* 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>
Renames what users see. The app, the installers, the window title, the
docs and all 21 locales now say VoiceStudio, with "(previously
OmniVoice-Studio)" noted near the title of each doc surface so people
recognise it.
Deliberately NOT renamed, because renaming any of them silently breaks
an existing install — there is no legacy-path fallback anywhere in this
codebase:
- bundle identifier com.debpalash.omnivoice-studio (MSI UpgradeCode,
macOS TCC grants, managed venv, WebView localStorage, the
single-instance lock)
- data directories OmniVoice / .omnivoice and omnivoice.db
- the ~150 OMNIVOICE_* environment variables
- the X-OmniVoice-* HTTP headers (a wire protocol)
- the published Docker image paths
- the OmniVoice ENGINE, which is a model name and not this product
tests/test_identity_paths_survive_the_rename.py pins every one of those
so a future well-meaning sweep cannot orphan a user's library.
Linux .deb users install a new package name and should apt remove
omnivoice-studio; that note is in the changelog.
Every performance question this week ("can we batch by cores?", "why is dubbing
slow?") was answerable only by measuring, and twice the intuitive answer was wrong:
* Concurrency on Apple Silicon buys NOTHING. Measured, 4 segments:
1 worker 19.3s | 2 workers 20.7s (0.93x) | 3 workers 19.2s (1.00x)
One GPU, already saturated — extra workers interleave. Scaling the GPU pool by
free RAM (the "intelligent batching" that sounds obviously right) would have
added OOM risk on a 16 GB box for zero throughput. _pick_gpu_workers()'s
hardcoded `MPS -> 1` is correct, and now provably so.
* The clone-prompt cache misses on every segment (a dub writes one reference per
segment: 166 distinct keys, cache can never hit). That looked like the dub's
hidden cost. It is 0.40s/segment — ~2% — and it is not even waste: each
reference is genuinely different audio, and encoding it is the *feature*
(per-line prosody). Dropping to per-speaker refs would save ~65s/dub and cost
quality. Not a free win; not taken.
What actually dominates is TTS itself, which scales with text length (3.2s for a
short line, 8.7s for a 2.5x longer one) and is GPU-bound on a GPU that one
inference already fills.
The profiler is deliberately gentle with memory, because a profiler that OOMs the
machine reproduces the very bug class it exists to fix (#1119): stages run one at a
time, models are unloaded between them, a stage is SKIPPED if free RAM is under the
floor rather than starting a load the OS would kill, and each measurement is a fixed
small number of passes — no looping to convergence.
Co-authored-by: mergetest <nizam4103@gmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
