ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering

• URL: http://arxiv.org/abs/2401.07333v1
• Date: Sun, 14 Jan 2024 17:43:55 GMT
• Title: ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering
• Authors: Yakun Song, Zhuo Chen, Xiaofei Wang, Ziyang Ma, Xie Chen
• Abstract summary: ELLA-V is a text-to-speech framework that enables fine-grained control over synthesized audio at the phoneme level. Our model outperforms VALL-E in terms of accuracy and delivers more stable results using both greedy and sampling-based decoding strategies.
• Score: 9.646664943647208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The language model (LM) approach based on acoustic and linguistic prompts, such as VALL-E, has achieved remarkable progress in the field of zero-shot audio generation. However, existing methods still have some limitations: 1) repetitions, transpositions, and omissions in the output synthesized speech due to limited alignment constraints between audio and phoneme tokens; 2) challenges of fine-grained control over the synthesized speech with autoregressive (AR) language model; 3) infinite silence generation due to the nature of AR-based decoding, especially under the greedy strategy. To alleviate these issues, we propose ELLA-V, a simple but efficient LM-based zero-shot text-to-speech (TTS) framework, which enables fine-grained control over synthesized audio at the phoneme level. The key to ELLA-V is interleaving sequences of acoustic and phoneme tokens, where phoneme tokens appear ahead of the corresponding acoustic tokens. The experimental findings reveal that our model outperforms VALL-E in terms of accuracy and delivers more stable results using both greedy and sampling-based decoding strategies. The code of ELLA-V will be open-sourced after cleanups. Audio samples are available at https://ereboas.github.io/ELLAV/.

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