Instructions to use Ar4ikov/Wav2Vec2ForSpeechClassification73 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ar4ikov/Wav2Vec2ForSpeechClassification73 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2Classification processor = AutoProcessor.from_pretrained("Ar4ikov/Wav2Vec2ForSpeechClassification73") model = Wav2Vec2Classification.from_pretrained("Ar4ikov/Wav2Vec2ForSpeechClassification73", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ar4ikov/Wav2Vec2ForSpeechClassification73: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/Ar4ikov/Wav2Vec2ForSpeechClassification73/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ar4ikov/Wav2Vec2ForSpeechClassification73/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ar4ikov/Wav2Vec2ForSpeechClassification73/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- a7d567f2ea05ab2019028f7a699f7f7c92b0f6694b41e0ae8fca2e4cf7bec7ff
- Size of remote file:
- 1.27 GB
- SHA256:
- 72568f4c7a3e801745ae44d1186483dbdcb76c66f170aaddf12011250651db1e
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