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        Baixar Pesos
      
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        Inferência por Linha de Comando
      
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        1. Obter tokens VQ do áudio de referência
      
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        2. Gerar tokens Semânticos a partir do texto:
      
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        3. Gerar vocais a partir de tokens semânticos:
      
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<h1 id="inferencia">Inferência</h1>
<p>O modelo Fish Audio S2 requer uma grande quantidade de VRAM. Recomendamos o uso de uma GPU com pelo menos 24GB para inferência.</p>
<h2 id="baixar-pesos">Baixar Pesos</h2>
<p>Primeiro, você precisa baixar os pesos do modelo:</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>hf<span class="w"> </span>download<span class="w"> </span>fishaudio/s2-pro<span class="w"> </span>--local-dir<span class="w"> </span>checkpoints/s2-pro
</span></code></pre></div>
<h2 id="inferencia-por-linha-de-comando">Inferência por Linha de Comando</h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Se você planeja deixar o modelo escolher aleatoriamente um timbre de voz, pode pular esta etapa.</p>
</div>
<h3 id="1-obter-tokens-vq-do-audio-de-referencia">1. Obter tokens VQ do áudio de referência</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>python<span class="w"> </span>fish_speech/models/dac/inference.py<span class="w"> </span><span class="se">\</span>
</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="w">    </span>-i<span class="w"> </span><span class="s2">&quot;test.wav&quot;</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="w">    </span>--checkpoint-path<span class="w"> </span><span class="s2">&quot;checkpoints/s2-pro/codec.pth&quot;</span>
</span></code></pre></div>
<p>Você deve obter um <code>fake.npy</code> e um <code>fake.wav</code>.</p>
<h3 id="2-gerar-tokens-semanticos-a-partir-do-texto">2. Gerar tokens Semânticos a partir do texto:</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>python<span class="w"> </span>fish_speech/models/text2semantic/inference.py<span class="w"> </span><span class="se">\</span>
</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="w">    </span>--text<span class="w"> </span><span class="s2">&quot;O texto que você deseja converter&quot;</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="w">    </span>--prompt-text<span class="w"> </span><span class="s2">&quot;Seu texto de referência&quot;</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="w">    </span>--prompt-tokens<span class="w"> </span><span class="s2">&quot;fake.npy&quot;</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-2-5"><a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a><span class="w">    </span><span class="c1"># --compile</span>
</span></code></pre></div>
<p>Este comando criará um arquivo <code>codes_N</code> no diretório de trabalho, onde N é um número inteiro começando em 0.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Você pode querer usar <code>--compile</code> para fundir kernels CUDA para uma inferência mais rápida. No entanto, recomendamos usar nossa otimização de aceleração de inferência sglang.
Da mesma forma, se você não planeja usar aceleração, pode comentar o parâmetro <code>--compile</code>.</p>
</div>
<div class="admonition info">
<p class="admonition-title">Info</p>
<p>Para GPUs que não suportam bf16, você pode precisar usar o parâmetro <code>--half</code>.</p>
</div>
<h3 id="3-gerar-vocais-a-partir-de-tokens-semanticos">3. Gerar vocais a partir de tokens semânticos:</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>python<span class="w"> </span>fish_speech/models/dac/inference.py<span class="w"> </span><span class="se">\</span>
</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a><span class="w">    </span>-i<span class="w"> </span><span class="s2">&quot;codes_0.npy&quot;</span><span class="w"> </span><span class="se">\</span>
</span></code></pre></div>
<p>Depois disso, você obterá um arquivo <code>fake.wav</code>.</p>
<h2 id="inferencia-webui">Inferência WebUI</h2>
<h3 id="1-gradio-webui">1. Gradio WebUI</h3>
<p>Para manter a compatibilidade, mantemos a interface Gradio WebUI anterior.</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>python<span class="w"> </span>tools/run_webui.py<span class="w"> </span><span class="c1"># --compile se você precisar de aceleração</span>
</span></code></pre></div>
<h3 id="2-awesome-webui">2. Awesome WebUI</h3>
<p>A Awesome WebUI é uma interface web moderna baseada em TypeScript, oferecendo funcionalidades mais ricas e uma melhor experiência do usuário.</p>
<p><strong>Construir a WebUI:</strong></p>
<p>Você precisa ter o Node.js e o npm instalados em seu computador local ou servidor.</p>
<ol>
<li>Entre no diretório <code>awesome_webui</code>:
   <div class="language-bash highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="nb">cd</span><span class="w"> </span>awesome_webui
</span></code></pre></div></li>
<li>Instale as dependências:
   <div class="language-bash highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>npm<span class="w"> </span>install
</span></code></pre></div></li>
<li>Construa a WebUI:
   <div class="language-bash highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>npm<span class="w"> </span>run<span class="w"> </span>build
</span></code></pre></div></li>
</ol>
<p><strong>Iniciar o Servidor Backend:</strong></p>
<p>Após a construção da WebUI, retorne ao diretório raiz do projeto e inicie o servidor API:</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a>python<span class="w"> </span>tools/api_server.py<span class="w"> </span>--listen<span class="w"> </span><span class="m">0</span>.0.0.0:8888<span class="w"> </span>--compile
</span></code></pre></div>
<p><strong>Acesso:</strong></p>
<p>Após o servidor ser iniciado, você pode acessá-lo através do navegador no seguinte endereço:
<code>http://localhost:8888/ui</code></p>


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