Instructions to use Drigoro/audio_wav2vec_largeRobust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/audio_wav2vec_largeRobust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Drigoro/audio_wav2vec_largeRobust")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Drigoro/audio_wav2vec_largeRobust") model = AutoModelForAudioClassification.from_pretrained("Drigoro/audio_wav2vec_largeRobust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Drigoro/audio_wav2vec_largeRobust: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Drigoro/audio_wav2vec_largeRobust/resolve/main/training_args.bin
- Command line
-
hf download hf://Drigoro/audio_wav2vec_largeRobust/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Drigoro/audio_wav2vec_largeRobust/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 36bbbec52e1c4c111fa78e4e99c66311902ff675b8dc59b97d0e6feeafd16688
- Size of remote file:
- 5.11 kB
- SHA256:
- 62c6187e5f4d5b0621b742db27eeef459511f72468e6c7404927c6060237b651
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