Xu Meng
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README-CN.md
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README-CN.md
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|[1703.10135](https://arxiv.org/pdf/1703.10135.pdf) | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN)
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|[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | 本代码库 |
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## 常見問題(FQ&A)
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#### 1.數據集哪裡下載?
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## 常见问题(FQ&A)
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#### 1.数据集在哪里下载?
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| 数据集 | OpenSLR地址 | 其他源 (Google Drive, Baidu网盘等) |
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| --- | ----------- | ---------------|
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| aidatatang_200zh | [OpenSLR](http://www.openslr.org/62/) | [Google Drive](https://drive.google.com/file/d/110A11KZoVe7vy6kXlLb6zVPLb_J91I_t/view?usp=sharing) |
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| magicdata | [OpenSLR](http://www.openslr.org/68/) | [Google Drive (Dev set)](https://drive.google.com/file/d/1g5bWRUSNH68ycC6eNvtwh07nX3QhOOlo/view?usp=sharing) |
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| aishell3 | [OpenSLR](https://www.openslr.org/93/) | [Google Drive](https://drive.google.com/file/d/1shYp_o4Z0X0cZSKQDtFirct2luFUwKzZ/view?usp=sharing) |
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| data_aishell | [OpenSLR](https://www.openslr.org/33/) | |
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> 解壓 aidatatang_200zh 後，還需將 `aidatatang_200zh\corpus\train`下的檔案全選解壓縮
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> 解压 aidatatang_200zh 后，还需将 `aidatatang_200zh\corpus\train`下的文件全选解压缩
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#### 2.`<datasets_root>`是什麼意思?
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假如數據集路徑為 `D:\data\aidatatang_200zh`，那麼 `<datasets_root>`就是 `D:\data`
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假如数据集路径为 `D:\data\aidatatang_200zh`，那么 `<datasets_root>`就是 `D:\data`
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#### 3.訓練模型顯存不足
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訓練合成器時：將 `synthesizer/hparams.py`中的batch_size參數調小
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#### 3.训练模型显存不足
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训练合成器时：将 `synthesizer/hparams.py`中的batch_size参数调小
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```
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//調整前
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//调整前
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tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule
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(2, 5e-4, 40_000, 12), # (r, lr, step, batch_size)
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(2, 2e-4, 80_000, 12), #
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(2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames
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(2, 3e-5, 320_000, 12), # synthesized for each decoder iteration)
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(2, 1e-5, 640_000, 12)], # lr = learning rate
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//調整後
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//调整后
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tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule
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(2, 5e-4, 40_000, 8), # (r, lr, step, batch_size)
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(2, 2e-4, 80_000, 8), #
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@ -180,15 +180,15 @@ tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule
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(2, 1e-5, 640_000, 8)], # lr = learning rate
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```
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聲碼器-預處理數據集時：將 `synthesizer/hparams.py`中的batch_size參數調小
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声码器-预处理数据集时：将 `synthesizer/hparams.py`中的batch_size参数调小
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```
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//調整前
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//调整前
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### Data Preprocessing
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max_mel_frames = 900,
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rescale = True,
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rescaling_max = 0.9,
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synthesis_batch_size = 16, # For vocoder preprocessing and inference.
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//調整後
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//调整后
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### Data Preprocessing
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max_mel_frames = 900,
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rescale = True,
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@ -196,16 +196,16 @@ tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule
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synthesis_batch_size = 8, # For vocoder preprocessing and inference.
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```
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聲碼器-訓練聲碼器時：將 `vocoder/wavernn/hparams.py`中的batch_size參數調小
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声码器-训练声码器时：将 `vocoder/wavernn/hparams.py`中的batch_size参数调小
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```
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//調整前
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//调整前
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# Training
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voc_batch_size = 100
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voc_lr = 1e-4
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voc_gen_at_checkpoint = 5
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voc_pad = 2
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//調整後
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//调整后
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# Training
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voc_batch_size = 6
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voc_lr = 1e-4
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@ -214,13 +214,13 @@ voc_pad =2
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```
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#### 4.碰到`RuntimeError: Error(s) in loading state_dict for Tacotron: size mismatch for encoder.embedding.weight: copying a param with shape torch.Size([70, 512]) from checkpoint, the shape in current model is torch.Size([75, 512]).`
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請參照 issue [#37](https://github.com/babysor/MockingBird/issues/37)
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请参照 issue [#37](https://github.com/babysor/MockingBird/issues/37)
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#### 5.如何改善CPU、GPU佔用率?
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適情況調整batch_size參數來改善
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#### 5.如何改善CPU、GPU占用率?
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视情况调整batch_size参数来改善
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#### 6.發生 `頁面文件太小，無法完成操作`
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請參考這篇[文章](https://blog.csdn.net/qq_17755303/article/details/112564030)，將虛擬內存更改為100G(102400)，例如:档案放置D槽就更改D槽的虚拟内存
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#### 6.发生 `页面文件太小，无法完成操作`
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请参考这篇[文章](https://blog.csdn.net/qq_17755303/article/details/112564030)，将虚拟内存更改为100G(102400)，例如:文件放置D盘就更改D盘的虚拟内存
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#### 7.什么时候算训练完成？
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首先一定要出现注意力模型，其次是loss足够低，取决于硬件设备和数据集。拿本人的供参考，我的注意力是在 18k 步之后出现的，并且在 50k 步之后损失变得低于 0.4
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