English | 中文

Features

🌍 Chinese supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata, aishell3, 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

DEMO VIDEO

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.

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.
• 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. Train synthesizer with your dataset

• Download aidatatang_200zh or other dataset and unzip: make sure you can access all .wav in train folder
• Preprocess with the audios and the mel spectrograms: python pre.py <datasets_root> Allow parameter --dataset {dataset} to support aidatatang_200zh, magicdata, aishell3

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.

• 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/.

FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.

2.2 Use pretrained model of synthesizer

Thanks to the community, some models will be shared:

author	Download link	Preview Video	Info
@FawenYo	https://drive.google.com/file/d/1H-YGOUHpmqKxJ9FRc6vAjPuqQki24UbC/view?usp=sharing Baidu Pan Code：1024	input output	200k steps with local accent of Taiwan
@miven	https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ code：2021	https://www.bilibili.com/video/BV1uh411B7AD/

A link to my early trained model: Baidu Yun Code：aid4

2.3 Train vocoder (Optional)

• Preprocess the data: python vocoder_preprocess.py <datasets_root>

• Train the vocoder: python vocoder_train.py mandarin <datasets_root>

3. Launch the Toolbox

You can then try the toolbox:

python demo_toolbox.py -d <datasets_root> or python demo_toolbox.py

Good news🤩: Chinese Characters are supported

Reference

This repository is forked from Real-Time-Voice-Cloning which only support English.

URL	Designation	Title	Implementation source
2010.05646	HiFi-GAN (vocoder)	Generative Adversarial Networks for Efficient and High Fidelity Speech 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?

aidatatang_200zh、magicdata、aishell3

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