ParakeetEricRoss/examples/transformer_tts

TransformerTTS 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_wavenet. Run the command below to preprocess the dataset.

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

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 direcctory (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_wavenet/ --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 text file, a sentence per line, and --output directory a directory to save the synthesized mel spectrogram(log magnitude) in .npy format. The mel spectrogram can be used with Waveflow to generate waveforms.

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

--device specifiies to device to run synthesis. Due to the autoregressiveness of wavenet, using cpu may be faster.

python synthesize.py --input=sentence.txt --output=mels/ --checkpoint_path='step-310000' --device="gpu" --verbose