MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915315754991616 |
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| author | Song, Yakun Chen, Jiawei Zhuang, Xiaobin Du, Chenpeng Ma, Ziyang Wu, Jian Cong, Jian Jia, Dongya Chen, Zhuo Wang, Yuping Wang, Yuxuan Chen, Xie |
| author_facet | Song, Yakun Chen, Jiawei Zhuang, Xiaobin Du, Chenpeng Ma, Ziyang Wu, Jian Cong, Jian Jia, Dongya Chen, Zhuo Wang, Yuping Wang, Yuxuan Chen, Xie |
| contents | Neural audio codecs have made significant strides in efficiently mapping raw audio waveforms into discrete token representations, which are foundational for contemporary audio generative models. However, most existing codecs are optimized primarily for reconstruction quality, often at the expense of the downstream modelability of the encoded tokens. Motivated by the need to overcome this bottleneck, we introduce $\textbf{MagiCodec}$, a novel single-layer, streaming Transformer-based audio codec. MagiCodec is designed with a multistage training pipeline that incorporates Gaussian noise injection and latent regularization, explicitly targeting the enhancement of semantic expressiveness in the generated codes while preserving high reconstruction fidelity. We analytically derive the effect of noise injection in the frequency domain, demonstrating its efficacy in attenuating high-frequency components and fostering robust tokenization. Extensive experimental evaluations show that MagiCodec surpasses state-of-the-art codecs in both reconstruction quality and downstream tasks. Notably, the tokens produced by MagiCodec exhibit Zipf-like distributions, as observed in natural languages, thereby improving compatibility with language-model-based generative architectures. The code and pre-trained models are available at https://github.com/Ereboas/MagiCodec. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00385 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation Song, Yakun Chen, Jiawei Zhuang, Xiaobin Du, Chenpeng Ma, Ziyang Wu, Jian Cong, Jian Jia, Dongya Chen, Zhuo Wang, Yuping Wang, Yuxuan Chen, Xie Sound Artificial Intelligence Machine Learning Audio and Speech Processing Neural audio codecs have made significant strides in efficiently mapping raw audio waveforms into discrete token representations, which are foundational for contemporary audio generative models. However, most existing codecs are optimized primarily for reconstruction quality, often at the expense of the downstream modelability of the encoded tokens. Motivated by the need to overcome this bottleneck, we introduce $\textbf{MagiCodec}$, a novel single-layer, streaming Transformer-based audio codec. MagiCodec is designed with a multistage training pipeline that incorporates Gaussian noise injection and latent regularization, explicitly targeting the enhancement of semantic expressiveness in the generated codes while preserving high reconstruction fidelity. We analytically derive the effect of noise injection in the frequency domain, demonstrating its efficacy in attenuating high-frequency components and fostering robust tokenization. Extensive experimental evaluations show that MagiCodec surpasses state-of-the-art codecs in both reconstruction quality and downstream tasks. Notably, the tokens produced by MagiCodec exhibit Zipf-like distributions, as observed in natural languages, thereby improving compatibility with language-model-based generative architectures. The code and pre-trained models are available at https://github.com/Ereboas/MagiCodec. |
| title | MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.00385 |