Palash Debnath
21105b24a5
VoxCPM2 ran only in-process, so using it meant installing voxcpm, and a torch of its choosing, into the app's own environment. It now has a one-click install into DATA_DIR/engines/voxcpm2/.venv and a self-contained sidecar (engines/voxcpm2_subprocess) that imports nothing from the app. tts_backend resolves the voxcpm2 id to the sidecar once that venv exists, and to the in-process class otherwise, so an existing pip install keeps working. voxcpm leaves torch unpinned. Resolved with the CUDA index, that paired PyPI's newest torch (CPU-only on Windows) with a +cu128 torchaudio, and uv's --torch-backend fell back to voxcpm 1.5.0 on Windows. A new torch_pins spec field pins the pair, and the host picks the build: +cu128 on CUDA hosts, +cpu on other Windows and Linux hosts, plain on macOS. Each was resolved with voxcpm==2.0.3. Not offered on Intel Macs, where torch 2.11 has no build. The parent keeps the reference-clip preparation and the trailing-silence trim, so output matches the in-process engine. The sidecar retries a transient weight download like the app's loader does.
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
Ctrl/Cmd+E), or pin one with
OMNIVOICE_TTS_BACKEND / OMNIVOICE_ASR_BACKEND.
When an engine reports itself unavailable, expand its row's Why? panel and use Learn more to jump straight to that engine's page here. The row's own message stays deliberately generic — an availability probe can carry local paths or credentials, so it is never shown verbatim — and the page below is where the actual requirements and setup steps live.
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.
Measured speed/VRAM numbers live in benchmarks; what each engine can do expressively in expressive-speech; sidecar disk footprints in disk-usage; the bar a new engine must clear in engine-acceptance.
New to VoiceStudio? Install the app first — macOS (first launch needs the one-time right-click → Open Gatekeeper approval), Windows, Linux, Docker.
Text-to-speech
| Engine | Guide | Runs on | Cloning | Enabled by |
|---|---|---|---|---|
| VoiceStudio (OmniVoice) — default | omnivoice | CUDA · MPS · CPU | ✅ | installed by default |
| VoxCPM2 | voxcpm2 | CUDA · MPS · CPU | ✅ + voice design | pip install "voxcpm>=2.0.3" |
| MOSS-TTS-Nano | moss-tts-nano | CUDA · CPU | ✅ (ref only) | clone + uv pip install -e . |
| KittenTTS | kittentts | CPU | — (8 preset voices) | pip install kittentts |
| MLX-Audio (Kokoro, CSM, Dia, …) | mlx-audio | Apple Silicon | model-dependent | pip install mlx-audio |
| CosyVoice 3 | cosyvoice | CUDA · CPU | ✅ | clone + requirements |
| GPT-SoVITS | gpt-sovits | external server | ✅ | its own API server |
| Sherpa-ONNX | sherpa-onnx | CUDA · CPU | — | pip install sherpa-onnx + model dir |
| IndexTTS 2.5 | indextts | CUDA · CPU | ✅ + emotion | one-click sidecar install |
| OmniVoice GGUF | omnivoice-gguf | CUDA · MPS · CPU | ✅ | bundled binary |
| Supertonic-3 | supertonic3 | CPU | — (7 preset voices) | uv sync --extra supertonic + license |
| MOSS-TTS-v1.5 (8B) | moss-tts-v15 | CUDA · CPU | ✅ | clone + env var |
| dots.tts (2B) | dots-tts | CUDA · CPU (not Windows) | ✅ | clone + env var |
| OmniVoice (subprocess) | omnivoice-subprocess | CUDA · MPS · CPU | ✅ | opt-in pick off MPS; automatic via default OmniVoice on MPS |
| PocketTTS (Kyutai) | pockettts | CPU (not Intel Mac) | ✅ | uv sync --extra pockettts + license |
| Confucius4-TTS | confucius4-tts | CUDA · CPU | ✅ | clone + env var |
| audio.cpp (Breeze-TTS-2) | audio-cpp | CPU + Vulkan/Metal/CUDA/HIP/ROCm where compiled | ✅ + voice design | prebuilt binary + env var (weights research/non-commercial) |
Speech-to-text
| Engine | Guide | Runs on | Best at | Enabled by |
|---|---|---|---|---|
| WhisperX | whisperx | CUDA · CPU | dubbing (word timestamps + diarization) | installed by default |
| Faster-Whisper | faster-whisper | CUDA · CPU | general transcription | installed by default |
| Faster-Whisper (isolated) | faster-whisper-isolated | CUDA · CPU | unattended batches | opt-in pick |
| MLX Whisper | mlx-whisper | Apple Silicon | Mac default | pip install mlx-whisper |
| PyTorch Whisper | pytorch-whisper | CUDA · MPS · CPU | ROCm hosts | installed by default |
| Parakeet TDT (NeMo) | nemo-parakeet | CUDA · CPU | 25 languages, fast CPU | separate venv (never the app's) |
| Parakeet TDT (MLX) | parakeet-mlx | Apple Silicon | dictation, 25 EU languages | default on mac-ARM source installs |
| Moonshine | moonshine | CPU | edge/low-power, no timestamps | pip install (see guide) |
| FunASR (SenseVoice) | funasr | CUDA · CPU | 50+ languages, inline diarization | pip install funasr |
| Sherpa-ONNX dictation | sherpa-onnx-asr | CPU | live streaming dictation | curated model download |
| OpenAI-compatible (local or remote) | openai-compatible-asr | network | a configured endpoint; loopback stays local | Model Catalogue |
Speaker diarization is not an engine registry of its own — the dub pipeline
uses pyannote (HF-gated; see diarization) and
FunASR can diarize inline with its cam++ speaker model.