What are Open Source Audio Models for Mobile Apps?
Open source audio models for mobile apps are specialized AI models designed to generate high-quality speech and audio content on resource-constrained mobile devices. Using advanced deep learning architectures like autoregressive transformers and streaming synthesis frameworks, these models convert text into natural-sounding speech with minimal latency and computational overhead. This technology enables developers to integrate powerful text-to-speech capabilities directly into mobile applications, supporting features like voice assistants, accessibility tools, language learning apps, and content narration. They foster innovation, reduce development costs, and democratize access to professional-grade voice synthesis for mobile platforms across diverse languages and use cases.
FunAudioLLM/CosyVoice2-0.5B
CosyVoice 2 is a streaming speech synthesis model based on a large language model, employing a unified streaming/non-streaming framework design. The model achieves ultra-low latency of 150ms in streaming mode while maintaining synthesis quality almost identical to non-streaming mode. With a 30%-50% reduction in pronunciation error rate compared to version 1.0 and an improved MOS score from 5.4 to 5.53, it offers fine-grained control over emotions and dialects across Chinese, English, Japanese, and Korean.
FunAudioLLM/CosyVoice2-0.5B: Ultra-Low Latency Mobile Champion
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. At just 0.5B parameters, it's optimized for mobile deployment. SiliconFlow pricing starts at $7.15 per M UTF-8 bytes.
Pros
- Ultra-low latency of 150ms ideal for real-time mobile apps.
- 30%-50% reduction in pronunciation error rate.
- Compact 0.5B parameters perfect for mobile devices.
Cons
- May have limitations in extremely nuanced emotional expression compared to larger models.
- Streaming quality, while excellent, requires stable connectivity.
Why We Love It
- It delivers professional-grade speech synthesis with breakthrough 150ms latency in a compact package perfectly sized for mobile apps, making real-time voice experiences accessible to all developers.
IndexTeam/IndexTTS-2
IndexTTS2 is a breakthrough auto-regressive zero-shot Text-to-Speech model that addresses precise duration control—critical for mobile apps like video dubbing and narration. It achieves disentanglement between emotional expression and speaker identity, enabling independent control over timbre and emotion. With state-of-the-art performance in word error rate, speaker similarity, and emotional fidelity, it features soft instruction mechanisms for intuitive emotion control via text descriptions.
IndexTeam/IndexTTS-2: Zero-Shot Emotion Control Pioneer
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. SiliconFlow pricing is $7.15 per M UTF-8 bytes for both input and output.
Pros
- Precise duration control for video dubbing and timed narration.
- Zero-shot capability—no training needed for new voices.
- Independent control of timbre and emotion.
Cons
- May require more computational resources than ultra-compact models.
- Zero-shot performance depends on quality of reference audio.
Why We Love It
- It revolutionizes mobile audio apps with breakthrough zero-shot voice cloning and emotion control, enabling developers to create personalized, emotionally rich voice experiences without extensive training data.
fishaudio/fish-speech-1.5
Fish Speech V1.5 is a leading open-source text-to-speech model employing an innovative DualAR architecture with dual autoregressive transformer design. With over 300,000 hours of training data for English and Chinese, and 100,000+ hours for Japanese, it achieved an ELO score of 1339 in TTS Arena evaluations. The model delivers exceptional accuracy with 3.5% WER and 1.2% CER for English, and 1.3% CER for Chinese characters—making it ideal for high-quality multilingual mobile applications.
fishaudio/fish-speech-1.5: Multilingual Accuracy Leader
Fish Speech V1.5 is a leading open-source text-to-speech (TTS) model. The model 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. This exceptional accuracy combined with comprehensive multilingual support makes Fish Speech V1.5 particularly valuable for mobile apps serving global audiences or requiring precise pronunciation in educational, accessibility, and professional contexts. SiliconFlow pricing is $15 per M UTF-8 bytes.
Pros
- Exceptional accuracy: 3.5% WER and 1.2% CER for English.
- Industry-leading ELO score of 1339 in TTS Arena.
- 300,000+ hours of English and Chinese training data.
Cons
- Higher SiliconFlow pricing at $15/M UTF-8 bytes.
- May require more processing power than ultra-compact alternatives.
Why We Love It
- It sets the gold standard for multilingual accuracy in mobile TTS, backed by massive training data and proven arena performance—perfect for apps where pronunciation precision is non-negotiable.
Audio Model Comparison
In this table, we compare 2025's leading open source audio models for mobile apps, each with a unique strength. For ultra-low latency real-time applications, FunAudioLLM/CosyVoice2-0.5B offers unmatched 150ms response times in a compact package. For advanced emotion control and zero-shot voice cloning, IndexTeam/IndexTTS-2 leads the way. For multilingual accuracy and arena-proven quality, fishaudio/fish-speech-1.5 stands out. This side-by-side view helps you choose the right model for your specific mobile application needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | FunAudioLLM/CosyVoice2-0.5B | FunAudioLLM | Text-to-Speech | $7.15/M UTF-8 bytes | 150ms latency, 0.5B mobile-optimized |
| 2 | IndexTeam/IndexTTS-2 | IndexTeam | Text-to-Speech | $7.15/M UTF-8 bytes | Zero-shot emotion & duration control |
| 3 | fishaudio/fish-speech-1.5 | fishaudio | Text-to-Speech | $15/M UTF-8 bytes | Multilingual accuracy (1339 ELO) |
Frequently Asked Questions
Our top three picks for 2025 are FunAudioLLM/CosyVoice2-0.5B, IndexTeam/IndexTTS-2, and fishaudio/fish-speech-1.5. Each of these models stood out for their mobile optimization, performance efficiency, and unique approach to solving challenges in text-to-speech synthesis for resource-constrained mobile environments.
Our in-depth analysis shows clear leaders for different mobile needs. FunAudioLLM/CosyVoice2-0.5B is the top choice for real-time voice assistants and live narration apps requiring ultra-low 150ms latency. For apps needing personalized voices and emotional expression like audiobook readers or character-based games, IndexTeam/IndexTTS-2 excels with zero-shot voice cloning and emotion control. For multilingual educational apps, accessibility tools, and global content platforms where pronunciation accuracy is critical, fishaudio/fish-speech-1.5 delivers arena-proven quality across English, Chinese, and Japanese.