Head to head · Text-to-speech · October 2026 research run
Ming-3.0-tts-tiny vs OmniVoice
OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface. Both do text-to-speech.
Best text-to-speech APIs for AI agents · All 76 tts comparisons
Best voice cloning and custom voice APIs for AI agents · All 76 cloning comparisons
Which one, for what
Best for Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT
Ahead on
- Reach, 50 against 40
- Risk, 55 against 35
- Agent interface, 60 against 50
Watch for
No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code
Best for Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold
Score by category
| Category | Weight this run | Ming-3.0-tts-tiny | OmniVoice | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 40 | Ming-3.0-tts-tiny +10 |
| Risk | 20%20 | 55 | 35 | Ming-3.0-tts-tiny +20 |
| Agent interface | 10%10 | 60 | 50 | Ming-3.0-tts-tiny +10 |
| Documentation and upkeep | 10%10 | 44 | 48 | OmniVoice +4 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 52.2/100 | C 56.9/100 |
Facts side by side
| Fact | Ming-3.0-tts-tiny | OmniVoice |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | k2-fsa (Next-gen Kaldi) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | CC-BY-NC (weights, per the card); Apache-2.0 (code) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-07-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.5/5 (4) | 2.3/5 (4) |
Verdicts
Ming-3.0-tts-tiny
A text-to-speech cousin for voice cloning, voice design and multi-speaker podcast audio in seven languages, under MIT. It needs the publisher's own code or SGLang-Omni and about 22 GB of GPU memory, with CUDA 11.8 pinned. The main risks are that loading runs repository code, no safety work is published and it can clone a voice from a short clip.
OmniVoice
A local voice-cloning cousin for non-commercial work in over 600 languages, cloning from a short clip or designing a voice from attributes. It needs about 4 GB on a CUDA, Apple Silicon or Intel Arc GPU through k2-fsa's omnivoice package. The main risk is the licence, changed from Apache 2.0 to non-commercial in July 2026 without a note.
Before you call either
Ming-3.0-tts-tiny
- Install from the GitHub repository,
git clone https://github.com/inclusionAI/Ming-3.0-tts.git && cd Ming-3.0-tts && pip install -r requirements-tiny.txt, in its own Python 3.10.15 environment, since the 0.5B model pins different versions - Quick start is
bash examples/one_click/run_seed.sh, which clones from the bundled reference clip - To serve, run
sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tinyfrom SGLang-Omni's main branch and POST to /v1/audio/speech with input, response_format and, for cloning, ref_audio plus ref_text - Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts
- Expect one complete audio body, since there is no streaming, and no timestamps
- Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config
- Run
python tools/preflight_tiny.py --model_path <dir>on the downloaded checkpoint before first use - Label output as synthetic speech where it could pass for a real recording, as the card asks
OmniVoice
- Install PyTorch for your accelerator, then
pip install omnivoice; load withOmniVoice.from_pretrained('k2-fsa/OmniVoice', device_map='cuda:0', dtype=torch.float16)(or mps, or xpu) - Clone with
model.generate(text=..., ref_audio='ref.wav', ref_text='...'); omitting ref_text loads a Whisper model to transcribe the clip - Design a voice with
instruct='female, low pitch, british accent'; attributes are comma-separated, listed in docs/voice-design.md - Output is a list of numpy arrays at 24 kHz; write with
sf.write('out.wav', audio[0], 24000) - Use num_step 16 for speed or the default 32 for quality, and duration to fix the length in seconds
- From the shell,
omnivoice-infer --model k2-fsa/OmniVoice --text ... --output out.wav;omnivoice-infer-batchspreads work across GPUs - Check the licence before any commercial use; the card says the weights are CC-BY-NC
- Only clone a voice you have the right to use
Questions
Which is better for AI agents, Ming-3.0-tts-tiny or OmniVoice?
OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface.
Other comparisons with Ming-3.0-tts-tiny or OmniVoice
Machine-readable
- This page as Markdown
/compare/ming-3-0-tts-tiny-vs-omnivoice.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ming-3-0-tts-tiny.json·/api/v1/tools/omnivoice.json - From a terminal
anchor compare ming-3-0-tts-tiny omnivoice(the CLI) - Over MCP
compare_tools {"a": "ming-3-0-tts-tiny", "b": "omnivoice"}at/mcp, no key