Instructions to use espnet/WavLabLM-MS-40k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/WavLabLM-MS-40k with ESPnet:
unknown model type (must be text-to-speech or automatic-speech-recognition)
- Notebooks
- Google Colab
- Kaggle
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Parent(s): c4a45e3
Create README.md
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README.md
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---
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tags:
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- espnet
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- audio
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- self-supervised-learning
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language: en
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datasets:
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- fleurs
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- babel
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- voxpopuli
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- commonvoice
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license: cc-by-4.0
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---
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## WavLabLM-MS 40k
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This model was trained by [William Chen] using ESPNet2's SSL recipe in [espnet](https://github.com/espnet/espnet/).
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### Citing ESPnet
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```BibTex
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@inproceedings{watanabe2018espnet,
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author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
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title={{ESPnet}: End-to-End Speech Processing Toolkit},
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year={2018},
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booktitle={Proceedings of Interspeech},
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pages={2207--2211},
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doi={10.21437/Interspeech.2018-1456},
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url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
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}
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```
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or arXiv:
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```bibtex
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@misc{watanabe2018espnet,
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title={ESPnet: End-to-End Speech Processing Toolkit},
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author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
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year={2018},
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eprint={1804.00015},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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