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 training_args.bin from duclee9x/wav2vec2-voa-example: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/duclee9x/wav2vec2-voa-example/resolve/main/training_args.bin
- Command line
-
hf download hf://duclee9x/wav2vec2-voa-example/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/duclee9x/wav2vec2-voa-example/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 9ad12c870c11fb9b2a57b974b0d19dd1574cb51c74c125e011eafc4fa66c8771
- Size of remote file:
- 3.18 kB
- SHA256:
- eba60022f9fc063a989f0b6d8878f33300fd5438eb94caf4d1de89a9306399d6
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