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:
- bba86ecb4b6a71d5de18c56ff82cfa82e66ab1eb509bfc3bc10f92050f216996
- Size of remote file:
- 9.1 MB
- SHA256:
- 7139e88343db88fbaf4bd4113ae2eb829f9c97451aedb1601281197844de9d3a
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