Instructions to use pinot/wav2vec2-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinot/wav2vec2-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pinot/wav2vec2-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("pinot/wav2vec2-test") model = AutoModelForCTC.from_pretrained("pinot/wav2vec2-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pinot/wav2vec2-test: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/pinot/wav2vec2-test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pinot/wav2vec2-test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pinot/wav2vec2-test/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- 67f6565a806a77212543e3d64dd8141191c5d444f7c38330c50438ed25bb8a87
- Size of remote file:
- 1.27 GB
- SHA256:
- e39f653c893397c04dbf2ecac9bf919e24ed0cbf8507ed00f3dd6fd1dda6f9b8
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