Instructions to use rdsmaia/wav2vec2_class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdsmaia/wav2vec2_class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="rdsmaia/wav2vec2_class")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("rdsmaia/wav2vec2_class") model = AutoModelForAudioClassification.from_pretrained("rdsmaia/wav2vec2_class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rdsmaia/wav2vec2_class: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/rdsmaia/wav2vec2_class/resolve/main/training_args.bin
- Command line
-
hf download hf://rdsmaia/wav2vec2_class/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/rdsmaia/wav2vec2_class/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- d62ee9437ef01e014609eff1620ff09a8845a5dc1f653b30903f3dd59b26fd28
- Size of remote file:
- 3.96 kB
- SHA256:
- 6c8059670cfecaf32e60ef43ce12764fcc1cfeca24b6e592f513e77e442e03df
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