Automatic Speech Recognition
Transformers
Safetensors
zero_swot_encoder
feature-extraction
zeroswot
speech translation
zero-shot
end-to-end
nllb
wav2vec2
custom_code
Instructions to use johntsi/ZeroSwot-Medium_asr-mustc_en-to-200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johntsi/ZeroSwot-Medium_asr-mustc_en-to-200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="johntsi/ZeroSwot-Medium_asr-mustc_en-to-200", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("johntsi/ZeroSwot-Medium_asr-mustc_en-to-200", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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If you find ZeroSwot useful for your research, please cite our paper :)
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}
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```
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If you find ZeroSwot useful for your research, please cite our paper :)
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```
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@inproceedings{tsiamas-etal-2024-pushing,
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title = {{Pushing the Limits of Zero-shot End-to-End Speech Translation}},
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author = "Tsiamas, Ioannis and
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G{\'a}llego, Gerard and
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Fonollosa, Jos{\'e} and
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Costa-juss{\`a}, Marta",
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editor = "Ku, Lun-Wei and
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Martins, Andre and
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Srikumar, Vivek",
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booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
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month = aug,
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year = "2024",
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address = "Bangkok, Thailand and virtual meeting",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2024.findings-acl.847",
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pages = "14245--14267",
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}
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```
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