Instructions to use OthmaneJ/distil-wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OthmaneJ/distil-wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="OthmaneJ/distil-wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("OthmaneJ/distil-wav2vec2") model = AutoModelForCTC.from_pretrained("OthmaneJ/distil-wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OthmaneJ/distil-wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 208 MB
-
https://huggingface.co/OthmaneJ/distil-wav2vec2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OthmaneJ/distil-wav2vec2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OthmaneJ/distil-wav2vec2/resolve/main/pytorch_model.bin
208 MB
- Xet hash:
- 5f973ad4e0dbdff8d4ee7930ebe982ca1d3ee3c1c541281468b887b165237f19
- Size of remote file:
- 208 MB
- SHA256:
- 083c4bc0c819fdca292b57c951eaa61f56b6ea76ea38b9d985f19f65f52aeb01
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