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## 实时语音克隆 - 中文/普通话
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[](http://choosealicense.com/licenses/mit/)
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### [English](README.md) | 中文
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### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)
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## 特性
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🌍 **中文** 支持普通话并使用多种中文数据集进行测试：aidatatang_200zh, magicdata, aishell3， biaobei，MozillaCommonVoice 等
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🤩 **PyTorch** 适用于 pytorch，已在 1.9.0 版本（最新于 2021 年 8 月）中测试，GPU Tesla T4 和 GTX 2060
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🌍 **Windows + Linux** 可在 Windows 操作系统和 linux 操作系统中运行（苹果系统M1版也有社区成功运行案例）
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🤩 **Easy & Awesome** 仅需下载或新训练合成器（synthesizer）就有良好效果，复用预训练的编码器/声码器，或实时的HiFi-GAN作为vocoder
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## 快速开始
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> 0训练新手友好版可以参考 [Quick Start (Newbie)](https://github.com/babysor/Realtime-Voice-Clone-Chinese/wiki/Quick-Start-(Newbie))
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### 1. 安装要求
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> 按照原始存储库测试您是否已准备好所有环境。
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**Python 3.7 或更高版本** 需要运行工具箱。
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* 安装 [PyTorch](https://pytorch.org/get-started/locally/)。
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> 如果在用 pip 方式安装的时候出现 `ERROR: Could not find a version that satisfies the requirement torch==1.9.0+cu102 (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2)` 这个错误可能是 python 版本过低，3.9 可以安装成功
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* 安装 [ffmpeg](https://ffmpeg.org/download.html#get-packages)。
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* 运行`pip install -r requirements.txt` 来安装剩余的必要包。
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* 安装 webrtcvad 用 `pip install webrtcvad-wheels`。
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### 2. 使用数据集训练合成器
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* 下载 数据集并解压：确保您可以访问 *train* 文件夹中的所有音频文件（如.wav）
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* 进行音频和梅尔频谱图预处理：
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`python pre.py <datasets_root>`
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可以传入参数 --dataset `{dataset}` 支持 aidatatang_200zh, magicdata, aishell3
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> 假如你下载的 `aidatatang_200zh`文件放在D盘，`train`文件路径为 `D:\data\aidatatang_200zh\corpus\train` , 你的`datasets_root`就是 `D:\data\`
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>假如發生 `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
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* 训练合成器：
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`python synthesizer_train.py mandarin <datasets_root>/SV2TTS/synthesizer`
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* 当您在训练文件夹 *synthesizer/saved_models/* 中看到注意线显示和损失满足您的需要时，请转到下一步。
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> 仅供参考，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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### 2.2 使用预先训练好的合成器
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> 实在没有设备或者不想慢慢调试，可以使用网友贡献的模型(欢迎持续分享):
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| 作者 | 下载链接 | 效果预览 | 信息 |
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| --- | ----------- | ----- | ----- |
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|@FawenYo | https://drive.google.com/file/d/1H-YGOUHpmqKxJ9FRc6vAjPuqQki24UbC/view?usp=sharing [百度盘链接](https://pan.baidu.com/s/1vSYXO4wsLyjnF3Unl-Xoxg) 提取码：1024 | [input](https://github.com/babysor/MockingBird/wiki/audio/self_test.mp3) [output](https://github.com/babysor/MockingBird/wiki/audio/export.wav) | 200k steps 台湾口音
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|@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ 提取码：2021 | https://www.bilibili.com/video/BV1uh411B7AD/ | 150k steps 旧版需根据[issue](https://github.com/babysor/MockingBird/issues/37)修复
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### 2.3 训练声码器 (Optional)
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* 预处理数据:
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`python vocoder_preprocess.py <datasets_root>`
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* 训练wavernn声码器:
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`python vocoder_train.py mandarin <datasets_root>`
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* 训练hifigan声码器:
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`python vocoder_train.py mandarin <datasets_root> hifigan`
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### 3. 启动工具箱
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然后您可以尝试使用工具箱：
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`python demo_toolbox.py -d <datasets_root>`
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> Good news🤩: 可直接使用中文
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## Release Note
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2021.9.8 新增Hifi-GAN Vocoder支持
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## 引用及论文
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> 该库一开始从仅支持英语的[Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) 分叉出来的，鸣谢作者。
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| URL | Designation | 标题 | 实现源码 |
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| --- | ----------- | ----- | --------------------- |
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| [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | 本代码库 |
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|[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | **SV2TTS** | **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis** | This repo |
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|[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |
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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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[aidatatang_200zh](http://www.openslr.org/62/)、[magicdata](http://www.openslr.org/68/)、[aishell3](http://www.openslr.org/93/)
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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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#### 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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#### 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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```
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//調整前
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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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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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(2, 1e-4, 160_000, 8), # r = reduction factor (# of mel frames
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(2, 3e-5, 320_000, 8), # synthesized for each decoder iteration)
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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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```
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//調整前
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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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### 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 = 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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```
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//調整前
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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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# Training
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voc_batch_size = 6
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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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```
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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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#### 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 |