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:
- 3c2e85d4922373ec117772788c4ec05c1b4c8c059b45668825075e152184ee07
- Size of remote file:
- 6.71 kB
- SHA256:
- e4cd5a964c9fba6a77d0effacdf8ff6112f22304689b71453318b9fb3477de67
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