{ "data": { "a": { "slug": "ming-3-0-tts-0-5b", "name": "Ming-3.0-tts-0.5B", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://github.com/inclusionAI", "kind": "weights", "category": "text-to-speech", "summary": "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.", "url": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b", "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.json", "repo": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ming-3.0-tts-0.5B" }, { "registry": "github", "name": "inclusionAI/Ming-3.0-tts" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The inference code is a public GitHub repository.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. Running it costs the hardware. The checkpoint is 2.96 GB in BF16, and inclusionAI says it tested inference on NVIDIA H800 80 GB and H20 96 GB with CUDA 12.4 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/inclusionAI/Ming-3.0-tts", "capabilities": [ "speech.tts", "speech.voices", "speech.languages", "voice.clone", "voice.design", "model.open-weights" ], "tags": [ "open-weights", "text-to-speech", "voice-cloning", "multilingual", "mit", "safetensors", "self-hosted", "python", "new" ], "lastRelease": "2026-10-19", "graded": true, "anchor": { "graded": true, "score": 57.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 12, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 66, "interface": 55, "reach": 55, "risk": 55, "upkeep": 44 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "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.", "bestFor": "Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence", "strengths": [ "MIT with nothing added, no gate and no account", "2.99 GB in BF16, about 3.6 GB to run, small enough for a laptop GPU", "Eight documented tasks with fixed control values, 100+ Chinese and five English voices, and 14 inline paralinguistic tags", "Safetensors and ONNX only, with a SHA-256 for every large file", "Card and inference code changed two days ago, with a commit history that says what changed" ], "weaknesses": [ "No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)", "Loading runs the publisher's code with trust_remote_code, and the prompt is built in code rather than a template", "One task per call, and a whole waveform back with no streaming or timestamps", "No safety evaluation, training data, quantised build or security route of its own", "No hosted route for an agent without a GPU" ], "agentNotes": [ "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", "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`", "Pass `--no_text_normalization` unless pynini 2.1.6 is installed, since text normalisation imports it", "Pick one task per call and pass only that task's controls, since controls from another task are rejected", "Run `python tools/preflight.py --model_path \u003cdir\u003e` to check the checkpoint before the first call", "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", "On Apple silicon, mlx-audio 0.6.1 or later loads mlx-community's conversion, which isn't inclusionAI's", "Clone only voices you have consent for and label the audio as synthetic, as the card asks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.8 } ], "editorialScores": { "capability": 66, "interface": 55, "reach": 55, "risk": 55, "upkeep": 44 }, "provenanceScore": 53, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts\npip install -r requirements.txt\npython infer.py --model_path inclusionAI/Ming-3.0-tts-0.5B --task IP \\\n --text \"The garden will remain open until sunset.\" \\\n --ip US_James --use_zero_spk_emb --no_text_normalization --output output/tts.wav" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/ming-3-0-tts-0-5b" }, "weights": { "trainedBy": "inclusionAI (Ant Group)", "builtOn": "Qwen2.5-0.5B", "params": "0.5B", "goodFor": "Text to speech with voice cloning, voice design, built-in voices and inline paralinguistic tags in seven languages.", "runtimes": [], "builds": [ { "name": "Ming-3.0-tts-0.5B", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B", "who": "publisher", "precision": "bf16", "sizeGB": 3, "memoryGB": 8, "format": "safetensors", "note": "Plus a 28 MB ONNX speaker encoder; runs with inclusionAI's PyTorch code, none of the runtimes listed for the machine" } ], "run": "git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts \u0026\u0026 pip install -r requirements.txt \u0026\u0026 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", "runBuild": "Ming-3.0-tts-0.5B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ming 3.0", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ming-3.0-tts-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny", "params": "7.9B total, about 1.3B active (backbone)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny" }, { "name": "Ming-3.0-audio-flash", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash", "params": "126B total, 6.5B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash" }, { "name": "Ming-3.0-audio-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny", "params": "9.3B total, 2.7B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny" }, { "name": "Ming-3.0-asr-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny", "params": "not stated on the card (Hub metadata counts 8,865,800,992 in the files)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny" } ], "olderCount": 54 }, "alsoIn": [ "voice-cloning" ], "area": "voice", "provenance": { "legalEntity": "inclusionAI (the LICENSE's copyright holder; source headers read Copyright (c) Ant Group)", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusionAI publishes through its Hugging Face organisation (not verified by Hugging Face) and its GitHub organisation, which link to each other. Neither links a website of its own, so no domain is recorded and no security.txt could be looked for.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, whose messages say what changed (dialect metrics updated on 15 October, the card's inference instructions aligned with the GitHub release on 19 October). The GitHub repository's history starts with the initial release on 19 October 2026.", "Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, read on 21 October 2026). Nothing on inclusionAI's card or sites links it." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.json" }, "answer": "Ming-3.0-tts-0.5B and OmniVoice score within a point of each other on agent readiness, 57.8 (C) and 56.9 (C). OmniVoice leads on capability.", "b": { "slug": "omnivoice", "name": "OmniVoice", "vendor": "k2-fsa (Next-gen Kaldi)", "vendorUrl": "https://k2-fsa.org", "kind": "weights", "category": "text-to-speech", "summary": "OmniVoice is k2-fsa's open-weight zero-shot text-to-speech model for over 600 languages, initialised from Qwen3-0.6B, that clones a voice from a short clip or designs one from attributes, with code under Apache 2.0 and weights under CC-BY-NC by the card.", "url": "https://www.anchorterminal.com/tools/omnivoice", "markdownUrl": "https://www.anchorterminal.com/tools/omnivoice.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/omnivoice.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/omnivoice.json", "repo": "https://huggingface.co/k2-fsa/OmniVoice", "license": "CC-BY-NC (weights, per the card); Apache-2.0 (code)", "transports": [], "packages": [ { "registry": "huggingface", "name": "k2-fsa/OmniVoice" }, { "registry": "pypi", "name": "omnivoice" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The omnivoice package and its source are public on PyPI and GitHub.", "pricing": "free", "pricingNotes": "Free to download. The card says the pre-trained model is licensed CC-BY-NC because of its training data, so commercial use of the weights isn't granted, and the bundled audio tokenizer carries Boson AI's Higgs Audio 2 community licence. The code is Apache 2.0. Running it costs the hardware, 3.3 GB of F32 safetensors with the tokenizer, on an NVIDIA GPU, Apple Silicon or an Intel Arc GPU per the README (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/k2-fsa/OmniVoice", "capabilities": [ "speech.tts", "speech.languages", "voice.clone", "model.open-weights" ], "tags": [ "open-weights", "model", "text-to-speech", "voice-cloning", "voice-design", "multilingual", "diffusion", "non-commercial", "safetensors", "self-hosted" ], "lastRelease": "2026-07-03", "graded": true, "anchor": { "graded": true, "score": 56.9, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 13, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 86, "interface": 50, "reach": 40, "risk": 35, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "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.", "bestFor": "Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU", "strengths": [ "Over 600 languages, with training sources and hours per language published", "Voice cloning from a short clip, voice design from attributes, or an automatic voice, in one API", "3.3 GB of files, about 4 GB in memory, on NVIDIA, Apple Silicon or Intel Arc GPUs", "Inline non-verbal tags and pinyin or phoneme pronunciation fixes", "Ungated, with hashed files in k2-fsa's own organisation and evaluation scripts in the repository" ], "weaknesses": [ "Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold", "The licence changed from Apache 2.0 on 3 July 2026 with no dated note, and recent licence questions have no reply", "Not in any released version of Transformers, vLLM, SGLang, llama.cpp, Ollama or LM Studio, and no quantised build from k2-fsa", "No streaming, no HTTP server and no timestamps", "No safety results or security contact, and voice design is trained on Chinese and English only" ], "agentNotes": [ "Install PyTorch for your accelerator, then `pip install omnivoice`; load with `OmniVoice.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-batch` spreads 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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.9 } ], "editorialScores": { "capability": 86, "interface": 50, "reach": 40, "risk": 35, "upkeep": 48 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install torch==2.8.0+cu128 torchaudio==2.8.0+cu128 --extra-index-url https://download.pytorch.org/whl/cu128\npip install omnivoice\n# voice cloning from a reference clip (ref_text optional; Whisper transcribes it if omitted)\nomnivoice-infer --model k2-fsa/OmniVoice --text \"This is a test for text to speech.\" --ref_audio ref.wav --ref_text \"Transcription of the reference audio.\" --output hello.wav" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/omnivoice" }, "weights": { "trainedBy": "k2-fsa (Next-gen Kaldi)", "builtOn": "Qwen/Qwen3-0.6B (initialised from it as the language-model backbone; Apache 2.0)", "params": "0.61B (main checkpoint) plus the Higgs Audio v2 tokenizer", "goodFor": "Zero-shot voice cloning and attribute-designed voices in over 600 languages, for non-commercial use.", "runtimes": [], "builds": [ { "name": "OmniVoice", "url": "https://huggingface.co/k2-fsa/OmniVoice", "who": "publisher", "precision": "fp32", "sizeGB": 3.27, "memoryGB": 8, "format": "safetensors", "note": "Includes the 0.81 GB Higgs Audio v2 tokenizer under Boson AI's licence. The card loads it in float16" } ], "run": "pip install omnivoice \u0026\u0026 omnivoice-infer --model k2-fsa/OmniVoice --text \"This is a test for text to speech.\" --instruct \"female, low pitch, british accent\" --output hello.wav", "runBuild": "OmniVoice", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "k2-fsa (Next-gen Kaldi)", "family": "OmniVoice", "url": "https://www.anchorterminal.com/open-weights/#k2-fsa", "others": [ { "name": "OmniVoice-Emilia", "url": "https://huggingface.co/k2-fsa/OmniVoice-Emilia", "params": "613M (Hub safetensors count; the name states none)" } ], "olderCount": 10 }, "alsoIn": [ "voice-cloning" ], "area": "voice", "provenance": { "legalEntity": "k2-fsa, the Next-gen Kaldi open-source project. The paper's authors are at Xiaomi Corp.; no company is named as the licensor", "domain": "k2-fsa.org", "domainRegistered": "2021-02-08", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/k2-fsa/OmniVoice/commits/master", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links github.com/k2-fsa/OmniVoice, and the GitHub README links back to huggingface.co/k2-fsa/OmniVoice. The k2-fsa organisation on Hugging Face isn't checked for verification here.", "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.", "The Hugging Face history is 18 commits from 30 March to 3 July 2026. The weights haven't changed since 30 March; later commits are card edits and the tokenizer's LICENSE. The GitHub repository has tags 0.1.2 to 0.2.1 (4 April to 16 July 2026) and its last commit on 24 August 2026.", "k2-fsa.org/.well-known/security.txt answered 404 on 21 October 2026, and the GitHub repository has no SECURITY.md. RDAP gives the domain's registration as 8 February 2021." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/omnivoice.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "k2-fsa (Next-gen Kaldi)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "CC-BY-NC (weights, per the card); Apache-2.0 (code)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-19", "b": "2026-07-03", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ming-3.0-tts-0.5B and OmniVoice score within a point of each other on agent readiness, 57.8 (C) and 56.9 (C). OmniVoice leads on capability.", "question": "Which is better for AI agents, Ming-3.0-tts-0.5B or OmniVoice?" } ], "goodFor": [ { "aheadOn": [ "Reach, 55 against 40", "Risk, 55 against 35", "Agent interface, 55 against 50" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5" ], "goodFor": "Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence", "slug": "ming-3-0-tts-0-5b", "watchFor": "No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)" }, { "aheadOn": [ "Capability, 86 against 66" ], "also": null, "goodFor": "Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU", "slug": "omnivoice", "watchFor": "Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold" } ], "job": { "capability": "speech.tts", "name": "Text-to-speech" }, "others": [ { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny.json", "title": "Ming-3.0-tts-0.5B vs Ming-3.0-tts-tiny", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.json", "title": "Ming-3.0-tts-0.5B vs Qwen3-TTS 12Hz 1.7B CustomVoice", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.json", "title": "Ming-3.0-tts-0.5B vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.json", "title": "Ming-3.0-tts-tiny vs OmniVoice", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice" }, { "json": "https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.json", "title": "OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice", "url": "https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice" }, { "json": "https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo.json", "title": "OmniVoice vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo" } ], "scores": [ { "by": 20, "edge": "omnivoice", "key": "capability", "ming-3-0-tts-0-5b": 66, "name": "Capability", "omnivoice": 86, "weight": 35 }, { "by": 15, "edge": "ming-3-0-tts-0-5b", "key": "reach", "ming-3-0-tts-0-5b": 55, "name": "Reach", "omnivoice": 40, "weight": 25 }, { "by": 20, "edge": "ming-3-0-tts-0-5b", "key": "risk", "ming-3-0-tts-0-5b": 55, "name": "Risk", "omnivoice": 35, "weight": 20 }, { "by": 5, "edge": "ming-3-0-tts-0-5b", "key": "interface", "ming-3-0-tts-0-5b": 55, "name": "Agent interface", "omnivoice": 50, "weight": 10 }, { "by": 4, "edge": "omnivoice", "key": "upkeep", "ming-3-0-tts-0-5b": 44, "name": "Documentation and upkeep", "omnivoice": 48, "weight": 10 } ], "summary": "Ming-3.0-tts-0.5B and OmniVoice score within a point of each other on agent readiness, 57.8 (C) and 56.9 (C). OmniVoice leads on capability. Both do text-to-speech.", "tutorials": [], "verdicts": { "ming-3-0-tts-0-5b": "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.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice", "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.min.md" }, "markdown": "Ming-3.0-tts-0.5B and OmniVoice score within a point of each other on agent readiness, 57.8 (C) and 56.9 (C). OmniVoice leads on capability. Both do text-to-speech.\n\n- Ming-3.0-tts-0.5B: grade C, 57.8/100, rank #69 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.json\n- OmniVoice: grade C, 56.9/100, rank #72 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/omnivoice.md · JSON https://www.anchorterminal.com/api/v1/tools/omnivoice.json\n- Best text-to-speech APIs for AI agents: https://www.anchorterminal.com/best/text-to-speech/index.md\n- All 76 tts comparisons: https://www.anchorterminal.com/compare/text-to-speech/index.md\n- Best voice cloning and custom voice APIs for AI agents: https://www.anchorterminal.com/best/voice-cloning/index.md\n- All 76 cloning comparisons: https://www.anchorterminal.com/compare/voice-cloning/index.md\n\n## Which one, for what\n\n### Ming-3.0-tts-0.5B (C)\n\nGood for: Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence\n\nAhead on:\n- Reach, 55 against 40\n- Risk, 55 against 35\n- Agent interface, 55 against 50\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)\n\n### OmniVoice (C)\n\nGood for: Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU\n\nAhead on:\n- Capability, 86 against 66\n\nWatch for: Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-tts-0.5B | OmniVoice | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 66 | 86 | OmniVoice +20 |\n| Reach | 25% (25 this run) | 55 | 40 | Ming-3.0-tts-0.5B +15 |\n| Risk | 20% (20 this run) | 55 | 35 | Ming-3.0-tts-0.5B +20 |\n| Agent interface | 10% (10 this run) | 55 | 50 | Ming-3.0-tts-0.5B +5 |\n| Documentation and upkeep | 10% (10 this run) | 44 | 48 | OmniVoice +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.8 · C** | **56.9 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-tts-0.5B | OmniVoice |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | k2-fsa (Next-gen Kaldi) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | CC-BY-NC (weights, per the card); Apache-2.0 (code) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-19 | 2026-07-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Ming-3.0-tts-0.5B.** 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.\n\n**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.\n\n## Before you call either\n\n### Ming-3.0-tts-0.5B\n\n1. 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\n2. 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`\n3. Pass `--no_text_normalization` unless pynini 2.1.6 is installed, since text normalisation imports it\n4. Pick one task per call and pass only that task's controls, since controls from another task are rejected\n5. Run `python tools/preflight.py --model_path \u003cdir\u003e` to check the checkpoint before the first call\n6. 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\n7. On Apple silicon, mlx-audio 0.6.1 or later loads mlx-community's conversion, which isn't inclusionAI's\n8. Clone only voices you have consent for and label the audio as synthetic, as the card asks\n\n### OmniVoice\n\n1. Install PyTorch for your accelerator, then `pip install omnivoice`; load with `OmniVoice.from_pretrained('k2-fsa/OmniVoice', device_map='cuda:0', dtype=torch.float16)` (or mps, or xpu)\n2. Clone with `model.generate(text=..., ref_audio='ref.wav', ref_text='...')`; omitting ref_text loads a Whisper model to transcribe the clip\n3. Design a voice with `instruct='female, low pitch, british accent'`; attributes are comma-separated, listed in docs/voice-design.md\n4. Output is a list of numpy arrays at 24 kHz; write with `sf.write('out.wav', audio[0], 24000)`\n5. Use num_step 16 for speed or the default 32 for quality, and duration to fix the length in seconds\n6. From the shell, `omnivoice-infer --model k2-fsa/OmniVoice --text ... --output out.wav`; `omnivoice-infer-batch` spreads work across GPUs\n7. Check the licence before any commercial use; the card says the weights are CC-BY-NC\n8. Only clone a voice you have the right to use\n\n## Questions\n\n### Which is better for AI agents, Ming-3.0-tts-0.5B or OmniVoice?\n\nMing-3.0-tts-0.5B and OmniVoice score within a point of each other on agent readiness, 57.8 (C) and 56.9 (C). OmniVoice leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-tts-0-5b\", \"b\": \"omnivoice\"}`. From a terminal: `anchor compare ming-3-0-tts-0-5b omnivoice`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.json and https://www.anchorterminal.com/api/v1/tools/omnivoice.json\n\n## Other comparisons with Ming-3.0-tts-0.5B or OmniVoice\n\n- [Ming-3.0-tts-0.5B vs Ming-3.0-tts-tiny](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny.md)\n- [Ming-3.0-tts-0.5B vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.md)\n- [Ming-3.0-tts-0.5B vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.md)\n- [Ming-3.0-tts-tiny vs OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.md)\n- [OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.md)\n- [OmniVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Ming-3.0-tts-0.5B vs OmniVoice", "url": "" } ], "description": "Ming-3.0-tts-0.5B and OmniVoice score within a point of each other for text-to-speech, 57.8 and 56.9 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Ming-3.0-tts-0.5B C 57.8", "OmniVoice C 56.9", "scores" ], "h1": "Ming-3.0-tts-0.5B vs OmniVoice", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-tts-0-5b-vs-omnivoice.png", "path": "/compare/ming-3-0-tts-0-5b-vs-omnivoice", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-tts-0.5B vs OmniVoice for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice" }, "tokens": { "markdown": 2050, "slim": 430 }, "version": 1 }