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efficient-nlp
/
stt-1b-en_fr-quantized

Automatic Speech Recognition
Moshi
GGUF
English
French
stt
audio
Model card Files Files and versions
xet
Community

Instructions to use efficient-nlp/stt-1b-en_fr-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Moshi

    How to use efficient-nlp/stt-1b-en_fr-quantized with Moshi:

    # pip install moshi
    # Run the interactive web server
    python -m moshi.server --hf-repo "efficient-nlp/stt-1b-en_fr-quantized"
    # 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("efficient-nlp/stt-1b-en_fr-quantized")
    
    # 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
stt-1b-en_fr-quantized
1.99 GB
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  • 1 contributor
History: 3 commits
luckyt's picture
luckyt
Upload tokenizer_en_fr_audio_8000.json
353f588 verified 9 months ago
  • .gitattributes
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  • README.md
    489 Bytes
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  • config.json
    1.26 kB
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  • mimi-pytorch-e351c8d8@125.safetensors
    385 MB
    xet
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  • model-q4k.gguf
    557 MB
    xet
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  • model-q80.gguf
    1.05 GB
    xet
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  • tokenizer_en_fr_audio_8000.json
    304 kB
    Upload tokenizer_en_fr_audio_8000.json 9 months ago
  • tokenizer_en_fr_audio_8000.model
    120 kB
    xet
    Upload folder using huggingface_hub 9 months ago