inclusionAI/Ming-omni-tts-tokenizer-12Hz

🌐Project Page ｜🤗 Hugging Face｜ 🤖 ModelScope | 🎮 Gradio Demo

Introduction

Ming-omni-tts is a high-performance unified audio generation model that achieves precise control over speech attributes and enables single-channel synthesis of speech, environmental sounds, and music. Powered by a custom 12.5Hz continuous tokenizer and Patch-by-Patch compression, it delivers competitive inference efficiency (3.1Hz). Additionally, the model features robust text normalization capabilities for the accurate and natural narration of complex mathematical and chemical expressions.

🚀 Core Capabilities

• 🔊 Fine-grained Vocal Control: The model supports precise control over speech rate, pitch, volume, emotion, and dialect through simple commands. Notably, its accuracy for Cantonese dialect control is as high as 93%, and its emotion control accuracy reaches 46.7%, surpassing CosyVoice3.
• 🌌 Intelligent Voice Design: Features 100+ premium built-in voices and supports zero-shot voice design through natural language descriptions. Its performance on the Instruct-TTS-Eval-zh benchmark is on par with Qwen3-TTS.
• 🎶 Immersive Unified Generation: The industry’s first autoregressive model to jointly generate speech, ambient sound, and music in a single channel. Built on a custom 12.5Hz continuous tokenizer and a DiT head architecture, it delivers a seamless, "in-the-scene" auditory experience.
• ⚡ High-efficiency Inference: Introduces a "Patch-by-Patch" compression strategy that reduces the LLM inference frame rate to 3.1Hz. This significantly cuts latency and enables podcast-style audio generation while preserving naturalness and audio detail.
• 🧪 Professional Text Normalization: The model accurately parses and narrates complex formats, including mathematical expressions and chemical equations, ensuring natural-sounding output for specialized applications.

Evaluation

• Reconstruction: The 12Hz tokenizer supports high-quality reconstruction across speech, music, and sound. Its performance is comparable to existing state-of-the-art methods across key fidelity metrics.
• Dialect Generation: Achieves 96% accuracy on WSYue-TTS-Eval and 86% WSC-TTS-Eval, outperforming CosyVoice3.
• Emotional Expressiveness: Delivers an average accuracy of 76.7% on CV3-Eval emotional sets and 46.7% on neutral emotion sets, significantly surpassing CosyVoice3-Base (40%) to reach SOTA levels.
• Instruction-based Voice Design: Scores 76.20% on InstructTTS-Eval-ZH. Its instruction-following capability is on par with Qwen3-TTS-VoiceDesign.
• Zero-shot Voice Clone: Exhibits exceptional stability on Seed-tts-eval (Chinese) with a WER of 0.83%, outperforming SeedTTS and GLM-TTS.
• Text Normalization (TN): On internal technical testsets, the model achieves a CER of 1.97% in normalized regions, delivering performance comparable to Gemini-2.5 Pro.

Example Usage

Audio Reconstruction

git clone https://github.com/inclusionAI/MingTok-Audio.git
cd MingTok-Audio
python3 test.py

Audio Generation

git clone https://github.com/inclusionAI/Ming-omni-tts.git
cd Ming-omni-tts
python3 cookbooks/test.py