pyproject.toml

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "omnivoice"
version = "0.2.7"
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",
    # pkg_resources is used by whisperx/faster-whisper at runtime.
    # On Python 3.12+ it's no longer bundled by default — pin a modern
    # setuptools to guarantee it's present (fixes #58).
    "setuptools>=75.0",
    "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",
    "scalar-fastapi",
    "uvicorn",
    "python-multipart",
    # Required by uvicorn for WebSocket support (real-time sidebar events).
    "websockets",
    # Offline translation — listed as builtin in the engine registry so the
    # "Argos (Local, Fast)" option in the Dub tab works out-of-the-box.
    "argostranslate>=1.9.0",
    # Phase 1 AUTH-02: Fernet symmetric encryption + scrypt KDF for the
    # at-rest HF token in the SQLite settings store. Pulled in directly so
    # we don't depend on a transitive arrival via pyannote/huggingface_hub
    # (Assumption A1 in RESEARCH.md was checked at execute-time and proved
    # false — `cryptography` is not on the install path today).
    "cryptography>=41",
]

[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",
]
# Phase 3 Plan 03-01 — Supertonic-3 opt-in engine. CPU-only ONNX TTS,
# 31 languages, ~99M params, ~400 MB model on first use. Default
# `uv sync --no-dev` does NOT install this; users opt in with
# `uv sync --extra supertonic` after accepting the OpenRAIL-M model
# license in Settings → Engines.
#
# Publisher verified per Plan 03-01 Task 1 (Package Legitimacy Audit):
#   • PyPI maintainers = Yu Yechan / Juheon Lee / Hyeongju Kim (Supertone Inc.)
#   • Repository = github.com/supertone-inc/supertonic-py
#   • Same publisher ships supertonic-js on npm (same maintainer email)
#   • Wheel inspected: pure-Python, no postinstall scripts, no subprocess/exec
#     at module top level. ``supertonic.config.MODEL_CONFIGS["supertonic-3"]``
#     itself pins the HF model revision by SHA — we re-pin to the same
#     SHA in backend/engines/supertonic3/constants.py for TTS-03.
supertonic = [
    "supertonic==1.3.1",
]

[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",
    "pytest-cov>=6.0",
]

[tool.pytest.ini_options]
# Bare `pytest` would otherwise walk into `research/` (1.2 GB of vendored
# upstream projects, each with its own test_*.py that calls sys.exit at
# module level) and INTERNALERROR. `backend/tests/` runs separately because
# it stubs `core.config` in sys.modules, which pollutes import state for
# other modules in the same session.
testpaths = ["tests"]
norecursedirs = [
    "research",
    "omnivoice/training",
    "omnivoice/eval",
    "frontend",
    "deploy",
    ".venv",
    "node_modules",
    "omnivoice_data",
    "backend/omnivoice_data",
]