Instructions to use facebook/mms-lid-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-lid-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="facebook/mms-lid-256")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("facebook/mms-lid-256") model = AutoModelForAudioClassification.from_pretrained("facebook/mms-lid-256") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c89c81703cb5f10add788a68847f9744255079e2889e199fd8ec67bdc9d14d95
- Size of remote file:
- 3.87 GB
- SHA256:
- a946279c9911273085f522d9e4edf2a5f60336a78ee3bf8fa931e5e2283d59df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.