Instructions to use kyutai/tts-1.6b-en_fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use kyutai/tts-1.6b-en_fr with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "kyutai/tts-1.6b-en_fr" # Then open https://localhost:8998 in your browser
# pip install moshi import torch from moshi.models import loaders # Load checkpoint info from HuggingFace checkpoint = loaders.CheckpointInfo.from_hf_repo("kyutai/tts-1.6b-en_fr") # Load the Mimi audio codec mimi = checkpoint.get_mimi(device="cuda") mimi.set_num_codebooks(8) # Encode audio (24kHz, mono) wav = torch.randn(1, 1, 24000 * 10) # [batch, channels, samples] with torch.no_grad(): codes = mimi.encode(wav.cuda()) decoded = mimi.decode(codes) - Notebooks
- Google Colab
- Kaggle
Link more clearly to tts-voices repo
Browse files
README.md
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See also the [project page](https://kyutai.org/next/tts),
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the [Colab example](https://colab.research.google.com/github/kyutai-labs/delayed-streams-modeling/blob/main/tts_pytorch.ipynb),
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This is a model for streaming text-to-speech (TTS).
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Unlike offline text-to-speech, where the model needs the entire text to produce the audio,
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See also the [project page](https://kyutai.org/next/tts),
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the [Colab example](https://colab.research.google.com/github/kyutai-labs/delayed-streams-modeling/blob/main/tts_pytorch.ipynb),
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the [GitHub repository](https://github.com/kyutai-labs/delayed-streams-modeling/),
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and the [repository of voices](https://huggingface.co/kyutai/tts-voices).
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Pre-print research paper is coming soon!
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This is a model for streaming text-to-speech (TTS).
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Unlike offline text-to-speech, where the model needs the entire text to produce the audio,
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