Instructions to use yapwithai/kyutai-stt-1b-en_fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use yapwithai/kyutai-stt-1b-en_fr with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "yapwithai/kyutai-stt-1b-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("yapwithai/kyutai-stt-1b-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
transformers support (#5)
Browse files- Update README.md (60f6dbeab22ee12fcd8dea0b36f6252c0dc6c1ae)
Co-authored-by: Eustache Le Bihan <eustlb@users.noreply.huggingface.co>
README.md
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# Model Card for Kyutai STT
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See also the [project page](https://kyutai.org/next/stt)
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and the [GitHub repository](https://github.com/kyutai-labs/delayed-streams-modeling/).
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# Model Card for Kyutai STT
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**Transformers support 🤗:** Starting with `transformers >= 4.53.0` and above, you can now run Kyutai STT natively!
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👉 Check it out here: [kyutai/stt-1b-en_fr-trfs](https://huggingface.co/kyutai/stt-1b-en_fr-trfs).
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See also the [project page](https://kyutai.org/next/stt)
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and the [GitHub repository](https://github.com/kyutai-labs/delayed-streams-modeling/).
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