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Clarinet

PaddlePaddle dynamic graph implementation of ClariNet, a convolutional network based vocoder. The implementation is based on the paper ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech.

Dataset

We experiment with the LJSpeech dataset. Download and unzip LJSpeech.

wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar xjvf LJSpeech-1.1.tar.bz2

Project Structure

├── data.py          data_processing
├── configs/         (example) configuration file
├── synthesis.py     script to synthesize waveform from mel_spectrogram
├── train.py         script to train a model
└── utils.py         utility functions

Train

Train the model using train.py, follow the usage displayed by python train.py --help.

usage: train.py [-h] [--config CONFIG] [--device DEVICE] [--output OUTPUT]
                [--data DATA] [--resume RESUME] [--wavenet WAVENET]

train a ClariNet model with LJspeech and a trained WaveNet model.

optional arguments:
  -h, --help         show this help message and exit
  --config CONFIG    path of the config file.
  --device DEVICE    device to use.
  --output OUTPUT    path to save student.
  --data DATA        path of LJspeech dataset.
  --resume RESUME    checkpoint to load from.
  --wavenet WAVENET  wavenet checkpoint to use.

--config is the configuration file to use. The provided configurations can be used directly. And you can change some values in the configuration file and train the model with a different config.
--data is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
--resume is the path of the checkpoint. If it is provided, the model would load the checkpoint before trainig.
--output is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.

├── checkpoints      # checkpoint
├── states           # audio files generated at validation
└── log              # tensorboard log

--device is the device (gpu id) to use for training. -1 means CPU.
--wavenet is the path of the wavenet checkpoint to load. If you do not specify --resume, then this must be provided.

Before you start training a ClariNet model, you should have trained a WaveNet model with single Gaussian output distribution. Make sure the config of the teacher model matches that of the trained model.

Example script:

python train.py --config=./configs/clarinet_ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0 --conditioner=wavenet_checkpoint/conditioner --conditioner=wavenet_checkpoint/teacher

You can monitor training log via tensorboard, using the script below.

cd experiment/log
tensorboard --logdir=.

Synthesis

usage: synthesis.py [-h] [--config CONFIG] [--device DEVICE] [--data DATA]
                    checkpoint output

train a ClariNet model with LJspeech and a trained WaveNet model.

positional arguments:
  checkpoint       checkpoint to load from.
  output           path to save student.

optional arguments:
  -h, --help       show this help message and exit
  --config CONFIG  path of the config file.
  --device DEVICE  device to use.
  --data DATA      path of LJspeech dataset.

--config is the configuration file to use. You should use the same configuration with which you train you model.
--data is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
checkpoint is the checkpoint to load.
output_path is the directory to save results. The output path contains the generated audio files (*.wav).
--device is the device (gpu id) to use for training. -1 means CPU.