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[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "omnivoice"
version = "0.2.4"
description = "OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models"
readme = "README.md"
license = "Apache-2.0"
requires-python = ">=3.11"
authors = [{name = "Han Zhu"}]
keywords = [
"tts",
"text-to-speech",
"speech-synthesis",
"zero-shot",
"multilingual",
"diffusion",
"voice-cloning",
]
classifiers = [
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Multimedia :: Sound/Audio :: Speech",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
]
dependencies = [
"torch>=2.4",
"torchaudio>=2.4",
"transformers>=5.3.0",
"accelerate",
"pydub",
"gradio",
"tensorboardX",
"webdataset",
"numpy",
"soundfile",
"psutil>=7.2.2",
# Pinned to 3.x — pyannote 4.x removed `use_auth_token` from `Inference`
# which whisperx 3.4.2 still passes, blowing up `whisperx.load_model()`
# with TypeError. whisperx tests against pyannote 3.3.2+, so we track
# that range and revisit when whisperx releases a 4-compatible build.
"pyannote-audio>=3.3.2,<4.0",
"pyinstaller>=6.19.0",
"imageio-ffmpeg>=0.6.0",
"pedalboard>=0.9.14",
# Primary ASR — cross-platform, CTranslate2-based under the hood. WhisperX
# adds wav2vec2 forced alignment (±10-30 ms word timing vs Whisper's own
# ±100-300 ms) which directly improves lip-sync on the dub pipeline.
# Pulls `faster-whisper` transitively, so a WhisperX install also provides
# the plain faster-whisper backend as a fallback for rare-language audio
# where no wav2vec2 alignment model exists.
"whisperx>=3.1.0",
"faster-whisper>=1.0.0",
# Apple Silicon-only speedup; skipped everywhere else so `uv sync` can
# succeed on Linux/Windows/mac-Intel (no mlx wheels exist for those).
"mlx-whisper>=0.2.1 ; sys_platform == 'darwin' and platform_machine == 'arm64'",
# Apple Silicon-only rich TTS library — 14+ engines (Kokoro, CSM, Dia,
# Qwen3-TTS, Chatterbox, MeloTTS, OuteTTS, Spark, Higgs-Audio, Voxtral,
# …). Gives mac-ARM users a broad engine picker in Settings. Also gated
# by platform markers because it depends on mlx (Apple Silicon only).
"mlx-audio>=0.3.0 ; sys_platform == 'darwin' and platform_machine == 'arm64'",
"demucs>=4.0.1",
"yt-dlp>=2024.12.13",
"alembic>=1.13",
# Lightweight English TTS "Turbo" tier — 25-80 MB ONNX model, 8 preset
# voices (Bella, Jasper, Luna, Bruno, Rosie, Hugo, Kiki, Leo), CPU
# realtime on any platform. Complements OmniVoice's 2.4 GB multilingual
# zero-shot clone: when the caller just needs fast English narration with
# no reference sample, this is ~100× smaller + ~10× faster. Pinned to
# the exact wheel because the project is in developer preview and the
# 0.x API is explicitly unstable.
"kittentts @ https://github.com/KittenML/KittenTTS/releases/download/0.8.1/kittentts-0.8.1-py3-none-any.whl",
# Invisible audio watermarking — embeds imperceptible neural watermarks
# in AI-generated speech for provenance detection (SynthID-like).
# MIT license, ~5ms per segment on CPU, 16-bit message payload.
"audioseal>=0.1.3",
# API server — always needed for the Studio UI.
"fastapi",
"uvicorn",
"python-multipart",
# Required by uvicorn for WebSocket support (real-time sidebar events).
"websockets",
]
[project.optional-dependencies]
eval = [
"jiwer==3.1.0", # WER
"librosa", # Audio processing
"s3prl", # Speech representation (HuBERT etc.)
"funasr", # ASR models
"zhconv", # Chinese character normalization
"zhon", # Chinese punctuation
"unidecode", # Unicode normalization
]
ui = [
"gradio",
"gradio_client",
"requests",
]
[project.scripts]
omnivoice-infer = "omnivoice.cli.infer:main"
omnivoice-infer-batch = "omnivoice.cli.infer_batch:main"
omnivoice-demo = "omnivoice.cli.demo:main"
omnivoice-dub = "omnivoice.cli.dub:main"
[project.urls]
Homepage = "https://github.com/k2-fsa/OmniVoice"
Repository = "https://github.com/k2-fsa/OmniVoice"
"Bug Tracker" = "https://github.com/k2-fsa/OmniVoice/issues"
[tool.uv.sources]
# Install PyTorch with CUDA support on Linux/Windows (CUDA doesn't exist for Mac).
# NOTE: We must explicitly request them as `dependencies` above. These improved
# versions will not be selected if they're only third-party dependencies.
torch = [
{ index = "pytorch-cuda", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
]
torchaudio = [
{ index = "pytorch-cuda", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
]
[[tool.uv.index]]
name = "pytorch-cuda"
# Use PyTorch built for NVIDIA Toolkit version 12.8.
# Available versions: https://pytorch.org/get-started/locally/
url = "https://download.pytorch.org/whl/cu128"
# Only use this index when explicitly requested by `tool.uv.sources`.
explicit = true
[tool.uv]
constraint-dependencies = [
"torch==2.8.0",
"torchaudio==2.8.0",
]
[tool.hatch.metadata]
# Needed so the KittenTTS wheel-URL dep in `project.dependencies` is accepted
# by hatchling's metadata validator. KittenTTS isn't on PyPI (dev preview),
# so pulling it via GH Releases URL is the only option today.
allow-direct-references = true
[tool.hatch.build.targets.sdist]
include = ["omnivoice"]
[tool.hatch.build.targets.wheel]
packages = ["omnivoice"]
[dependency-groups]
dev = [
"httpx>=0.28.1",
"pytest>=9.0.3",
"pytest-asyncio>=1.3.0",
]