{"repo": "5uck1ess/tts-bench", "files": {"report.py": {"e965ff34baa1dcb356925d7854eb6f19337e1cd9": {"t": 1785674302.3937914, "n": 72546, "h": "9cbf20b2388b3795", "c": "\"\"\"Generate a self-contained HTML report from a bench.py results directory.\n\nShows TTFA cold/warm + RTFx cold/warm per (model, device, prompt), with an\ninline <audio> player for the cold run of each cell so you can click-play\neach wav in the browser without leaving the page.\n\nFeatures:\n- dark/light theme toggle (persisted to localStorage)\n- sortable columns (click any header — cycles asc / desc / unsorted)\n- live text filter across all visible rows\n\nAll inline — no external CSS, JS, or fonts; works offline.\n\nAlso builds results/index.html — a top-level page listing every dated run.\n\nUsage:\n    python report.py results/2026-05-23_1409              # one report\n    python report.py results/2026-05-23_1409 --open       # ... and open it\n    python report.py --index                              # results/index.html only\n    python report.py --all                                # regenerate everything\n\"\"\"\n\nimport argparse\nimport csv\nimport json\nimport sys\nimport webbrowser\nfrom collections import defaultdict\nfrom html import escape\nfrom pathlib import Path\n\nREPO = Path(__file__).parent\nRESULTS = REPO / \"results\"\n\n# NAQ left the harness — it's now private lab-only R&D (see naq_lab/, gitignored).\n# The bench no longer computes it and results.csv carries no naq columns, so the\n# report has no quality lens / NAQ column. All NAQ scoring happens post-hoc in\n# naq_lab over the saved wavs.\n\ntry:\n    from bench import PROMPTS as _BENCH_PROMPTS\n    PROMPT_INFO = {str(pid): (lang, text) for pid, lang, text in _BENCH_PROMPTS}\nexcept Exception:\n    PROMPT_INFO = {}\n\n\nSTYLE = \"\"\"<style>\n  :root {\n    color-scheme: dark light;\n    --bg: #1a1a1a;\n    --panel: #242424;\n    --text: #e0e0e0;\n    --muted: #888;\n    --accent: #6cf;\n    --num: #cfc;\n    --fail: #f88;\n    --border: #333;\n    --row-hover: #2a2a2a;\n    --th-bg: #2c2c2c;\n    --th-hover: #353535;\n    --input-bg: #2a2a2a;\n    --input-border: #444;\n    --controls-border: #2a2a2a;\n    --dev-cpu: #ddd;\n    --dev-cuda: #9cf;\n    --dev-mps: #fc9;\n    --lang: #6cf;\n    --prompt-text: #9c9;\n    --code-bg: #2a2a2a;\n  }\n  [data-theme=\"light\"] {\n    color-scheme: light;\n    --bg: #f7f8fa;\n    --panel: #ffffff;\n    --text: #1a1d22;\n    --muted: #6b7280;\n    --accent: #1a73e8;\n    --num: #137333;\n    --fail: #c5221f;\n    --border: #e2e6eb;\n    --row-hover: #f0f4f8;\n    --th-bg: #eef1f4;\n    --th-hover: #e2e6eb;\n    --input-bg: #ffffff;\n    --input-border: #cdd3da;\n    --controls-border: #e2e6eb;\n    --dev-cpu: #555;\n    --dev-cuda: #1a73e8;\n    --dev-mps: #c2410c;\n    --lang: #1a73e8;\n    --prompt-text: #2d6a4f;\n    --code-bg: #eef1f4;\n  }\n  * { box-sizing: border-box; }\n  body { font-family: ui-monospace, \"SF Mono\", Menlo, Consolas, monospace;\n         background: var(--bg); color: var(--text);\n         margin: 0; padding: 1.5rem 2rem 3rem;\n         max-width: 1400px; margin-inline: auto;\n         transition: background 0.15s, color 0.15s; }\n  h1, h2 { color: var(--text); margin-top: 0; }\n  h1 { font-size: 1.5em; }\n  h2 { font-size: 1.05em; }\n  .meta { color: var(--muted); font-size: 0.88em; margin-bottom: 0.4rem; }\n  .prompt { background: var(--panel); padding: 1rem 1.2rem;\n            border-radius: 10px; margin-bottom: 1.4rem;\n            border: 1px solid var(--border); }\n  .prompt-text { color: var(--prompt-text); font-style: italic;\n                 display: block; margin: 0.2rem 0 0.8rem 0; }\n  .lang { color: var(--lang); font-style: normal; margin-right: 0.4rem;\n          font-weight: 500; }\n  table { border-collapse: collapse; width: 100%; font-size: 0.92em; }\n  th, td { padding: 7px 10px; text-align: left;\n           border-bottom: 1px solid var(--border); }\n  th { background: var(--th-bg); color: var(--text);\n       cursor: pointer; user-select: none; white-space: nowrap;\n       font-weight: 600; }\n  th:hover { background: var(--th-hover); }\n  td.num { text-align: right; font-variant-numeric: tabular-nums;\n           color: var(--num); }\n  th.num { text-align: right; }\n  td.fail { color: var(--fail); font-style: italic; }\n  td.dev-cuda { color: var(--dev-cuda); }\n  td.dev-mps  { color: var(--dev-mps); }\n  td.dev-cpu  { color: var(--dev-cpu); }\n  .muted { color: var(--muted); }\n  audio { width: 220px; height: 30px; vertical-align: middle; }\n  tr:hover td { background: var(--row-hover); }\n  code { background: var(--code-bg); padding: 1px 6px; border-radius: 3px;\n         font-size: 0.92em; }\n  a { color: var(--accent); text-decoration: none; }\n  a:hover { text-decoration: underline; }\n  .nav { margin-bottom: 0.6rem; }\n  .controls { position: sticky; top: 0; background: var(--bg); z-index: 10;\n              padding: 0.7rem 0 0.7rem; margin-bottom: 1rem;\n              display: flex; gap: 0.6rem; align-items: center; flex-wrap: wrap;\n              border-bottom: 1px solid var(--controls-border); }\n  .controls input { background: var(--input-bg); color: var(--text);\n                    border: 1px solid var(--input-border);\n                    border-radius: 6px; padding: 7px 11px; font: inherit;\n                    min-width: 320px; }\n  .controls input:focus { outline: none; border-color: var(--accent); }\n  .controls .hint { color: var(--muted); font-size: 0.85em; }\n  .controls button { background: var(--input-bg); color: var(--text);\n                     border: 1px solid var(--input-border);\n                     border-radius: 6px; padding: 6px 12px; font: inherit;\n                     cursor: pointer; transition: border-color 0.15s; }\n  .controls button:hover { border-color: var(--accent); }\n  .spacer { flex: 1; }\n\n  /* Lens picker grid */\n  .lens-grid { display: grid; grid-template-columns: repeat(3, 1fr);\n               gap: 1rem; margin: 1.5rem 0; }\n  @media (max-width: 760px) { .lens-grid { grid-template-columns: 1fr; } }\n  .lens-card { background: var(--panel); border: 1px solid var(--border);\n               border-radius: 10px; padding: 1.2rem 1.4rem;\n               transition: border-color 0.15s; }\n  .lens-card:hover { border-color: var(--accent); }\n  .lens-card a { display: block; text-decoration: none; color: var(--text); }\n  .lens-card h3 { margin: 0 0 0.4rem 0; font-size: 1.1em; color: var(--accent); }\n  .lens-card .desc { color: var(--muted); font-size: 0.9em; }\n\n  /* TLDR callout used by speed.html */\n  .tldr { background: var(--panel); border-left: 3px solid var(--accent);\n          padding: 0.8rem 1.1rem; margin: 1rem 0 1.4rem; border-radius: 4px; }\n  .tldr h2 { margin: 0 0 0.4rem 0; font-size: 1em; }\n  .tldr p { margin: 0.2rem 0; color: var(--text); }\n  .tldr strong { color: var(--accent); }\n\n  /* Secondary-axis \"badge\" pill cells */\n  td.pill { font-size: 0.82em; color: var(--muted); }\n  td.pill[data-sort] { font-variant-numeric: tabular-nums; }\n  td.pill a { color: inherit; }\n\n  /* Prompt jumper for samples.html */\n  .prompt-jumper { position: sticky; top: 3rem; background: var(--bg);\n                   padding: 0.4rem 0; margin-bottom: 0.8rem;\n                   border-bottom: 1px solid var(--border); font-size: 0.9em; }\n  .prompt-jumper a { margin-right: 0.6rem; }\n\n  /* Lens-to-lens nav tabs at top of each lens page */\n  .lens-tabs { display: inline-flex; gap: 0.3rem; margin-right: 0.6rem;\n               align-items: center; }\n  .lens-tab { display: inline-block; padding: 4px 12px; border-radius: 6px;\n              border: 1px solid var(--border); color: var(--text);\n              text-decoration: none; font-size: 0.92em;\n              transition: border-color 0.15s, background 0.15s; }\n  .lens-tab:hover { border-color: var(--accent); text-decoration: none; }\n  .lens-tab.active { background: var(--accent); color: var(--bg);\n                     border-color: var(--accent); }\n  .lens-arrow { display: inline-block; padding: 4px 10px; border-radius: 6px;\n                border: 1px solid var(--border); color: var(--muted);\n                text-decoration: none; font-size: 0.92em;\n                transition: border-color 0.15s, color 0.15s; }\n  .lens-arrow:hover { border-color: var(--accent); color: var(--accent);\n                      text-decoration: none; }\n  .lens-arrow-disabled { opacity: 0.35; cursor: default;\n                         border-style: dashed; }\n  .lens-arrow-disabled:hover { border-color: var(--border); color: var(--muted); }\n\n  /* \"Reading this report\" explainer above tables */\n  .reading-guide { background: var(--panel); border-left: 3px solid var(--muted);\n                   padding: 0.55rem 0.9rem; margin: 0.7rem 0 1rem;\n                   border-radius: 4px; font-size: 0.88em; color: var(--text); }\n  .reading-guide strong { color: var(--accent); }\n  .reading-guide a { color: var(--accent); }\n</style>\"\"\"\n\n\nCONTROLS = '''<div class=\"controls\">\n<input id=\"filter\" type=\"search\" placeholder=\"filter rows (model, device, value)…\" autocomplete=\"off\">\n<button type=\"button\" id=\"reset-sort\">reset sort</button>\n<span class=\"hint\">click any column header to sort</span>\n<span class=\"spacer\"></span>\n<button type=\"button\" id=\"theme-toggle\" title=\"Toggle theme\">☾ dark</button>\n</div>'''\n\n\nSCRIPT = r'''<script>\n(function(){\n  // ---------- theme toggle ----------\n  const themeBtn = document.getElementById('theme-toggle');\n  function applyTheme(name) {\n    document.documentElement.setAttribute('data-theme', name);\n    themeBtn.textContent = name === 'light' ? '☀ light' : '☾ dark';\n    try { localStorage.setItem('tts-bench-theme', name); } catch (e) {}\n  }\n  let stored = null;\n  try { stored = localStorage.getItem('tts-bench-theme'); } catch (e) {}\n  applyTheme(stored === 'light' ? 'light' : 'dark');\n  themeBtn.addEventListener('click', () => {\n    const cur = document.documentElement.getAttribute('data-theme') || 'dark';\n    applyTheme(cur === 'dark' ? 'light' : 'dark');\n  });\n\n  // ---------- sort + filter ----------\n  const tables = document.querySelectorAll('table');\n  const filterInput = document.getElementById('filter');\n  const resetBtn = document.getElementById('reset-sort');\n  const sortState = { col: -1, dir: 0 };\n\n  tables.forEach(table => {\n    table.querySelectorAll('thead th').forEach(th => { th.dataset.label = th.textContent; });\n    table.querySelectorAll('tbody tr').forEach((row, i) => { row.dataset.origIdx = String(i); });\n  });\n\n  function applyFilter() {\n    const q = filterInput.value.toLowerCase().trim();\n    tables.forEach(t => {\n      t.querySelectorAll('tbody tr').forEach(row => {\n        const text = row.textContent.toLowerCase();\n        row.style.display = (!q || text.includes(q)) ? '' : 'none';\n      });\n    });\n  }\n  filterInput.addEventListener('input', applyFilter);\n\n  function cellSortValue(row, colIdx) {\n    const cell = row.children[colIdx];\n    if (!cell) return Number.POSITIVE_INFINITY;\n    if (cell.hasAttribute('data-sort')) {\n      const raw = cell.getAttribute('data-sort');\n      if (raw === '' || raw === null) return Number.POSITIVE_INFINITY;\n      const v = parseFloat(raw);\n      return Number.isNaN(v) ? Number.POSITIVE_INFINITY : v;\n    }\n    return cell.textContent.toLowerCase();\n  }\n\n  function renderArrows() {\n    tables.forEach(t => {\n      t.querySelectorAll('thead th').forEach((th, i) => {\n        const arrow = (i === sortState.col && sortState.dir !== 0)\n                    ? (sortState.dir === 1 ? ' ▲' : ' ▼') : '';\n        th.textContent = th.dataset.label + arrow;\n      });\n    });\n  }\n\n  function restoreOrder() {\n    tables.forEach(table => {\n      const tbody = table.querySelector('tbody');\n      const rows = Array.from(tbody.querySelectorAll('tr'));\n      rows.sort((a, b) => parseInt(a.dataset.origIdx) - parseInt(b.dataset.origIdx));\n      rows.forEach(r => tbody.appendChild(r));\n    });\n  }\n\n  function sortAll(colIdx, explicitDir) {\n    if (explicitDir === 1 || explicitDir === -1) {\n      sortState.col = colIdx;\n      sortState.dir = explicitDir;\n    } else if (sortState.col === colIdx) {\n      sortState.dir = sortState.dir === 1 ? -1 : (sortState.dir === -1 ? 0 : 1);\n    } else {\n      sortState.col = colIdx;\n      sortState.dir = 1;\n    }\n    renderArrows();\n\n    if (sortState.dir === 0) {\n      restoreOrder();\n      return;\n    }\n\n    const dir = sortState.dir;\n    tables.forEach(table => {\n      const tbody = table.querySelector('tbody');\n      const rows = Array.from(tbody.querySelectorAll('tr'));\n      rows.sort((a, b) => {\n        const aSpan = a.querySelector('td[colspan]') !== null;\n        const bSpan = b.querySelector('td[colspan]') !== null;\n        if (aSpan !== bSpan) return aSpan ? 1 : -1;\n        const av = cellSortValue(a, colIdx);\n        const bv = cellSortValue(b, colIdx);\n        if (av < bv) return -dir;\n        if (av > bv) return dir;\n        return 0;\n      });\n      rows.forEach(r => tbody.appendChild(r));\n    });\n  }\n\n  tables.forEach(table => {\n    table.querySelectorAll('thead th').forEach((th, idx) => {\n      th.addEventListener('click', () => sortAll(idx));\n    });\n  });\n\n  resetBtn.addEventListener('click', () => {\n    sortState.col = -1; sortState.dir = 0;\n    renderArrows();\n    restoreOrder();\n    filterInput.value = '';\n    applyFilter();\n  });\n\n  // Page-load default sort: pages can set window.__defaultSort = {colIdx, dir}\n  // (set in a tiny inline <script> earlier in the page).\n  if (window.__defaultSort &&\n      typeof window.__defaultSort.colIdx === 'number' &&\n      (window.__defaultSort.dir === 1 || window.__defaultSort.dir === -1)) {\n    sortAll(window.__defaultSort.colIdx, window.__defaultSort.dir);\n  }\n})();\n</script>'''\n\n\ndef _fmt_ttfa(ms):\n    if ms is None:\n        return \"—\"\n    if ms >= 1000:\n        return f\"{ms/1000:.2f}s\"\n    return f\"{ms:.0f}ms\"\n\n\ndef _fmt_rtf(x):\n    if x is None:\n        return \"—\"\n    return f\"{x:.2f}×\"\n\n\ndef _fmt_mb(x):\n    \"\"\"Format MiB compactly: <1000 MB shows as 'NNN MB', >=1024 as 'X.XX GB'.\"\"\"\n    if x is None:\n        return \"—\"\n    if x >= 1024:\n        return f\"{x/1024:.2f} GB\"\n    return f\"{x:.0f} MB\"\n\n\ndef _humanize_error(err):\n    \"\"\"Turn a raw runner error into a reader-friendly reason so a failed cell\n    doesn't read like a hardware fault. An OOM means the model is too big for\n    this device's memory, not that the rig broke.\"\"\"\n    e = (err or \"\").strip()\n    low = e.lower()\n    if not e or e == \"no successful run\":\n        return \"no successful run\"\n    # \"invalid buffer size\" is how MPS reports an allocation it can't satisfy —\n    # an out-of-memory by another name.\n    if \"outofmemory\" in low or \"out of memory\" in low or \"invalid buffer size\" in low:\n        if \"cuda\" in low or \"gpu\" in low:\n            return \"Skipped — out of GPU memory (model exceeds this GPU's VRAM)\"\n        return \"Skipped — out of memory (model exceeds available RAM)\"\n    if \"timeout\" in low and \"s\" in low:\n        # \"timeout 600s\" → 600\n        import re\n        m = re.search(r\"timeout\\s+(\\d+)s\", low)\n        if m:\n            secs = int(m.group(1))\n            return f\"Timed out after {secs // 60} min — model too slow at this prompt length\"\n        return \"Timed out — model too slow at this prompt length\"\n    if \"torchcodec\" in low or \"libtorchcodec\" in low:\n        return \"Audio loader needs FFmpeg DLLs (not present on this platform)\"\n    if \"flash_attn\" in low and (\"not installed\" in low or \"no module\" in low):\n        return \"Needs flash_attn (no wheel for this Python/torch/OS combo)\"\n    if \"no module named\" in low and \"luxtts\" in low or \"no module named 'zipvoice'\" in low:\n        return \"LuxTTS install failed (piper-phonemize has no Windows wheels)\"\n    if \"no module named\" in low:\n        import re\n        m = re.search(r\"no module named ['\\\"]?([^'\\\"\\s]+)['\\\"]?\", low)\n        if m:\n            return f\"Missing dependency '{m.group(1)}' — install incomplete on this platform\"\n        return \"Missing dependency — install incomplete on this platform\"\n    if \"repo id must use alphanumeric\" in low:\n        return \"transformers/Windows repo-id path bug (workaround pending)\"\n    if \"mutable default\" in low and \"override\" in low:\n        return \"hydra-core/omegaconf too old for Python 3.11 (deps need bump)\"\n    if (\"typeerror\" in low and \"'nonetype' object cannot be interpreted as an integer\" in low):\n        return \"Upstream config field missing — dep version mismatch\"\n    if \"cuda error\" in low or \"cuda runtime\" in low:\n        return \"CUDA runtime error — likely driver/version mismatch\"\n    if \"cuda requested but not available\" in low:\n        return \"CUDA not available on this rig\"\n    if \"mps requested but not available\" in low:\n        return \"MPS not available on this rig\"\n    if \"cuda only\" in low or \"cuda-only\" in low:\n        return \"Model is GPU-only — CPU/MPS not supported\"\n    if \"reference\" in low and (\"not found\" in low or \"missing\" in low):\n        return \"Reference wav missing for voice cloning\"\n    # Fallback: surface the first line, trimmed.\n    return f\"Failed: {e.splitlines()[0][:140]}\"\n\n\n# Display sizes for the Size column. Numbers are the model's weight count\n# (params), not weights-on-disk. Hand-curated — keep in sync with README.\n# Display name shown in the rendered HTML for each venv-keyed model.\n# CSV column still uses the raw venv key; this is presentation-only.\n# Keys not present here fall through to the raw key (Title-cased by convention).\nMODEL_DISPLAY_NAMES = {\n    \"sesame\":        \"Sesame CSM-1B\",\n    \"miso\":          \"Miso TTS 8B\",\n    \"longcat_1b\":    \"LongCat-AudioDiT 1B\",\n    \"longcat_3p5b\":  \"LongCat-AudioDiT 3.5B\",\n    \"orpheus\":       \"Orpheus-TTS 3B\",\n    \"cosyvoice\":     \"CosyVoice 3 0.5B\",\n    \"lfm2_audio\":    \"LFM2.5-Audio 1.5B\",\n    \"miotts_01b\":    \"MioTTS 0.1B\",\n    \"miotts_06b\":    \"MioTTS 0.6B\",\n    \"wavtts\":        \"WavTTS 0.67B\",\n    \"coqui\":         \"Coqui XTTS-v2\",\n    \"vibevoice\":     \"VibeVoice Realtime 0.5B\",\n    \"vibevoice_15b\": \"VibeVoice 1.5B\",\n    \"qwentts\":       \"Qwen3-TTS 1.7B Base\",\n    \"qwentts_06b\":   \"Qwen3-TTS 0.6B Base\",\n    \"qwentts_06b_custom\": \"Qwen3-TTS 0.6B CustomVoice\",\n    \"qwentts_fast\":  \"Qwen3-TTS 1.7B (CUDA-graph)\",\n    \"neutts_air\":    \"NeuTTS Air\",\n    \"neutts_nano\":   \"NeuTTS Nano\",\n    \"voxcpm\":        \"VoxCPM2 2B\",\n    \"mars5\":         \"Mars5-TTS\",\n    \"pocket\":        \"Pocket-TTS\",\n    \"magpie\":        \"Magpie-TTS\",\n    \"indextts\":      \"IndexTTS-2\",\n    \"f5tts\":         \"F5-TTS v1\",\n    \"chatterbox\":       \"Chatterbox\",\n    \"chatterbox_turbo\": \"Chatterbox Turbo\",\n    \"dia\":           \"Dia 1.6B-0626\",\n    \"omnivoice\":     \"OmniVoice\",\n    \"zipvoice\":      \"ZipVoice 123M\",\n    \"piper\":         \"Piper\",\n    \"kokoro\":        \"Kokoro\",\n    \"kokoro_mlx\":    \"Kokoro (MLX)\",\n    \"kittentts\":     \"KittenTTS Nano 0.1\",\n    \"scyllasband\":   \"Scylla's Band\",\n    \"inflect_nano\":  \"Inflect-Nano v2\",\n    \"inflect_micro\": \"Inflect-Micro v2\",\n    \"soprano\":       \"Soprano 1.1 80M\",\n    \"moss_tts_nano\": \"MOSS-TTS-Nano\",\n    \"moss_tts\":      \"MOSS-TTS v1.0\",\n    \"moss_tts_v15\":  \"MOSS-TTS v1.5\",\n    \"supertonic\":    \"Supertonic 3\",\n    \"luxtts\":        \"LuxTTS\",\n    \"maya1\":         \"Maya1\",\n    \"voxtral\":       \"Voxtral 4B TTS\",\n    \"fish_15\":       \"Fish Speech 1.5\",\n    \"fish_s2\":       \"Fish Speech S2-Pro\",\n    \"zonos\":         \"Zonos v0.1\",\n    \"zonos2\":        \"Zonos2\",\n    \"openvoice\":     \"OpenVoice v2\",\n    \"styletts2\":     \"StyleTTS 2\",\n    \"vibevoice_7b\":  \"VibeVoice 7B\",\n    \"metavoice\":     \"MetaVoice-1B\",\n    \"step_editx\":    \"Step-Audio-EditX\",\n    \"echo\":          \"Echo-TTS\",\n    \"miratts\":       \"MiraTTS\",\n    \"outetts\":       \"OuteTTS 1.0 1B\",\n    \"parler\":        \"Parler-TTS Mini v1\",\n    \"melotts\":       \"MeloTTS\",\n    \"higgs_v3\":      \"Higgs Audio v3 TTS\",\n    \"dramabox\":      \"DramaBox\",\n    \"dots_tts\":      \"dots.tts (soar)\",\n}\n\n\ndef _display_name(model):\n    \"\"\"Return display name for a model venv key; falls back to raw key.\"\"\"\n    return MODEL_DISPLAY_NAMES.get(model, model)\n\n\nMODEL_SIZE = {\n    \"pocket\":        \"100M\",\n    \"neutts_air\":    \"748M\",\n    \"neutts_nano\":   \"229M\",\n    \"luxtts\":        \"123M\",\n    \"chatterbox\":       \"1.2B\",\n    \"chatterbox_turbo\": \"744M\",\n    \"f5tts\":         \"330M\",\n    \"coqui\":         \"750M\",\n    \"vibevoice\":     \"0.5B\",\n    \"vibevoice_15b\": \"1.5B\",\n    \"omnivoice\":     \"~1B\",\n    \"zipvoice\":      \"123M\",\n    \"voxcpm\":        \"2B\",\n    \"magpie\":        \"357M\",\n    \"qwentts\":       \"1.7B\",\n    \"qwentts_06b\":   \"0.6B\",\n    \"qwentts_06b_custom\": \"0.6B\",\n    \"qwentts_fast\":  \"1.7B\",\n    \"indextts\":      \"1.5B\",\n    \"sesame\":        \"1B\",\n    \"miso\":          \"8.2B\",\n    \"longcat_1b\":    \"1.42B\",\n    \"longcat_3p5b\":  \"3.83B\",\n    \"orpheus\":       \"3.3B\",   # Llama-3.2-3B backbone + SNAC decoder\n    \"cosyvoice\":     \"0.5B\",   # Fun-CosyVoice3-0.5B-2512\n    \"lfm2_audio\":    \"1.5B\",   # 1.2B LFM2.5 LM + 115M FastConformer encoder\n    \"miotts_01b\":    \"0.1B\",   # Falcon-H1-Tiny-Multilingual-100M backbone\n    \"miotts_06b\":    \"0.6B\",   # Qwen3-0.6B-Base backbone\n    \"wavtts\":        \"0.67B\",  # WavTTS_Large DiT (1152x28), 673M from checkpoint\n    \"mars5\":         \"1.2B\",\n    \"dia\":           \"1.6B\",\n    \"kokoro\":        \"82M\",\n    \"kokoro_mlx\":    \"82M\",\n    \"kittentts\":     \"<100M\",\n    \"piper\":         \"~25MB\",\n    \"scyllasband\":   \"~103M\",\n    # Complete text-to-waveform totals from the model cards (the integrated 24 kHz\n    # decoder is inside these numbers, not bolted on as a separate vocoder).\n    \"inflect_nano\":  \"3.96M\",\n    \"inflect_micro\": \"9.36M\",\n    \"soprano\":       \"80M\",\n    \"moss_tts_nano\": \"100M\",\n    \"moss_tts\":      \"8B\",\n    \"moss_tts_v15\":  \"8B\",\n    \"supertonic\":    \"99M\",\n    \"maya1\":         \"3B\",\n    \"voxtral\":       \"4B\",\n    \"fish_15\":       \"~500M\",\n    \"fish_s2\":       \"4B\",\n    \"zonos\":         \"1.6B\",\n    \"zonos2\":        \"8B (MoE, ~900M active)\",\n    \"openvoice\":     \"~100M\",\n    \"styletts2\":     \"~148M\",\n    \"vibevoice_7b\":  \"7B\",\n    \"metavoice\":     \"1.2B\",\n    \"step_editx\":    \"3B\",\n    \"echo\":          \"2.8B\",   # DiT generative model (safetensors count); +695M S1-DAC codec not counted (matches fish_15 convention)\n    \"miratts\":       \"0.5B\",   # HF card \"Model size 0.5B params\" (BF16); FastBiCodec + FlashSR not counted\n    \"outetts\":       \"1B\",     # Llama-3.2-1B backbone\n    \"parler\":        \"878M\",   # parler-tts-mini-v1 safetensors total; large variant = 2.33B\n    \"melotts\":       \"~52M\",   # MeloTTS-English checkpoint.pth (~208 MB fp32 / 4)\n    \"higgs_v3\":      \"4B\",     # Qwen3 ~4B backbone (HF card); Higgs audio tokenizer not counted\n    \"dramabox\":      \"3.3B\",   # LTX-2.3 audio-only DiT (IC-LoRA merged); 12B 4-bit text encoder not counted\n    \"dots_tts\":      \"2B\",     # semantic encoder + LLM + flow-matching acoustic head (soar/SCA checkpoint)\n}\n\n# Canonical URL of the EXACT checkpoint each runner loads (HF model card, or the\n# GitHub repo when that's the pinned source). Extracted from runners/*.py and the\n# installed venv packages — keep in sync with the README model tables, which carry\n# the same links. Consumed by arena/build_manifest.py for the post-vote reveal.\n_HF = \"https://huggingface.co/\"\nMODEL_URL = {\n    \"pocket\":        \"https://github.com/kyutai-labs/pocket-tts\",\n    \"neutts_air\":    _HF + \"neuphonic/neutts-air\",\n    \"neutts_nano\":   _HF + \"neuphonic/neutts-nano-q4-gguf\",\n    \"luxtts\":        \"https://github.com/ysharma3501/LuxTTS\",\n    \"chatterbox\":       _HF + \"ResembleAI/chatterbox\",\n    \"chatterbox_turbo\": _HF + \"ResembleAI/chatterbox-turbo\",\n    \"f5tts\":         _HF + \"SWivid/F5-TTS\",\n    \"coqui\":         _HF + \"coqui/XTTS-v2\",\n    \"vibevoice\":     _HF + \"microsoft/VibeVoice-Realtime-0.5B\",\n    \"vibevoice_15b\": _HF + \"microsoft/VibeVoice-1.5B\",\n    \"vibevoice_7b\":  _HF + \"vibevoice/VibeVoice-7B\",\n    \"omnivoice\":     _HF + \"k2-fsa/OmniVoice\",\n    \"zipvoice\":      _HF + \"k2-fsa/ZipVoice\",\n    \"voxcpm\":        _HF + \"openbmb/VoxCPM2\",\n    \"magpie\":        _HF + \"nvidia/magpie_tts_multilingual_357m\",\n    \"qwentts\":       _HF + \"Qwen/Qwen3-TTS-12Hz-1.7B-Base\",\n    \"qwentts_06b\":   _HF + \"Qwen/Qwen3-TTS-12Hz-0.6B-Base\",\n    \"qwentts_06b_custom\": _HF + \"Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice\",\n    \"qwentts_fast\":  _HF + \"Qwen/Qwen3-TTS-12Hz-1.7B-Base\",\n    \"indextts\":      _HF + \"IndexTeam/IndexTTS-2\",\n    \"sesame\":        _HF + \"sesame/csm-1b\",\n    \"miso\":          _HF + \"MisoLabs/MisoTTS\",\n    \"longcat_1b\":    _HF + \"meituan-longcat/LongCat-AudioDiT-1B\",\n    \"longcat_3p5b\":  _HF + \"meituan-longcat/LongCat-AudioDiT-3.5B\",\n    \"orpheus\":       _HF + \"canopylabs/orpheus-3b-0.1-ft\",\n    \"cosyvoice\":     _HF + \"FunAudioLLM/Fun-CosyVoice3-0.5B-2512\",\n    \"lfm2_audio\":    _HF + \"LiquidAI/LFM2.5-Audio-1.5B\",\n    \"miotts_01b\":    _HF + \"Aratako/MioTTS-0.1B\",\n    \"miotts_06b\":    _HF + \"Aratako/MioTTS-0.6B\",\n    \"mars5\":         _HF + \"Camb-ai/mars5-tts\",\n    \"dia\":           _HF + \"nari-labs/Dia-1.6B-0626\",\n    \"kokoro\":        _HF + \"hexgrad/Kokoro-82M\",\n    \"kokoro_mlx\":    _HF + \"prince-canuma/Kokoro-82M\",\n    \"kittentts\":     _HF + \"KittenML/kitten-tts-nano-0.1\",\n    \"piper\":         \"https://github.com/OHF-Voice/piper1-gpl\",\n    \"scyllasband\":   _HF + \"spybyscript/scyllasband\",\n    \"inflect_nano\":  _HF + \"owensong/Inflect-Nano-v2\",\n    \"inflect_micro\": _HF + \"owensong/Inflect-Micro-v2\",\n    \"soprano\":       _HF + \"ekwek/Soprano-1.1-80M\",\n    \"moss_tts_nano\": _HF + \"OpenMOSS-Team/MOSS-TTS-Nano\",\n    \"moss_tts\":      _HF + \"OpenMOSS-Team/MOSS-TTS\",\n    \"moss_tts_v15\":  _HF + \"OpenMOSS-Team/MOSS-TTS-v1.5\",\n    \"supertonic\":    _HF + \"Supertone/supertonic-3\",\n    \"maya1\":         _HF + \"maya-research/maya1\",\n    \"voxtral\":       _HF + \"mistralai/Voxtral-4B-TTS-2603\",\n    \"fish_15\":       _HF + \"fishaudio/fish-speech-1.5\",\n    \"fish_s2\":       _HF + \"fishaudio/s2-pro\",\n    \"echo\":          _HF + \"jordand/echo-tts-base\",\n    \"zonos\":         _HF + \"Zyphra/Zonos-v0.1-transformer\",\n    \"zonos2\":        _HF + \"Zyphra/Zonos2\",\n    \"openvoice\":     _HF + \"myshell-ai/OpenVoiceV2\",\n    \"styletts2\":     \"https://github.com/yl4579/StyleTTS2\",\n    \"metavoice\":     _HF + \"metavoiceio/metavoice-1B-v0.1\",\n    \"step_editx\":    _HF + \"stepfun-ai/Step-Audio-EditX\",\n    \"miratts\":       _HF + \"YatharthS/MiraTTS\",\n    \"outetts\":       _HF + \"OuteAI/Llama-OuteTTS-1.0-1B\",\n    \"parler\":        _HF + \"parler-tts/parler-tts-mini-v1\",\n    \"melotts\":       _HF + \"myshell-ai/MeloTTS-English\",\n    \"higgs_v3\":      _HF + \"bosonai/higgs-audio-v3-tts-4b\",\n    \"dramabox\":      \"https://github.com/resemble-ai/DramaBox\",\n    \"dots_tts\":      _HF + \"rednote-hilab/dots.tts-soar\",\n    \"wavtts\":        \"https://github.com/cwx-worst-one/WavTTS\",\n}\n\n# Whether a model supports zero-shot voice cloning at runtime.\n# \"predefined\": fixed speaker(s) / preset voices only.\n# \"cloning\":    accepts a reference wav (or wav+text) at inference and matches that voice.\nMODEL_KIND = {\n    \"piper\":         \"predefined\",\n    \"kokoro\":        \"predefined\",\n    \"kokoro_mlx\":    \"predefined\",\n    \"kittentts\":     \"predefined\",\n    \"magpie\":        \"predefined\",\n    \"vibevoice\":     \"predefined\",\n    \"vibevoice_15b\": \"cloning\",   # no preset voice — clones from a reference wav\n    \"scyllasband\":   \"predefined\",\n    \"inflect_nano\":  \"predefined\",   # one fixed synthetic voice, no wav cloning\n    \"inflect_micro\": \"predefined\",\n    \"soprano\":       \"predefined\",\n    \"supertonic\":    \"predefined\",\n    \"luxtts\":        \"predefined\",\n    \"pocket\":        \"cloning\",\n    \"chatterbox\":       \"cloning\",\n    \"chatterbox_turbo\": \"cloning\",\n    \"f5tts\":         \"cloning\",\n    \"indextts\":      \"cloning\",\n    \"omnivoice\":     \"cloning\",\n    \"zipvoice\":      \"cloning\",\n    \"voxcpm\":        \"cloning\",\n    \"coqui\":         \"cloning\",\n    \"qwentts\":       \"cloning\",\n    \"qwentts_06b\":   \"cloning\",\n    \"qwentts_06b_custom\": \"predefined\",\n    \"qwentts_fast\":  \"cloning\",\n    \"sesame\":        \"cloning\",\n    \"miso\":          \"cloning\",\n    \"longcat_1b\":    \"cloning\",\n    \"longcat_3p5b\":  \"cloning\",\n    \"orpheus\":       \"predefined\",   # named preset voices (tara, ...), no wav cloning\n    \"cosyvoice\":     \"cloning\",\n    \"lfm2_audio\":    \"predefined\",   # 4 preset voices (US/UK x m/f) via system prompt; no wav cloning\n    \"miotts_01b\":    \"cloning\",      # zero-shot clone; no-reference run = bundled chris clip\n    \"miotts_06b\":    \"cloning\",\n    \"mars5\":         \"cloning\",\n    \"dia\":           \"cloning\",\n    \"neutts_air\":    \"cloning\",\n    \"neutts_nano\":   \"cloning\",\n    \"moss_tts_nano\": \"cloning\",\n    \"moss_tts\":      \"cloning\",\n    \"moss_tts_v15\":  \"cloning\",\n    \"maya1\":         \"predefined\",   # voice-description preset, no wav cloning\n    \"voxtral\":       \"cloning\",      # cuda/vLLM path clones; MLX path preset-only\n    \"fish_15\":       \"cloning\",\n    \"fish_s2\":       \"cloning\",\n    \"zonos\":         \"cloning\",\n    \"zonos2\":        \"cloning\",   # no preset voice — zero-shot clones from a reference wav\n    \"openvoice\":     \"cloning\",\n    \"styletts2\":     \"cloning\",\n    \"vibevoice_7b\":  \"cloning\",\n    \"metavoice\":     \"cloning\",\n    \"step_editx\":    \"cloning\",\n    \"echo\":          \"cloning\",   # no preset voice — zero-shot clones from a reference wav\n    \"miratts\":       \"cloning\",   # no preset voice — zero-shot clones from a reference wav\n    \"outetts\":       \"cloning\",   # clones from a reference wav; ALSO has preset voices (default lens)\n    \"parler\":        \"predefined\",   # voice set by a text description, no wav cloning\n    \"melotts\":       \"predefined\",   # VITS preset speakers (EN-US), no wav cloning\n    \"higgs_v3\":      \"cloning\",   # zero-shot cloning; no-reference run = its own default voice\n    \"dramabox\":      \"cloning\",   # 10s+ wav cloning; no-reference run = prompt-described voice\n    \"dots_tts\":      \"cloning\",   # zero-shot cloning (+ ref transcript); no-reference run = bundled chris clip\n    \"wavtts\":        \"cloning\",   # no preset voice — zero-shot clones from ref wav + sibling .txt\n}\n\n\n# Release month (YYYY-MM) per model. Sourced programmatically from the canonical\n# checkpoint repo's creation date — HF `createdAt` / GitHub `created_at` — which is\n# the first public appearance of the weights, a faithful release proxy at month\n# granularity. Variants sharing a checkpoint share its date (qwentts/qwentts_fast).\n# NOTE: piper is dated to the ORIGINAL rhasspy/piper engine (2023-01); MODEL_URL\n# points at the later piper1-gpl GPL re-home (2025), which would misdate a classic.\nMODEL_RELEASE = {\n    \"pocket\":        \"2026-01\",\n    \"neutts_air\":    \"2025-09\",\n    \"neutts_nano\":   \"2025-12\",\n    \"luxtts\":        \"2026-01\",\n    \"chatterbox\":       \"2025-04\",\n    \"chatterbox_turbo\": \"2025-12\",\n    \"f5tts\":         \"2024-10\",\n    \"coqui\":         \"2023-10\",\n    \"vibevoice\":     \"2025-12\",\n    \"vibevoice_15b\": \"2025-08\",\n    \"vibevoice_7b\":  \"2025-09\",\n    \"omnivoice\":     \"2026-03\",\n    \"zipvoice\":      \"2025-06\",\n    \"voxcpm\":        \"2026-04\",\n    \"magpie\":        \"2025-12\",\n    \"qwentts\":       \"2026-01\",\n    \"qwentts_06b\":   \"2026-01\",\n    \"qwentts_06b_custom\": \"2026-01\",\n    \"qwentts_fast\":  \"2026-01\",\n    \"indextts\":      \"2025-06\",\n    \"sesame\":        \"2025-03\",\n    \"miso\":          \"2026-05\",\n    \"longcat_1b\":    \"2026-03\",\n    \"longcat_3p5b\":  \"2026-03\",\n    \"orpheus\":       \"2025-03\",\n    \"cosyvoice\":     \"2025-12\",\n    \"lfm2_audio\":    \"2025-12\",\n    \"miotts_01b\":    \"2026-02\",\n    \"miotts_06b\":    \"2026-02\",\n    \"mars5\":         \"2024-06\",\n    \"dia\":           \"2025-06\",\n    \"kokoro\":        \"2024-12\",\n    \"kokoro_mlx\":    \"2024-12\",   # same Kokoro-82M checkpoint (MLX backend) — shares its date\n    \"kittentts\":     \"2025-08\",\n    \"piper\":         \"2023-01\",   # original rhasspy/piper engine, not the piper1-gpl re-home\n    \"scyllasband\":   \"2026-07\",\n    \"inflect_nano\":  \"2026-07\",   # v2.0.0 2026-07-24 (v1 was 2026-06)\n    \"inflect_micro\": \"2026-07\",\n    \"soprano\":       \"2026-01\",\n    \"moss_tts_nano\": \"2026-04\",\n    \"moss_tts\":      \"2026-02\",\n    \"moss_tts_v15\":  \"2026-05\",\n    \"supertonic\":    \"2026-05\",\n    \"maya1\":         \"2025-10\",\n    \"voxtral\":       \"2025-11\",\n    \"fish_15\":       \"2024-11\",\n    \"fish_s2\":       \"2026-03\",\n    \"echo\":          \"2025-12\",\n    \"zonos\":         \"2025-02\",\n    \"zonos2\":        \"2026-06\",\n    \"openvoice\":     \"2024-04\",\n    \"styletts2\":     \"2023-06\",\n    \"metavoice\":     \"2024-02\",\n    \"step_editx\":    \"2025-10\",\n    \"miratts\":       \"2025-12\",\n    \"outetts\":       \"2025-04\",\n    \"parler\":        \"2024-06\",\n    \"melotts\":       \"2024-02\",\n    \"higgs_v3\":      \"2026-06\",\n    \"dramabox\":      \"2026-04\",\n    \"dots_tts\":      \"2026-06\",\n    \"wavtts\":        \"2026-05\",\n}\n\n_MONTHS = (\"\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n           \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\")\n\n\ndef _release_label(model):\n    \"\"\"Human label for the Released column, e.g. 'May 2026'; '' when unknown.\"\"\"\n    ym = MODEL_RELEASE.get(model)\n    if not ym:\n        return \"\"\n    y, m = ym.split(\"-\")\n    return f\"{_MONTHS[int(m)]} {y}\"\n\n\ndef _release_sort(model):\n    \"\"\"Numeric YYYYMM for the data-sort attr (parseFloat-friendly, chronological);\n    None when unknown so the cell sorts to the bottom like other missing values.\"\"\"\n    ym = MODEL_RELEASE.get(model)\n    if not ym:\n        return None\n    y, m = ym.split(\"-\")\n    return int(y) * 100 + int(m)\n\n\ndef _release_td(model, cls=\"muted\"):\n    \"\"\"A sortable Released table cell: 'May 2026' display, numeric YYYYMM data-sort.\"\"\"\n    s = _release_sort(model)\n    ds = f' data-sort=\"{s}\"' if s is not None else ' data-sort=\"\"'\n    return f'<td class=\"{cls}\"{ds}>{escape(_release_label(model) or \"—\")}</td>'\n\n\n# ---------------------------------------------------------------------------\n# Capability matrix data — the structured source behind the Capabilities page.\n# Lifted from the curated README model tables (single values, kept in sync with\n# them). License / SR / Expressive / Langs mirror the README cells verbatim; the\n# README stays hand-maintained for now (single-sourcing it from these dicts is a\n# deferred follow-up). MODEL_CROSSLINGUAL is curated separately (see below).\n# ---------------------------------------------------------------------------\n\n# Output sample rate, as shown in the README (e.g. \"44.1k\").\nMODEL_SR = {\n    \"kittentts\": \"24k\", \"kokoro\": \"24k\", \"kokoro_mlx\": \"24k\", \"lfm2_audio\": \"24k\", \"luxtts\": \"22.05k\",\n    \"magpie\": \"22.05k\", \"maya1\": \"24k\", \"melotts\": \"44.1k\", \"orpheus\": \"24k\",\n    \"outetts\": \"44.1k\", \"parler\": \"44.1k\", \"piper\": \"22.05k\", \"scyllasband\": \"24k\", \"soprano\": \"32k\",\n    \"inflect_nano\": \"24k\", \"inflect_micro\": \"24k\",\n    \"supertonic\": \"24k\", \"vibevoice\": \"24k\", \"voxtral\": \"24k\", \"chatterbox\": \"24k\",\n    \"chatterbox_turbo\": \"24k\", \"coqui\": \"24k\", \"cosyvoice\": \"24k\", \"dia\": \"44.1k\",\n    \"dots_tts\": \"48k\", \"dramabox\": \"48k\", \"echo\": \"44.1k\", \"f5tts\": \"24k\",\n    \"fish_15\": \"44.1k\", \"fish_s2\": \"44.1k\", \"higgs_v3\": \"24k\", \"indextts\": \"24k\",\n    \"longcat_1b\": \"24k\", \"longcat_3p5b\": \"24k\", \"mars5\": \"24k\", \"metavoice\": \"48k\",\n    \"miotts_01b\": \"44.1k\", \"miotts_06b\": \"44.1k\", \"miratts\": \"48k\", \"miso\": \"24k\",\n    \"moss_tts\": \"24k\", \"moss_tts_v15\": \"24k\", \"moss_tts_nano\": \"48k\",\n    \"neutts_air\": \"24k\", \"neutts_nano\": \"24k\", \"omnivoice\": \"24k\", \"openvoice\": \"22.05k\",\n    \"pocket\": \"24k\", \"qwentts\": \"24k\", \"qwentts_06b\": \"24k\", \"qwentts_06b_custom\": \"24k\",\n    \"qwentts_fast\": \"24k\", \"sesame\": \"24k\",\n    \"step_editx\": \"24k\", \"styletts2\": \"24k\", \"vibevoice_15b\": \"24k\", \"vibevoice_7b\": \"24k\",\n    \"voxcpm\": \"48k\", \"wavtts\": \"16k\", \"zipvoice\": \"24k\", \"zonos\": \"44.1k\", \"zonos2\": \"44.1k\",\n}\n\n# Explicit emotion/delivery control offered (tags / desc / knob / emo-ref / —);\n# \"*\" = a caveat applies (see docs/expressive-control.md). \"—\" = none.\nMODEL_EXPRESSIVE = {\n    \"kittentts\": \"—\", \"kokoro\": \"—\", \"kokoro_mlx\": \"—\", \"lfm2_audio\": \"—\", \"luxtts\": \"—\",\n    \"magpie\": \"emotion voices*\", \"maya1\": \"tags + desc\", \"melotts\": \"—\", \"orpheus\": \"tags\",\n    \"outetts\": \"—\", \"parler\": \"desc*\", \"piper\": \"—\", \"scyllasband\": \"6 knobs\",\n    # speed + variation (latent noise scale). No emotion/style control.\n    \"inflect_nano\": \"2 knobs\", \"inflect_micro\": \"2 knobs\",\n    \"soprano\": \"—\", \"supertonic\": \"tags\",\n    \"vibevoice\": \"—\", \"voxtral\": \"—\", \"chatterbox\": \"knob\", \"chatterbox_turbo\": \"tags*\",\n    \"coqui\": \"—\", \"cosyvoice\": \"desc\", \"dia\": \"tags\", \"dots_tts\": \"—\", \"dramabox\": \"desc\",\n    \"echo\": \"tags\", \"f5tts\": \"—\", \"fish_15\": \"—\", \"fish_s2\": \"tags\", \"higgs_v3\": \"tags\",\n    \"indextts\": \"emo-ref + desc + knob\", \"longcat_1b\": \"—\", \"longcat_3p5b\": \"—\", \"mars5\": \"—\",\n    \"metavoice\": \"—\", \"miotts_01b\": \"—\", \"miotts_06b\": \"—\", \"miratts\": \"knob\", \"miso\": \"—\",\n    \"moss_tts\": \"—\", \"moss_tts_v15\": \"tags (pause)\", \"moss_tts_nano\": \"—\", \"neutts_air\": \"—\",\n    \"neutts_nano\": \"—\", \"omnivoice\": \"tags*\", \"openvoice\": \"knob\", \"pocket\": \"—\",\n    \"qwentts\": \"—\", \"qwentts_06b\": \"—\",\n    # CustomVoice instruct control is unverified pending a runtime check (card/table conflict).\n    \"qwentts_06b_custom\": \"—\", \"qwentts_fast\": \"—\", \"sesame\": \"—\", \"step_editx\": \"tags + desc\",\n    \"styletts2\": \"knob\", \"vibevoice_15b\": \"—\", \"vibevoice_7b\": \"—\", \"voxcpm\": \"desc\",\n    \"wavtts\": \"—\", \"zipvoice\": \"—\", \"zonos\": \"emo-ref + knob\", \"zonos2\": \"knob\",\n}\n\n# License string as shown in the README (the precise truth; the commercial-OK\n# filter is a coarse heuristic derived from it — see _is_commercial).\nMODEL_LICENSE = {\n    \"kittentts\": \"Apache 2.0\", \"kokoro\": \"Apache 2.0\", \"kokoro_mlx\": \"Apache 2.0\",\n    \"lfm2_audio\": \"LFM Open v1.0\",\n    \"luxtts\": \"MIT\", \"magpie\": \"NVIDIA OML\", \"maya1\": \"Apache 2.0\", \"melotts\": \"MIT\",\n    \"orpheus\": \"Apache 2.0\", \"outetts\": \"CC-BY-NC-SA 4.0 + Llama 3.2\", \"parler\": \"Apache 2.0\",\n    \"piper\": \"GPL-3.0\", \"scyllasband\": \"Apache 2.0\", \"soprano\": \"Apache 2.0\",\n    \"inflect_nano\": \"Apache 2.0\", \"inflect_micro\": \"Apache 2.0\",\n    \"supertonic\": \"MIT + OpenRAIL-M\",\n    \"vibevoice\": \"MIT\", \"voxtral\": \"CC-BY-NC 4.0\", \"chatterbox\": \"MIT\",\n    \"chatterbox_turbo\": \"MIT\", \"coqui\": \"CPML (non-commercial)\", \"cosyvoice\": \"Apache 2.0\",\n    \"dia\": \"Apache 2.0\", \"dots_tts\": \"Apache 2.0\", \"dramabox\": \"LTX-2 Community (NC)\",\n    \"echo\": \"CC-BY-NC-SA 4.0\", \"f5tts\": \"CC-BY-NC\", \"fish_15\": \"CC-BY-NC-SA 4.0\",\n    \"fish_s2\": \"Research (non-commercial)\", \"higgs_v3\": \"Research (NC)\", \"indextts\": \"Apache 2.0\",\n    \"longcat_1b\": \"MIT\", \"longcat_3p5b\": \"MIT\", \"mars5\": \"AGPL-3.0\", \"metavoice\": \"Apache 2.0\",\n    \"miotts_01b\": \"Falcon-LLM\", \"miotts_06b\": \"Apache 2.0\", \"miratts\": \"MIT\", \"miso\": \"Modified MIT\",\n    \"moss_tts\": \"Apache 2.0\", \"moss_tts_v15\": \"Apache 2.0\", \"moss_tts_nano\": \"Apache 2.0\",\n    \"neutts_air\": \"Apache 2.0\", \"neutts_nano\": \"Apache 2.0\",\n    \"omnivoice\": \"Apache 2.0 code / CC-BY-NC weights\",\n    \"openvoice\": \"MIT\", \"pocket\": \"Apache 2.0\", \"qwentts\": \"Apache 2.0\",\n    \"qwentts_06b\": \"Apache 2.0\", \"qwentts_06b_custom\": \"Apache 2.0\", \"qwentts_fast\": \"MIT\",\n    \"sesame\": \"Apache 2.0\", \"step_editx\": \"Apache 2.0\", \"styletts2\": \"MIT\", \"vibevoice_15b\": \"MIT\",\n    \"vibevoice_7b\": \"MIT\", \"voxcpm\": \"Apache 2.0\", \"wavtts\": \"MIT code / CC-BY-NC 4.0 weights\",\n    \"zipvoice\": \"Apache 2.0\", \"zonos\": \"Apache 2.0\", \"zonos2\": \"Apache 2.0\",\n}\n\n# Language coverage cell from the README: \"✓\" multilingual, \"✓ (N)\"/\"✓ (zh+en)\"\n# with detail, \"—\"/\"— (en)\" English-only. _is_multilingual keys off the leading ✓.\nMODEL_LANGS = {\n    \"kittentts\": \"—\", \"kokoro\": \"✓\", \"kokoro_mlx\": \"✓\", \"lfm2_audio\": \"— (en)\", \"luxtts\": \"—\",\n    \"magpie\": \"✓ (9)\", \"maya1\": \"—\", \"melotts\": \"— (en)\", \"orpheus\": \"— (en)\",\n    \"outetts\": \"✓ (12)\", \"parler\": \"—\", \"piper\": \"✓\", \"scyllasband\": \"✓ (4)\",\n    \"inflect_nano\": \"— (en)\", \"inflect_micro\": \"— (en)\",\n    \"soprano\": \"—\",\n    \"supertonic\": \"✓ (31)\", \"vibevoice\": \"—\", \"voxtral\": \"✓\", \"chatterbox\": \"—\",\n    \"chatterbox_turbo\": \"—\", \"coqui\": \"✓ (17)\", \"cosyvoice\": \"✓\", \"dia\": \"—\",\n    \"dots_tts\": \"✓ (24)\", \"dramabox\": \"— (en)\", \"echo\": \"—\", \"f5tts\": \"✓\",\n    \"fish_15\": \"✓\", \"fish_s2\": \"—\", \"higgs_v3\": \"✓ (100)\", \"indextts\": \"✓\",\n    \"longcat_1b\": \"✓ (zh+en)\", \"longcat_3p5b\": \"✓ (zh+en)\", \"mars5\": \"—\", \"metavoice\": \"—\",\n    \"miotts_01b\": \"✓ (en+ja)\", \"miotts_06b\": \"✓ (en+ja)\", \"miratts\": \"—\", \"miso\": \"— (en)\",\n    \"moss_tts\": \"✓ (20)\", \"moss_tts_v15\": \"✓ (31)\", \"moss_tts_nano\": \"✓ (zh+en)\",\n    \"neutts_air\": \"—\", \"neutts_nano\": \"—\", \"omnivoice\": \"✓ (600+)\", \"openvoice\": \"✓\",\n    \"pocket\": \"—\", \"qwentts\": \"✓\", \"qwentts_06b\": \"✓\", \"qwentts_06b_custom\": \"✓\",\n    \"qwentts_fast\": \"✓\", \"sesame\": \"—\", \"step_editx\": \"—\",\n    \"styletts2\": \"—\", \"vibevoice_15b\": \"—\", \"vibevoice_7b\": \"—\", \"voxcpm\": \"✓ (30)\",\n    \"wavtts\": \"✓ (zh+en)\", \"zipvoice\": \"✓ (zh+en)\", \"zonos\": \"✓\", \"zonos2\": \"—\",\n}\n\n# --- Registry-drift guard -------------------------------------------------\n# Every model must appear in ALL capability registries: a missing key silently\n# renders \"—\" cells on Capabilities and mislabels licenses via _is_commercial.\n# Fails at import time (publish.py imports this module) so drift can't ship.\n_REGISTRIES = {\n    \"MODEL_DISPLAY_NAMES\": MODEL_DISPLAY_NAMES, \"MODEL_SIZE\": MODEL_SIZE,\n    \"MODEL_URL\": MODEL_URL, \"MODEL_KIND\": MODEL_KIND, \"MODEL_RELEASE\": MODEL_RELEASE,\n    \"MODEL_SR\": MODEL_SR, \"MODEL_EXPRESSIVE\": MODEL_EXPRESSIVE,\n    \"MODEL_LICENSE\": MODEL_LICENSE, \"MODEL_LANGS\": MODEL_LANGS,\n}\n# The expected set MUST include harness.MODELS, not just the union of the\n# registries. A model absent from EVERY registry contributes nothing to that\n# union, so it was invisible to this check — the guard caught partial\n# registration but not zero registration. `scyllasband` shipped to gh-pages\n# exactly that way (2026-07-20): it rendered as the raw slug with empty\n# size/kind/license/SR/language cells, and nothing failed. harness.MODELS is\n# the real source of truth for \"what models exist\", so fold it in.\nimport harness as _harness  # stdlib-only at module level; no import cost, no cycle\n\n_HARNESS_MODELS = {_m[0] for _m in _harness.MODELS}\n_ALL_REGISTRY_MODELS = set().union(\n    *(d.keys() for d in _REGISTRIES.values())) | _HARNESS_MODELS\n_REGISTRY_DRIFT = {name: sorted(_ALL_REGISTRY_MODELS - d.keys())\n                   for name, d in _REGISTRIES.items() if _ALL_REGISTRY_MODELS - d.keys()}\nif _REGISTRY_DRIFT:\n    raise AssertionError(\n        \"model registry drift — these registries are missing models that \"\n        \"harness.MODELS or another registry declares. Every model in \"\n        \"harness.MODELS must appear in ALL registries, or it renders on the \"\n        f\"board as a raw slug with empty capability cells: {_REGISTRY_DRIFT}\")\n\n\n# Tokens in a license string that signal NON-commercial / research-only use.\n_NONCOMMERCIAL_TOKENS = (\"NC\", \"non-commercial\", \"Non-commercial\", \"CPML\", \"Research\")\n\n\ndef _is_multilingual(model):\n    \"\"\"True if the model synthesizes more than English (README ✓ in the langs cell).\"\"\"\n    return MODEL_LANGS.get(model, \"\").startswith(\"✓\")\n\n\ndef _is_commercial(model):\n    \"\"\"Coarse 'commercial use permitted' flag derived from the license string.\n    The Capabilities table always shows the exact license too — this only drives\n    the convenience filter, so it errs toward the literal NC/Research signal.\"\"\"\n    lic = MODEL_LICENSE.get(model, \"\")\n    return bool(lic) and not any(tok in lic for tok in _NONCOMMERCIAL_TOKENS)\n\n\ndef _sr_hz(model):\n    \"\"\"Numeric sample rate in Hz from the 'NN.Nk' README cell, for sortable cells.\"\"\"\n    s = MODEL_SR.get(model, \"\")\n    try:\n        return int(float(s.rstrip(\"k\")) * 1000) if s.endswith(\"k\") else None\n    except ValueError:\n        return None\n\n\n# Cross-lingual voice cloning: clones a voice from a reference in one language and\n# speaks a DIFFERENT one in that same voice. CURATED, not derived — each entry was\n# verified against the model's card/paper (2026-06-30). A multilingual cloner is NOT\n# automatically cross-lingual: longcat (1B/3.5B), miotts (0.1B/0.6B), and voxtral are\n# multilingual cloners documented to clone only within a single language, so they are\n# omitted. Multilingual cloners with no documented cross-lingual transfer are also\n# left out (fish_15, moss_tts_nano, voxcpm, wavtts, zipvoice) — flagged only on\n# positive evidence. Everything not listed (incl. all English-only cloners) is False.\nMODEL_CROSSLINGUAL = {\n    \"coqui\", \"cosyvoice\", \"dots_tts\", \"f5tts\", \"higgs_v3\", \"indextts\", \"moss_tts\",\n    \"moss_tts_v15\", \"omnivoice\", \"openvoice\", \"outetts\", \"qwentts\", \"qwentts_06b\",\n    \"qwentts_fast\", \"zonos\",\n}\n\n\ndef _is_crosslingual(model):\n    \"\"\"True if the model can clone a voice and then speak a different language in it\n    (curated per-model — see MODEL_CROSSLINGUAL).\"\"\"\n    return model in MODEL_CROSSLINGUAL\n\n\ndef _ds(val):\n    \"\"\"data-sort attribute for numeric cells; empty when None.\"\"\"\n    return f' data-sort=\"{val}\"' if val is not None else ' data-sort=\"\"'\n\n\ndef _read_meta(run_dir):\n    \"\"\"Read meta.json from a results dir; return None if missing or invalid.\"\"\"\n    p = run_dir / \"meta.json\"\n    if not p.exists():\n        return None\n    try:\n        return json.loads(p.read_text(encoding=\"utf-8\"))\n    except (OSError, json.JSONDecodeError):\n        return None\n\n\ndef _rig_summary(meta):\n    \"\"\"One-line human summary like 'AMD Ryzen 9 9950X3D · RTX 5090 32GB · 128 GB RAM · Windows 11'.\"\"\"\n    if not meta:\n        return \"\"\n    parts = []\n    if meta.get(\"cpu\"):\n        cpu = meta[\"cpu\"]\n        if meta.get(\"cpu_cores_physical\"):\n            cpu = f\"{cpu} ({meta['cpu_cores_physical']}C)\"\n        parts.append(cpu)\n    if meta.get(\"gpu\"):\n        gpu = meta[\"gpu\"]\n        if meta.get(\"gpu_vram_gb\"):\n            gpu = f\"{gpu} {meta['gpu_vram_gb']}GB\"\n        parts.append(gpu)\n    if meta.get(\"ram_gb\"):\n        parts.append(f\"{meta['ram_gb']} GB RAM\")\n    if meta.get(\"os\"):\n        parts.append(meta[\"os\"])\n    return \" · \".join(parts)\n\n\ndef _read_csv(csv_path):\n    rows = []\n    with csv_path.open(encoding=\"utf-8\") as f:\n        for r in csv.DictReader(f):\n            for k in (\"ttfa_ms\", \"gen_s\", \"audio_s\", \"rtf\",\n                      \"peak_mem_mb\", \"peak_vram_mb\", \"wall_s\"):\n                v = r.get(k)\n                r[k] = float(v) if v not in (None, \"\") else None\n            r[\"run_index\"] = int(r.get(\"run_index\") or 0)\n            r[\"is_cold\"] = str(r.get(\"is_cold\")).lower() == \"true\"\n            r[\"ok\"] = str(r.get(\"ok\")).lower() == \"true\"\n            rows.append(r)\n    return rows\n\n\ndef _sort_prompt_ids(ids):\n    return sorted(ids, key=lambda s: int(s) if str(s).isdigit() else 1_000_000)\n\n\ndef _build_context(rows, run_dir, meta):\n    \"\"\"Pre-compute everything the four lens renderers need from a CSV row list.\n\n    Returns a dict with:\n      per_cell:    {(prompt_id, model, device) -> {\"cold\": row|None, \"warms\": [row], \"fail\": row|None}}\n                   Note: only the FIRST failure row per cell is retained in \"fail\".\n                   Subsequent failures for the same cell are dropped.\n      per_model:   {(model, device) -> aggregated dict with avg TTFA/RTF, peak mem/vram,\n                    n_ok, n_fail, n_total}\n      per_prompt:  {prompt_id -> [{model, device, ttfa_warm, rtf_warm, wav, wav_exists}\n                    ranked by warm TTFA asc (fastest first), successful-cold rows only]}\n      tldr_speed:  {\"predefined\": (model, device, rtf_warm, ttfa_warm) | None,\n                    \"cloning\":    (model, device, rtf_warm, ttfa_warm) | None}\n      prompts_seen, models_seen, devices_seen, runs_per_cell, meta, run_name, run_dir\n    \"\"\"\n    cells = defaultdict(lambda: {\"cold\": None, \"warms\": [], \"fail\": None})\n    for r in rows:\n        key = (r[\"prompt_id\"], r[\"model\"], r[\"device\"])\n        if not r[\"ok\"]:\n            if cells[key][\"fail\"] is None:\n                cells[key][\"fail\"] = r\n        elif r[\"is_cold\"]:\n            cells[key][\"cold\"] = r\n        else:\n            cells[key][\"warms\"].append(r)\n\n    prompts_seen = _sort_prompt_ids({r[\"prompt_id\"] for r in rows})\n    models_seen = sorted({r[\"model\"] for r in rows})\n    devices_seen = sorted({r[\"device\"] for r in rows})\n    # Intentionally caps fail at 1 per cell (matches the first-failure-wins above)\n    # so the \"N runs per cell\" badge reflects warm-run count, not failure noise.\n    runs_per_cell = max(\n        (1 if c[\"cold\"] else 0) + len(c[\"warms\"]) + (1 if c[\"fail\"] else 0)\n        for c in cells.values()\n    )\n\n    def _rtf(r):\n        a, g = r.get(\"audio_s\"), r.get(\"gen_s\")\n        return (a / g) if (a and g) else None\n\n    per_model = {}\n    for (pid, model, dev), c in cells.items():\n        key = (model, dev)\n        per_model.setdefault(key, {\n            \"ttfa_cold\": [], \"ttfa_warm\": [], \"rtf_cold\": [], \"rtf_warm\": [],\n            \"peak_mem\": [], \"peak_vram\": [],\n            \"ok_prompts\": set(), \"fail_prompts\": set(), \"all_prompts\": set(),\n        })\n        bucket = per_model[key]\n        bucket[\"all_prompts\"].add(pid)\n        if c[\"cold\"]:\n            bucket[\"ok_prompts\"].add(pid)\n            bucket[\"ttfa_cold\"].append(c[\"cold\"][\"ttfa_ms\"])\n            rc = _rtf(c[\"cold\"])\n            if rc is not None:\n                bucket[\"rtf_cold\"].append(rc)\n            if c[\"cold\"].get(\"peak_mem_mb\") is not None:\n                bucket[\"peak_mem\"].append(c[\"cold\"][\"peak_mem_mb\"])\n            if c[\"cold\"].get(\"peak_vram_mb\") is not None:\n                bucket[\"peak_vram\"].append(c[\"cold\"][\"peak_vram_mb\"])\n            if c[\"warms\"]:\n                bucket[\"ttfa_warm\"].extend(w[\"ttfa_ms\"] for w in c[\"warms\"])\n                warm_rtfs = [v for v in (_rtf(w) for w in c[\"warms\"]) if v is not None]\n                bucket[\"rtf_warm\"].extend(warm_rtfs)\n        elif c[\"fail\"]:\n            bucket[\"fail_prompts\"].add(pid)\n\n    def _avg(xs):\n        return (sum(xs) / len(xs)) if xs else None\n\n    def _maxv(xs):\n        return max(xs) if xs else None\n\n    agg = {}\n    for key, b in per_model.items():\n        agg[key] = {\n            \"ttfa_cold\": _avg(b[\"ttfa_cold\"]),\n            \"ttfa_warm\": _avg(b[\"ttfa_warm\"]),\n            \"rtf_cold\":  _avg(b[\"rtf_cold\"]),\n            \"rtf_warm\":  _avg(b[\"rtf_warm\"]),\n            \"peak_mem\":  _maxv(b[\"peak_mem\"]),\n            \"peak_vram\": _maxv(b[\"peak_vram\"]),\n            \"n_ok\":      len(b[\"ok_prompts\"]),\n            \"n_fail\":    len(b[\"fail_prompts\"]),\n            \"n_total\":   len(b[\"all_prompts\"]),\n        }\n\n    per_prompt = {}\n    for pid in prompts_seen:\n        items = []\n        for (p2, model, dev), c in cells.items():\n            if p2 != pid:\n                continue\n            if not c[\"cold\"]:\n                continue\n            cold = c[\"cold\"]\n            warm_ttfas = [w[\"ttfa_ms\"] for w in c[\"warms\"]]\n            ttfa_warm = (sum(warm_ttfas) / len(warm_ttfas)) if warm_ttfas else None\n            warm_rtfs = [v for v in (_rtf(w) for w in c[\"warms\"]) if v is not None]\n            rtf_warm = (sum(warm_rtfs) / len(warm_rtfs)) if warm_rtfs else None\n            items.append({\n                \"model\": model,\n                \"device\": dev,\n                \"ttfa_warm\": ttfa_warm,\n                \"rtf_warm\": rtf_warm,\n                \"wav\": f\"{model}_{dev}_p{pid}.wav\",\n                \"wav_exists\": (run_dir / f\"{model}_{dev}_p{pid}.wav\").exists(),\n            })\n        # Sort by warm TTFA asc (fastest first); missing values sink to the bottom.\n        items.sort(key=lambda it: (it[\"ttfa_warm\"] is None,\n                                   it[\"ttfa_warm\"] or float(\"inf\")))\n        per_prompt[pid] = items\n\n    def _pick_best_speed(kind):\n        best = None\n        for (model, dev), a in agg.items():\n            if MODEL_KIND.get(model) != kind:\n                continue\n            if a[\"rtf_warm\"] is None:\n                continue\n            if best is None or a[\"rtf_warm\"] > best[2]:\n                best = (model, dev, a[\"rtf_warm\"], a[\"ttfa_warm\"])\n        return best\n\n    return {\n        \"per_cell\": cells,\n        \"per_model\": agg,\n        \"per_prompt\": per_prompt,\n        \"tldr_speed\": {\n            \"predefined\": _pick_best_speed(\"predefined\"),\n            \"cloning\":    _pick_best_speed(\"cloning\"),\n        },\n        \"prompts_seen\": prompts_seen,\n        \"models_seen\": models_seen,\n        \"devices_seen\": devices_seen,\n        \"runs_per_cell\": runs_per_cell,\n        \"meta\": meta,\n        \"run_name\": run_dir.name,\n        \"run_dir\": run_dir,\n    }\n\n\ndef _render_lens_picker(ctx):\n    \"\"\"Render the per-rig landing card with three lens links.\"\"\"\n    meta = ctx[\"meta\"]\n    out = ['<!doctype html>',\n           '<html lang=\"en\"><head><meta charset=\"utf-8\">',\n           f'<title>TTS Bench — {escape(ctx[\"run_name\"])}</title>',\n           STYLE,\n           '</head><body>',\n           CONTROLS,\n           '<div class=\"nav\"><a href=\"../index.html\">← all runs</a></div>',\n           f'<h1>TTS Bench — {escape(ctx[\"run_name\"])}</h1>']\n    if meta:\n        out.append(f'<div class=\"meta\"><strong>Rig:</strong> '\n                   f'<code>{escape(meta.get(\"rig\") or \"?\")}</code> — '\n                   f'{escape(_rig_summary(meta))}</div>')\n        if meta.get(\"label\"):\n            ref = meta.get(\"reference\")\n            ref_html = f' — ref <code>{escape(ref)}</code>' if ref else \"\"\n            out.append(f'<div class=\"meta\"><strong>Label:</strong> '\n                       f'{escape(meta[\"label\"])}{ref_html}</div>')\n    n_models = len(ctx[\"models_seen\"])\n    n_devices = len(ctx[\"devices_seen\"])\n    n_prompts = len(ctx[\"prompts_seen\"])\n    out.append(\n        f'<div class=\"meta\">{n_models} model(s) · '\n        f'{n_devices} device(s) · '\n        f'{n_prompts} prompt(s) · '\n        f'{ctx[\"runs_per_cell\"]} run(s) per cell</div>'\n    )\n    out.append('<div class=\"lens-grid\">')\n    out.append(\n        '<div class=\"lens-card\"><a href=\"speed.html\">'\n        '<h3>▶ Speed</h3>'\n        f'<div class=\"desc\">TTFA, RTFx, memory · {n_models} models · sortable</div>'\n        '</a></div>'\n    )\n    out.append(\n        '<div class=\"lens-card\"><a href=\"samples.html\">'\n        '<h3>▶ Samples</h3>'\n        '<div class=\"desc\">By-prompt gallery · A/B every model</div>'\n        '</a></div>'\n    )\n    out.append('</div>')\n    out.append('<div class=\"meta\">'\n               '<a href=\"results.csv\">results.csv</a> · '\n               '<a href=\"meta.json\">meta.json</a></div>')\n    out.append(SCRIPT)\n    out.append('</body></html>')\n    return \"\\n\".join(out)\n\n\n_LENSES = ((\"speed\", \"Speed\"), (\"samples\", \"Samples\"))\n\n_READING_GUIDE = {\n    \"speed\": (\n        '<div class=\"reading-guide\">'\n        '<strong>TTFA</strong> = time to first audio (ms; lower is better). '\n        '<strong>RTFx</strong> = realtime speed (× realtime; higher is better; e.g. 10× means '\n        '10 sec of audio generated per 1 sec of compute). '\n        '<strong>Cold</strong> = first run after process start; <strong>warm</strong> = subsequent runs.'\n        '</div>'\n    ),\n    \"samples\": (\n        '<div class=\"reading-guide\">'\n        'Each prompt section shows every model\\'s audio output, ordered by '\n        '<strong>warm TTFA</strong> (fastest first). '\n        'Click any audio player to hear that model\\'s rendering.'\n        '</div>'\n    ),\n}\n\n\ndef _lens_nav(active):\n    \"\"\"Emit the nav strip with prev/next arrows + lens tabs + 'all runs' link.\n\n    `active` is one of the slugs in _LENSES. Arrows are bounded — at the\n    first lens the ← arrow renders disabled (no link); at the last lens\n    the → arrow renders disabled. No wraparound, so the report tour ends\n    at the natural edges instead of cycling infinitely.\n    \"\"\"\n    slugs = [s for s, _ in _LENSES]\n    labels = {s: l for s, l in _LENSES}\n    idx = slugs.index(active)\n    prev_slug = slugs[idx - 1] if idx > 0 else None\n    next_slug = slugs[idx + 1] if idx < len(slugs) - 1 else None\n\n    parts = ['<div class=\"nav\"><span class=\"lens-tabs\">']\n    if prev_slug is not None:\n        parts.append(\n            f'<a class=\"lens-arrow\" href=\"{prev_slug}.html\" '\n            f'title=\"Previous lens: {labels[prev_slug]}\">← {labels[prev_slug]}</a>'\n        )\n    else:\n        parts.append('<span class=\"lens-arrow lens-arrow-disabled\">← </span>')\n    for slug, label in _LENSES:\n        cls = \"lens-tab active\" if slug == active else \"lens-tab\"\n        parts.append(f'<a class=\"{cls}\" href=\"{slug}.html\">{label}</a>')\n    if next_slug is not None:\n        parts.append(\n            f'<a class=\"lens-arrow\" href=\"{next_slug}.html\" '\n            f'title=\"Next lens: {labels[next_slug]}\">{labels[next_slug]} →</a>'\n        )\n    else:\n        parts.append('<span class=\"lens-arrow lens-arrow-disabled\"> →</span>')\n    parts.append('</span> · <a href=\"../index.html\">all runs</a></div>')\n    return \"\".join(parts)\n\n\ndef _speed_table_html(ctx):\n    \"\"\"Return (table_html, rtf_warm_col_idx) for one run's speed view.\n\n    Just the <table> (aggregated per model/device, no audio) — shared by the\n    per-rig speed.html and the cross-rig speed hub on the published landing.\n    The caller decides where to place it and whether to set window.__defaultSort\n    (rtf_warm_col_idx is returned so the caller can default-sort by warm RTF).\"\"\"\n    cols = (\"Model\", \"Device\", \"TTFA cold\", \"TTFA warm\",\n            \"RTFx cold\", \"RTFx warm\", \"Peak RAM\", \"Peak VRAM\", \"Size\", \"Released\")\n    num_cols = {\"TTFA cold\", \"TTFA warm\", \"RTFx cold\", \"RTFx warm\",\n                \"Peak RAM\", \"Peak VRAM\"}\n    rtf_warm_idx = cols.index(\"RTFx warm\")\n    out = ['<table><thead><tr>']\n    for c in cols:\n        cls = ' class=\"num\"' if c in num_cols else ''\n        out.append(f'<th{cls}>{c}</th>')\n    out.append('</tr></thead><tbody>')\n\n    # Sort rows by RTFx warm desc for stable origIdx ordering\n    keys_sorted = sorted(\n        ctx[\"per_model\"].keys(),\n        key=lambda k: (ctx[\"per_model\"][k][\"rtf_warm\"] is None,\n                       -(ctx[\"per_model\"][k][\"rtf_warm\"] or 0))\n    )\n    for (model, dev) in keys_sorted:\n        a = ctx[\"per_model\"][(model, dev)]\n        row_id = f\"speed-{model}-{dev}\".lower().replace(\"/\", \"-\")\n        dev_class = f\"dev-{dev}\"\n        size_str = MODEL_SIZE.get(model, \"—\")\n        n_ok = a[\"n_ok\"]; n_total = a[\"n_total\"]\n        partial = (n_ok < n_total) and n_ok > 0\n        partial_tag = (f' <span class=\"muted\">({n_ok}/{n_total} ok)</span>'\n                       if partial else \"\")\n        if n_ok == 0:\n            err_row = next(\n                (c[\"fail\"] for k, c in ctx[\"per_cell\"].items()\n                 if k[1] == model and k[2] == dev and c[\"fail\"]),\n                None,\n            )\n            err = (err_row.get(\"error\") if err_row else \"\") or \"no successful run\"\n            out.append(f'<tr id=\"{escape(row_id)}\">'\n                       f'<td>{escape(_display_name(model))}</td>'\n                       f'<td class=\"{dev_class}\">{escape(dev)}</td>'\n                       f'<td colspan=\"{len(cols)-2}\" class=\"fail\">{escape(_humanize_error(err))}</td>'\n                       '</tr>')\n            continue\n        out.append(f'<tr id=\"{escape(row_id)}\">')\n        out.append(f'<td>{escape(_display_name(model))}{partial_tag}</td>'\n                   f'<td class=\"{dev_class}\">{escape(dev)}</td>')\n        out.append(f'<td class=\"num\"{_ds(a[\"ttfa_cold\"])}>{_fmt_ttfa(a[\"ttfa_cold\"])}</td>')\n        out.append(f'<td class=\"num\"{_ds(a[\"ttfa_warm\"])}>{_fmt_ttfa(a[\"ttfa_warm\"])}</td>')\n        out.append(f'<td class=\"num\"{_ds(a[\"rtf_cold\"])}>{_fmt_rtf(a[\"rtf_cold\"])}</td>')\n        out.append(f'<td class=\"num\"{_ds(a[\"rtf_warm\"])}>{_fmt_rtf(a[\"rtf_warm\"])}</td>')\n        out.append(f'<td class=\"num\"{_ds(a[\"peak_mem\"])}>{_fmt_mb(a[\"peak_mem\"])}</td>')\n        out.append(f'<td class=\"num\"{_ds(a[\"peak_vram\"])}>{_fmt_mb(a[\"peak_vram\"])}</td>')\n        out.append(f'<td class=\"muted\">{escape(size_str)}</td>')\n        out.append(_release_td(model))\n        out.append('</tr>')\n    out.append('</tbody></table>')\n    return \"\".join(out), rtf_warm_idx\n\n\ndef _render_speed(ctx):\n    \"\"\"Render speed.html — aggregated per (model, device), no audio.\"\"\"\n    meta = ctx[\"meta\"]\n    has_ref = bool((meta or {}).get(\"reference\"))\n    out = ['<!doctype html>',\n           '<html lang=\"en\"><head><meta charset=\"utf-8\">',\n           f'<title>TTS Bench — Speed — {escape(ctx[\"run_name\"])}</title>',\n           STYLE,\n           '</head><body>',\n           CONTROLS,\n           _lens_nav(\"speed\"),\n           f'<h1>TTS Bench — Speed — {escape(ctx[\"run_name\"])}</h1>']\n    if meta:\n        out.append(f'<div class=\"meta\"><strong>Rig:</strong> '\n                   f'<code>{escape(meta.get(\"rig\") or \"?\")}</code> — '\n                   f'{escape(_rig_summary(meta))}</div>')\n        if meta.get(\"label\"):\n            ref = meta.get(\"reference\")\n            ref_html = f' — ref <code>{escape(ref)}</code>' if ref else \"\"\n            out.append(f'<div class=\"meta\"><strong>Label:</strong> '\n                       f'{escape(meta[\"label\"])}{ref_html}</div>')\n\n    out.append(_READING_GUIDE[\"speed\"])\n\n    # TLDR\n    out.append('<div class=\"tldr\"><h2>Speed winners</h2>')\n    tldr = ctx[\"tldr_speed\"]\n    def _fmt_tldr(entry, label):\n        if not entry:\n            return f'<p>{label}: <span class=\"muted\">no data</span></p>'\n        model, dev, rtf, ttfa = entry\n        return (f'<p>{label}: <strong>{escape(_display_name(model))}</strong> ({escape(dev)}) — '\n                f'{_fmt_rtf(rtf)} warm RTFx, {_fmt_ttfa(ttfa)} warm TTFA</p>')\n    if has_ref:\n        # Cloning-only run; collapse to single line\n        best = tldr[\"cloning\"] or tldr[\"predefined\"]\n        out.append(_fmt_tldr(best, \"Fastest (this cloning run)\"))\n    else:\n        out.append(_fmt_tldr(tldr[\"predefined\"], \"Fastest predefined-voice\"))\n        out.append(_fmt_tldr(tldr[\"cloning\"],    \"Fastest cloning-capable\"))\n    out.append('</div>')\n\n    # Table (shared builder — same markup the cross-rig speed hub uses)\n    table_html, rtf_warm_idx = _speed_table_html(ctx)\n    out.append(table_html)\n\n    # Default sort: RTFx warm desc\n    out.append(f'<script>window.__defaultSort = {{colIdx: {rtf_warm_idx}, dir: -1}};</script>')\n    out.append(SCRIPT)\n    out.append('</body></html>')\n    return \"\\n\".join(out)\n\n\ndef _render_samples(ctx):\n    \"\"\"Render samples.html — by-prompt gallery, successful rows only, ordered by warm TTFA.\"\"\"\n    meta = ctx[\"meta\"]\n    prompts = ctx[\"prompts_seen\"]\n    out = ['<!doctype html>',\n           '<html lang=\"en\"><head><meta charset=\"utf-8\">',\n           f'<title>TTS Bench — Samples — {escape(ctx[\"run_name\"])}</title>',\n           STYLE,\n           '</head><body>',\n           CONTROLS,\n           _lens_nav(\"samples\"),\n           f'<h1>TTS Bench — Samples — {escape(ctx[\"run_name\"])}</h1>']\n    if meta:\n        out.append(f'<div class=\"meta\"><strong>Rig:</strong> '\n                   f'<code>{escape(meta.get(\"rig\") or \"?\")}</code> — '\n                   f'{escape(_rig_summary(meta))}</div>')\n        if meta.get(\"label\"):\n            ref = meta.get(\"reference\")\n            ref_html = f' — ref <code>{escape(ref)}</code>' if ref else \"\"\n            out.append(f'<div class=\"meta\"><strong>Label:</strong> '\n                       f'{escape(meta[\"label\"])}{ref_html}</div>')\n    rank_basis = \"warm TTFA (fastest first)\"\n    out.append(f'<div class=\"meta\">{len(prompts)} prompt(s) · '\n               f'one section per prompt · all models ranked by {rank_basis} within each</div>')\n\n    out.append(_READING_GUIDE[\"samples\"])\n\n    # Reference-voice player for cloning runs: each model in the table below\n    # is trying to imitate this voice, so the listener needs to hear it to\n    # judge fidelity. publish.py copies the source wav into the slug dir as\n    # `_reference.wav`; if absent (older runs / non-cloning runs) we skip.\n    ref_meta = (meta or {}).get(\"reference\") if meta else None\n    if ref_meta and (ctx[\"run_dir\"] / \"_reference.wav\").exists():\n        out.append(\n            '<div class=\"prompt\" style=\"border-left: 3px solid var(--accent);\">'\n            '<h2>Reference voice</h2>'\n            f'<div class=\"meta\">Each model below was given this clip + transcript as the voice to imitate. '\n            f'Source: <code>{escape(ref_meta)}</code></div>'\n            '<audio controls preload=\"metadata\" src=\"_reference.wav\" '\n            'style=\"width: 100%; max-width: 480px;\"></audio>'\n            '</div>'\n        )\n\n    if len(prompts) > 3:\n        out.append('<nav class=\"prompt-jumper\">Jump to: ')\n        out.append(\" · \".join(f'<a href=\"#p{escape(pid)}\">P{escape(pid)}</a>' for pid in prompts))\n        out.append('</nav>')\n\n    cols = (\"Rank\", \"Model\", \"Device\", \"TTFA warm\", \"Audio\")\n    num_cols = {\"Rank\", \"TTFA warm\"}\n    for pid in prompts:\n        # Samples only lists clips we can actually play. A successful row normally\n        # ships its cold-run wav; a speed-only model (e.g. kokoro_mlx, benched for\n        # timing but publishing no Mac clip) lands here with no wav — skip it rather\n        # than render a dead \"missing\" row.\n        items = [it for it in ctx[\"per_prompt\"].get(pid, []) if it.get(\"wav_exists\")]\n        out.append(f'<div class=\"prompt\" id=\"p{escape(pid)}\"><h2>Prompt {escape(pid)}</h2>')\n        ptext = PROMPT_INFO.get(pid)\n        if ptext:\n            lang, text = ptext\n            out.append(f'<span class=\"prompt-text\"><span class=\"lang\">[{escape(lang)}]</span>'\n                       f'\"{escape(text)}\"</span>')\n        if not items:\n            out.append('<div class=\"meta\">No successful samples for this prompt.</div></div>')\n            continue\n        out.append('<table><thead><tr>')\n        for c in cols:\n            cls = ' class=\"num\"' if c in num_cols else ''\n            out.append(f'<th{cls}>{c}</th>')\n        out.append('</tr></thead><tbody>')\n        for rank, it in enumerate(items, 1):\n            row_id = f\"sample-p{pid}-{it['model']}-{it['device']}\".lower().replace(\"/\", \"-\")\n            dev_class = f\"dev-{it['device']}\"\n            audio_html = (\n                f'<audio controls preload=\"none\" src=\"{escape(it[\"wav\"])}\"></audio>'\n                if it[\"wav_exists\"] else '<span class=\"muted\">missing</span>'\n            )\n            out.append(f'<tr id=\"{escape(row_id)}\">')\n            out.append(f'<td class=\"num\" data-sort=\"{rank}\">{rank}</td>')\n            out.append(f'<td>{escape(_display_name(it[\"model\"]))}</td>')\n            out.append(f'<td class=\"{dev_class}\">{escape(it[\"device\"])}</td>')\n            out.append(f'<td class=\"num pill\"{_ds(it[\"ttfa_warm\"])}>{_fmt_ttfa(it[\"ttfa_warm\"])}</td>')\n            out.append(f'<td>{audio_html}</td>')\n            out.append('</tr>')\n        out.append('</tbody></table></div>')\n\n    out.append(SCRIPT)\n    out.append('</body></html>')\n    return \"\\n\".join(out)\n\n\ndef build_report(run_dir: Path) -> Path:\n    \"\"\"Emit index.html + speed.html + samples.html from results.csv.\n    Returns the path to index.html (the lens picker).\"\"\"\n    csv_path = run_dir / \"results.csv\"\n    if not csv_path.exists():\n        raise SystemExit(f\"No results.csv in {run_dir}\")\n    rows = _read_csv(csv_path)\n    if not rows:\n        raise SystemExit(f\"Empty results.csv in {run_dir}\")\n\n    meta = _read_meta(run_dir)\n    ctx = _build_context(rows, run_dir, meta)\n\n    (run_dir / \"index.html\").write_text(_render_lens_picker(ctx), encoding=\"utf-8\")\n    (run_dir / \"speed.html\").write_text(_render_speed(ctx), encoding=\"utf-8\")\n    # NAQ left the harness — drop any quality.html left over from an older run.\n    (run_dir / \"quality.html\").unlink(missing_ok=True)\n    (run_dir / \"samples.html\").write_text(_render_samples(ctx), encoding=\"utf-8\")\n\n    # Legacy report.html — emit a small redirect stub so any external link still works.\n    legacy = run_dir / \"report.html\"\n    legacy.write_text(\n        '<!doctype html><html><head><meta charset=\"utf-8\">'\n        '<meta http-equiv=\"refresh\" content=\"0; url=index.html\">'\n        '<title>Redirecting…</title></head>'\n        '<body><a href=\"index.html\">Lens picker</a></body></html>',\n        encoding=\"utf-8\",\n    )\n    return run_dir / \"index.html\"\n\n\ndef build_index() -> Path:\n    if not RESULTS.exists():\n        raise SystemExit(f\"No results dir at {RESULTS}\")\n\n    runs = []\n    for d in sorted(RESULTS.iterdir(), reverse=True):\n        if not d.is_dir():\n            continue\n        csv_path = d / \"results.csv\"\n        if not csv_path.exists():\n            continue\n        try:\n            rows = _read_csv(csv_path)\n        except Exception:\n            continue\n        if not rows:\n            continue\n        meta = _read_meta(d)\n        runs.append({\n            \"name\": d.name,\n            \"models\": sorted({r[\"model\"] for r in rows}),\n            \"devices\": sorted({r[\"device\"] for r in rows}),\n            \"prompts\": _sort_prompt_ids({r[\"prompt_id\"] for r in rows}),\n            \"rows\": len(rows),\n            \"ok\": sum(1 for r in rows if r[\"ok\"]),\n            \"has_index\": (d / \"index.html\").exists(),\n            \"rig\": (meta or {}).get(\"rig\"),\n            \"rig_full\": _rig_summary(meta),\n            \"label\": (meta or {}).get(\"label\"),\n        })\n\n    out = ['<!doctype html>',\n           '<html lang=\"en\"><head><meta charset=\"utf-8\">',\n           '<title>TTS Bench — All Runs</title>',\n           STYLE,\n           '</head><body>',\n           CONTROLS,\n           '<h1>TTS Bench — All Runs</h1>',\n           f'<div class=\"meta\">{len(runs)} run(s) with <code>results.csv</code></div>',\n           '<table><thead><tr>']\n    num_cols = {\"Prompts\", \"Rows\", \"OK\"}\n    for col in (\"Run\", \"Label\", \"Rig\", \"Models\", \"Devices\", \"Prompts\", \"Rows\", \"OK\", \"Report\"):\n        cls = ' class=\"num\"' if col in num_cols else ''\n        out.append(f'<th{cls}>{col}</th>')\n    out.append('</tr></thead><tbody>')\n\n    for r in runs:\n        models = (\", \".join(r[\"models\"])\n                  if len(r[\"models\"]) <= 5\n                  else f\"{len(r['models'])} models\")\n        if r[\"has_index\"]:\n            parts = [f'<a href=\"{escape(r[\"name\"])}/speed.html\">speed</a>']\n            parts.append(f'<a href=\"{escape(r[\"name\"])}/samples.html\">samples</a>')\n            link = \" · \".join(parts)\n        elif (RESULTS / r[\"name\"] / \"report.html\").exists():\n            link = f'<a href=\"{escape(r[\"name\"])}/report.html\">view (legacy)</a>'\n        else:\n            link = '<span class=\"muted\">no report</span>'\n        rig_cell = (f'<code title=\"{escape(r[\"rig_full\"])}\">{escape(r[\"rig\"])}</code>'\n                    if r[\"rig\"] else '<span class=\"muted\">—</span>')\n        label_cell = (escape(r[\"label\"])\n                      if r[\"label\"]\n                      else '<span class=\"muted\">—</span>')\n        out.append('<tr>')\n        if r[\"has_index\"]:\n            out.append(f\"<td><a href='{escape(r['name'])}/index.html'>{escape(r['name'])}</a></td>\")\n        else:\n            out.append(f\"<td>{escape(r['name'])}</td>\")\n        out.append(f\"<td>{label_cell}</td>\")\n        out.append(f\"<td>{rig_cell}</td>\")\n        out.append(f\"<td{_ds(len(r['models']))}>{escape(models)}</td>\")\n        out.append(f\"<td>{escape(', '.join(r['devices']))}</td>\")\n        out.append(f\"<td class='num'{_ds(len(r['prompts']))}>{len(r['prompts'])}</td>\")\n        out.append(f\"<td class='num'{_ds(r['rows'])}>{r['rows']}</td>\")\n        out.append(f\"<td class='num'{_ds(r['ok'])}>{r['ok']}/{r['rows']}</td>\")\n        out.append(f'<td>{link}</td>')\n        out.append('</tr>')\n    out.append('</tbody></table>')\n    out.append(SCRIPT)\n    out.append('</body></html>')\n\n    index_path = RESULTS / \"index.html\"\n    index_path.write_text(\"\\n\".join(out), encoding=\"utf-8\")\n    return index_path\n\n\ndef main() -> int:\n    p = argparse.ArgumentParser(description=\"Generate HTML reports from bench.py CSVs.\")\n    p.add_argument(\"run_dir\", nargs=\"?\", help=\"Results subdir (e.g. results/2026-05-23_1409).\")\n    p.add_argument(\"--index\", action=\"store_true\", help=\"Build only results/index.html.\")\n    p.add_argument(\"--all\", action=\"store_true\", help=\"Regenerate report.html for every run + index.\")\n    p.add_argument(\"--open\", action=\"store_true\", help=\"Open the generated HTML in a browser.\")\n    args = p.parse_args()\n\n    if args.all:\n        for d in sorted(RESULTS.iterdir(), reverse=True):\n            if not d.is_dir() or not (d / \"results.csv\").exists():\n                continue\n            html = build_report(d)\n            print(f\"  wrote {html.relative_to(REPO)} (+ 3 lens pages)\")\n        idx = build_index()\n        print(f\"wrote {idx.relative_to(REPO)}\")\n        if args.open:\n            webbrowser.open(idx.as_uri())\n        return 0\n\n    if args.index:\n        idx = build_index()\n        print(f\"wrote {idx.relative_to(REPO)}\")\n        if args.open:\n            webbrowser.open(idx.as_uri())\n        return 0\n\n    if not args.run_dir:\n        p.error(\"Provide a run dir, --index, or --all.\")\n    run_dir = Path(args.run_dir)\n    if not run_dir.is_absolute():\n        run_dir = REPO / args.run_dir\n    if not run_dir.exists():\n        raise SystemExit(f\"Not found: {run_dir}\")\n\n    html = build_report(run_dir)\n    print(f\"wrote {html.relative_to(REPO)} (+ speed.html, samples.html)\")\n    build_index()\n    if args.open:\n        webbrowser.open(html.as_uri())\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}}}}