lbqin / parallel_wavenet_vocoder

Parallel WaveNet Vocoder Based on ClariNet

Parallel WaveNet vocoder

About the samples, i will add later, now the samples is not much good.

Note: the code is adapted from r9y9's wavenet vocoder, u can get more information about wavenet at there.

To Do List

• add gaussian distribution to origin wavenet
• obtain a good single gaussian teacher (430K now)
• add gaussian student
• obtain a good single gaussian student (training, 70k, i use 330k as the teacher)
• test share upsample conv(now not share)

Quick Start

Prepare Data

python preprocess.py \
    ljspeech \  # data name, i use ljspeech as defalut
    your_data_dir \
    the_dir_to_save_data/\
    --preset=presets/ljspeech_gaussian.json \

Train Autoregressive WaveNet(Teacher)

python train.py \
    --preset=presets/ljspeech_gaussian.json \
    --data-root=your_data_dir \
    --hparams='batch_size=9,' \  # in my expreiment, i use 3 gpus(1080Ti)
    --checkpoint-dir=checkpoint-ljspeech \
    --log-event-path=log-ljspeech

Synthesis Using Teacher

python synthesis.py \
    --conditional your_local_condition_path \
    --preset=presets/ljspeech_gaussian.json \
    your_teacher_checkpoint_path \
    your_save_dir

Train Distillation WaveNet(Student)

python train_student.py \
    --preset=presets/ljspeech_gaussian.json \
    --data-root=your_data_dir \
    --hparams='batch_size=8,' \  # in my expreiment, i use 4 gpus(1080Ti)
    --checkpoint-dir=checkpoint-ljspeech_student \
    --log-event-path=log-ljspeech_student \
    --checkpoint_teacher=your_teacher_checkpoint_path

Synthesis Using Student

python synthesis_student.py \
    --conditional your_local_condition_path \
    --preset=presets/ljspeech_gaussian.json \
    your_checkpoint_path \
    your_save_dir

References

• ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech