Instructions to use facebook/mms-lid-126 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-lid-126 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="facebook/mms-lid-126")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("facebook/mms-lid-126") model = AutoModelForAudioClassification.from_pretrained("facebook/mms-lid-126", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mms-lid-126: direct link, hf CLI and curl.
- Browser
- Download file 3.86 GB
-
https://huggingface.co/facebook/mms-lid-126/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mms-lid-126/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mms-lid-126/resolve/main/pytorch_model.bin
3.86 GB
- Xet hash:
- 142920a13f69b7416764dd25758c316a648cb8015585b91561d9f771c04e3fe4
- Size of remote file:
- 3.86 GB
- SHA256:
- ae4656bddd75fdac0765c21c1cf496ef8eeb6190682d06ad68f7043105d12d9f
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