Palash DebnathandClaude Fable 5
030d5ea01f
* 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>
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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).
- 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 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 / #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).
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); an optional transcript of the clip improves conditioning.
Known limits
- No voice design from a text description — use VoxCPM2 for that.
- Below the 6 GB VRAM floor, expect very slow renders or budget timeouts; prefer OmniVoice GGUF or a CPU engine such as PocketTTS.
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.
See also: benchmarks.md, performance.md, expressive-speech.md, disk usage.