Instructions to use duclee9x/wav2vec2-voa-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duclee9x/wav2vec2-voa-example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="duclee9x/wav2vec2-voa-example")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("duclee9x/wav2vec2-voa-example") model = AutoModelForCTC.from_pretrained("duclee9x/wav2vec2-voa-example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from duclee9x/wav2vec2-voa-example: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/duclee9x/wav2vec2-voa-example/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://duclee9x/wav2vec2-voa-example/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/duclee9x/wav2vec2-voa-example/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 15e8d7319a05aa88b2db775ec138354769496772cc20760e983f2370451303b5
- Size of remote file:
- 378 MB
- SHA256:
- 8d6de8e8607d54f42e75121b9a9ddb6e148077f2f0992b6d7e3757431db4a265
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