{
  "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-tiny and Audio8-TTS-Realistic 0.6B score within a point of each other on agent readiness, 52.2 (D) and 51.5 (D). Audio8-TTS-Realistic 0.6B leads on reach and documentation and upkeep.",
    "b": {
      "slug": "ming-3-0-tts-tiny",
      "name": "Ming-3.0-tts-tiny",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "text-to-speech",
      "summary": "Ming-3.0-tts-tiny is inclusionAI's open-weight text-to-speech model, a Ling V3 mixture of experts with 7.9B backbone parameters and about 1.3B active, for voice cloning, voice design and controlled speech in seven languages, under MIT.",
      "url": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny",
      "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-tiny.json",
      "repo": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ming-3.0-tts-tiny"
        }
      ],
      "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). Inference needs the publisher's GitHub repository, which is public.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. inclusionAI sells no per-call access to these weights. Running them costs the hardware, about 17.7 GB of BF16 safetensors, on an NVIDIA H800 as the publisher recommends (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",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "text-to-speech",
        "voice-cloning",
        "multilingual",
        "mit",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.2,
        "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": 17,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 44
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A text-to-speech cousin for voice cloning, voice design and multi-speaker podcast audio in seven languages, under MIT. It needs the publisher's own code or SGLang-Omni and about 22 GB of GPU memory, with CUDA 11.8 pinned. The main risks are that loading runs repository code, no safety work is published and it can clone a voice from a short clip.",
        "bestFor": "Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT",
        "strengths": [
          "Plain MIT licence, no gate and no account",
          "BF16 files of 18.0 GB fit a 24 GB GPU",
          "Ten task modes documented with the inputs each accepts, from cloning to voice design and podcast scripts",
          "An OpenAI-compatible /v1/audio/speech endpoint through SGLang-Omni, with the command on the card",
          "Safetensors with a SHA-256 for every large file, and active upkeep in its first week"
        ],
        "weaknesses": [
          "No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code",
          "Loading runs the publisher's Python code, pinned to transformers 4.46.3, torch 2.7.1 and CUDA 11.8",
          "No quantised build from inclusionAI and no hosted provider",
          "No training data or safety evaluation, and no security route of its own",
          "One task per call, no streaming and no timestamps",
          "The card's generate() temperature disagrees with the config files"
        ],
        "agentNotes": [
          "Install from the GitHub repository, `git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts \u0026\u0026 pip install -r requirements-tiny.txt`, in its own Python 3.10.15 environment, since the 0.5B model pins different versions",
          "Quick start is `bash examples/one_click/run_seed.sh`, which clones from the bundled reference clip",
          "To serve, run `sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tiny` from SGLang-Omni's main branch and POST to /v1/audio/speech with input, response_format and, for cloning, ref_audio plus ref_text",
          "Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts",
          "Expect one complete audio body, since there is no streaming, and no timestamps",
          "Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config",
          "Run `python tools/preflight_tiny.py --model_path \u003cdir\u003e` on the downloaded checkpoint before first use",
          "Label output as synthetic speech where it could pass for a real recording, as the card asks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.2
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 44
        },
        "provenanceScore": 64,
        "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": "git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts\nconda create -n ming-tiny python=3.10.15 -y \u0026\u0026 conda activate ming-tiny\npip install -r requirements-tiny.txt\n# zero-shot cloning from the bundled reference clip (writes outputs/seed.wav)\nbash examples/one_click/run_seed.sh",
        "http": "# after: sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tiny\ncurl -X POST http://127.0.0.1:8000/v1/audio/speech \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"inclusionAI/Ming-3.0-tts-tiny\", \"input\": \"Get the trust fund to the bank early.\", \"response_format\": \"wav\", \"seed\": 123}' \\\n  --output speech.wav"
      },
      "letme": {
        "capability": "https://letme.dev/speech.tts",
        "tool": "https://letme.dev/ming-3-0-tts-tiny"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "lbs-27b",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "builtOn": "Ling V3",
        "params": "7.9B",
        "active": "1.3B",
        "goodFor": "Text to speech with voice cloning, voice design and controlled speech in seven languages, on a mixture-of-experts backbone.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ming-3.0-tts-tiny",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "Runs with inclusionAI's code or SGLang-Omni, 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-tiny.txt \u0026\u0026 bash examples/one_click/run_seed.sh",
        "runBuild": "Ming-3.0-tts-tiny",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "Ming 3.0",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "Ming-3.0-tts-0.5B",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B",
            "params": "0.5B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b"
          },
          {
            "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, Ant Group (named in the site footer of inclusion-ai.org; the LICENSE copyright line reads inclusionAI)",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI, and the GitHub organisation's profile describes inclusionAI as Ant Group's open-source projects. The footer reads \"© 2026 inclusionAI, Ant Group\". No registered company name was found.",
          "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 24 messages say what changed (a corrected parameter description, updated metrics, the SGLang-Omni instructions).",
          "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026. Domain registration date from RDAP (rdap.org) on 21 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": 64
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.json",
      "live": {
        "slug": "ming-3-0-tts-tiny",
        "securityTxt": {
          "url": "https://inclusion-ai.org/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-10-30T15:39:10.82845107Z"
        },
        "domain": {
          "domain": "inclusion-ai.org",
          "registered": "2025-04-03",
          "source": "https://rdap.publicinterestregistry.org/rdap/domain/inclusion-ai.org",
          "checkedAt": "2026-10-25T14:04:43.857656282Z"
        },
        "pages": [
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        "question": "Which is better for AI agents, Audio8-TTS-Realistic 0.6B or Ming-3.0-tts-tiny?"
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        "goodFor": "Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT",
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        "json": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-fish-audio-drama-3.json",
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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-0-5b.json",
        "title": "Audio8-TTS-Realistic 0.6B vs Ming-3.0-tts-0.5B",
        "url": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-0-5b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-omnivoice.json",
        "title": "Audio8-TTS-Realistic 0.6B vs OmniVoice",
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/fish-audio-drama-3-vs-ming-3-0-tts-tiny.json",
        "title": "Fish Audio Drama 3 vs Ming-3.0-tts-tiny",
        "url": "https://www.anchorterminal.com/compare/fish-audio-drama-3-vs-ming-3-0-tts-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/fish-audio-s2-pro-vs-ming-3-0-tts-tiny.json",
        "title": "Fish Audio S2 Pro vs Ming-3.0-tts-tiny",
        "url": "https://www.anchorterminal.com/compare/fish-audio-s2-pro-vs-ming-3-0-tts-tiny"
      },
      {
        "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-tiny-vs-omnivoice.json",
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        "by": 13,
        "edge": "audio8-tts-realistic-0-6b",
        "key": "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-tiny": "A text-to-speech cousin for voice cloning, voice design and multi-speaker podcast audio in seven languages, under MIT. It needs the publisher's own code or SGLang-Omni and about 22 GB of GPU memory, with CUDA 11.8 pinned. The main risks are that loading runs repository code, no safety work is published and it can clone a voice from a short clip."
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  "markdown": "Ming-3.0-tts-tiny and Audio8-TTS-Realistic 0.6B score within a point of each other on agent readiness, 52.2 (D) and 51.5 (D). Audio8-TTS-Realistic 0.6B leads on reach 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-tiny: grade D, 52.2/100, rank #101 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-tiny.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- Reach, 55 against 50\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-tiny (D)\n\nGood for: Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT\n\nAhead on:\n- Risk, 55 against 40\n\nWatch for: No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code\n\n\n## Score by category\n\n| Category | Weight | Audio8-TTS-Realistic 0.6B | Ming-3.0-tts-tiny | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 50 | Audio8-TTS-Realistic 0.6B +5 |\n| Risk | 20% (20 this run) | 40 | 55 | Ming-3.0-tts-tiny +15 |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\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** | **52.2 · D** | |\n\n## Facts side by side\n\n| Fact | Audio8-TTS-Realistic 0.6B | Ming-3.0-tts-tiny |\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 | 2026-10-20 |\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 | 2.5/5 (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-tiny.** A text-to-speech cousin for voice cloning, voice design and multi-speaker podcast audio in seven languages, under MIT. It needs the publisher's own code or SGLang-Omni and about 22 GB of GPU memory, with CUDA 11.8 pinned. The main risks are that loading runs repository code, no safety work is published and it can clone a voice from a short clip.\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-tiny\n\n1. Install from the GitHub repository, `git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts \u0026\u0026 pip install -r requirements-tiny.txt`, in its own Python 3.10.15 environment, since the 0.5B model pins different versions\n2. Quick start is `bash examples/one_click/run_seed.sh`, which clones from the bundled reference clip\n3. To serve, run `sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tiny` from SGLang-Omni's main branch and POST to /v1/audio/speech with input, response_format and, for cloning, ref_audio plus ref_text\n4. Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts\n5. Expect one complete audio body, since there is no streaming, and no timestamps\n6. Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config\n7. Run `python tools/preflight_tiny.py --model_path \u003cdir\u003e` on the downloaded checkpoint before first use\n8. Label output as synthetic speech where it could pass for a real recording, 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-tiny?\n\nMing-3.0-tts-tiny and Audio8-TTS-Realistic 0.6B score within a point of each other on agent readiness, 52.2 (D) and 51.5 (D). Audio8-TTS-Realistic 0.6B leads on reach 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-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-tiny.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-tiny\"}`. From a terminal: `anchor compare audio8-tts-realistic-0-6b ming-3-0-tts-tiny`\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-tiny.json\n\n## Other comparisons with Audio8-TTS-Realistic 0.6B or Ming-3.0-tts-tiny\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-0.5B](https://www.anchorterminal.com/compare/audio8-tts-realistic-0-6b-vs-ming-3-0-tts-0-5b.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-tiny](https://www.anchorterminal.com/compare/fish-audio-drama-3-vs-ming-3-0-tts-tiny.md)\n- [Fish Audio S2 Pro vs Ming-3.0-tts-tiny](https://www.anchorterminal.com/compare/fish-audio-s2-pro-vs-ming-3-0-tts-tiny.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-tiny vs OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.md)\n- [Ming-3.0-tts-tiny vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-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",
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