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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f3fbaf9e8b58b3c8ac4366a1fc16bd5cf92f2647d6ea79748d94b26f6cd9c3d
- Size of remote file:
- 9.11 MB
- SHA256:
- 3841b25e8100aeb59a72f081770be040267a0e61e8902fe0eada6139e518c71a
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