Instructions to use modhp/wav2vec2-model1-torgo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modhp/wav2vec2-model1-torgo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="modhp/wav2vec2-model1-torgo")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("modhp/wav2vec2-model1-torgo") model = AutoModelForCTC.from_pretrained("modhp/wav2vec2-model1-torgo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from modhp/wav2vec2-model1-torgo: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/modhp/wav2vec2-model1-torgo/resolve/main/training_args.bin
- Command line
-
hf download hf://modhp/wav2vec2-model1-torgo/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/modhp/wav2vec2-model1-torgo/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- f36b34fd195a5f7a3551221476508c8cbe37c0ac5062e0fc84b8ec39a32daf00
- Size of remote file:
- 2.99 kB
- SHA256:
- c76f58cf86c8bf9a8d77adfd24d0df2f1bf6e0cafc1477c748e7519c7f796bc1
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