Instructions to use Ar4ikov/W2VFSC2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ar4ikov/W2VFSC2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ar4ikov/W2VFSC2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ar4ikov/W2VFSC2") model = AutoModelForAudioClassification.from_pretrained("Ar4ikov/W2VFSC2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ar4ikov/W2VFSC2: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/Ar4ikov/W2VFSC2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ar4ikov/W2VFSC2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ar4ikov/W2VFSC2/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- 63603bb0d30da238d0d829f04a36c283d0fb115f500319c936b35f2a5410bc87
- Size of remote file:
- 1.27 GB
- SHA256:
- 70fcb7bd2169732c8ede5ba4d8582e9399a50d86d6375821cad8357b05148b17
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