will create a virtual environment where necessary dependencies are installed. Switch to the new environment by conda activate env_name and enjoy it.
  > env.yml only includes the necessary dependencies to run the project，temporarily without monotonic-align. You can check the official website to install the GPU version of pytorch.
#### 1.2 Setup with a M1 Mac
The following steps are a workaround to directly use the original demo_toolbox.pywithout the changing of codes.

>  Since the major issue comes with the PyQt5 packages used in demo_toolbox.py not compatible with M1 chips, were one to attempt on training models with the M1 chip, either that person can forgo demo_toolbox.py, or one can try the web.py in the project.
##### 1.2.1 Install PyQt5, with ref here.
  * Create and open a Rosetta Terminal, with ref here.
  * Use system Python to create a virtual environment for the project

* Upgrade pip and install PyQt5

##### 1.2.2 Install pyworld and ctc-segmentation
Both packages seem to be unique to this project and are not seen in the original Real-Time Voice Cloning project. When installing with pip install, both packages lack wheels so the program tries to directly compile from c code and could not find Python.h.
  * Install pyworld
      * brew install python Python.h can come with Python installed by brew
      * export CPLUS_INCLUDE_PATH=/opt/homebrew/Frameworks/Python.framework/Headers The filepath of brew-installed Python.h is unique to M1 MacOS and listed above. One needs to manually add the path to the environment variables.
      * pip install pyworld that should do.

  * Installctc-segmentation
    > Same method does not apply to ctc-segmentation, and one needs to compile it from the source code on github.
    * git clone https://github.com/lumaku/ctc-segmentation.git
    * cd ctc-segmentation
    * source /PathToMockingBird/venv/bin/activate If the virtual environment hasn't been deployed, activate it.
    * cythonize -3 ctc_segmentation/ctc_segmentation_dyn.pyx
    * /usr/bin/arch -x86_64 python setup.py build Build with x86 architecture.
    * /usr/bin/arch -x86_64 python setup.py install --optimize=1 --skip-buildInstall with x86 architecture.
##### 1.2.3 Other dependencies
  * /usr/bin/arch -x86_64 pip install torch torchvision torchaudio Pip installing PyTorch as an example, articulate that it's installed with x86 architecture
  * pip install ffmpeg  Install ffmpeg
  * pip install -r requirements.txt Install other requirements.
##### 1.2.4 Run the Inference Time (with Toolbox)
  > To run the project on x86 architecture. ref.
  * vim /PathToMockingBird/venv/bin/pythonM1 Create an executable file pythonM1 to condition python interpreter at /PathToMockingBird/venv/bin.
  * Write in the following content:

* chmod +x pythonM1 Set the file as executable.
  * If using PyCharm IDE, configure project interpreter to pythonM1(steps here), if using command line python, run /PathToMockingBird/venv/bin/pythonM1 demo_toolbox.py

2. Prepare your models  Copy Section

Note that we are using the pretrained encoder/vocoder but not synthesizer, since the original model is incompatible with the Chinese symbols. It means the demo_cli is not working at this moment, so additional synthesizer models are required.
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 train.py --type=synth 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 4j5d  |  | 75k steps trained by multiple datasets
| @author | https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw  Baidu code：om7f  |  | 25k steps trained by multiple datasets, only works under version 0.0.1
|@FawenYo | https://yisiou-my.sharepoint.com/:u:/g/personal/lawrence_cheng_fawenyo_onmicrosoft_com/EWFWDHzee-NNg9TWdKckCc4BC7bK2j9cCbOWn0-_tK0nOg?e=n0gGgC  | input output | 200k steps with local accent of Taiwan, only works under version 0.0.1
|@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ code: 2021 https://www.aliyundrive.com/s/AwPsbo8mcSP code: z2m0 | 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，<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  Copy Section

#### 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>
#### 3.3 Using the command line
You can then try the command:
python gen_voice.py <text_file.txt> your_wav_file.wav
you may need to install cn2an by "pip install cn2an" for better digital number result.
Reference  Copy Chapter

This repository is forked from Real-Time-Voice-Cloning which only support English.
| URL | Designation | Title | Implementation source |
| --- | ----------- | ----- | --------------------- |
| 1803.09017 | GlobalStyleToken (synthesizer)| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | This repo |
| 2010.05646 | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | This repo |
| 2106.02297 | Fre-GAN (vocoder)| Fre-GAN: Adversarial Frequency-consistent Audio Synthesis | This repo |
|1806.04558 | SV2TTS | Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis | This repo |
|1802.08435 | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | fatchord/WaveRNN |
|1703.10135 | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | fatchord/WaveRNN
|1710.10467 | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | This repo |
F Q&A  Copy Chapter

#### 1.Where can I download the dataset?
| Dataset | Original Source | Alternative Sources |
| --- | ----------- | ---------------|
| aidatatang_200zh | OpenSLR | Google Drive |
| magicdata | OpenSLR | Google Drive (Dev set) |
| aishell3 | OpenSLR | Google Drive |
| data_aishell | OpenSLR |  |
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> isD:\data
#### 3.Not enough VRAM
Train the synthesizer：adjust the batch_size in synthesizer/hparams.py

Train Vocoder-Preprocess the data：adjust the batch_size in synthesizer/hparams.py

Train Vocoder-Train the vocoder：adjust the batch_size in vocoder/wavernn/hparams.py