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:
- d6900b7cb17cbe61892c8ebef2eacbc899525f605bf4cf68aaaece474dad5d1f
- Size of remote file:
- 9.26 MB
- SHA256:
- 535373f255986fe56cbb1a56aea4ec1c73198e520ecfd078d36fb0695fccc467
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