Instructions to use dg845/univnet-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dg845/univnet-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dg845/univnet-dev")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("dg845/univnet-dev") model = AutoModel.from_pretrained("dg845/univnet-dev") - Notebooks
- Google Colab
- Kaggle
Upload preprocessor_config.json
Browse filesUpdate feature extractor preprocessor_config.json (rename UnivNetFeatureExtractor to UnivNetGanFeatureExtractor)
- preprocessor_config.json +1 -1
preprocessor_config.json
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"compression_clip_val": 1e-05,
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"compression_factor": 1.0,
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"do_normalize": false,
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"feature_extractor_type": "
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"feature_size": 1,
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"filter_length": 1024,
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"fmax": 12000.0,
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"compression_clip_val": 1e-05,
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"compression_factor": 1.0,
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"do_normalize": false,
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"feature_extractor_type": "UnivNetGanFeatureExtractor",
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"feature_size": 1,
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"filter_length": 1024,
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"fmax": 12000.0,
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