CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
In our prior works, we introduced a scalable streaming speech synthesis model, CosyVoice 2, which integrates a large language model (LLM) and a chunk-aware flow matching (FM) model, and achieves low-latency bi-streaming speech synthesis and human-parity quality. Despite these advancements, CosyVoice 2 exhibits limitations in language coverage, domain diversity, data volume, text formats, and post-training techniques. In this paper, we present CosyVoice 3, an improved model designed for zero-shot multilingual speech synthesis in the wild, surpassing its predecessor in content consistency, speaker similarity, and prosody naturalness. Key features of CosyVoice 3 include: 1) A novel speech tokenizer to improve prosody naturalness, developed via supervised multi-task training, including automatic speech recognition, speech emotion recognition, language identification, audio event detection, and speaker analysis. 2) A new differentiable reward model for post-training applicable not only to CosyVoice 3 but also to other LLM-based speech synthesis models. 3) Dataset Size Scaling: Training data is expanded from ten thousand hours to one million hours, encompassing 9 languages and 18 Chinese dialects across various domains and text formats. 4) Model Size Scaling: Model parameters are increased from 0.5 billion to 1.5 billion, resulting in enhanced performance on our multilingual benchmark due to the larger model capacity. These advancements contribute significantly to the progress of speech synthesis in the wild. We encourage readers to listen to the demo at https://funaudiollm.github.io/cosyvoice3.
Key facts. Cited by 150 papers in the map (data through 14 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 52 of its 57 references that are in the map. First posted on arXiv 23 May 2025.
Builds on
52 of 57 references.
Closest ancestors · 8
Same direction, newest first
- 2025F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization3 Apr
- 2025DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation6 Feb
- 2024CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models13 Dec
- 2024F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching9 Oct
- 2024NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models5 Mar
- 2023E3 TTS: Easy End-to-End Diffusion-based Text to Speech2 Nov
- 2023VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching10 Sep
- 2023Matcha-TTS: A fast TTS architecture with conditional flow matching6 Sep
Other references · 44
- 2022Scalable Diffusion Models with Transformerscited by 6,054
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- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
- 2021SoundStream: An End-to-End Neural Audio Codeccited by 771
- 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 752
- 2025Qwen2.5-Omni Technical Reportcited by 736
- 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 717
- 2017Tacotron: Towards End-to-End Speech Synthesiscited by 490
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- 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 347
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- 2023EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesiscited by 98
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- 2021Large-scale Self-Supervised Speech Representation Learning for Automatic Speaker Verificationcited by 78
- 2018ClariNet: Parallel Wave Generation in End-to-End Text-to-Speechcited by 70
- 2024FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applicationscited by 68
- 2017Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learningcited by 67
- 2024Autoregressive Speech Synthesis without Vector Quantizationcited by 65
- 2024ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reorderingcited by 50
- 2023An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verificationcited by 42
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- 2023SECap: Speech Emotion Captioning with Large Language Modelcited by 33
- 2025IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech Systemcited by 28
- 2024RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesiscited by 26
- 2024VALL-T: Decoder-Only Generative Transducer for Robust and Decoding-Controllable Text-to-Speechcited by 23
- 2024TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touchcited by 10
- 2025Unispeaker: A Unified Approach for Multimodality-driven Speaker Generationcited by 2
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Citing papers per year: 2025: 24; 2026: 126.
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