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

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arxiv 2406.05763 v3 pith:MCVQJPPX submitted 2024-06-09 eess.AS

Linhan Ma , Dake Guo , Kun Song , Yuepeng Jiang , Shuai Wang , Liumeng Xue , Weiming Xu , Huan Zhao
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Binbin Zhang Lei Xie
This is my paper · ORCID
classification eess.AS
keywords corpuswenetspeech4ttsdatasegmentsystemsbenchmarklargemandarin
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SimulS2ST-Omni: Data-Efficient Streaming Speech-to-Speech Translation via Explicit Trajectory Supervision

    cs.SD 2026-07 conditional novelty 6.0 of 10

    A joint text-code trajectory supervision recipe lets a two-stream speech LM achieve competitive long-form streaming S2ST with ~2k hours of paired speech.

  2. FlowTTS-GRPO: Online Reinforcement Learning with Multi-Objective Reward Optimization for Flow-Matching Based Text-to-Speech

    eess.AS 2026-06 unverdicted novelty 6.0 of 10

    FlowTTS-GRPO fine-tunes open-source flow-matching TTS models with multi-objective online RL via ODE-to-SDE conversion, improving speaker similarity and quality on CosyVoice 3.0 and F5-TTS.

  3. MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition

    cs.CL 2025-06 conditional novelty 6.0 of 10

    MFLA adds finite look-ahead attention plus a CIF-based token counter to Whisper, enabling streaming recognition with a wait-k latency-quality trade-off.

  4. Adaptive Duration Model for Text Speech Alignment

    cs.SD 2025-07 conditional novelty 5.0 of 10

    DurFormer, an adaptive duration prediction model with speed, scene, and semantic conditioning, improves phoneme-level duration accuracy and lowers word error rate in Mandarin text-to-speech.

  5. UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic information

    cs.SD 2025-05 conditional novelty 5.0 of 10

    The authors propose DistilCodec, a 32,768-code single-codebook audio codec, and UniTTS, a Qwen2.5-7B TTS model trained with audio, text, and cross-modal autoregressive tasks on interleaved prompts.

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