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 pytorch_model.bin from modhp/wav2vec2-model1-torgo: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/modhp/wav2vec2-model1-torgo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://modhp/wav2vec2-model1-torgo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/modhp/wav2vec2-model1-torgo/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 9147be559ca9193a2f0c7af56ef4a091ab925bee6f325ded83d11af0049b07a1
- Size of remote file:
- 378 MB
- SHA256:
- 972c84a3f1fbcec2e85237506cf0f1f3cecf156554e863f2ae74e5d6b1fae8d7
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