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caizexin committed Oct 4, 2020
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14 changes: 14 additions & 0 deletions .gitignore
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synthesizer/saved_models
synthesizer/pre_trained
feedback_synthesizer/saved_models
vocoder/saved_models
vocoder/pretrained
deep_speaker/data_vox12/vox12_train_aug.csv
deep_speaker/vox12_resnet34
deep_speaker/log
deep_speaker/database
datasets/vctk/wavs
datasets/vctk/synthesizer/audio
datasets/vctk/synthesizer/embeds
datasets/vctk/synthesizer/mels
!.gitignore
17 changes: 17 additions & 0 deletions README
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1. speaker verification model
- cd deep_speaker
- CUDA_VISIBLE_DEVICES=1 python train.py

1. synthesizer without feedback control:
- extract audio feature using process_audio.ipynb (remember to split validation set and test set out for performance evaluation)
- extract embedding using script deep_speaker/get_gvector.ipynb
- train baseline multispeaker TTS system using synthesizer_train.py (CUDA_VISIBLE_DEVICES=1 python synthesizer_train.py vctk datasets/vctk/synthesizer)
- validate and synthesize result using syn.ipynb at any time during training
- using pre-trained WaveRNN vocoder in vocoder/pretrained, or train new one using vocoder_train.py. (CUDA_VISIBLE_DEVICES=1 python vocoder_train.py -g --syn_dir datasets/vctk/synthesizer vctk datasets/vctk) For better performance, please use GTA Mel-spectrogram obtained by vocoder_preprocess.py.

2. synthesizer with feedback constraint:
- load two model (speaker verification model and multispeaker synthesizer) by changing the path in feedback_synthesizer/hparams.py
- CUDA_VISIBLE_DEVICES=1 python fc_synthesizer_train.py
- evaluate with feedback_syn.ipynb


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