![mockingbird](https://user-images.githubusercontent.com/12797292/131216767-6eb251d6-14fc-4951-8324-2722f0cd4c63.jpg)


[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/)

> English | [中文](README-CN.md)

## Features
🌍 **Chinese** supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata, aishell3, data_aishell, and etc.

🤩 **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060

🌍 **Windows + Linux** run in both Windows OS and linux OS (even in M1 MACOS)

🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder

🌍 **Webserver Ready** to serve your result with remote calling

### [DEMO VIDEO](https://www.bilibili.com/video/BV17Q4y1B7mY/)

## Quick Start

### 1. Install Requirements
#### 1.1 General Setup
> 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)

or
- install dependencies with `conda` or `mamba`

  ```conda env create -n env_name -f env.yml```

  ```mamba env create -n env_name -f env.yml```

  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.py`without 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](https://stackoverflow.com/a/68038451/20455983) here.
  * Create and open a Rosetta Terminal, with [ref](https://dev.to/courier/tips-and-tricks-to-setup-your-apple-m1-for-development-547g) here.
  * Use system Python to create a virtual environment for the project
    ```
    /usr/bin/python3 -m venv /PathToMockingBird/venv
    source /PathToMockingBird/venv/bin/activate
    ```
  * Upgrade pip and install `PyQt5`
    ```
    pip install --upgrade pip
    pip 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](https://github.com/CorentinJ/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.


  * Install`ctc-segmentation`
    > Same method does not apply to `ctc-segmentation`, and one needs to compile it from the source code on [github](https://github.com/lumaku/ctc-segmentation).
    * `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-build`Install 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](https://youtrack.jetbrains.com/issue/PY-46290/Allow-running-Python-under-Rosetta-2-in-PyCharm-for-Apple-Silicon).
  * `vim /PathToMockingBird/venv/bin/pythonM1` Create an executable file `pythonM1` to condition python interpreter at `/PathToMockingBird/venv/bin`.
  * Write in the following content:
    ```
    #!/usr/bin/env zsh
    mydir=${0:a:h}
    /usr/bin/arch -x86_64 $mydir/python "$@"
    ```
  * `chmod +x pythonM1` Set the file as executable.
  * If using PyCharm IDE, configure project interpreter to `pythonM1`([steps here](https://www.jetbrains.com/help/pycharm/configuring-python-interpreter.html#add-existing-interpreter)), if using command line python, run `/PathToMockingBird/venv/bin/pythonM1 demo_toolbox.py`


### 2. Prepare your models
> 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](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：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](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: 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
#### 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
> 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 |
| [2106.02297](https://arxiv.org/abs/2106.02297) | Fre-GAN (vocoder)| Fre-GAN: Adversarial Frequency-consistent Audio 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：adjust 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
                (2,  3e-5, 320_000,  8),   #     synthesized for each decoder iteration)
                (2,  1e-5, 640_000,  8)],  # lr = learning rate
```

Train Vocoder-Preprocess the data：adjust the batch_size in `synthesizer/hparams.py`
```
//Before
### Data Preprocessing
        max_mel_frames = 900,
        rescale = True,
        rescaling_max = 0.9,
        synthesis_batch_size = 16,                  # For vocoder preprocessing and inference.
//After
### Data Preprocessing
        max_mel_frames = 900,
        rescale = True,
        rescaling_max = 0.9,
        synthesis_batch_size = 8,                  # For vocoder preprocessing and inference.
```

Train Vocoder-Train the vocoder：adjust the batch_size in `vocoder/wavernn/hparams.py`
```
//Before
# Training
voc_batch_size = 100
voc_lr = 1e-4
voc_gen_at_checkpoint = 5
voc_pad = 2

//After
# Training
voc_batch_size = 6
voc_lr = 1e-4
voc_gen_at_checkpoint = 5
voc_pad =2
```

#### 4.If it happens `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]).`
Please refer to issue [#37](https://github.com/babysor/MockingBird/issues/37)

#### 5. How to improve CPU and GPU occupancy rate?
Adjust the batch_size as appropriate to improve


#### 6. What if it happens `the page file is too small to complete the operation`
Please refer to this [video](https://www.youtube.com/watch?v=Oh6dga-Oy10&ab_channel=CodeProf) and change the virtual memory to 100G (102400), for example : When the file is placed in the D disk, the virtual memory of the D disk is changed.

#### 7. When should I stop during training?
FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.
![attention_step_20500_sample_1](https://user-images.githubusercontent.com/7423248/128587252-f669f05a-f411-4811-8784-222156ea5e9d.png)
![step-135500-mel-spectrogram_sample_1](https://user-images.githubusercontent.com/7423248/128587255-4945faa0-5517-46ea-b173-928eff999330.png)