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
a1ebcf3 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/a1ebcf32202b48d1c03c2fb5861749cc539dad03/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification@a1ebcf32202b48d1c03c2fb5861749cc539dad03/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ConformerForAudioFrameClassification/resolve/a1ebcf32202b48d1c03c2fb5861749cc539dad03/pytorch_model.bin
201 kB
- Xet hash:
- a1ddd7309f5a60c926d72bf273048b761b174ca28529187c798d6ceea93778b5
- Size of remote file:
- 201 kB
- SHA256:
- 447cb81dd5e11280b5c88713de29f21cff1542b548d73960aeaed47316826a3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.