The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge

This paper presents the NPU-HWC system submitted to the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge 2024 (ICAGC). Our system consists of two modules: a speech generator for Track 1 and a background audio generator for Track 2. In Track 1, we employ Single-Codec to tokenize the speech into discrete tokens and use a language-model-based approach to achieve zero-shot speaking style cloning. The Single-Codec effectively decouples timbre and speaking style at the token level, reducing the acoustic modeling burden on the autoregressive language model. Additionally, we use DSPGAN to upsample 16 kHz mel-spectrograms to high-fidelity 48 kHz waveforms. In Track 2, we propose a background audio gen-erator based on large language models (LLMs). This system produces scene-appropriate accompaniment descriptions, synthesizes background audio with Tango 2, and integrates it with the speech generated by our Track 1 system. Our submission achieves the second place and the first place in Track 1 and Track 2 respectively.

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Publication Details

Published
2024-11-07
DOI
https://doi.org/10.1109/iscslp63861.2024.10800697
Citations
2
Primary Topic
Speech and Audio Processing
Type
article
Field-Weighted Citation Impact
0.70
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article

The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge

Jie Liu, Jineng Yao, Lei Xie, Ziyu Zhang et al.
2 citations
Speech and Audio Processing
0.70
article

The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge

Jie Liu, Jineng Yao, Lei Xie, Ziyu Zhang, Dake Guo, Kun Xia, Xinfa Zhu, Zhao Guo, Yao Wang
article en
2 citations

Abstract

This paper presents the NPU-HWC system submitted to the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge 2024 (ICAGC). Our system consists of two modules: a speech generator for Track 1 and a background audio generator for Track 2. In Track 1, we employ Single-Codec to tokenize the speech into discrete tokens and use a language-model-based approach to achieve zero-shot speaking style cloning. The Single-Codec effectively decouples timbre and speaking style at the token level, reducing the acoustic modeling burden on the autoregressive language model. Additionally, we use DSPGAN to upsample 16 kHz mel-spectrograms to high-fidelity 48 kHz waveforms. In Track 2, we propose a background audio gen-erator based on large language models (LLMs). This system produces scene-appropriate accompaniment descriptions, synthesizes background audio with Tango 2, and integrates it with the speech generated by our Track 1 system. Our submission achieves the second place and the first place in Track 1 and Track 2 respectively.

Northwestern Polytechnical University (CN)
Openalex Percentile: Top 29%
Speech and Audio Processing
0.70
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