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# Wavenet
# WaveNet
Paddle implementation of wavenet in dynamic graph, a convolutional network based vocoder. Wavenet is proposed in [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499), but in thie experiment, the implementation follows the teacher model in [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](arxiv.org/abs/1807.07281).
PaddlePaddle dynamic graph implementation of WaveNet, a convolutional network based vocoder. WaveNet is originally proposed in [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499). However, in this experiment, the implementation follows the teacher model in [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](arxiv.org/abs/1807.07281).
## Dataset
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## Train
Train the model using train.py, follow the usage displayed by `python train.py --help`.
Train the model using train.py. For help on usage, try `python train.py --help`.
```text
usage: train.py [-h] [--data DATA] [--config CONFIG] [--output OUTPUT]
[--device DEVICE] [--resume RESUME]
Train a wavenet model with LJSpeech.
Train a WaveNet model with LJSpeech.
optional arguments:
-h, --help show this help message and exit
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1. `--config` is the configuration file to use. The provided configurations 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.
3. `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before training.
4. `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
```text
@ -53,13 +53,13 @@ optional arguments:
5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
example script:
Example script:
```bash
python train.py --config=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
```
You can monitor training log via tensorboard, using the script below.
You can monitor training log via TensorBoard, using the script below.
```bash
cd experiment/log
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usage: synthesis.py [-h] [--data DATA] [--config CONFIG] [--device DEVICE]
checkpoint output
Synthesize valid data from LJspeech with a wavenet model.
Synthesize valid data from LJspeech with a WaveNet model.
positional arguments:
checkpoint checkpoint to load.
@ -85,12 +85,12 @@ optional arguments:
```
1. `--config` is the configuration file to use. You should use the same configuration with which you train you model.
2. `--data` is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
2. `--data` is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
3. `checkpoint` is the checkpoint to load.
4. `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`).
5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
example script:
Example script:
```bash
python synthesis.py --config=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --device=0 experiment/checkpoints/step_500000 generated