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:
- b8f4b1b65cce318f3da98095d92867ffd496cb65e8f5a8dea1960c8a87e30a95
- Size of remote file:
- 9.13 MB
- SHA256:
- 0e787938eb3c28e1b3a7f7ef5a9e1ced9feabd85481c0c19dba0e2921e711500
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