Instructions to use facebook/mms-lid-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-lid-1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="facebook/mms-lid-1024")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("facebook/mms-lid-1024") model = AutoModelForAudioClassification.from_pretrained("facebook/mms-lid-1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mms-lid-1024: direct link, hf CLI and curl.
- Browser
- Download file 3.87 GB
-
https://huggingface.co/facebook/mms-lid-1024/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mms-lid-1024/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mms-lid-1024/resolve/main/pytorch_model.bin
3.87 GB
- Xet hash:
- 0d127544c479484136885e4630c416ef0a1109e7627b73bd5fa18e3a58582799
- Size of remote file:
- 3.87 GB
- SHA256:
- 1aea1f212158f6524cecbb2c3d62776b71c9f6298d1e32ccd35b5dcecb477dd3
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