🚧 While I no longer actively update this repo, you can find me continuously pushing this tech forward to good side and open-source. I’m also building an optimized and cloud hosted version: https://noiz.ai/ and we’re hiring.

English | 中文| 中文Linux

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

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.

  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.
• Run pip install -r requirements.txt to install the remaining necessary packages.

  The recommended environment here is Repo Tag 0.0.1 Pytorch1.9.0 with Torchvision0.10.0 and cudatoolkit10.2 requirements.txt webrtcvad-wheels because requirements. txt was exported a few months ago, so it doesn’t work with newer versions

• 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.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

  /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 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:

  #!/usr/bin/env zsh
  mydir=${0h}
  /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), 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 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

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 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

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

//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

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 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.