What are Open Source Models for Multilingual Speech Recognition?
Open source models for multilingual speech recognition are specialized AI systems designed to understand, process, and generate speech across multiple languages and dialects. These models use advanced deep learning architectures like dual autoregressive transformers to convert text to natural-sounding speech or recognize spoken language with high accuracy. They support diverse linguistic scenarios including cross-lingual synthesis, dialect recognition, and mixed-language processing. This technology democratizes access to powerful multilingual speech capabilities, enabling developers to create inclusive applications for global audiences while fostering collaboration and innovation in speech AI research.
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 impressive accuracy rates: 3.5% WER and 1.2% CER for English, and 1.3% CER for Chinese characters.
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
- Exceptional ELO score of 1339 in TTS Arena evaluations.
- Low error rates: 3.5% WER and 1.2% CER for English.
- Massive training data: 300K+ hours for English and Chinese.
Cons
- Higher pricing compared to other TTS models.
- Limited to three primary languages (English, Chinese, Japanese).
Why We Love It
- It delivers industry-leading multilingual TTS performance with exceptional accuracy and innovative architecture, making it ideal for high-quality speech synthesis applications.
CosyVoice2-0.5B
CosyVoice 2 is a streaming speech synthesis model based on large language model architecture, employing unified streaming/non-streaming framework design. It achieves ultra-low latency of 150ms in streaming mode while maintaining quality. Compared to v1.0, it reduces pronunciation errors by 30%-50% and improves MOS score from 5.4 to 5.53. It supports Chinese (including Cantonese, Sichuan, Shanghainese, Tianjin dialects), English, Japanese, Korean, and cross-lingual scenarios.
CosyVoice2-0.5B: Advanced Streaming Speech Synthesis
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 speech token codebook utilization through finite scalar quantization (FSQ) and develops a chunk-aware causal streaming matching model. In streaming mode, it achieves ultra-low latency of 150ms while maintaining synthesis quality almost identical to 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), English, Japanese, Korean, and cross-lingual scenarios.
Pros
- Ultra-low latency of 150ms in streaming mode.
- 30%-50% reduction in pronunciation error rates.
- Improved MOS score from 5.4 to 5.53.
Cons
- Smaller model size (0.5B parameters) may limit complexity.
- Streaming quality dependent on network conditions.
Why We Love It
- It combines real-time streaming capabilities with exceptional dialect diversity, making it perfect for live multilingual applications requiring low latency and high quality.
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 introduces novel speech duration control methods supporting explicit token specification and auto-regressive generation modes. The model achieves disentanglement between emotional expression and speaker identity, enabling independent control via separate prompts. It incorporates GPT latent representations and utilizes a three-stage training paradigm for enhanced emotional speech clarity.
IndexTTS-2: Revolutionary Zero-Shot Duration 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. 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
- Breakthrough zero-shot capabilities without speaker training.
- Precise duration control for video dubbing applications.
- Independent control over timbre and emotional expression.
Cons
- Complex architecture may require more computational resources.
- Three-stage training paradigm increases implementation complexity.
Why We Love It
- It revolutionizes speech synthesis with zero-shot capabilities and precise duration control, making it ideal for professional applications like video dubbing and content creation.
Multilingual Speech Recognition Model Comparison
In this table, we compare 2025's leading multilingual speech recognition models, each with unique strengths. Fish Speech V1.5 excels in multilingual accuracy with extensive training data. CosyVoice2-0.5B offers real-time streaming with exceptional dialect support. IndexTTS-2 provides breakthrough zero-shot capabilities with precise duration control. This side-by-side comparison helps you choose the right model for your specific multilingual speech recognition needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | Fish Speech V1.5 | fishaudio | Text-to-Speech | $15/M UTF-8 bytes | Leading multilingual accuracy |
| 2 | CosyVoice2-0.5B | FunAudioLLM | Text-to-Speech | $7.15/M UTF-8 bytes | Ultra-low latency streaming |
| 3 | IndexTTS-2 | IndexTeam | Text-to-Speech | $7.15/M UTF-8 bytes | Zero-shot 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, multilingual performance, and unique approach to solving challenges in text-to-speech synthesis and cross-language speech generation.
Our analysis shows different leaders for specific needs. Fish Speech V1.5 is best for high-accuracy multilingual TTS with extensive language training data. CosyVoice2-0.5B excels in real-time applications requiring low latency and dialect support. IndexTTS-2 is ideal for applications requiring zero-shot capabilities and precise duration control like video dubbing.