Instructions to use ta012/SSLAM_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ta012/SSLAM_pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ta012/SSLAM_pretrain", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ta012/SSLAM_pretrain", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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- Audio
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- SSL
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- SSLAM
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library_name: transformers
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## 🙌 Acknowledgments
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This repository builds on the EAT implementation for Hugging Face models. We remap SSLAM weights to that interface.
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- Paper: EAT: Self supervised pretraining with Efficient Audio Transformer
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- Code: https://github.com/cwx-worst-one/EAT
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We are not affiliated with the EAT authors. All credit for the original implementation belongs to them.
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## 📚 Citation
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- Audio
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- SSL
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- SSLAM
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- AudioEncoder
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library_name: transformers
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## 🙌 Acknowledgments
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This repository builds on the [EAT](https://github.com/cwx-worst-one/EAT) implementation for Hugging Face models. We remap SSLAM weights to that interface. We are not affiliated with the EAT authors. All credit for the original implementation belongs to them.
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## 📚 Citation
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