  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.
