Instructions to use Ramnathan/wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ramnathan/wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Ramnathan/wav2vec2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Ramnathan/wav2vec2") model = AutoModelForCTC.from_pretrained("Ramnathan/wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ramnathan/wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Ramnathan/wav2vec2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ramnathan/wav2vec2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ramnathan/wav2vec2/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- ee9071962fb3483ae21c9c84e8412fd454e48ed16a3775e3a74ab54811805ff4
- Size of remote file:
- 378 MB
- SHA256:
- e24407e4223941523105c74c4b0334f33dafe5510ab574ae7174adbd04dc8b11
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