# 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](https://arxiv.org/abs/1712.05884). ## Dataset We experiment with the LJSpeech dataset. Download and unzip [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). ```bash 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 1. **source path**. 2. preprocess the dataset. 3. train the model. 4. synthesize mels. ```bash ./run.sh ``` ### Preprocess the dataset ```bash ./local/preprocess.sh ${conf_path} ``` ### Train the model `./local/train.sh` calls `${BIN_DIR}/train.py`. ```bash CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} ``` Here's the complete help message. ```text usage: train.py [-h] [--config FILE] [--data DATA_DIR] [--output OUTPUT_DIR] [--checkpoint_path CHECKPOINT_PATH] [--ngpu NGPU] [--opts ...] optional arguments: -h, --help show this help message and exit --config FILE path of the config file to overwrite to default config with. --data DATA_DIR path to the datatset. --output OUTPUT_DIR path to save checkpoint and logs. --checkpoint_path CHECKPOINT_PATH path of the checkpoint to load --ngpu NGPU if ngpu == 0, use cpu. --opts ... options to overwrite --config file and the default config, passing in KEY VALUE pairs ``` If you want to train on CPU, just set `--ngpu=0`. If you want to train on multiple GPUs, just set `--ngpu` as num of GPU. By default, training will be resumed from the latest checkpoint in `--output`, if you want to start a new training, please use a new `${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 `${BIN_DIR}/synthesize.py`, which synthesize **mels** from text_list here. ```bash CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${train_output_path} ${ckpt_name} ``` ```text 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](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0) 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. 1. This model use a binary classifier to predict the stop token. [tacotron2_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.3.zip) 2. 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](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.3_alternative.zip)