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
d82ee5f 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/d82ee5f98fd675b0868ab06369ff5b14de2f8a1a/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification@d82ee5f98fd675b0868ab06369ff5b14de2f8a1a/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification/resolve/d82ee5f98fd675b0868ab06369ff5b14de2f8a1a/pytorch_model.bin
201 kB
- Xet hash:
- b71b26d43e17683f3223ad4f361335866caf588a39cb86c9b87bc36dc04541e0
- Size of remote file:
- 201 kB
- SHA256:
- 5c44a30c5b2ad2ff88b33dbeec19e2e05e7fe75a0a13d6d93c01ed043b95c8e1
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