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:
- c1b44b654bb2910c9f49aa31a30f9c758786d5b1e7cffe423e326e33fbcca58e
- Size of remote file:
- 9.08 MB
- SHA256:
- 06ba01eb9300126833437e24c3b00908c0e90af1b793f520a7fe5f6b642bc0a0
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