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.

我们提出了一种快速高质量的并行音频生成编解码语言模型，并采用组遮罩语言模型和组迭代并行解码来实现高效的并行音频生成，通过有效建模组内条件依赖关系，使用跨注意力机制来捕捉提示语音的说话风格并提高计算效率，在基于提示的音频生成中验证实验结果表明，我们提出的模型优于基线。