# WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmark

arXiv: https://arxiv.org/abs/2406.05763 · HTML: https://mlsanity.com/paper/2406.05763/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2024.

## Built on by (most cited first, 30 of 40)

- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [Kimi-Audio Technical Report](https://mlsanity.com/paper/2504.18425/) (2025)
- [Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation](https://mlsanity.com/paper/2407.05361/) (2024)
- [Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens](https://mlsanity.com/paper/2503.01710/) (2025)
- [Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis](https://mlsanity.com/paper/2502.04128/) (2025)
- [VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language Model](https://mlsanity.com/paper/2505.03739/) (2025)
- [F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization](https://mlsanity.com/paper/2504.02407/) (2025)
- [Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation](https://mlsanity.com/paper/2501.15907/) (2025)
- [ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark](https://mlsanity.com/paper/2507.05727/) (2025)
- [TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch](https://mlsanity.com/paper/2412.08237/) (2024)
- [Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis](https://mlsanity.com/paper/2504.10352/) (2025)
- [WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing](https://mlsanity.com/paper/2509.18004/) (2025)
- [UniSS: Unified Expressive Speech-to-Speech Translation with Your Voice](https://mlsanity.com/paper/2509.21144/) (2025)
- [Step-Audio-EditX Technical Report](https://mlsanity.com/paper/2511.03601/) (2025)
- [DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue](https://mlsanity.com/paper/2504.14482/) (2025)
- [FlowTTS-GRPO: Online Reinforcement Learning with Multi-Objective Reward Optimization for Flow-Matching Based Text-to-Speech](https://mlsanity.com/paper/2606.23190/) (2026)
- [TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch](https://mlsanity.com/paper/2412.15622/) (2024)
- [VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing](https://mlsanity.com/paper/2511.12347/) (2025)
- [The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge](https://mlsanity.com/paper/2410.23815/) (2024)
- [UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic information](https://mlsanity.com/paper/2505.17426/) (2025)
- [TELEVAL: A Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios](https://mlsanity.com/paper/2507.18061/) (2025)
- [Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets](https://mlsanity.com/paper/2508.15442/) (2025)
- [Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching](https://mlsanity.com/paper/2409.04859/) (2024)
- [The ISCSLP 2024 Conversational Voice Clone (CoVoC) Challenge: Tasks, Results and Findings](https://mlsanity.com/paper/2411.00064/) (2024)
- [MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition](https://mlsanity.com/paper/2506.03722/) (2025)
- [Eliminating stability hallucinations in llm-based tts models via attention guidance](https://mlsanity.com/paper/2509.19852/) (2025)
- [A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction](https://mlsanity.com/paper/2601.04960/) (2026)
- [Eureka-Audio: Triggering Audio Intelligence in Compact Language Models](https://mlsanity.com/paper/2602.13954/) (2026)
- [Efficient Training for Cross-lingual Speech Language Models](https://mlsanity.com/paper/2604.11096/) (2026)
- [VITA-QinYu: Expressive Spoken Language Model for Role-Playing and Singing](https://mlsanity.com/paper/2605.06765/) (2026)

## Abstract (arXiv)

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.