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inclusionAI / Ming-omni-tts

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Ming-omni-tts: Simple and Efficient Unified Generation of Speech, Music, and Sound with Precise Control

165
9
100% credibility
Found Feb 18, 2026 at 19 stars 9x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Ming-omni-tts is a unified AI model that generates controllable speech, music, and sound effects from text prompts, supporting voice cloning, emotions, dialects, and text normalization.

How It Works

1
🔍 Discover Ming-omni-tts

You hear about a fun tool that turns your words into lifelike speech, music, or sounds with custom voices and feelings.

2
🌐 Try the online demo

Head to the demo page to type a phrase and instantly hear it spoken in different voices or with background music.

3
🎤 Pick your voice style

Choose a built-in voice, describe a new one like 'cheerful grandma', or upload a short clip of someone's voice to clone it.

4
✏️ Craft your prompt

Write your text, add simple instructions like 'speak slowly with joy' or 'add rain sounds in the background'.

5
▶️ Generate your audio

Hit play and watch as realistic speech, tunes, or effects come alive exactly how you imagined.

🎉 Enjoy and share

Download your custom audio clip to use in stories, videos, or podcasts, delighting friends and family.

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AI-Generated Review

What is Ming-omni-tts?

Ming-omni-tts is a Python-based unified TTS system that generates speech, music, and sound effects from text prompts with precise control over attributes like rate, pitch, volume, emotion, and dialects. It solves the hassle of juggling separate models for voice synthesis, ambient audio, and background music by handling all in a single channel via a custom 12.5Hz tokenizer. Users get efficient, low-latency output (3.1Hz inference) ideal for podcast-style generation or immersive scenes.

Why is it gaining traction?

It stands out with top benchmarks in zero-shot TTS (WER 0.83%), Cantonese dialect accuracy (93%), and emotion control (up to 76.7%), beating CosyVoice3 and matching Qwen3-TTS. The simple API supports 100+ voices, zero-shot cloning, and text normalization for math/chem formulas, plus joint speech-music-sound generation that's rare in autoregressive models. Developers dig the Hugging Face/ModelScope integration and Gradio demo for quick testing.

Who should use this?

Audio engineers crafting voice assistants or games with dynamic soundscapes, ML devs building multilingual TTS apps (strong in Chinese/Cantonese), and content creators automating podcasts with emotional narration. It's for those needing precise control without model-switching overhead.

Verdict

Promising for experimentation given strong evals, but low maturity (19 stars, 1.0% credibility) means expect rough edges in docs and stability. Try the 0.5B model on HF for proofs-of-concept; skip for production until more polish.

(198 words)

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