PaddleSpeech/examples/ljspeech/voc0/local/waveflow

WaveFlow with LJSpeech

Dataset

Download the datasaet.

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

Extract the dataset.

tar xjvf LJSpeech-1.1.tar.bz2

Preprocess the dataset.

Assume the path to save the preprocessed dataset is ljspeech_waveflow. Run the command below to preprocess the dataset.

python preprocess.py --input=LJSpeech-1.1/  --output=ljspeech_waveflow

Train the model

The training script requires 4 command line arguments. --data is the path of the training dataset, --output is the path of the output directory (we recommend to use a subdirectory in runs to manage different experiments.)

--device should be "cpu" or "gpu", --nprocs is the number of processes to train the model in parallel.

python train.py --data=ljspeech_waveflow/ --output=runs/test --device="gpu" --nprocs=1

If you want distributed training, set a larger --nprocs (e.g. 4). Note that distributed training with cpu is not supported yet.

Synthesize

Synthesize waveform. We assume the --input is a directory containing several mel spectrograms(log magnitude) in .npy format. The output would be saved in --output directory, containing several .wav files, each with the same name as the mel spectrogram does.

--checkpoint_path should be the path of the parameter file (.pdparams) to load. Note that the extention name .pdparmas is not included here.

--device specifies to device to run synthesis on.

python synthesize.py --input=mels/ --output=wavs/ --checkpoint_path='step-2000000' --device="gpu" --verbose

Pretrained Model

Pretrained Model with residual channel equals 128 can be downloaded here. waveflow_ljspeech_ckpt_0.3.zip.