  With the development of large text-to-speech (TTS) models and scale-up of the
training data, state-of-the-art TTS systems have achieved impressive
performance. In this paper, we present WenetSpeech4TTS, a multi-domain Mandarin
corpus derived from the open-sourced WenetSpeech dataset. Tailored for the
text-to-speech tasks, we refined WenetSpeech by adjusting segment boundaries,
enhancing the audio quality, and eliminating speaker mixing within each
segment. Following a more accurate transcription process and quality-based data
filtering process, the obtained WenetSpeech4TTS corpus contains $12,800$ hours
of paired audio-text data. Furthermore, we have created subsets of varying
sizes, categorized by segment quality scores to allow for TTS model training
and fine-tuning. VALL-E and NaturalSpeech 2 systems are trained and fine-tuned
on these subsets to validate the usability of WenetSpeech4TTS, establishing
baselines on benchmark for fair comparison of TTS systems. The corpus and
corresponding benchmarks are publicly available on huggingface.
