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:
- d1a0abf9f92ee0987f9547224cb22d3cbeb8c78e2b9864af93ab4e09d2563b3a
- Size of remote file:
- 9.21 MB
- SHA256:
- fe15b4ac3feb2b58a22c4010fc0400f99d98e45e7112a187cc6c94a1609a449c
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