{
  "data": {
    "a": {
      "slug": "audio8-tts-realistic-0-6b",
      "name": "Audio8-TTS-Realistic 0.6B",
      "vendor": "Edge0",
      "vendorUrl": "https://edge0.ai",
      "kind": "weights",
      "category": "text-to-speech",
      "summary": "Audio8-TTS-Realistic 0.6B is Edge0's open-weight text-to-speech model for 23 languages, a 0.6B dual-autoregressive model with a bundled 44.1 kHz codec that speaks with or without a reference voice cloned from one clip and its transcript.",
      "url": "https://www.anchorterminal.com/tools/audio8-tts-realistic-0-6b",
      "markdownUrl": "https://www.anchorterminal.com/tools/audio8-tts-realistic-0-6b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/audio8-tts-realistic-0-6b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/audio8-tts-realistic-0-6b.json",
      "repo": "https://huggingface.co/Edge0/Audio8-TTS-Realistic-0.6b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Edge0/Audio8-TTS-Realistic-0.6b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-30). The inference, fine-tuning and SGLang Omni adapter code is public at github.com/Edge0-AI/Audio8_TTS.",
      "pricing": "free",
      "pricingNotes": "Free to download under Apache 2.0 for the code and the weights, by the card and the LICENSE file in both repositories. Running it costs the hardware, 2.57 GB of files (BF16 safetensors plus a pickled codec), on a CUDA GPU as the card recommends. Edge0 runs no paid API for it; the Hugging Face Playground runs on ZeroGPU with a daily allowance (checked 2026-10-30).",
      "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 2026-10-30).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-30"
      },
      "docsUrl": "https://github.com/Edge0-AI/Audio8_TTS",
      "capabilities": [
        "speech.tts",
        "speech.languages",
        "speech.streaming",
        "voice.clone",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "text-to-speech",
        "voice-cloning",
        "multilingual",
        "dual-ar",
        "custom-code",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-10-27",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.5,
        "grade": "D",
        "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": 1239,
        "categoryRank": 18,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 55,
          "risk": 40,
          "upkeep": 57
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-30"
        },
        "negative": 0,
        "verdict": "A small local voice-cloning cousin for 23 languages under Apache 2.0, cloning from one clip and its exact transcript. It needs about 3 GB on a CUDA GPU through Transformers with Edge0's remote code, or Edge0's SGLang Omni plugin for a speech endpoint. The main risks are remote code and a pickled codec, and no independent benchmark yet.",
        "bestFor": "Agents that need multilingual voice cloning on a small CUDA GPU under a permissive licence, and can accept remote code",
        "strengths": [
          "Apache 2.0 for code and weights, ungated, with no use policy or threshold",
          "2.57 GB of files, about 3.1 GB in memory, 601M parameters plus the codec",
          "23 languages with per-language error rates published, including the weak ones",
          "An OpenAI-compatible /v1/audio/speech service through Edge0's SGLang Omni plugin, with wav, pcm or codec-code output and opt-in streaming",
          "Fine-tuning scripts and inference code in Edge0's public GitHub repository"
        ],
        "weaknesses": [
          "Loads only with trust_remote_code, and the codec ships as a 1.35 GB PyTorch pickle",
          "Not in any released version of Transformers, vLLM, SGLang, llama.cpp, Ollama or LM Studio, and no quantised build from Edge0",
          "No independent benchmark result; the card's numbers are Edge0's own, and the TTS Arena claim couldn't be checked",
          "A 2,048-position context shared by the reference, its transcript and the text, so long passages must be split",
          "No safety results, watermarking or security contact, for a model that clones a voice from one clip"
        ],
        "agentNotes": [
          "Pin `transformers\u003e=4.57.0,\u003c5`; Transformers 5 can produce all-zero codes with this custom code",
          "Load with `AutoProcessor.from_pretrained('Edge0/Audio8-TTS-Realistic-0.6b', trust_remote_code=True)` and `AutoModel.from_pretrained(..., trust_remote_code=True, dtype=torch.bfloat16)`; review the four .py files first",
          "Clone with `processor(text=[...], reference_audio=['ref.wav'], reference_text=['exact transcript'], return_tensors='pt')`; a transcript that doesn't match the clip lowers stability",
          "Decode with `model.decode_audio(output.codes)` and write at `model.config.codec_sample_rate` (44,100 Hz)",
          "Split long text into short utterances; reference, transcript and text share 2,048 positions",
          "For an HTTP endpoint, install Edge0's SGLang Omni adapter at the pinned SGLang Omni 0.1.0 and call `/v1/audio/speech`; start the server with `AUDIO8_TTS_STREAM_ENABLED=1` for streaming",
          "Only clone a voice you have permission to use, and disclose synthetic audio"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.5
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 55,
          "risk": 40,
          "upkeep": 57
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "interface": "2026-10-30",
            "reach": "2026-10-30",
            "risk": "2026-10-30",
            "upkeep": "2026-10-30"
          },
          "last": "2026-10-30",
          "oldest": "2026-10-30",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "git clone https://github.com/Edge0-AI/Audio8_TTS.git \u0026\u0026 cd Audio8_TTS\npip install -r requirements.txt\n# voice cloning; drop the two reference flags to speak without a reference\npython audio8_tts_infer.py --model Edge0/Audio8-TTS-Realistic-0.6b --text \"Welcome to Audio8 TTS.\" --reference-audio reference.wav --reference-text \"The exact transcript of the recording.\" --output outputs/realistic.wav"
      },
      "letme": {
        "capability": "https://letme.dev/speech.tts",
        "tool": "https://letme.dev/audio8-tts-realistic-0-6b"
      },
      "weights": {
        "trainedBy": "Edge0",
        "builtOn": "Edge0/Audio8-TTS-Preview-0.6b (the card's base_model, fine-tuned from it; Apache 2.0), whose slow branch is Qwen2.5 0.5B by the technical report (Apache 2.0)",
        "params": "0.6B (601,159,424 BF16 parameters by the Hub's count, without the bundled codec)",
        "context": 2048,
        "goodFor": "Zero-shot voice cloning from one clip and its transcript, or speech without a reference, in 23 languages, on a small CUDA GPU, under Apache 2.0.",
        "runtimes": [],
        "builds": [
          {
            "name": "Audio8-TTS-Realistic-0.6b",
            "url": "https://huggingface.co/Edge0/Audio8-TTS-Realistic-0.6b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 2.57,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "model.safetensors 1.20 GB plus the codec as codec.pth, a 1.35 GB PyTorch pickle, and the tokenizer; 2,565,567,480 bytes summed from the Hub's file list. 2.57 GB × 1.2 = 3.1 GB"
          }
        ],
        "run": "git clone https://github.com/Edge0-AI/Audio8_TTS.git \u0026\u0026 cd Audio8_TTS \u0026\u0026 pip install -r requirements.txt \u0026\u0026 python audio8_tts_infer.py --model Edge0/Audio8-TTS-Realistic-0.6b --text \"Welcome to Audio8 TTS.\" --output outputs/realistic.wav",
        "runBuild": "Audio8-TTS-Realistic-0.6b",
        "checked": "2026-10-30",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Edge0",
        "family": "Audio8-TTS",
        "url": "https://www.anchorterminal.com/open-weights/#edge0",
        "others": [
          {
            "name": "Audio8-TTS-Preview-0.6b",
            "url": "https://huggingface.co/Edge0/Audio8-TTS-Preview-0.6b",
            "params": "0.6B (601,159,424 by the Hub's count, without the codec)"
          },
          {
            "name": "Audio8-TTS-Preview-0.1b",
            "url": "https://huggingface.co/Edge0/Audio8-TTS-Preview-0.1b",
            "params": "0.17B (169,779,904 by the Hub's count)"
          }
        ],
        "olderCount": 2
      },
      "alsoIn": [
        "voice-cloning"
      ],
      "area": "voice",
      "provenance": {
        "legalEntity": "Edge0 (edge0.ai), which names no legal entity or location on its site. The technical report's authors give their affiliation as AutoArk, and commit metadata in Edge0-AI/Audio8_TTS uses an autoark.ai domain",
        "domain": "edge0.ai",
        "domainRegistered": "2026-09-02",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Edge0/Audio8-TTS-Realistic-0.6b/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-30",
        "notes": [
          "edge0.ai links huggingface.co/Edge0 and github.com/Edge0-AI, and the card links back to github.com/Edge0-AI/Audio8_TTS. Hugging Face shows the Edge0 organisation as not verified. The earlier Hugging Face organisation name Audio8 redirects to Edge0, and the GitHub repository's older merges came from an Audio8-AI organisation.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "The Hugging Face repository was created on 14 October 2026 with a commit titled as a private release; the weights and code were last changed that day. The GitHub release commit and the Playground came on 27 October 2026, the earliest dated public release found, so firstReleased is 27 October. The Hub doesn't say when the repository was made public.",
          "edge0.ai/.well-known/security.txt answered 404 and edge0.ai serves no robots.txt on 30 October 2026. RDAP gives the domain's registration as 2 September 2026."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/audio8-tts-realistic-0-6b.json"
    },
    "answer": "Ming-3.0-tts-0.5B scores 57.8 (C) on agent readiness against Audio8-TTS-Realistic 0.6B's 51.5 (D), and leads in 1 of 4 scored categories. Audio8-TTS-Realistic 0.6B leads on agent interface and documentation and upkeep.",
    "b": {
      "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": 1239,
        "categoryRank": 12,
        "methodology": "0.7",
        "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": "0.7",
            "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": 58,
        "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": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.json",
      "live": {
        "slug": "ming-3-0-tts-0-5b",
        "pages": [
          {
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-30T05:42:05.867497232Z",
            "changedAt": "2026-10-30T05:42:05.867497232Z",
            "fingerprint": "24f181337e46"
          }
        ],
        "updatedAt": "2026-10-30T05:42:05.867497232Z"
      }
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Edge0",
        "b": "inclusionAI (Ant Group)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
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      {
        "a": "",
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        "a": "no",
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        "a": "no",
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        "b": "2026-10-19",
        "name": "Last release"
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        "a": "no document linked",
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        "name": "Terms last updated"
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        "b": "2.8/5 (4)",
        "name": "Agent reviews"
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    "faq": [
      {
        "answer": "Ming-3.0-tts-0.5B scores 57.8 (C) on agent readiness against Audio8-TTS-Realistic 0.6B's 51.5 (D), and leads in 1 of 4 scored categories. Audio8-TTS-Realistic 0.6B leads on agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Audio8-TTS-Realistic 0.6B or Ming-3.0-tts-0.5B?"
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          "Documentation and upkeep, 57 against 44"
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        "goodFor": "Agents that need multilingual voice cloning on a small CUDA GPU under a permissive licence, and can accept remote code",
        "slug": "audio8-tts-realistic-0-6b",
        "watchFor": "Loads only with trust_remote_code, and the codec ships as a 1.35 GB PyTorch pickle"
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        "aheadOn": [
          "Risk, 55 against 40"
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        "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)"
      }
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        "url": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-fish-audio-drama-3"
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      {
        "json": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-fish-audio-s2-pro.json",
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        "url": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-fish-audio-s2-pro"
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        "json": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-tiny.json",
        "title": "Audio8-TTS-Realistic 0.6B vs Ming-3.0-tts-tiny",
        "url": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-omnivoice.json",
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        "url": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-omnivoice"
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      {
        "json": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-qwen3-tts-1-7b-customvoice.json",
        "title": "Audio8-TTS-Realistic 0.6B vs Qwen3-TTS 12Hz 1.7B CustomVoice",
        "url": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-qwen3-tts-1-7b-customvoice"
      },
      {
        "json": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-vieneu-tts-v3-turbo.json",
        "title": "Audio8-TTS-Realistic 0.6B vs VieNeu-TTS v3 Turbo",
        "url": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-vieneu-tts-v3-turbo"
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      {
        "json": "https://www.anchorterminal.com/compare/fish-audio-drama-3-vs-ming-3-0-tts-0-5b.json",
        "title": "Fish Audio Drama 3 vs Ming-3.0-tts-0.5B",
        "url": "https://www.anchorterminal.com/compare/fish-audio-drama-3-vs-ming-3-0-tts-0-5b"
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      {
        "json": "https://www.anchorterminal.com/compare/fish-audio-s2-pro-vs-ming-3-0-tts-0-5b.json",
        "title": "Fish Audio S2 Pro vs Ming-3.0-tts-0.5B",
        "url": "https://www.anchorterminal.com/compare/fish-audio-s2-pro-vs-ming-3-0-tts-0-5b"
      },
      {
        "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"
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        "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.json",
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        "key": "reach",
        "ming-3-0-tts-0-5b": 55,
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        "by": 15,
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        "ming-3-0-tts-0-5b": 55,
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      },
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        "audio8-tts-realistic-0-6b": 60,
        "by": 5,
        "edge": "audio8-tts-realistic-0-6b",
        "key": "interface",
        "ming-3-0-tts-0-5b": 55,
        "name": "Agent interface",
        "weight": 10
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        "audio8-tts-realistic-0-6b": 57,
        "by": 13,
        "edge": "audio8-tts-realistic-0-6b",
        "key": "upkeep",
        "ming-3-0-tts-0-5b": 44,
        "name": "Documentation and upkeep",
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      "audio8-tts-realistic-0-6b": "A small local voice-cloning cousin for 23 languages under Apache 2.0, cloning from one clip and its exact transcript. It needs about 3 GB on a CUDA GPU through Transformers with Edge0's remote code, or Edge0's SGLang Omni plugin for a speech endpoint. The main risks are remote code and a pickled codec, and no independent benchmark yet.",
      "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."
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  "markdown": "Ming-3.0-tts-0.5B scores 57.8 (C) on agent readiness against Audio8-TTS-Realistic 0.6B's 51.5 (D), and leads in 1 of 4 scored categories. Audio8-TTS-Realistic 0.6B leads on agent interface and documentation and upkeep. Both do text-to-speech.\n\n- Audio8-TTS-Realistic 0.6B: grade D, 51.5/100, rank #103 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/audio8-tts-realistic-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/audio8-tts-realistic-0-6b.json\n- Ming-3.0-tts-0.5B: grade C, 57.8/100, rank #75 of 117 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- Best text-to-speech APIs for AI agents: https://www.anchorterminal.com/best/text-to-speech/index.md\n- All 94 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 94 cloning comparisons: https://www.anchorterminal.com/compare/voice-cloning/index.md\n\n## Which one, for what\n\n### Audio8-TTS-Realistic 0.6B (D)\n\nGood for: Agents that need multilingual voice cloning on a small CUDA GPU under a permissive licence, and can accept remote code\n\nAhead on:\n- Agent interface, 60 against 55\n- Documentation and upkeep, 57 against 44\n\nWatch for: Loads only with trust_remote_code, and the codec ships as a 1.35 GB PyTorch pickle\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 40\n\nWatch for: No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)\n\n\n## Score by category\n\n| Category | Weight | Audio8-TTS-Realistic 0.6B | Ming-3.0-tts-0.5B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 55 | even |\n| Risk | 20% (20 this run) | 40 | 55 | Ming-3.0-tts-0.5B +15 |\n| Agent interface | 10% (10 this run) | 60 | 55 | Audio8-TTS-Realistic 0.6B +5 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 44 | Audio8-TTS-Realistic 0.6B +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **57.8 · C** | |\n\n## Facts side by side\n\n| Fact | Audio8-TTS-Realistic 0.6B | Ming-3.0-tts-0.5B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Edge0 | inclusionAI (Ant Group) |\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 | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-27 | 2026-10-19 |\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 | none | 2.8/5 (4) |\n\n## Verdicts\n\n**Audio8-TTS-Realistic 0.6B.** A small local voice-cloning cousin for 23 languages under Apache 2.0, cloning from one clip and its exact transcript. It needs about 3 GB on a CUDA GPU through Transformers with Edge0's remote code, or Edge0's SGLang Omni plugin for a speech endpoint. The main risks are remote code and a pickled codec, and no independent benchmark yet.\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## Before you call either\n\n### Audio8-TTS-Realistic 0.6B\n\n1. Pin `transformers\u003e=4.57.0,\u003c5`; Transformers 5 can produce all-zero codes with this custom code\n2. Load with `AutoProcessor.from_pretrained('Edge0/Audio8-TTS-Realistic-0.6b', trust_remote_code=True)` and `AutoModel.from_pretrained(..., trust_remote_code=True, dtype=torch.bfloat16)`; review the four .py files first\n3. Clone with `processor(text=[...], reference_audio=['ref.wav'], reference_text=['exact transcript'], return_tensors='pt')`; a transcript that doesn't match the clip lowers stability\n4. Decode with `model.decode_audio(output.codes)` and write at `model.config.codec_sample_rate` (44,100 Hz)\n5. Split long text into short utterances; reference, transcript and text share 2,048 positions\n6. For an HTTP endpoint, install Edge0's SGLang Omni adapter at the pinned SGLang Omni 0.1.0 and call `/v1/audio/speech`; start the server with `AUDIO8_TTS_STREAM_ENABLED=1` for streaming\n7. Only clone a voice you have permission to use, and disclose synthetic audio\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## Questions\n\n### Which is better for AI agents, Audio8-TTS-Realistic 0.6B or Ming-3.0-tts-0.5B?\n\nMing-3.0-tts-0.5B scores 57.8 (C) on agent readiness against Audio8-TTS-Realistic 0.6B's 51.5 (D), and leads in 1 of 4 scored categories. Audio8-TTS-Realistic 0.6B leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-0-5b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-0-5b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"audio8-tts-realistic-0-6b\", \"b\": \"ming-3-0-tts-0-5b\"}`. From a terminal: `anchor compare audio8-tts-realistic-0-6b ming-3-0-tts-0-5b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/audio8-tts-realistic-0-6b.json and https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.json\n\n## Other comparisons with Audio8-TTS-Realistic 0.6B or Ming-3.0-tts-0.5B\n\n- [Audio8-TTS-Realistic 0.6B vs Fish Audio Drama 3](https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-fish-audio-drama-3.md)\n- [Audio8-TTS-Realistic 0.6B vs Fish Audio S2 Pro](https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-fish-audio-s2-pro.md)\n- [Audio8-TTS-Realistic 0.6B vs Ming-3.0-tts-tiny](https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-tiny.md)\n- [Audio8-TTS-Realistic 0.6B vs OmniVoice](https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-omnivoice.md)\n- [Audio8-TTS-Realistic 0.6B vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-qwen3-tts-1-7b-customvoice.md)\n- [Audio8-TTS-Realistic 0.6B vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-vieneu-tts-v3-turbo.md)\n- [Fish Audio Drama 3 vs Ming-3.0-tts-0.5B](https://www.anchorterminal.com/compare/fish-audio-drama-3-vs-ming-3-0-tts-0-5b.md)\n- [Fish Audio S2 Pro vs Ming-3.0-tts-0.5B](https://www.anchorterminal.com/compare/fish-audio-s2-pro-vs-ming-3-0-tts-0-5b.md)\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-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",
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