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
ab93dd1 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/ab93dd1a08f8a9903339bd61eb8b1815c3074dd1/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification@ab93dd1a08f8a9903339bd61eb8b1815c3074dd1/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification/resolve/ab93dd1a08f8a9903339bd61eb8b1815c3074dd1/pytorch_model.bin
201 kB
- Xet hash:
- c11969bde4228f22199204d82de84c2774f93a3fb6554a5139d2543c9c7ee2ad
- Size of remote file:
- 201 kB
- SHA256:
- 669fe6e24ac17e728205c76eb36b6cf61ad6ff729336ec0c77ff2f7e678b08f9
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