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:
- 8157427127aeac91b73c157857683d7d4d05719983d13300a4f3e7d4386c1292
- Size of remote file:
- 9.14 MB
- SHA256:
- 54189301808029020ace908f7cc3771c6d5679c0e3da6ed59b8a579435cbcf30
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.