{ "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": "VieNeu-TTS v3 Turbo and Ming-3.0-tts-0.5B score within a point of each other on agent readiness, 58.5 (C) and 57.8 (C). Ming-3.0-tts-0.5B leads on risk.", "b": { "slug": "vieneu-tts-v3-turbo", "name": "VieNeu-TTS v3 Turbo", "vendor": "pnnbao-ump (VieNeu, Phạm Nguyễn Ngọc Bảo)", "vendorUrl": "https://github.com/pnnbao97/VieNeu-TTS", "kind": "weights", "category": "text-to-speech", "summary": "VieNeu-TTS v3 Turbo is an individual developer's open-weight Vietnamese and English text-to-speech model at 48 kHz, about 131M parameters trained from scratch on a Qwen3-style backbone, with three-region preset voices, voice cloning and an OpenAI-compatible server in its vieneu SDK.", "url": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo", "markdownUrl": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vieneu-tts-v3-turbo.json", "repo": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "pnnbao-ump/VieNeu-TTS-v3-Turbo" }, { "registry": "pypi", "name": "vieneu" } ], "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 training dataset it names, pnnbao-ump/VieNeu-TTS-10k-ENVI, is gated, but it isn't needed to run the model. The audio codec, OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano, downloads separately and isn't gated.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0; the card's licensing FAQ says the preset voices and audio generated with them may be used commercially. Running it costs very little hardware, under 0.5 GB of files, on a CPU through ONNX Runtime or an NVIDIA GPU through PyTorch, about 1 GB of VRAM at 16 streams on an RTX 3060 per the publisher (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/pnnbao97/VieNeu-TTS", "capabilities": [ "speech.tts", "speech.voices", "speech.languages", "speech.streaming", "voice.clone", "model.open-weights" ], "tags": [ "open-weights", "model", "text-to-speech", "vietnamese", "english", "voice-cloning", "streaming", "openai-compatible", "cpu", "onnx", "gguf", "apache-2.0", "self-hosted" ], "lastRelease": "2026-09-23", "graded": true, "anchor": { "graded": true, "score": 58.5, "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": 10, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 65, "risk": 50, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version.", "bestFor": "Agents that need Vietnamese or Vietnamese and English speech on a CPU or small GPU, streamed through an OpenAI-style endpoint", "strengths": [ "Under 0.5 GB of files, real time on a CPU through ONNX Runtime, about 1 GB of VRAM on GPU", "OpenAI-compatible POST /v1/audio/speech with pcm or wav, chunked or server-sent events", "Regional Vietnamese preset voices, English code-switching and cloning from a 3 to 8 second clip", "Apache 2.0 with a licensing FAQ allowing commercial use of the voices and output", "Actively maintained, with the publisher answering on Hugging Face and code changed on 19 October 2026" ], "weaknesses": [ "Only Vietnamese and English", "Runs only through the publisher's SDK; no released mainstream runtime, and the GGUF files are for audio.cpp", "Weights replaced in place many times without notes, and the card lags the SDK's voice list", "One individual maintainer, with no security contact or safety results", "Training data is a gated set with an undisclosed pipeline" ], "agentNotes": [ "On CPU, `pip install vieneu`, then `from vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer(text, voice='Mai Anh'), 'out.wav')`; `Vieneu(precision='int8')` is about twice as fast on a CPU with VNNI", "On GPU, install torch 2.8 for CUDA 12.8 and transformers 4.57.6 before vieneu; it switches to PyTorch with batching and stream scheduling", "Serve with `uv run python -m apps.openai_speech` from a clone of pnnbao97/VieNeu-TTS, then POST to http://localhost:8000/v1/audio/speech with model vieneu-v3-turbo, voice and response_format pcm or wav", "Call `list_preset_voices()` for the voice names of your installed version; pin the SDK version if the roster matters", "Clone with `ref_audio='voice.wav', denoise=True`, or enrol once with `add_voice(name, path)` and reuse the name", "Keep temperature near 0.8; the style argument is ignored on v3 Turbo", "Only clone a voice you have the right to use" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.5 } ], "editorialScores": { "interface": 60, "reach": 65, "risk": 50, "upkeep": 58 }, "provenanceScore": 53, "checked": { "categories": { "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 vieneu # CPU, torch-free ONNX path; for CUDA install torch 2.8 and transformers 4.57.6 first\npython -c \"from vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer('Xin chào, đây là VieNeu-TTS.', voice='Mai Anh'), 'out.wav')\"", "http": "# after: git clone https://github.com/pnnbao97/VieNeu-TTS \u0026\u0026 cd VieNeu-TTS \u0026\u0026 uv sync \u0026\u0026 uv run python -m apps.openai_speech\ncurl -X POST http://localhost:8000/v1/audio/speech \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"vieneu-v3-turbo\", \"voice\": \"Mai Anh\", \"input\": \"Xin chào!\", \"response_format\": \"wav\"}' \\\n --output speech.wav" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/vieneu-tts-v3-turbo" }, "weights": { "trainedBy": "Phạm Nguyễn Ngọc Bảo (pnnbao-ump)", "params": "0.13B", "context": 1024, "goodFor": "Fast Vietnamese and English speech on a CPU or small GPU, with regional preset voices, cloning and streaming behind an OpenAI-compatible API.", "runtimes": [], "builds": [ { "name": "VieNeu-TTS-v3-Turbo update/ (safetensors)", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/tree/main/update", "who": "publisher", "precision": "bf16", "sizeGB": 0.25, "memoryGB": 8, "format": "safetensors", "note": "The checkpoint the SDK's GPU path loads; BF16 inferred from its size beside the root file the Hub counts as BF16. Needs the 0.09 GB MOSS-Audio-Tokenizer-Nano codec, loaded with trust_remote_code" }, { "name": "VieNeu-TTS-v3-Turbo onnx_update/ (FP32)", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/tree/main/onnx_update", "who": "publisher", "precision": "fp32", "sizeGB": 0.48, "memoryGB": 8, "format": "onnx", "note": "The SDK's default CPU path" }, { "name": "VieNeu-TTS-v3-Turbo onnx_int8/ (INT8)", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/tree/main/onnx_int8", "who": "publisher", "precision": "int8", "sizeGB": 0.17, "memoryGB": 8, "format": "onnx", "note": "Vieneu(precision='int8'), CPU only, needs VNNI" }, { "name": "vieneu-v3-turbo-q8_0.gguf", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/blob/main/gguf/vieneu-v3-turbo-q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.19, "memoryGB": 8, "format": "gguf", "note": "For audio.cpp, not llama.cpp. A BF16 GGUF (0.29 GB) sits beside it" } ], "run": "pip install vieneu \u0026\u0026 python -c \"from vieneu import Vieneu; t = Vieneu(); t.save(t.infer('Xin chào!', voice='Mai Anh'), 'out.wav')\"", "runBuild": "VieNeu-TTS-v3-Turbo onnx_update/ (FP32)", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "VieNeu (pnnbao-ump)", "family": "VieNeu-TTS v3", "url": "https://www.anchorterminal.com/open-weights/#vieneu", "others": [ { "name": "VieNeu-TTS-v3-Nano", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Nano", "params": "not stated (no safetensors count on the Hub)" } ], "olderCount": 7 }, "alsoIn": [ "voice-cloning" ], "area": "voice", "provenance": { "legalEntity": "Phạm Nguyễn Ngọc Bảo, an individual (named on the card as author; Hugging Face account pnnbao-ump, GitHub account pnnbao97). No company found", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/pnnbao97/VieNeu-TTS/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links github.com/pnnbao97/VieNeu-TTS, and that README links back to huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo, so the two accounts are the same publisher's.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. There is no domain of the publisher's own.", "The Hugging Face history is 65 commits from 5 June to 23 September 2026. Many replace model.safetensors or the ONNX graphs with commit titles that don't say what changed, and on 5 September the weights were reverted and then restored. The GitHub repository is tagged up to v3.8.3 (23 September 2026) and last changed on 19 October 2026.", "The GitHub repository has no SECURITY.md (its .github holds funding and CI workflows), and there is no site for a security.txt." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "pnnbao-ump (VieNeu, Phạm Nguyễn Ngọc Bảo)", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-19", "b": "2026-09-23", "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": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "VieNeu-TTS v3 Turbo and Ming-3.0-tts-0.5B score within a point of each other on agent readiness, 58.5 (C) and 57.8 (C). Ming-3.0-tts-0.5B leads on risk.", "question": "Which is better for AI agents, Ming-3.0-tts-0.5B or VieNeu-TTS v3 Turbo?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 50" ], "also": null, "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": [ "Reach, 65 against 55", "Agent interface, 60 against 55", "Documentation and upkeep, 58 against 44" ], "also": null, "goodFor": "Agents that need Vietnamese or Vietnamese and English speech on a CPU or small GPU, streamed through an OpenAI-style endpoint", "slug": "vieneu-tts-v3-turbo", "watchFor": "Only Vietnamese and English" } ], "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-omnivoice.json", "title": "Ming-3.0-tts-0.5B vs OmniVoice", "url": "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-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-tiny-vs-vieneu-tts-v3-turbo.json", "title": "Ming-3.0-tts-tiny vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-vs-vieneu-tts-v3-turbo.json", "title": "Qwen3-TTS 12Hz 1.7B CustomVoice vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-vs-vieneu-tts-v3-turbo" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "vieneu-tts-v3-turbo", "key": "reach", "ming-3-0-tts-0-5b": 55, "name": "Reach", "vieneu-tts-v3-turbo": 65, "weight": 25 }, { "by": 5, "edge": "ming-3-0-tts-0-5b", "key": "risk", "ming-3-0-tts-0-5b": 55, "name": "Risk", "vieneu-tts-v3-turbo": 50, "weight": 20 }, { "by": 5, "edge": "vieneu-tts-v3-turbo", "key": "interface", "ming-3-0-tts-0-5b": 55, "name": "Agent interface", "vieneu-tts-v3-turbo": 60, "weight": 10 }, { "by": 14, "edge": "vieneu-tts-v3-turbo", "key": "upkeep", "ming-3-0-tts-0-5b": 44, "name": "Documentation and upkeep", "vieneu-tts-v3-turbo": 58, "weight": 10 } ], "summary": "VieNeu-TTS v3 Turbo and Ming-3.0-tts-0.5B score within a point of each other on agent readiness, 58.5 (C) and 57.8 (C). Ming-3.0-tts-0.5B leads on risk. 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.", "vieneu-tts-v3-turbo": "A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version." } }, "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-vieneu-tts-v3-turbo", "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.min.md" }, "markdown": "VieNeu-TTS v3 Turbo and Ming-3.0-tts-0.5B score within a point of each other on agent readiness, 58.5 (C) and 57.8 (C). Ming-3.0-tts-0.5B leads on risk. 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- VieNeu-TTS v3 Turbo: grade C, 58.5/100, rank #66 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.md · JSON https://www.anchorterminal.com/api/v1/tools/vieneu-tts-v3-turbo.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- Risk, 55 against 50\n\nWatch for: No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)\n\n### VieNeu-TTS v3 Turbo (C)\n\nGood for: Agents that need Vietnamese or Vietnamese and English speech on a CPU or small GPU, streamed through an OpenAI-style endpoint\n\nAhead on:\n- Reach, 65 against 55\n- Agent interface, 60 against 55\n- Documentation and upkeep, 58 against 44\n\nWatch for: Only Vietnamese and English\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-tts-0.5B | VieNeu-TTS v3 Turbo | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 65 | VieNeu-TTS v3 Turbo +10 |\n| Risk | 20% (20 this run) | 55 | 50 | Ming-3.0-tts-0.5B +5 |\n| Agent interface | 10% (10 this run) | 55 | 60 | VieNeu-TTS v3 Turbo +5 |\n| Documentation and upkeep | 10% (10 this run) | 44 | 58 | VieNeu-TTS v3 Turbo +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.8 · C** | **58.5 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-tts-0.5B | VieNeu-TTS v3 Turbo |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | pnnbao-ump (VieNeu, Phạm Nguyễn Ngọc Bảo) |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-19 | 2026-09-23 |\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) | 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**VieNeu-TTS v3 Turbo.** A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version.\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### VieNeu-TTS v3 Turbo\n\n1. On CPU, `pip install vieneu`, then `from vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer(text, voice='Mai Anh'), 'out.wav')`; `Vieneu(precision='int8')` is about twice as fast on a CPU with VNNI\n2. On GPU, install torch 2.8 for CUDA 12.8 and transformers 4.57.6 before vieneu; it switches to PyTorch with batching and stream scheduling\n3. Serve with `uv run python -m apps.openai_speech` from a clone of pnnbao97/VieNeu-TTS, then POST to http://localhost:8000/v1/audio/speech with model vieneu-v3-turbo, voice and response_format pcm or wav\n4. Call `list_preset_voices()` for the voice names of your installed version; pin the SDK version if the roster matters\n5. Clone with `ref_audio='voice.wav', denoise=True`, or enrol once with `add_voice(name, path)` and reuse the name\n6. Keep temperature near 0.8; the style argument is ignored on v3 Turbo\n7. 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 VieNeu-TTS v3 Turbo?\n\nVieNeu-TTS v3 Turbo and Ming-3.0-tts-0.5B score within a point of each other on agent readiness, 58.5 (C) and 57.8 (C). Ming-3.0-tts-0.5B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-tts-0-5b\", \"b\": \"vieneu-tts-v3-turbo\"}`. From a terminal: `anchor compare ming-3-0-tts-0-5b vieneu-tts-v3-turbo`\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/vieneu-tts-v3-turbo.json\n\n## Other comparisons with Ming-3.0-tts-0.5B or VieNeu-TTS v3 Turbo\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 OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.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-tiny vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.md)\n- [OmniVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo.md)\n- [Qwen3-TTS 12Hz 1.7B CustomVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-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 VieNeu-TTS v3 Turbo", "url": "" } ], "description": "VieNeu-TTS v3 Turbo and Ming-3.0-tts-0.5B score within a point of each other for text-to-speech, 58.5 and 57.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Ming-3.0-tts-0.5B C 57.8", "VieNeu-TTS v3 Turbo C 58.5", "scores" ], "h1": "Ming-3.0-tts-0.5B vs VieNeu-TTS v3 Turbo", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.png", "path": "/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-tts-0.5B vs VieNeu-TTS v3 Turbo for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo" }, "tokens": { "markdown": 2100, "slim": 480 }, "version": 1 }