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:
- 2a279d97d99556485a90d93c51261e054131fe09483e232f1df3aaf930f880c2
- Size of remote file:
- 166 kB
- SHA256:
- edebe2c2c28cca01aa67a26fce0363e2ae9d597a4dbc202ebc66504c06d99fec
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