flykhan
2536c937e3
翻译自英文原版 maths-cs-ai-compendium，共 20 章全部完成。 第01章 向量 | 第02章 矩阵 | 第03章 微积分 第04章 统计学 | 第05章 概率论 | 第06章 机器学习 第07章 计算语言学 | 第08章 计算机视觉 | 第09章 音频与语音 第10章 多模态学习 | 第11章 自主系统 | 第12章 图神经网络 第13章 计算与操作系统 | 第14章 数据结构与算法 第15章 生产级软件工程 | 第16章 SIMD与GPU编程 第17章 AI推理 | 第18章 ML系统设计 第19章 应用人工智能 | 第20章 前沿人工智能 翻译说明： - 所有数学公式 $...$ / $$...$$、代码块、图片引用完整保留 - mkdocs.yml 配置中文导航 + language: zh - README.md 已翻译为中文（兼 docs/index.md） - docs/ 目录包含指向各章文件的 symlink - 约 29,000 行中文内容，排除 .cache/ 构建缓存
115 lines
7.8 KiB
XML
115 lines
7.8 KiB
XML
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<!-- Title -->
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<text x="350" y="22" text-anchor="middle" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="#333">Tacotron 2 Architecture</text>
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<text x="107" y="55" text-anchor="middle" font-family="Arial, sans-serif" font-size="11" font-weight="bold" fill="#3498db">Encoder</text>
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<!-- 3 Conv Layers -->
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<text x="107" y="127" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" fill="#333">3 Conv Layers + BN + ReLU</text>
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<!-- BiLSTM -->
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<text x="107" y="165" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#3498db">Bidirectional LSTM</text>
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<!-- ===== ATTENTION (Centre) ===== -->
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<!-- Arrow from attention to decoder -->
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<!-- ===== DECODER (Right) ===== -->
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<text x="466" y="40" text-anchor="middle" font-family="Arial, sans-serif" font-size="11" font-weight="bold" fill="#e74c3c">Decoder (Autoregressive)</text>
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<!-- Pre-Net -->
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<text x="466" y="71" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" fill="#333">Pre-Net (2 FC + dropout)</text>
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<!-- 2 LSTM Layers -->
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<text x="466" y="107" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#e74c3c">2 Uni-directional LSTMs</text>
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<!-- Linear Projection -->
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<text x="466" y="143" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" fill="#333">Linear Projection</text>
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<!-- ===== MEL OUTPUT ===== -->
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<!-- Mel Spectrogram output -->
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<text x="625" y="76" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#27ae60">Mel</text>
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<text x="625" y="89" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#27ae60">Spectrogram</text>
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<text x="625" y="101" text-anchor="middle" font-family="Arial, sans-serif" font-size="8" fill="#666">(80 channels)</text>
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<!-- ===== POST-NET (Below decoder) ===== -->
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<text x="524" y="212" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#f39c12">Post-Net: 5 Conv Layers (residual refinement)</text>
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<text x="524" y="226" text-anchor="middle" font-family="Arial, sans-serif" font-size="8" fill="#666">Predicts residual to add to mel spectrogram for finer detail</text>
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<!-- Arrow from post-net back label -->
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<text x="640" y="155" text-anchor="start" font-family="Arial, sans-serif" font-size="8" fill="#f39c12" font-style="italic">refine</text>
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<!-- Autoregressive feedback loop -->
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<text x="575" y="120" text-anchor="start" font-family="Arial, sans-serif" font-size="7" fill="#9b59b6" transform="rotate(90, 575, 120)">prev frame</text>
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<!-- Feedback arrow from mel back to decoder pre-net -->
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<text x="520" y="42" text-anchor="middle" font-family="Arial, sans-serif" font-size="7" fill="#9b59b6">previous mel frame (teacher forcing)</text>
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<!-- Note box -->
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<text x="35" y="265" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#333">Note:</text>
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<text x="70" y="265" font-family="Arial, sans-serif" font-size="9" fill="#666">Autoregressive: generates one mel frame at a time, attending to the full encoder output. At inference, each predicted</text>
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<text x="35" y="278" font-family="Arial, sans-serif" font-size="9" fill="#666">frame is fed back through the Pre-Net as input for the next step. A WaveNet or HiFi-GAN vocoder converts mel to audio.</text>
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