Instructions to use facebook/mms-1b-fl102 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-1b-fl102 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/mms-1b-fl102")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/mms-1b-fl102") model = AutoModelForCTC.from_pretrained("facebook/mms-1b-fl102") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f679b09a7b39cc5ec12d54553847fe41e9049eba279cf177059e8b6f61dfb7e4
- Size of remote file:
- 20.7 MB
- SHA256:
- 6155baed5881a48ccb848bf84b4807c46c3dc5cbcd390f908cff1cf81d3213f3
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