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:
- ef272e6fd11a1158acf55d47dcd23937afd712a72242387f15c105ba613e26e5
- Size of remote file:
- 9.03 MB
- SHA256:
- c6303fb3e4d439fabf6f2d517eda76286c38d23b64a22ec3ae3f25f7baabc26e
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