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# Deepvoice 3
# Deep Voice 3
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).
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](https://arxiv.org/abs/1710.07654).
We implement Deepvoice 3 in paddle fluid with dynamic graph, which is convenient for flexible network architectures.
We implement Deep Voice 3 using Paddle Fluid with dynamic graph, which is convenient for building flexible network architectures.
## Dataset
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## Model Architecture
![DeepVoice3 model architecture](./images/model_architecture.png)
![Deep Voice 3 model architecture](./images/model_architecture.png)
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.
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
@ -37,7 +37,7 @@ Train the model using train.py, follow the usage displayed by `python train.py -
```text
usage: train.py [-h] [-c CONFIG] [-s DATA] [-r RESUME] [-o OUTPUT] [-g DEVICE]
Train a deepvoice 3 model with LJSpeech dataset.
Train a Deep Voice 3 model with LJSpeech dataset.
optional arguments:
-h, --help show this help message and exit
@ -55,7 +55,7 @@ optional arguments:
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.
2. `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
3. `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before trainig.
4. `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
4. `--output` is the directory to save results, all results are saved in this directory. The structure of the output directory is shown below.
```text
├── checkpoints # checkpoint
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5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
example script:
Example script:
```bash
python train.py --config=./ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
@ -86,7 +86,7 @@ tensorboard --logdir=.
```text
usage: synthesis.py [-h] [-c CONFIG] [-g DEVICE] checkpoint text output_path
Synthsize waveform with a checkpoint.
Synthsize waveform from a checkpoint.
positional arguments:
checkpoint checkpoint to load.
@ -107,7 +107,7 @@ optional arguments:
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.
5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
example script:
Example script:
```bash
python synthesis.py --config=./ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated