Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-Wav2Vec2Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bab8ed0990e92dfbf351ba2300d83f57fcc8b825b1705b1edf8be9a7440c2513
- Size of remote file:
- 211 kB
- SHA256:
- c40f9df88c640fbd9bfbdd9a27517e814f71fcd504a0b3dfc74b0d220efc7413
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.