Audio-to-Audio
Moshi
English
contextspan
full-duplex
spoken-dialogue
speech-to-speech
retrieval
retrieval-augmented-generation
tool-calling
context-spanning
personaplex
Instructions to use mindlogicinc/context-spanning-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use mindlogicinc/context-spanning-7b with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "mindlogicinc/context-spanning-7b" # 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("mindlogicinc/context-spanning-7b") # 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
Commit History
30 days ago
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model card: add arXiv 2609.33443 link and tag
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code repo renamed to Context-Spanning
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license note: main voice MIT
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model card: default voice main, shorter card
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model card: badges, license notes
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model card wording
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model card: released checkpoint and results
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Super-squash branch 'main' using huggingface_hub
Co-authored-by: jaeho-mindlogic <jaeho-mindlogic@users.noreply.huggingface.co>9b3801e