k2-fsa/OmniVoice

OmniVoice is a state-of-the-art massively multilingual zero-shot text-to-speech (TTS) model supporting over 600 languages. Built on a novel diffusion language model-style architecture, it generates high-quality speech with superior inference speed, supporting voice cloning and voice design.

Contents: Key Features | Installation | Quick Start | Python API | Command-Line Tools | Training & Evaluation | Discussion | Citation

• 600+ Languages Supported: The broadest language coverage among zero-shot TTS models (full list).
• Voice Cloning: State-of-the-art voice cloning quality.
• Voice Design: Control voices via assigned speaker attributes (gender, age, pitch, dialect/accent, whisper, etc.).
• Fine-grained Control: Non-verbal symbols (e.g., [laughter]) and pronunciation correction via pinyin or phonemes.
• Fast Inference: RTF as low as 0.025 (40x faster than real-time).
• Diffusion Language Model-style Architecture: A clean, streamlined, and scalable design that delivers both quality and speed.

Choose one of the following methods: pip or uv.

We recommend using a fresh virtual environment (e.g., conda, venv, etc.) to avoid conflicts.

Step 1: Install PyTorch

# Install pytorch with your CUDA version, e.g.
pip install torch==2.8.0+cu128 torchaudio==2.8.0+cu128 --extra-index-url https://download.pytorch.org/whl/cu128

pip install torch==2.8.0 torchaudio==2.8.0

pip install torch torchaudio --index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

python -c "import torch; print(torch.xpu.is_available(), torch.xpu.device_count())"

Step 2: Install OmniVoice (choose one)

# From PyPI (stable release)
pip install omnivoice

# From the latest source on GitHub (no need to clone)
pip install git+https://github.com/k2-fsa/OmniVoice.git

# For development (clone first, editable install)
git clone https://github.com/k2-fsa/OmniVoice.git
cd OmniVoice
pip install -e .

Clone the repository and sync dependencies:

git clone https://github.com/k2-fsa/OmniVoice.git
cd OmniVoice
uv sync

Tip: Can use mirror with uv sync --default-index "https://mirrors.aliyun.com/pypi/simple"

Try OmniVoice without coding:

• Launch the local web UI: omnivoice-demo --ip 0.0.0.0 --port 8001

• Or try it directly on HuggingFace Space

• Or run it in Google Colab:

If you have trouble connecting to HuggingFace when downloading the pre-trained models, set export HF_ENDPOINT="https://hf-mirror.com" before running.

For full usage, see the Python API and Command-Line Tools sections below.

OmniVoice supports three generation modes. All features in this section are also available via command-line tools.

Clone a voice from a short reference audio. Provide ref_audio and ref_text:

from omnivoice import OmniVoice
import soundfile as sf
import torch

model = OmniVoice.from_pretrained(
    "k2-fsa/OmniVoice",
    device_map="cuda:0",
    dtype=torch.float16
)
# Apple Silicon users: use device_map="mps" instead
# Intel Arc GPU users: use device_map="xpu" instead

audio = model.generate(
    text="Hello, this is a test of zero-shot voice cloning.",
    ref_audio="ref.wav",
    ref_text="Transcription of the reference audio.",
) # audio is a list of `np.ndarray` with shape (T,) at 24 kHz.

# If you don't want to input `ref_text` manually, you can directly omit the `ref_text`.
# The model will use Whisper ASR to auto-transcribe it. To use a local copy (or
# a different Whisper model), pass `asr_model_name="..."` to `from_pretrained`.
# To control which device Whisper is loaded on (e.g. another GPU in multi-GPU
# setups, or the CPU), pass `asr_device="cuda:1"` (or `"cpu"`).

sf.write("out.wav", audio[0], 24000)

Encode the reference audio once, save the resulting prompt, and skip the audio loading / auto-transcription steps in later sessions:

prompt = model.create_voice_clone_prompt(
    ref_audio="ref.wav", ref_text="Transcription of the reference audio."
)
prompt.save("my_voice.pt")

# Later, in a new session:
from omnivoice import VoiceClonePrompt

prompt = VoiceClonePrompt.load("my_voice.pt")
audio = model.generate(text="Hello again!", voice_clone_prompt=prompt)

Tips

• Use a 3–10 seconds reference audio clip. Longer audio slows down inference and may degrade cloning quality.

• For standard pronunciation, use a reference audio in the same language as the target speech. In cross-lingual voice cloning (i.e., the reference audio and target speech are in different languages), the generated speech will carry an accent from the reference audio's language.

• For better results with Arabic numerals, normalize them to words first (e.g., "123" → "one hundred twenty-three"). You can pass normalize_text=True to generate() to do this automatically (opt-in; install the extra with pip install "omnivoice[tn]", which pulls in WeTextProcessing):

  # "I have 2345 apples." is read correctly instead of digit-by-digit.
  audio = model.generate(text="I have 2345 apples.", normalize_text=True)

  Chinese and English use WeTextProcessing; other languages fall back to num2words for integers. Inline control syntax ([laughter], [B EY1 S], pinyin tone markers) is preserved. On macOS (Apple Silicon), pynini has no wheel — install it via conda install -c conda-forge pynini first.

For more tips, see docs/tips.md.

Describe the desired voice with speaker attributes — no reference audio needed. Supported attributes: gender (male/female), age (child to elderly), pitch (very low to very high), style (whisper), English accent (American, British, etc.), and Chinese dialect (四川话, 陕西话, etc.). Attributes are comma-separated and freely combinable across categories.

audio = model.generate(
    text="Hello, this is a test of zero-shot voice design.",
    instruct="female, low pitch, british accent",
)

Note: The model is primarily trained on the voice cloning task, so voice cloning is the most stable mode. Voice design is trained on Chinese and English data only. It can generalize to other languages, but may produce unstable results for some low-resource languages or edge cases.

See docs/voice-design.md for the full attribute reference, Chinese equivalents, and usage tips.

Let the model choose a voice automatically:

audio = model.generate(text="This is a sentence without any voice prompt.")

All above three modes share the same model.generate() API. You can further control the generation behavior via keyword arguments:

audio = model.generate(
    text="...",
    num_step=32,  # diffusion steps (or 16 for faster inference)
    speed=1.0,     # speed factor (>1.0 faster, <1.0 slower)
    duration=10.0, # fixed output duration in seconds (overrides speed)
    # ... more options
)

See more detailed control in docs/generation-parameters.md.

OmniVoice supports inline non-verbal symbols and pronunciation correction within the input text.

Non-verbal symbols: Insert tags like [laughter] directly in the text to add expressive non-verbal sounds.

audio = model.generate(text="[laughter] You really got me. I didn't see that coming at all.")

Supported tags: [laughter], [sigh], [confirmation-en], [question-en], [question-ah], [question-oh], [question-ei], [question-yi], [surprise-ah], [surprise-oh], [surprise-wa], [surprise-yo], [dissatisfaction-hnn].

Pronunciation control (Chinese): Use pinyin with tone numbers to correct specific character pronunciations.

audio = model.generate(text="这批货物打ZHE2出售后他严重SHE2本了，再也经不起ZHE1腾了。")

Pronunciation control (English): Use CMU pronunciation dictionary (uppercase, in brackets) to override default English pronunciations.

audio = model.generate(text="He plays the [B EY1 S] guitar while catching a [B AE1 S] fish.")

Three CLI entry points are provided. The CLI tools support all features available in the Python API (voice cloning, voice design, auto voice, generation parameters, etc.) — all controlled via command-line arguments.

omnivoice-demo --ip 0.0.0.0 --port 8001

Provides a web UI for voice cloning and voice design. See omnivoice-demo --help for all options.

# Voice Cloning
# ref_text can be omitted (Whisper will auto-transcribe ref_audio to get it).
omnivoice-infer \
    --model k2-fsa/OmniVoice \
    --text "This is a test for text to speech." \
    --ref_audio ref.wav \
    --ref_text "Transcription of the reference audio." \
    --output hello.wav

# Voice Design
omnivoice-infer --model k2-fsa/OmniVoice \
    --text "This is a test for text to speech." \
    --instruct "male, British accent" \
    --output hello.wav

# Auto Voice
omnivoice-infer \
    --model k2-fsa/OmniVoice \
    --text "This is a test for text to speech."\
    --output hello.wav

omnivoice-infer-batch can distribute batch inference across multiple GPUs, designed for large-scale TTS tasks.

omnivoice-infer-batch \
    --model k2-fsa/OmniVoice \
    --test_list test.jsonl \
    --res_dir results/

The test list is a JSONL file where each line is a JSON object:

{"id": "sample_001", "text": "Hello world", "ref_audio": "/path/to/ref.wav", "ref_text": "Reference transcript", "instruct": "female, british accent", "language_id": "en", "duration": 10.0, "speed": 1.0}

Only id and text are mandatory fields. ref_audio and ref_text are used in voice cloning mode. instruct is used in voice design mode. If no reference audio or instruct are provided, the model will generate text in a random voice.

language_id, duration, and speed are optional. duration (in seconds) fixes the output length; speed controls the speaking rate. If duration and speed are both provided, speed will be ignored.

Inference can be accelerated ~2-2.9x losslessly with FlashInfer kernels (sequence packing for the CFG cond/uncond pair, fused RMSNorm/RoPE/GEMM kernels, and optional CUDA graphs).

Installation (NVIDIA GPUs; pick the index matching your CUDA version, e.g. cu128 for PyTorch built with CUDA 12.8):

pip install flashinfer-python==0.6.15.post1 "flashinfer-jit-cache==0.6.15.post1+cu128" \
    --extra-index-url https://flashinfer.ai/whl/cu128/

Usage with the batch inference CLI:

omnivoice-infer-batch \
    --model k2-fsa/OmniVoice \
    --test_list test.jsonl \
    --res_dir results/ \
    --batch_size 8 \
    --enable_flashinfer true

or with the Python API:

from omnivoice.models.omnivoice_flashinfer import apply_flashinfer

model = OmniVoice.from_pretrained("k2-fsa/OmniVoice", device_map="cuda", dtype=torch.float16)
apply_flashinfer(model)                          # throughput / batched inference
apply_flashinfer(model, enable_cuda_graph=True)  # recommended for batch=1 (low latency)

CUDA graphs are recommended for single-stream (batch=1) usage, where kernel-launch overhead dominates; at batch >= 4 the plain FlashInfer path is already the fastest configuration.

Benchmark (seed-tts zh testset, 2020 samples / 3.3h audio, voice cloning, single H100, fp16, num_step=32; Average RTF as reported by omnivoice-infer-batch, outputs ASR-verified lossless):

See examples/ for the complete pipeline — from data preparation to training, evaluation, and finetuning.

You can directly discuss on GitHub Issues.

You can also scan the QR code to join our wechat group or follow our wechat official account.

Wechat Group	Wechat Official Account

OmniVoice is supported by a growing ecosystem of community projects. Explore them in Community Projects.

@article{zhu2026omnivoice,
      title={OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models},
      author={Zhu, Han and Ye, Lingxuan and Kang, Wei and Yao, Zengwei and Guo, Liyong and Kuang, Fangjun and Han, Zhifeng and Zhuang, Weiji and Lin, Long and Povey, Daniel},
      journal={arXiv preprint arXiv:2604.00688},
      year={2026}
}

Users are strictly prohibited from using this model for unauthorized voice cloning, voice impersonation, fraud, scams, or any other illegal or unethical activities. All users shall ensure full compliance with applicable local laws, regulations, and ethical standards. The developers assume no liability for any misuse of this model and advocate for responsible AI development and use, encouraging the community to uphold safety and ethical principles in AI research and applications.