Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification") model = AutoModelForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hf-transformers-bot HF Staff
Update tiny models for Wav2Vec2ConformerForAudioFrameClassification
301312a Download pytorch_model.bin from hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification: direct link, hf CLI and curl.
- Browser
- Download file 201 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification/resolve/301312a1d0704bf238c233a9bef3f07d2eb55c84/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification@301312a1d0704bf238c233a9bef3f07d2eb55c84/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification/resolve/301312a1d0704bf238c233a9bef3f07d2eb55c84/pytorch_model.bin
201 kB
- Xet hash:
- 06eaaf0554c1a1a76a256f6af825f470edde1016f4a8d34a317f7779ed73d5b9
- Size of remote file:
- 201 kB
- SHA256:
- eb4fe0dbb859d311700ae064fd30612bc812853f2a95536d2e001bee1f7b4b98
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