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Tacotron2 with LJSpeech

PaddlePaddle dynamic graph implementation of Tacotron2, a neural network architecture for speech synthesis directly from text. The implementation is based on Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions.

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

Get Started

Assume the path to the dataset is ~/datasets/LJSpeech-1.1. Run the command below to

source path.
preprocess the dataset,
train the model.
synthesize mels.

./run.sh

Preprocess the dataset

./local/preprocess.sh ${conf_path}

Train the model

./local/train.sh calls ${BIN_DIR}/train.py.

CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}

Here's the complete help message.

usage: train.py [-h] [--config FILE] [--data DATA_DIR] [--output OUTPUT_DIR]
                [--checkpoint_path CHECKPOINT_PATH] [--ngpu NGPU] [--opts ...]

optional arguments:
  -h, --help            show this help message and exit
  --config FILE         path of the config file to overwrite to default config
                        with.
  --data DATA_DIR       path to the datatset.
  --output OUTPUT_DIR   path to save checkpoint and logs.
  --checkpoint_path CHECKPOINT_PATH
                        path of the checkpoint to load
  --ngpu NGPU           if ngpu == 0, use cpu.
  --opts ...            options to overwrite --config file and the default
                        config, passing in KEY VALUE pairs

If you want to train on CPU, just set --ngpu=0. If you want to train on multiple GPUs, just set --ngpu as num of GPU. By default, training will be resumed from the latest checkpoint in --output, if you want to start a new training, please use a new ${OUTPUTPATH} with no checkpoint. And if you want to resume from an other existing model, you should set checkpoint_path to be the checkpoint path you want to load. Note: The checkpoint path cannot contain the file extension.

Synthesize

./local/synthesize.sh calls ${BIN_DIR}/synthesize.py, which synthesize mels from text_list here.

CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${train_output_path} ${ckpt_name}

usage: synthesize.py [-h] [--config FILE] [--checkpoint_path CHECKPOINT_PATH]
                     [--input INPUT] [--output OUTPUT] [--ngpu NGPU]
                     [--opts ...] [-v]

generate mel spectrogram with TransformerTTS.

optional arguments:
  -h, --help            show this help message and exit
  --config FILE         extra config to overwrite the default config
  --checkpoint_path CHECKPOINT_PATH
                        path of the checkpoint to load.
  --input INPUT         path of the text sentences
  --output OUTPUT       path to save outputs
  --ngpu NGPU           if ngpu == 0, use cpu.
  --opts ...            options to overwrite --config file and the default
                        config, passing in KEY VALUE pairs
  -v, --verbose         print msg

Ps. You can use waveflow as the neural vocoder to synthesize mels to wavs. (Please refer to synthesize.sh in our LJSpeech waveflow example)

Pretrained Models

Pretrained Models can be downloaded from links below. We provide 2 models with different configurations.

This model use a binary classifier to predict the stop token. tacotron2_ljspeech_ckpt_0.3.zip
This model does not have a stop token predictor. It uses the attention peak position to decided whether all the contents have been uttered. Also guided attention loss is used to speed up training. This model is trained with configs/alternative.yaml.tacotron2_ljspeech_ckpt_0.3_alternative.zip

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