Instructions to use facebook/mms-lid-2048 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-lid-2048 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="facebook/mms-lid-2048")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("facebook/mms-lid-2048") model = AutoModelForAudioClassification.from_pretrained("facebook/mms-lid-2048", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mms-lid-2048: direct link, hf CLI and curl.
- Browser
- Download file 3.87 GB
-
https://huggingface.co/facebook/mms-lid-2048/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mms-lid-2048/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mms-lid-2048/resolve/main/pytorch_model.bin
3.87 GB
- Xet hash:
- b9a1bfbf37a018656c921c113bfcd15d6ce219fb73e29d0fced75b307d1b28a1
- Size of remote file:
- 3.87 GB
- SHA256:
- 87ce12fd5c2e86d1bdeedb26b651f9367091a5d64962c5806f0ff767e79ad971
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