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<li class="toctree-l2"><a class="reference internal" href="%E6%95%B0%E6%8D%AE%E9%9B%86.html#alimeeting">Alimeeting数据集介绍</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#id3">快速开始</a></li>
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<section id="id1">
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<h1>基线<a class="headerlink" href="#id1" title="此标题的永久链接">¶</a></h1>
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<section id="id2">
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<h2>基线概述<a class="headerlink" href="#id2" title="此标题的永久链接">¶</a></h2>
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<p>我们将提供一个在<a class="reference external" href="https://github.com/alibaba-damo-academy/FunASR">FunASR</a>上实现的端到端SA-ASR系统作为基线。该模型的结构如图3所示。SpeakerEncoder用<a class="reference external" href="https://modelscope.cn/home">ModelScope</a>中预先训练好的<a class="reference external" href="https://modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary">说话人确认模型</a>作为初始化。这个说话人确认模型也被用来提取说话人档案中的说话人嵌入。</p>
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<p><img alt="model archietecture" src="_images/sa_asr_arch.png" /></p>
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</section>
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<section id="id3">
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<h2>快速开始<a class="headerlink" href="#id3" title="此标题的永久链接">¶</a></h2>
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<p>首先需要安装FunASR和ModelScope. (<a class="reference external" href="https://github.com/alibaba-damo-academy/FunASR#installation">installation</a>)<br />
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基线系统有训练和测试两个脚本,<code class="docutils literal notranslate"><span class="pre">run.sh</span></code>是用于训练基线系统并在M2MeT的验证与测试集上评估的，而<code class="docutils literal notranslate"><span class="pre">run_m2met_2023_infer.sh</span></code>用于此次竞赛预备开放的全新测试集上测试同时生成符合竞赛最终提交格式的文件。
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在运行 <code class="docutils literal notranslate"><span class="pre">run.sh</span></code>前，需要自行下载并解压<a class="reference external" href="http://www.openslr.org/119/">AliMeeting</a>数据集并放置于<code class="docutils literal notranslate"><span class="pre">./dataset</span></code>目录下：</p>
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<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>dataset
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<span class="p">|</span>——<span class="w"> </span>Eval_Ali_far
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<span class="p">|</span>——<span class="w"> </span>Eval_Ali_near
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<span class="p">|</span>——<span class="w"> </span>Test_Ali_far
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<span class="p">|</span>——<span class="w"> </span>Test_Ali_near
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<span class="p">|</span>——<span class="w"> </span>Train_Ali_far
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<span class="p">|</span>——<span class="w"> </span>Train_Ali_near
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</pre></div>
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</div>
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<p>在运行<code class="docutils literal notranslate"><span class="pre">run_m2met_2023_infer.sh</span></code>前, 需要将测试集<code class="docutils literal notranslate"><span class="pre">Test_2023_Ali_far</span></code>（仅包含音频，将于6.16发布）放置于<code class="docutils literal notranslate"><span class="pre">./dataset</span></code>目录下。然后将主办方提供的<code class="docutils literal notranslate"><span class="pre">wav.scp</span></code>，<code class="docutils literal notranslate"><span class="pre">wav_raw.scp</span></code>，<code class="docutils literal notranslate"><span class="pre">segments</span></code>，<code class="docutils literal notranslate"><span class="pre">utt2spk</span></code>和<code class="docutils literal notranslate"><span class="pre">spk2utt</span></code>放置于<code class="docutils literal notranslate"><span class="pre">./data/Test_2023_Ali_far</span></code>目录下。</p>
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<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>data/Test_2023_Ali_far
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<span class="p">|</span>——<span class="w"> </span>wav.scp
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<span class="p">|</span>——<span class="w"> </span>wav_raw.scp
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<span class="p">|</span>——<span class="w"> </span>segments
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<span class="p">|</span>——<span class="w"> </span>utt2spk
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<span class="p">|</span>——<span class="w"> </span>spk2utt
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</pre></div>
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</div>
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<p>更多基线系统详情见<a class="reference external" href="https://github.com/alibaba-damo-academy/FunASR/blob/main/egs/alimeeting/sa-asr/README.md">此处</a></p>
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</section>
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<section id="id4">
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<h2>基线结果<a class="headerlink" href="#id4" title="此标题的永久链接">¶</a></h2>
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<p>基线系统的结果如表3所示。在训练期间，说话人档案采用了真实说话人嵌入。然而由于在评估过程中缺乏真实说话人标签，因此使用了由额外的谱聚类提供的说话人特征。同时我们还提供了在评估和测试集上使用真实说话人档案的结果，以显示说话人档案准确性的影响。</p>
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<p><img alt="baseline_result" src="_images/baseline_result.png" /></p>
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