From f57d1a69b66c5c8a33aadfff84e93524c6df88ae Mon Sep 17 00:00:00 2001 From: Xu Meng Date: Sat, 6 Aug 2022 23:51:34 +0800 Subject: [PATCH] Translate update README-CN.md (#698) Fix: Traditional Chinese to Simplified Chinese --- README-CN.md | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/README-CN.md b/README-CN.md index 738b37f..cb5c583 100644 --- a/README-CN.md +++ b/README-CN.md @@ -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.``是什麼意思? -假如數據集路徑為 `D:\data\aidatatang_200zh`，那麼 ``就是 `D:\data` +假如数据集路径为 `D:\data\aidatatang_200zh`，那么 ``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)，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 +#### 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)，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 #### 7.什么时候算训练完成？ 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