Instructions to use arunps/wav2vec2-base-adsids with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arunps/wav2vec2-base-adsids with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="arunps/wav2vec2-base-adsids")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("arunps/wav2vec2-base-adsids") model = AutoModelForAudioClassification.from_pretrained("arunps/wav2vec2-base-adsids") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72f02a2a71bfb25263d0da270cc0f97a12c76d2df173fd318f9e0fb379c72644
- Size of remote file:
- 3.57 kB
- SHA256:
- af686f6630e938794b1ca2e4dc1e9bb35e9df172060776634f52fbde574dcfcc
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