声音克隆代码，让你在5秒钟内克隆声音

 [](http://choosealicense.com/licenses/mit/) > English | [涓枃](README-CN.md) ## Features 馃實 **Chinese** supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata, aishell3, data_aishell, and etc. 馃ぉ **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060 馃實 **Windows + Linux** run in both Windows OS and linux OS (even in M1 MACOS) 馃ぉ **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder 馃實 **Webserver Ready** to serve your result with remote calling ### [DEMO VIDEO](https://www.bilibili.com/video/BV17Q4y1B7mY/) ## Quick Start ### 1. Install Requirements > Follow the original repo to test if you got all environment ready. **Python 3.7 or higher ** is needed to run the toolbox. * Install [PyTorch](https://pytorch.org/get-started/locally/). > If you get an `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 )` This error is probably due to a low version of python, try using 3.9 and it will install successfully * Install [ffmpeg](https://ffmpeg.org/download.html#get-packages). * Run `pip install -r requirements.txt` to install the remaining necessary packages. * Install webrtcvad `pip install webrtcvad-wheels`(If you need) > Note that we are using the pretrained encoder/vocoder but synthesizer, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment. ### 2. Prepare your models You can either train your models or use existing ones: #### 2.1 Train encoder with your dataset (Optional) * Preprocess with the audios and the mel spectrograms: `python encoder_preprocess.py <datasets_root>` Allowing parameter `--dataset {dataset}` to support the datasets you want to preprocess. Only the train set of these datasets will be used. Possible names: librispeech_other, voxceleb1, voxceleb2. Use comma to sperate multiple datasets. * Train the encoder: `python encoder_train.py my_run <datasets_root>/SV2TTS/encoder` > For training, the encoder uses visdom. You can disable it with `--no_visdom`, but it's nice to have. Run "visdom" in a separate CLI/process to start your visdom server. #### 2.2 Train synthesizer with your dataset * Download dataset and unzip: make sure you can access all .wav in folder * Preprocess with the audios and the mel spectrograms: `python pre.py <datasets_root>` Allowing parameter `--dataset {dataset}` to support aidatatang_200zh, magicdata, aishell3, data_aishell, etc.If this parameter is not passed, the default dataset will be aidatatang_200zh. * Train the synthesizer: `python synthesizer_train.py mandarin <datasets_root>/SV2TTS/synthesizer` * Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*. #### 2.3 Use pretrained model of synthesizer > Thanks to the community, some models will be shared: | author | Download link | Preview Video | Info | | --- | ----------- | ----- |----- | | @author | https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g [Baidu](https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g) 4j5d | | 75k steps trained by multiple datasets | @author | https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw [Baidu](https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw) code锛歰m7f | | 25k steps trained by multiple datasets, only works under version 0.0.1 |@FawenYo | https://drive.google.com/file/d/1H-YGOUHpmqKxJ9FRc6vAjPuqQki24UbC/view?usp=sharing https://u.teknik.io/AYxWf.pt | [input](https://github.com/babysor/MockingBird/wiki/audio/self_test.mp3) [output](https://github.com/babysor/MockingBird/wiki/audio/export.wav) | 200k steps with local accent of Taiwan, only works under version 0.0.1 |@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ code锛�2021 | https://www.bilibili.com/video/BV1uh411B7AD/ | only works under version 0.0.1 #### 2.4 Train vocoder (Optional) > note: vocoder has little difference in effect, so you may not need to train a new one. * Preprocess the data: `python vocoder_preprocess.py <datasets_root> -m <synthesizer_model_path>` > `<datasets_root>` replace with your dataset root锛宍<synthesizer_model_path>`replace with directory of your best trained models of sythensizer, e.g. *sythensizer\saved_mode\xxx* * Train the wavernn vocoder: `python vocoder_train.py mandarin <datasets_root>` * Train the hifigan vocoder `python vocoder_train.py mandarin <datasets_root> hifigan` ### 3. Launch #### 3.1 Using the web server You can then try to run:`python web.py` and open it in browser, default as `http://localhost:8080` #### 3.2 Using the Toolbox You can then try the toolbox: `python demo_toolbox.py -d <datasets_root>` ## Reference > This repository is forked from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) which only support English. | URL | Designation | Title | Implementation source | | --- | ----------- | ----- | --------------------- | | [1803.09017](https://arxiv.org/abs/1803.09017) | GlobalStyleToken (synthesizer)| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | This repo | | [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | This repo | |[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | **SV2TTS** | **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis** | This repo | |[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) | |[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 | This repo | ## F Q&A #### 1.Where can I download the dataset? | Dataset | Original Source | Alternative Sources | | --- | ----------- | ---------------| | 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/) | | > After unzip aidatatang_200zh, you need to unzip all the files under `aidatatang_200zh\corpus\train` #### 2.What is`<datasets_root>`? If the dataset path is `D:\data\aidatatang_200zh`,then `<datasets_root>` is`D:\data` #### 3.Not enough VRAM Train the synthesizer锛歛djust the batch_size in `synthesizer/hparams.py` ``` //Before 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 //After 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), # (2, 1e-4, 160_000, 8), # r = reduction factor (# of mel frames