Instructions to use bezzam/xcodec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bezzam/xcodec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bezzam/xcodec2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bezzam/xcodec2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload feature extractor
Browse files- preprocessor_config.json +0 -1
preprocessor_config.json
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"padding_value": 1,
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"pre_padding_value": 0.0,
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"return_attention_mask": true,
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"sample_rate": 16000,
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"sampling_rate": 16000,
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"stride": 2
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}
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"padding_value": 1,
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"pre_padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 16000,
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"stride": 2
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}
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