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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c401e91b7c88a1b5bde2313854ed23122f6a8b26bd87088c7af93eaced74e5c
- Size of remote file:
- 378 MB
- SHA256:
- 38d874ce9a69a29e4bac9835dc66d73a29bd2f6ef05dae4a5b60a0df8a5129bd
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