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
Moshi Streaming Speech-to-Text (Quantized)
This is a quantized version of Kyutaiโs stt-1b-en_fr model. The original model is a 1B parameter streaming speech-to-text model for English and French. This fork contains the same model, quantized to Q8_0 and Q4_K GGUF formats for reduced memory usage and faster inference.
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Hardware compatibility
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