Efficient Parallel Audio Generation Using Group Masked Language Modeling
We present a fast and high-quality codec language model for parallel audio generation. While SoundStorm, a state-of-the-art parallel audio generation model, accelerates inference speed compared to autoregressive models, it still suffers from slow inference due to iterative sampling. To resolve this...
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Veröffentlicht in: | IEEE signal processing letters 2024, Vol.31, p.979-983 |
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Sprache: | eng |
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Zusammenfassung: | We present a fast and high-quality codec language model for parallel audio generation. While SoundStorm, a state-of-the-art parallel audio generation model, accelerates inference speed compared to autoregressive models, it still suffers from slow inference due to iterative sampling. To resolve this problem, we propose Group-Masked Language Modeling (G-MLM) and Group Iterative Parallel Decoding (G-IPD) for efficient parallel audio generation. Both the training and sampling schemes enable the model to synthesize high-quality audio with a small number of iterations by effectively modeling the group-wise conditional dependencies. In addition, our model employs a cross-attention-based architecture to capture the speaker style of the prompt voice and improves computational efficiency. Experimental results demonstrate that our proposed model outperforms the baselines in prompt-based audio generation. |
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ISSN: | 1070-9908 1558-2361 |
DOI: | 10.1109/LSP.2024.3381910 |