Instructions to use zylin12/wav2-noise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zylin12/wav2-noise with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="zylin12/wav2-noise")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("zylin12/wav2-noise") model = AutoModelForAudioClassification.from_pretrained("zylin12/wav2-noise", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a82620b03603432c9f534fb13e42347e2e7e4dd4485220f4e09a363e1bdb5b4
- Size of remote file:
- 378 MB
- SHA256:
- 39156be68d313306c91bd61232b186df4f9dc8b770c2249a7f8b601cd87508cf
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