Instructions to use facebook/mms-1b-l1107 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-1b-l1107 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/mms-1b-l1107")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/mms-1b-l1107") model = AutoModelForCTC.from_pretrained("facebook/mms-1b-l1107") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b6f9f86bae83135da9ca56a18c67a616f998aed555dd8a33e33d7c0bc200c2b
- Size of remote file:
- 8.84 MB
- SHA256:
- 13531f392acb32a56f99afa9897642ac1a172bc941a254a596a4de682b4c327c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.