ParakeetRebeccaRosario/examples/deepvoice3

Deep Voice 3

PaddlePaddle dynamic graph implementation of Deep Voice 3, a convolutional network based text-to-speech generative model. The implementation is based on Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.

We implement Deep Voice 3 using Paddle Fluid with dynamic graph, which is convenient for building flexible network architectures.

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

Model Architecture

The model consists of an encoder, a decoder and a converter (and a speaker embedding for multispeaker models). The encoder and the decoder together form the seq2seq part of the model, and the converter forms the postnet part.

Project Structure

├── data.py          data_processing
├── configs/         (example) configuration files
├── sentences.txt    sample sentences
├── synthesis.py     script to synthesize waveform from text
├── train.py         script to train a model
└── utils.py         utility functions

Saving & Loading

train.py and synthesis.py have 3 arguments in common, --checkpooint, iteration and output.

1. output is the directory for saving results. During training, checkpoints are saved in checkpoints/ in output and tensorboard log is save in log/ in output. Other possible outputs are saved in states/ in outuput. During synthesizing, audio files and other possible outputs are save in synthesis/ in output. So after training and synthesizing with the same output directory, the file structure of the output directory looks like this.

├── checkpoints/      # checkpoint directory (including *.pdparams, *.pdopt and a text file `checkpoint` that records the latest checkpoint)
├── states/           # audio files generated at validation and other possible outputs
├── log/              # tensorboard log
└── synthesis/        # synthesized audio files and other possible outputs

2. --checkpoint and --iteration for loading from existing checkpoint. Loading existing checkpoiont follows the following rule: If --checkpoint is provided, the checkpoint specified by --checkpoint is loaded. If --checkpoint is not provided, we try to load the model specified by --iteration from the checkpoint directory. If --iteration is not provided, we try to load the latested checkpoint from checkpoint directory.

Train

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

usage: train.py [-h] [--config CONFIG] [--data DATA] [--device DEVICE]
                [--checkpoint CHECKPOINT | --iteration ITERATION]
                output

Train a Deep Voice 3 model with LJSpeech dataset.

positional arguments:
  output                        path to save results

optional arguments:
  -h, --help                    show this help message and exit
  --config CONFIG               experimrnt config
  --data DATA                   The path of the LJSpeech dataset.
  --device DEVICE               device to use
  --checkpoint CHECKPOINT       checkpoint to resume from.
  --iteration ITERATION         the iteration of the checkpoint to load from output directory

• --config is the configuration file to use. The provided ljspeech.yaml 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).
• --device is the device (gpu id) to use for training. -1 means CPU.
• --checkpoint is the path of the checkpoint.
• --iteration is the iteration of the checkpoint to load from output directory. See Saving-&-Loading for details of checkpoint loading.
• output is the directory to save results, all results are saved in this directory. The structure of the output directory is shown below.

├── checkpoints      # checkpoint
├── log              # tensorboard log
└── states           # train and evaluation results
    ├── alignments   # attention
    ├── lin_spec     # linear spectrogram
    ├── mel_spec     # mel spectrogram
    └── waveform     # waveform (.wav files)

Example script:

python train.py \
    --config=configs/ljspeech.yaml \
    --data=./LJSpeech-1.1/ \
    --device=0 \
    experiment

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]
                    [--checkpoint CHECKPOINT | --iteration ITERATION]
                    text output

Synthsize waveform with a checkpoint.

positional arguments:
  text                          text file to synthesize
  output                        path to save synthesized audio

optional arguments:
  -h, --help                    show this help message and exit
  --config CONFIG               experiment config
  --device DEVICE               device to use
  --checkpoint CHECKPOINT       checkpoint to resume from
  --iteration ITERATION         the iteration of the checkpoint to load from output directory

• --config is the configuration file to use. You should use the same configuration with which you train you model.

• --device is the device (gpu id) to use for training. -1 means CPU.

• --checkpoint is the path of the checkpoint.

• --iteration is the iteration of the checkpoint to load from output directory. See Saving-&-Loading for details of checkpoint loading.

• textis the text file to synthesize.

• output is the directory to save results. The generated audio files (*.wav) and attention plots (*.png) for are save in synthesis/ in ouput directory.

Example script:

python synthesis.py \
    --config=configs/ljspeech.yaml \
    --device=0 \
    --checkpoint="experiment/checkpoints/model_step_005000000" \
    sentences.txt experiment

or

python synthesis.py \
    --config=configs/ljspeech.yaml \
    --device=0 \
    --iteration=005000000 \
    sentences.txt experiment