniVoiceBackend):
# The OSS default engine has an extended native surface;
# preserving it is required for a gallery preview and a
# GPU-worker take to share the same voice identity.
return _run_inference(
backend._model, text, language, ref_audio, ref_text,
instruct, duration, num_step, guidance_scale, speed,
params.get("t_shift"), denoise, postprocess_output,
params.get("layer_penalty_factor"),
params.get("position_temperature"),
params.get("class_temperature"), used_seed,
effect_preset, max_chunk_chars, crossfade_ms,
)
return _run_backend_inference(
backend, text, language, ref_audio, ref_text, instruct,
duration, num_step, guidance_scale, speed, denoise,
postprocess_output, used_seed, effect_preset,
max_chunk_chars, crossfade_ms,
)
except Exception as exc:
from worker import errors as worker_errors # noqa: PLC0415
+4
View File
@@ -57,6 +57,10 @@ REQUIRED_FEATURES = frozenset({
"task_progress_v1",
"task_inputs_v1",
"remote_model_download_v1",
# A generic backend.generate() call accepts the same wire shape but drops
# profile conditioning controls. Require the canonical worker render path
# so an older peer cannot successfully return a different voice.
"remote_tts_render_v1",
})
+22 -8
View File
@@ -1,7 +1,7 @@
# VoiceStudio
**The open-source ElevenLabs alternative.** Real-time dictation, zero-shot voice
cloning, and cinematic video dubbing — fully local, no API keys, no accounts.
cloning, and cinematic video dubbing — fully local, with no cloud API keys or accounts.
**646 languages.**
[![Docker Pulls](https://img.shields.io/docker/pulls/palashdeb/omnivoice-studio?logo=docker&color=2496ED)](https://hub.docker.com/r/palashdeb/omnivoice-studio)
@@ -30,6 +30,14 @@ weights + cache (20 GB+ comfortable), and optionally a GPU — 4 GB VRAM works
the entire pipeline runs on CPU, just slower. Pull size: ~5 GB compressed
(CUDA/CPU image), ~15 GB for the `:rocm` variant.
## See it in action
![Switching TTS engines from the VoiceStudio status bar](https://raw.githubusercontent.com/debpalash/VoiceStudio/main/docs/media/0.5.0/quick-switch.gif)
| Model catalogue | Save a gallery voice |
|---|---|
| ![VoiceStudio Model Catalogue](https://raw.githubusercontent.com/debpalash/VoiceStudio/main/docs/media/0.5.0/catalogue.png) | ![Saving a gallery voice as a local profile](https://raw.githubusercontent.com/debpalash/VoiceStudio/main/docs/media/0.5.0/gallery-save.png) |
---
## Quick start (CPU)
@@ -90,12 +98,12 @@ There's also a Compose file in the repo with `cpu` / `gpu` / `rocm` profiles
|-----|--------------|
| `:latest` | **Rolling preview** — latest commit on `main`, at or ahead of the last release. This is the preview channel; pin `:stable` for production. |
| `:stable` | Most recent versioned release (updated on every `v*` git tag) |
| `:0.4.1` | Exact release version |
| `:0.4` | Latest patch within the `0.4` minor |
| `:0.5.0` | Exact release version |
| `:0.5` | Latest patch within the `0.5` minor |
| `:main` | Alias of the same rolling `main` build as `:latest` |
| `:sha-xxxxxxx` | A specific commit (produced by manual workflow dispatch) |
| `:rocm` | **AMD GPU (ROCm) build** of the rolling preview — the ROCm analogue of `:latest` |
| `:stable-rocm`, `:0.4.1-rocm`, `:0.4-rocm`, `:sha-xxxxxxx-rocm` | ROCm builds of the corresponding tags above |
| `:stable-rocm`, `:0.5.0-rocm`, `:0.5-rocm`, `:sha-xxxxxxx-rocm` | ROCm builds of the corresponding tags above |
Preview builds always come from `main` and never version-sort below `:stable`,
so upgrades flow naturally. The same images and tags
@@ -143,11 +151,17 @@ more), auto-detected and selectable in Settings.
- The image ships with `OMNIVOICE_SERVER_MODE=1`, which relaxes the desktop-only
loopback-origin gate so the admin UI works through Docker's NAT. Set it to `0`
if you front the container with your own loopback auth proxy.
- For LAN or internet-facing deployments, set a long random
`OMNIVOICE_API_KEY` and pass the same key through the browser's login prompt.
A six-digit share PIN is also available for casual LAN access, but it does
not authorize administration or dictation; see the
[API authentication guide](https://github.com/debpalash/VoiceStudio/blob/main/docs/api-auth.md).
> **Security:** VoiceStudio ships **no authentication**. Anything that can reach the
> URL can use the app. Before exposing it beyond localhost, put it behind a
> reverse proxy with auth (Caddy `basic_auth`, nginx + htpasswd) or a private
> overlay (Tailscale, ZeroTier).
> **Security:** Loopback-only publishing is the safe default. Before exposing
> VoiceStudio on a trusted LAN, configure `OMNIVOICE_API_KEY`. On any untrusted
> network, plain HTTP is not safe for the API key or session cookie. Keep the
> backend on an encrypted private overlay such as Tailscale/ZeroTier; do not
> expose it directly to the public internet.
---
+1 -1
View File
@@ -342,7 +342,7 @@ arbitrary path merely because it ends in `/ws/events` or `/ws/transcribe`.
| Code | Meaning | What to do |
|---|---|---|
| **401** | Consumption auth failed — `{"detail": "PIN required"}` or `{"detail": "API key required"}`. | Supply the PIN / key (header, cookie, or query param above). A WebSocket surfaces this as close code **1008**. |
| **403** | Authorization failed: loopback/native access was required, cookie Origin/CSRF validation failed, a server-mode mutation lacked an admin credential, or a native path capability was invalid/expired. | A PIN cannot grant admin or filesystem access. Re-authenticate the UI; scripts should use the API-key header; run native operations from the desktop app. |
| **403** | Authorization failed: loopback/native access was required, cookie Origin/CSRF validation failed, a server-mode mutation lacked an admin credential, or a native path capability was invalid/expired. | A PIN cannot grant admin or filesystem access. Re-authenticate the UI; scripts should use the API-key header; run native operations from the desktop app. The admin gate names the key only when one can satisfy it: server mode with `OMNIVOICE_API_KEY` configured answers `{"detail": "loopback origin or admin API key required"}` (the bundled UI routes it to the API-key login form); PIN-only/no-key server mode and the desktop build answer `{"detail": "loopback origin required"}` (only loopback can satisfy the gate). |
| **429** | A failed administrator-session exchange exceeded its per-client limit, the GPU pool is saturated, or a model download is rate-limited. Ships with `Retry-After`; workload throttles also carry `X-VoiceStudio-Retryable: true`. | Back off for `Retry-After` seconds. For authentication, verify the master before retrying; a correct master is never locked out. |
---
+56
View File
@@ -0,0 +1,56 @@
# Benchmarks
Measured numbers per engine and device — how long a generation actually
takes on real hardware. Every number here is produced by the in-repo
harness, on named hardware, at a named version; nothing is estimated.
## How numbers are measured
```bash
# stop the app first — a running backend holds a model and skews numbers
uv run python scripts/bench_pipeline.py # everything
uv run python scripts/bench_pipeline.py tts # just the TTS stage
```
`scripts/bench_pipeline.py` profiles each pipeline stage one at a time,
memory-safely: it refuses to start a stage without enough free RAM and
unloads models between stages. See [performance.md](performance.md) for
what each stage spends its time on.
The `tts` stage emits the two values this table collects:
- **RTF** (real-time factor) — seconds of compute per second of generated
audio, printed next to each warm measurement. RTF < 1 means faster than
real time. Use the **short line (warm)** RTF for the table.
- **Peak VRAM** — printed on CUDA only. MPS is unified memory and CPU has
no VRAM; subprocess-isolated engines allocate outside the harness's view
(it prints `n/a` for them). Leave the column blank in all those cases.
## Results
No verified rows yet — this table fills from maintainer runs and community
submissions.
| Engine | Device | RTF (warm) | Peak VRAM (GB) | App version | Source |
|---|---|---|---|---|---|
| _none yet — contribute yours below_ | | | | | |
Column meanings: **Engine** — the TTS engine the harness resolved (printed
at stage start). **Device** — one string naming what ran the model, e.g.
`RTX 3060 12 GB`, `Apple M2 Pro`, `Ryzen 7 5800X (CPU)`. **RTF (warm)**
the short-line warm RTF from the harness. **Peak VRAM** — the harness's
CUDA peak, blank on MPS/CPU. **App version** — from `Settings → About`.
**Source** — a link to the PR that added the row.
## Contributing a row
1. Run the harness on an otherwise-idle machine (app stopped) and copy its
summary table.
2. Open a PR adding one row using the column meanings above, and paste the
raw harness output into the PR description — that PR link becomes the
row's **Source**.
3. One row per engine+device pair; a newer app version replaces the old row.
Numbers from different machines aren't directly comparable — that's fine.
The point is honest expectations ("this engine on this class of GPU ≈ this
fast"), not a leaderboard.
+61
View File
@@ -0,0 +1,61 @@
# Engine guides
One page per engine: what it's for, what it needs, how to enable it, and its
quirks. Select engines in **Model Catalogue → Engines** (or quick-switch with
<kbd>Ctrl</kbd>/<kbd>Cmd</kbd>+<kbd>E</kbd>), or pin one with
`OMNIVOICE_TTS_BACKEND` / `OMNIVOICE_ASR_BACKEND`.
The compute device (CUDA/ROCm/MPS/CPU) is auto-detected; pin it under
**Settings → Performance & Device** (or `OMNIVOICE_DEVICE`) if auto-detect
picks wrong — see [performance](../performance.md).
Measured speed/VRAM numbers live in [benchmarks](../benchmarks.md); what each
engine can do expressively in [expressive-speech](../expressive-speech.md);
sidecar disk footprints in [disk-usage](disk-usage.md); the bar a new engine
must clear in [engine-acceptance](../engine-acceptance.md).
New to VoiceStudio? Install the app first — [macOS](../install/macos.md)
(first launch needs the one-time right-click → **Open** Gatekeeper
approval), [Windows](../install/windows.md), [Linux](../install/linux.md),
[Docker](../install/docker.md).
## Text-to-speech
| Engine | Guide | Runs on | Cloning | Enabled by |
|---|---|---|---|---|
| VoiceStudio (OmniVoice) — **default** | [omnivoice](omnivoice.md) | CUDA · MPS · CPU | ✅ | installed by default |
| VoxCPM2 | [voxcpm2](voxcpm2.md) | CUDA · MPS · CPU | ✅ + voice design | `pip install "voxcpm>=2.0.3"` |
| MOSS-TTS-Nano | [moss-tts-nano](moss-tts-nano.md) | CUDA · CPU | ✅ (ref only) | clone + `uv pip install -e .` |
| KittenTTS | [kittentts](kittentts.md) | CPU | — (8 preset voices) | `pip install kittentts` |
| MLX-Audio (Kokoro, CSM, Dia, …) | [mlx-audio](mlx-audio.md) | Apple Silicon | model-dependent | `pip install mlx-audio` |
| CosyVoice 3 | [cosyvoice](cosyvoice.md) | CUDA · CPU | ✅ | clone + requirements |
| GPT-SoVITS | [gpt-sovits](gpt-sovits.md) | external server | ✅ | its own API server |
| Sherpa-ONNX | [sherpa-onnx](sherpa-onnx.md) | CUDA · CPU | — | `pip install sherpa-onnx` + model dir |
| IndexTTS 2.5 | [indextts](indextts.md) | CUDA · CPU | ✅ + emotion | one-click sidecar install |
| OmniVoice GGUF | [omnivoice-gguf](omnivoice-gguf.md) | CUDA · MPS · CPU | ✅ | bundled binary |
| Supertonic-3 | [supertonic3](supertonic3.md) | CPU | — (7 preset voices) | `uv sync --extra supertonic` + license |
| MOSS-TTS-v1.5 (8B) | [moss-tts-v15](moss-tts-v15.md) | CUDA · CPU | ✅ | clone + env var |
| dots.tts (2B) | [dots-tts](dots-tts.md) | CUDA · CPU (not Windows) | ✅ | clone + env var |
| OmniVoice (subprocess) | [omnivoice-subprocess](omnivoice-subprocess.md) | CUDA · MPS · CPU | ✅ | opt-in pick, no install |
| PocketTTS (Kyutai) | [pockettts](pockettts.md) | CPU (not Intel Mac) | ✅ | `uv sync --extra pockettts` + license |
| Confucius4-TTS | [confucius4-tts](confucius4-tts.md) | CUDA · CPU | ✅ | clone + env var |
## Speech-to-text
| Engine | Guide | Runs on | Best at | Enabled by |
|---|---|---|---|---|
| WhisperX | [whisperx](whisperx.md) | CUDA · CPU | dubbing (word timestamps + diarization) | installed by default |
| Faster-Whisper | [faster-whisper](faster-whisper.md) | CUDA · CPU | general transcription | installed by default |
| Faster-Whisper (isolated) | [faster-whisper-isolated](faster-whisper-isolated.md) | CUDA · CPU | unattended batches | opt-in pick |
| MLX Whisper | [mlx-whisper](mlx-whisper.md) | Apple Silicon | Mac default | `pip install mlx-whisper` |
| PyTorch Whisper | [pytorch-whisper](pytorch-whisper.md) | CUDA · MPS · CPU | ROCm hosts | installed by default |
| Parakeet TDT (NeMo) | [nemo-parakeet](nemo-parakeet.md) | CUDA · CPU | 25 languages, fast CPU | separate venv (never the app's) |
| Parakeet TDT (MLX) | [parakeet-mlx](parakeet-mlx.md) | Apple Silicon | dictation, 25 EU languages | default on mac-ARM source installs |
| Moonshine | [moonshine](moonshine.md) | CPU | edge/low-power, no timestamps | `pip install` (see guide) |
| FunASR (SenseVoice) | [funasr](funasr.md) | CUDA · CPU | 50+ languages, inline diarization | `pip install funasr` |
| Sherpa-ONNX dictation | [sherpa-onnx-asr](sherpa-onnx-asr.md) | CPU | live streaming dictation | curated model download |
| OpenAI-compatible (remote) | [openai-compatible-asr](openai-compatible-asr.md) | network | offloading to a server (audio leaves the machine) | Model Catalogue |
Speaker diarization is not an engine registry of its own — the dub pipeline
uses pyannote (HF-gated; see [diarization](../features/diarization.md)) and
FunASR can diarize inline with its `cam++` speaker model.
+61
View File
@@ -0,0 +1,61 @@
# VoiceStudio — Faster-Whisper (Crash-Isolated) Engine
The same CTranslate2 Whisper engine as [faster-whisper](faster-whisper.md),
run in a **separate child process** ("sidecar"). CTranslate2's GPU teardown
can segfault — the endemic faster-whisper crash — and a hung or crashed
transcribe in-process takes the whole backend down with it. Isolated, the
child can crash or be force-killed to reclaim a hung transcribe and its VRAM
while the backend stays up
([#730](https://github.com/debpalash/VoiceStudio/issues/730)).
There is nothing extra to install: the sidecar reuses the app's own venv —
only the process boundary is new.
## Selecting it
- **Model Catalogue → Engines**, ASR tab → **Use** on the crash-isolated row, or
- pin it with `OMNIVOICE_ASR_BACKEND=faster-whisper-isolated`.
It is never picked by auto-detect — it's an explicit opt-in escape hatch.
## Best at
- **Long batch runs** where one bad file must not kill the backend.
- Machines where in-process faster-whisper has crashed or hung before:
a sidecar crash fails only that job, and the next transcribe respawns a
fresh sidecar automatically.
## Platform support
Same as faster-whisper: CUDA float16 or CPU int8 on macOS, Windows, and
Linux. The sidecar picks cuda/cpu itself and walks the same
float16 → int8_float16 → int8 degrade chain on GPUs without efficient fp16
([#551](https://github.com/debpalash/VoiceStudio/issues/551)).
## Model selection
- `ASR_MODEL_FASTER` — the shared model selection, same as the in-process
engine: set it once and both variants load the same weights.
- `ASR_MODEL_FW` — optional sidecar-only override; when set it wins over
`ASR_MODEL_FASTER` for this engine. Default `large-v3`.
- `ASR_COMPUTE_TYPE` — optional: pin the sidecar to one CTranslate2 compute
type instead of the automatic degrade chain.
Weights download on first load — see
[downloading-models](../downloading-models.md).
## Trade-offs and quirks
- **Slightly slower per call** than in-process faster-whisper (IPC overhead);
the model stays warm inside the sidecar between calls, so the cost is per
request, not per chunk of audio.
- Word timestamps are Whisper-native (±100300 ms) — no forced alignment.
For dubbing lip-sync, use [whisperx](whisperx.md) or
[mlx-whisper](mlx-whisper.md).
- If the sidecar dies mid-transcription the job fails with a clear
"sidecar crashed" error and the backend stays up — retry to respawn.
- **cuDNN 8 is still required on CUDA** — same CTranslate2 requirement as the
in-process engine. It's checked up front so a missing cuDNN 8 shows as
"unavailable" in Model Catalogue → Engines instead of a sidecar that
silently fails every transcribe
([#1371](https://github.com/debpalash/VoiceStudio/issues/1371)).
+70
View File
@@ -0,0 +1,70 @@
# VoiceStudio — Faster-Whisper Engine
Faster-Whisper runs Whisper on CTranslate2 — the same transcription core
WhisperX uses, **without** the wav2vec2 forced-alignment pass. It's the safe
cross-platform fallback when whisperx isn't installed, and the capture/dictation
fallback on non-Apple machines.
## Selecting it
- **Model Catalogue → Engines**, ASR tab → **Use** on the Faster-Whisper row, or
- pin it with `OMNIVOICE_ASR_BACKEND=faster-whisper`.
Auto-detect only picks it when [whisperx](whisperx.md) is unavailable.
## Best at
- **Subtitles, dictation buffers, and batch transcription** where Whisper's
native word timing (±100300 ms) is good enough.
- For dubbing lip-sync, prefer [whisperx](whisperx.md) (or
[mlx-whisper](mlx-whisper.md) on Apple Silicon) — their forced alignment is
an order of magnitude tighter on word boundaries.
## Platform support
- **CUDA** — float16, with automatic degradation (below).
- **CPU** — int8 on macOS, Windows, and Linux.
- **Apple Silicon GPU / ROCm** — not supported: CTranslate2 has no Metal or
HIP build, so those hosts run on CPU
([#1529](https://github.com/debpalash/VoiceStudio/issues/1529)); auto-detect
routes them to mlx-whisper / pytorch-whisper instead.
## Model selection
`ASR_MODEL_FASTER` — default `Systran/faster-whisper-large-v3`. Accepts the
size aliases (`tiny``large-v3`, `distil-large-v3`) or any CTranslate2
Whisper repo on HF. Weights download on first load — see
[downloading-models](../downloading-models.md).
Segments are cleaned up by faster-whisper's built-in Silero VAD before
transcription.
## Degradation chains
- GPUs without efficient fp16 (older Maxwell/Pascal, GTX 16xx, or a
CTranslate2/cuDNN mismatch) fail at model construction with a compute-type
error; the engine walks float16 → int8_float16 → int8 instead of failing
every chunk ([#551](https://github.com/debpalash/VoiceStudio/issues/551)).
- A CUDA out-of-memory falls back to CPU (slower, same model and accuracy) —
flushing the resident TTS model frees VRAM for GPU-speed ASR
([#255](https://github.com/debpalash/VoiceStudio/issues/255)).
## Quirks
- **cuDNN 8 required on CUDA** — a missing cuDNN 8 would fast-fail the whole
process, so the engine checks up front and reports itself unavailable
instead ([#1371](https://github.com/debpalash/VoiceStudio/issues/1371)).
pytorch-whisper covers that case on torch's bundled cuDNN 9.
- On some hardened Linux kernels the CTranslate2 native library is rejected
with "cannot enable executable stack" (an OSError, not an ImportError) —
reported as unavailable rather than crashing engine selection
([#692](https://github.com/debpalash/VoiceStudio/issues/692)).
- CTranslate2's GPU teardown can rarely segfault the process at unload. If
you hit that, switch to the crash-isolated variant —
[faster-whisper-isolated](faster-whisper-isolated.md)
([#730](https://github.com/debpalash/VoiceStudio/issues/730)).
- Transcribes are time-bounded: `OMNIVOICE_TRANSCRIBE_CHUNK_TIMEOUT_S`
(default 120 s per dub chunk) and `OMNIVOICE_ASR_TRANSCRIBE_TIMEOUT_S`
(default 300 s whole-file).
Speed comparisons across engines live in [performance](../performance.md).
+62
View File
@@ -0,0 +1,62 @@
# VoiceStudio — FunASR (SenseVoice) Engine
FunASR drives Alibaba's SenseVoiceSmall with FSMN-VAD: an all-in-one
multilingual pipeline — transcription with punctuation and inverse text
normalization across **50+ languages**, plus optional **inline speaker
diarization** via the cam++ speaker model. It's the opt-in alternative to
WhisperX ([#182](https://github.com/debpalash/VoiceStudio/issues/182));
WhisperX remains the cross-platform default.
## Selecting it
- Install it into the app venv: `uv pip install funasr`.
- Then **Model Catalogue → Engines**, ASR tab → **Use** on the FunASR row, or
`OMNIVOICE_ASR_BACKEND=funasr`.
Auto-detect never picks it; it's an explicit opt-in.
## Best at
- **Multi-speaker transcription without any HuggingFace token.** This is the
only ASR engine with diarization built in: cam++ labels each sentence
(`Speaker 1`, `Speaker 2`, ...) in the same pass — no gated pyannote
model, no license click-through. Compare
[diarization](../features/diarization.md) for the pyannote/WhisperX route
and what each buys you.
- **Broad language coverage** beyond Whisper's strongest languages, with
punctuation included.
## Not suited for
- **Lip-sync dubbing** — FunASR returns sentence-level timestamps, not
word-level ones. Use [whisperx](whisperx.md) /
[mlx-whisper](mlx-whisper.md) when word timing matters.
## Platform support
CUDA or CPU, on macOS, Windows, and Linux.
## Model selection
| Variable | Default | Role |
| --- | --- | --- |
| `ASR_MODEL_FUNASR` | `iic/SenseVoiceSmall` | main ASR model |
| `ASR_FUNASR_VAD` | `fsmn-vad` | VAD segmentation model |
| `ASR_FUNASR_SPK` | `cam++` | speaker model; set to empty (`ASR_FUNASR_SPK=`) to disable diarization and use the dub pipeline's pyannote/heuristic path instead |
Weights download on first load (through FunASR's own model hub) — see
[downloading-models](../downloading-models.md).
## Quirks
- With the speaker model enabled, long recordings are transcribed in **one
call** and split by FunASR's internal VAD — cam++ assigns speaker cluster
IDs per call, so this is what keeps "Speaker 1" meaning the same person
across the whole file.
- The engine runs with `spk_mode="vad_segment"`: FunASR 1.3.1's default
(`punc_segment`) requires a separate punctuation model and crashes when
SenseVoice is loaded without one.
- SenseVoice's rich-token markup (language/emotion/event tags around the
text) is stripped from the output automatically.
- Language detection is automatic (`language: auto`); the detected language
is reported per file.
+78
View File
@@ -0,0 +1,78 @@
# VoiceStudio — GPT-SoVITS Engine
GPT-SoVITS (RVC-Boss) is one of the most popular open-source voice-cloning
systems (57k+ GitHub stars, MIT-licensed). It does zero-shot and few-shot
cloning with excellent naturalness in Chinese, English, Japanese, Cantonese,
and Korean, and it is very fast (RTF ~0.014 on suitable hardware).
Unlike VoiceStudio's other engines, GPT-SoVITS does not run inside the app.
It ships as a standalone API server, and VoiceStudio connects to it over
HTTP.
## When to pick it
- You already run (or want to run) a GPT-SoVITS server, e.g. with few-shot
fine-tuned voices.
- You need fast, natural cloning in zh/en/ja/yue/ko.
## Setup
1. Install and start the GPT-SoVITS API server (upstream project):
```bash
cd GPT-SoVITS
python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml
```
2. Select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=gpt-sovits`.
VoiceStudio marks the engine available only when the server responds
(2-second reachability probe).
## Configuration
| Variable | Default | Meaning |
| --- | --- | --- |
| `OMNIVOICE_GPTSOVITS_URL` | `http://127.0.0.1:9880` | API server URL |
| `OMNIVOICE_TRUSTED_NETWORKS` | (unset) | Required to allow a non-loopback server |
**Remote servers:** by default VoiceStudio only talks to loopback addresses
— part of the local-first guarantee. To point at a server on another
machine (e.g. a GPU box on your LAN), add its network to
`OMNIVOICE_TRUSTED_NETWORKS`; otherwise the connection is refused as an
untrusted endpoint.
Prefer `https://` (or a private tunnel such as Tailscale/WireGuard) for any
non-loopback server: with plain `http://` the text you synthesize and the
audio that comes back cross the network unencrypted. VoiceStudio does not
disable certificate verification, so a TLS endpoint needs a certificate the
system trusts.
## Behaviour notes
- Output is 32 kHz mono (server output is resampled if needed).
- Cloning passes your reference clip path and optional transcript to the
server; the reference path must be readable **by the server process**, so
remote servers need the clip on their own filesystem.
- Speed control is forwarded as the server's `speed_factor`.
- The GPU is whatever the GPT-SoVITS server itself uses (CUDA preferred);
VoiceStudio's side is just an HTTP client.
## Known limits
- Five languages only; for broader coverage use
[OmniVoice](omnivoice.md) ([languages.md](../languages.md)).
- No voice design; server availability is your responsibility — if the
server stops, generations fail with a "server not reachable" error.
## Troubleshooting
- "GPT-SoVITS server not reachable": start the server with the command
above, or fix `OMNIVOICE_GPTSOVITS_URL`.
- "endpoint is outside loopback or OMNIVOICE_TRUSTED_NETWORKS": see
Configuration above.
- Other issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[expressive-speech.md](../expressive-speech.md).
+75
View File
@@ -0,0 +1,75 @@
# VoiceStudio — KittenTTS Engine
KittenTTS (KittenML) is the lightweight English "flash" tier: a 2580 MB
ONNX model with 8 preset voices that runs realtime on any CPU — no torch, no
CUDA, no GPU of any kind. Use it when you just need quick English narration
(voiceovers, demo reads, short phrases) with no reference sample.
## When to pick it
- English-only content where speed and a tiny install matter more than
cloning.
- Machines with no usable GPU.
The trade-off against [OmniVoice](omnivoice.md): no voice cloning, English
only — but a much faster and much smaller install.
## Setup
```bash
pip install kittentts
```
Then select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=kittentts`.
## Voices
Eight preset voices, four male/female pairs:
```text
expr-voice-2-m expr-voice-2-f (default: expr-voice-2-f)
expr-voice-3-m expr-voice-3-f
expr-voice-4-m expr-voice-4-f
expr-voice-5-m expr-voice-5-f
```
An unknown voice id logs an info message and falls back to the default.
## Model selection
| Variable | Default | Meaning |
| --- | --- | --- |
| `OMNIVOICE_KITTENTTS_MODEL` | `KittenML/kitten-tts-mini-0.8` | HuggingFace checkpoint to load |
The ~80 MB model downloads from HuggingFace on first use (retried once on a
flaky connection). See [downloading-models.md](../downloading-models.md).
## Behaviour notes
- Output is 24 kHz mono.
- CPU-only by design — the ONNX graph has no CUDA/MPS path.
- Non-English `language` values are ignored with a log line pointing at
OmniVoice; reference audio is likewise ignored (no cloning).
- **Long-input hardening
([#1173](https://github.com/debpalash/VoiceStudio/issues/1173)):** the
shipped ONNX graph has a hard 512-token cap, and phonemization can expand
text massively (digits especially). VoiceStudio pre-measures every chunk
with the model's own tokenizer and splits oversized chunks at word
boundaries, so long or digit-heavy inputs no longer abort inside
onnxruntime with an opaque "invalid expand shape" error.
## Known limits
- English only; no cloning, no voice design, no emotion controls
(see [expressive-speech.md](../expressive-speech.md)).
- Preset voices only — speed is the one knob.
## Troubleshooting
- Engine unavailable: `pip install kittentts` into VoiceStudio's Python
environment and restart.
- Other issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[disk usage](disk-usage.md).
+76
View File
@@ -0,0 +1,76 @@
# VoiceStudio — MLX-Audio Engine (Apple Silicon)
MLX-Audio (Blaizzy/mlx-audio) wraps 14+ TTS engines — Kokoro, CSM, Dia,
Qwen3-TTS, Chatterbox, MeloTTS, OuteTTS, and more — behind a single adapter
that runs on Apple's MLX framework. It is **Apple Silicon only**: the engine
is not shipped on Linux, Windows, or Intel Macs, and a stray wheel on those
platforms never reports as available
([#390](https://github.com/debpalash/VoiceStudio/issues/390)).
## When to pick it
- You're on an M-series Mac and want small, fast models tuned for it.
- You want one of the specific hosted models (Kokoro for small multilingual,
CSM for cloning, Qwen3-TTS for voice design, Dia for dialogue, …).
## Setup
```bash
pip install mlx-audio
```
Then select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=mlx-audio`.
## Model selection
One backend hosts many models. The curated set:
| Key | Model | Niche |
| --- | --- | --- |
| `kokoro` (default) | `mlx-community/Kokoro-82M-bf16` | small multilingual |
| `csm` | `mlx-community/csm-1b-8bit` | voice cloning |
| `qwen3-tts` | `mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-4bit` | voice design |
| `dia` | `mlx-community/Dia-1.6B` | dialogue |
| `chatterbox` | `mlx-community/Chatterbox-TTS-4bit` | expressive |
| `melotts` | `mlx-community/MeloTTS-English-v3-MLX` | lightweight VITS |
| `outetts` | `mlx-community/Llama-OuteTTS-1.0-1B-4bit` | LM-based |
Pick a model in the **Model Catalogue → Engines** curated picker
([#981](https://github.com/debpalash/VoiceStudio/issues/981)) or set
`OMNIVOICE_MLX_AUDIO_MODEL` to either a curated key (`kokoro`) or any full
HF repo id. The env var overrides the persisted UI choice.
## Behaviour notes
- Output is 24 kHz mono for most hosted models.
- **Cloning works only with the `csm` model** — it is the only curated model
confirmed to accept a reference clip. Other models silently ignore
reference audio, so the engine reports cloning support only when CSM is
selected (dub/batch jobs gate on this).
- Voice design (text description → voice) is available through the
Qwen3-TTS VoiceDesign model.
- Language support is per-model (Kokoro ~8 languages, others vary). An
unsupported language for Kokoro produces a clear error naming what it
does support ([#977](https://github.com/debpalash/VoiceStudio/issues/977))
— leave language on Auto or switch to a multilingual engine.
## Platform notes
This engine is exempt from cross-platform parity as a platform-only
capability behind explicit opt-in: it exists only where Apple's MLX runtime
exists. On any other platform the engine picker shows it unavailable with
the reason.
## Troubleshooting
- Unavailable on an M-series Mac: `pip install mlx-audio` into
VoiceStudio's Python environment; in a packaged app build, MLX's native
libraries may fail to load — the engine reports unavailable rather than
crashing.
- Other issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[languages.md](../languages.md),
[downloading-models.md](../downloading-models.md),
[disk usage](disk-usage.md).
+57
View File
@@ -0,0 +1,57 @@
# VoiceStudio — MLX Whisper Engine
MLX Whisper runs Whisper on the Apple Silicon GPU via MLX. It exists because
CTranslate2 (whisperx / faster-whisper) has **no Metal build** — on a Mac
those engines transcribe on the CPU no matter what GPU is present. Measured
on an M2 with whisper-large-v3, one 30 s dub chunk: **90.4 s on WhisperX
(CPU) vs 20.5 s on MLX (GPU)** — which is why auto-detect picks MLX Whisper
on every Apple Silicon machine
([#1127](https://github.com/debpalash/VoiceStudio/issues/1127)).
## Selecting it
- Nothing to do on Apple Silicon — auto-detect prefers it there.
- Or explicitly: **Model Catalogue → Engines**, ASR tab → **Use**, or
`OMNIVOICE_ASR_BACKEND=mlx-whisper`.
## Best at
- **Dubbing on a Mac** — it layers the same wav2vec2 forced alignment
WhisperX uses on top of the GPU transcription, so word timing (±1030 ms)
and therefore lip-sync accuracy are unchanged. Same model, same alignment,
~4x the speed.
- **Dictation/capture** — the capture path automatically swaps in
`mlx-community/whisper-large-v3-turbo` (~5x faster than large-v3) unless a
sherpa dictation model or [parakeet-mlx](parakeet-mlx.md) is preferred.
## Platform support
**Apple Silicon only.** A shared platform gate refuses Linux, Windows, and
Intel Macs before any package import, so a stray `mlx-whisper` wheel on the
wrong platform never reports itself available
([#390](https://github.com/debpalash/VoiceStudio/issues/390)). All other
platforms use the CUDA/CPU engines instead.
## Model selection
- `ASR_MODEL` — default `mlx-community/whisper-large-v3-mlx`. Any MLX-format
Whisper repo works. Weights download on first load — see
[downloading-models](../downloading-models.md).
- `OMNIVOICE_ALIGN_DEVICE` — force the wav2vec2 aligner's device. The aligner
runs on MPS when it can and falls back to CPU; languages without a bundled
aligner (~20 major languages have one) keep Whisper's native word
timestamps.
## Quirks
- Audio is decoded through VoiceStudio's validated ffmpeg rather than the
bare `ffmpeg` PATH lookup mlx-whisper would do on its own — a clean
from-source install with no system ffmpeg works fine
([#479](https://github.com/debpalash/VoiceStudio/issues/479)).
- The model is warmed into unified memory in the background, so the first
transcribe after startup doesn't pay the load cost.
- In a packaged app, a native MLX library that fails to load is reported as
"unavailable" (with fallback to another engine) rather than crashing the
engine list.
Speed comparisons across engines live in [performance](../performance.md).
+48
View File
@@ -0,0 +1,48 @@
# VoiceStudio — Moonshine Engine
Moonshine is an edge-optimized ASR family built for CPU-only machines.
Unlike Whisper it processes variable-length audio (no padding everything to
30 s), which keeps latency low on short clips — sub-200 ms class on capture
buffers. It's the lightest local option for quick transcription on hardware
where even int8 whisper-large is too slow.
## Selecting it
- Install one of the runtimes into the app venv:
`uv pip install moonshine-onnx` (lighter, tried first) or
`moonshine-voice`.
- Then **Model Catalogue → Engines**, ASR tab → **Use** on the Moonshine row,
or `OMNIVOICE_ASR_BACKEND=moonshine`.
Auto-detect never picks it; it's an explicit opt-in.
## Best at
- **Quick notes and short-clip transcription on low-power CPU machines.**
- Environments where a sub-1 GB footprint matters more than word timing or
language coverage.
## Not suited for
- **Dubbing.** Output is plain text as a **single segment spanning the whole
file — no word or segment timestamps** — so there's nothing for lip-sync
or subtitle timing to work with. Use a Whisper-family engine or
[sherpa-onnx-asr](sherpa-onnx-asr.md) for those jobs.
- Multilingual work: results report English; for broad language coverage use
[whisperx](whisperx.md) or [funasr](funasr.md).
## Platform support
CPU only, by design — macOS, Windows, and Linux. It claims no GPU.
## Model selection
`ASR_MODEL_MOONSHINE` — default `moonshine/base`. Weights download on first
load — see [downloading-models](../downloading-models.md).
## Quirks
- The engine tries `moonshine_onnx` first and falls back to
`moonshine_voice` — installing either one is enough.
- Segment bounds are synthesized from the audio duration (start 0, end =
file length), since the model reports none.
+78
View File
@@ -0,0 +1,78 @@
# VoiceStudio — MOSS-TTS-Nano Engine
MOSS-TTS-Nano (OpenMOSS) is the low-resource, broad-language pick: a
100M-parameter autoregressive codec LM that runs realtime on a 4-core CPU —
no GPU required — with native 48 kHz output and 20 languages under an
Apache-2.0 license. It fills the "runs on a fanless laptop" tier while still
covering languages like Arabic, Hebrew, Persian, Korean, and Turkish.
## When to pick it
- CPU-only or low-power hardware, but you still need cloning and non-English
coverage.
- Your language is among: Chinese, English, German, Spanish, French,
Japanese, Italian, Hebrew, Korean, Russian, Persian, Arabic, Polish,
Portuguese, Czech, Danish, Swedish, Hungarian, Greek, Turkish.
## Setup
The package is **not on PyPI** — install it from the upstream repo into
VoiceStudio's Python environment:
```bash
git clone https://github.com/OpenMOSS/MOSS-TTS-Nano.git
cd MOSS-TTS-Nano
uv pip install -e .
```
Then select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=moss-tts-nano`.
## Model selection
| Variable | Default | Meaning |
| --- | --- | --- |
| `OMNIVOICE_MOSS_TTS_MODEL` | `OpenMOSS-Team/MOSS-TTS-Nano` | HuggingFace checkpoint to load |
The first use downloads the weights (retried once on a truncated download).
See [downloading-models.md](../downloading-models.md).
## Behaviour notes
- **Cloning is reference-only**: pass a reference clip. Style instructions,
preset speakers, and speed control are not supported and are silently
ignored, so mixed-engine call sites keep working.
- The model emits 48 kHz stereo; VoiceStudio downmixes to mono, matching the
rest of the pipeline (the dub mixer treats TTS output as mono per
segment).
- Runs on CPU or CUDA.
## Upstream is unpinned
The upstream repo is installed straight from git with no pinned release, and
the model class it exports has changed before
([#1287](https://github.com/debpalash/VoiceStudio/issues/1287)). VoiceStudio
therefore verifies that a usable model class actually exists — not just that
the package imports — before reporting the engine as ready. If the engine
shows unavailable with a "does not expose a usable model class" message,
pull the latest upstream and re-run `uv pip install -e .`, or open an issue
with the version you have.
## Known limits
- No voice design, no instruct, no speed control — cloning from a reference
clip only.
- Quality sits below the large engines; see
[benchmarks.md](../benchmarks.md).
## Troubleshooting
- "moss_tts_nano package not installed": run the clone + `uv pip install -e .`
steps above.
- Entry-point errors after an upstream update: see "Upstream is unpinned"
above.
- General issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [languages.md](../languages.md),
[expressive-speech.md](../expressive-speech.md),
[disk usage](disk-usage.md).
+58
View File
@@ -0,0 +1,58 @@
# VoiceStudio — Parakeet TDT (NVIDIA NeMo) Engine
NVIDIA's Parakeet TDT via the NeMo toolkit: a FastConformer encoder with a
Token-and-Duration Transducer decoder. It beats Whisper large-v3 on English
benchmarks (~6% WER) and supports **25 (mostly European) languages** with
automatic language detection. The 0.6B model is fast even on CPU — measured
RTF 0.080.23 on an Apple Silicon M2 CPU (2026-07-02), ~20x faster than
faster-whisper large-v3 int8 on the same host.
## Do not install NeMo into the app venv
`nemo_toolkit`'s ASR extras pin `transformers>=4.57,<4.58`, which conflicts
with VoiceStudio's own `transformers>=5.3` requirement and **will break the
backend** (ImportError on startup) if installed into the shared venv. There
is currently no safe in-app install path for this engine; in-app isolation
is tracked separately.
If you want the Parakeet models without a separate environment, use these
instead — same model family, no NeMo dependency:
- **Apple Silicon:** [parakeet-mlx](parakeet-mlx.md) (installed by default on
mac-ARM source installs).
- **Any platform, CPU:** [sherpa-onnx-asr](sherpa-onnx-asr.md) — its default
dictation model is an int8 ONNX export of Parakeet TDT v3.
## Selecting it
Only meaningful if you've set up `nemo_toolkit[asr]` in a **separate,
dedicated Python environment** that runs the backend:
- **Model Catalogue → Engines**, ASR tab → **Use** on the Parakeet TDT row, or
- `OMNIVOICE_ASR_BACKEND=nemo-parakeet`.
Auto-detect never picks it; it's an explicit opt-in.
## Best at
- **English and European-language transcription** where WER matters more
than word-level subtitle timing.
- **CPU-only hosts** — faster than realtime without any GPU.
## Platform support
CUDA or CPU (the old hard CUDA gate was removed — see the RTF numbers
above). Availability is a pure dependency check on `nemo.collections.asr`.
## Model selection
`ASR_MODEL_NEMO` — default `nvidia/parakeet-tdt-0.6b-v3`. Weights download
on first load — see [downloading-models](../downloading-models.md).
## Quirks
- Output is a **single segment** for the whole file (NeMo doesn't VAD-split
like Whisper), with word timestamps when the model exposes them — fine for
dictation and plain transcripts, not ideal for long-form subtitles.
- The detected language isn't exposed cleanly by NeMo, so results report
`en` regardless of the actual (auto-detected) language.
+83
View File
@@ -0,0 +1,83 @@
# VoiceStudio — OmniVoice GGUF Engine
OmniVoice GGUF runs the same OmniVoice model as the [default
engine](omnivoice.md), but through a bundled native binary
(`bin/omnivoice-tts-<platform>`) loading quantized GGUF weights. It is
hardware-adaptive: a probe picks the quantization that fits your machine, so
small GPUs and CPU-only hosts get a working OmniVoice instead of a paging,
timing-out one.
## When to pick it
- Your GPU is below the default engine's 6 GB VRAM floor.
- CPU-only machines that still want OmniVoice's voice and language coverage.
- You want generation isolated in a separate process (a crash or leak never
takes the app down — each generation spawns the binary fresh).
## Quantization selection
Weights come from the `Serveurperso/OmniVoice-GGUF` HuggingFace repo, pinned
to an exact revision. The hardware probe selects:
| Hardware | Quant | Approx. VRAM use |
| --- | --- | --- |
| 12 GB+ VRAM | BF16 | ~1.6 GB (quality-first) |
| 412 GB VRAM | Q8_0 | ~945 MB (recommended balance) |
| 14 GB VRAM | Q4_K_M | ~659 MB (minimal footprint) |
| CPU-only | Q4_K_M | RAM-bound, latency-tolerable |
You can override the selection from Settings; overrides are allow-listed
against the same table (an F32 reference quant, ~3.2 GB, is override-only).
## Setup
Nothing to install: installer and CI builds bundle the binary for your
platform. Select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=omnivoice-gguf`. The quant weights download on first
use (see [downloading-models.md](../downloading-models.md)) — install them
ahead of time from **Model Catalogue → Models** if you want the first
generation to be quick; a long first render is the download, not a hang.
**Source checkouts:** the repo ships zero-byte placeholders in `bin/` — real
binaries come from CI or the installer. The engine detects a placeholder and
reports unavailable with instructions
([#1172](https://github.com/debpalash/VoiceStudio/issues/1172)) instead of
failing at spawn time; build one with
`scripts/build-omnivoice-tts.sh --platform <slug>` or use the default
in-process engine.
## Integrity and self-healing
Before reporting ready, the engine:
- verifies the binary against the SHA-256 manifest (`bin/checksums.sha256`);
- detects macOS Gatekeeper quarantine and prints the exact
`xattr -cr '/Applications/VoiceStudio.app'` fix;
- restores a missing execute bit (a git clone or zip extract on POSIX can
drop `+x`, which used to surface as a permission error mislabeled as
out-of-memory — [#437](https://github.com/debpalash/VoiceStudio/issues/437)).
The chmod runs only after the SHA check confirms it's the right file.
## Behaviour notes
- Output is 24 kHz mono — same model, same rate as in-process OmniVoice.
- Cloning from a reference clip (with optional transcript) and style
instructions are supported; no voice design.
- Same multilingual surface as OmniVoice ([languages.md](../languages.md)).
- Because generation runs in another process, the app's own GPU counters
don't see its allocations — diagnostics label it accordingly.
| Variable | Default | Meaning |
| --- | --- | --- |
| `OMNIVOICE_GGUF_GENERATE_TIMEOUT_S` | (generous built-in) | Per-generation timeout for the spawned binary |
## Troubleshooting
- "GGUF binary missing": this build doesn't bundle the runtime for your
platform — use the default engine.
- Checksum mismatch or quarantine messages: follow the printed fix, or
reinstall.
- Other issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[performance.md](../performance.md), [disk usage](disk-usage.md).
+100
View File
@@ -0,0 +1,100 @@
# VoiceStudio — OmniVoice Engine (default)
OmniVoice (k2-fsa/OmniVoice) is VoiceStudio's default TTS engine — the one a
fresh install uses without any configuration. It does zero-shot voice cloning
across 600+ languages and outputs 24 kHz mono audio. Voice cloning, dubbing,
and dictation all run on it out of the box.
## When to pick it
- You want cloning plus the broadest language coverage (see
[languages.md](../languages.md)).
- You have a GPU (CUDA or Apple Silicon MPS) with ~6 GB VRAM or more.
- You just installed VoiceStudio — it's already selected.
For low-VRAM or CPU-only machines, the
[OmniVoice GGUF](omnivoice-gguf.md) variant runs the same model through a
quantized native binary with a much smaller memory footprint.
## Requirements
- Runs on CUDA, MPS (Apple Silicon), or CPU — auto-detected.
- Recommended VRAM floor: **6 GB** on a dedicated GPU. This is the only
engine with a measured floor: on 4 GB cards (GTX 1650 Ti, Quadro P2000 —
issues [#1226](https://github.com/debpalash/VoiceStudio/issues/1226) /
[#1222](https://github.com/debpalash/VoiceStudio/issues/1222)) the driver
pages to system RAM and a render that should take seconds runs for minutes
until the compute budget kills it. The UI warns before you wait; nothing
hard-blocks, since short inputs can still fit.
- No extra install — the model ships with the app and downloads its weights
on first use (see [downloading-models.md](../downloading-models.md)).
## Selecting the engine
OmniVoice is the default, so normally there is nothing to do. If you switched
away and want it back:
- **Model Catalogue → Engines**, or
- set `OMNIVOICE_TTS_BACKEND=omnivoice`.
The env var overrides the persisted UI choice.
## Behaviour notes
- Weights load lazily on first use and are shared with the rest of the app
(dubbing, dictation) — the model is never double-loaded.
- On CUDA the model runs fp16 with `torch.compile`; a speech recognizer is
co-loaded for the cloning path.
- Output is 24 kHz mono; the shared mastering chain (highpass + compressor)
is tuned for this rate and applied automatically.
- Cloning takes a short reference clip (`ref_audio`); 310 seconds is the
sweet spot. A transcript of the clip improves conditioning — if the profile
has none, VoiceStudio transcribes the clip automatically on first use and
saves the result to the profile.
- Encoded voice references persist on disk (`prompt_cache/` in the app data
dir), so the first generation with a known voice after a restart skips the
re-encode and any transcription pass. Set `OMNIVOICE_PROMPT_DISK_CACHE=0`
to keep the cache in memory only.
- Style attributes (`instruct`) and a reference clip can be **combined**:
when they agree, the instruct stabilizes cloning for the attributes it
names (upstream documents dialect cloning as the canonical case — dialect
reference + matching dialect instruct). When they conflict, the reference
audio wins.
- Inline pronunciation control: Chinese via pinyin with tone numbers
(`打ZHE2出售`), English via bracketed CMU phonemes (`[B EY1 S]`). Non-verbal
tags like `[laughter]` are covered in
[expressive-speech.md](../expressive-speech.md).
- Voice design works from attributes (gender, age, pitch, whisper, English
accents, Chinese dialects) via the Design tab — no reference audio needed.
- Optional FlashInfer acceleration on CUDA: set `OMNIVOICE_FLASHINFER=1`
(or `=graph` for CUDA-graph capture, best for one render at a time) after
installing the `flashinfer-python` package — see
[performance.md](../performance.md). Off by default; if the package is
missing or a kernel fails, the app logs why and continues on the standard
path.
## Known limits
- Voice design understands only the fixed attribute vocabulary — free-form
design *prose* is mapped onto those attributes, and wording outside them
is ignored. Design is trained on English and Chinese and can be unstable
in low-resource languages; for description-driven design in other cases
try [VoxCPM2](voxcpm2.md).
- Below the 6 GB VRAM floor, expect very slow renders or budget timeouts;
prefer [OmniVoice GGUF](omnivoice-gguf.md) or a CPU engine such as
[PocketTTS](pockettts.md).
## Troubleshooting
- "Too heavy for the available compute" on a small GPU: see the VRAM floor
above — switch to OmniVoice GGUF or close other GPU apps.
- First generation is slow: the first call downloads multi-GB weights. To
keep the first render quick, install the model ahead of time from
**Model Catalogue → Models** — a long first generate is almost always the
download, not a hang.
- General install issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[performance.md](../performance.md),
[expressive-speech.md](../expressive-speech.md),
[disk usage](disk-usage.md).
+58
View File
@@ -0,0 +1,58 @@
# VoiceStudio — Parakeet TDT v3 (MLX) Engine
NVIDIA's Parakeet TDT v3 on the Apple Silicon GPU, via the small pure-Python
`parakeet-mlx` package. It gives Macs the Parakeet tier CUDA/CPU users get
through NeMo or sherpa-onnx: **25 European languages**, word timestamps from
the TDT decoder itself (no wav2vec2 alignment pass needed), ~1.2 GB download,
~2 GB unified memory, dictation-grade speed on the GPU.
Unlike [nemo-parakeet](nemo-parakeet.md) it needs no `nemo_toolkit` (whose
transformers pin conflicts with the app's) — it is **installed by default on
Apple Silicon source installs since 0.3.22**.
## Selecting it
- **Model Catalogue → Engines**, ASR tab → **Use** on the Parakeet TDT v3
(MLX) row, or `OMNIVOICE_ASR_BACKEND=parakeet-mlx`.
- **Dictation prefers it automatically**: once the model weights are
installed (Model Catalogue → Models — the auto-pick never triggers a
download), live dictation/capture uses it whenever your system language is
one of the 25 covered European languages. Other languages keep the
multilingual Whisper engine, so dictation coverage never regresses.
## Best at
- **Live dictation on a Mac** — TDT decoding is fast enough for the capture
path, at Parakeet's better-than-Whisper English WER.
- **European-language transcription** with word timestamps at a fraction of
whisper-large-v3's memory and compute.
For languages outside the 25 (CJK, Arabic, ...), use
[mlx-whisper](mlx-whisper.md) instead.
## Platform support
**Apple Silicon only** — the same shared MLX platform gate as mlx-whisper
refuses Linux, Windows, and Intel Macs before any import
([#390](https://github.com/debpalash/VoiceStudio/issues/390)). It runs on the
unified-memory GPU; there is no CPU tier.
## Model selection
`ASR_MODEL_PARAKEET_MLX` — default `mlx-community/parakeet-tdt-0.6b-v3`.
Weights download on first load — see
[downloading-models](../downloading-models.md).
## Quirks
- Long files are processed in 120 s chunks internally to bound unified-memory
use; short dictation buffers and dub chunks are unaffected.
- Parakeet v3 auto-detects among its 25 languages but doesn't expose the
pick, so the reported language is the one you requested (or none) — it is
never hardcoded to English.
- Word timestamps are merged from the decoder's subword tokens — good for
subtitles and dictation; for lip-sync-critical dubbing the wav2vec2-aligned
engines ([mlx-whisper](mlx-whisper.md), [whisperx](whisperx.md)) remain the
accuracy tier.
Speed comparisons across engines live in [performance](../performance.md).
+82
View File
@@ -0,0 +1,82 @@
# VoiceStudio — PocketTTS Engine
PocketTTS (kyutai-labs/pocket-tts, 100M parameters) is the fastest-CPU-render
pick: small, low-latency, CPU-only, with zero-shot voice cloning from a
reference clip. It covers six languages — English, French, German,
Portuguese, Italian, Spanish — with one model per language, and measures
roughly 89x real-time on an Apple M3 Pro.
It complements the quality engines: where they fall back to CPU, PocketTTS
is built for it. CPU-only is deliberate — upstream observes no GPU speedup
for this model.
## When to pick it
- CPU-only machines that need fast rendering *and* voice cloning.
- Latency-sensitive use (dictation-style, short utterances) in one of the
six languages.
## Setup
1. Install the optional dependency:
```bash
uv sync --extra pockettts
```
(Or enable it from **Model Catalogue → Engines**.)
2. **Accept the license in-app**
([#1306](https://github.com/debpalash/VoiceStudio/issues/1306)). The code
is MIT and the weights are CC-BY-4.0, but the weights are **gated on
HuggingFace** behind an access agreement with an acceptable-use clause.
VoiceStudio surfaces this before first use: the engine stays unavailable
until you review and accept in **Model Catalogue → Engines → PocketTTS**.
You also need HuggingFace access to the gated repo (see
[downloading-models.md](../downloading-models.md) for token setup).
3. Select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=pockettts`.
## Platform notes
- Works on Linux, Windows, macOS Apple Silicon — CPU only everywhere.
- **Not available on Intel Macs**: the required PyTorch version has no
macOS x86_64 wheel. The engine reports this plainly instead of failing
mid-install.
## Behaviour notes
- Output is 24 kHz mono.
- Six languages, one model per language, chosen by the `language` you
request; cloning takes a short reference clip.
- Runs in a crash-isolated sidecar process (parent Python environment): a
wedged generation is hard-killed by a watchdog and its memory reclaimed —
something an in-process engine cannot do.
- The first use downloads the gated weights; the sidecar heartbeats
progress during the download so the watchdog doesn't fire.
| Variable | Default | Meaning |
| --- | --- | --- |
| `OMNIVOICE_POCKETTTS_RECV_TIMEOUT_S` | `600` | Sidecar response deadline in seconds (min 30; cold loads download weights) |
## Known limits
- No voice design, no emotion controls
(see [expressive-speech.md](../expressive-speech.md)).
- Six languages only — for broader coverage use
[OmniVoice](omnivoice.md) ([languages.md](../languages.md)).
- Revoking the license acceptance takes effect immediately, without a
restart — subsequent generations refuse.
## Troubleshooting
- "pocket_tts package not installed": run the `uv sync` above.
- "license not accepted": open **Model Catalogue → Engines → PocketTTS**
and review/accept.
- Timeouts on a slow connection: raise
`OMNIVOICE_POCKETTTS_RECV_TIMEOUT_S` for the first (download-heavy) run.
- Other issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[performance.md](../performance.md), [disk usage](disk-usage.md).
+61
View File
@@ -0,0 +1,61 @@
# VoiceStudio — PyTorch Whisper Engine
Whisper through the plain `transformers` pipeline, riding torch itself. No
extra install — transformers ships with the app — and because it runs on
torch's own stack (including torch's bundled cuDNN 9), it works on machines
where the CTranslate2 engines can't load. It is also the engine that
genuinely uses **AMD ROCm** GPUs, so auto-detect picks it on ROCm hosts
([#1529](https://github.com/debpalash/VoiceStudio/issues/1529)).
## Selecting it
- **Model Catalogue → Engines**, ASR tab → **Use** on the PyTorch Whisper
row, or `OMNIVOICE_ASR_BACKEND=pytorch-whisper`.
- Auto-detect picks it on ROCm, and as the last resort everywhere else.
## Best at
- **ROCm dubbing/transcription** — the only Whisper engine that uses the HIP
GPU (CTranslate2 has no HIP build, MLX is Apple-only).
- **Rescue engine** when whisperx/faster-whisper can't load — e.g. the
missing-cuDNN-8 case
([#255](https://github.com/debpalash/VoiceStudio/issues/255)) — since it
needs neither CTranslate2 nor cuDNN 8.
For lip-sync-grade word timing prefer [whisperx](whisperx.md) or
[mlx-whisper](mlx-whisper.md); this engine returns the pipeline's own word
timestamps.
## Platform support
CUDA, Apple Silicon (MPS), ROCm (HIP), and CPU — wherever torch runs, on
macOS, Windows, and Linux.
## Model selection
`OMNIVOICE_PYTORCH_ASR_MODEL` — default `openai/whisper-large-v3-turbo`. Any
transformers-format Whisper repo works. Weights download on first load — see
[downloading-models](../downloading-models.md).
## VRAM preflight
whisper-large-v3-turbo needs roughly 3.2 GiB before generation adds its
workspace; loading it onto a nearly-full card "succeeds" and then the first
transcribe OOMs with zero segments. So on CUDA the engine checks free VRAM
against a 5 GB budget before loading and uses the CPU instead when the card
is too full (flush the TTS model to restore GPU-speed ASR). Disable with
`OMNIVOICE_ASR_VRAM_PREFLIGHT=0`.
## Quirks
- If the pipeline fails to import (`AutoFeatureExtractor` errors), the cause
is either an incomplete transformers install or a torch/torchvision
version mismatch — the error message names the exact reinstall command;
the trio has to move together at the pinned versions
([#549](https://github.com/debpalash/VoiceStudio/issues/549),
[#1376](https://github.com/debpalash/VoiceStudio/issues/1376)).
- Transcribes are time-bounded like every local engine:
`OMNIVOICE_TRANSCRIBE_CHUNK_TIMEOUT_S` (default 120 s per dub chunk),
`OMNIVOICE_ASR_TRANSCRIBE_TIMEOUT_S` (default 300 s whole-file).
Speed comparisons across engines live in [performance](../performance.md).
+71
View File
@@ -0,0 +1,71 @@
# VoiceStudio — Sherpa-ONNX Dictation Engine
The k2-fsa/sherpa-onnx ONNX runtime as a **live dictation** engine: small
int8 models that transcribe faster than realtime on CPU, with identical
behavior on macOS (arm64 + x86_64), Windows, and Linux — no CUDA dependency.
Streaming models emit partial text frame-by-frame as you speak; offline
models re-transcribe a growing buffer on a short cadence, so you see live
partials either way.
## Selecting it
- Ensure `sherpa-onnx` is installed (`uv add sherpa-onnx` on source installs).
- Pick a dictation model in the app (Model Catalogue → Models lists the
curated set below), or **Model Catalogue → Engines**, ASR tab → **Use**, or
pin `OMNIVOICE_ASR_BACKEND=sherpa-onnx-asr`.
- `OMNIVOICE_SHERPA_ASR_MODEL` selects the model — default
`sherpa-parakeet-tdt-v3`.
## Best at
- **Live dictation on CPU** — the whole point of this engine. Fast partials,
automatic endpointing on silence, no GPU required.
- It also honors the regular offline `transcribe` contract, so any of its
models can transcribe a file — plain text, single segment, no word
timestamps, which makes it a dictation/notes tool rather than a dubbing
engine.
## The 7 curated models
| Id | Type | Languages | Download |
| --- | --- | --- | --- |
| `sherpa-parakeet-tdt-v3` (default) | offline | 25 European languages | 0.67 GB |
| `sherpa-parakeet-tdt-v2` | offline | English | 0.66 GB |
| `sherpa-zipformer-bilingual-zh-en` | streaming | Chinese + English | 0.20 GB |
| `sherpa-paraformer-bilingual-zh-en` | streaming | Chinese + English | 0.24 GB |
| `sherpa-zipformer-en-20m` | streaming | English | 0.044 GB |
| `sherpa-zipformer-zh-14m` | streaming | Chinese | 0.025 GB |
| `sherpa-whisper-tiny` | offline | 90+ languages (auto-detect) | 0.104 GB |
Sizes are measured on-disk download sizes. Weights are int8 ONNX checkpoints
that download on first use through the same HF cache as everything else —
see [downloading-models](../downloading-models.md). Peak RAM for the 0.6B
Parakeets is noticeably higher than their download size (onnxruntime's arena
allocator holds onto freed blocks).
## Platform support
CPU on every platform, by the strict cross-platform default-parity rule.
`OMNIVOICE_SHERPA_ASR_PROVIDER` can override the ONNX provider on a verified
GPU build, but the default never diverges.
## Tuning
- `OMNIVOICE_SHERPA_ASR_THREADS` — decode threads (default 2; the 0.6B
Parakeets automatically use up to 4 when the host has the cores, so decode
keeps ahead of the speaker).
- `OMNIVOICE_DICTATION_ENDPOINT_R1` / `OMNIVOICE_DICTATION_ENDPOINT_R2`
streaming endpoint rules in seconds (defaults 1.0 / 0.6: text commits
~0.6 s after you stop speaking). Applied without a restart.
## Quirks
- The recognizer is **pre-warmed in the background** so the first dictation
session doesn't pay the 1.32.5 s ONNX session load
([#888](https://github.com/debpalash/VoiceStudio/issues/888)); it's then
shared warm across sessions.
- On Apple Silicon, installing the [parakeet-mlx](parakeet-mlx.md) model
makes dictation prefer the GPU Parakeet automatically for the 25 covered
languages; an explicitly selected sherpa model still wins.
- The offline `transcribe` path reports `language: auto` — per-file language
detection is only meaningful for the Whisper Tiny model.
+75
View File
@@ -0,0 +1,75 @@
# VoiceStudio — Sherpa-ONNX Engine
Sherpa-ONNX (k2-fsa/sherpa-onnx) is a unified C++ ONNX runtime that wraps
20+ TTS model families (VITS, MeloTTS, Piper, Kokoro, Matcha, and more)
behind one API, with pre-built wheels for Linux, Windows, and macOS (x86 and
ARM). You bring the model: point VoiceStudio at any downloaded sherpa-onnx
TTS model directory.
## When to pick it
- You want a specific community model (e.g. a Piper or VITS voice for your
language) that no other engine hosts.
- You need a dependable CPU engine with optional CUDA acceleration.
## Setup
1. Install the runtime:
```bash
pip install sherpa-onnx
```
2. Download a TTS model from the
[sherpa-onnx releases](https://github.com/k2-fsa/sherpa-onnx/releases)
and unpack it somewhere permanent.
3. Point VoiceStudio at the model directory and restart:
```bash
export OMNIVOICE_SHERPA_MODEL=/path/to/model-dir
```
4. Select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=sherpa-onnx`.
The directory must contain `model.onnx` and `tokens.txt`. Sherpa-ONNX ships
no bundled default model, so the engine reports unavailable — with the
reason — until `OMNIVOICE_SHERPA_MODEL` points at a valid directory. (Before
this gate, selecting the engine unconfigured produced a failure mislabeled
as out-of-memory —
[#919](https://github.com/debpalash/VoiceStudio/issues/919).)
## Configuration
| Variable | Default | Meaning |
| --- | --- | --- |
| `OMNIVOICE_SHERPA_MODEL` | (unset) | Directory containing `model.onnx` + `tokens.txt` |
## Behaviour notes
- Output defaults to 22.05 kHz (the VITS default); once a model is loaded,
its own sample rate is used.
- CPU is the universal baseline; the CUDA onnxruntime provider is available
on Linux/Windows installs.
- **No cloning**: voices come from the model itself. Multi-speaker VITS
models select a voice by numeric speaker id; speed is supported.
- Languages depend entirely on the model you download.
## Known limits
- One model at a time — switching models means changing
`OMNIVOICE_SHERPA_MODEL` and restarting.
- No voice design, no reference-audio cloning, no emotion controls
(see [expressive-speech.md](../expressive-speech.md)).
## Troubleshooting
- "OMNIVOICE_SHERPA_MODEL not set" / "No model.onnx in …": follow Setup
above — the variable must point at the *unpacked* model directory, not
the archive.
- Other issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[languages.md](../languages.md),
[disk usage](disk-usage.md).
+76
View File
@@ -0,0 +1,76 @@
# VoiceStudio — Supertonic-3 Engine
Supertonic-3 (Supertone Inc.) is a ~99M-parameter ONNX TTS engine covering
31 languages with 7 preset voices at native 44.1 kHz. It is CPU-only by
design — pure ONNX Runtime on the CPU execution provider, with no CUDA or
MPS path in the upstream SDK — and runs in its own sidecar process so
crashes and cold init never block the rest of VoiceStudio.
## When to pick it
- Broad language coverage on machines with no usable GPU.
- Preset-voice narration at a higher sample rate than the default engine.
## Setup
1. Install the optional dependency into VoiceStudio's environment:
```bash
uv sync --extra supertonic
```
(Or enable it from **Model Catalogue → Engines**, which installs the
pinned `supertonic` wheel for you.)
2. **Accept the license in-app.** First use is gated behind an explicit
acceptance dialog: the inference SDK is MIT, but the model weights are
**OpenRAIL-M**, which carries use restrictions. The engine stays
unavailable until you review and accept in **Model Catalogue → Engines →
Supertonic-3**.
3. Select the engine via **Model Catalogue → Engines** or
`OMNIVOICE_TTS_BACKEND=supertonic3`.
The first synthesis cold-downloads ~400 MB of model weights, pinned to an
exact HuggingFace revision SHA so the bytes match what the SDK was validated
against. See [downloading-models.md](../downloading-models.md).
## Voices
Seven preset voices are surfaced: `M1` (default), `M3`, `M4`, `M5`, `F3`,
`F4`, `F5`. The SDK itself accepts the full `M1``M5` / `F1``F5` set if a
caller passes one explicitly; unknown ids fall back to the default with a
log line.
## Behaviour notes
- Output is 44.1 kHz mono.
- Runs as a long-lived sidecar in the parent Python environment (its
dependencies — onnxruntime, numpy, soundfile — already match
VoiceStudio's pins); subsequent calls reuse the warm ONNX session.
- `speed` is clamped to 0.72.0; quality steps clamp to 512.
- Language is an ISO 639-1 code; Auto engages the SDK's multilingual
fallback.
## Known limits
- **No cloning and no voice design** — preset voices only. Dub/batch jobs
that need cloning won't select it.
- CPU-only: hardware acceleration is a property of the upstream SDK, not a
VoiceStudio limitation.
- OpenRAIL-M weights are not covered by VoiceStudio's blanket
commercial-use statement — review the model license terms in the
acceptance dialog.
## Troubleshooting
- "supertonic package not installed": run the `uv sync` above or enable
from the Model Catalogue.
- "license not accepted": open **Model Catalogue → Engines → Supertonic-3**
and accept.
- Other issues: [install/troubleshooting.md](../install/troubleshooting.md).
See also: [benchmarks.md](../benchmarks.md),
[languages.md](../languages.md),
[expressive-speech.md](../expressive-speech.md),
[disk usage](disk-usage.md).
+76
View File
@@ -0,0 +1,76 @@
# VoiceStudio — VoxCPM2 Engine
VoxCPM2 (OpenBMB) is the studio-quality option: native 48 kHz output,
zero-shot voice cloning, and — uniquely among VoiceStudio's engines —
**voice design**: creating a synthetic voice from a text description
("young female, warm tone, British accent") with no reference audio at all.
## When to pick it
- You want voice design without a reference clip.
- You want the highest output sample rate (48 kHz vs OmniVoice's 24 kHz).
- Your language is among its 30 supported languages: Arabic, Burmese,
Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew,
Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay,
Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish,
Tagalog, Thai, Turkish, Vietnamese.
## Requirements
- Python ≥ 3.10, PyTorch ≥ 2.5.
- CUDA ≥ 12 recommended for full speed; MPS (Apple Silicon) and CPU also
work.
## Setup
Install the package into VoiceStudio's Python environment:
```bash
pip install "voxcpm>=2.0.3"
```
That is a version **floor**, not a pin — an older install still works, but
the engine logs an upgrade hint at load time. Then select the engine via
**Model Catalogue → Engines** or `OMNIVOICE_TTS_BACKEND=voxcpm2`.
## Model selection
| Variable | Default | Meaning |
| --- | --- | --- |
| `OMNIVOICE_VOXCPM_MODEL` | `openbmb/VoxCPM2` | HuggingFace checkpoint to load |
The first use downloads a multi-GB checkpoint from HuggingFace. A download
interrupted near the end used to abort the load outright
([#1224](https://github.com/debpalash/VoiceStudio/issues/1224)); the load is
now retried once with a fresh client. See
[downloading-models.md](../downloading-models.md).
## Behaviour notes
- **Voice design:** provide a description and no reference audio.
- **Cloning:** the reference clip is prepared before use (edge-silence trim
and length cap) so dead air in a raw clip doesn't condition the output; on
any prep problem the raw clip is used as-is.
- **Style instructions** are passed as an inline prefix to the text.
- VoxCPM2 emits mastered, studio-grade audio, so VoiceStudio **skips its
shared mastering chain** (which is tuned for 24 kHz engines) — only benign
loudness normalization applies.
- A trailing-silence guard trims long near-silent tails from generations,
keeping a short natural tail.
## Known limits
- Slower than the lightweight CPU engines — see
[benchmarks.md](../benchmarks.md) and [performance.md](../performance.md).
- Language coverage is 30 languages; for anything else use the default
[OmniVoice](omnivoice.md) engine ([languages.md](../languages.md)).
## Troubleshooting
- Engine shows unavailable: the `voxcpm` package isn't installed — run the
`pip install` above and restart VoiceStudio.
- Repeated first-download failures: check connectivity/HF access, then see
[install/troubleshooting.md](../install/troubleshooting.md).
See also: [expressive-speech.md](../expressive-speech.md),
[disk usage](disk-usage.md).
+80
View File
@@ -0,0 +1,80 @@
# VoiceStudio — WhisperX Engine
WhisperX is the default ASR engine on CUDA and plain-CPU hosts: faster-whisper
(CTranslate2) transcription plus a **wav2vec2 forced-alignment** pass that
snaps word boundaries to ±1030 ms (Whisper's own timestamps are ±100300 ms).
That word timing is what dubbing lip-sync depends on, which is why auto-detect
prefers it wherever CTranslate2 can use the GPU.
## Selecting it
- **Model Catalogue → Engines**, ASR tab → **Use** on the WhisperX row, or
- pin it with `OMNIVOICE_ASR_BACKEND=whisperx` (the env var always wins over
the Settings pick; with neither set, auto-detect chooses per-hardware).
## Best at
- **Dubbing** — the forced alignment is the accuracy tier lip-sync needs.
- **Batch transcription** with word-level subtitles.
- Multi-speaker work: it pairs with pyannote speaker diarization — see
[diarization](../features/diarization.md).
## Platform support
| Host | What happens |
| --- | --- |
| NVIDIA CUDA | GPU, float16 (degrades automatically, see below) |
| CPU (any OS) | int8 — works, but slow for large-v3 |
| Apple Silicon | CPU only — CTranslate2 has no Metal build, so auto-detect prefers [mlx-whisper](mlx-whisper.md) there ([#1127](https://github.com/debpalash/VoiceStudio/issues/1127)) |
| AMD ROCm | CPU only — CTranslate2 has no HIP build, so auto-detect prefers [pytorch-whisper](pytorch-whisper.md) there ([#1529](https://github.com/debpalash/VoiceStudio/issues/1529)) |
## Model selection
- `ASR_MODEL_WHISPERX` — default `large-v3`. Accepts the usual size aliases
(`tiny``large-v3`, `distil-large-v3`) or a full HF repo id. Weights
download on first load — see [downloading-models](../downloading-models.md).
- `OMNIVOICE_ALIGN_DEVICE` — force the wav2vec2 aligner's device. Aligners
exist for ~20 major languages; other languages keep Whisper's native word
timestamps instead of failing.
## VRAM preflight and degradation
Loading fp16 large-v3 onto a nearly-full 8 GB card dies as a *native* CUDA
abort — no Python exception, the whole backend goes down
([#723](https://github.com/debpalash/VoiceStudio/issues/723)). So before every
load the engine checks free VRAM against per-compute-type budgets
(float16 5.0 GB, int8_float16 3.5 GB, int8 3.0 GB, scaled down for smaller
models) and degrades the compute type — or falls to CPU int8 — instead of
starting a load that would kill the process. Disable with
`OMNIVOICE_ASR_VRAM_PREFLIGHT=0`.
Two more fallback chains run at load time:
- GPUs without efficient fp16 (older Maxwell/Pascal, GTX 16xx) raise a
compute-type error — the engine retries int8_float16, then int8
([#551](https://github.com/debpalash/VoiceStudio/issues/551)).
- A genuine CUDA OOM retries on CPU int8, so dubbing still completes
(slower, same model and accuracy).
## Quirks
- **cuDNN 8 required on CUDA.** CTranslate2 links cuDNN 8; if it's missing the
process fast-fails with no traceback, so the engine is reported unavailable
up front and selection falls through to pytorch-whisper, which uses torch's
own cuDNN 9 ([#1371](https://github.com/debpalash/VoiceStudio/issues/1371)).
- On some hardened Linux kernels CTranslate2's native library is rejected with
"cannot enable executable stack" — reported as unavailable, not a crash
([#692](https://github.com/debpalash/VoiceStudio/issues/692)).
- A partially-installed environment (interrupted sync, antivirus quarantine)
can break WhisperX's deep import chain (whisperx → pyannote →
lightning_fabric). The engine is then reported unavailable with a repair
hint — reinstall, or `uv sync --reinstall` on a source checkout
([#1185](https://github.com/debpalash/VoiceStudio/issues/1185)).
- Audio is decoded through VoiceStudio's validated ffmpeg, not a bare `ffmpeg`
PATH lookup ([#479](https://github.com/debpalash/VoiceStudio/issues/479)).
- Transcribes are time-bounded: each dub chunk by
`OMNIVOICE_TRANSCRIBE_CHUNK_TIMEOUT_S` (default 120 s), whole files by
`OMNIVOICE_ASR_TRANSCRIBE_TIMEOUT_S` (default 300 s). Raise them for very
long files on slow hardware.
Speed comparisons across engines live in [performance](../performance.md).
+19 -8
View File
@@ -13,12 +13,12 @@ and [`palashdeb/omnivoice-studio` on Docker Hub](https://hub.docker.com/r/palash
> |-----|--------------|
> | `:latest` | **Rolling preview** — latest commit on `main`, at or ahead of the last release. This is the preview channel; pin `:stable` for production. |
> | `:stable` | Most recent versioned release (updated on every `v*` git tag) |
> | `:0.4.1` | Exact release version |
> | `:0.4` | Latest patch within the 0.4 minor |
> | `:0.5.0` | Exact release version |
> | `:0.5` | Latest patch within the 0.5 minor |
> | `:main` | Alias of the same rolling `main` build as `:latest` |
> | `:sha-xxxxxxx` | Specific commit (produced by manual workflow dispatch) |
> | `:rocm` | **AMD GPU (ROCm) build** of the rolling preview — the ROCm analogue of `:latest` |
> | `:stable-rocm`, `:0.4.1-rocm`, `:0.4-rocm`, `:sha-xxxxxxx-rocm` | ROCm builds of the corresponding CUDA tags above |
> | `:stable-rocm`, `:0.5.0-rocm`, `:0.5-rocm`, `:sha-xxxxxxx-rocm` | ROCm builds of the corresponding CUDA tags above |
>
> Versioning rule: preview builds always come from `main` and never
> version-sort below `:stable` — upgrades flow naturally.
@@ -89,7 +89,7 @@ PublishPort=127.0.0.1:3900:3900
Volume=omnivoice-data:/app/omnivoice_data
```
Release pins exist too: `:stable-rocm`, `:0.4.1-rocm`, `:0.4-rocm` mirror
Release pins exist too: `:stable-rocm`, `:0.5.0-rocm`, `:0.5-rocm` mirror
the CUDA tags exactly.
> **Consumer cards and APUs (RX 6000/7000, Strix Point/Halo):** the backend
@@ -192,10 +192,14 @@ docker run -e OMNIVOICE_PUBLIC_API_BASE=https://api.your-host.example \
> may instead bake `VITE_OMNIVOICE_API` at build time, but the runtime var above
> is simpler and image-agnostic.
> **Security:** VoiceStudio ships no authentication. Anything on your LAN with
> the URL can use the app. Put it behind a reverse proxy with `basic_auth`
> (Caddy / nginx + htpasswd) or a private network overlay (Tailscale, ZeroTier)
> before exposing publicly.
> **Security:** Loopback-only publishing is the safe default. On a trusted LAN,
> set a long random `OMNIVOICE_API_KEY` with `docker run -e` or Compose; the
> browser will prompt for it. The optional six-digit share PIN permits casual
> consumption access but does not authorize administration or dictation. On any
> untrusted network, plain HTTP is not safe for the API key or session cookie.
> Keep the backend on an encrypted private overlay such as Tailscale/ZeroTier;
> do not expose it directly to the public internet. See [API
> authentication](../api-auth.md) for the complete access model.
## Volume mounts
@@ -214,6 +218,13 @@ Two paths are worth persisting across container restarts:
The running version is now shown in **Settings → About → Version** (read live
from the backend), so the web UI no longer displays a dash in Docker.
- **Checking which version is running:** `docker exec <container> python3 -c "import importlib.metadata; print(importlib.metadata.version('omnivoice'))"`, or hit the `/health` endpoint — it returns `{"status": "ok", "device": ..., "version": "0.3.x"}`. Use the container name listed by `docker compose ps` (or `omnivoice` for the `docker run` examples).
- **Watching startup:** the port answers within about a second of container
start, but heavy initialization (PyTorch, API routes, database migration)
continues in the background. During that window `/health` returns **503**
with the current step, and `GET /startup/progress` returns the full
step-by-step ledger (`status`, current `step`/`label`, per-step states) —
useful when a start seems slow and you want to see where it actually is.
The Docker `HEALTHCHECK` flips healthy only once `/health` is 200.
- **"Loopback origin required" errors (and a blank version):** the desktop
build restricts the `/system/*` and `/api/settings/*` routes to a loopback
origin, but Docker's NAT makes every request look non-loopback, so the gate
+6
View File
@@ -80,6 +80,9 @@ None of them are required — the defaults are chosen for the common case.
| Variable | Default | What it does |
|---|---|---|
| `OMNIVOICE_DEVICE` | `auto` | Pin the compute device (`cuda` / `rocm` / `xpu` / `mps` / `cpu`) instead of auto-detect. Same control lives in **Settings → Performance & Device** (the env var wins over the UI pick). Honored only for devices the host actually has — a family that isn't detected is noted and ignored, never obeyed blindly. Applies at the next backend start. |
| `OMNIVOICE_FLASHINFER` | `0` | CUDA-only accelerated decoding for the default engine via [FlashInfer](https://github.com/flashinfer-ai/flashinfer) kernels (packed CFG attention, fused RMSNorm/RoPE/GEMM) — ~2x on upstream's benchmarks. `1` enables it; `graph` also captures CUDA graphs (best when you render one thing at a time). Requires installing the optional `flashinfer-python` package into the backend environment first (`uv pip install flashinfer-python flashinfer-jit-cache --extra-index-url https://flashinfer.ai/whl/cu128/`, matching your CUDA build). Replaces `torch.compile` for that session, pins inference to a single GPU thread (the FlashInfer attention plan is per-generation state), and keeps fused copies of the attention/MLP weights resident (~roughly half the LLM's weight size extra VRAM) — leave it off on tight-VRAM cards. If the package is missing or a FlashInfer/CUDA-graph kernel fails at runtime, the app logs the reason and falls back to the standard path; failures outside those kernels (e.g. a genuine out-of-memory) surface normally. |
| `OMNIVOICE_PROMPT_DISK_CACHE` | `1` | Persist encoded voice-clone references (`prompt_cache/` in the app data dir, ~10 KB per voice, 32 newest kept) so the first generation with a known voice after a restart skips the reference re-encode and any auto-transcription. Set `0` to keep the cache in memory only. |
| `OMNIVOICE_IDLE_TIMEOUT_S` | `900` | Seconds of idle before the TTS model unloads to free memory. Raise it (e.g. `3600`) if you generate in bursts and dislike the ~8 s reload; lower it on tight-memory machines. |
| `OMNIVOICE_SIDECAR_IDLE_TIMEOUT_S` | `300` | Same idea for sidecar engines (IndexTTS 2.5 etc.). |
| `OMNIVOICE_LLM_CONCURRENCY` | `6` | Parallel LLM translation calls during a dub. Raise for a fast API endpoint, lower if your provider rate-limits. |
@@ -229,6 +232,9 @@ uv run python scripts/bench_pipeline.py tts clone # just these stages
If you report a performance issue, pasting its table (plus your platform and
RAM/VRAM) turns a guessing game into a bisect.
Measured results per engine/device — and how to contribute yours — live in
[benchmarks.md](benchmarks.md).
## Things that look like knobs but aren't
- **Deleting and re-adding a voice** doesn't speed anything up; the reference
+13 -2
View File
@@ -153,6 +153,15 @@ fallback is reported once. ASR, diarization and translation also remain local. D
runs here, deliberately and permanently, because there latency *is* the
feature. The remaining operations are being ported one at a time.
### Voice identity parity
For TTS, the worker receives the complete local rendering contract: the voice
profile's reference audio and transcript, its pinned seed, model quality
controls, text chunking/crossfade settings, and output effect preset. The
worker runs the same native or generic rendering pipeline as local
`/generate`; selecting a gallery voice therefore does not turn it into a new
random voice merely because it was rendered on another GPU.
The picker knows this. It resolves against the surface you are on, so a chosen
worker reads **Local** on a tab whose work has no remote path yet and names the
reason, instead of showing a green dot next to a GPU that receives nothing. The
@@ -210,10 +219,12 @@ what is genuinely still in flight.
**Version or feature mismatch.** The protocol keeps a two-release compatibility
window, but release numbers alone do not prove that a worker understands every
additive command. Registration therefore also declares named features for task
inputs, progress leases, and remote model downloads. A worker outside the
inputs, progress leases, remote model downloads, and the voice-identity render
pipeline. A worker outside the
version window, or one missing a required feature, is refused with
`UPGRADE_REQUIRED` and an update instruction before any task runs. It can never
silently render without reference audio or leave a download stuck at 0%.
silently render without reference audio, substitute a different voice, or leave
a download stuck at 0%.
Every remote failure includes a concrete next step. Capacity, missing models,
expired leases or sessions, authentication, rejected inputs, and result upload
+4
View File
@@ -16,6 +16,10 @@ For another device on the same network — e.g. opening the web UI on your phone
You can also drive this from **Settings → Sharing & Remote Access**.
Desktop installers include the web interface used by the LAN address; another
device does not need VoiceStudio installed and the host does not need a source
checkout or a separate frontend development server.
### How the PIN works
- A fresh 6-digit PIN is generated each time you enable sharing; it is never written to disk.
- The QR encodes the PIN (`…/?pin=######`) so scanning connects in one step. Typing the bare URL instead prompts for the PIN.
@@ -0,0 +1,479 @@
# Frontend Responsiveness: Persistence Write-Amplification Remediation Plan
| Field | Decision |
| --- | --- |
| Status | Implemented in draft PR #1541; CI and review pending |
| Target | One focused frontend PR |
| Priority | P1 responsiveness and data-safety hardening |
| Risk | Medium: persistence timing changes, persisted formats do not |
| Dependencies | None |
| Rollback | Revert the PR; the existing keys and schemas remain readable |
## Executive decision
The first optimization PR should remove synchronous JSON serialization and `localStorage` writes from high-frequency interaction paths. It should preserve the existing `omnivoice.app` and `omni_ui` contracts, coalesce each burst to the latest value, flush within a bounded window, and prevent deferred writes from undoing Factory Reset.
This is the best first change because it addresses a measured, cross-workspace bottleneck without combining it with a storage migration, backend change, or `App.jsx` rewrite. Incremental-dub scheduling, transactional undo, and workspace decomposition remain separate follow-ups with their own evidence and rollback boundaries.
## Evidence and diagnosis
### Static path
Two independent persistence paths run on the browser main thread:
1. Every Zustand `set` invokes the persist middleware. The middleware runs `partialize`, serializes the complete persisted projection, and calls synchronous `localStorage.setItem('omnivoice.app', ...)`, even when the mutation only changes transient state.
2. `useAppData` has a broad effect that serializes and writes `omni_ui` whenever text, dub segments, transcript, tracks, history, or a related preference changes.
The resulting hot path is:
`input -> store update -> render/effects -> full projection -> JSON.stringify -> localStorage.setItem`
The cost scales with document size rather than with the small field the user changed. `localStorage` is synchronous, so both serialization and the physical write compete with the next frame.
### Local runtime baseline
The following measurements are diagnostic baselines from commit `3e3189d04d2d6dba69b4dd07fefc8725b9c94af6`, not portable CI thresholds. Each scenario performs 20 UI-scale interactions; raw storage timing excludes `JSON.stringify`, so it is a lower bound. Two unrelated contact-key writes were excluded from the target-key counts but included in the aggregate raw timing.
| Fixture | Writes to target keys | Input-to-next-frame | Raw `setItem` time |
| --- | ---: | ---: | ---: |
| Small local state | 40 `omnivoice.app` + 20 `omni_ui` | 13.9 ms average, 18.9 ms max | 1.9 ms |
| 1,800 dub segments + 400 story tracks | 40 + 20 | 23.6 ms average, 39.0 ms max | 50.7 ms |
| 3,000 dub segments + 3,000 story tracks | 40 + 20 | Repeated 56-114 ms long tasks | 809 ms |
Representative serialized sizes were approximately 156 KB for `omnivoice.app` and 1.5 MB for `omni_ui`. A direct text-edit probe also produced one write to each key for each change.
### Baseline verification
- Baseline commit: `3e3189d04d2d6dba69b4dd07fefc8725b9c94af6`.
- `bun run test -- src/utils/prefKeys.test.js src/test/omniUiSchema.test.js src/test/dubStepRestoreClamp.test.js src/store/uiScaleMigration.test.ts src/test/dubPerLangTranslations.test.jsx src/test/dubVoiceMatchRequest.test.jsx` passes: 6 files, 35 tests.
- The production build passes. The main application chunk is approximately 381.82 KB minified / 116.27 KB gzip.
- `backend/api/routers/mcp_bindings.py` is not implicated: its list handler is a thin delegation, and the bindings panel already loads bindings and profiles concurrently.
- The large Settings/OpenAPI chunk is lazy and is not the interaction-time bottleneck targeted here.
## Goal
For a rapid sequence of edits, perform no JSON serialization or physical storage write in the originating interaction task and persist only the newest value after the burst, while retaining synchronous hydration and the current recovery formats.
## Scope
### In scope
- One shared, typed, coalescing JSON writer for browser `localStorage`.
- A Zustand-compatible structured storage adapter that defers serialization itself.
- Deferred `omni_ui` persistence with its exact current field set.
- Trailing flush, maximum-wait flush, and page-lifecycle flush.
- Single-writer protection for the standalone Tauri capture widget.
- Factory Reset cancellation so pending values cannot recreate deleted keys.
- Deterministic unit/integration tests, a before/after browser trace, and an Unreleased changelog entry.
### Explicitly out of scope
- IndexedDB, workers, new storage keys, schema changes, or a Zustand version bump.
- Removing duplicated fields from `omni_ui` or changing restore precedence.
- Backend/API/database changes, including MCP bindings.
- Debouncing `/tools/incremental` in this PR.
- Changing undo/redo semantics or snapshot representation.
- Splitting stores, decomposing `App.jsx`, or moving workspace imports.
- New dependencies, user-visible strings, locale files, or an app version bump.
- Hardware-sensitive timing assertions in CI.
## Compatibility and safety invariants
The implementation must preserve all of the following:
| Contract | Required invariant |
| --- | --- |
| Zustand key | `omnivoice.app` |
| Zustand envelope | `{ state, version: 7 }`, serialized with normal `JSON.stringify` semantics |
| Zustand projection | Existing `partialize` fields and transient-field stripping remain semantically unchanged |
| Zustand migration | Existing v1-v7 migration behavior remains unchanged |
| Legacy recovery key | `omni_ui` |
| Legacy recovery shape | Exact current field names, omission behavior, and `sanitizeOmniUi` restore path |
| Hydration | Synchronous; no loading gate or async race is introduced |
| Durability | When serialization/storage succeeds and the browser runs timers, a dirty key is attempted within 1,000 ms of its first unflushed change |
| Lifecycle | `pagehide` and hidden-document events attempt pending values; both events together cause at most one physical write per unchanged generation |
| Reset | A removed preference key cannot be recreated by old or newly queued work before the reset reload |
| Desktop windows | Persistence starts in an unknown/read-only role; the resolved main webview is activated as the only writer and the standalone widget stays read-only |
| Privacy | Logs may contain a key and error name, never persisted user content |
| Platform parity | Same default behavior on macOS, Windows, Linux, browser, and Docker |
Direct consumers such as `utils/donationMoments.js`, E2E state seeding, long-form recovery, and the preference-key registry must continue to parse the existing envelope without changes. The donation opt-out's primary `omnivoice.donate.optOut` flag remains an immediate, separate write; add a compatibility assertion that its immediate behavior and the flushed legacy-envelope fallback both remain valid.
Concurrent browser/Docker tabs are explicitly not promoted to a coordinated multi-writer system in this PR. They retain unsupported last-physical-writer-wins behavior. The PR description must state that boundary; adding cross-tab revisions or `BroadcastChannel` arbitration would be a separate data-consistency design.
## Proposed design
### 1. Shared coalescing writer
Create `frontend/src/utils/coalescedJsonStorage.ts` with an injectable core and one application singleton. The public contract should be small:
| API | Contract |
| --- | --- |
| `queueJsonWrite(key, readLatestValue)` | Mark `key` dirty and replace its lazy provider; return a generation-bound disposer that can cancel only this registration |
| `createZustandJsonStorage()` | Return a `PersistStorage` adapter whose `getItem` is synchronous and whose `setItem` queues the structured `StorageValue` |
| `flushPendingWrites()` | Synchronously serialize and attempt every pending write; return a summary for tests/diagnostics |
| `discardPendingWrites(predicate?)` | Cancel timers and pending values matching a key predicate |
| `suspendJsonWrites(predicate)` | Discard matching work and reject later matching queues until the returned resume callback is used |
| `configurePersistenceRole(role)` | Resolve the singleton from initial `unknown` to `main` or `readonly`; activate staged main work or discard all staged widget work |
| Adapter `removeItem(key)` | Cancel/stage-remove that key before raw removal; propagate main-window removal errors; remain inert in a read-only widget |
| `installPersistenceLifecycleFlush()` | Install the singleton listener pair once for the main bootstrap owner; cleanup is idempotent and reserved for tests/HMR teardown |
Required scheduling semantics:
- Quiet delay: 250 ms after the latest value for a key.
- Hard maximum: 1,000 ms from the first unflushed value for that key; continuous input must not starve persistence.
- Last scheduled value wins.
- The queued provider is evaluated on the JavaScript thread only at flush, so the value serialized is the latest application value at flush time rather than a deep-cloned event-time object.
- The quiet timer resets on replacement; the maximum timer does not.
- A successful maximum flush starts a new window for later updates.
- Use standard timers. Do not make `requestIdleCallback` part of the correctness path; availability differs across the supported webviews.
- Do not wrap `createJSONStorage`. It stringifies before calling the adapter and would leave the main cost inside the interaction path.
Flush behavior:
1. Read the latest provider and serialize only at flush time.
2. Compare the serialized value with the currently durable raw value and skip an identical physical write.
3. Call `setItem` once at most for each dirty key in that flush.
4. Mark the entry clean only after a successful write or confirmed identical value.
5. Ensure an old timer cannot commit after a newer value, cancellation, or removal.
`getItem` must evaluate and return the latest pending structured value when one exists; otherwise it must synchronously parse the durable raw value. This keeps explicit Zustand `rehydrate()` calls internally consistent without changing cold-start hydration.
The lazy-provider contract avoids copying a 1.5 MB document on every input. Task 0 must audit every persisted nested container for in-place mutation. React/Zustand setters are expected to publish replacements; any isolated violation must be fixed or explicitly converted to a safe value provider before wiring this scheduler. If the audit reveals a broad mutable-data convention, stop and redesign this PR rather than hiding a state-model refactor inside it. A deterministic test must pin current-at-flush semantics: mutate/replace the provider's source without serializing, then flush and verify the current value is written.
### 2. Failure semantics
- `JSON.stringify` or storage failures must not escape through a Zustand setter, React effect, or lifecycle event.
- A serialization failure discards that invalid value after a warning; a later valid update can proceed.
- Every flush attempt clears both timers first.
- A quota/security/write failure leaves the previous durable blob untouched and keeps the newest value dirty, but disarms automatic retry. A later queue starts a fresh 250/1,000 ms window; an explicit/lifecycle flush attempts it once. Advancing timers alone must not create a retry loop.
- A multi-key flush is isolated per key: successful keys become clean; a failed key remains dirty; retrying the failed key must not rewrite successful siblings.
- Warn once per key/operation/error class to avoid console floods.
- Never log the value, text, segment data, or serialized payload.
- Adapter `removeItem` and Factory Reset remain truthful: cancel pending work first, then allow a main-window raw removal failure to reach the caller.
- The 1,000 ms durability statement applies only when the browser schedules the timer and storage succeeds. Timer throttling, quota denial, a crashed process, or a failed lifecycle write cannot be promised durable; these cases are observable and non-crashing.
### 3. Main-window ownership
The Tauri widget imports the same Zustand store in a separate webview and calls setters for runtime dictation state. Today those transient setters can persist an older projection over the main window's current preferences.
Do not duplicate widget detection inside the storage utility. `detectIsWidget()` already resolves the initialization marker, Tauri `getCurrentWindow().label`, and legacy development URL. `bootstrapApp()` must pass that exact resolved result to `configurePersistenceRole()` before React renders.
The singleton begins in `unknown`: hydration reads work, but writes/removals can only be staged and no timer, serialization, or raw mutation may run. Resolving `main` replays only the latest staged operation per key and starts its 250/1,000 ms clocks at activation; time spent awaiting role detection does not count against a window in which writing was forbidden. Resolving `readonly` discards staged work and makes both `setItem` and `removeItem` inert. This is necessary because the store is statically imported before asynchronous window detection completes. The in-page browser capture pill shares the main document and remains writable.
Tests that import the store without `bootstrapApp()` must use an isolated writer or explicitly configure `main` in setup and reset role, staged work, suspensions, timers, and listeners in teardown. Existing migration tests must clear scheduler state before seeding raw fixtures; otherwise a staged pending value can mask the fixture during `persist.rehydrate()`.
### 4. Lifecycle ownership
After `detectIsWidget()` resolves, `bootstrapApp()` should configure the role and install lifecycle flushing before rendering only for the main window. Bootstrap is the sole production owner; an isolated writer instance or explicit teardown resets listeners in tests.
- Flush on `pagehide`.
- Flush on `visibilitychange` only when `document.visibilityState === 'hidden'`.
- Do not add `beforeunload`; it is unnecessary and can interfere with back/forward caching.
- Lifecycle flush uses the same generation/cancellation checks as timer flushes. If hidden visibility and `pagehide` both fire, the second invocation observes a clean generation and performs no second serialization/write.
### 5. Zustand integration
In `frontend/src/store/index.ts`:
- Replace `createJSONStorage(() => localStorage)` with the structured coalescing adapter.
- Preserve `name`, `partialize`, `version: 7`, and `migrate` semantically unchanged.
- Keep the long-form projection and removal of `generating`/`audioUrl` intact.
- Do not add `text`, dub segments, or other legacy recovery fields to this key.
This PR deliberately leaves `partialize` synchronous. If post-change profiling shows its `storyTracks.map(...)` is still material, optimize projection scheduling in a separate change rather than replacing hydration and migration machinery here.
### 6. `omni_ui` integration
In `frontend/src/hooks/useAppData.js`:
- Build the same recovery object with the same property order and values.
- Replace direct `JSON.stringify` + `localStorage.setItem` with a lazy `queueJsonWrite('omni_ui', readLatestOmniUi)` provider.
- Keep synchronous parsing, `sanitizeOmniUi`, legacy `clone`/`design` handling, and dub-step clamping unchanged.
- Add an explicit `omniUiRestoreComplete` readiness state. The initial persistence effect must queue nothing; the restore effect sets all recovered values and flips readiness in the same batch, and the subsequent render supplies the first writable value.
- Prove an immediate lifecycle event between the initial effects and the restored render cannot persist defaults.
- Feed a lazy latest-value provider to the writer and invoke its generation-bound disposer in effect cleanup. An obsolete StrictMode/unmounted effect may cancel only its own registration, never a newer mount's provider. Do not deep-clone at queue time; the immutability audit and current-at-flush contract above define ownership.
### 7. Factory Reset integration
In `clearLocalPreferences`:
1. Suspend and discard every pending key for which `isPrefKey(key)` is true.
2. Enumerate and remove durable preference keys exactly as today.
3. Preserve connection credentials and user-data keys exactly as today.
The suspension lasts for the remainder of the successful reset session, because background store activity can occur during the 400 ms before reload. Wrap the entire enumerate-and-remove transaction, including `length`, `key()`, and key filtering/access, so any failure resumes writes before rethrowing. This prevents the existing reset error path from leaving persistence silently disabled. This ordering is mandatory: a stale timer, a new post-reset store update, or the later `pagehide` could otherwise resurrect `omnivoice.app` or `omni_ui` after deletion. Tests that simulate a successful reset without a real reload must explicitly reset the isolated writer afterward.
## File-level change budget
| File | Change |
| --- | --- |
| `frontend/src/utils/coalescedJsonStorage.ts` | New lazy scheduler, Zustand adapter, role configuration, suspension, and lifecycle ownership |
| `frontend/src/utils/coalescedJsonStorage.test.ts` | New deterministic scheduler/failure/lifecycle/widget tests |
| `frontend/src/store/index.ts` | Swap storage adapter only; preserve projection and migrations |
| `frontend/src/store/persistenceScheduling.test.ts` | New Zustand envelope, coalescing, hydration, and long-form projection tests |
| `frontend/src/hooks/useAppData.js` | Gate restore readiness and queue the existing `omni_ui` value provider |
| `frontend/src/hooks/useAppData.persistence.test.jsx` | New restore and burst-write integration tests |
| `frontend/src/main-app.jsx` | Configure the resolved window role, then install main-only lifecycle flushing |
| `frontend/src/main-app.test.jsx` | Extend label/marker/URL role-order coverage |
| `frontend/src/utils/prefKeys.js` | Suspend pending and future preference writes across successful reset |
| `frontend/src/utils/prefKeys.test.js` | Add no-resurrection coverage |
| `frontend/src/utils/donationMoments.test.js` | Preserve immediate primary opt-out and flushed legacy fallback behavior |
| `frontend/e2e-perf/responsiveness.spec.ts` | Add opt-in production-bundle fixture, route mocks, instrumentation, and JSON artifact; no wall-clock CI assertions |
| `frontend/playwright.perf.config.ts` | Add cross-platform production-preview benchmark config derived from the existing prod smoke config |
| `CHANGELOG.md` | One Unreleased performance/fix line once the PR number exists |
No backend, locale, package manifest, lockfile, or persisted-schema file should change.
## Implementation sequence
### Task 0: Freeze the current contracts
- [ ] Record the parent commit SHA and rerun the browser baseline with identical fixtures.
- [ ] Add characterization assertions for the exact Zustand envelope, version, legacy snapshot keys, direct readers, and reset key registry; these must pass before production changes.
- [ ] Add integration assertions for burst write counts and initial-default overwrite behavior; these must fail on the current immediate writer for the expected reason.
- [ ] Confirm existing direct readers (`donationMoments`, E2E helpers) against the frozen fixture.
- [ ] Audit the persisted Zustand projection and every `omni_ui` nested value for in-place mutation. Record the search paths in the PR; resolve any hit before adopting lazy providers.
- [ ] Keep the current 35 targeted tests green while adding fail-before cases.
Exit condition: characterization tests pass; behavioral integration tests fail only because writes are immediate/repeated or startup persistence is ungated. Scheduler-specific unit tests are introduced with the new utility rather than pretending to fail before their seam exists.
### Task 1: Implement the storage primitive
- [ ] Implement per-key quiet and maximum timers with injected clock/storage/serializer dependencies.
- [ ] Make value materialization and serialization lazy and deduplicate against the durable raw string.
- [ ] Implement synchronous pending/durable reads.
- [ ] Implement generation-bound provider disposers plus flush, discard, and removal guards.
- [ ] Implement predicate-based suspension for destructive reset windows.
- [ ] Define failed attempts as timer-disarmed; a later queue starts a new maximum window.
- [ ] Isolate partial failures across multiple dirty keys.
- [ ] Recover from throwing providers, durable reads, malformed JSON, and raw writes without poisoning later valid operations.
- [ ] Deduplicate warnings and prove no value, serialized payload, or error message containing user content is logged.
- [ ] Contain and deduplicate errors without logging payloads.
- [ ] Add `unknown -> main|readonly` role configuration; unknown work cannot reach raw storage.
- [ ] Make adapter removal obey cancellation, role, and error-propagation contracts.
- [ ] Add single-owner lifecycle installation and idempotent teardown.
- [ ] Add a full isolated-writer reset hook for tests: role, staged operations, suspensions, timers, listeners, and warning registry.
Exit condition: all utility tests pass without importing React or the application store.
### Task 2: Wire Zustand without changing its contract
- [ ] Replace `createJSONStorage` with the structured adapter.
- [ ] Keep `partialize`, `version`, and `migrate` unchanged except for any mechanical key constant extraction needed by tests.
- [ ] Prove that 100 rapid transient updates cause zero synchronous serializations/writes and at most one trailing write.
- [ ] Prove the final JSON contains the latest persisted update and `{ version: 7 }`.
- [ ] Prove `persist.clearStorage()` cannot be undone by timers/lifecycle and is inert in the widget role.
- [ ] Update raw-seeded migration tests to reset pending/staged writer state before `rehydrate()`.
- [ ] Prove long-form fields round-trip while `generating` and `audioUrl` remain excluded.
- [ ] Prove v6-to-v7 and older accepted fixtures still hydrate synchronously.
Exit condition: existing store migration tests plus the new scheduling suite pass.
### Task 3: Wire `omni_ui`
- [ ] Extract snapshot construction only if needed for a precise shape test; do not redesign ownership.
- [ ] Add the restore-complete state gate, then queue a latest-value provider rather than serializing in the effect.
- [ ] Add a seeded-restore test proving the initial defaults never become the durable winner.
- [ ] Dispatch lifecycle flush before the post-restore render and prove it writes no defaults.
- [ ] Add a burst test proving the latest text and dub segment data win after one write.
- [ ] Cover StrictMode double effects plus unmount/remount before the quiet timer; no obsolete provider may win.
- [ ] Re-run schema, legacy-mode, and restored-dub-step tests unchanged.
Exit condition: a reload after explicit flush restores a deep-equal latest snapshot through `sanitizeOmniUi`.
### Task 4: Close lifecycle and reset races
- [ ] Configure the exact `detectIsWidget()` result before render, then install main-window lifecycle flushing.
- [ ] Prove marker, Tauri-label-only, and legacy-URL detection; pre-role setters cannot leak from a widget.
- [ ] Prove unknown-role set→remove ends removed, remove→set activates the set, and the 1-second clock starts at main-role activation.
- [ ] Prove duplicate installation does not duplicate listeners and teardown removes the exact callbacks.
- [ ] Prove hidden visibility plus `pagehide` produce at most one serialization/write for an unchanged pending generation.
- [ ] Suspend pending and future preference values before Factory Reset removal.
- [ ] Queue another store update, advance every fake timer, and dispatch lifecycle events after reset; both target keys must remain absent.
- [ ] Prove raw removal and enumeration/access failures resume normal persistence before propagating the error.
- [ ] Prove preserved connection/data keys remain untouched.
- [ ] Prove standalone-widget setters and `persist.clearStorage()` cannot mutate durable state, while main-window operations still work.
Exit condition: neither stale timers, lifecycle events, StrictMode, nor the widget can overwrite newer or deliberately removed durable state.
### Task 5: Verify and document
- [ ] Run targeted tests during iteration.
- [ ] Run frontend typecheck, lint, format check, full Vitest, build, and production-bundle smoke.
- [ ] Run the repository's backend suites offline before landing, despite no backend diff, because they are merge gates.
- [ ] Check in the opt-in Playwright benchmark with deterministic fixture generation and JSON output.
- [ ] Run an alternating parent/implementation/parent (A/B/A) benchmark sequence with five repeats per leg; repeat if same-commit variance exceeds 5%.
- [ ] Attach counts, payload sizes, p50/p95/max interaction latency, and long-task evidence to the PR.
- [ ] Open the draft PR to obtain its number, then add/amend the Unreleased changelog line before requesting review.
Exit condition: deterministic acceptance criteria pass; build/merge gates are green; browser timing is attached as reproducible decision evidence rather than a hardware-sensitive CI gate.
## Required deterministic tests
| Scenario | Required result |
| --- | --- |
| 100 replacements in one burst | 0 synchronous provider/serializer/write calls; 1 trailing write with value 100 |
| Lazy provider source changes before flush | Current-at-flush value is written; no deep clone or serialization occurred while queueing |
| Obsolete provider disposer | Cancels only its generation; it cannot cancel a newer provider for the same key |
| Continuous updates beyond 1 second | A maximum-wait flush occurs; later updates start a new window |
| Identical durable value | Serialization may occur at flush; physical `setItem` is skipped |
| Explicit `getItem` before flush | Latest pending structured value is returned synchronously |
| Hidden document followed by `pagehide` | At most one serialization/write for the unchanged pending generation |
| Cancel/remove followed by all timers | Deleted key stays absent |
| Zustand `persist.clearStorage()` | Pending/staged key is cancelled; timer/lifecycle cannot resurrect it |
| Successful reset followed by a new store update | Matching writes remain suspended and deleted keys stay absent until reload |
| Failed reset removal | Error propagates and write suspension is released |
| Reset enumeration/access failure | Error propagates and write suspension is released |
| Serialization error | Caller does not throw; invalid entry does not poison a later valid update |
| Provider throws | Caller/lifecycle does not crash; invalid entry is discarded and a later valid provider succeeds |
| Durable `getItem` throws | Hydration falls back to defaults without crashing; a later valid queue can persist |
| Malformed durable JSON | Hydration follows the current safe fallback/migration behavior and later persistence repairs it |
| Quota/security error | Caller does not throw; old durable value remains; timers do not retry; one later queue starts one new window |
| Two-key partial failure | Successful key stays clean; failed key alone retries later |
| Unknown staged set→remove / remove→set | Only the final operation activates on `main`; its clocks start at activation |
| Unknown/standalone-widget update and removal | Reads work; no raw mutation before role resolution or after read-only resolution |
| Duplicate lifecycle installation/teardown | One listener set; exact callbacks are removed once |
| Zustand transient burst | At most one `omnivoice.app` write and unchanged v7 envelope |
| Legacy recovery burst | At most one `omni_ui` write with latest text/segments |
| Seeded initial recovery | Defaults never overwrite restored state, including immediate lifecycle and StrictMode/unmount races |
| Factory Reset race | Both pending target keys remain absent after timers and lifecycle events |
| Donation opt-out compatibility | Primary opt-out remains immediately visible; flushed v7 legacy fallback remains readable |
| Repeated warning | One warning per key/operation/error class; no value, serialized payload, or content-bearing error message appears |
Do not use elapsed milliseconds as Vitest pass/fail assertions. Use fake timers and call counts for CI; use browser traces for performance evidence.
## Verification commands
Run targeted tests while iterating:
```powershell
cd frontend
bun run test -- src/utils/coalescedJsonStorage.test.ts src/store/persistenceScheduling.test.ts src/hooks/useAppData.persistence.test.jsx src/main-app.test.jsx src/utils/prefKeys.test.js src/utils/donationMoments.test.js src/test/omniUiSchema.test.js src/test/dubStepRestoreClamp.test.js src/store/uiScaleMigration.test.ts
```
Run the frontend landing gate:
```powershell
cd frontend
bun run typecheck:ci
bun run lint
bun run format:check
bun run test
bun run test:prod-bundle
bun run test:legacy
```
`test:prod-bundle` already performs the production build before its smoke test, so a separate `bun run build` would only duplicate work. Run it separately only when build output is needed during iteration.
Run the backend CI-equivalent suites from the repository root with a genuinely empty Hugging Face cache:
```powershell
$previousOffline = $env:HF_HUB_OFFLINE
$previousCache = $env:HF_HUB_CACHE
$tempRoot = [IO.Path]::GetFullPath([IO.Path]::GetTempPath())
$emptyHfCache = [IO.Path]::GetFullPath((Join-Path $tempRoot ("omnivoice-hf-empty-" + [guid]::NewGuid())))
if (-not $emptyHfCache.StartsWith($tempRoot, [StringComparison]::OrdinalIgnoreCase)) { throw 'Unsafe cache path' }
New-Item -ItemType Directory -Path $emptyHfCache | Out-Null
try {
if (@(Get-ChildItem -LiteralPath $emptyHfCache -Force).Count -ne 0) { throw 'HF cache is not empty' }
$env:HF_HUB_OFFLINE = '1'
$env:HF_HUB_CACHE = $emptyHfCache
uv run --no-sync pytest tests/ -q --tb=short
if ($LASTEXITCODE -ne 0) { throw "tests/ failed with exit code $LASTEXITCODE" }
uv run --no-sync pytest backend/tests/ -q --tb=short
if ($LASTEXITCODE -ne 0) { throw "backend/tests/ failed with exit code $LASTEXITCODE" }
} finally {
$env:HF_HUB_OFFLINE = $previousOffline
$env:HF_HUB_CACHE = $previousCache
Remove-Item -LiteralPath $emptyHfCache -Recurse -Force
}
```
These are repository landing gates, not evidence that the frontend optimization itself works. The unique cache and restored environment prevent a populated developer cache or leaked shell state from masking failures.
## Browser validation protocol
Check in `frontend/e2e-perf/responsiveness.spec.ts` and `frontend/playwright.perf.config.ts` as a non-CI production-bundle benchmark harness. Keeping it outside `e2e-prod/` ensures the existing production-smoke CI command cannot discover this manual benchmark. The config must mirror `playwright.prod.config.ts`: build the real `dist/`, serve it with `vite preview` on a dedicated strict port, honor `PLAYWRIGHT_CHROMIUM`, use `/usr/bin/chromium` only when it exists, and otherwise fall back to Playwright's bundled browser. It must not use the dev-server E2E config.
The spec must generate fixtures from fixed seeds, install all required API/WebSocket route mocks or a deterministic bootstrap bypass before navigation, and make no assumption that a backend is running on port 3900. It must use `page.addInitScript` before application code to wrap target-key storage writes and `PerformanceObserver`, drive selectors rather than arbitrary sleeps, and emit machine-readable JSON under Playwright's `test-results` directory. It asserts final state and observable deterministic write counts, but it does not assert elapsed milliseconds or claim to observe serializer task identity. The injected Vitest scheduler tests own the stronger “no provider/serializer execution in the originating task” assertion.
Run it with:
```powershell
cd frontend
node ./node_modules/@playwright/test/cli.js test --config=playwright.perf.config.ts responsiveness.spec.ts --repeat-each=5 --reporter=line
```
Run `bun install --frozen-lockfile` first. The command above is verified from `frontend/` to resolve the installed Playwright 1.61.0 CLI by exact package path; do not replace it with `bun x playwright` or a global `bun run` shim, which can select another Playwright version, fetch a package, or even resolve a stale Windows shim. If `PLAYWRIGHT_CHROMIUM` is unset and no supported system Chromium exists, install the pinned browser once with `node ./node_modules/@playwright/test/cli.js install chromium`. This adds no project dependency, and the dedicated config provides the cross-platform executable fallback. The config owns port 4174 and never reuses an existing listener, so a stale preview fails loudly and every successful run tears down the exact server it started.
Use the same browser version, build mode, machine power state, and fixture on both commits.
1. Instrument target-key `setItem` count, serialized byte length, and call duration before the app loads.
2. Observe long tasks and event-to-next-`requestAnimationFrame` latency.
3. Seed 1,800 dub segments and 400 story tracks using the current v7/legacy formats.
4. Run 20 UI-scale updates 25 ms apart, keeping the complete burst below the hard maximum.
5. Run 20 Studio text updates under the same cadence.
6. End each burst, wait 1,250 ms, and verify the durable latest values by parsing both keys.
7. Run A/B/A (parent, implementation, parent), five repeats per leg; compare median p95 and retain every JSON artifact.
8. Run the 3,000/3,000 fixture once as a diagnostic stress case, not as a product limit.
Deterministic merge gates:
- A sub-1-second 20-event burst produces no more than one physical write per target key after the burst: at least a 96% reduction from the measured 60 target writes.
- Injected utility/integration tests prove no target-key provider, serialization, or write executes in the originating input task; the browser harness independently verifies observable physical writes.
- Both parsed durable values contain the final interaction's state.
Manual decision thresholds, not CI merge gates:
- Target at least 20% lower median p95 input-to-frame latency on the representative fixture.
- Target no more than 5% median-p95 regression on the small fixture.
- Expect no greater-than-50-ms task during the interaction burst with persistence work in its trace stack.
- If either target is missed or same-commit A/A variance exceeds 5%, treat the timing as inconclusive, attach the raw artifacts, and re-profile. Do not widen this PR merely to manufacture a favorable number.
## Acceptance criteria
The PR is ready for review only when all are true:
- [ ] Existing keys, field sets, JSON envelope, version, migrations, and restore behavior are unchanged.
- [ ] One burst yields at most one trailing write per dirty key and the newest value wins.
- [ ] Normal continuous input schedules an attempt within 1 second; failure and timer-throttling limits are documented accurately.
- [ ] With healthy storage, orderly hide/navigation flushes synchronously and a hard process termination can lose at most the scheduled unflushed window; failure/throttling exceptions are documented.
- [ ] Factory Reset cannot be undone by pending work.
- [ ] Unknown-role work cannot reach raw storage, and the standalone widget cannot write or remove main-window preferences.
- [ ] Storage failures cannot crash input handling and never leak user content to logs.
- [ ] Deterministic tests meet merge gates; the checked-in A/B/A benchmark and raw timing artifacts are attached as non-CI decision evidence.
- [ ] Frontend and backend merge gates pass.
- [ ] No dependency, lockfile, locale, backend, persisted-version, or package-version change is present.
- [ ] The PR remains reviewable as one persistence concern; no opportunistic refactor is included.
## Risks and mitigations
| Risk | Mitigation |
| --- | --- |
| Up to the scheduled window of edits lost on a hard process kill | 250 ms quiet flush, 1,000 ms maximum attempt, hidden/pagehide flush; disclose timer/storage limitations |
| Pending or newly queued write recreates reset data | Suspend by `isPrefKey` before raw removal; post-reset update + timer + lifecycle regression test |
| Widget flushes stale main-window state | Resolve the existing detector before render; unknown cannot write; widget set/remove operations stay read-only |
| Older timer overwrites a newer value | Per-key generation token and last-value-wins tests |
| Mutable data changes before deferred serialization | Lazy current-value provider plus a documented mutation audit; never claim event-time snapshot semantics |
| Quota or disabled storage breaks the UI | Contain write errors, preserve the previous durable blob, disarm timers, retry only on later activity/explicit flush |
| Concurrent browser tabs overwrite each other | Keep the unsupported last-physical-writer boundary explicit; do not add an incomplete conflict protocol here |
| Trailing flush is still expensive for pathological documents | Measure it; do not hide it. Escalate to document storage/worker design in a separate PR if representative flush exceeds the budget |
| Middleware contract accidentally changes | Exact envelope/fixture tests plus existing migration and direct-reader suites |
| Lifecycle listeners duplicate in development/tests | One production owner, isolated test instances, idempotent teardown, and duplicate-install test |
| Timing benchmark flakes in CI | Keep wall-clock evidence informational/manual; gate deterministic operation counts |
## Rollback plan
No data rollback or migration is required. Reverting the adapter wiring restores immediate writes, and both old and new builds read the same `omnivoice.app` v7 envelope and `omni_ui` object. If a release-only issue appears, revert the PR rather than introducing a second persistence mode or format.
## Follow-up queue
These are intentionally not part of the first PR:
1. **Incremental dub scheduling.** Add a 300 ms debounce, pass `AbortController.signal` through `apiPost`, use a monotonic request revision, cancel outside Dub, and prove one request per burst plus stale-response rejection.
2. **Transactional dub undo.** Profile `pushUndo`, which currently stringifies the complete segment array per edit and retains up to 50 snapshots. If material, group edits by segment/field and focus or idle boundary while preserving one-step undo behavior.
3. **Workspace isolation.** Profile React commits after persistence remediation; then extract one workspace at a time, moving heavy hooks/imports behind lazy boundaries. Source length and selector count alone are not success metrics.
4. **Document storage migration.** Consider IndexedDB or a worker only if representative post-PR flushes remain over budget. That work requires an independent migration, downgrade, reset, quota, and async-hydration design.
Each follow-up must begin from a fresh trace. None should be pulled into this PR merely because it is nearby.
+529
View File
@@ -0,0 +1,529 @@
import { expect, test, type Page, type TestInfo } from '@playwright/test';
import { writeFile } from 'node:fs/promises';
const APP_STORE_KEY = 'omnivoice.app';
const OMNI_UI_KEY = 'omni_ui';
const TARGET_KEYS = [APP_STORE_KEY, OMNI_UI_KEY] as const;
const UPDATE_COUNT = 20;
const UPDATE_INTERVAL_MS = 25;
const TRAILING_FLUSH_SETTLE_MS = 1_250;
type TargetKey = (typeof TARGET_KEYS)[number];
interface PhysicalWrite {
phase: string;
key: TargetKey;
atMs: number;
bytes: number;
durationMs: number;
}
interface LongTaskSample {
phase: string;
atMs: number;
durationMs: number;
}
interface InputFrameSample {
phase: string;
target: 'ui-scale' | 'studio-text';
atMs: number;
durationMs: number;
}
interface BrowserMetrics {
phase: string;
writes: PhysicalWrite[];
longTasks: LongTaskSample[];
inputToNextRaf: InputFrameSample[];
}
declare global {
interface Window {
__OV_WINDOW__?: string;
__OMNIVOICE_API_BASE__?: string;
__ovResponsivenessMetrics?: BrowserMetrics;
__ovSetResponsivenessPhase?: (phase: string) => void;
}
}
function makeStoryTracks() {
return Array.from({ length: 400 }, (_, index) => ({
id: index + 1,
character: index % 2 === 0 ? 'narrator' : 'guest',
text: `Story track ${index.toString().padStart(3, '0')} ${'narration '.repeat(8)}`,
profileId: null,
emotion: index % 3 === 0 ? 'warm' : null,
speed: 1,
}));
}
function makeDubSegments() {
return Array.from({ length: 1_800 }, (_, index) => ({
id: `segment-${index.toString().padStart(4, '0')}`,
start: index * 2.5,
end: index * 2.5 + 2.25,
speaker: index % 2 === 0 ? 'SPEAKER_00' : 'SPEAKER_01',
text_original: `Original line ${index} ${'source '.repeat(7)}`,
text: `Translated line ${index} ${'target '.repeat(7)}`,
profile_id: null,
direction: '',
}));
}
function persistedFixtures() {
return {
app: {
state: {
mode: 'settings',
defineMethod: 'audio',
uiScale: 1,
uiScaleConfigured: true,
navStyle: 'rail',
locale: 'en',
localeChosen: true,
langPromptSeen: true,
storyTracks: makeStoryTracks(),
},
version: 7,
},
omniUi: {
uiScale: 1,
text: 'Seeded studio text',
mode: 'settings',
defineMethod: 'audio',
vdStates: {
Gender: 'Auto',
Age: 'Auto',
Pitch: 'Auto',
Style: 'Auto',
EnglishAccent: 'Auto',
ChineseDialect: 'Auto',
},
language: 'Auto',
isSidebarCollapsed: false,
sidebarTab: 'projects',
dubJobId: 'responsiveness-fixture',
dubFilename: 'responsiveness-fixture.mp4',
dubDuration: 4_500,
dubSegments: makeDubSegments(),
dubLang: 'English',
dubLangCode: 'en',
dubTracks: [],
dubStep: 'editing',
dubTranscript: '',
exportTracks: {},
preserveBg: true,
defaultTrack: 'dialogue',
exportHistory: [],
speed: 1,
steps: 16,
cfg: 2,
denoise: true,
showOverrides: false,
},
};
}
async function installDeterministicBrowserState(page: Page): Promise<Set<string>> {
const fixtures = persistedFixtures();
const unexpectedRequests = new Set<string>();
await page.addInitScript(
({ appKey, omniUiKey, app, omniUi }) => {
// Fix window identity and API routing before any application module runs.
window.__OV_WINDOW__ = 'main';
window.__OMNIVOICE_API_BASE__ = window.location.origin;
// Seed through the native method so fixture setup is not counted as an
// application write. Both payloads intentionally match production schema.
const nativeSetItem = Storage.prototype.setItem;
nativeSetItem.call(localStorage, appKey, JSON.stringify(app));
nativeSetItem.call(localStorage, omniUiKey, JSON.stringify(omniUi));
nativeSetItem.call(localStorage, 'omnivoice.settings.category', 'appearance');
const targetKeys = new Set([appKey, omniUiKey]);
const metrics: BrowserMetrics = {
phase: 'startup',
writes: [],
longTasks: [],
inputToNextRaf: [],
};
window.__ovResponsivenessMetrics = metrics;
window.__ovSetResponsivenessPhase = (phase) => {
metrics.phase = phase;
};
Storage.prototype.setItem = function setItem(key: string, value: string): void {
const startedAt = performance.now();
try {
nativeSetItem.call(this, key, value);
} finally {
if (targetKeys.has(key)) {
const durationMs = performance.now() - startedAt;
metrics.writes.push({
phase: metrics.phase,
key: key as TargetKey,
atMs: startedAt,
// Encode after the native call so byte accounting is excluded
// from the measured physical-storage duration.
bytes: new TextEncoder().encode(value).byteLength,
durationMs,
});
}
}
};
document.addEventListener(
'input',
(event) => {
const target = event.target;
if (!(target instanceof HTMLElement)) return;
const sampleTarget = target.matches('.appearance-panel input[type="range"]')
? 'ui-scale'
: target.matches('textarea.studio-script-input')
? 'studio-text'
: null;
if (!sampleTarget) return;
const startedAt = performance.now();
requestAnimationFrame(() => {
metrics.inputToNextRaf.push({
phase: metrics.phase,
target: sampleTarget,
atMs: startedAt,
durationMs: performance.now() - startedAt,
});
});
},
true,
);
if (
'PerformanceObserver' in window &&
PerformanceObserver.supportedEntryTypes?.includes('longtask')
) {
const observer = new PerformanceObserver((list) => {
for (const entry of list.getEntries()) {
metrics.longTasks.push({
phase: metrics.phase,
atMs: entry.startTime,
durationMs: entry.duration,
});
}
});
observer.observe({ type: 'longtask', buffered: true });
}
// Keep the realtime hook deterministic and fully local while preserving
// the handler and EventTarget surfaces used by capture/realtime clients.
class DeterministicWebSocket extends EventTarget {
static readonly CONNECTING = 0;
static readonly OPEN = 1;
static readonly CLOSING = 2;
static readonly CLOSED = 3;
readonly url: string;
readyState = DeterministicWebSocket.CONNECTING;
onopen: ((event: Event) => void) | null = null;
onmessage: ((event: MessageEvent) => void) | null = null;
onerror: ((event: Event) => void) | null = null;
onclose: ((event: CloseEvent) => void) | null = null;
constructor(url: string | URL) {
super();
this.url = String(url);
queueMicrotask(() => {
if (this.readyState !== DeterministicWebSocket.CONNECTING) return;
this.readyState = DeterministicWebSocket.OPEN;
const event = new Event('open');
this.dispatchEvent(event);
this.onopen?.(event);
});
}
send(): void {}
close(): void {
if (this.readyState === DeterministicWebSocket.CLOSED) return;
this.readyState = DeterministicWebSocket.CLOSED;
const event = new CloseEvent('close', { code: 1000, wasClean: true });
this.dispatchEvent(event);
this.onclose?.(event);
}
}
Object.defineProperty(window, 'WebSocket', {
configurable: true,
writable: true,
value: DeterministicWebSocket,
});
},
{
appKey: APP_STORE_KEY,
omniUiKey: OMNI_UI_KEY,
app: fixtures.app,
omniUi: fixtures.omniUi,
},
);
// Production resolves API calls to the preview origin. Fulfil every
// fetch/XHR deterministically, while allowing HTML, chunks, fonts and CSS to
// come from the real production bundle under test.
await page.route('**/*', async (route) => {
const request = route.request();
if (!['fetch', 'xhr'].includes(request.resourceType())) {
await route.continue();
return;
}
const path = new URL(request.url()).pathname;
const responseByPath: Record<string, unknown> = {
'/health': { status: 'ok' },
'/setup/status': {
models_ready: true,
missing: [],
hf_cache_dir: '/deterministic/models',
disk_free_gb: 100,
min_free_gb: 1,
enough_disk: true,
},
'/model/status': { status: 'idle', sub_stage: null, detail: '', error: null, progress: null },
'/profiles': [],
'/personalities': [],
'/history': [],
'/dub/history': [],
'/projects': [],
'/export/history': [],
'/engines': {
tts: { active: null, backends: [] },
asr: { active: null, backends: [] },
llm: { active: null, backends: [] },
},
'/sysinfo': { cpu: 0, ram: 0, total_ram: 32, vram: 0, gpu_active: false },
'/system/info': { platform: 'benchmark', device: 'deterministic' },
'/system/notifications': { notifications: [] },
'/system/last-run-crash': { record: null, acknowledged: false },
'/system/logs': { path: '', exists: false, lines: [] },
'/system/logs/tauri': { path: '', exists: false, lines: [] },
'/system/network/state': { enabled: false },
'/dictation/prefs': {
enabled: false,
mode: 'toggle',
model_id: 'sherpa-parakeet-tdt-v3',
},
'/workers': { enabled: false, running: false, workers: [] },
'/workers/target': {
target: 'local',
active: { remote: false },
targets: [
{ id: 'local', label: 'Local', is_local: true, status: 'ready', available: true },
],
},
'/api/settings/analytics': { available: false, prompted: true, opted_in: false },
'/donation_progress.json': {
raised: 10,
goal: 200,
currency: 'USD',
sponsorCount: 1,
updated: '2026-06-17',
},
};
const responseBody = responseByPath[path];
if (responseBody === undefined) {
unexpectedRequests.add(`${request.method()} ${path}`);
await route.fulfill({
status: 501,
contentType: 'application/json',
headers: { 'x-omnivoice-backend': '1' },
body: JSON.stringify({ detail: 'Unhandled deterministic benchmark route' }),
});
return;
}
await route.fulfill({
status: 200,
contentType: 'application/json',
headers: { 'x-omnivoice-backend': '1' },
body: JSON.stringify(responseBody),
});
});
return unexpectedRequests;
}
async function setPhase(page: Page, phase: string): Promise<void> {
await page.evaluate((nextPhase) => window.__ovSetResponsivenessPhase?.(nextPhase), phase);
}
async function driveNativeInputBurst(
page: Page,
selector: string,
values: string[],
): Promise<void> {
await page.locator(selector).evaluate(
async (node, burst) => {
const element = node as HTMLInputElement | HTMLTextAreaElement;
const prototype =
element instanceof HTMLTextAreaElement
? HTMLTextAreaElement.prototype
: HTMLInputElement.prototype;
const nativeValueSetter = Object.getOwnPropertyDescriptor(prototype, 'value')?.set;
if (!nativeValueSetter) throw new Error(`No native value setter for ${element.tagName}`);
await new Promise<void>((resolve) => {
// Schedule against one common origin. Measuring UI work must not add
// another 25 ms after every handler and accidentally turn a 475 ms
// burst into a >1 s stream that rightfully crosses the max-flush gate.
burst.values.forEach((value, index) => {
setTimeout(() => {
nativeValueSetter.call(element, value);
element.dispatchEvent(new Event('input', { bubbles: true, composed: true }));
if (index === burst.values.length - 1) resolve();
}, index * burst.intervalMs);
});
});
},
{ values, intervalMs: UPDATE_INTERVAL_MS },
);
}
async function readDurableValues(page: Page) {
return page.evaluate(
({ appKey, omniUiKey }) => ({
app: JSON.parse(localStorage.getItem(appKey) || 'null'),
omniUi: JSON.parse(localStorage.getItem(omniUiKey) || 'null'),
}),
{ appKey: APP_STORE_KEY, omniUiKey: OMNI_UI_KEY },
);
}
function writesFor(metrics: BrowserMetrics, phase: string, key: TargetKey): PhysicalWrite[] {
return metrics.writes.filter((write) => write.phase === phase && write.key === key);
}
async function writeReport(testInfo: TestInfo, report: unknown): Promise<void> {
const artifactPath = testInfo.outputPath('responsiveness.json');
await writeFile(artifactPath, `${JSON.stringify(report, null, 2)}\n`, 'utf8');
await testInfo.attach('responsiveness.json', {
path: artifactPath,
contentType: 'application/json',
});
}
test('coalesces large-state persistence during rapid UI input', async ({ page }, testInfo) => {
const unexpectedRequests = await installDeterministicBrowserState(page);
await page.goto('/', { waitUntil: 'domcontentloaded' });
const listenerProbe = await page.evaluate(async () => {
const socket = new WebSocket('ws://benchmark.invalid');
let onceCalls = 0;
let removedCalls = 0;
const removedListener = () => {
removedCalls += 1;
};
socket.addEventListener(
'open',
() => {
onceCalls += 1;
},
{ once: true },
);
socket.addEventListener('open', removedListener);
socket.removeEventListener('open', removedListener);
await Promise.resolve();
socket.dispatchEvent(new Event('open'));
socket.close();
return { onceCalls, removedCalls };
});
expect(listenerProbe).toEqual({ onceCalls: 1, removedCalls: 0 });
const scaleSelector = '.appearance-panel input[type="range"]';
await expect(page.locator(scaleSelector)).toBeVisible();
// Let startup restoration and its trailing persistence window fully settle;
// subsequent records are phase-labelled and attributable to one burst.
await page.waitForTimeout(TRAILING_FLUSH_SETTLE_MS);
const scaleValues = Array.from({ length: UPDATE_COUNT }, (_, index) =>
(0.65 + index * 0.05).toFixed(2),
);
const finalScale = Number(scaleValues.at(-1));
await setPhase(page, 'ui-scale');
await driveNativeInputBurst(page, scaleSelector, scaleValues);
await page.waitForTimeout(TRAILING_FLUSH_SETTLE_MS);
const afterScale = await readDurableValues(page);
expect(afterScale.app?.state?.uiScale).toBe(finalScale);
expect(afterScale.omniUi?.uiScale).toBe(finalScale);
// Navigate through the real production UI. Waiting before phase assignment
// prevents the navigation write from being counted as a text-input write.
await setPhase(page, 'navigation');
await page.locator('.nav-rail button[aria-label="Voice"]').click();
const textSelector = 'textarea.studio-script-input';
await expect(page.locator(textSelector)).toBeVisible();
await page.waitForTimeout(TRAILING_FLUSH_SETTLE_MS);
const textValues = Array.from(
{ length: UPDATE_COUNT },
(_, index) => `responsiveness-${index.toString().padStart(2, '0')}-${'voice '.repeat(8)}`,
);
const finalText = textValues.at(-1);
await setPhase(page, 'studio-text');
await driveNativeInputBurst(page, textSelector, textValues);
await page.waitForTimeout(TRAILING_FLUSH_SETTLE_MS);
const durable = await readDurableValues(page);
const metrics = await page.evaluate(() => window.__ovResponsivenessMetrics as BrowserMetrics);
const report = {
schemaVersion: 1,
fixture: { appStoreVersion: 7, storyTracks: 400, dubSegments: 1_800 },
burst: { updates: UPDATE_COUNT, requestedIntervalMs: UPDATE_INTERVAL_MS },
durable: {
appUiScale: durable.app?.state?.uiScale,
omniUiScale: durable.omniUi?.uiScale,
omniUiText: durable.omniUi?.text,
},
phases: {
uiScale: {
writes: Object.fromEntries(
TARGET_KEYS.map((key) => [key, writesFor(metrics, 'ui-scale', key)]),
),
inputToNextRaf: metrics.inputToNextRaf.filter((sample) => sample.phase === 'ui-scale'),
longTasks: metrics.longTasks.filter((sample) => sample.phase === 'ui-scale'),
},
studioText: {
writes: Object.fromEntries(
TARGET_KEYS.map((key) => [key, writesFor(metrics, 'studio-text', key)]),
),
inputToNextRaf: metrics.inputToNextRaf.filter((sample) => sample.phase === 'studio-text'),
longTasks: metrics.longTasks.filter((sample) => sample.phase === 'studio-text'),
},
},
startup: {
writes: metrics.writes.filter((write) => write.phase === 'startup'),
longTasks: metrics.longTasks.filter((sample) => sample.phase === 'startup'),
},
network: { unexpectedRequests: [...unexpectedRequests].sort() },
};
await writeReport(testInfo, report);
expect(durable.omniUi?.text).toBe(finalText);
expect([...unexpectedRequests].sort(), 'every fetch/XHR must have an explicit fixture').toEqual(
[],
);
expect(metrics.inputToNextRaf.filter((sample) => sample.phase === 'ui-scale')).toHaveLength(
UPDATE_COUNT,
);
expect(metrics.inputToNextRaf.filter((sample) => sample.phase === 'studio-text')).toHaveLength(
UPDATE_COUNT,
);
for (const phase of ['ui-scale', 'studio-text']) {
for (const key of TARGET_KEYS) {
expect(
writesFor(metrics, phase, key).length,
`${phase} should physically write ${key} no more than once`,
).toBeLessThanOrEqual(1);
}
}
});
+47
View File
@@ -0,0 +1,47 @@
import { defineConfig, devices } from '@playwright/test';
import { existsSync } from 'node:fs';
// Opt-in production-bundle responsiveness benchmark. Keep it separate from
// playwright.prod.config.ts: the smoke suite is a CI correctness gate, while
// this harness records machine-dependent timing diagnostics for local review.
const PORT = Number(process.env.E2E_PERF_PORT || 4174);
// An explicit browser wins; Linux CI/dev containers commonly provide a system
// Chromium; contributors on Windows/macOS fall back to Playwright's bundle.
const SYSTEM_CHROMIUM = '/usr/bin/chromium';
const browserPath =
process.env.PLAYWRIGHT_CHROMIUM || (existsSync(SYSTEM_CHROMIUM) ? SYSTEM_CHROMIUM : undefined);
export default defineConfig({
testDir: './e2e-perf',
testMatch: 'responsiveness.spec.ts',
timeout: 120_000,
expect: { timeout: 15_000 },
fullyParallel: false,
// `--repeat-each=5` is a variance sample, not five independent load tests.
// Keep repeats serial so they do not contend with each other or distort the
// input/long-task evidence on high-core development machines.
workers: 1,
retries: 0,
reporter: [['list']],
outputDir: 'test-results/responsiveness',
use: {
baseURL: `http://localhost:${PORT}`,
headless: true,
trace: 'retain-on-failure',
...(browserPath ? { launchOptions: { executablePath: browserPath } } : {}),
},
projects: [{ name: 'chromium', use: { ...devices['Desktop Chrome'] } }],
webServer: {
// Playwright launches through the platform shell. Invoke the repo-pinned
// Vite binary directly so Windows does not depend on whichever global Bun
// shim happens to precede the checked-in toolchain on PATH.
command: `node ./node_modules/vite/bin/vite.js build && node ./node_modules/vite/bin/vite.js preview --port ${PORT} --strictPort`,
url: `http://localhost:${PORT}`,
// Always own the production preview used for a measurement. Reusing an
// arbitrary listener can benchmark stale dist bytes and leaves teardown
// ownership ambiguous; a stale 4174 listener should fail loudly instead.
reuseExistingServer: false,
timeout: 180_000,
},
});
+3
View File
@@ -100,3 +100,6 @@ zbus = "5.16"
# Scoped-reset tests build real directory trees to prove the delete guard only
# ever removes paths inside a validated OmniVoice root.
tempfile = "3"
# MockRuntime app for the backend-lifecycle fault-injection harness
# (tests/backend_lifecycle.rs) — feature-unifies onto the main dep.
tauri = { version = "2.11.0", features = ["test"] }
+16
View File
@@ -44,5 +44,21 @@ fn main() {
ensure_sidecar_placeholder("uv");
ensure_sidecar_placeholder("ffmpeg");
ensure_sidecar_placeholder("ffprobe");
// Windows test binaries need the Common-Controls v6 manifest that
// tauri-build embeds into the app binary but cargo gives tests none of:
// without it the loader resolves comctl32 v5 (no TaskDialogIndirect —
// imported by tauri's dialog/tray stack) and every integration-test
// binary dies at load with STATUS_ENTRYPOINT_NOT_FOUND (0xc0000139).
// See tests/windows-test.manifest.
if std::env::var("CARGO_CFG_TARGET_OS").as_deref() == Ok("windows") {
let manifest = PathBuf::from(std::env::var("CARGO_MANIFEST_DIR").unwrap_or_else(|_| ".".into()))
.join("tests")
.join("windows-test.manifest");
println!("cargo:rerun-if-changed={}", manifest.display());
println!("cargo:rustc-link-arg-tests=/MANIFEST:EMBED");
println!("cargo:rustc-link-arg-tests=/MANIFESTINPUT:{}", manifest.display());
}
tauri_build::build();
}
+240 -29
View File
@@ -100,6 +100,52 @@ pub fn backend_deep_healthy(port: u16) -> bool {
}
}
/// Readiness = identity AND capability. The shallow probe proves the
/// responder is OUR backend; the deep probe proves it can actually serve a
/// DB-backed route. Declaring Ready on the shallow probe alone announced a
/// backend whose install/DB was broken underneath as up — the UI looked
/// alive while every real request 500'd or dead-ended on "can't reach the
/// backend". Both Ready transitions (startup poll, supervisor respawn wait)
/// gate on this; the supervisor's DEATH detection stays process-exit-only
/// (`try_wait`), so a busy-but-alive backend is still never killed.
pub fn backend_ready(port: u16) -> bool {
backend_healthy(port) && backend_deep_healthy(port)
}
/// Startup progress from the backend's early-bind `/startup/progress`
/// endpoint: `(status, step, label)`, e.g. `("starting", "ml_imports",
/// "Loading ML runtime (PyTorch)…")`. `None` when nothing answers, when the
/// responder lacks the `x-omnivoice-backend` marker header (a foreign
/// process on our port must not narrate our splash), or on an old backend
/// without the endpoint — callers fall back to the legacy probes.
pub fn startup_progress(port: u16) -> Option<(String, String, String)> {
let url = format!("http://127.0.0.1:{}/startup/progress", port);
let resp = raw_http_get(&url, Duration::from_millis(800)).ok()?;
if parse_http_status(&resp) != Some(200) {
return None;
}
let head_end = resp.find("\r\n\r\n").unwrap_or(resp.len());
if !resp[..head_end].to_ascii_lowercase().contains("x-omnivoice-backend") {
return None;
}
let body = &resp[resp.find("\r\n\r\n").map(|i| i + 4).unwrap_or(0)..];
let status = parse_json_string_field(body, "status")?;
let step = parse_json_string_field(body, "step").unwrap_or_default();
let label = parse_json_string_field(body, "label").unwrap_or_default();
Some((status, step, label))
}
/// First `"key": "value"` string field in a JSON body — same dependency-free
/// sniffing style as `parse_app_version`. `None` for absent or non-string
/// (e.g. `null`) values.
fn parse_json_string_field(body: &str, key: &str) -> Option<String> {
let needle = format!("\"{key}\"");
let rest = &body[body.find(&needle)? + needle.len()..];
let rest = rest[rest.find(':')? + 1..].trim_start();
let rest = rest.strip_prefix('"')?;
Some(rest[..rest.find('"')?].to_string())
}
/// Status code from a raw HTTP response ("HTTP/1.1 200 OK" → 200).
fn parse_http_status(response: &str) -> Option<u16> {
let line = response.lines().next()?;
@@ -243,6 +289,16 @@ pub fn kill_orphan_on_port(port: u16) {
// ── Log paths ─────────────────────────────────────────────────────────────
pub fn backend_log_path() -> PathBuf {
// Support/test override: point logs (and the crash-marker store, which
// derives from this path) somewhere explicit. The fault-injection
// harness gives every scenario its own tempdir through this.
if let Ok(dir) = std::env::var("OMNIVOICE_LOG_DIR") {
if !dir.trim().is_empty() {
let log_dir = PathBuf::from(dir);
let _ = fs::create_dir_all(&log_dir);
return log_dir.join("backend.log");
}
}
let log_dir = if cfg!(target_os = "macos") {
let home = std::env::var("HOME").unwrap_or_else(|_| "/tmp".to_string());
PathBuf::from(home).join("Library/Logs/OmniVoice")
@@ -471,6 +527,29 @@ fn analytics_env(baked_token: Option<&str>, baked_host: Option<&str>) -> Vec<(St
out
}
/// Parse the `OMNIVOICE_BACKEND_CMD` override: a JSON array (`["prog","a"]`)
/// when it starts with `[` — the form the harness uses, so paths with spaces
/// survive — else whitespace-split. `None` for unset/empty/unparseable.
pub fn parse_backend_cmd_override(raw: &str) -> Option<Vec<String>> {
let raw = raw.trim();
if raw.is_empty() {
return None;
}
let argv: Vec<String> = if raw.starts_with('[') {
serde_json::from_str(raw).ok()?
} else {
raw.split_whitespace().map(str::to_string).collect()
};
if argv.is_empty() || argv[0].trim().is_empty() {
return None;
}
Some(argv)
}
fn backend_cmd_override() -> Option<Vec<String>> {
parse_backend_cmd_override(&std::env::var("OMNIVOICE_BACKEND_CMD").ok()?)
}
pub fn spawn_backend<R: tauri::Runtime>(app: &tauri::AppHandle<R>, progress: Option<&Arc<Mutex<BootstrapStage>>>) -> Option<Child> {
let log_path = backend_log_path();
let err_path = log_path.with_file_name("backend_err.log");
@@ -480,12 +559,22 @@ pub fn spawn_backend<R: tauri::Runtime>(app: &tauri::AppHandle<R>, progress: Opt
err_path.display(),
);
let (python, backend_dir) = match ensure_venv_ready(app, progress) {
Some(x) => x,
None => {
log::error!("Venv bootstrap failed — backend not started");
return None;
}
// Fault-injection / QA seam: OMNIVOICE_BACKEND_CMD runs the given argv
// as "the backend". Venv bootstrap and ffmpeg resolution are skipped
// (they can install toolchains or touch the network); everything else —
// the err-log run offset, the drainer threads, env pinning, real OS
// pipes, the spawn-failure diagnostic — stays exactly real, which is
// the point: the lifecycle harness exercises genuine process deaths.
let cmd_override = backend_cmd_override();
let (python, backend_dir) = match cmd_override {
Some(ref argv) => (PathBuf::from(&argv[0]), PathBuf::new()),
None => match ensure_venv_ready(app, progress) {
Some(x) => x,
None => {
log::error!("Venv bootstrap failed — backend not started");
return None;
}
},
};
if let Some(p) = progress {
@@ -564,18 +653,20 @@ pub fn spawn_backend<R: tauri::Runtime>(app: &tauri::AppHandle<R>, progress: Opt
}
// Analytics destination (#1123) — see analytics_env() below for why.
env.extend(analytics_env(option_env!("VITE_POSTHOG_KEY"), option_env!("VITE_POSTHOG_HOST")));
let app_data = app.path().app_local_data_dir().unwrap_or_default();
if let Some(ffmpeg_path) = resolve_ffmpeg(app, &app_data) {
env.push(("FFMPEG_PATH".into(), ffmpeg_path.to_string_lossy().into()));
}
if let Some(ffprobe_path) = resolve_ffprobe(app, &app_data) {
let ffprobe_str: String = ffprobe_path.to_string_lossy().into();
env.push(("FFPROBE_PATH".into(), ffprobe_str.clone()));
// Issue #76: OMNIVOICE_FFPROBE_PATH is the canonical name going
// forward — explicit, namespaced, and unambiguously the path of a
// file (not a PATH-style command name). FFPROBE_PATH stays for
// backward compat with prior backend releases.
env.push(("OMNIVOICE_FFPROBE_PATH".into(), ffprobe_str));
if cmd_override.is_none() {
let app_data = app.path().app_local_data_dir().unwrap_or_default();
if let Some(ffmpeg_path) = resolve_ffmpeg(app, &app_data) {
env.push(("FFMPEG_PATH".into(), ffmpeg_path.to_string_lossy().into()));
}
if let Some(ffprobe_path) = resolve_ffprobe(app, &app_data) {
let ffprobe_str: String = ffprobe_path.to_string_lossy().into();
env.push(("FFPROBE_PATH".into(), ffprobe_str.clone()));
// Issue #76: OMNIVOICE_FFPROBE_PATH is the canonical name going
// forward — explicit, namespaced, and unambiguously the path of a
// file (not a PATH-style command name). FFPROBE_PATH stays for
// backward compat with prior backend releases.
env.push(("OMNIVOICE_FFPROBE_PATH".into(), ffprobe_str));
}
}
let mut cmd = Command::new(&python);
cmd.env_remove("PYTHONHOME").env_remove("PYTHONPATH").env_remove("LD_LIBRARY_PATH");
@@ -594,18 +685,25 @@ pub fn spawn_backend<R: tauri::Runtime>(app: &tauri::AppHandle<R>, progress: Opt
// nvidia-smi probe already uses (setup.rs).
cmd.creation_flags(0x0800_0000 | 0x0000_0200);
}
match cmd_override {
Some(ref argv) => {
cmd.args(&argv[1..]);
}
None => {
cmd.args([
"-m",
"uvicorn",
"main:app",
"--app-dir",
backend_dir.to_string_lossy().as_ref(),
"--host",
"127.0.0.1",
"--port",
&backend_port().to_string(),
]);
}
}
let mut child = match cmd
.args([
"-m",
"uvicorn",
"main:app",
"--app-dir",
backend_dir.to_string_lossy().as_ref(),
"--host",
"127.0.0.1",
"--port",
&backend_port().to_string(),
])
.stdout(Stdio::piped())
.stderr(Stdio::piped())
.spawn()
@@ -742,6 +840,119 @@ mod tests {
std::env::remove_var("OMNIVOICE_INSTALL_CHANNEL");
}
/// Loopback responder for the /startup/progress probe tests.
fn spawn_progress_stub(with_marker: bool, body: &'static str) -> u16 {
use std::io::{Read, Write};
let listener = std::net::TcpListener::bind("127.0.0.1:0").expect("bind");
let port = listener.local_addr().unwrap().port();
std::thread::spawn(move || {
for stream in listener.incoming() {
let Ok(mut stream) = stream else { break };
let mut buf = [0u8; 512];
let _ = stream.read(&mut buf);
let marker = if with_marker {
"x-omnivoice-backend: 0.0.0\r\n"
} else {
""
};
let resp = format!(
"HTTP/1.1 200 OK\r\n{marker}Content-Length: {}\r\n\r\n{body}",
body.len()
);
let _ = stream.write_all(resp.as_bytes());
}
});
port
}
/// Loopback HTTP responder for the probe tests: answers `/system/info`
/// with a genuine-looking backend body and `/profiles` with the given
/// status — the exact shape of a zombie whose install/DB broke while
/// `/system/info` kept answering from memory.
fn spawn_probe_stub(profiles_status: u16) -> u16 {
use std::io::{Read, Write};
let listener = std::net::TcpListener::bind("127.0.0.1:0").expect("bind");
let port = listener.local_addr().unwrap().port();
std::thread::spawn(move || {
for stream in listener.incoming() {
let Ok(mut stream) = stream else { break };
let mut buf = [0u8; 512];
let n = stream.read(&mut buf).unwrap_or(0);
let req = String::from_utf8_lossy(&buf[..n]);
let resp = if req.starts_with("GET /system/info") {
"HTTP/1.1 200 OK\r\nContent-Length: 19\r\n\r\n{\"data_dir\": \"/x\"}\n".to_string()
} else {
format!("HTTP/1.1 {profiles_status} X\r\nContent-Length: 2\r\n\r\n[]")
};
let _ = stream.write_all(resp.as_bytes());
}
});
port
}
#[test]
fn backend_cmd_override_parses_json_and_whitespace_forms() {
// JSON form (the harness's): paths with spaces survive.
assert_eq!(
parse_backend_cmd_override(r#"["/tmp/my dir/prog", "arg1"]"#),
Some(vec!["/tmp/my dir/prog".into(), "arg1".into()])
);
// Whitespace form (manual QA): OMNIVOICE_BACKEND_CMD="/bin/false x".
assert_eq!(
parse_backend_cmd_override("/bin/false x"),
Some(vec!["/bin/false".into(), "x".into()])
);
// Unset/empty/garbage never activates the seam — production behavior
// is byte-identical without the env var.
assert_eq!(parse_backend_cmd_override(""), None);
assert_eq!(parse_backend_cmd_override(" "), None);
assert_eq!(parse_backend_cmd_override("[not json"), None);
assert_eq!(parse_backend_cmd_override("[]"), None);
assert_eq!(parse_backend_cmd_override(r#"[""]"#), None);
}
#[test]
fn startup_progress_parses_fields_and_requires_the_marker() {
const BODY: &str =
r#"{"status": "starting", "step": "ml_imports", "label": "Loading ML runtime (PyTorch)…", "error": null}"#;
// Marker present → the tuple the poll loops narrate from.
let port = spawn_progress_stub(true, BODY);
assert_eq!(
startup_progress(port),
Some((
"starting".into(),
"ml_imports".into(),
"Loading ML runtime (PyTorch)…".into()
))
);
// No marker header → a foreign responder must not narrate our splash.
let foreign = spawn_progress_stub(false, BODY);
assert_eq!(startup_progress(foreign), None);
// Ready body with null step/label → status still parses, step empty.
let ready = spawn_progress_stub(true, r#"{"status": "ready", "step": null, "label": null}"#);
assert_eq!(startup_progress(ready), Some(("ready".into(), String::new(), String::new())));
// Nothing listening → None (old backend / dead port fall back).
assert_eq!(startup_progress(1), None);
}
#[test]
fn ready_requires_the_deep_probe_not_just_identity() {
// Regression for the shallow-Ready class: a backend that identifies
// itself on /system/info but 500s a DB-backed route must NOT be
// announced Ready — that zombie looked alive while every real
// request dead-ended on "can't reach the backend".
let broken = spawn_probe_stub(500);
assert!(backend_healthy(broken), "identity probe should pass");
assert!(!backend_deep_healthy(broken), "deep probe must fail on 500");
assert!(!backend_ready(broken), "Ready must gate on the deep probe");
let ok = spawn_probe_stub(200);
assert!(backend_ready(ok), "identity + working DB route is Ready");
// Nothing listening at all: no probe passes.
assert!(!backend_ready(1)); // port 1 — never bindable by us
}
#[test]
fn spawn_failure_diagnostic_surfaces_path_error_and_hint() {
let err = io::Error::new(io::ErrorKind::NotFound, "No such file or directory");
+384 -18
View File
@@ -293,14 +293,20 @@ pub fn respawn_backend(
/// the venv — is removed and the bootstrap re-runs once, recreating it through
/// the normal `CreatingVenv` / `InstallingDeps` setup path instead of
/// surfacing the same dead-end failure on every retry.
pub fn spawn_backend_and_wait(app: &tauri::AppHandle, stage_handle: &Arc<Mutex<BootstrapStage>>) {
pub fn spawn_backend_and_wait<R: tauri::Runtime>(app: &tauri::AppHandle<R>, stage_handle: &Arc<Mutex<BootstrapStage>>) {
let mut venv_heal_attempted = false;
'bootstrap: loop {
let child = crate::backend::spawn_backend(app, Some(stage_handle));
track_backend_child(app, child);
let start = std::time::Instant::now();
while start.elapsed() < Duration::from_secs(300) {
if crate::backend::backend_healthy(backend_port()) {
// Early-bind narration: the backend answers /startup/progress within
// ~1s of spawn, long before it is Ready — surface each step change
// as a log line so the splash shows "Loading ML runtime (PyTorch)…"
// instead of a silent 300s wait. An old backend (no endpoint) yields
// None and the wait looks exactly as it did before.
let mut last_step = String::new();
while start.elapsed() < startup_budget() {
if crate::backend::backend_ready(backend_port()) {
set_stage(stage_handle, BootstrapStage::Ready);
// #567/#570/#571: once Ready, keep watching the backend child
// and respawn it if it dies mid-session, so a crash self-heals
@@ -441,13 +447,25 @@ pub fn spawn_backend_and_wait(app: &tauri::AppHandle, stage_handle: &Arc<Mutex<B
set_stage(stage_handle, BootstrapStage::Failed { message: msg });
return;
}
if let Some((status, step, label)) =
crate::backend::startup_progress(backend_port())
{
if status == "starting" && !step.is_empty() && step != last_step {
last_step = step;
emit_log(app, "starting_backend", &format!("Startup: {label}"));
}
}
std::thread::sleep(Duration::from_millis(500));
}
let err_tail = crate::backend::read_error_log_tail_for_run(20);
let msg = if err_tail.is_empty() {
"Backend did not respond within 300 s".to_string()
format!("Backend did not respond within {} s", startup_budget().as_secs())
} else {
format!("Backend did not respond within 300 s. Last stderr output:\n{}", err_tail)
format!(
"Backend did not respond within {} s. Last stderr output:\n{}",
startup_budget().as_secs(),
err_tail
)
};
set_stage(stage_handle, BootstrapStage::Failed { message: msg });
return;
@@ -476,6 +494,15 @@ static SUPERVISOR_ACTIVE: AtomicBool = AtomicBool::new(false);
/// moment a fresh child is spawned and tracked (`track_backend_child`).
static BACKEND_KILL_INTENDED: AtomicBool = AtomicBool::new(false);
/// Bumped every time `track_backend_child` installs a new child. The
/// supervisor snapshots it when it observes a death; a change during its
/// backoff pause means ANOTHER flow (Retry / Clean & Retry) spawned and
/// tracked a replacement — ownership has transferred, whether or not that
/// replacement is still alive when sampled (the flag and a liveness check
/// can both be missed inside one 500ms window; the generation cannot).
static BACKEND_SPAWN_GENERATION: std::sync::atomic::AtomicU64 =
std::sync::atomic::AtomicU64::new(0);
pub fn set_backend_kill_intended(value: bool) {
BACKEND_KILL_INTENDED.store(value, Ordering::SeqCst);
}
@@ -499,7 +526,7 @@ const CRASH_STDERR_TAIL_LINES: usize = 40;
const MAX_RESTARTS: usize = 3;
const RESTART_WINDOW: Duration = Duration::from_secs(600);
fn app_is_quitting(app: &tauri::AppHandle) -> bool {
fn app_is_quitting<R: tauri::Runtime>(app: &tauri::AppHandle<R>) -> bool {
app.try_state::<AppFlags>()
.map(|f| f.quitting.load(Ordering::SeqCst))
.unwrap_or(false)
@@ -508,7 +535,7 @@ fn app_is_quitting(app: &tauri::AppHandle) -> bool {
/// Store the freshly spawned backend child (and its spawn time, for the crash
/// marker's `uptime_s`), and re-arm the death watchers: any deliberate-kill
/// window ends the moment a new child is tracked.
fn track_backend_child(app: &tauri::AppHandle, child: Option<std::process::Child>) {
fn track_backend_child<R: tauri::Runtime>(app: &tauri::AppHandle<R>, child: Option<std::process::Child>) {
let state = app.state::<BackendState>();
if let Ok(mut guard) = state.process.lock() {
*guard = child;
@@ -516,11 +543,12 @@ fn track_backend_child(app: &tauri::AppHandle, child: Option<std::process::Child
if let Ok(mut spawned) = state.spawned_at.lock() {
*spawned = Some(Instant::now());
}
BACKEND_SPAWN_GENERATION.fetch_add(1, Ordering::SeqCst);
set_backend_kill_intended(false);
}
/// Seconds since the tracked backend child was spawned (0 when unknown).
fn backend_uptime_s(app: &tauri::AppHandle) -> u64 {
fn backend_uptime_s<R: tauri::Runtime>(app: &tauri::AppHandle<R>) -> u64 {
app.try_state::<BackendState>()
.and_then(|s| s.spawned_at.lock().ok().and_then(|g| *g))
.map(|t| t.elapsed().as_secs())
@@ -530,7 +558,7 @@ fn backend_uptime_s(app: &tauri::AppHandle) -> u64 {
/// Returns `Some(BackendExit)` if the tracked backend child has exited,
/// `None` if it is still running (or none is tracked — which we never treat as
/// a death to respawn, to avoid fighting a deliberate teardown).
fn backend_child_exit(app: &tauri::AppHandle) -> Option<BackendExit> {
fn backend_child_exit<R: tauri::Runtime>(app: &tauri::AppHandle<R>) -> Option<BackendExit> {
let state = app.try_state::<BackendState>()?;
let mut guard = state.process.lock().ok()?;
match guard.as_mut() {
@@ -543,6 +571,30 @@ fn backend_child_exit(app: &tauri::AppHandle) -> Option<BackendExit> {
}
}
/// How long the launch poll waits for the backend to become Ready before
/// declaring Failed. 300s in production; `OMNIVOICE_STARTUP_BUDGET_S`
/// exists for the fault-injection harness (a slow-start scenario must not
/// sleep five minutes in CI) and for support triage on pathological disks.
fn startup_budget() -> Duration {
std::env::var("OMNIVOICE_STARTUP_BUDGET_S")
.ok()
.and_then(|v| v.trim().parse::<u64>().ok())
.filter(|&s| s > 0)
.map(Duration::from_secs)
.unwrap_or(Duration::from_secs(300))
}
/// The supervisor's death-detection poll interval. 2s in production;
/// `OMNIVOICE_SUPERVISOR_POLL_MS` shrinks it for the harness only.
fn supervisor_poll() -> Duration {
std::env::var("OMNIVOICE_SUPERVISOR_POLL_MS")
.ok()
.and_then(|v| v.trim().parse::<u64>().ok())
.filter(|&ms| ms > 0)
.map(Duration::from_millis)
.unwrap_or(Duration::from_secs(2))
}
/// Drop restart timestamps older than `RESTART_WINDOW` and report whether the
/// remaining count has hit the cap. Pure so the backoff policy is unit-tested
/// without spawning real processes.
@@ -551,19 +603,40 @@ fn restart_budget_exhausted(times: &mut Vec<Instant>, now: Instant) -> bool {
times.len() >= MAX_RESTARTS
}
/// Escalating pause before a respawn, keyed on how many restarts already
/// happened inside `RESTART_WINDOW`. The FIRST respawn stays immediate (a
/// one-off crash should self-heal fast); repeat deaths get breathing room so
/// a tight crash loop doesn't burn the whole 3-in-600s budget in seconds —
/// back-to-back torch-import storms are exactly what pushes a
/// memory-pressured machine over the edge again. Pure for unit testing.
fn restart_backoff_delay(recent_restarts: usize) -> Duration {
match recent_restarts {
0 => Duration::ZERO,
1 => Duration::from_secs(5),
_ => Duration::from_secs(15),
}
}
/// After the backend is Ready, watch its process and respawn it on an
/// unexpected exit. Runs on the (otherwise-returning) bootstrap thread and
/// stops the instant the app is quitting so it never resurrects the backend
/// during shutdown. Death is detected only via a *confirmed process exit*
/// (`try_wait`), never a slow health probe, so a busy-but-alive backend is
/// never killed.
fn supervise_backend(app: &tauri::AppHandle, stage_handle: &Arc<Mutex<BootstrapStage>>) {
fn supervise_backend<R: tauri::Runtime>(app: &tauri::AppHandle<R>, stage_handle: &Arc<Mutex<BootstrapStage>>) {
let mut restart_times: Vec<Instant> = Vec::new();
loop {
std::thread::sleep(Duration::from_secs(2));
std::thread::sleep(supervisor_poll());
if app_is_quitting(app) {
return;
}
// Snapshot the spawn generation BEFORE observing the exit: sampled
// after, a replacement tracked in the gap between `try_wait` and the
// load would be baked into the snapshot and the transfer missed
// (third-pass review find). Sampled before, any tracking that
// happens from here on — even one whose child we are about to see
// exit — reads as a generation change and yields.
let observed_generation = BACKEND_SPAWN_GENERATION.load(Ordering::SeqCst);
let exit = match backend_child_exit(app) {
Some(exit) => exit,
None => continue, // still running
@@ -604,6 +677,10 @@ fn supervise_backend(app: &tauri::AppHandle, stage_handle: &Arc<Mutex<BootstrapS
set_stage(stage_handle, BootstrapStage::Failed { message: msg });
return;
}
// Backoff BEFORE this restart is recorded: `restart_times` was just
// pruned to the window, so its length is the number of recent
// respawns already attempted.
let backoff = restart_backoff_delay(restart_times.len());
restart_times.push(Instant::now());
log::warn!("Backend process exited unexpectedly ({exit_info}) — restarting it (#567)");
emit_log(app, "starting_backend", "Backend stopped unexpectedly — restarting it automatically");
@@ -611,6 +688,51 @@ fn supervise_backend(app: &tauri::AppHandle, stage_handle: &Arc<Mutex<BootstrapS
// poll has already stopped post-Ready, so the stage alone won't show).
let _ = app.emit("backend-restarting", exit_info.clone());
set_stage(stage_handle, BootstrapStage::StartingBackend);
// The banner is already up, so the pause reads as "reconnecting", not
// as a hang. Chunked so quitting (or a deliberate retry-flow kill,
// which owns the respawn) is honored within 500 ms.
if !backoff.is_zero() {
log::info!(
"Backend died {} time(s) in the last {} min — waiting {}s before respawning",
restart_times.len(),
RESTART_WINDOW.as_secs() / 60,
backoff.as_secs()
);
let waited = Instant::now();
while waited.elapsed() < backoff {
if app_is_quitting(app) {
return;
}
if backend_kill_intended() {
log::info!("Deliberate replace during restart backoff — supervisor yielding");
return;
}
// A completed Retry/Clean&Retry sets the deliberate-kill flag
// and then `track_backend_child` CLEARS it — possibly both
// between two of these samples, so the flag alone can be
// missed. The durable tell is the spawn GENERATION: it bumps
// when a replacement is tracked and never un-bumps, so it is
// observed even if the replacement has itself already exited
// by the time we sample. Yield promptly (not at backoff end)
// so the retry's own spawn_backend_and_wait can claim the
// supervisor slot at Ready — and so we never free_port() a
// replacement out from under the flow that owns it.
if BACKEND_SPAWN_GENERATION.load(Ordering::SeqCst) != observed_generation {
log::info!(
"A replacement backend was tracked during restart backoff — supervisor yielding"
);
return;
}
std::thread::sleep(Duration::from_millis(500));
}
}
// Last look before touching the port — covers the zero-backoff first
// respawn (which never enters the pause loop) and the tail of the
// pause itself. After this point we own the respawn.
if BACKEND_SPAWN_GENERATION.load(Ordering::SeqCst) != observed_generation {
log::info!("A replacement backend was tracked — supervisor yielding to its flow");
return;
}
// Clear any orphan still holding the port before the respawn. #1223:
// if it can't be cleared, respawning just reproduces the bind failure
// — stop and say so rather than burning a restart attempt.
@@ -642,11 +764,12 @@ fn supervise_backend(app: &tauri::AppHandle, stage_handle: &Arc<Mutex<BootstrapS
// Wait (bounded) for the respawn to become healthy. If it dies again
// immediately, bail early so the next loop counts it toward the cap.
let start = Instant::now();
let mut last_step = String::new();
while start.elapsed() < Duration::from_secs(120) {
if app_is_quitting(app) {
return;
}
if crate::backend::backend_healthy(backend_port()) {
if crate::backend::backend_ready(backend_port()) {
set_stage(stage_handle, BootstrapStage::Ready);
let _ = app.emit("backend-restored", ());
log::info!("Backend restarted and healthy again");
@@ -655,6 +778,16 @@ fn supervise_backend(app: &tauri::AppHandle, stage_handle: &Arc<Mutex<BootstrapS
if backend_child_exit(app).is_some() {
break;
}
// Same early-bind narration as the launch poll: name the startup
// step in the reconnecting window instead of a silent wait.
if let Some((status, step, label)) =
crate::backend::startup_progress(backend_port())
{
if status == "starting" && !step.is_empty() && step != last_step {
last_step = step;
emit_log(app, "starting_backend", &format!("Startup: {label}"));
}
}
std::thread::sleep(Duration::from_millis(500));
}
}
@@ -712,6 +845,59 @@ pub fn copy_dir_recursive(src: &Path, dst: &Path) -> io::Result<()> {
Ok(())
}
/// Install the production SPA beside `backend/`, where the Python server's
/// static-file mount resolves it for Network Sharing clients.
fn sync_packaged_frontend(resource_root: &Path, project_dir: &Path) -> io::Result<()> {
let source = resource_root.join("frontend").join("dist");
if !source.join("index.html").is_file() {
return Err(io::Error::new(
io::ErrorKind::NotFound,
"bundled frontend is missing index.html",
));
}
let destination = project_dir.join("frontend").join("dist");
let frontend_dir = destination.parent().expect("frontend dist has a parent");
let staging = frontend_dir.join(".dist-staging");
let backup = frontend_dir.join(".dist-backup");
fs::create_dir_all(frontend_dir)?;
if staging.exists() {
fs::remove_dir_all(&staging)?;
}
// A previous process may have died after moving the live shell aside but
// before installing staging. Restore the only known-good SPA before doing
// any new work; never discard that recovery copy merely because startup
// retried.
if !destination.exists() && backup.exists() {
fs::rename(&backup, &destination)?;
}
if let Err(error) = copy_dir_recursive(&source, &staging) {
let _ = fs::remove_dir_all(&staging);
return Err(error);
}
if destination.exists() {
if backup.exists() {
// An interrupted cleanup can leave an incomplete backup. Remove
// it before touching the known-working destination; if cleanup
// fails, abort with the live shell still intact.
fs::remove_dir_all(&backup)?;
}
fs::rename(&destination, &backup)?;
}
if let Err(error) = fs::rename(&staging, &destination) {
if backup.exists() {
let _ = fs::rename(&backup, &destination);
}
let _ = fs::remove_dir_all(&staging);
return Err(error);
}
if backup.exists() {
fs::remove_dir_all(backup)?;
}
Ok(())
}
/// Refresh `pyproject.toml` + `uv.lock` in the project dir from the bundled
/// resources, so an upgraded app never runs freshly-synced backend code against
/// the stale dependency manifests from when the venv was first created (#307 —
@@ -1406,11 +1592,13 @@ manually, then relaunch.",
if let Some(ref res) = resource_dir {
let flat = res.clone();
let up2 = res.join("_up_").join("_up_");
let (res_omni, res_backend) = if flat.join("pyproject.toml").is_file() {
(flat.join("omnivoice"), flat.join("backend"))
let res_root = if flat.join("pyproject.toml").is_file() {
flat
} else {
(up2.join("omnivoice"), up2.join("backend"))
up2
};
let res_omni = res_root.join("omnivoice");
let res_backend = res_root.join("backend");
if res_omni.is_dir() {
let omnivoice_dir = project_dir.join("omnivoice");
let _ = fs::remove_dir_all(&omnivoice_dir);
@@ -1428,6 +1616,11 @@ manually, then relaunch.",
}
log::info!("Synced backend/ from bundle");
}
if let Err(e) = sync_packaged_frontend(&res_root, &project_dir) {
fail(progress, &format!("Failed to sync frontend/dist: {}", e));
return None;
}
log::info!("Synced frontend/dist from bundle");
// #307: the source dirs above track the bundle, so the
// dependency manifests must too — otherwise an upgrade runs
// new code against a venv that predates newly added deps.
@@ -1526,6 +1719,17 @@ the existing venv; newly added dependencies may be missing (#307)",
// copies from when the venv was first created.
if let Ok(res) = app.path().resource_dir() {
let _ = refresh_project_manifests(&res, &project_dir);
let flat = res.clone();
let up2 = res.join("_up_").join("_up_");
let res_root = if flat.join("pyproject.toml").is_file() {
flat
} else {
up2
};
if let Err(e) = sync_packaged_frontend(&res_root, &project_dir) {
fail(progress, &format!("Failed to sync frontend/dist: {}", e));
return None;
}
}
let mut repair_cmd = Command::new(&uv_path);
scrub_python_env(&mut repair_cmd); // #144: don't inherit AppImage's bundled Python
@@ -1630,16 +1834,22 @@ the existing venv; newly added dependencies may be missing (#307)",
let flat = resource_dir.clone();
let up2 = resource_dir.join("_up_").join("_up_");
let (resource_pyproject, resource_uvlock, resource_readme, resource_changelog, resource_omnivoice, resource_backend) = if flat.join("pyproject.toml").is_file() {
(flat.join("pyproject.toml"), flat.join("uv.lock"), flat.join("README.md"), flat.join("CHANGELOG.md"), flat.join("omnivoice"), flat.join("backend"))
let resource_root = if flat.join("pyproject.toml").is_file() {
flat
} else if up2.join("pyproject.toml").is_file() {
(up2.join("pyproject.toml"), up2.join("uv.lock"), up2.join("README.md"), up2.join("CHANGELOG.md"), up2.join("omnivoice"), up2.join("backend"))
up2
} else {
fail(progress, &format!(
"Missing bootstrap resources — checked flat={} and _up_={}",
flat.display(), up2.display()));
return None;
};
let resource_pyproject = resource_root.join("pyproject.toml");
let resource_uvlock = resource_root.join("uv.lock");
let resource_readme = resource_root.join("README.md");
let resource_changelog = resource_root.join("CHANGELOG.md");
let resource_omnivoice = resource_root.join("omnivoice");
let resource_backend = resource_root.join("backend");
if !resource_pyproject.is_file() || !resource_backend.is_dir() {
fail(progress, &format!(
@@ -1688,6 +1898,10 @@ the existing venv; newly added dependencies may be missing (#307)",
fail(progress, &format!("copy backend/: {}", e));
return None;
}
if let Err(e) = sync_packaged_frontend(&resource_root, &project_dir) {
fail(progress, &format!("copy frontend/dist: {}", e));
return None;
}
let uv_path = match resolve_uv(app, &app_data, progress) {
Ok(p) => p,
@@ -1918,6 +2132,118 @@ mod tests {
use super::*;
use std::collections::HashMap;
#[test]
fn packaged_frontend_is_installed_for_the_lan_server() {
let resources = tempfile::tempdir().unwrap();
let project = tempfile::tempdir().unwrap();
let source = resources.path().join("frontend").join("dist");
fs::create_dir_all(source.join("assets")).unwrap();
fs::write(source.join("index.html"), "new shell").unwrap();
fs::write(source.join("assets").join("client.js"), "new client").unwrap();
let installed = project.path().join("frontend").join("dist");
fs::create_dir_all(&installed).unwrap();
fs::write(installed.join("index.html"), "stale shell").unwrap();
sync_packaged_frontend(resources.path(), project.path()).unwrap();
assert_eq!(
fs::read_to_string(installed.join("index.html")).unwrap(),
"new shell"
);
assert_eq!(
fs::read_to_string(installed.join("assets").join("client.js")).unwrap(),
"new client"
);
}
#[test]
fn packaged_frontend_error_does_not_expose_resource_path() {
let resources = tempfile::tempdir().unwrap();
let project = tempfile::tempdir().unwrap();
let error = sync_packaged_frontend(resources.path(), project.path()).unwrap_err();
assert_eq!(error.kind(), io::ErrorKind::NotFound);
assert_eq!(error.to_string(), "bundled frontend is missing index.html");
assert!(!error.to_string().contains(&resources.path().display().to_string()));
}
#[cfg(unix)]
#[test]
fn failed_packaged_frontend_copy_preserves_installed_shell() {
use std::os::unix::fs::symlink;
let resources = tempfile::tempdir().unwrap();
let project = tempfile::tempdir().unwrap();
let source = resources.path().join("frontend").join("dist");
fs::create_dir_all(source.join("assets")).unwrap();
fs::write(source.join("index.html"), "new shell").unwrap();
symlink("missing-client.js", source.join("assets").join("client.js")).unwrap();
let installed = project.path().join("frontend").join("dist");
fs::create_dir_all(&installed).unwrap();
fs::write(installed.join("index.html"), "working shell").unwrap();
sync_packaged_frontend(resources.path(), project.path()).unwrap_err();
assert_eq!(
fs::read_to_string(installed.join("index.html")).unwrap(),
"working shell"
);
}
#[cfg(unix)]
#[test]
fn interrupted_frontend_swap_recovers_backup_before_a_later_copy_failure() {
use std::os::unix::fs::symlink;
let resources = tempfile::tempdir().unwrap();
let project = tempfile::tempdir().unwrap();
let source = resources.path().join("frontend").join("dist");
fs::create_dir_all(source.join("assets")).unwrap();
fs::write(source.join("index.html"), "new shell").unwrap();
symlink("missing-client.js", source.join("assets").join("client.js")).unwrap();
let frontend = project.path().join("frontend");
let installed = frontend.join("dist");
let backup = frontend.join(".dist-backup");
fs::create_dir_all(&backup).unwrap();
fs::write(backup.join("index.html"), "working backup shell").unwrap();
sync_packaged_frontend(resources.path(), project.path()).unwrap_err();
assert_eq!(
fs::read_to_string(installed.join("index.html")).unwrap(),
"working backup shell"
);
}
#[test]
fn interrupted_backup_cleanup_failure_preserves_working_destination() {
let resources = tempfile::tempdir().unwrap();
let project = tempfile::tempdir().unwrap();
let source = resources.path().join("frontend").join("dist");
fs::create_dir_all(&source).unwrap();
fs::write(source.join("index.html"), "new shell").unwrap();
let frontend = project.path().join("frontend");
let installed = frontend.join("dist");
let backup = frontend.join(".dist-backup");
fs::create_dir_all(&installed).unwrap();
fs::write(installed.join("index.html"), "working shell").unwrap();
// A non-directory at the interrupted backup path makes cleanup fail
// and would also prevent the live destination from being renamed.
fs::write(&backup, "partial backup").unwrap();
sync_packaged_frontend(resources.path(), project.path()).unwrap_err();
assert_eq!(
fs::read_to_string(installed.join("index.html")).unwrap(),
"working shell"
);
}
#[test]
fn update_drift_sync_preserves_user_installed_engines() {
// #1029: the routine update sync must carry --inexact so a
@@ -2024,6 +2350,46 @@ mod tests {
assert!(aged.is_empty(), "stale timestamps should have been dropped");
}
/// Env-mutating tests in THIS module serialize on their own lock (cargo
/// runs tests in threads; the harness binary has its own).
static ENV_LOCK: std::sync::Mutex<()> = std::sync::Mutex::new(());
#[test]
fn timing_overrides_default_to_production_values() {
// The env overrides exist for the fault-injection harness only —
// production timing must not drift when they are unset.
let _g = ENV_LOCK.lock().unwrap_or_else(|e| e.into_inner());
std::env::remove_var("OMNIVOICE_STARTUP_BUDGET_S");
std::env::remove_var("OMNIVOICE_SUPERVISOR_POLL_MS");
assert_eq!(startup_budget(), Duration::from_secs(300));
assert_eq!(supervisor_poll(), Duration::from_secs(2));
// Zero/garbage never yields a degenerate loop.
std::env::set_var("OMNIVOICE_STARTUP_BUDGET_S", "0");
std::env::set_var("OMNIVOICE_SUPERVISOR_POLL_MS", "abc");
assert_eq!(startup_budget(), Duration::from_secs(300));
assert_eq!(supervisor_poll(), Duration::from_secs(2));
std::env::set_var("OMNIVOICE_STARTUP_BUDGET_S", "6");
assert_eq!(startup_budget(), Duration::from_secs(6));
std::env::remove_var("OMNIVOICE_STARTUP_BUDGET_S");
std::env::remove_var("OMNIVOICE_SUPERVISOR_POLL_MS");
}
#[test]
fn restart_backoff_escalates_but_first_respawn_is_immediate() {
// A one-off crash self-heals with zero added latency; repeat deaths
// inside the window get an escalating pause so a tight crash loop
// can't burn the whole 3-in-600s budget in seconds.
assert_eq!(restart_backoff_delay(0), Duration::ZERO);
assert_eq!(restart_backoff_delay(1), Duration::from_secs(5));
assert_eq!(restart_backoff_delay(2), Duration::from_secs(15));
// Monotonic, and capped rather than unbounded — the budget check is
// what ends a hopeless loop, not an ever-growing sleep.
assert_eq!(restart_backoff_delay(50), Duration::from_secs(15));
for n in 0..10 {
assert!(restart_backoff_delay(n) <= restart_backoff_delay(n + 1));
}
}
#[test]
fn torch_download_failure_is_detected_for_targeted_help() {
// #569: the cu128 torch wheel host (and a torch-named download/fetch
+2 -1
View File
@@ -79,7 +79,8 @@
"../../README.md",
"../../CHANGELOG.md",
"../../omnivoice",
"../../backend"
"../../backend",
"../../frontend/dist"
],
"externalBin": [
"binaries/uv",
@@ -0,0 +1,549 @@
//! Backend-lifecycle fault-injection harness.
//!
//! Runs `spawn_backend_and_wait` / `supervise_backend` against REAL dying
//! child processes (via the `OMNIVOICE_BACKEND_CMD` seam) and asserts the
//! user receives the CORRECT NAMED DIAGNOSIS — not merely that recovery
//! happened. Diagnosis quality is the bar: 61% of the historical "can't
//! reach the backend" class was closed undiagnosed.
//!
//! The scenario "backend" is this test binary re-invoking itself
//! (`scenario_child`), so exit codes, Unix signals, and pipe-close ordering
//! are the genuine OS articles on all three platforms — no system python,
//! no mocks of the behaviors under test.
//!
//! Every test mutates process-global state (env vars, the crash store, the
//! kill-intended flag), so they hold one mutex AND CI runs this binary with
//! `--test-threads=1`.
use std::io::{Read, Write};
use std::sync::atomic::AtomicBool;
use std::sync::{Arc, Mutex, MutexGuard};
use std::time::{Duration, Instant};
use tauri::Listener;
use tauri::Manager;
use app_lib::bootstrap::{
spawn_backend_and_wait, BootstrapStage, BootstrapState, LogPayload,
set_backend_kill_intended,
};
use app_lib::{AppFlags, BackendState, CaptureDispatchState};
static HARNESS: Mutex<()> = Mutex::new(());
// ── Scenario child ────────────────────────────────────────────────────────
/// Not a real test: when `OMNIVOICE_SCENARIO` is set, this plays the backend
/// — optionally serving minimal HTTP on `OMNIVOICE_PORT`, printing a stderr
/// script, then dying the scripted death. A no-op in a normal test pass.
#[test]
fn scenario_child() {
// The gate value is the PID of the process that ARMED the scenario (the
// parent harness). The parent's own libtest also runs this test — in a
// parallel local `cargo test` it could observe the armed env and start
// fault-injecting itself (binding the port, idling 600s). Only a
// DIFFERENT process — the spawned child — may play the backend.
match std::env::var("OMNIVOICE_SCENARIO") {
Ok(v) if v.parse::<u32>() == Ok(std::process::id()) => return, // the parent itself
Ok(_) => {}
Err(_) => return,
}
let get = |k: &str| std::env::var(k).unwrap_or_default();
let get_ms = |k: &str| get(k).parse::<u64>().ok();
if let Some(delay) = get_ms("OMNIVOICE_SCENARIO_START_DELAY_MS") {
std::thread::sleep(Duration::from_millis(delay));
}
// Serve /system/info + /profiles (the two probes behind backend_ready)
// and /startup/progress (marker-stamped) for the given window; 0 = serve
// forever.
if let Some(serve_ms) = get_ms("OMNIVOICE_SCENARIO_SERVE_MS") {
let port: u16 = get("OMNIVOICE_PORT").parse().expect("OMNIVOICE_PORT");
let progress_only = get("OMNIVOICE_SCENARIO_PROGRESS_ONLY") == "1";
let listener = std::net::TcpListener::bind(("127.0.0.1", port)).expect("bind scenario port");
listener.set_nonblocking(true).unwrap();
let deadline = if serve_ms == 0 {
None
} else {
Some(Instant::now() + Duration::from_millis(serve_ms))
};
loop {
if let Some(d) = deadline {
if Instant::now() >= d {
break;
}
}
match listener.accept() {
Ok((mut stream, _)) => {
let mut buf = [0u8; 512];
let _ = stream.set_read_timeout(Some(Duration::from_millis(200)));
let n = stream.read(&mut buf).unwrap_or(0);
let req = String::from_utf8_lossy(&buf[..n]);
let resp = if req.starts_with("GET /startup/progress") {
let body = r#"{"status": "starting", "step": "ml_imports", "label": "Loading ML runtime (PyTorch)_"}"#;
format!(
"HTTP/1.1 200 OK\r\nx-omnivoice-backend: 0.0.0\r\nContent-Length: {}\r\n\r\n{}",
body.len(), body
)
} else if progress_only {
"HTTP/1.1 503 X\r\nContent-Length: 0\r\n\r\n".to_string()
} else if req.starts_with("GET /system/info") {
let body = r#"{"data_dir": "/x", "app_version": "0.0.0"}"#;
format!("HTTP/1.1 200 OK\r\nContent-Length: {}\r\n\r\n{}", body.len(), body)
} else {
"HTTP/1.1 200 OK\r\nContent-Length: 2\r\n\r\n[]".to_string()
};
let _ = stream.write_all(resp.as_bytes());
}
Err(_) => std::thread::sleep(Duration::from_millis(20)),
}
}
}
let stderr_script = get("OMNIVOICE_SCENARIO_STDERR");
if !stderr_script.is_empty() {
// \n-encoded so a multi-line traceback fits in one env var.
eprintln!("{}", stderr_script.replace("\\n", "\n"));
let _ = std::io::stderr().flush();
// Let the shell's drainer thread pull the pipe before death.
std::thread::sleep(Duration::from_millis(150));
}
#[cfg(unix)]
if get("OMNIVOICE_SCENARIO_SIGNAL") == "9" {
unsafe { libc::raise(libc::SIGKILL) };
}
if let Some(code) = get_ms("OMNIVOICE_SCENARIO_EXIT") {
std::process::exit(code as i32);
}
// Scripted to serve forever / be killed externally: idle out.
std::thread::sleep(Duration::from_secs(600));
}
// ── Harness plumbing ──────────────────────────────────────────────────────
struct Scenario<'a> {
stderr: &'a str,
exit: Option<i32>,
signal9: bool,
serve_ms: Option<u64>,
progress_only: bool,
}
impl Default for Scenario<'_> {
fn default() -> Self {
Scenario { stderr: "", exit: None, signal9: false, serve_ms: None, progress_only: false }
}
}
const SCENARIO_ENV: &[&str] = &[
"OMNIVOICE_SCENARIO",
"OMNIVOICE_SCENARIO_STDERR",
"OMNIVOICE_SCENARIO_EXIT",
"OMNIVOICE_SCENARIO_SIGNAL",
"OMNIVOICE_SCENARIO_SERVE_MS",
"OMNIVOICE_SCENARIO_PROGRESS_ONLY",
"OMNIVOICE_SCENARIO_START_DELAY_MS",
"OMNIVOICE_BACKEND_CMD",
"OMNIVOICE_LOG_DIR",
"OMNIVOICE_PORT",
"OMNIVOICE_STARTUP_BUDGET_S",
"OMNIVOICE_SUPERVISOR_POLL_MS",
];
struct TestApp {
app: tauri::App<tauri::test::MockRuntime>,
stage: Arc<Mutex<BootstrapStage>>,
logs: Arc<Mutex<Vec<LogPayload>>>,
_logdir: tempfile::TempDir,
_guard: MutexGuard<'static, ()>,
}
impl TestApp {
fn new(scenario: &Scenario) -> Self {
let guard = HARNESS.lock().unwrap_or_else(|e| e.into_inner());
for k in SCENARIO_ENV {
std::env::remove_var(k);
}
// Reset the retry-flow flag a previous scenario may have left set.
set_backend_kill_intended(false);
let logdir = tempfile::tempdir().expect("logdir");
std::env::set_var("OMNIVOICE_LOG_DIR", logdir.path());
// Fresh ephemeral port per scenario.
let port = {
let l = std::net::TcpListener::bind("127.0.0.1:0").unwrap();
l.local_addr().unwrap().port()
};
std::env::set_var("OMNIVOICE_PORT", port.to_string());
std::env::set_var("OMNIVOICE_STARTUP_BUDGET_S", "6");
std::env::set_var("OMNIVOICE_SUPERVISOR_POLL_MS", "100");
let exe = std::env::current_exe().expect("current_exe");
std::env::set_var(
"OMNIVOICE_BACKEND_CMD",
serde_json::to_string(&[
exe.to_string_lossy().as_ref(),
"scenario_child",
"--exact",
"--nocapture",
])
.unwrap(),
);
// Armed with OUR pid: the in-process scenario_child test sees its own
// pid and stays inert; only the spawned child (a different pid) runs.
std::env::set_var("OMNIVOICE_SCENARIO", std::process::id().to_string());
if !scenario.stderr.is_empty() {
std::env::set_var("OMNIVOICE_SCENARIO_STDERR", scenario.stderr);
}
if let Some(code) = scenario.exit {
std::env::set_var("OMNIVOICE_SCENARIO_EXIT", code.to_string());
}
if scenario.signal9 {
std::env::set_var("OMNIVOICE_SCENARIO_SIGNAL", "9");
}
if let Some(ms) = scenario.serve_ms {
std::env::set_var("OMNIVOICE_SCENARIO_SERVE_MS", ms.to_string());
}
if scenario.progress_only {
std::env::set_var("OMNIVOICE_SCENARIO_PROGRESS_ONLY", "1");
}
let app = tauri::test::mock_builder()
.build(tauri::test::mock_context(tauri::test::noop_assets()))
.expect("mock app");
app.manage(BackendState { process: Mutex::new(None), spawned_at: Mutex::new(None) });
app.manage(AppFlags {
quitting: AtomicBool::new(false),
dictating: AtomicBool::new(false),
capture: Mutex::new(CaptureDispatchState { ready: false, pending: None }),
});
let stage = Arc::new(Mutex::new(BootstrapStage::Checking));
let logs: Arc<Mutex<Vec<LogPayload>>> = Arc::new(Mutex::new(Vec::new()));
app.manage(BootstrapState { stage: stage.clone(), logs: logs.clone() });
TestApp { app, stage, logs, _logdir: logdir, _guard: guard }
}
fn handle(&self) -> tauri::AppHandle<tauri::test::MockRuntime> {
self.app.handle().clone()
}
/// Run the bootstrap on a thread; the returned closure joins it with a
/// hard timeout so a wiring regression fails red instead of hanging CI.
fn run_bootstrap(&self) -> std::thread::JoinHandle<()> {
let handle = self.handle();
let stage = self.stage.clone();
std::thread::spawn(move || spawn_backend_and_wait(&handle, &stage))
}
fn stage_snapshot(&self) -> BootstrapStage {
self.stage.lock().unwrap_or_else(|e| e.into_inner()).clone()
}
fn failed_message(&self) -> Option<String> {
match self.stage_snapshot() {
BootstrapStage::Failed { message } => Some(message),
_ => None,
}
}
fn markers(&self) -> app_lib::crash::CrashStore {
app_lib::crash::load_store_from(&app_lib::crash::markers_path())
}
fn record_events(&self, name: &'static str) -> Arc<Mutex<Vec<String>>> {
let seen: Arc<Mutex<Vec<String>>> = Arc::new(Mutex::new(Vec::new()));
let seen2 = seen.clone();
self.app.handle().listen(name, move |_ev| {
seen2.lock().unwrap_or_else(|e| e.into_inner()).push(name.to_string());
});
seen
}
fn kill_tracked_child(&self) {
let state = self.app.state::<BackendState>();
let guard = state.process.lock();
if let Ok(mut guard) = guard {
if let Some(child) = guard.as_mut() {
let _ = child.kill();
let _ = child.wait();
}
}
}
fn quit(&self) {
self.app
.state::<AppFlags>()
.quitting
.store(true, std::sync::atomic::Ordering::SeqCst);
}
}
impl Drop for TestApp {
fn drop(&mut self) {
self.quit(); // stop any still-running supervisor loop promptly
self.kill_tracked_child();
for k in SCENARIO_ENV {
std::env::remove_var(k);
}
set_backend_kill_intended(false);
}
}
fn wait_until(timeout: Duration, mut pred: impl FnMut() -> bool) -> bool {
let start = Instant::now();
while start.elapsed() < timeout {
if pred() {
return true;
}
std::thread::sleep(Duration::from_millis(100));
}
false
}
fn join_with_timeout(h: std::thread::JoinHandle<()>, timeout: Duration, what: &str) {
let start = Instant::now();
while !h.is_finished() {
assert!(
start.elapsed() < timeout,
"{what}: bootstrap thread still running after {timeout:?} — a lifecycle \
regression is hanging instead of diagnosing"
);
std::thread::sleep(Duration::from_millis(100));
}
let _ = h.join();
}
// ── Scenarios ─────────────────────────────────────────────────────────────
/// S1 — the backend exits EXIT_PORT_IN_USE: the user must read a port
/// conflict (in the exact phrasing BootstrapSplash.detectHints localizes),
/// not a traceback whose one meaningful line is an OS-translated errno.
#[test]
fn port_conflict_is_named_as_a_port_conflict() {
let t = TestApp::new(&Scenario {
stderr: "FATAL: port is already in use",
exit: Some(app_lib::backend::EXIT_PORT_IN_USE),
..Default::default()
});
let h = t.run_bootstrap();
join_with_timeout(h, Duration::from_secs(30), "port conflict");
let msg = t.failed_message().expect("stage must be Failed");
assert!(
msg.contains("is already in use, so the backend could not"),
"diagnosis must carry the detectHints-matchable port phrasing, got: {msg}"
);
let store = t.markers();
assert_eq!(store.markers.len(), 1, "one real death → one marker");
assert_eq!(store.markers.last().unwrap().exit_code, Some(app_lib::backend::EXIT_PORT_IN_USE));
}
/// S3 — generic startup traceback: the Failed message must carry the stderr
/// tail INCLUDING the chained-traceback root cause, and the marker must
/// record the death's shape.
#[test]
fn generic_traceback_surfaces_the_root_cause() {
let t = TestApp::new(&Scenario {
stderr: "Traceback (most recent call last):\\n File \"main.py\", line 1\\nImportError: libcublas.so.12: cannot open shared object file\\n\\nThe above exception was the direct cause of the following exception:\\n\\nTraceback (most recent call last):\\n File \"wrapper.py\", line 9\\nRuntimeError: failed to initialize CUDA backend",
exit: Some(1),
..Default::default()
});
let h = t.run_bootstrap();
join_with_timeout(h, Duration::from_secs(30), "generic traceback");
let msg = t.failed_message().expect("stage must be Failed");
assert!(msg.contains("Backend process exited"), "got: {msg}");
assert!(
msg.contains("libcublas.so.12"),
"the root-cause line must survive into the diagnosis, got: {msg}"
);
let store = t.markers();
assert_eq!(store.markers.len(), 1);
let m = store.markers.last().unwrap();
assert_eq!(m.exit_code, Some(1));
assert!(m.last_stderr.contains("Traceback"), "marker carries the evidence");
assert!(m.last_stderr.contains("libcublas.so.12"));
}
/// S4 — spawn failure (the program does not exist): the spawn diagnostic
/// must reach the user, and NO crash marker is written — nothing ever ran.
#[test]
fn spawn_failure_diagnoses_and_writes_no_bogus_marker() {
let t = TestApp::new(&Scenario::default());
// Point the seam at a program that cannot exist.
let missing = t._logdir.path().join("no-such-backend");
std::env::set_var(
"OMNIVOICE_BACKEND_CMD",
serde_json::to_string(&[missing.to_string_lossy().as_ref()]).unwrap(),
);
let h = t.run_bootstrap();
join_with_timeout(h, Duration::from_secs(30), "spawn failure");
let msg = t.failed_message().expect("stage must be Failed");
assert!(
msg.contains("Failed to launch the backend process"),
"spawn_failure_diagnostic must reach the user, got: {msg}"
);
assert_eq!(
t.markers().markers.len(),
0,
"never-started is not a crash — no marker may be written"
);
}
/// S5 — slow start past the budget: the timeout diagnosis must name the
/// budget and carry the last stderr, and no death marker exists (the
/// process is alive, just slow).
#[test]
fn slow_start_times_out_with_the_last_stderr() {
let t = TestApp::new(&Scenario {
stderr: "Loading checkpoint shards_ 10%",
serve_ms: None,
..Default::default()
});
// The child prints, then idles far past the 6s budget without serving.
let h = t.run_bootstrap();
join_with_timeout(h, Duration::from_secs(60), "slow start");
let msg = t.failed_message().expect("stage must be Failed");
assert!(msg.contains("did not respond within 6 s"), "got: {msg}");
assert!(
msg.contains("Loading checkpoint shards"),
"the last stderr must ride along so triage sees WHERE it was, got: {msg}"
);
assert_eq!(t.markers().markers.len(), 0, "no death → no marker");
}
/// S6 — post-Ready crash loop: markers are recorded BEFORE each restart,
/// restarts are announced, and budget exhaustion lands on a Failed message
/// naming the pattern and the last exit.
#[test]
fn crash_loop_exhausts_the_budget_with_a_named_diagnosis() {
let t = TestApp::new(&Scenario {
stderr: "RuntimeError: CUDA error: out of memory",
exit: Some(1),
serve_ms: Some(1500),
..Default::default()
});
let restarts = t.record_events("backend-restarting");
let gave_up = t.record_events("backend-restart-failed");
let h = t.run_bootstrap();
assert!(
wait_until(Duration::from_secs(20), || matches!(
t.stage_snapshot(),
BootstrapStage::Ready | BootstrapStage::StartingBackend | BootstrapStage::Failed { .. }
)),
"backend never reached Ready"
);
join_with_timeout(h, Duration::from_secs(120), "crash loop");
let msg = t.failed_message().expect("budget exhaustion must land on Failed");
assert!(msg.contains("kept crashing"), "got: {msg}");
assert!(msg.contains("exit code 1"), "the last death must be named, got: {msg}");
assert_eq!(restarts.lock().unwrap().len(), 3, "3 respawns before giving up");
assert_eq!(gave_up.lock().unwrap().len(), 1);
let store = t.markers();
assert!(
!store.markers.is_empty(),
"every real death records forensics BEFORE the restart decision"
);
assert!(
store.markers.iter().all(|m| m.exit_code == Some(1)),
"markers carry the actual exit"
);
assert!(
store.markers.last().unwrap().last_stderr.contains("out of memory"),
"the OOM evidence must be in the marker"
);
}
/// S7 (unix) — SIGKILL (the OS OOM killer's signature): the death must be
/// named as signal 9, not exit-code noise.
#[cfg(unix)]
#[test]
fn sigkill_is_named_as_signal_nine() {
let t = TestApp::new(&Scenario {
signal9: true,
serve_ms: Some(1500),
..Default::default()
});
let h = t.run_bootstrap();
join_with_timeout(h, Duration::from_secs(120), "sigkill loop");
let msg = t.failed_message().expect("stage must be Failed");
assert!(msg.contains("signal 9"), "signal deaths must be named, got: {msg}");
let store = t.markers();
let m = store.markers.last().expect("marker written");
assert_eq!(m.exit_code, None);
assert_eq!(m.signal, Some(9));
}
/// S8 — a deliberate kill (Retry/Clean&Retry owns the respawn): the
/// supervisor must yield silently — no crash marker, no restart, the stage
/// never Failed.
#[test]
fn deliberate_kill_yields_without_a_crash_marker() {
let t = TestApp::new(&Scenario {
serve_ms: Some(0), // serve forever
..Default::default()
});
let restarts = t.record_events("backend-restarting");
let h = t.run_bootstrap();
assert!(
wait_until(Duration::from_secs(20), || matches!(
t.stage_snapshot(),
BootstrapStage::Ready
)),
"backend never reached Ready"
);
let before = t.markers().markers.len();
set_backend_kill_intended(true);
t.kill_tracked_child();
join_with_timeout(h, Duration::from_secs(30), "deliberate kill");
assert_eq!(t.markers().markers.len(), before, "no marker for an intentional kill");
assert_eq!(restarts.lock().unwrap().len(), 0, "no respawn — the retry flow owns it");
assert!(
matches!(t.stage_snapshot(), BootstrapStage::Ready),
"the stage must never flip to Failed for a deliberate replace"
);
}
/// S9 — early-bind narration + a deferred-startup FATAL: the splash log
/// narrates the step the backend reported, and when it dies the named step
/// reaches both the user-facing diagnosis and the crash forensics.
#[test]
fn deferred_startup_failure_names_the_step() {
let t = TestApp::new(&Scenario {
stderr: "Traceback (most recent call last):\\n File \"main.py\"\\nImportError: torch\\nFATAL: backend startup failed during 'ml_imports': ImportError: torch",
exit: Some(1),
serve_ms: Some(1500),
progress_only: true, // /startup/progress answers; health probes do not
..Default::default()
});
let h = t.run_bootstrap();
join_with_timeout(h, Duration::from_secs(60), "deferred FATAL");
let msg = t.failed_message().expect("stage must be Failed");
assert!(
msg.contains("FATAL: backend startup failed during 'ml_imports'"),
"the named step must reach the user, got: {msg}"
);
let store = t.markers();
assert!(store
.markers
.last()
.expect("marker written")
.last_stderr
.contains("failed during 'ml_imports'"));
let logs = t.logs.lock().unwrap_or_else(|e| e.into_inner());
assert!(
logs.iter().any(|l| l.line.contains("Startup: Loading ML runtime")),
"the launch poll must narrate the step the backend reported; logs: {:?}",
logs.iter().map(|l| &l.line).collect::<Vec<_>>()
);
}
@@ -0,0 +1,23 @@
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<!-- Manifest embedded into TEST binaries on Windows (build.rs,
rustc-link-arg-tests). tauri-build embeds the app's manifest into the
application binary, but cargo test binaries get none — so the loader
resolves comctl32 v5, which lacks the TaskDialogIndirect entry point
tauri's dialog/tray stack imports, and every integration-test binary
dies at load with STATUS_ENTRYPOINT_NOT_FOUND (0xc0000139) before a
single test runs. Declaring the Common-Controls v6 dependency here is
the documented remedy. -->
<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
<dependency>
<dependentAssembly>
<assemblyIdentity
type="win32"
name="Microsoft.Windows.Common-Controls"
version="6.0.0.0"
processorArchitecture="*"
publicKeyToken="6595b64144ccf1df"
language="*"
/>
</dependentAssembly>
</dependency>
</assembly>
+68 -3
View File
@@ -182,15 +182,16 @@ describe('apiFetch 401 routing', () => {
dispatch.mockRestore();
});
const stub401 = (detail: string) =>
const stubStatus = (status: number, statusText: string, detail: string) =>
vi.fn(() =>
Promise.resolve({
ok: false,
status: 401,
statusText: 'Unauthorized',
status,
statusText,
text: async () => JSON.stringify({ detail }),
}),
) as any;
const stub401 = (detail: string) => stubStatus(401, 'Unauthorized', detail);
const authEvent = () =>
dispatch.mock.calls.map((c) => c[0]).find((e) => (e as Event).type === 'ov:auth-required');
@@ -227,6 +228,70 @@ describe('apiFetch 401 routing', () => {
expect(authEvent()).toBeTruthy();
expect((authEvent() as any).detail.mode).toBe('pin');
});
const stub403 = (detail: string) => stubStatus(403, 'Forbidden', detail);
it('dispatches ov:auth-required {mode:"apikey"} on an admin-gate 403 (#1525)', async () => {
globalThis.fetch = stub403('loopback origin or admin API key required');
const { apiFetch } = await import('./client');
try {
await apiFetch('/system/info');
} catch {
/* ApiError expected */
}
expect(authEvent()).toBeTruthy();
expect((authEvent() as any).detail.mode).toBe('apikey');
});
it('does not dispatch ov:auth-required on other 403s (CSRF / desktop-only)', async () => {
globalThis.fetch = stub403('browser origin rejected');
const { apiFetch } = await import('./client');
try {
await apiFetch('/system/info');
} catch {
/* ApiError expected */
}
expect(authEvent()).toBeFalsy();
});
it('a stale 403 neither clears a new session nor reopens the auth gate (PR #1569 race)', async () => {
// The request goes out with an old credential; while it is in flight the
// user completes another key exchange. A late 403 may only invalidate the
// credentials the failed request actually carried — wiping the fresh
// session or reopening the gate would undo the successful login.
sessionStorage.setItem(
ADMIN_SESSION_STORAGE_KEY,
JSON.stringify({
token: `ovs_admin_session_${'O'.repeat(43)}`,
expiresAt: Date.now() / 1000 + 3600,
apiBase: API,
}),
);
globalThis.fetch = vi.fn(() => {
sessionStorage.setItem(
ADMIN_SESSION_STORAGE_KEY,
JSON.stringify({
token: `ovs_admin_session_${'N'.repeat(43)}`,
expiresAt: Date.now() / 1000 + 3600,
apiBase: API,
}),
);
return Promise.resolve({
ok: false,
status: 403,
statusText: 'Forbidden',
text: async () => JSON.stringify({ detail: 'loopback origin or admin API key required' }),
});
}) as any;
const { apiFetch } = await import('./client');
try {
await apiFetch('/system/info');
} catch {
/* ApiError expected */
}
expect(authEvent()).toBeFalsy();
expect(sessionStorage.getItem(ADMIN_SESSION_STORAGE_KEY)).not.toBeNull();
});
});
describe('apiFetch 404 from a non-VoiceStudio server (#1385)', () => {
+25 -3
View File
@@ -528,15 +528,37 @@ export async function apiFetch(path: string, opts: ApiFetchOptions = {}): Promis
// "API key required" (BearerKeyMiddleware, OMNIVOICE_API_KEY) vs anything
// else, i.e. "PIN required" (NetworkAccessMiddleware). Both are 401; the
// detail is the only discriminator (only two 401 sites exist backend-side).
if (backendTarget && res.status === 401 && typeof window !== 'undefined') {
// The router-level admin gates answer 403 "loopback origin or admin API
// key required" (require_admin/require_admin_action) — same situation, the
// client just isn't admin-authenticated — so it routes to the API-key form
// too. Other 403s (CSRF "browser origin rejected", loopback-only routes)
// are NOT credential gaps; presenting a key won't help, so they stay plain
// errors.
const adminGate403 =
res.status === 403 &&
typeof detail === 'string' &&
detail.toLowerCase().includes('admin api key');
if (backendTarget && (res.status === 401 || adminGate403) && typeof window !== 'undefined') {
// readError's declared `string` return isn't guaranteed at runtime —
// `j.detail` can be a structured object/array on a future 401. Match only
// real strings (avoids both a `.toLowerCase()` crash and `String()` itself
// throwing on a malformed object); anything else falls back to PIN.
// (No adminGate403 arm here: "admin api key" ⊇ "api key", so the sniff
// below already yields 'apikey' for every admin-gate 403.)
const mode =
typeof detail === 'string' && detail.toLowerCase().includes('api key') ? 'apikey' : 'pin';
if (mode === 'apikey') clearAdminSession();
window.dispatchEvent(new CustomEvent('ov:auth-required', { detail: { mode } }));
// A failed response may only invalidate the credentials it actually
// carried (`session` is captured at send time). Clearing blindly let
// a stale 403 that landed after a key exchange wipe the fresh
// session, reloading a successful login straight back into the gate.
const currentSession = getAdminSession(API);
const staleAdminResponse = mode === 'apikey' && currentSession?.token !== session?.token;
if (mode === 'apikey' && !staleAdminResponse && session) {
clearAdminSession();
}
if (!staleAdminResponse) {
window.dispatchEvent(new CustomEvent('ov:auth-required', { detail: { mode } }));
}
}
// Structured details (e.g. the typed asr_model_missing 409) carry a
// human-readable `message` — use it for the Error message instead of
@@ -11,8 +11,7 @@ import {
hasCrashEvidence,
isSentinelMarker,
} from '../utils/backendCrash';
import { openExternal } from '../api/external';
import { buildBugReportUrl } from '../utils/bugReport';
import { openBugReport } from '../utils/bugReport';
/**
* BackendCrashNotice the honest half of #941.
@@ -129,13 +128,11 @@ export default function BackendCrashNotice() {
// A sentinel report must not claim a crash in its title
// the marker's whole point is that it cannot know
// (CodeRabbit on #1380). The evidence still rides along.
await openExternal(
await buildBugReportUrl({
title: sentinel
? '[Crash] Backend ended uncleanly (previous run)'
: `[Crash] Backend died (${exit})`,
}),
);
await openBugReport({
title: sentinel
? '[Crash] Backend ended uncleanly (previous run)'
: `[Crash] Backend died (${exit})`,
});
} catch (e) {
// Same class as BackendStartFailureNotice (#1177): a Report
// click that silently does nothing reads as a broken button.
@@ -15,7 +15,7 @@ vi.mock('../utils/backendCrash', async (importOriginal) => {
};
});
vi.mock('../utils/bugReport', () => ({
buildBugReportUrl: vi.fn().mockResolvedValue('https://example.test/issues/new'),
openBugReport: vi.fn().mockResolvedValue(undefined),
}));
vi.mock('../api/external', () => ({
openExternal: vi.fn().mockResolvedValue(undefined),
@@ -138,9 +138,9 @@ describe('BackendCrashNotice — sentinel evidence gate (#1375)', () => {
// The report's TITLE must not claim a death the sentinel cannot attest to
// "Backend died (process ended uncleanly )" states as fact what the
// marker only suspects.
const { buildBugReportUrl } = await import('../utils/bugReport');
await waitFor(() => expect(buildBugReportUrl).toHaveBeenCalled());
const { title } = buildBugReportUrl.mock.calls[0][0];
const { openBugReport } = await import('../utils/bugReport');
await waitFor(() => expect(openBugReport).toHaveBeenCalled());
const { title } = openBugReport.mock.calls[0][0];
expect(title).toMatch(/ended uncleanly/);
expect(title).not.toMatch(/died/);
});
@@ -3,8 +3,7 @@ import { useTranslation } from 'react-i18next';
import { AlertTriangle, X } from 'lucide-react';
import toast from 'react-hot-toast';
import { Button, Dialog } from '../ui';
import { openExternal } from '../api/external';
import { buildBugReportUrl } from '../utils/bugReport';
import { openBugReport } from '../utils/bugReport';
import { detectHints, isUnrecoverableFailure } from './BootstrapSplash';
/**
@@ -101,12 +100,10 @@ export default function BackendStartFailureNotice() {
// buildBugReportUrl scrubs the Error text again and attaches
// the environment block, so the report arrives WITH the
// evidence and WITHOUT the user's home path.
await openExternal(
await buildBugReportUrl({
title: '[Backend] Backend failed to start',
error: new Error(message),
}),
);
await openBugReport({
title: '[Backend] Backend failed to start',
error: new Error(message),
});
} catch (e) {
// Never fail silently: the user clicked Report and must be
// told it didn't open, plus the fallback that still works
@@ -1,15 +1,14 @@
import { describe, it, expect, vi, beforeEach } from 'vitest';
import { render, screen, fireEvent, waitFor } from '@testing-library/react';
import BackendStartFailureNotice from './BackendStartFailureNotice';
import { buildBugReportUrl } from '../utils/bugReport';
import { openExternal } from '../api/external';
import { openBugReport } from '../utils/bugReport';
import toast from 'react-hot-toast';
// #1177: the shell's `Failed { message }` diagnosis must reach the user AFTER
// the bootstrap splash is gone the window in which a start failure used to
// collapse into the evidence-free "Can't reach the local VoiceStudio backend".
vi.mock('../utils/bugReport', () => ({
buildBugReportUrl: vi.fn().mockResolvedValue('https://example.test/issues/new'),
openBugReport: vi.fn().mockResolvedValue(undefined),
}));
vi.mock('../api/external', () => ({
openExternal: vi.fn().mockResolvedValue(undefined),
@@ -70,16 +69,15 @@ describe('BackendStartFailureNotice', () => {
fireEvent.click(await screen.findByRole('button', { name: /see why/i }));
fireEvent.click(await screen.findByRole('button', { name: /report/i }));
await waitFor(() => expect(buildBugReportUrl).toHaveBeenCalled());
await waitFor(() => expect(openBugReport).toHaveBeenCalled());
// The evidence rides along on the report, not just on screen.
expect(buildBugReportUrl.mock.calls[0][0].error.message).toContain('ModuleNotFoundError');
expect(openExternal).toHaveBeenCalledWith('https://example.test/issues/new');
expect(openBugReport.mock.calls[0][0].error.message).toContain('ModuleNotFoundError');
});
// A Report click that silently does nothing reads as a broken button the
// user is left with no idea whether anything was sent.
it('tells the user when the report cannot be opened', async () => {
buildBugReportUrl.mockRejectedValueOnce(new Error('no browser'));
openBugReport.mockRejectedValueOnce(new Error('no browser'));
render(<BackendStartFailureNotice />);
emit(DIAGNOSIS);
fireEvent.click(await screen.findByRole('button', { name: /see why/i }));
+2 -2
View File
@@ -3,7 +3,7 @@ import { AlertCircle, BookOpen, Bug, RefreshCw, Search } from 'lucide-react';
import i18next from 'i18next';
import { classifyError, openDocsFor } from '../utils/errorDocsMap';
import { openExternal } from '../api/external';
import { buildBugReportUrl, buildIssueSearchUrl } from '../utils/bugReport';
import { buildIssueSearchUrl, openBugReport } from '../utils/bugReport';
import { Button } from '../ui';
export default class ErrorBoundary extends React.Component {
@@ -42,7 +42,7 @@ export default class ErrorBoundary extends React.Component {
// Prefilled GitHub Issues URL with the scrubbed error attached the
// user reviews everything on github.com before anything is submitted.
try {
await openExternal(await buildBugReportUrl({ error: this.state.error }));
await openBugReport({ error: this.state.error });
} catch (err) {
console.warn('[ErrorBoundary] report failed', err);
}
+2 -3
View File
@@ -16,8 +16,7 @@
import { useState } from 'react';
import { Bug } from 'lucide-react';
import { Button } from '../ui';
import { openExternal } from '../api/external';
import { buildBugReportUrl } from '../utils/bugReport';
import { openBugReport } from '../utils/bugReport';
import { useTranslation } from 'react-i18next';
export default function ReportBugButton({ size = 'sm', variant = 'subtle', label, error }) {
@@ -28,7 +27,7 @@ export default function ReportBugButton({ size = 'sm', variant = 'subtle', label
const handleClick = async () => {
setBuilding(true);
try {
await openExternal(await buildBugReportUrl({ error }));
await openBugReport({ error });
} finally {
setBuilding(false);
}
@@ -0,0 +1,164 @@
/**
* Settings Performance: the compute-device override.
*
* Lets the user pin which device family the backend uses (auto / CUDA /
* ROCm / XPU / MPS / CPU) instead of trusting auto-detect the fix for the
* "auto-detect picked wrong" issue class. Options are limited to families
* that actually exist on this host (plus Auto and CPU, which always do);
* the pick applies at the next backend start, same restart contract as the
* rest of this tab. `OMNIVOICE_DEVICE` pins the value and disables the
* control rather than pretending the UI choice would win.
*
* Endpoints:
* GET /api/settings/compute-device
* {value, applied, restart_required, effective_family, auto_family,
* available_families, env_pinned, choices}
* PUT /api/settings/compute-device body {"value": "auto"|family}
*/
import React, { useCallback, useEffect, useState } from 'react';
import { MonitorCog } from 'lucide-react';
import { useTranslation } from 'react-i18next';
import { apiJson, apiFetch } from '../../api/client';
import { Select } from '../../ui';
import { SettingsSection, SettingRow } from './primitives';
import RestartBadge from './RestartBadge';
// English fallbacks; the rendered label comes from the locale files
// (settings.device_family_*) so localized builds stay localized.
const FAMILY_FALLBACKS = {
cuda: 'NVIDIA GPU (CUDA)',
rocm: 'AMD GPU (ROCm)',
xpu: 'Intel GPU (XPU)',
mps: 'Apple GPU (MPS)',
cpu: 'CPU',
};
export default function ComputeDevicePanel() {
const { t } = useTranslation();
const [state, setState] = useState(null);
const [saving, setSaving] = useState(false);
const [error, setError] = useState(null);
const refresh = useCallback(async () => {
setError(null);
try {
setState(await apiJson('/api/settings/compute-device'));
} catch (e) {
setError(
e?.message ||
t('settings.device_load_failed', { defaultValue: 'Failed to load device setting' }),
);
}
}, [t]);
useEffect(() => {
refresh();
}, [refresh]);
const onChange = async (e) => {
const value = e.target.value;
setSaving(true);
setError(null);
try {
const res = await apiFetch('/api/settings/compute-device', {
method: 'PUT',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ value }),
});
const body = await res.json().catch(() => null);
if (body?.value) setState(body);
else refresh();
} catch (err) {
setError(
err?.message || t('settings.perf_save_failed', { defaultValue: 'Failed to save setting' }),
);
// Re-sync so the UI never shows a pick that didn't persist but keep
// the save error visible (refresh() would clear it).
try {
setState(await apiJson('/api/settings/compute-device'));
} catch {
/* the save error already on screen covers this */
}
} finally {
setSaving(false);
}
};
const label = t('settings.compute_device', { defaultValue: 'Compute device' });
const families = state?.available_families || [];
const familyLabel = (f) =>
t(`settings.device_family_${f}`, { defaultValue: FAMILY_FALLBACKS[f] || f });
return (
<SettingsSection
icon={MonitorCog}
title={t('settings.compute_device_title', { defaultValue: 'Compute device' })}
description={t('settings.compute_device_desc', {
defaultValue: 'Which device the backend runs models on. Auto is right for almost everyone.',
})}
>
{error && (
<div className="perfpanel__error" role="alert">
{error}
</div>
)}
<SettingRow
title={
<>
{label}
<RestartBadge />
</>
}
subtitle={(() => {
const pinned = state?.env_pinned
? t('settings.compute_device_env_pinned', {
defaultValue: 'Pinned by the OMNIVOICE_DEVICE environment variable',
})
: null;
const ignored = state?.override_ignored
? t('settings.compute_device_ignored', {
defaultValue: 'That device was not detected on this machine — Auto is in effect',
})
: null;
// An env pin naming absent hardware needs BOTH facts: why the
// control is disabled, and that the pin is not actually in effect.
if (pinned && ignored) return `${pinned} · ${ignored}`;
if (pinned) return pinned;
if (ignored) return ignored;
return state?.restart_required
? t('settings.compute_device_restart', {
defaultValue: 'Takes effect after the app restarts',
})
: undefined;
})()}
note={t('settings.compute_device_note', {
defaultValue:
'Only devices detected on this machine are listed. CPU always works; pinning a device never invents hardware.',
})}
control={
<Select
size="sm"
value={state?.value ?? 'auto'}
onChange={onChange}
disabled={!state || saving || state?.env_pinned}
aria-label={label}
data-testid="compute-device-select"
>
<option value="auto">
{t('settings.compute_device_auto', {
defaultValue: 'Auto (recommended)',
})}
{state?.auto_family ? `${familyLabel(state.auto_family)}` : ''}
</option>
{families.map((f) => (
<option key={f} value={f}>
{familyLabel(f)}
</option>
))}
</Select>
}
/>
</SettingsSection>
);
}
@@ -0,0 +1,104 @@
import { describe, it, expect, vi, beforeEach } from 'vitest';
import { fireEvent, render, screen, waitFor } from '@testing-library/react';
import React from 'react';
function mockFetchSequence(...responses) {
const fn = vi.fn();
for (const r of responses) {
fn.mockResolvedValueOnce({
ok: r.status >= 200 && r.status < 300,
status: r.status,
json: async () => r.body,
text: async () => JSON.stringify(r.body),
});
}
return fn;
}
import ComputeDevicePanel from './ComputeDevicePanel';
const CUDA_HOST = {
value: 'auto',
applied: 'auto',
restart_required: false,
effective_family: 'cuda',
auto_family: 'cuda',
available_families: ['cuda', 'cpu'],
env_pinned: false,
choices: ['auto', 'cuda', 'rocm', 'xpu', 'mps', 'cpu'],
};
describe('ComputeDevicePanel', () => {
beforeEach(() => {
vi.restoreAllMocks();
});
it('offers only the detected families plus Auto', async () => {
global.fetch = mockFetchSequence({ status: 200, body: CUDA_HOST });
render(<ComputeDevicePanel />);
// Wait for the LOADED state (3 options), not just the select it
// renders disabled with only Auto before the GET resolves.
await waitFor(() => expect(screen.getByTestId('compute-device-select').options.length).toBe(3));
const options = [...screen.getByTestId('compute-device-select').options].map((o) => o.value);
// No mps/rocm/xpu on a CUDA host an override can steer, not invent.
expect(options).toEqual(['auto', 'cuda', 'cpu']);
});
it('changing the pick PUTs the value and shows the restart note', async () => {
const fetchMock = mockFetchSequence(
{ status: 200, body: CUDA_HOST }, // initial GET
{
status: 200,
body: { ...CUDA_HOST, value: 'cpu', restart_required: true },
}, // PUT echo
);
global.fetch = fetchMock;
render(<ComputeDevicePanel />);
await waitFor(() => expect(screen.getByTestId('compute-device-select').options.length).toBe(3));
fireEvent.change(screen.getByTestId('compute-device-select'), { target: { value: 'cpu' } });
await waitFor(() => {
const put = fetchMock.mock.calls.find(([_u, opts]) => opts && opts.method === 'PUT');
expect(put).toBeTruthy();
expect(put[0]).toMatch(/\/api\/settings\/compute-device$/);
expect(JSON.parse(put[1].body)).toEqual({ value: 'cpu' });
});
expect(screen.getByText(/after the app restarts/i)).toBeInTheDocument();
});
it('an OMNIVOICE_DEVICE pin disables the control and says so', async () => {
global.fetch = mockFetchSequence({
status: 200,
body: { ...CUDA_HOST, value: 'cpu', applied: 'cpu', env_pinned: true },
});
render(<ComputeDevicePanel />);
await waitFor(() => {
expect(screen.getByTestId('compute-device-select')).toBeDisabled();
});
expect(screen.getByText(/OMNIVOICE_DEVICE/)).toBeInTheDocument();
});
it('re-syncs from the server when the PUT fails, keeping the error visible', async () => {
const fetchMock = mockFetchSequence(
{ status: 200, body: CUDA_HOST }, // initial GET
{ status: 500, body: { detail: 'nope' } }, // PUT fails
{ status: 200, body: CUDA_HOST }, // re-sync GET
);
global.fetch = fetchMock;
render(<ComputeDevicePanel />);
await waitFor(() => expect(screen.getByTestId('compute-device-select').options.length).toBe(3));
fireEvent.change(screen.getByTestId('compute-device-select'), { target: { value: 'cpu' } });
await waitFor(() => {
// Three calls: GET, failed PUT, re-sync GET the select ends on the
// server's truth (auto), never a pick that didn't persist.
expect(fetchMock.mock.calls.length).toBe(3);
});
expect(screen.getByTestId('compute-device-select')).toHaveValue('auto');
// The save error must survive the re-sync a silent snap-back reads
// as "the app ignored me".
expect(screen.getByRole('alert')).toBeInTheDocument();
});
});
@@ -18,6 +18,7 @@ import { Badge } from '../../ui';
import { SettingsSection } from './primitives';
import Row from './Row';
import PerformancePanel from './PerformancePanel';
import ComputeDevicePanel from './ComputeDevicePanel';
export default function PerformanceDeviceTab() {
const { t } = useTranslation();
@@ -29,6 +30,8 @@ export default function PerformanceDeviceTab() {
<>
<PerformancePanel />
<ComputeDevicePanel />
<SettingsSection
icon={Gauge}
title={t('settings.device', { defaultValue: 'Device & compute' })}
@@ -196,6 +196,12 @@ export const GROUPS = [
'vram',
'compute',
'platform',
'cuda',
'rocm',
'mps',
'cpu',
'xpu',
'intel',
],
},
{
@@ -67,6 +67,7 @@ describe('restart flag ↔ RestartBadge lockstep', () => {
'RemoteBackendPanel.jsx': 'sharing',
'AudioToolsPanel.jsx': 'audio-tools',
'PerformancePanel.jsx': 'performance',
'ComputeDevicePanel.jsx': 'performance',
};
const panelsUsingRestartBadge = fs
+66 -34
View File
@@ -1,4 +1,4 @@
import { useState, useEffect, useCallback, useRef } from 'react';
import { useState, useEffect, useLayoutEffect, useCallback, useRef } from 'react';
import { useAppStore } from '../store';
import { listProfiles } from '../api/profiles';
import { listHistory } from '../api/generate';
@@ -10,6 +10,7 @@ import { useModelStatus } from '../api/hooks';
import useRealtimeEvents from './useRealtimeEvents';
import { mergeDescribedAttrs } from '../utils/voiceInstruct';
import { sanitizeOmniUi } from '../utils/omniUiSchema';
import { queueJsonWrite } from '../utils/coalescedJsonStorage';
/**
* Encapsulates all data-loading effects, localStorage persistence,
@@ -24,6 +25,7 @@ import { sanitizeOmniUi } from '../utils/omniUiSchema';
// reinstall doesn't clear the webview's localStorage). Only settled states
// come back.
const STABLE_DUB_STEPS = new Set(['idle', 'editing', 'done']);
export const OMNI_UI_KEY = 'omni_ui';
/** Clamp a persisted dubStep to a state that is valid after a cold start.
* Stable steps pass through; transient (and unknown/corrupt) values fall
@@ -89,6 +91,45 @@ export default function useAppData() {
const [studioProjects, setStudioProjects] = useState([]);
const [exportHistory, setExportHistory] = useState([]);
const [showOverrides, setShowOverrides] = useState(false);
const [omniUiRestoreComplete, setOmniUiRestoreComplete] = useState(false);
const omniUiWriteDisposerRef = useRef(null);
const omniUiSnapshotRef = useRef(null);
const omniUiSnapshot = {
uiScale,
text,
mode,
defineMethod,
vdStates,
language,
isSidebarCollapsed,
sidebarTab,
dubJobId,
dubFilename,
dubDuration,
dubSegments,
dubLang,
dubLangCode,
dubTracks,
dubStep,
dubTranscript,
exportTracks,
preserveBg,
defaultTrack,
exportHistory,
speed,
steps,
cfg,
denoise,
showOverrides,
};
// Only expose committed state to the deferred writer. Publishing during
// render would let a timer or lifecycle flush observe a concurrent render
// that React later abandons. Layout effects run before the passive effect
// that registers the provider, without copying any nested document data.
useLayoutEffect(() => {
omniUiSnapshotRef.current = omniUiSnapshot;
});
// ── Model status (TanStack Query) ──
// Sysinfo lives in Header (the only consumer) so its 5s poll doesn't
@@ -196,7 +237,7 @@ export default function useAppData() {
// was healed per-field; this closes it generically — malformed values
// are dropped up front instead of throwing mid-restore and silently
// discarding every field after the bad one).
const saved = sanitizeOmniUi(JSON.parse(localStorage.getItem('omni_ui') || '{}'));
const saved = sanitizeOmniUi(JSON.parse(localStorage.getItem(OMNI_UI_KEY) || '{}'));
if (saved.uiScale) setUiScale(saved.uiScale);
if (saved.text) setText(saved.text);
// Legacy shim (voice-studio-unification P4): the old 'clone'/'design'
@@ -240,7 +281,14 @@ export default function useAppData() {
if (saved.cfg) setCfg(saved.cfg);
if (saved.denoise !== undefined) setDenoise(saved.denoise);
if (saved.showOverrides !== undefined) setShowOverrides(saved.showOverrides);
} catch (e) {}
} catch (e) {
// Preserve the existing fail-open recovery behavior: malformed or
// inaccessible legacy state falls back to the initialized defaults.
} finally {
// The initial persistence effect closes over `false` and therefore
// cannot flush defaults. The restored render becomes the first writer.
setOmniUiRestoreComplete(true);
}
return () => {
cancelled = true;
};
@@ -248,38 +296,14 @@ export default function useAppData() {
// ── Persist to localStorage ──
useEffect(() => {
localStorage.setItem(
'omni_ui',
JSON.stringify({
uiScale,
text,
mode,
defineMethod,
vdStates,
language,
isSidebarCollapsed,
sidebarTab,
dubJobId,
dubFilename,
dubDuration,
dubSegments,
dubLang,
dubLangCode,
dubTracks,
dubStep,
dubTranscript,
exportTracks,
preserveBg,
defaultTrack,
exportHistory,
speed,
steps,
cfg,
denoise,
showOverrides,
}),
);
if (!omniUiRestoreComplete) return undefined;
// Replacements must retain the scheduler's original maximum-wait window.
// React runs a dependency effect's cleanup before every new setup, so the
// generation disposer is intentionally reserved for a true unmount below.
omniUiWriteDisposerRef.current = queueJsonWrite(OMNI_UI_KEY, () => omniUiSnapshotRef.current);
return undefined;
}, [
omniUiRestoreComplete,
uiScale,
text,
mode,
@@ -308,6 +332,14 @@ export default function useAppData() {
showOverrides,
]);
useEffect(
() => () => {
omniUiWriteDisposerRef.current?.();
omniUiWriteDisposerRef.current = null;
},
[],
);
return {
profiles,
history,
@@ -0,0 +1,342 @@
import React, { StrictMode, Suspense } from 'react';
import { act, cleanup, render, renderHook } from '@testing-library/react';
import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
const persistenceProbe = vi.hoisted(() => ({
flushOnNextOmniQueue: false,
providerReads: 0,
queuedProviders: [],
disposers: [],
materializedValues: [],
}));
// Keep the real scheduler in this integration suite. The wrapper adds one
// deterministic test seam: forcing the first omni_ui registration to flush
// synchronously catches an initial-default provider before React can replace
// it with the restored render's provider.
vi.mock('../utils/coalescedJsonStorage', async (importOriginal) => {
const actual = await importOriginal();
return {
...actual,
queueJsonWrite(key, readLatestValue) {
const trackedProvider = () => {
persistenceProbe.providerReads += 1;
const value = readLatestValue();
persistenceProbe.materializedValues.push(value);
return value;
};
const disposeActual = actual.queueJsonWrite(key, trackedProvider);
const dispose = vi.fn(disposeActual);
persistenceProbe.queuedProviders.push({ key, provider: trackedProvider });
persistenceProbe.disposers.push(dispose);
if (key === 'omni_ui' && persistenceProbe.flushOnNextOmniQueue) {
persistenceProbe.flushOnNextOmniQueue = false;
actual.flushPendingWrites();
}
return dispose;
},
};
});
const systemApi = vi.hoisted(() => ({ modelStatus: vi.fn() }));
vi.mock('../api/system', () => systemApi);
vi.mock('../api/hooks', () => ({
useModelStatus: () => ({ data: { status: 'idle' } }),
}));
vi.mock('./useRealtimeEvents', () => ({ default: vi.fn() }));
import useAppData from './useAppData';
import { useAppStore } from '../store';
import {
configurePersistenceRole,
discardPendingWrites,
flushPendingWrites,
resetCoalescedJsonStorageForTests,
} from '../utils/coalescedJsonStorage';
const initialStoreState = useAppStore.getInitialState();
const OMNI_UI_KEYS = [
'uiScale',
'text',
'mode',
'defineMethod',
'vdStates',
'language',
'isSidebarCollapsed',
'sidebarTab',
'dubJobId',
'dubFilename',
'dubDuration',
'dubSegments',
'dubLang',
'dubLangCode',
'dubTracks',
'dubStep',
'dubTranscript',
'exportTracks',
'preserveBg',
'defaultTrack',
'exportHistory',
'speed',
'steps',
'cfg',
'denoise',
'showOverrides',
];
function resetProbe() {
persistenceProbe.flushOnNextOmniQueue = false;
persistenceProbe.providerReads = 0;
persistenceProbe.queuedProviders.length = 0;
persistenceProbe.disposers.length = 0;
persistenceProbe.materializedValues.length = 0;
}
function seedOmniUi(value) {
localStorage.setItem('omni_ui', JSON.stringify(value));
}
function watchStorageWrites() {
return vi.spyOn(localStorage, 'setItem');
}
function omniWrites(setItemSpy) {
return setItemSpy.mock.calls
.filter(([key]) => key === 'omni_ui')
.map(([, raw]) => JSON.parse(raw));
}
beforeEach(() => {
vi.useFakeTimers();
resetCoalescedJsonStorageForTests();
configurePersistenceRole('main');
useAppStore.setState(initialStoreState, true);
useAppStore.setState({
text: 'initial default that must never win',
mode: 'studio',
dubSegments: [],
dubStep: 'idle',
});
discardPendingWrites();
localStorage.clear();
resetProbe();
// Keep the backend-readiness loop parked without scheduling retries or
// allowing unrelated list responses to update the hook after a test ends.
systemApi.modelStatus.mockReset().mockImplementation(() => new Promise(() => {}));
});
afterEach(() => {
cleanup();
resetCoalescedJsonStorageForTests();
localStorage.clear();
vi.useRealTimers();
vi.restoreAllMocks();
});
describe('useAppData omni_ui persistence', () => {
it('never exposes initial defaults to a lifecycle flush while restoring seeded state', () => {
seedOmniUi({
uiScale: 1.15,
text: 'restored script',
mode: 'dub',
defineMethod: 'design',
language: 'Spanish',
isSidebarCollapsed: true,
sidebarTab: 'projects',
dubJobId: 'job-restored',
dubFilename: 'clip.mp4',
dubDuration: 42,
dubSegments: [{ id: '1', text: 'Hola', start: 0, end: 2 }],
dubLang: 'Spanish',
dubLangCode: 'es',
dubTracks: ['es'],
dubStep: 'generating',
dubTranscript: 'Hola',
exportTracks: { original: true, es: true },
preserveBg: false,
defaultTrack: 'es',
exportHistory: [{ id: 'export-1' }],
speed: 1.2,
steps: 24,
cfg: 2.5,
denoise: false,
showOverrides: true,
});
const setItemSpy = watchStorageWrites();
setItemSpy.mockClear();
persistenceProbe.flushOnNextOmniQueue = true;
const { result } = renderHook(() => useAppData());
const writes = omniWrites(setItemSpy);
expect(writes).toHaveLength(1);
expect(writes[0]).toMatchObject({
text: 'restored script',
mode: 'dub',
dubJobId: 'job-restored',
dubStep: 'editing',
exportHistory: [{ id: 'export-1' }],
showOverrides: true,
});
expect(writes[0].dubSegments).toEqual([
{ id: '1', text: 'Hola', text_original: 'Hola', start: 0, end: 2 },
]);
expect(result.current.showOverrides).toBe(true);
expect(Object.keys(persistenceProbe.materializedValues[0])).toEqual(OMNI_UI_KEYS);
expect(persistenceProbe.queuedProviders[0].key).toBe('omni_ui');
});
it('keeps serialization and physical writes out of a burst and flushes only the latest value', () => {
const setItemSpy = watchStorageWrites();
renderHook(() => useAppData());
flushPendingWrites();
setItemSpy.mockClear();
persistenceProbe.providerReads = 0;
persistenceProbe.materializedValues.length = 0;
for (let index = 1; index <= 20; index += 1) {
act(() => {
useAppStore.getState().setText(`draft-${index}`);
useAppStore
.getState()
.setDubSegments([
{ id: '1', text: `segment-${index}`, text_original: 'source', start: 0, end: 1 },
]);
});
}
expect(persistenceProbe.providerReads).toBe(0);
expect(omniWrites(setItemSpy)).toHaveLength(0);
act(() => {
vi.advanceTimersByTime(249);
});
expect(omniWrites(setItemSpy)).toHaveLength(0);
act(() => {
vi.advanceTimersByTime(1);
});
const writes = omniWrites(setItemSpy);
expect(writes).toHaveLength(1);
expect(writes[0].text).toBe('draft-20');
expect(writes[0].dubSegments[0].text).toBe('segment-20');
expect(persistenceProbe.providerReads).toBe(1);
});
it('preserves the original hard deadline during continuous edits', () => {
const setItemSpy = watchStorageWrites();
renderHook(() => useAppData());
flushPendingWrites();
setItemSpy.mockClear();
persistenceProbe.providerReads = 0;
act(() => {
useAppStore.getState().setText('continuous-1');
});
for (let index = 2; index <= 5; index += 1) {
act(() => {
vi.advanceTimersByTime(200);
useAppStore.getState().setText(`continuous-${index}`);
});
}
// Every edit arrived before the 250 ms quiet delay. The maximum timer
// still belongs to the window opened at t=0 and must not be restarted by
// React's dependency-effect cleanup/re-registration cycle.
act(() => {
vi.advanceTimersByTime(199);
});
expect(omniWrites(setItemSpy)).toHaveLength(0);
expect(persistenceProbe.providerReads).toBe(0);
act(() => {
vi.advanceTimersByTime(1);
});
const writes = omniWrites(setItemSpy);
expect(writes).toHaveLength(1);
expect(writes[0].text).toBe('continuous-5');
expect(persistenceProbe.providerReads).toBe(1);
});
it('never persists state from a render that React abandons', () => {
let suspendNextRender = false;
const neverSettles = new Promise(() => {});
function ConcurrentHarness() {
useAppData();
if (suspendNextRender) throw neverSettles;
return null;
}
render(
<Suspense fallback={null}>
<ConcurrentHarness />
</Suspense>,
);
act(() => {
flushPendingWrites();
useAppStore.getState().setText('last committed script');
});
// The store notification starts a render which suspends before commit.
// A render-time ref assignment exposed this value to the already-pending
// provider even though React never published it to the UI.
suspendNextRender = true;
act(() => {
useAppStore.getState().setText('abandoned candidate');
});
act(() => {
flushPendingWrites();
});
expect(JSON.parse(localStorage.getItem('omni_ui')).text).toBe('last committed script');
});
it('uses generation-bound cleanup across StrictMode updates and unmount', () => {
const setItemSpy = watchStorageWrites();
const { unmount } = renderHook(() => useAppData(), { wrapper: StrictMode });
expect(persistenceProbe.disposers.length).toBeGreaterThan(0);
const obsoleteDisposer = persistenceProbe.disposers.at(-1);
act(() => {
useAppStore.getState().setText('newer provider');
});
// Dependency changes replace the provider without disposing the prior
// registration; disposing here would restart the scheduler's hard window.
expect(obsoleteDisposer).not.toHaveBeenCalled();
// A repeated/stale cleanup must not cancel the replacement registration.
obsoleteDisposer();
act(() => {
flushPendingWrites();
});
expect(omniWrites(setItemSpy).at(-1)?.text).toBe('newer provider');
setItemSpy.mockClear();
act(() => {
useAppStore.getState().setText('cancel on unmount');
});
const activeDisposer = persistenceProbe.disposers.at(-1);
unmount();
expect(activeDisposer).toHaveBeenCalledOnce();
act(() => {
vi.runAllTimers();
flushPendingWrites();
});
expect(omniWrites(setItemSpy)).toHaveLength(0);
});
it('completes restore readiness after malformed legacy JSON', () => {
localStorage.setItem('omni_ui', '{malformed');
const setItemSpy = watchStorageWrites();
setItemSpy.mockClear();
expect(() => renderHook(() => useAppData())).not.toThrow();
act(() => {
vi.advanceTimersByTime(250);
});
const writes = omniWrites(setItemSpy);
expect(writes).toHaveLength(1);
expect(writes[0].text).toBe('initial default that must never win');
});
});
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} متصل",
"workers_token_expired": "انتهت الصلاحية — أنشئ رمزًا جديدًا",
"workers_token_expires_in": "تنتهي الصلاحية خلال {{time}}",
"workers_token_qr_hint": "على الجهاز الآخر: الإعدادات ← النظام ← العاملون البعيدون ← انضمام، ثم امسح أو الصق."
"workers_token_qr_hint": "على الجهاز الآخر: الإعدادات ← النظام ← العاملون البعيدون ← انضمام، ثم امسح أو الصق.",
"compute_device": "جهاز الحوسبة",
"compute_device_title": "جهاز الحوسبة",
"compute_device_desc": "الجهاز الذي يشغّل عليه الخادم النماذج. الوضع التلقائي مناسب للجميع تقريبًا.",
"compute_device_env_pinned": "مثبّت بواسطة متغيّر البيئة OMNIVOICE_DEVICE",
"compute_device_ignored": "لم يُكتشف هذا الجهاز على هذا الحاسوب — الوضع التلقائي هو المعمول به",
"compute_device_restart": "يسري بعد إعادة تشغيل التطبيق",
"compute_device_note": "تُعرض فقط الأجهزة المكتشفة على هذا الحاسوب. تعمل CPU دائمًا؛ تثبيت جهاز لا يخترع عتادًا أبدًا.",
"compute_device_auto": "تلقائي (مستحسن)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "تعذّر تحميل إعداد الجهاز",
"perf_save_failed": "تعذّر حفظ الإعداد"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "الإبلاغ عن خطأ",
"title": "يفتح صفحة مشكلات GitHub المملوءة مسبقًا في متصفحك. لا يتم إرسال أي شيء حتى تقوم بالنقر فوق إرسال."
"title": "يفتح صفحة مشكلات GitHub المملوءة مسبقًا في متصفحك. لا يتم إرسال أي شيء حتى تقوم بالنقر فوق إرسال.",
"staleTitle": "يتوفر تحديث",
"staleMessage": "أنت تستخدم VoiceStudio {{current}}، لكن الإصدار {{latest}} متوفر بالفعل — قد يكون هذا الخطأ مُصلحًا فيه. هل تريد الاطلاع على أحدث إصدار قبل الإبلاغ؟",
"staleView": "عرض التحديث",
"staleFileAnyway": "الإبلاغ على أي حال"
},
"app": {
"loading": "جارٍ التحميل…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Abgelaufen — erzeugen Sie ein neues",
"workers_token_expires_in": "Läuft ab in {{time}}",
"workers_token_qr_hint": "Auf dem anderen Rechner: Einstellungen → System → Remote-Worker → Beitreten, dann scannen oder einfügen."
"workers_token_qr_hint": "Auf dem anderen Rechner: Einstellungen → System → Remote-Worker → Beitreten, dann scannen oder einfügen.",
"compute_device": "Rechengerät",
"compute_device_title": "Rechengerät",
"compute_device_desc": "Auf welchem Gerät das Backend Modelle ausführt. Auto ist für fast alle richtig.",
"compute_device_env_pinned": "Durch die Umgebungsvariable OMNIVOICE_DEVICE festgelegt",
"compute_device_ignored": "Dieses Gerät wurde auf diesem Rechner nicht erkannt — Auto ist aktiv",
"compute_device_restart": "Wird nach dem Neustart der App wirksam",
"compute_device_note": "Es werden nur auf diesem Rechner erkannte Geräte angezeigt. CPU funktioniert immer; ein festgelegtes Gerät erfindet keine Hardware.",
"compute_device_auto": "Auto (empfohlen)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Geräteeinstellung konnte nicht geladen werden",
"perf_save_failed": "Einstellung konnte nicht gespeichert werden"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Melden Sie einen Fehler",
"title": "Öffnet eine vorab ausgefüllte GitHub-Problemseite in Ihrem Browser. Es wird nichts gesendet, bis Sie auf „Senden“ klicken."
"title": "Öffnet eine vorab ausgefüllte GitHub-Problemseite in Ihrem Browser. Es wird nichts gesendet, bis Sie auf „Senden“ klicken.",
"staleTitle": "Update verfügbar",
"staleMessage": "Sie verwenden VoiceStudio {{current}}, aber {{latest}} ist bereits erschienen dieser Fehler ist dort möglicherweise schon behoben. Vor dem Melden die neueste Version ansehen?",
"staleView": "Update ansehen",
"staleFileAnyway": "Trotzdem melden"
},
"app": {
"loading": "Laden…",
+20 -2
View File
@@ -890,7 +890,21 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Expired — generate a new one",
"workers_token_expires_in": "Expires in {{time}}",
"workers_token_qr_hint": "On the other machine: Settings → System → Remote workers → Join, then scan or paste."
"workers_token_qr_hint": "On the other machine: Settings → System → Remote workers → Join, then scan or paste.",
"compute_device": "Compute device",
"compute_device_title": "Compute device",
"compute_device_desc": "Which device the backend runs models on. Auto is right for almost everyone.",
"compute_device_env_pinned": "Pinned by the OMNIVOICE_DEVICE environment variable",
"compute_device_ignored": "That device was not detected on this machine — Auto is in effect",
"compute_device_restart": "Takes effect after the app restarts",
"compute_device_note": "Only devices detected on this machine are listed. CPU always works; pinning a device never invents hardware.",
"compute_device_auto": "Auto (recommended)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Failed to load device setting"
},
"about": {
"app": "App",
@@ -2790,7 +2804,11 @@
},
"reportBug": {
"label": "Report a bug",
"title": "Opens a prefilled GitHub Issues page in your browser. Nothing is sent until you click Submit."
"title": "Opens a prefilled GitHub Issues page in your browser. Nothing is sent until you click Submit.",
"staleTitle": "Update available",
"staleMessage": "You're running VoiceStudio {{current}}, but {{latest}} is already out — this bug may be fixed there. See the latest release before filing?",
"staleView": "View update",
"staleFileAnyway": "File anyway"
},
"app": {
"loading": "Loading…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} en línea",
"workers_token_expired": "Caducado — genera uno nuevo",
"workers_token_expires_in": "Caduca en {{time}}",
"workers_token_qr_hint": "En el otro equipo: Ajustes → Sistema → Trabajadores remotos → Unirse, y luego escanea o pega."
"workers_token_qr_hint": "En el otro equipo: Ajustes → Sistema → Trabajadores remotos → Unirse, y luego escanea o pega.",
"compute_device": "Dispositivo de cómputo",
"compute_device_title": "Dispositivo de cómputo",
"compute_device_desc": "En qué dispositivo ejecuta los modelos el backend. Auto es lo correcto para casi todos.",
"compute_device_env_pinned": "Fijado por la variable de entorno OMNIVOICE_DEVICE",
"compute_device_ignored": "Ese dispositivo no se detectó en esta máquina — Auto está en efecto",
"compute_device_restart": "Surte efecto tras reiniciar la aplicación",
"compute_device_note": "Solo se listan los dispositivos detectados en esta máquina. La CPU siempre funciona; fijar un dispositivo nunca inventa hardware.",
"compute_device_auto": "Auto (recomendado)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "No se pudo cargar el ajuste del dispositivo",
"perf_save_failed": "No se pudo guardar el ajuste"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Informar un error",
"title": "Abre una página de Problemas de GitHub precargada en su navegador. No se envía nada hasta que haga clic en Enviar."
"title": "Abre una página de Problemas de GitHub precargada en su navegador. No se envía nada hasta que haga clic en Enviar.",
"staleTitle": "Actualización disponible",
"staleMessage": "Está usando VoiceStudio {{current}}, pero {{latest}} ya está disponible; puede que este error ya esté corregido. ¿Ver la última versión antes de informar?",
"staleView": "Ver actualización",
"staleFileAnyway": "Informar de todos modos"
},
"app": {
"loading": "Cargando…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} en ligne",
"workers_token_expired": "Expiré — générez-en un nouveau",
"workers_token_expires_in": "Expire dans {{time}}",
"workers_token_qr_hint": "Sur l'autre machine : Paramètres → Système → Workers distants → Rejoindre, puis scannez ou collez."
"workers_token_qr_hint": "Sur l'autre machine : Paramètres → Système → Workers distants → Rejoindre, puis scannez ou collez.",
"compute_device": "Périphérique de calcul",
"compute_device_title": "Périphérique de calcul",
"compute_device_desc": "Sur quel périphérique le backend exécute les modèles. Auto convient à presque tout le monde.",
"compute_device_env_pinned": "Épinglé par la variable d'environnement OMNIVOICE_DEVICE",
"compute_device_ignored": "Ce périphérique n'a pas été détecté sur cette machine — Auto est en vigueur",
"compute_device_restart": "Prend effet après le redémarrage de l'application",
"compute_device_note": "Seuls les périphériques détectés sur cette machine sont listés. Le CPU fonctionne toujours ; épingler un périphérique n'invente jamais de matériel.",
"compute_device_auto": "Auto (recommandé)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Impossible de charger le réglage du périphérique",
"perf_save_failed": "Impossible d'enregistrer le réglage"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Signaler un bug",
"title": "Ouvre une page de problèmes GitHub pré-remplie dans votre navigateur. Rien n'est envoyé jusqu'à ce que vous cliquiez sur Soumettre."
"title": "Ouvre une page de problèmes GitHub pré-remplie dans votre navigateur. Rien n'est envoyé jusqu'à ce que vous cliquiez sur Soumettre.",
"staleTitle": "Mise à jour disponible",
"staleMessage": "Vous utilisez VoiceStudio {{current}}, mais {{latest}} est déjà sortie — ce bug y est peut-être déjà corrigé. Voir la dernière version avant de signaler ?",
"staleView": "Voir la mise à jour",
"staleFileAnyway": "Signaler quand même"
},
"app": {
"loading": "Chargement…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} ऑनलाइन",
"workers_token_expired": "समाप्त हो गया — नया बनाएँ",
"workers_token_expires_in": "{{time}} में समाप्त होगा",
"workers_token_qr_hint": "दूसरी मशीन पर: सेटिंग्स → सिस्टम → रिमोट वर्कर → जुड़ें, फिर स्कैन या पेस्ट करें।"
"workers_token_qr_hint": "दूसरी मशीन पर: सेटिंग्स → सिस्टम → रिमोट वर्कर → जुड़ें, फिर स्कैन या पेस्ट करें।",
"compute_device": "कंप्यूट डिवाइस",
"compute_device_title": "कंप्यूट डिवाइस",
"compute_device_desc": "बैकएंड मॉडल किस डिवाइस पर चलाता है। लगभग सभी के लिए Auto सही है।",
"compute_device_env_pinned": "OMNIVOICE_DEVICE पर्यावरण चर द्वारा पिन किया गया",
"compute_device_ignored": "वह डिवाइस इस मशीन पर नहीं मिला — Auto प्रभावी है",
"compute_device_restart": "ऐप के पुनरारंभ के बाद प्रभावी होगा",
"compute_device_note": "केवल इस मशीन पर पाए गए डिवाइस सूचीबद्ध हैं। CPU हमेशा काम करता है; डिवाइस पिन करने से हार्डवेयर कभी नहीं बनता।",
"compute_device_auto": "Auto (अनुशंसित)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "डिवाइस सेटिंग लोड नहीं हो सकी",
"perf_save_failed": "सेटिंग सहेजी नहीं जा सकी"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "बग की रिपोर्ट करें",
"title": "आपके ब्राउज़र में पहले से भरा हुआ GitHub इश्यूज़ पेज खोलता है। जब तक आप सबमिट पर क्लिक नहीं करते तब तक कुछ भी नहीं भेजा जाता है।"
"title": "आपके ब्राउज़र में पहले से भरा हुआ GitHub इश्यूज़ पेज खोलता है। जब तक आप सबमिट पर क्लिक नहीं करते तब तक कुछ भी नहीं भेजा जाता है।",
"staleTitle": "अपडेट उपलब्ध है",
"staleMessage": "आप VoiceStudio {{current}} चला रहे हैं, लेकिन {{latest}} पहले ही आ चुका है — हो सकता है यह बग वहाँ ठीक हो चुका हो। रिपोर्ट करने से पहले नवीनतम रिलीज़ देखें?",
"staleView": "अपडेट देखें",
"staleFileAnyway": "फिर भी रिपोर्ट करें"
},
"app": {
"loading": "लोड हो रहा है...",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Kedaluwarsa — buat yang baru",
"workers_token_expires_in": "Kedaluwarsa dalam {{time}}",
"workers_token_qr_hint": "Di mesin yang lain: Pengaturan → Sistem → Pekerja jarak jauh → Gabung, lalu pindai atau tempelkan."
"workers_token_qr_hint": "Di mesin yang lain: Pengaturan → Sistem → Pekerja jarak jauh → Gabung, lalu pindai atau tempelkan.",
"compute_device": "Perangkat komputasi",
"compute_device_title": "Perangkat komputasi",
"compute_device_desc": "Di perangkat mana backend menjalankan model. Auto tepat untuk hampir semua orang.",
"compute_device_env_pinned": "Disematkan oleh variabel lingkungan OMNIVOICE_DEVICE",
"compute_device_ignored": "Perangkat itu tidak terdeteksi di mesin ini — Auto yang berlaku",
"compute_device_restart": "Berlaku setelah aplikasi dimulai ulang",
"compute_device_note": "Hanya perangkat yang terdeteksi di mesin ini yang ditampilkan. CPU selalu berfungsi; menyematkan perangkat tidak pernah mengarang perangkat keras.",
"compute_device_auto": "Auto (disarankan)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Gagal memuat pengaturan perangkat",
"perf_save_failed": "Gagal menyimpan pengaturan"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Laporkan bug",
"title": "Membuka halaman Masalah GitHub yang telah diisi sebelumnya di browser Anda. Tidak ada yang dikirim sampai Anda mengklik Kirim."
"title": "Membuka halaman Masalah GitHub yang telah diisi sebelumnya di browser Anda. Tidak ada yang dikirim sampai Anda mengklik Kirim.",
"staleTitle": "Pembaruan tersedia",
"staleMessage": "Anda menggunakan VoiceStudio {{current}}, tetapi {{latest}} sudah dirilis — bug ini mungkin sudah diperbaiki di sana. Lihat rilis terbaru sebelum melapor?",
"staleView": "Lihat pembaruan",
"staleFileAnyway": "Tetap laporkan"
},
"app": {
"loading": "Memuat…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Scaduto — generane uno nuovo",
"workers_token_expires_in": "Scade tra {{time}}",
"workers_token_qr_hint": "Sull'altro computer: Impostazioni → Sistema → Worker remoti → Collegati, poi scansiona o incolla."
"workers_token_qr_hint": "Sull'altro computer: Impostazioni → Sistema → Worker remoti → Collegati, poi scansiona o incolla.",
"compute_device": "Dispositivo di calcolo",
"compute_device_title": "Dispositivo di calcolo",
"compute_device_desc": "Su quale dispositivo il backend esegue i modelli. Auto va bene per quasi tutti.",
"compute_device_env_pinned": "Bloccato dalla variabile d'ambiente OMNIVOICE_DEVICE",
"compute_device_ignored": "Quel dispositivo non è stato rilevato su questa macchina — è attivo Auto",
"compute_device_restart": "Ha effetto dopo il riavvio dell'app",
"compute_device_note": "Sono elencati solo i dispositivi rilevati su questa macchina. La CPU funziona sempre; fissare un dispositivo non inventa mai hardware.",
"compute_device_auto": "Auto (consigliato)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Impossibile caricare l'impostazione del dispositivo",
"perf_save_failed": "Impossibile salvare l'impostazione"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Segnala un bug",
"title": "Apre una pagina dei problemi di GitHub precompilata nel browser. Non viene inviato nulla finché non si fa clic su Invia."
"title": "Apre una pagina dei problemi di GitHub precompilata nel browser. Non viene inviato nulla finché non si fa clic su Invia.",
"staleTitle": "Aggiornamento disponibile",
"staleMessage": "Stai usando VoiceStudio {{current}}, ma {{latest}} è già disponibile: questo bug potrebbe essere già stato corretto. Vedere l'ultima versione prima di segnalare?",
"staleView": "Vedi aggiornamento",
"staleFileAnyway": "Segnala comunque"
},
"app": {
"loading": "Caricamento…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} 台オンライン",
"workers_token_expired": "期限切れ — 新しく生成してください",
"workers_token_expires_in": "あと {{time}} で期限切れ",
"workers_token_qr_hint": "もう一方のマシンで: 設定 → システム → リモートワーカー → 参加 を開き、スキャンするか貼り付けてください。"
"workers_token_qr_hint": "もう一方のマシンで: 設定 → システム → リモートワーカー → 参加 を開き、スキャンするか貼り付けてください。",
"compute_device": "計算デバイス",
"compute_device_title": "計算デバイス",
"compute_device_desc": "バックエンドがモデルを実行するデバイス。ほとんどの場合は「自動」が最適です。",
"compute_device_env_pinned": "環境変数 OMNIVOICE_DEVICE により固定されています",
"compute_device_ignored": "そのデバイスはこのマシンで検出されませんでした — 「自動」が適用されています",
"compute_device_restart": "アプリの再起動後に有効になります",
"compute_device_note": "このマシンで検出されたデバイスのみ表示されます。CPU は常に動作します。デバイスを固定してもハードウェアが増えることはありません。",
"compute_device_auto": "自動(推奨)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "デバイス設定を読み込めませんでした",
"perf_save_failed": "設定を保存できませんでした"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "バグを報告する",
"title": "ブラウザで事前に入力された GitHub の問題ページを開きます。 「送信」をクリックするまでは何も送信されません。"
"title": "ブラウザで事前に入力された GitHub の問題ページを開きます。 「送信」をクリックするまでは何も送信されません。",
"staleTitle": "アップデートがあります",
"staleMessage": "VoiceStudio {{current}} を使用していますが、{{latest}} が既に公開されています。このバグは修正済みかもしれません。報告する前に最新リリースを確認しますか?",
"staleView": "アップデートを見る",
"staleFileAnyway": "そのまま報告する"
},
"app": {
"loading": "読み込み中…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}}대 온라인",
"workers_token_expired": "만료됨 — 새로 생성하세요",
"workers_token_expires_in": "{{time}} 후 만료",
"workers_token_qr_hint": "다른 컴퓨터에서: 설정 → 시스템 → 원격 워커 → 참여로 이동한 뒤 스캔하거나 붙여 넣으세요."
"workers_token_qr_hint": "다른 컴퓨터에서: 설정 → 시스템 → 원격 워커 → 참여로 이동한 뒤 스캔하거나 붙여 넣으세요.",
"compute_device": "연산 장치",
"compute_device_title": "연산 장치",
"compute_device_desc": "백엔드가 모델을 실행할 장치입니다. 거의 모든 경우 '자동'이 적합합니다.",
"compute_device_env_pinned": "OMNIVOICE_DEVICE 환경 변수로 고정됨",
"compute_device_ignored": "이 컴퓨터에서 해당 장치를 찾지 못했습니다 — '자동'이 적용 중입니다",
"compute_device_restart": "앱을 다시 시작한 후 적용됩니다",
"compute_device_note": "이 컴퓨터에서 감지된 장치만 표시됩니다. CPU는 항상 작동하며, 장치를 고정해도 없는 하드웨어가 생기지는 않습니다.",
"compute_device_auto": "자동 (권장)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "장치 설정을 불러오지 못했습니다",
"perf_save_failed": "설정을 저장하지 못했습니다"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "버그 신고",
"title": "브라우저에서 미리 채워진 GitHub 문제 페이지를 엽니다. 제출을 클릭할 때까지 아무 것도 전송되지 않습니다."
"title": "브라우저에서 미리 채워진 GitHub 문제 페이지를 엽니다. 제출을 클릭할 때까지 아무 것도 전송되지 않습니다.",
"staleTitle": "업데이트 사용 가능",
"staleMessage": "VoiceStudio {{current}}을(를) 사용 중이지만 {{latest}}이(가) 이미 출시되었습니다. 이 버그는 이미 수정되었을 수 있습니다. 신고하기 전에 최신 릴리스를 확인할까요?",
"staleView": "업데이트 보기",
"staleFileAnyway": "그래도 신고"
},
"app": {
"loading": "로드 중…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Verlopen — genereer een nieuwe",
"workers_token_expires_in": "Verloopt over {{time}}",
"workers_token_qr_hint": "Op de andere machine: Instellingen → Systeem → Externe workers → Koppelen, en scan of plak daar."
"workers_token_qr_hint": "Op de andere machine: Instellingen → Systeem → Externe workers → Koppelen, en scan of plak daar.",
"compute_device": "Rekenapparaat",
"compute_device_title": "Rekenapparaat",
"compute_device_desc": "Op welk apparaat de backend modellen draait. Auto is voor bijna iedereen juist.",
"compute_device_env_pinned": "Vastgezet door de omgevingsvariabele OMNIVOICE_DEVICE",
"compute_device_ignored": "Dat apparaat is op deze machine niet gedetecteerd — Auto is van kracht",
"compute_device_restart": "Wordt van kracht na het herstarten van de app",
"compute_device_note": "Alleen op deze machine gedetecteerde apparaten worden getoond. CPU werkt altijd; een vastgezet apparaat verzint nooit hardware.",
"compute_device_auto": "Auto (aanbevolen)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Apparaatinstelling kon niet worden geladen",
"perf_save_failed": "Instelling kon niet worden opgeslagen"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Rapporteer een bug",
"title": "Opent een vooraf ingevulde GitHub Issues-pagina in uw browser. Er wordt niets verzonden totdat u op Verzenden klikt."
"title": "Opent een vooraf ingevulde GitHub Issues-pagina in uw browser. Er wordt niets verzonden totdat u op Verzenden klikt.",
"staleTitle": "Update beschikbaar",
"staleMessage": "U gebruikt VoiceStudio {{current}}, maar {{latest}} is al uit — deze bug is daar mogelijk al verholpen. De nieuwste versie bekijken voordat u rapporteert?",
"staleView": "Update bekijken",
"staleFileAnyway": "Toch rapporteren"
},
"app": {
"loading": "Laden…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Wygasł — wygeneruj nowy",
"workers_token_expires_in": "Wygasa za {{time}}",
"workers_token_qr_hint": "Na drugim komputerze: Ustawienia → Systemu → Zdalne workery → Dołącz, potem zeskanuj albo wklej."
"workers_token_qr_hint": "Na drugim komputerze: Ustawienia → Systemu → Zdalne workery → Dołącz, potem zeskanuj albo wklej.",
"compute_device": "Urządzenie obliczeniowe",
"compute_device_title": "Urządzenie obliczeniowe",
"compute_device_desc": "Na którym urządzeniu backend uruchamia modele. Auto jest właściwe dla niemal wszystkich.",
"compute_device_env_pinned": "Przypięte przez zmienną środowiskową OMNIVOICE_DEVICE",
"compute_device_ignored": "Tego urządzenia nie wykryto na tej maszynie — obowiązuje Auto",
"compute_device_restart": "Zacznie obowiązywać po ponownym uruchomieniu aplikacji",
"compute_device_note": "Wyświetlane są tylko urządzenia wykryte na tej maszynie. CPU zawsze działa; przypięcie urządzenia nigdy nie wymyśla sprzętu.",
"compute_device_auto": "Auto (zalecane)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Nie udało się wczytać ustawienia urządzenia",
"perf_save_failed": "Nie udało się zapisać ustawienia"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Zgłoś błąd",
"title": "Otwiera wstępnie wypełnioną stronę problemów z GitHub w przeglądarce. Nic nie zostanie wysłane, dopóki nie klikniesz Prześlij."
"title": "Otwiera wstępnie wypełnioną stronę problemów z GitHub w przeglądarce. Nic nie zostanie wysłane, dopóki nie klikniesz Prześlij.",
"staleTitle": "Dostępna aktualizacja",
"staleMessage": "Używasz VoiceStudio {{current}}, ale dostępna jest już wersja {{latest}} — ten błąd może być tam już naprawiony. Zobaczyć najnowsze wydanie przed zgłoszeniem?",
"staleView": "Zobacz aktualizację",
"staleFileAnyway": "Zgłoś mimo to"
},
"app": {
"loading": "Ładowanie…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Expirado — gere um novo",
"workers_token_expires_in": "Expira em {{time}}",
"workers_token_qr_hint": "Na outra máquina: Configurações → Sistema → Workers remotos → Associar, depois escaneie ou cole."
"workers_token_qr_hint": "Na outra máquina: Configurações → Sistema → Workers remotos → Associar, depois escaneie ou cole.",
"compute_device": "Dispositivo de computação",
"compute_device_title": "Dispositivo de computação",
"compute_device_desc": "Em qual dispositivo o backend executa os modelos. Auto é o certo para quase todos.",
"compute_device_env_pinned": "Fixado pela variável de ambiente OMNIVOICE_DEVICE",
"compute_device_ignored": "Esse dispositivo não foi detectado nesta máquina — Auto está em vigor",
"compute_device_restart": "Entra em vigor após reiniciar o aplicativo",
"compute_device_note": "Apenas dispositivos detectados nesta máquina são listados. A CPU sempre funciona; fixar um dispositivo nunca inventa hardware.",
"compute_device_auto": "Auto (recomendado)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Falha ao carregar a configuração do dispositivo",
"perf_save_failed": "Falha ao salvar a configuração"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Reportar um bug",
"title": "Abre uma página pré-preenchida de problemas do GitHub em seu navegador. Nada é enviado até você clicar em Enviar."
"title": "Abre uma página pré-preenchida de problemas do GitHub em seu navegador. Nada é enviado até você clicar em Enviar.",
"staleTitle": "Atualização disponível",
"staleMessage": "Você está usando o VoiceStudio {{current}}, mas a {{latest}} já foi lançada — este bug pode já estar corrigido. Ver a versão mais recente antes de reportar?",
"staleView": "Ver atualização",
"staleFileAnyway": "Reportar mesmo assim"
},
"app": {
"loading": "Carregando…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} в сети",
"workers_token_expired": "Истёк — создайте новый",
"workers_token_expires_in": "Истекает через {{time}}",
"workers_token_qr_hint": "На другой машине: Настройки → Система → Удалённые воркеры → Подключиться, затем отсканируйте или вставьте."
"workers_token_qr_hint": "На другой машине: Настройки → Система → Удалённые воркеры → Подключиться, затем отсканируйте или вставьте.",
"compute_device": "Устройство вычислений",
"compute_device_title": "Устройство вычислений",
"compute_device_desc": "На каком устройстве backend выполняет модели. Auto подходит почти всем.",
"compute_device_env_pinned": "Закреплено переменной окружения OMNIVOICE_DEVICE",
"compute_device_ignored": "Это устройство не обнаружено на этой машине — действует Auto",
"compute_device_restart": "Вступит в силу после перезапуска приложения",
"compute_device_note": "Показаны только устройства, обнаруженные на этой машине. CPU работает всегда; закрепление устройства не создаёт несуществующее оборудование.",
"compute_device_auto": "Auto (рекомендуется)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Не удалось загрузить настройку устройства",
"perf_save_failed": "Не удалось сохранить настройку"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Сообщить об ошибке",
"title": "Открывает предварительно заполненную страницу проблем GitHub в вашем браузере. Ничего не будет отправлено, пока вы не нажмете «Отправить»."
"title": "Открывает предварительно заполненную страницу проблем GitHub в вашем браузере. Ничего не будет отправлено, пока вы не нажмете «Отправить».",
"staleTitle": "Доступно обновление",
"staleMessage": "Вы используете VoiceStudio {{current}}, но уже вышла версия {{latest}} — возможно, эта ошибка там исправлена. Посмотреть последнюю версию перед отправкой?",
"staleView": "Посмотреть обновление",
"staleFileAnyway": "Всё равно сообщить"
},
"app": {
"loading": "Загрузка…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} online",
"workers_token_expired": "Har gått ut — skapa en ny",
"workers_token_expires_in": "Går ut om {{time}}",
"workers_token_qr_hint": "På den andra maskinen: Inställningar → System → Fjärrarbetare → Anslut, skanna eller klistra sedan in."
"workers_token_qr_hint": "På den andra maskinen: Inställningar → System → Fjärrarbetare → Anslut, skanna eller klistra sedan in.",
"compute_device": "Beräkningsenhet",
"compute_device_title": "Beräkningsenhet",
"compute_device_desc": "Vilken enhet backend kör modeller på. Auto är rätt för nästan alla.",
"compute_device_env_pinned": "Låst av miljövariabeln OMNIVOICE_DEVICE",
"compute_device_ignored": "Den enheten hittades inte på den här datorn — Auto gäller",
"compute_device_restart": "Träder i kraft efter omstart av appen",
"compute_device_note": "Endast enheter som hittats på den här datorn visas. CPU fungerar alltid; att låsa en enhet hittar aldrig på hårdvara.",
"compute_device_auto": "Auto (rekommenderas)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Kunde inte läsa in enhetsinställningen",
"perf_save_failed": "Kunde inte spara inställningen"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Rapportera ett fel",
"title": "Öppnar en förifylld GitHub-problemsida i din webbläsare. Inget skickas förrän du klickar på Skicka."
"title": "Öppnar en förifylld GitHub-problemsida i din webbläsare. Inget skickas förrän du klickar på Skicka.",
"staleTitle": "Uppdatering tillgänglig",
"staleMessage": "Du använder VoiceStudio {{current}}, men {{latest}} är redan ute — det här felet kan redan vara åtgärdat där. Se den senaste versionen innan du rapporterar?",
"staleView": "Visa uppdatering",
"staleFileAnyway": "Rapportera ändå"
},
"app": {
"loading": "Laddar...",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "ออนไลน์ {{count}} เครื่อง",
"workers_token_expired": "หมดอายุแล้ว — สร้างใหม่",
"workers_token_expires_in": "หมดอายุใน {{time}}",
"workers_token_qr_hint": "บนเครื่องอีกเครื่อง: การตั้งค่า → ระบบ → ผู้ปฏิบัติงานระยะไกล → เข้าร่วม แล้วสแกนหรือวาง"
"workers_token_qr_hint": "บนเครื่องอีกเครื่อง: การตั้งค่า → ระบบ → ผู้ปฏิบัติงานระยะไกล → เข้าร่วม แล้วสแกนหรือวาง",
"compute_device": "อุปกรณ์ประมวลผล",
"compute_device_title": "อุปกรณ์ประมวลผล",
"compute_device_desc": "แบ็กเอนด์รันโมเดลบนอุปกรณ์ใด อัตโนมัติ เหมาะสำหรับเกือบทุกคน",
"compute_device_env_pinned": "ถูกกำหนดโดยตัวแปรสภาพแวดล้อม OMNIVOICE_DEVICE",
"compute_device_ignored": "ไม่พบอุปกรณ์นั้นบนเครื่องนี้ — ใช้ อัตโนมัติ แทน",
"compute_device_restart": "มีผลหลังจากรีสตาร์ตแอป",
"compute_device_note": "แสดงเฉพาะอุปกรณ์ที่ตรวจพบบนเครื่องนี้ CPU ใช้ได้เสมอ การกำหนดอุปกรณ์ไม่เคยสร้างฮาร์ดแวร์ที่ไม่มีอยู่",
"compute_device_auto": "อัตโนมัติ (แนะนำ)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "โหลดการตั้งค่าอุปกรณ์ไม่สำเร็จ",
"perf_save_failed": "บันทึกการตั้งค่าไม่สำเร็จ"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "รายงานข้อบกพร่อง",
"title": "เปิดหน้าปัญหา GitHub ที่กรอกไว้ล่วงหน้าในเบราว์เซอร์ของคุณ ไม่มีการส่งสิ่งใดจนกว่าคุณจะคลิกส่ง"
"title": "เปิดหน้าปัญหา GitHub ที่กรอกไว้ล่วงหน้าในเบราว์เซอร์ของคุณ ไม่มีการส่งสิ่งใดจนกว่าคุณจะคลิกส่ง",
"staleTitle": "มีอัปเดตพร้อมใช้งาน",
"staleMessage": "คุณกำลังใช้ VoiceStudio {{current}} แต่ {{latest}} ออกแล้ว — ข้อบกพร่องนี้อาจได้รับการแก้ไขแล้ว ดูรุ่นล่าสุดก่อนรายงานหรือไม่?",
"staleView": "ดูอัปเดต",
"staleFileAnyway": "รายงานต่อไป"
},
"app": {
"loading": "กำลังโหลด...",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} çevrimiçi",
"workers_token_expired": "Süresi doldu — yenisini oluşturun",
"workers_token_expires_in": "{{time}} içinde süresi dolacak",
"workers_token_qr_hint": "Diğer makinede: Ayarlar → Sistem → Uzak işçiler → Katıl, sonra tarayın veya yapıştırın."
"workers_token_qr_hint": "Diğer makinede: Ayarlar → Sistem → Uzak işçiler → Katıl, sonra tarayın veya yapıştırın.",
"compute_device": "Hesaplama aygıtı",
"compute_device_title": "Hesaplama aygıtı",
"compute_device_desc": "Backend'in modelleri hangi aygıtta çalıştıracağı. Auto neredeyse herkes için doğrudur.",
"compute_device_env_pinned": "OMNIVOICE_DEVICE ortam değişkeniyle sabitlendi",
"compute_device_ignored": "Bu aygıt bu makinede algılanmadı — Auto geçerli",
"compute_device_restart": "Uygulama yeniden başlatıldıktan sonra geçerli olur",
"compute_device_note": "Yalnızca bu makinede algılanan aygıtlar listelenir. CPU her zaman çalışır; bir aygıtı sabitlemek asla donanım uydurmaz.",
"compute_device_auto": "Auto (önerilen)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Aygıt ayarı yüklenemedi",
"perf_save_failed": "Ayar kaydedilemedi"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Hata bildir",
"title": "Tarayıcınızda önceden doldurulmuş bir GitHub Sorunları sayfasını açar. Siz Gönder'i tıklatana kadar hiçbir şey gönderilmez."
"title": "Tarayıcınızda önceden doldurulmuş bir GitHub Sorunları sayfasını açar. Siz Gönder'i tıklatana kadar hiçbir şey gönderilmez.",
"staleTitle": "Güncelleme mevcut",
"staleMessage": "VoiceStudio {{current}} kullanıyorsunuz, ancak {{latest}} zaten yayında — bu hata orada düzeltilmiş olabilir. Bildirmeden önce en son sürüme bakmak ister misiniz?",
"staleView": "Güncellemeyi görüntüle",
"staleFileAnyway": "Yine de bildir"
},
"app": {
"loading": "Yükleniyor…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} у мережі",
"workers_token_expired": "Сплив — створіть новий",
"workers_token_expires_in": "Спливає через {{time}}",
"workers_token_qr_hint": "На іншій машині: Налаштування → система → Віддалені воркери → Підключитися, потім відскануйте або вставте."
"workers_token_qr_hint": "На іншій машині: Налаштування → система → Віддалені воркери → Підключитися, потім відскануйте або вставте.",
"compute_device": "Пристрій обчислень",
"compute_device_title": "Пристрій обчислень",
"compute_device_desc": "На якому пристрої backend виконує моделі. Auto підходить майже всім.",
"compute_device_env_pinned": "Закріплено змінною середовища OMNIVOICE_DEVICE",
"compute_device_ignored": "Цей пристрій не виявлено на цій машині — діє Auto",
"compute_device_restart": "Набуде чинності після перезапуску застосунку",
"compute_device_note": "Показано лише пристрої, виявлені на цій машині. CPU працює завжди; закріплення пристрою ніколи не вигадує обладнання.",
"compute_device_auto": "Auto (рекомендовано)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Не вдалося завантажити налаштування пристрою",
"perf_save_failed": "Не вдалося зберегти налаштування"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Повідомити про помилку",
"title": "Відкриває попередньо заповнену сторінку проблем GitHub у вашому браузері. Нічого не буде надіслано, доки ви не натиснете Надіслати."
"title": "Відкриває попередньо заповнену сторінку проблем GitHub у вашому браузері. Нічого не буде надіслано, доки ви не натиснете Надіслати.",
"staleTitle": "Доступне оновлення",
"staleMessage": "Ви використовуєте VoiceStudio {{current}}, але вже вийшла версія {{latest}} — можливо, цю помилку там виправлено. Переглянути останній випуск перед надсиланням?",
"staleView": "Переглянути оновлення",
"staleFileAnyway": "Все одно повідомити"
},
"app": {
"loading": "Завантаження…",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} trực tuyến",
"workers_token_expired": "Đã hết hạn — hãy tạo mã mới",
"workers_token_expires_in": "Hết hạn sau {{time}}",
"workers_token_qr_hint": "Trên máy kia: Cài đặt → Hệ thống → Máy phụ từ xa → Tham gia, rồi quét hoặc dán."
"workers_token_qr_hint": "Trên máy kia: Cài đặt → Hệ thống → Máy phụ từ xa → Tham gia, rồi quét hoặc dán.",
"compute_device": "Thiết bị tính toán",
"compute_device_title": "Thiết bị tính toán",
"compute_device_desc": "Backend chạy mô hình trên thiết bị nào. Tự động phù hợp với hầu hết mọi người.",
"compute_device_env_pinned": "Được ghim bởi biến môi trường OMNIVOICE_DEVICE",
"compute_device_ignored": "Không phát hiện thiết bị đó trên máy này — Tự động đang có hiệu lực",
"compute_device_restart": "Có hiệu lực sau khi khởi động lại ứng dụng",
"compute_device_note": "Chỉ liệt kê các thiết bị được phát hiện trên máy này. CPU luôn hoạt động; ghim một thiết bị không bao giờ bịa ra phần cứng.",
"compute_device_auto": "Tự động (khuyên dùng)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "Không tải được cài đặt thiết bị",
"perf_save_failed": "Không lưu được cài đặt"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "Báo cáo lỗi",
"title": "Mở trang Vấn đề GitHub được điền sẵn trong trình duyệt của bạn. Không có gì được gửi cho đến khi bạn nhấp vào Gửi."
"title": "Mở trang Vấn đề GitHub được điền sẵn trong trình duyệt của bạn. Không có gì được gửi cho đến khi bạn nhấp vào Gửi.",
"staleTitle": "Có bản cập nhật",
"staleMessage": "Bạn đang dùng VoiceStudio {{current}}, nhưng {{latest}} đã ra mắt — lỗi này có thể đã được sửa ở đó. Xem bản phát hành mới nhất trước khi báo cáo?",
"staleView": "Xem bản cập nhật",
"staleFileAnyway": "Vẫn báo cáo"
},
"app": {
"loading": "Đang tải…",
+21 -2
View File
@@ -551,7 +551,22 @@
"workers_summary_online": "{{count}} 台在线",
"workers_token_expired": "已过期——请重新生成",
"workers_token_expires_in": "{{time}} 后过期",
"workers_token_qr_hint": "在另一台机器上:设置 → 系统 → 远程工作机 → 加入,然后扫描或粘贴。"
"workers_token_qr_hint": "在另一台机器上:设置 → 系统 → 远程工作机 → 加入,然后扫描或粘贴。",
"compute_device": "计算设备",
"compute_device_title": "计算设备",
"compute_device_desc": "后端在哪个设备上运行模型。绝大多数情况下选「自动」即可。",
"compute_device_env_pinned": "已被环境变量 OMNIVOICE_DEVICE 固定",
"compute_device_ignored": "本机未检测到该设备——当前实际使用「自动」",
"compute_device_restart": "重启应用后生效",
"compute_device_note": "仅列出本机检测到的设备。CPU 始终可用;固定设备不会凭空造出硬件。",
"compute_device_auto": "自动(推荐)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "无法加载设备设置",
"perf_save_failed": "无法保存设置"
},
"about": {
"app": "应用",
@@ -2090,7 +2105,11 @@
},
"reportBug": {
"label": "报告错误",
"title": "在浏览器中打开预填的 GitHub issue 页面。在你点击“提交”之前不会发送任何内容。"
"title": "在浏览器中打开预填的 GitHub issue 页面。在你点击“提交”之前不会发送任何内容。",
"staleTitle": "有可用更新",
"staleMessage": "你正在使用 VoiceStudio {{current}},但 {{latest}} 已经发布——这个错误可能已在新版本中修复。报告前先查看最新版本?",
"staleView": "查看更新",
"staleFileAnyway": "仍然报告"
},
"app": {
"loading": "加载中...",
+21 -2
View File
@@ -315,7 +315,22 @@
"workers_summary_online": "{{count}} 台上線",
"workers_token_expired": "已過期——請重新產生",
"workers_token_expires_in": "{{time}} 後過期",
"workers_token_qr_hint": "在另一台機器上:設定 → 系統 → 遠端工作機 → 加入,然後掃描或貼上。"
"workers_token_qr_hint": "在另一台機器上:設定 → 系統 → 遠端工作機 → 加入,然後掃描或貼上。",
"compute_device": "運算裝置",
"compute_device_title": "運算裝置",
"compute_device_desc": "後端在哪個裝置上執行模型。絕大多數情況下選「自動」即可。",
"compute_device_env_pinned": "已由環境變數 OMNIVOICE_DEVICE 固定",
"compute_device_ignored": "本機未偵測到該裝置——目前實際使用「自動」",
"compute_device_restart": "重新啟動應用程式後生效",
"compute_device_note": "僅列出本機偵測到的裝置。CPU 永遠可用;固定裝置不會憑空生出硬體。",
"compute_device_auto": "自動(建議)",
"device_family_cuda": "NVIDIA GPU (CUDA)",
"device_family_rocm": "AMD GPU (ROCm)",
"device_family_xpu": "Intel GPU (XPU)",
"device_family_mps": "Apple GPU (MPS)",
"device_family_cpu": "CPU",
"device_load_failed": "無法載入裝置設定",
"perf_save_failed": "無法儲存設定"
},
"bootstrap": {
"title": "VoiceStudio",
@@ -2083,7 +2098,11 @@
},
"reportBug": {
"label": "報告錯誤",
"title": "在浏览器中打开预填充的 GitHub 问题页面。在您单击“提交之前,不会发送任何容。"
"title": "在瀏覽器中開啟預先填寫的 GitHub 問題頁面。在您按一下「提交之前,不會傳送任何容。",
"staleTitle": "有可用更新",
"staleMessage": "您正在使用 VoiceStudio {{current}},但 {{latest}} 已經發布——這個錯誤可能已在新版本中修復。回報前先查看最新版本?",
"staleView": "查看更新",
"staleFileAnyway": "仍然回報"
},
"app": {
"loading": "加載中...",
+8 -2
View File
@@ -22,6 +22,10 @@ import DesktopCaptureShortcutBridge from './components/DesktopCaptureShortcutBri
import CaptureWidget from './components/CaptureWidget.jsx';
import { installConsoleCapture } from './utils/consoleBuffer.js';
import { installGlobalErrorHandlers } from './utils/globalErrorHandlers.js';
import {
configurePersistenceRole,
installPersistenceLifecycleFlush,
} from './utils/coalescedJsonStorage';
installConsoleCapture();
// After console capture so the underlying console.error of each uncaught
@@ -43,7 +47,7 @@ const queryClient = new QueryClient({
// declaring `"url": "/?window=widget"` in tauri.conf.json silently failed to
// create the widget window. So both windows load the same index.html and we
// differentiate by window label via the Tauri JS API.
async function detectIsWidget() {
export async function detectIsWidget(locationSearch = window.location.search) {
// The widget window stamps this from an initialization_script (lib.rs)
// before any page script runs, so it is always here and never races.
//
@@ -62,12 +66,14 @@ async function detectIsWidget() {
} catch {
// Non-Tauri context (browser dev, Docker) fall back to URL query for
// legacy `bun dev:frontend` workflows that may still rely on it.
return window.location.search.includes('window=widget');
return locationSearch.includes('window=widget');
}
}
export async function bootstrapApp() {
const isWidget = await detectIsWidget();
configurePersistenceRole(isWidget ? 'readonly' : 'main');
if (!isWidget) installPersistenceLifecycleFlush();
const isDesktopShell = typeof window !== 'undefined' && '__TAURI_INTERNALS__' in window;
// The widget window is `transparent: true` (tauri.conf.json), but it loads

Some files were not shown because too many files have changed in this diff Show More