Instructions to use BinaryLight1011/Musicbeat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BinaryLight1011/Musicbeat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="BinaryLight1011/Musicbeat")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BinaryLight1011/Musicbeat", device_map="auto") - PEFT
How to use BinaryLight1011/Musicbeat with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Jefferson de Azevedo commited on
Upload processor_config.json with huggingface_hub
Browse files- processor_config.json +13 -0
processor_config.json
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{
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"feature_extractor": {
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"chunk_length_s": null,
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"feature_extractor_type": "EncodecFeatureExtractor",
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"feature_size": 1,
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"overlap": null,
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"padding_side": "left",
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"padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 32000
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},
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"processor_class": "MusicgenProcessor"
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}
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