Instructions to use rdsmaia/wav2vec2_class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdsmaia/wav2vec2_class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="rdsmaia/wav2vec2_class")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("rdsmaia/wav2vec2_class") model = AutoModelForAudioClassification.from_pretrained("rdsmaia/wav2vec2_class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rdsmaia/wav2vec2_class: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/rdsmaia/wav2vec2_class/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rdsmaia/wav2vec2_class/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rdsmaia/wav2vec2_class/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- a53f8daafc216e2878ab278d3ba2f5b954af6d2dfe4b3728de14771a8f70e82d
- Size of remote file:
- 378 MB
- SHA256:
- c6bee8a8b24b0c051b6c310cd2d4cf68338ea06b14f5396252102ecced4b4ee3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.