Instructions to use modhp/wav2vec2-model2-torgo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modhp/wav2vec2-model2-torgo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="modhp/wav2vec2-model2-torgo")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("modhp/wav2vec2-model2-torgo") model = AutoModelForCTC.from_pretrained("modhp/wav2vec2-model2-torgo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from modhp/wav2vec2-model2-torgo: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/modhp/wav2vec2-model2-torgo/resolve/main/training_args.bin
- Command line
-
hf download hf://modhp/wav2vec2-model2-torgo/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/modhp/wav2vec2-model2-torgo/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 2aefebbd271271c459f326fa3ccaf9c0c9bcfc30d76c5503687a2d0699465476
- Size of remote file:
- 2.99 kB
- SHA256:
- 93063da824cca3ee661b3443e7db631be65a80a1ae74139719926bdac2d54b17
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