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🌍 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

• Major upgrade on GUI/Client and unifying web and toolbox [X] Init framework ./mkgui and tech design [X] Add demo part of Voice Cloning and Conversion [X] Add preprocessing and training for Voice Conversion [ ] Add preprocessing and training for Encoder/Synthesizer/Vocoder
• Major upgrade on model backend based on ESPnet2(not yet started)

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 symbols. It means the demo_cli is not working at this moment.

You can either train your models or use existing ones:

• 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.

• 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/.

Thanks to the community, some models will be shared:

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

You can then try to run:python web.py and open it in browser, default as http://localhost:8080

You can then try the toolbox: python demo_toolbox.py -d <datasets_root>

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.

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

After unzip aidatatang_200zh, you need to unzip all the files under aidatatang_200zh\corpus\train

If the dataset path is D:\data\aidatatang_200zh,then <datasets_root> isD:\data

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

Please refer to issue #37

Adjust the batch_size as appropriate to improve

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.

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