Instructions to use Ar4ikov/W2VFSC3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ar4ikov/W2VFSC3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ar4ikov/W2VFSC3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ar4ikov/W2VFSC3") model = AutoModelForAudioClassification.from_pretrained("Ar4ikov/W2VFSC3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ar4ikov/W2VFSC3: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/Ar4ikov/W2VFSC3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ar4ikov/W2VFSC3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ar4ikov/W2VFSC3/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- 1e0cd1e6fdd515330af7a5c00ff2d369acf96efe4b5fe87e9c56cee8ecba95fd
- Size of remote file:
- 1.27 GB
- SHA256:
- 5f783eb37dda4f6c2c84f24a7bee2072444d8f80700c4022330a3260cb54e8dc
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