Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ConformerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ConformerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Wav2Vec2ConformerModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ConformerModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ConformerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b43ba25e845babad71a0d3632aca10d644955401efcdd03d350869bd7df3cba
- Size of remote file:
- 197 kB
- SHA256:
- 92ce4d33e5a7b5d2be19b7208f639b60a5b636e54f51c76940cb04dc7ec80929
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.