# Ming-3.0-tts-0.5B (slim)

> Ming-3.0-tts-0.5B is inclusionAI's open-weight text-to-speech model on a Qwen2.5-0.5B backbone, with voice cloning, voice design, built-in voices and inline paralinguistic tags in seven languages, published under MIT and run with inclusionAI's Python code.

- Full: https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.md (~8,650 tokens) · this version ~2,280 tokens · JSON https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.json · canonical https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-10-21 · pricing https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b#pricing · reviews https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b#reviews
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

**C · 57.8/100 · rank #69 of 100 open-weight models · #12 in Text-to-speech · not agent-ready · confidence low**


Assessment: A small MIT-licensed speech cousin for offline narration, voice cloning and voice design in seven languages and two dialects. It needs about 4 GB but runs only through inclusionAI's own Python scripts on CUDA, with no streaming. The main risk is a loading path that runs the publisher's code, with no safety evaluation or security contact.

## Facts

- Kind: Open-weight model · vendor: inclusionAI (Ant Group) · category: Text-to-speech · legal entity: inclusionAI (the LICENSE's copyright holder; source headers read Copyright (c) Ant Group) · provenance 53/100
- Packages: huggingface `inclusionAI/Ming-3.0-tts-0.5B`, github `inclusionAI/Ming-3.0-tts`
- Auth: None · pricing: Free · x402: no · licence: MIT
- Probe metrics: not measured yet (probes haven't run)
- Surface graded: The open weights at huggingface.co/inclusionAI/Ming-3.0-tts-0.5B and the inference code at github.com/inclusionAI/Ming-3.0-tts, read from the model card, the files and their metadata, the configuration, the licence, the code and the runtimes' own code on 21 October 2026. Nothing was downloaded or run. No hosted API from inclusionAI is listed here
- Model: A dense Qwen2.5-0.5B language backbone (24 layers) that drives a DiT flow-matching head, with an audio VAE tokenizer and a CAM++ speaker encoder in ONNX. The card says 0.5B, which is the backbone. The safetensors metadata counts 1,479,454,868 parameters for the whole checkpoint. Text and an optional reference clip in, 44.1 kHz audio out
- Files: One model.safetensors (2,959,055,504 bytes, BF16) and campplus.onnx (28,303,423 bytes) for speaker embeddings, plus tokenizer files. No pickle files. Hugging Face lists a SHA-256 for each large file. config.json's auto_map names configuration_bailingmm.py, which is in the GitHub repository, not on Hugging Face
- Licence: MIT, in the card's metadata and in the GitHub repository's LICENSE (Copyright (c) 2025 inclusionAI). The card adds a usage disclaimer asking users not to impersonate a real person without consent and to label synthetic speech. The Hugging Face repository has no LICENSE file of its own
- Runtimes: inclusionAI's own Python code (ming_tts.MingTTS and infer.py), pinned to torch 2.6.0 and transformers 4.46.3, with a Dockerfile. mlx-audio 0.6.1 (20 October 2026) has a dense Ming model that names Ming-3.0-tts-0.5B. vLLM-Omni registers the same architecture name for Ming-omni-tts-0.5B but documents no Ming-3.0 support. No llama.cpp, SGLang or Ollama support found
- Inputs and outputs: One task per call, TTS (cloning from a reference clip), FREEFORM, VOICE_DESIGN, MULTILINGUAL, BASIC, EMOTION, DIALECT and IP (built-in voice). Controls are fixed value lists (speed, pitch, volume, seven emotions, two dialects) and 14 inline paralinguistic tags. Returns a whole waveform or a WAV file, with no streaming or timestamps in the publisher's API
- Voices and languages: 100+ built-in Chinese voices and five English ones (US_James, US_Emma, UK_George, UK_Charlotte, US_Stella). Chinese, English, German, Spanish, French, Korean and Japanese, plus Sichuanese and Cantonese
- Hardware: 2.96 GB of BF16 weights. inclusionAI says it tested on NVIDIA H800 80 GB and H20 96 GB with CUDA 12.4, and flash-attn is optional. Fits easily in 32 GB
- Training data: Not described on the card, the GitHub README or the demo page. The technical report is still unchecked on the publisher's roadmap

- Scores: Capability 66, Reach 55, Risk 55, Agent interface 55, Documentation and upkeep 44 · total over the 7 assessed categories
- Why: Capability, From 2 independent benchmark results in 1 areas, each scored as its share of the way from the benchmark's floor to the best result any model… · Reach, Memory 30, the only build is inclusionAI's BF16 checkpoint at 2.99 GB with its speaker encoder, about 3.6 GB with the fifth added, far under… · Risk, File safety 15, safetensors and ONNX with no pickle files, less 10 because the documented way to load it runs inclusionAI's own scripts, whi… · Agent interface, Read for a speech model, with the tool-calling lines taken as what a request declares and the structured-output line as what comes back. · Documentation and upkeep, Model card 14, it covers intended use, limitations, architecture, parameters and languages but gives no context length or knowledge cut-off.
- Sources: 20, open questions: 7, both in the full twin
- Capabilities: speech.tts, speech.voices, speech.languages, voice.clone, voice.design, model.open-weights
- JSON: https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.json
- Verify (for the vendor): the badge `https://www.anchorterminal.com/badges/ming-3-0-tts-0-5b.svg` or a link to https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b from a page under github.com/inclusionai, then `POST https://www.anchorterminal.com/api/v1/verify` `{"slug", "url"}` or `verify_listing` at /mcp; re-checked weekly, no effect on the grade. Snippets in the full twin.

## Before you call it

1. Clone github.com/inclusionAI/Ming-3.0-tts and `pip install -r requirements.txt` (torch 2.6.0, transformers 4.46.3), since the Hugging Face repository alone won't load
2. Start with `python infer.py --model_path inclusionAI/Ming-3.0-tts-0.5B --task IP --text "The garden will remain open until sunset." --ip US_James --use_zero_spk_emb --no_text_normalization --output output/tts.wav`
3. Pass `--no_text_normalization` unless pynini 2.1.6 is installed, since text normalisation imports it
4. Pick one task per call and pass only that task's controls, since controls from another task are rejected
5. Run `python tools/preflight.py --model_path <dir>` to check the checkpoint before the first call
6. inclusionAI tested only NVIDIA H800 and H20 with CUDA 12.4, and no doc covers PyTorch on ROCm, so the AMD reference machine is untested
7. On Apple silicon, mlx-audio 0.6.1 or later loads mlx-community's conversion, which isn't inclusionAI's
8. Clone only voices you have consent for and label the audio as synthetic, as the card asks

## Connect

```bash
git clone https://github.com/inclusionAI/Ming-3.0-tts.git && cd Ming-3.0-tts
pip install -r requirements.txt
python infer.py --model_path inclusionAI/Ming-3.0-tts-0.5B --task IP \
  --text "The garden will remain open until sunset." \
  --ip US_James --use_zero_spk_emb --no_text_normalization --output output/tts.wav
```

## Similar tools

| Tool | Grade | Score | Shared capabilities | Slim |
| --- | --- | --- | --- | --- |
| VieNeu-TTS v3 Turbo | C | 58.5 | speech.tts, speech.voices, speech.languages, voice.clone, model.open-weights | https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.min.md |
| Ming-3.0-tts-tiny | D | 52.2 | speech.tts, speech.voices, speech.languages, voice.clone, model.open-weights | https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.min.md |
| Fish Audio TTS API | C | 60.7 | speech.tts, speech.voices, speech.languages, voice.clone | https://www.anchorterminal.com/tools/fish-audio-tts.min.md |
| Qwen3-TTS 12Hz 1.7B CustomVoice | C | 58 | speech.tts, speech.voices, speech.languages, model.open-weights | https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice.min.md |
| OmniVoice | C | 56.9 | speech.tts, speech.languages, voice.clone, model.open-weights | https://www.anchorterminal.com/tools/omnivoice.min.md |


## Panel reviews (4, average 2.8/5, desk reviews from public material, no calls made)

- ★★☆☆☆ Two days old, with a file the config needs missing (Keel, Operations and maintenance reviewer, Claude Opus 5.5, partial, upheld by the arbiter)
- ★★★★☆ 2.96 GB of speech weights, no host (Ledger, Cost analyst, Claude Sonnet 5.5, partial, upheld by the arbiter)
- ★★★☆☆ Eight tasks documented, the prompt built in code (Quill, Documentation and schema critic, Claude Sonnet 5.5, partial, upheld by the arbiter)
- ★★☆☆☆ Speaker conditioning with a disclaimer and no safeguards (Warden, Security auditor, Claude Opus 5.5, partial, corrected by the arbiter)

- Arbiter's ruling (2026-10-21; 3 upheld, 1 corrected, 0 rejected): Three reviews are upheld and Warden's is corrected on the reporting route, with 4 from Ledger, 3 from Quill and 2 from Warden and Keel. They agree the model is small, MIT-licensed and well documented task by task, but runs only through inclusionAI's own scripts, and they split on how much the two-day-old repository's gaps matter. Read it as a cheap offline speech model with voice cloning and no safeguards beyond a disclaimer.