Instructions to use hsali/wav2vec2_testing2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsali/wav2vec2_testing2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("hsali/wav2vec2_testing2") model = Wav2Vec2ForSpeechClassification.from_pretrained("hsali/wav2vec2_testing2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hsali/wav2vec2_testing2: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/hsali/wav2vec2_testing2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hsali/wav2vec2_testing2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hsali/wav2vec2_testing2/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 7ec9024baca943c104f2181616e16ec25472b2b3857a6ba5d80b4279244187ae
- Size of remote file:
- 378 MB
- SHA256:
- b4e90f38b2a75f2a062254e6948895a1a5a955411137f5f7e6e6ff13287774b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.