Transformers
PyTorch
multilingual
seamless_crossattention
audio
text
multimodal
seamless
subtitle-editing-time-prediction
cross-attention
attention-mechanism
Instructions to use videoloc/seamless-crossattention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use videoloc/seamless-crossattention with Transformers:
# Load model directly from transformers import HFSeamlessCrossAttention model = HFSeamlessCrossAttention.from_pretrained("videoloc/seamless-crossattention", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- attention-mechanism
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library_name: transformers
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base_model: facebook/hf-seamless-m4t-medium
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---
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# videoloc/seamless-crossattention
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- **[seamless-basic](https://huggingface.co/videoloc/seamless-basic)**: Basic audio+text model without attention mechanisms
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- **[seamless-translation](https://huggingface.co/videoloc/seamless-translation)**: Includes translation awareness but no cross-attention
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- **[seamless-langpairs](https://huggingface.co/videoloc/seamless-langpairs)**: Includes language pair embeddings but no cross-attention
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- attention-mechanism
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library_name: transformers
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base_model: facebook/hf-seamless-m4t-medium
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license: cc-by-nc-4.0
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---
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# videoloc/seamless-crossattention
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- **[seamless-basic](https://huggingface.co/videoloc/seamless-basic)**: Basic audio+text model without attention mechanisms
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- **[seamless-translation](https://huggingface.co/videoloc/seamless-translation)**: Includes translation awareness but no cross-attention
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- **[seamless-langpairs](https://huggingface.co/videoloc/seamless-langpairs)**: Includes language pair embeddings but no cross-attention
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