Instructions to use Drigoro/audio_wav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/audio_wav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Drigoro/audio_wav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Drigoro/audio_wav2vec") model = AutoModelForAudioClassification.from_pretrained("Drigoro/audio_wav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Drigoro/audio_wav2vec: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Drigoro/audio_wav2vec/resolve/main/training_args.bin
- Command line
-
hf download hf://Drigoro/audio_wav2vec/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/Drigoro/audio_wav2vec/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- ff5fe829b1ac1564237c8e5a3d7694b5732ce7b53af43ca25a992faceda0a1ac
- Size of remote file:
- 5.11 kB
- SHA256:
- 8c4a7c4ce70932f4cb28e76ecab035a5e94d150eb6023ee32bae335c2e21ccf5
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