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:
- 1b69124404b90b9cf5670ddef052acc820c5aaa5a0bc64befba921cb966e3b0c
- Size of remote file:
- 9.25 MB
- SHA256:
- 87e0331347bab0468850cc7253400c2292cf63f87723944cf22cf5589a71d3ea
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