modelscope · LauraGPT · Aug 21, 2026 · Aug 21, 2026
diff --git a/docs/community_growth_20k.md b/docs/community_growth_20k.md
@@ -34,14 +34,20 @@ This plan focuses on useful adoption work rather than vanity marketing: if more
 
 ## Current campaign snapshot
 
-As of 2026-08-17 00:48 UTC, the ecosystem has 36,584 combined GitHub stars, or 5,360 additional stars since the 31,224 baseline. Exceeding the +20,000 target requires another 14,641 stars to reach at least 51,225 by 2026-09-30, or roughly 326 stars/day across the remaining 45 days.
+As of 2026-08-21 03:58 UTC, the ecosystem has 36,713 combined GitHub stars, or 5,489 additional stars since the 31,224 baseline. Exceeding the +20,000 target requires another 14,511 stars to reach at least 51,224 by 2026-09-30, or roughly 363 stars/day across the remaining 40 days. Public PyPI reports 121,164 downloads over the latest 7 days and 515,283 over the latest 30 days through 2026-08-20.
 
 | Repository | Stars | Forks | Open issues | Open PRs | Last push |
 |---|---:|---:|---:|---:|---|
-| `modelscope/FunASR` | 19,870 | 1,990 | 4 | 0 | 2026-08-17 |
-| `QwenAudio/Fun-ASR` | 1,478 | 146 | 0 | 0 | 2026-07-24 |
-| `QwenAudio/SenseVoice` | 9,084 | 808 | 0 | 0 | 2026-08-12 |
-| `modelscope/FunClip` | 6,152 | 736 | 0 | 0 | 2026-08-03 |
+| `modelscope/FunASR` | 19,944 | 1,996 | 5 | 2 | 2026-08-21 |
+| `QwenAudio/Fun-ASR` | 1,483 | 147 | 0 | 0 | 2026-08-19 |
+| `QwenAudio/SenseVoice` | 9,118 | 808 | 1 | 0 | 2026-08-18 |
+| `modelscope/FunClip` | 6,168 | 738 | 0 | 0 | 2026-08-19 |
+
+### 2026-08-21 FunASR v1.4.3 stable release
+
+- FunASR PR [#3519](https://github.com/modelscope/FunASR/pull/3519) merged as `eedd4e22d10dc2e81d9c2bb321edb3750253964b`. The release adds the optional `AutoModel(vad_model="silero-vad")` adapter with millisecond segments, threshold controls, 8/16 kHz input, ONNX mode, and bounded segment lengths. Large embedding sets with a known speaker count now use fixed-K clustering instead of dense spectral clustering.
+- Exact-main validation passed 167 source tests, compileall, PEP 517 wheel/sdist builds, Twine, source-to-artifact diff checks, and isolated installs with no dependencies and full dependencies. The public wheel SHA-256 is `4b1491643a5bc6ccdd8acfc3ec438f9d9b9aaca8c2bf2d0e71f737dfc03025b9`; the sdist SHA-256 is `74a9a60eac4f05b7cba25d31bdc0f6bba5d70dbe9fd326291b362a5e425ba01a`.
+- Signed tag `v1.4.3` peels exactly to the merge commit. The unique public [GitHub Release](https://github.com/modelscope/FunASR/releases/tag/v1.4.3) has the wheel, sdist, nine `runtime-llamacpp-v0.2.0` platform archives, and `SHA256SUMS-v1.4.3`, exactly 12 assets. Public PyPI and GitHub no-cache downloads matched every expected size and SHA-256, and a fresh public-wheel installation reported version 1.4.3.
 
 ### 2026-08-16 product-site attribution and claim audit
 

diff --git a/web-pages/product-site/content/legacy-manifest.json b/web-pages/product-site/content/legacy-manifest.json
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diff --git a/web-pages/product-site/legacy/blog/funasr-v1-4-3-pypi-release.html b/web-pages/product-site/legacy/blog/funasr-v1-4-3-pypi-release.html
@@ -0,0 +1,108 @@
+<!DOCTYPE html>
+<html lang="zh">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width,initial-scale=1">
+  <title>FunASR v1.4.3：可选 Silero VAD 与大规模固定 K 说话人聚类 | FunASR</title>
+  <meta name="description" content="FunASR v1.4.3 新增可选 Silero VAD 适配器，并让已知说话人数的大规模 embedding 使用固定 K 聚类；PyPI 与 12 个 GitHub Release 资产均可回源校验。">
+  <meta name="keywords" content="FunASR v1.4.3,PyPI,Silero VAD,AutoModel,说话人分离,KMeans,llama.cpp,GGUF">
+  <link rel="icon" href="/logo.png">
+  <meta property="og:title" content="FunASR v1.4.3：可选 Silero VAD 与大规模固定 K 说话人聚类">
+  <meta property="og:description" content="毫秒级 VAD 片段、可配置阈值与 ONNX 模式，以及更节省内存的大规模已知说话人数聚类。">
+  <meta property="og:type" content="article">
+  <meta property="og:url" content="https://www.funasr.com/blog/funasr-v1-4-3-pypi-release.html">
+  <meta property="og:image" content="https://www.funasr.com/img/banner.4f436d19.png">
+  <link rel="canonical" href="https://www.funasr.com/blog/funasr-v1-4-3-pypi-release.html">
+  <link rel="alternate" hreflang="en" href="https://www.funasr.com/en/blog/funasr-v1-4-3-pypi-release.html">
+  <link rel="alternate" hreflang="zh" href="https://www.funasr.com/blog/funasr-v1-4-3-pypi-release.html">
+  <link rel="alternate" hreflang="x-default" href="https://www.funasr.com/en/blog/funasr-v1-4-3-pypi-release.html">
+  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&amp;display=swap" rel="stylesheet">
+  <script type="application/ld+json">
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+  </script>
+  <style>
+  :root{--primary:#2563eb;--primary-dark:#1d4ed8;--accent:#047857;--bg:#fff;--surface:#f8fafc;--border:#e2e8f0;--text:#0f172a;--text-soft:#475569;--text-muted:#94a3b8}
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+  .container{max-width:800px;margin:0 auto;padding:0 24px}
+  .nav{position:fixed;top:0;left:0;right:0;z-index:100;background:rgba(255,255,255,0.94);backdrop-filter:blur(12px);border-bottom:1px solid var(--border);padding:14px 0}
+  .nav .container{max-width:1120px;display:flex;align-items:center;gap:24px}
+  .nav-logo{font-size:1.2rem;font-weight:800;color:var(--text)}.nav-logo span{color:var(--primary)}
+  .nav-links{display:flex;gap:20px;margin-left:auto;flex-wrap:wrap}.nav-links a{font-size:.85rem;font-weight:500;color:var(--text-soft)}
+  .nav-btn{padding:7px 16px;background:var(--primary);color:#fff;border-radius:7px;font-size:.83rem;font-weight:600}
+  article{padding:116px 0 80px}
+  h1{font-size:2rem;font-weight:800;line-height:1.3;margin-bottom:14px}
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+  h3{font-size:1.05rem;font-weight:700;margin:24px 0 10px}
+  p{margin-bottom:14px;color:var(--text-soft)}
+  ul{margin:0 0 16px 22px;color:var(--text-soft)}li{margin-bottom:7px}
+  table{width:100%;border-collapse:collapse;margin:18px 0;font-size:.88rem}
+  th,td{padding:11px 12px;text-align:left;vertical-align:top;border-bottom:1px solid var(--border)}th{font-weight:600;background:var(--surface);color:var(--text)}
+  pre{background:#172033;color:#e2e8f0;padding:18px;border-radius:8px;overflow-x:auto;font-size:.82rem;line-height:1.7;margin:14px 0}
+  code{font-family:ui-monospace,Menlo,monospace}p code,li code,td code{background:var(--surface);padding:2px 6px;border-radius:5px;font-size:.85em;color:var(--primary-dark)}
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+  .lead{font-size:1.05rem;color:var(--text);margin:22px 0}
+  .hero-media{display:block;width:100%;aspect-ratio:2.04/1;object-fit:cover;border-radius:8px;border:1px solid var(--border);margin:0 0 24px}
+  .proof{border-left:4px solid var(--accent);background:#f0fdf4;padding:14px 16px;margin:18px 0;color:#14532d}
+  .cta{background:var(--surface);border:1px solid var(--border);border-radius:8px;padding:24px;margin:30px 0;text-align:center}.cta p{margin-bottom:16px}.cta a.btn{display:inline-block;padding:10px 22px;background:var(--primary);color:#fff;border-radius:7px;font-weight:600}
+  footer{background:#0f172a;color:#94a3b8;padding:28px 0;text-align:center;font-size:.8rem}footer a{color:#94a3b8}
+  @media(max-width:900px){.nav-links{display:none}.nav .container{gap:16px}.nav-logo{margin-right:auto}h1{font-size:1.65rem}table{font-size:.8rem}th,td{padding:9px 8px}}
+  </style>
+</head>
+<body>
+<nav class="nav"><div class="container"><a href="/" class="nav-logo">Fun<span>ASR</span></a><div class="nav-links"><a href="/#capabilities">核心能力</a><a href="/#usage">如何使用</a><a href="/#benchmark">性能</a><a href="/#demo">演示</a><a href="https://modelscope.github.io/FunASR/zh/" target="_blank">文档</a><a href="/quickstart.html">快速上手</a><a href="/models.html">模型</a><a href="/ecosystem.html">生态</a><a href="/blog/">技术博客</a><a href="/vs-whisper.html">对比 Whisper</a><a href="/donors.html">功德榜</a></div><a href="/en/blog/funasr-v1-4-3-pypi-release.html" style="font-size:.83rem;font-weight:500;color:var(--text-soft)">EN</a><a href="https://github.com/modelscope/FunASR" target="_blank" rel="noopener" class="nav-btn">GitHub ★</a></div></nav>
+<article><div class="container">
+  <h1>FunASR v1.4.3：可选 Silero VAD 与大规模固定 K 说话人聚类</h1>
+  <p class="post-meta">2026-08-21 · FunASR 每周正式发布</p>
+  <img class="hero-media" src="/img/banner.4f436d19.png?v=1.4.3" alt="FunASR v1.4.3 发布视觉图">
+  <p class="lead">FunASR v1.4.3 为 <code>AutoModel(vad_model="silero-vad")</code> 增加可选 Silero VAD 适配器，直接返回 FunASR 兼容的毫秒级片段，并支持阈值、8/16 kHz 输入、ONNX 模式和最长片段限制。</p>
+  <p>已知说话人数的说话人分离在大规模 embedding 输入下改用固定 K 聚类，避免内存开销较高的稠密谱聚类。签名 GitHub Release 同时提供 wheel、sdist、九个平台的 llama.cpp / GGUF 运行时和 <code>SHA256SUMS-v1.4.3</code>，共 12 个资产。</p>
+
+  <h2>本次更新</h2>
+  <table>
+    <thead><tr><th>能力</th><th>v1.4.3 内容</th><th>用户收益</th></tr></thead>
+    <tbody>
+      <tr><td>可选 VAD</td><td><code>silero-vad</code> 与 <code>silero_vad</code> 别名接入现有 AutoModel VAD 流程</td><td>无需改变后续 ASR 流程即可选择 Silero VAD</td></tr>
+      <tr><td>输入与输出</td><td>8/16 kHz waveform 输入，输出毫秒级区间；支持阈值、最短静音、ONNX 与最长片段参数</td><td>片段边界可直接供 FunASR 的长音频识别使用</td></tr>
+      <tr><td>说话人聚类</td><td>已知说话人数且 embedding 较大时使用固定 K 聚类</td><td>避开稠密谱聚类的平方级内存压力</td></tr>
+      <tr><td>默认行为</td><td>Silero 作为 <code>funasr[silero]</code> 可选依赖，原有 FSMN VAD 不变</td><td>基础安装与既有生产配置保持兼容</td></tr>
+    </tbody>
+  </table>
+
+  <h2>1. 安装并确认版本</h2>
+  <p>从 public PyPI 固定正式版：</p>
+  <pre><code>python -m pip install -U "funasr==1.4.3"
+funasr --version</code></pre>
+  <p>启用 Silero VAD：</p>
+  <pre><code>python -m pip install -U "funasr[silero]==1.4.3"
+
+model = AutoModel(
+    model="paraformer-zh",
+    vad_model="silero-vad",
+    device="cpu",
+    vad_kwargs={"silero_threshold": 0.5, "silero_min_silence_duration_ms": 100},
+)
+result = model.generate(input="audio.wav")</code></pre>
+  <p>public PyPI 只发布 <code>funasr-1.4.3-py3-none-any.whl</code> 与 <code>funasr-1.4.3.tar.gz</code>。它们从 exact main 独立构建，并再次 no-cache 下载、隔离安装和 smoke。</p>
+  <div class="proof">exact-main 源码测试 167 项全部通过；compileall、PEP 517 构建、Twine、源码与产物 diff、无依赖与完整依赖隔离安装均通过。public wheel、sdist 与 GitHub 12 个资产已重新下载并逐项核对 size 和 SHA-256。</div>
+
+  <h2>2. 九个平台运行时与校验清单</h2>
+  <p>同一发布页复用并验证了 <a href="https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.0" target="_blank" rel="noopener">runtime-llamacpp-v0.2.0</a> 的全部九个平台资产：</p>
+  <ul>
+    <li><code>linux-arm64</code>、<code>linux-x64</code>、<code>linux-x64-avx2</code>、<code>linux-x64-vulkan</code></li>
+    <li><code>macos-arm64</code></li>
+    <li><code>windows-x64</code>、<code>windows-x64-avx2</code>、<code>windows-x64-cuda</code>、<code>windows-x64-vulkan</code></li>
+  </ul>
+  <p>下载后在同一目录校验：</p>
+  <pre><code>sha256sum -c SHA256SUMS-v1.4.3</code></pre>
+  <p>模型、命令与硬件选择见<a href="/llama-cpp.html"> llama.cpp / GGUF 专页</a>。manifest 覆盖 wheel、sdist 和九个 runtime；manifest 自身的 GitHub digest 也已核验。</p>
+
+  <div class="cta">
+    <p>先用固定版本在真实音频上复现，再按 SHA-256 选择适合硬件的运行时。遇到问题请附模型、设备、输入边界和可复现命令。</p>
+    <a class="btn" href="https://github.com/modelscope/FunASR/releases/tag/v1.4.3" target="_blank" rel="noopener">查看 v1.4.3 发布与 12 个资产</a>
+  </div>
+</div></article>
+<footer><div class="container">© 2024-2026 Tongyi Lab, Alibaba Group · <a href="https://github.com/modelscope/FunASR/blob/main/LICENSE">MIT License</a></div></footer>
+</body>
+</html>