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    مقدمة
  

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        1. إعداد مجموعة البيانات
      
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        2. الاستخراج الدفعي للرموز الدلالية (semantic tokens)
      
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        3. حزم مجموعة البيانات في protobuf
      
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        4. أخيرًا، الضبط الدقيق باستخدام LoRA
      
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        الضبط الدقيق لـ LLAMA
      
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        1. إعداد مجموعة البيانات
      
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    <a href="https://github.com/fishaudio/fish-speech/blob/main/docs/ar/finetune.md" title="عدل الصفحة" class="md-content__button md-icon" rel="edit">
      
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<h1 id="fine-tuning">الضبط الدقيق (Fine-tuning)</h1>
<p>من الواضح أنك عندما فتحت هذه الصفحة، لم تكن راضيًا عن أداء النموذج المدرب مسبقًا في وضع zero-shot. أنت ترغب في إجراء ضبط دقيق لنموذج لتحسين أدائه على مجموعة البيانات الخاصة بك.</p>
<p>في الإصدار الحالي، ما عليك سوى إجراء الضبط الدقيق لجزء 'LLAMA'.</p>
<h2 id="llama">الضبط الدقيق لـ LLAMA</h2>
<h3 id="1">1. إعداد مجموعة البيانات</h3>
<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>.
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>├── SPK1
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>│   ├── 21.15-26.44.lab
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>│   ├── 21.15-26.44.mp3
</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>│   ├── 27.51-29.98.lab
</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a>│   ├── 27.51-29.98.mp3
</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a>│   ├── 30.1-32.71.lab
</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a>│   └── 30.1-32.71.mp3
</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a>└── SPK2
</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>    ├── 38.79-40.85.lab
</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a>    └── 38.79-40.85.mp3
</span></code></pre></div>
<p>تحتاج إلى تحويل مجموعة البيانات الخاصة بك إلى التنسيق أعلاه ووضعها تحت مجلد <code>data</code>. يمكن أن يكون للملف الصوتي الامتدادات <code>.mp3</code>، <code>.wav</code>، أو <code>.flac</code>، ويجب أن يكون لملف التعليقات التوضيحية الامتداد <code>.lab</code>.</p>
<div class="admonition info">
<p class="admonition-title">تنسيق مجموعة البيانات</p>
<p>يحتاج ملف التعليقات التوضيحية <code>.lab</code> فقط إلى احتواء النص المكتوب للمقطع الصوتي، دون الحاجة إلى تنسيق خاص. على سبيل المثال، إذا كان محتوى <code>hi.mp3</code> هو "مرحبًا، وداعًا"، فسيحتوي ملف <code>hi.lab</code> على سطر واحد من النص: "مرحبًا، وداعًا".</p>
</div>
<div class="admonition warning">
<p class="admonition-title">تحذير</p>
<p>يوصى بتطبيق تسوية جهارة الصوت (loudness normalization) على مجموعة البيانات. يمكنك استخدام <a href="https://github.com/fishaudio/audio-preprocess">fish-audio-preprocess</a> للقيام بذلك.
<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>fap<span class="w"> </span>loudness-norm<span class="w"> </span>data-raw<span class="w"> </span>data<span class="w"> </span>--clean
</span></code></pre></div></p>
</div>
<h3 id="2-semantic-tokens">2. الاستخراج الدفعي للرموز الدلالية (semantic tokens)</h3>
<p>تأكد من أنك قمت بتنزيل أوزان VQGAN. إذا لم تكن قد فعلت، قم بتشغيل الأمر التالي:</p>
<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>huggingface-cli<span class="w"> </span>download<span class="w"> </span>fishaudio/openaudio-s1-mini<span class="w"> </span>--local-dir<span class="w"> </span>checkpoints/openaudio-s1-mini
</span></code></pre></div>
<p>يمكنك بعد ذلك تشغيل الأمر التالي لاستخراج الرموز الدلالية:</p>
<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>tools/vqgan/extract_vq.py<span class="w"> </span>data<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>--num-workers<span class="w"> </span><span class="m">1</span><span class="w"> </span>--batch-size<span class="w"> </span><span class="m">16</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="w">    </span>--config-name<span class="w"> </span><span class="s2">&quot;modded_dac_vq&quot;</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="w">    </span>--checkpoint-path<span class="w"> </span><span class="s2">&quot;checkpoints/openaudio-s1-mini/codec.pth&quot;</span>
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">ملاحظة</p>
<p>يمكنك ضبط <code>--num-workers</code> و <code>--batch-size</code> لزيادة سرعة الاستخراج، ولكن يرجى التأكد من عدم تجاوز حد ذاكرة وحدة معالجة الرسومات (GPU) الخاصة بك.</p>
</div>
<p>سيقوم هذا الأمر بإنشاء ملفات <code>.npy</code> في مجلد <code>data</code>، كما هو موضح أدناه:</p>
<div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>.
</span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>├── SPK1
</span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a>│   ├── 21.15-26.44.lab
</span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a>│   ├── 21.15-26.44.mp3
</span><span id="__span-4-5"><a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a>│   ├── 21.15-26.44.npy
</span><span id="__span-4-6"><a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a>│   ├── 27.51-29.98.lab
</span><span id="__span-4-7"><a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a>│   ├── 27.51-29.98.mp3
</span><span id="__span-4-8"><a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a>│   ├── 27.51-29.98.npy
</span><span id="__span-4-9"><a id="__codelineno-4-9" name="__codelineno-4-9" href="#__codelineno-4-9"></a>│   ├── 30.1-32.71.lab
</span><span id="__span-4-10"><a id="__codelineno-4-10" name="__codelineno-4-10" href="#__codelineno-4-10"></a>│   ├── 30.1-32.71.mp3
</span><span id="__span-4-11"><a id="__codelineno-4-11" name="__codelineno-4-11" href="#__codelineno-4-11"></a>│   └── 30.1-32.71.npy
</span><span id="__span-4-12"><a id="__codelineno-4-12" name="__codelineno-4-12" href="#__codelineno-4-12"></a>└── SPK2
</span><span id="__span-4-13"><a id="__codelineno-4-13" name="__codelineno-4-13" href="#__codelineno-4-13"></a>    ├── 38.79-40.85.lab
</span><span id="__span-4-14"><a id="__codelineno-4-14" name="__codelineno-4-14" href="#__codelineno-4-14"></a>    ├── 38.79-40.85.mp3
</span><span id="__span-4-15"><a id="__codelineno-4-15" name="__codelineno-4-15" href="#__codelineno-4-15"></a>    └── 38.79-40.85.npy
</span></code></pre></div>
<h3 id="3-protobuf">3. حزم مجموعة البيانات في protobuf</h3>
<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>python<span class="w"> </span>tools/llama/build_dataset.py<span class="w"> </span><span class="se">\</span>
</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="w">    </span>--input<span class="w"> </span><span class="s2">&quot;data&quot;</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="w">    </span>--output<span class="w"> </span><span class="s2">&quot;data/protos&quot;</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="w">    </span>--text-extension<span class="w"> </span>.lab<span class="w"> </span><span class="se">\</span>
</span><span id="__span-5-5"><a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="w">    </span>--num-workers<span class="w"> </span><span class="m">16</span>
</span></code></pre></div>
<p>بعد انتهاء تنفيذ الأمر، يجب أن ترى ملف <code>protos</code> في مجلد <code>data</code>.</p>
<h3 id="4-lora">4. أخيرًا، الضبط الدقيق باستخدام LoRA</h3>
<p>بالمثل، تأكد من أنك قمت بتنزيل أوزان <code>LLAMA</code>. إذا لم تكن قد فعلت، قم بتشغيل الأمر التالي:</p>
<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>huggingface-cli<span class="w"> </span>download<span class="w"> </span>fishaudio/openaudio-s1-mini<span class="w"> </span>--local-dir<span class="w"> </span>checkpoints/openaudio-s1-mini
</span></code></pre></div>
<p>أخيرًا، يمكنك بدء الضبط الدقيق عن طريق تشغيل الأمر التالي:</p>
<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>python<span class="w"> </span>fish_speech/train.py<span class="w"> </span>--config-name<span class="w"> </span>text2semantic_finetune<span class="w"> </span><span class="se">\</span>
</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a><span class="w">    </span><span class="nv">project</span><span class="o">=</span><span class="nv">$project</span><span class="w"> </span><span class="se">\</span>
</span><span id="__span-7-3"><a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a><span class="w">    </span>+lora@model.model.lora_config<span class="o">=</span>r_8_alpha_16
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">ملاحظة</p>
<p>يمكنك تعديل معلمات التدريب مثل <code>batch_size</code>، <code>gradient_accumulation_steps</code>، وما إلى ذلك لتناسب ذاكرة وحدة معالجة الرسومات الخاصة بك عن طريق تعديل <code>fish_speech/configs/text2semantic_finetune.yaml</code>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">ملاحظة</p>
<p>لمستخدمي Windows، يمكنك استخدام <code>trainer.strategy.process_group_backend=gloo</code> لتجنب مشكلات <code>nccl</code>.</p>
</div>
<p>بعد اكتمال التدريب، يمكنك الرجوع إلى قسم <a href="../inference/">الاستدلال (inference)</a> لاختبار نموذجك.</p>
<div class="admonition info">
<p class="admonition-title">معلومات</p>
<p>بشكل افتراضي، سيتعلم النموذج فقط أنماط كلام المتحدث وليس جرس الصوت (timbre). لا تزال بحاجة إلى استخدام التلقينات (prompts) لضمان استقرار جرس الصوت.
إذا كنت ترغب في تعلم جرس الصوت، يمكنك زيادة عدد خطوات التدريب، ولكن هذا قد يؤدي إلى الإفراط في التخصيص (overfitting).</p>
</div>
<p>بعد التدريب، تحتاج إلى تحويل أوزان LoRA إلى أوزان عادية قبل إجراء الاستدلال.</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/llama/merge_lora.py<span class="w"> </span><span class="se">\</span>
</span><span id="__span-8-2"><a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a><span class="w">    </span>--lora-config<span class="w"> </span>r_8_alpha_16<span class="w"> </span><span class="se">\</span>
</span><span id="__span-8-3"><a id="__codelineno-8-3" name="__codelineno-8-3" href="#__codelineno-8-3"></a><span class="w">    </span>--base-weight<span class="w"> </span>checkpoints/openaudio-s1-mini<span class="w"> </span><span class="se">\</span>
</span><span id="__span-8-4"><a id="__codelineno-8-4" name="__codelineno-8-4" href="#__codelineno-8-4"></a><span class="w">    </span>--lora-weight<span class="w"> </span>results/<span class="nv">$project</span>/checkpoints/step_000000010.ckpt<span class="w"> </span><span class="se">\</span>
</span><span id="__span-8-5"><a id="__codelineno-8-5" name="__codelineno-8-5" href="#__codelineno-8-5"></a><span class="w">    </span>--output<span class="w"> </span>checkpoints/openaudio-s1-mini-yth-lora/
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">ملاحظة</p>
<p>يمكنك أيضًا تجربة نقاط تحقق (checkpoints) أخرى. نقترح استخدام أقدم نقطة تحقق تلبي متطلباتك، حيث إنها غالبًا ما تؤدي أداءً أفضل على البيانات خارج التوزيع (OOD).</p>
</div>


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