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
File size: 498 Bytes
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"feature_extractor_type": "Xcodec2FeatureExtractor",
"feature_size": 80,
"hop_length": 320,
"mel_floor": 1.192092955078125e-07,
"n_channels": 1,
"n_fft": 512,
"num_mel_bins": 80,
"padding_side": "right",
"padding_value": 1,
"pre_padding_value": 0.0,
"preemphasis": 0.97,
"return_attention_mask": true,
"sampling_rate": 16000,
"spec_center": false,
"spec_hop_length": 160,
"spec_power": 2.0,
"spec_remove_dc_offset": true,
"stride": 2,
"window_length": 400
}
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