Model Details Distill NeuCodec is a version of NeuCodec with a compatible, distilled encoder. The distilled encoder is 10x smaller in parameter count and uses ~7.5x less MACs at inference time. The distilled model makes the following adjustments to the model: Swap the notoriuously slow BigCodec acoustic encoder for the SQCodec acoustic encoder (70m → 36m) Swap the w2v bert 2.0 semantic encoder for DistilHuBERT (600m → 21m) Our work is largely based on extending the work of X Codec2.0 and SQCodec. Developed by: Neuphonic Model type: Neural Audio Codec License: apache 2.0 Repository: https://github.com/neuphonic/neucodec Paper: arXiv Pre encoded Datasets: Emilia YODAS EN More coming soon! Get Started Use the code below to get started with the model. To install from pypi in a dedicated environment, using Python 3.10 or above: Then, to use in python: Training Details The model was trained using the same data as the full model, with an additional distillation loss (MSE between distilled and original encoder ouputs).
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