Instructions to use Ar4ikov/W2VSC2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ar4ikov/W2VSC2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2Classification processor = AutoProcessor.from_pretrained("Ar4ikov/W2VSC2") model = Wav2Vec2Classification.from_pretrained("Ar4ikov/W2VSC2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ar4ikov/W2VSC2: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/Ar4ikov/W2VSC2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ar4ikov/W2VSC2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ar4ikov/W2VSC2/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- f829f355da73ee2e444e2fe8f60ac3a348e77dd295330a932cbcae7c523cda6e
- Size of remote file:
- 1.27 GB
- SHA256:
- 36604c3f8a4a10821728025623dceecf734681b28f7c31d20cac0431f7ea3693
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