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
2008a72 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/2008a721861993df97eb04c27dbfff3b0392de36/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification@2008a721861993df97eb04c27dbfff3b0392de36/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification/resolve/2008a721861993df97eb04c27dbfff3b0392de36/pytorch_model.bin
201 kB
- Xet hash:
- 47e9550f50a375cc217329c25e85e2d39716e2fd8d5ee5cd08a985e8c98e3961
- Size of remote file:
- 201 kB
- SHA256:
- f4e162464499bbf5d3f11bfa23979b8f4ea9ad83482f04249a9e9585844992b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.