The results illustrate the step-by-step development of CosyVoice 2 from its predecessor. By replacing the randomly initialized language model with a pretrained LLM, we achieved relative improvements in content consistency of 18.46% and 15.40% on the test-zh and test-hard sets, respectively. Next, we removed the speaker embedding from the text-to-speech language model, which helps prevent information leakage and disturbances in in-context learning. This change resulted in a significant reduction in content errors while maintaining speaker similarity, indicating that content information is primarily modeled by the LM, and speaker information is mainly recovered by the flow matching model. Finally, by replacing VQ with FSQ, we achieved the CosyVoice 2 model, noting much higher content consistency and unchanged speaker similarity. By fully utilizing the codebook, FSQ captures more content information and context variation, leading to better alignment between text and speech tokens. Furthermore, we conducted a comparative experiment by incorporating pitch loss as a constraint during the training of the FSQ-based speech tokenizer. We found that this approach led to improved performance in downstream TTS tasks. In future versions of CosyVoice, we plan to carry out more detailed experiments and analyses. We also conducted another modular analysis to evaluate the impact of streaming modules on the synthesis performance. The results show the results for content consistency and speaker similarity. We fount that the streaming LM has a minimal impact on typical cases from the test-zh and test-en sets, indicating the effectiveness of our unified training framework. The primary impact of the streaming LM is observed in challenging cases from the test-hard set, likely due to the loss of contextual information in streaming mode. Interestingly, the streaming flow matching model results in slightly higher speaker similarity compared to the offline mode. This may be due to the higher prompt-to-generation ratio of initial chunks in streaming mode, whereas the prompt-to-generation ratio in offline mode can be very low, with many padding tokens. The negative effect of the streaming flow matching model on content consistency is much less pronounced compared to streaming LMs, thanks to the semantic-acoustic decoupled modeling in CosyVoice 2. Results on Japanese and Korean Benchmarks In addition to Chinese and English, CosyVoice 2 also supports Japanese and Korean. We evaluated the content consistency, speaker similarity and speech quality on our constructed Japanese and Korean test sets. The results show that CosyVoice 2 performs significantly better on Korean than on Japanese across all evaluation metrics. This discrepancy is primarily due to the overlap in the character set between Japanese and Chinese, which leads to Chinese pronunciations in Japanese contexts. In the future work, we plan to explore ways to enhance linguistic context for multilingual synthesis. Since Korean does not have character overlap with other languages, its speech synthesis achieves much better performance. Another issue is data imbalance. We believe that increasing the volume of training data could further improve synthesis performance for both Japanese and Korean. Results on Instructed Generation To evaluate the performance of instructed generation, we have created a Chinese test set comprising 290 samples. This set includes 29 types of instructions, each with 10 different input texts. We utilize five audio prompts and speaker embeddings from five speakers (three female and two male) as conditions for the flow matching model. Our testing is conducted in offline mode. We objectively evaluate content consistency (CER), speaker similarity (SS), and speech quality (NMOS). Subjectively, we assess the accuracy and naturalness of instruction using the Mean Opinion Score for Instruction (MOS-I), which ranges from 1 to 5. Each sample is assessed by 10 native Chinese speakers, with scores assigned in increments of 0.5. The evaluation criteria focus on whether the speech adheres to all specified instructions, such as emotional expression, speech rate adjustment, dialect usage, and role-playing. Fine-grained controls, including the insertion of laughter, speaking with laughter, breath control, and emphasis, are evaluated for naturalness and accuracy. The results show that CosyVoice 2 exhibits superior content consistency (CER), speaker similarity (SS), and accuracy and naturalness in instruction control (MOS-I), while maintaining comparable speech quality to CosyVoice-Instruct. When input instructions are removed from CosyVoice 2, there is a notable decline in MOS-I; however, improvements are observed in content consistency (CER), speaker similarity (SS), and speech quality (NMOS). This indicates that instruction controllability is difficult to implicitly emerge from content text. Results on Speaker Fine-tuned Models  During the fine-tuning phase, we employ unsupervised clustering on the speaker embeddings of the same speaker to ensure the stability of the speaker's timbre. We have demonstrated that a target speaker with as few as 400 audio recordings can achieve reasonably good speech synthesis performance, with only slight variations in objective metrics observed among different speakers. Our experiments indicate that most speakers can inherit the zero-shot TTS model's robust contextual understanding and perception, thereby naturally expressing various moods and emotions in response to the input text. LM Fine-tuning with Reinforcement Learning  Although the SFT can improve the performance on most speakers, the results of one speaker, Spk E, are still worse than the base model especially on English. Because Spk E has a more complex voice and faster speech speed. Additionally, only Chinese recordings are available for Spk E. So we apply the reinforcement learning on Spk E for further improvements. For DPO, we synthesis 10 thousand samples-pairs by the SFT models to change the preference biasing of the LM by the ASR and SS rewards. We also use the differentiable ASR rewards to optimize the LM parameters. After RL, we evaluate the model with content consistency (WER), speaker similarity (SS) and speech quality (NMOS) on the test set of Spk E and further evaluated the WER on the SeedTTS test sets to explore whether the model can maintain robustness to out-of-domain or cross-lingual input text. Compared to the pre-trained base model, the SFT model shows higher speaker similarity and speech quality, however, the WER could be worse than the base model. We find that the audio synthesized by the base model always has a slower speed than the SFT and ground truth, which is more friendly to the ASR systems. For the target speaker dataset, both preference biasing and differentiable rewards can reduce the WER with little harmful effect on the other two metrics. But for the SEED test sets, the DPO based reinforcement only benefits the Chinese and English subset, while the hard samples will be worse. The reason could be that the hard samples contain many repeated words or phrases, they could be regarded as rejected samples during DPO training. However, the differentiable ASR reward will not suffer this problem, as it can directly optimize the TTS system by the ASR posterior. This means that the differentiable ASR reward has a better generalization ability in the out-of-domain situations. Finally, we can combine them with each other for further improvements. Conclusion Building on the success of CosyVoice, this report presents CosyVoice 2, an improved streaming speech synthesis model that leverages large language models. By unifying streaming and non-streaming synthesis within a single framework, CosyVoice 2 achieves human-parity naturalness, minimal response latency, and virtually lossless synthesis quality in streaming mode. Key innovations include finite scalar quantization for full codebook utilization, a simplified text-to-speech language model architecture that incorporates pre-trained textual LLMs, and the development of a chunk-aware causal flow matching model to support diverse synthesis scenarios. Additionally, improvements in instructed TTS capacity allow for versatile and vivid speech generation with fine-grained control over emotion, accent, role style, and vocal bursts. Through systematic modifications and optimizations, CosyVoice 2 not only delivers superior synthesis quality but also loosens deployment requirements, making it suitable for both streaming and non-streaming applications. We believe that CosyVoice 2 represents a significant advancement in scalable, high-quality, and interactive text-to-speech synthesis. Limitations CosyVoice 2 has several limitations that need to be addressed. First, it supports only a limited number of languages. For languages with overlapping character sets, synthesis performance may degrade, presenting an open challenge for future research. Second, CosyVoice 2 cannot control acoustic characteristics, such as timbre, through textual instructions, which could be a fascinating area of exploration for role-playing applications. Additionally, CosyVoice does not perform well when tasked with singing.