- Scores 93.2% in Pronunciation Robustness, surpassing commercial models like Eleven v4
- Offers two variants: a 0.5B parameter dense model and a 7.9B Mixture-of-Experts (MoE) model
- Supports preset voices and stateless voice cloning; weights are open-sourced on Hugging Face under MIT License
AI CURATED HOT STORY · 10/20/2026
Ant Group Open-Sources Ming-3.0-TTS, Tops Voice Synthesis Leaderboard
Ant Group has officially open-sourced the Ming-3.0 voice series, with its core component, Ming-3.0-TTS, ranking first among open-source models on the Artificial Analysis controlled speech and pronunciation robustness leaderboard. Available in 0.5B dense and 7.9B Mixture-of-Experts (MoE) versions under the MIT license, this release marks a significant performance leap for domestic open-source voice technology, surpassing several commercial competitors.
Cross-referenced reports highlight that the Ming-3.0 series introduces an innovative "Director Mode," allowing developers to precisely control emotion, stress, and rhythm via text descriptions. The model achieves a pronunciation robustness score of 93.2%, outperforming mainstream commercial models like Eleven v4. Additionally, the accompanying ASR and audio understanding modules optimize long-audio structured processing, supporting dialect recognition and preserving non-textual contextual information, thereby creating a comprehensive technical ecosystem.
This launch holds substantial ecological significance for the AI voice sector. By lowering the barrier to high-quality voice synthesis and enabling modular workflow construction, it accelerates the adoption of voice interaction in vertical domains. The release of high-performance open weights shifts industry focus from pure technical competition to application ecosystem building.
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Total 2 reports · Latest first- Artificial Analysis (@ArtificialAnlys)T1·78 pts
- IT之家 · 智能时代T2·78 pts
- Ming-3.0-tts supports text-based voice design and 'Director Mode' to control performance details like emotion, stress, and rhythm.
- Ming-3.0-audio-flash/tiny directly processes long audio for transcription, summarization, and opinion analysis, preserving non-textual context.
- Ming-3.0-asr-tiny leverages LLM bases and hotword lists to improve recognition accuracy in dialects and vertical domains.