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:
- dbad56224606e11850bcc9c3f0f0d0157644c09c43e23ac5d7b11cd8059f4d2b
- Size of remote file:
- 9.04 MB
- SHA256:
- 854fcdb0128b5a06e46faa215a8869feebb9ccf2d7ab170b8492e4b6a9bb56f6
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