What are Open Source Audio Generation Models?
Open source audio generation models are specialized AI systems designed to create high-quality speech and audio from text descriptions. Using advanced deep learning architectures like dual autoregressive transformers and large language models, they translate natural language into realistic speech with various voices, emotions, and languages. This technology allows developers and creators to generate, modify, and build upon audio content with unprecedented freedom. They foster collaboration, accelerate innovation, and democratize access to powerful text-to-speech tools, enabling a wide range of applications from voice assistants to video dubbing and enterprise audio solutions.
Fish Speech V1.5
Fish Speech V1.5 is a leading open-source text-to-speech (TTS) model employing an innovative DualAR architecture with dual autoregressive transformer design. It supports multiple languages, with over 300,000 hours of training data for English and Chinese, and over 100,000 hours for Japanese. In TTS Arena evaluations, it achieved an exceptional ELO score of 1339, with word error rates of 3.5% for English and character error rates of 1.2% for English and 1.3% for Chinese.
Fish Speech V1.5: Leading Multilingual TTS Performance
Fish Speech V1.5 is a leading open-source text-to-speech (TTS) model that employs an innovative DualAR architecture, featuring a dual autoregressive transformer design. It supports multiple languages, with over 300,000 hours of training data for both English and Chinese, and over 100,000 hours for Japanese. In independent evaluations by TTS Arena, the model performed exceptionally well, with an ELO score of 1339. The model achieved a word error rate (WER) of 3.5% and a character error rate (CER) of 1.2% for English, and a CER of 1.3% for Chinese characters.
Pros
- Industry-leading ELO score of 1339 in TTS Arena.
- Extensive multilingual support with 300k+ hours training data.
- Low error rates: 3.5% WER and 1.2% CER for English.
Cons
- Higher pricing at $15/M UTF-8 bytes on SiliconFlow.
- Limited to text-to-speech functionality only.
Why We Love It
- It delivers exceptional multilingual performance with industry-leading accuracy scores, making it the gold standard for high-quality text-to-speech generation.
CosyVoice2-0.5B
CosyVoice 2 is a streaming speech synthesis model based on large language models, featuring unified streaming/non-streaming framework design. It achieves ultra-low latency of 150ms in streaming mode while maintaining quality. Compared to v1.0, it reduced pronunciation errors by 30-50% and improved MOS scores from 5.4 to 5.53. It supports Chinese dialects, English, Japanese, Korean, and cross-lingual scenarios with fine-grained emotion and dialect control.
CosyVoice2-0.5B: Ultra-Low Latency Streaming TTS
CosyVoice 2 is a streaming speech synthesis model based on a large language model, employing a unified streaming/non-streaming framework design. The model enhances the utilization of the speech token codebook through finite scalar quantization (FSQ), simplifies the text-to-speech language model architecture, and develops a chunk-aware causal streaming matching model that supports different synthesis scenarios. In streaming mode, the model achieves ultra-low latency of 150ms while maintaining synthesis quality almost identical to that of non-streaming mode. Compared to version 1.0, the pronunciation error rate has been reduced by 30%-50%, the MOS score has improved from 5.4 to 5.53, and fine-grained control over emotions and dialects is supported. The model supports Chinese (including dialects: Cantonese, Sichuan dialect, Shanghainese, Tianjin dialect, etc.), English, Japanese, Korean, and supports cross-lingual and mixed-language scenarios.
Pros
- Ultra-low latency of 150ms in streaming mode.
- 30-50% reduction in pronunciation errors vs v1.0.
- Improved MOS score from 5.4 to 5.53.
Cons
- Smaller 0.5B parameter model may limit complexity.
- Focused primarily on Asian languages and English.
Why We Love It
- It combines streaming efficiency with quality improvements, offering real-time speech synthesis with fine-grained control over emotions and dialects.
IndexTTS-2
IndexTTS2 is a breakthrough auto-regressive zero-shot Text-to-Speech model addressing precise duration control challenges in large-scale TTS systems. It supports explicit token specification for precise duration and free auto-regressive generation. The model achieves disentanglement between emotional expression and speaker identity, enabling independent control over timbre and emotion. It incorporates GPT latent representations and features soft instruction mechanisms for emotional control, outperforming state-of-the-art models in word error rate, speaker similarity, and emotional fidelity.
IndexTTS-2: Advanced Zero-Shot TTS with Emotion Control
IndexTTS2 is a breakthrough auto-regressive zero-shot Text-to-Speech (TTS) model designed to address the challenge of precise duration control in large-scale TTS systems, which is a significant limitation in applications like video dubbing. It introduces a novel, general method for speech duration control, supporting two modes: one that explicitly specifies the number of generated tokens for precise duration, and another that generates speech freely in an auto-regressive manner. Furthermore, IndexTTS2 achieves disentanglement between emotional expression and speaker identity, enabling independent control over timbre and emotion via separate prompts. To enhance speech clarity in highly emotional expressions, the model incorporates GPT latent representations and utilizes a novel three-stage training paradigm. To lower the barrier for emotional control, it also features a soft instruction mechanism based on text descriptions, developed by fine-tuning Qwen3, to effectively guide the generation of speech with the desired emotional tone. Experimental results show that IndexTTS2 outperforms state-of-the-art zero-shot TTS models in word error rate, speaker similarity, and emotional fidelity across multiple datasets.
Pros
- Precise duration control for video dubbing applications.
- Independent control over timbre and emotional expression.
- Zero-shot capabilities with superior performance metrics.
Cons
- More complex setup due to advanced feature set.
- Higher computational requirements for optimal performance.
Why We Love It
- It revolutionizes TTS with precise duration control and emotion-timbre disentanglement, perfect for professional audio production and video dubbing applications.
Audio AI Model Comparison
In this table, we compare 2025's leading open source audio generation models, each with unique strengths. For multilingual excellence, Fish Speech V1.5 provides industry-leading accuracy. For real-time applications, CosyVoice2-0.5B offers ultra-low latency streaming. For advanced control, IndexTTS-2 delivers zero-shot capabilities with emotion and duration control. This side-by-side view helps you choose the right tool for your specific audio generation needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | Fish Speech V1.5 | fishaudio | Text-to-Speech | $15/M UTF-8 bytes | Industry-leading multilingual accuracy |
| 2 | CosyVoice2-0.5B | FunAudioLLM | Text-to-Speech | $7.15/M UTF-8 bytes | Ultra-low latency streaming (150ms) |
| 3 | IndexTTS-2 | IndexTeam | Text-to-Speech | $7.15/M UTF-8 bytes | Zero-shot with emotion & duration control |
Frequently Asked Questions
Our top three picks for 2025 are Fish Speech V1.5, CosyVoice2-0.5B, and IndexTTS-2. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in text-to-speech synthesis, multilingual support, and advanced audio control capabilities.
Our in-depth analysis shows several leaders for different needs. Fish Speech V1.5 is the top choice for multilingual accuracy with industry-leading performance scores. For real-time applications requiring minimal latency, CosyVoice2-0.5B excels with 150ms streaming capability. For professional applications needing precise control, IndexTTS-2 offers zero-shot capabilities with emotion and duration control.