@ -148,30 +148,30 @@
|[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 )
|[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | 本代码库 |
## 常見問題 (FQ& A)
#### 1.數據集哪裡下載 ?
## 常见问题 (FQ& A)
#### 1.数据集在哪里下载 ?
| 数据集 | OpenSLR地址 | 其他源 (Google Drive, Baidu网盘等) |
| --- | ----------- | ---------------|
| aidatatang_200zh | [OpenSLR ](http://www.openslr.org/62/ ) | [Google Drive ](https://drive.google.com/file/d/110A11KZoVe7vy6kXlLb6zVPLb_J91I_t/view?usp=sharing ) |
| magicdata | [OpenSLR ](http://www.openslr.org/68/ ) | [Google Drive (Dev set) ](https://drive.google.com/file/d/1g5bWRUSNH68ycC6eNvtwh07nX3QhOOlo/view?usp=sharing ) |
| aishell3 | [OpenSLR ](https://www.openslr.org/93/ ) | [Google Drive ](https://drive.google.com/file/d/1shYp_o4Z0X0cZSKQDtFirct2luFUwKzZ/view?usp=sharing ) |
| data_aishell | [OpenSLR ](https://www.openslr.org/33/ ) | |
> 解壓 aidatatang_200zh 後，還需將 `aidatatang_200zh\corpus\train` 下的檔案全選解壓縮
> 解压 aidatatang_200zh 后，还需将 `aidatatang_200zh\corpus\train` 下的文件全选解压缩
#### 2.`< datasets_root > `是什麼意思?
假如數據集路徑為 `D:\data\aidatatang_200zh` ，那麼 `<datasets_root>` 就是 `D:\data`
假如数据集路径为 `D:\data\aidatatang_200zh` ，那么 `<datasets_root>` 就是 `D:\data`
#### 3.訓練模型顯 存不足
訓練合成器時：將 `synthesizer/hparams.py` 中的batch_size參數調 小
#### 3.训练模型显 存不足
训练合成器时：将 `synthesizer/hparams.py` 中的batch_size参数调 小
```
//調 整前
//调 整前
tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule
(2, 5e-4, 40_000, 12), # (r, lr, step, batch_size)
(2, 2e-4, 80_000, 12), #
(2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames
(2, 3e-5, 320_000, 12), # synthesized for each decoder iteration)
(2, 1e-5, 640_000, 12)], # lr = learning rate
//調整後
//调整后
tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule
(2, 5e-4, 40_000, 8), # (r, lr, step, batch_size)
(2, 2e-4, 80_000, 8), #
@ -180,15 +180,15 @@ tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule
(2, 1e-5, 640_000, 8)], # lr = learning rate
```
聲碼器-預處理數據集時：將 `synthesizer/hparams.py` 中的batch_size參數調 小
声码器-预处理数据集时：将 `synthesizer/hparams.py` 中的batch_size参数调 小
```
//調 整前
//调 整前
### Data Preprocessing
max_mel_frames = 900,
rescale = True,
rescaling_max = 0.9,
synthesis_batch_size = 16, # For vocoder preprocessing and inference.
//調整後
//调整后
### Data Preprocessing
max_mel_frames = 900,
rescale = True,
@ -196,16 +196,16 @@ tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule
synthesis_batch_size = 8, # For vocoder preprocessing and inference.
```
聲碼器-訓練聲碼器時：將 `vocoder/wavernn/hparams.py` 中的batch_size參數調 小
声码器-训练声码器时：将 `vocoder/wavernn/hparams.py` 中的batch_size参数调 小
```
//調 整前
//调 整前
# Training
voc_batch_size = 100
voc_lr = 1e-4
voc_gen_at_checkpoint = 5
voc_pad = 2
//調整後
//调整后
# Training
voc_batch_size = 6
voc_lr = 1e-4
@ -214,13 +214,13 @@ voc_pad =2
```
#### 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]).`
請參 照 issue [#37 ](https://github.com/babysor/MockingBird/issues/37 )
请参 照 issue [#37 ](https://github.com/babysor/MockingBird/issues/37 )
#### 5.如何改善CPU、GPU佔 用率?
適情況調整batch_size參數來 改善
#### 5.如何改善CPU、GPU占 用率?
视情况调整batch_size参数来 改善
#### 6.發生 `頁面文件太小，無 法完成操作`
請參考這篇[文章](https://blog.csdn.net/qq_17755303/article/details/112564030)， 的虚拟内存
#### 6.发生 `页面文件太小，无 法完成操作`
请参考这篇[文章](https://blog.csdn.net/qq_17755303/article/details/112564030)， 的虚拟内存
#### 7.什么时候算训练完成？
首先一定要出现注意力模型， ， ，
Xu Meng
3 years ago
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