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:
- 0fc31a2a90283820be5a94fcc43bb71b8f0c6ede5137a366cbc71c70288155c2
- Size of remote file:
- 9.1 MB
- SHA256:
- 408aeba5d0141c672af203eae3b80ec967e6cb04ddc55e9a1611d4daf88c852b
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