What are Open Source AI Models for Dubbing?
Open source AI models for dubbing are specialized text-to-speech (TTS) systems designed to create natural-sounding voice overs from text scripts. Using advanced deep learning architectures like dual autoregressive transformers and streaming synthesis models, they translate written dialogue into synchronized speech for video dubbing applications. These models support multiple languages, precise duration control, and emotional expression control—essential features for professional dubbing workflows. They foster collaboration, accelerate innovation, and democratize access to powerful voice synthesis tools, enabling everything from indie film dubbing to large-scale multilingual content localization.
fishaudio/fish-speech-1.5
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 independent TTS Arena evaluations, it achieved an exceptional ELO score of 1339, with impressive accuracy rates of 3.5% WER and 1.2% CER for English.
fishaudio/fish-speech-1.5: Multilingual TTS Excellence
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. The model 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
- Exceptional ELO score of 1339 in TTS Arena evaluations.
- Multilingual support with extensive training data.
- Low error rates: 3.5% WER and 1.2% CER for English.
Cons
- Higher pricing at $15/M UTF-8 bytes from SiliconFlow.
- Limited to three primary languages (English, Chinese, Japanese).
Why We Love It
- It delivers exceptional multilingual dubbing quality with proven performance metrics and extensive training data, making it ideal for professional dubbing workflows.
FunAudioLLM/CosyVoice2-0.5B
CosyVoice 2 is a streaming speech synthesis model based on a large language model, employing unified streaming/non-streaming framework design. It achieves ultra-low latency of 150ms in streaming mode while maintaining synthesis quality. The model features 30%-50% reduced pronunciation error rates, improved MOS score from 5.4 to 5.53, and supports fine-grained control over emotions and dialects across Chinese, English, Japanese, and Korean.
FunAudioLLM/CosyVoice2-0.5B: Real-Time Dubbing Powerhouse
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 for real-time dubbing.
- 30%-50% reduction in pronunciation error rates.
- Improved MOS score from 5.4 to 5.53.
Cons
- Smaller 0.5B parameter model compared to larger alternatives.
- Limited emotional control compared to specialized emotion models.
Why We Love It
- It excels in real-time dubbing applications with ultra-low latency and extensive dialect support, perfect for live dubbing and streaming scenarios.
IndexTeam/IndexTTS-2
IndexTTS2 is a breakthrough zero-shot Text-to-Speech model designed specifically for video dubbing applications with precise duration control. It features disentangled emotional expression and speaker identity control, enabling independent control over timbre and emotion. The model incorporates GPT latent representations and utilizes a novel three-stage training paradigm, outperforming state-of-the-art zero-shot TTS models in word error rate, speaker similarity, and emotional fidelity.
IndexTeam/IndexTTS-2: Professional Dubbing 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. 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 specifically for video dubbing.
- Disentangled emotional expression and speaker identity control.
- Zero-shot capability requiring no speaker-specific training.
Cons
- More complex setup due to advanced control features.
- Higher computational requirements for zero-shot synthesis.
Why We Love It
- It solves the critical challenge of precise duration control in video dubbing while offering unprecedented emotional and voice control, making it the ideal choice for professional dubbing studios.
AI Dubbing Model Comparison
In this table, we compare 2025's leading open source AI models for dubbing, each with unique strengths for professional voice synthesis. For multilingual excellence, fishaudio/fish-speech-1.5 provides top-tier accuracy. For real-time dubbing, FunAudioLLM/CosyVoice2-0.5B offers ultra-low latency streaming. For precise video dubbing control, IndexTeam/IndexTTS-2 delivers duration control and emotional disentanglement. This side-by-side view helps you choose the right model for your specific dubbing workflow.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | fishaudio/fish-speech-1.5 | fishaudio | Text-to-Speech | $15/M UTF-8 bytes | Multilingual accuracy leader |
| 2 | FunAudioLLM/CosyVoice2-0.5B | FunAudioLLM | Text-to-Speech | $7.15/M UTF-8 bytes | Ultra-low latency streaming |
| 3 | IndexTeam/IndexTTS-2 | IndexTeam | Text-to-Speech | $7.15/M UTF-8 bytes | Precise dubbing duration control |
Frequently Asked Questions
Our top three picks for 2025 are fishaudio/fish-speech-1.5, FunAudioLLM/CosyVoice2-0.5B, and IndexTeam/IndexTTS-2. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in text-to-speech synthesis and professional dubbing applications.
Our analysis shows different leaders for various dubbing needs. fishaudio/fish-speech-1.5 excels in multilingual dubbing with proven accuracy metrics. FunAudioLLM/CosyVoice2-0.5B is ideal for real-time dubbing with 150ms latency. IndexTeam/IndexTTS-2 is perfect for professional video dubbing requiring precise duration control and emotional expression management.