Instructions to use sourabharsh/wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sourabharsh/wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sourabharsh/wav2vec2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sourabharsh/wav2vec2") model = AutoModelForCTC.from_pretrained("sourabharsh/wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sourabharsh/wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/sourabharsh/wav2vec2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sourabharsh/wav2vec2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sourabharsh/wav2vec2/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 4bfdf137cf5050680e1191935b70859e9a91cd825b61b0768d85f4c84a00eb03
- Size of remote file:
- 378 MB
- SHA256:
- 43917d3dfe3c01f7cce6a8f4644aebd16fd7023de0eedc229b4fc3b79566e425
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