Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification") model = AutoModelForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28d6e93a5ada0f9268f68e37dc40c36b169138972782b1d8c3bf3e93989cb9d9
- Size of remote file:
- 133 kB
- SHA256:
- 503e32bd2fb7e31f3b03a9f7066298f3b58b8db1d45ef4516ec967d5427d93bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.