Instructions to use pinot/wav2vec2-test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinot/wav2vec2-test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pinot/wav2vec2-test2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("pinot/wav2vec2-test2") model = AutoModelForCTC.from_pretrained("pinot/wav2vec2-test2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pinot/wav2vec2-test2: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/pinot/wav2vec2-test2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pinot/wav2vec2-test2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pinot/wav2vec2-test2/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- 73366ebeba50b23d87a27cd0b0b675c878288ed5d6508f62b0c553f5155a4ef2
- Size of remote file:
- 1.27 GB
- SHA256:
- 6adcaedf99cf72694c50468038f728d6f64ce4c65f965388fe791510097ce21b
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