115 lines
4.2 KiB
Markdown
115 lines
4.2 KiB
Markdown
# Deepvoice 3
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Paddle implementation of deepvoice 3 in dynamic graph, a convolutional network based text-to-speech synthesis model. The implementation is based on [Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://arxiv.org/abs/1710.07654).
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We implement Deepvoice 3 in paddle fluid with dynamic graph, which is convenient for flexible network architectures.
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## Dataset
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We experiment with the LJSpeech dataset. Download and unzip [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
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```bash
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wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
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tar xjvf LJSpeech-1.1.tar.bz2
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```
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## Model Architecture
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The model consists of an encoder, a decoder and a converter (and a speaker embedding for multispeaker models). The encoder, together with the decoder forms the seq2seq part of the model, and the converter forms the postnet part.
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## Project Structure
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```text
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├── data.py data_processing
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├── ljspeech.yaml (example) configuration file
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├── sentences.txt sample sentences
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├── synthesis.py script to synthesize waveform from text
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├── train.py script to train a model
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└── utils.py utility functions
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```
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## Train
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Train the model using train.py, follow the usage displayed by `python train.py --help`.
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```text
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usage: train.py [-h] [-c CONFIG] [-s DATA] [-r RESUME] [-o OUTPUT] [-g DEVICE]
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Train a deepvoice 3 model with LJSpeech dataset.
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optional arguments:
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-h, --help show this help message and exit
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-c CONFIG, --config CONFIG
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experimrnt config
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-s DATA, --data DATA The path of the LJSpeech dataset.
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-r RESUME, --resume RESUME
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checkpoint to load
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-o OUTPUT, --output OUTPUT
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The directory to save result.
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-g DEVICE, --device DEVICE
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device to use
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```
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1. `--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.
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2. `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
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3. `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before trainig.
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4. `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
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```text
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├── checkpoints # checkpoint
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├── log # tensorboard log
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└── states # train and evaluation results
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├── alignments # attention
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├── lin_spec # linear spectrogram
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├── mel_spec # mel spectrogram
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└── waveform # waveform (.wav files)
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```
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5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
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example script:
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```bash
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python train.py --config=./ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
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```
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You can monitor training log via tensorboard, using the script below.
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```bash
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cd experiment/log
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tensorboard --logdir=.
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```
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## Synthesis
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```text
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usage: synthesis.py [-h] [-c CONFIG] [-g DEVICE] checkpoint text output_path
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Synthsize waveform with a checkpoint.
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positional arguments:
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checkpoint checkpoint to load.
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text text file to synthesize
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output_path path to save results
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optional arguments:
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-h, --help show this help message and exit
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-c CONFIG, --config CONFIG
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experiment config.
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-g DEVICE, --device DEVICE
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device to use
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```
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1. `--config` is the configuration file to use. You should use the same configuration with which you train you model.
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2. `checkpoint` is the checkpoint to load.
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3. `text`is the text file to synthesize.
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4. `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`) and attention plots (*.png) for each sentence.
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5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
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example script:
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```bash
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python synthesis.py --config=./ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated
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```
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