Instructions to use Hamzaaa/wav2vec2-base-finetuned-crema with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hamzaaa/wav2vec2-base-finetuned-crema with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hamzaaa/wav2vec2-base-finetuned-crema")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hamzaaa/wav2vec2-base-finetuned-crema") model = AutoModelForAudioClassification.from_pretrained("Hamzaaa/wav2vec2-base-finetuned-crema", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20fb1a66515e752a6fbff823ead988b52d4a9b5bbe0c12692fb52cff26352882
- Size of remote file:
- 378 MB
- SHA256:
- 3cdec8096c039de18f9ab713e6f63fa19b8a4448bc7727bb94b8262e046a7953
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.