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:
- 4f18bf7a7f50576fdb35c882d6ae07e814fac741ae7f31a87ee449b3e75b629e
- Size of remote file:
- 9.17 MB
- SHA256:
- 1a1cdb2326873efcdd4b2dcfb16bb4dda4c46ef6149d64a8ea81aedeef45fee4
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