Instructions to use voidful/mhubert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/mhubert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="voidful/mhubert-base")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("voidful/mhubert-base") model = AutoModel.from_pretrained("voidful/mhubert-base") - Notebooks
- Google Colab
- Kaggle
add preprocessor_config.json
Browse files- preprocessor_config.json +9 -0
preprocessor_config.json
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{
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"do_normalize": true,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0,
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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