Ming-3.0-tts-tiny

This repository contains Ming-3.0-tts-tiny, the Ming-3.0-tts mixture-of-experts checkpoint built on Ling V3. Its Ling V3 backbone has 7.9B total parameters and activates about 1.3B parameters per token. The repository includes sharded weights, supporting model code, audio tokenizer, speaker encoder and text tokenizer.

Inference requires the Ming-3.0-tts GitHub repository. This Hugging Face repository is for checkpoint distribution and does not provide a standalone inference setup. Clone the GitHub repository and follow its README for the current dependencies, inference code and usage instructions. The CLI scripts and Python APIs referenced below are provided by the GitHub repository.

Model Details

Capabilities

• Zero-shot voice cloning from a short reference clip
• Voice Design from a natural-language speaker description
• Free-form control of delivery and speaking style
• 100+ built-in Chinese voices and five built-in English voices
• Multilingual synthesis and Sichuanese/Cantonese dialect control
• 14 inline tags for breath, cough, sigh, laughter, hesitation, surprise and question/confirmation sounds in Chinese or English
• Explicit emotion, speed, pitch and volume control
• Multi-speaker dialogue from a script and speaker references (podcast)

Quick Start

Installation

git clone https://github.com/inclusionAI/Ming-3.0-tts.git
cd Ming-3.0-tts
conda create -n ming-tiny python=3.10.15 -y
conda activate ming-tiny
pip install -r requirements-tiny.txt

Tiny uses a separate reference inference environment: Python 3.10.15, PyTorch and torchaudio 2.7.1+cu118 (CUDA 11.8 wheels), and transformers==4.46.3. Install the pinned dependencies from requirements-tiny.txt in a dedicated environment, as other dependency versions differ from 0.5B. We recommend using NVIDIA H800 for inference.

Download

pip install "huggingface_hub[cli]"
hf download inclusionAI/Ming-3.0-tts-tiny --local-dir Ming-3.0-tts-tiny

CLI Inference

python infer_tiny.py \
  --model_path inclusionAI/Ming-3.0-tts-tiny \
  --task dialect \
  --text "今日天气几好，我哋食完饭一齐去海边行下啦。" \
  --dialect 广粤话 \
  --prompt_wav data/wavs/10002287-00000094.wav \
  --output_path output/tiny.wav

Python API

Loading configures the release runtime (bf16, portable MoE loop, fp32 recurrent state) and checks the checkpoint against it; generate() defaults to cfg_scale=2.0, temperature=0, flow_steps=10, and sigma=0.25.

from ming_tiny import MingTinyTTS

tts = MingTinyTTS.from_pretrained("inclusionAI/Ming-3.0-tts-tiny", device="cuda:0")
tts.generate(
    task="seed",
    text="傍晚的风从山口吹过来，远处的云慢慢染成了金色。",
    prompt_wav="data/wavs/10002287-00000094.wav",
    prompt_text="在此奉劝大家别乱打美白针。",
    target_language="Chinese",          # English for English text
    output_path="outputs/seed.wav",
)

Supported Tasks

Each call selects one task and accepts the inputs listed below:

Freeform always requires a reference clip and cannot be combined with voice design, a built-in voice, emotion, dialect or attribute controls. For podcast, start every dialogue line with speaker_N: and provide one reference audio clip and transcript per speaker, in speaker order. Repeat --prompt_wav and --prompt_text on the command line.

See the Tiny examples for a Podcast Python example and examples/one_click/ for scripts for every task. python infer_tiny.py --help lists the CLI arguments.

Verify the Checkpoint

python tools/preflight_tiny.py --model_path /path/to/Ming-3.0-tts-tiny

Performance

Benchmark comparisons from the project README.

Bold and underline mark the best and second-best values among the listed open-source models; ties share a mark. Seed-TTS and NVbench-GT are unranked reference rows.

Zero-shot TTS (Seed-TTS testset)

Multilingual (MiniMax test set, selected languages)

Evaluated on the German, Spanish, French, Korean, and Japanese subsets of MiniMaxAI/TTS-Multilingual-Test-Set.

Paralinguistic Control (NVBench)

Dialect Control

Emotion Control (CV3)

Emotion Control (CV3N, hard emotion control)

Voice Design (InstructTTSEval-ZH)

Basic Attribute Control

Podcast

Limitations

• Korean CER (6.000%) is the clearest multilingual content-accuracy gap for this checkpoint.
• Speaker similarity remains weaker than content accuracy on several multilingu