Instructions to use facebook/mms-1b-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-1b-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/mms-1b-all")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/mms-1b-all") model = AutoModelForCTC.from_pretrained("facebook/mms-1b-all") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by skydheere - opened
README.md
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- ms
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- zu
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- za
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license:
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datasets:
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- google/fleurs
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metrics:
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- MMS base checkpoints:
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- [facebook/mms-1b](https://huggingface.co/facebook/mms-1b)
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- [facebook/mms-300m](https://huggingface.co/facebook/mms-300m)
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- [Official Space](https://huggingface.co/spaces/facebook/MMS)
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- ms
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- zu
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- za
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license: mit
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datasets:
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- google/fleurs
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metrics:
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- MMS base checkpoints:
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- [facebook/mms-1b](https://huggingface.co/facebook/mms-1b)
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- [facebook/mms-300m](https://huggingface.co/facebook/mms-300m)
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- [Official Space](https://huggingface.co/spaces/facebook/MMS)
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