119 lines
5.0 KiB
Markdown
119 lines
5.0 KiB
Markdown
# Parallel WaveGAN with the Baker dataset
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This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
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## Preprocess the dataset
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Download the dataset from the [official website of data-baker](https://www.data-baker.com/data/index/source) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/BZNSYP`.
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Run the script for preprocessing.
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```bash
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bash preprocess.sh
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── stats.npy
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```
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The dataset is split into 3 parts, namely train, dev and test, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains log magnitude of mel spectrogram of each utterances, while the norm folder contains normalized spectrogram. The statistics used to normalize the spectrogram is computed from the training set, which is located in `dump/train/stats.npy`.
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Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
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## Train the model
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To train the model use the `run.sh`. It is an example script to run `train.py`.
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```bash
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bash run.sh
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```
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Or you can use the `train.py` directly. Here's the complete help message to run it.
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```text
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usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
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[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
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[--device DEVICE] [--nprocs NPROCS] [--verbose VERBOSE]
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Train a Parallel WaveGAN model with Baker Mandrin TTS dataset.
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optional arguments:
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-h, --help show this help message and exit
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--config CONFIG config file to overwrite default config
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--train-metadata TRAIN_METADATA
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training data
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--dev-metadata DEV_METADATA
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dev data
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--output-dir OUTPUT_DIR
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output dir
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--device DEVICE device type to use
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--nprocs NPROCS number of processes
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--verbose VERBOSE verbose
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```
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1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
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2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
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3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
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4. `--device` is the type of the device to run the experiment, 'cpu' or 'gpu' are supported.
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5. `--nprocs` is the number of processes to run in parallel, note that nprocs > 1 is only supported when `--device` is 'gpu'.
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## Pretrained Models
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Pretrained models can be downloaded here:
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1. Parallel WaveGAN checkpoint. [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip), which is used as a vocoder in the end-to-end inference script.
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Parallel WaveGAN checkpoint contains files listed below.
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```text
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pwg_baker_ckpt_0.4
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├── pwg_default.yaml # default config used to train parallel wavegan
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├── pwg_snapshot_iter_400000.pdz # generator parameters of parallel wavegan
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└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
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```
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## Synthesize
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When training is done or pretrained models are downloaded. You can run `synthesize.py` to synthsize.
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```text
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usage: synthesize.py [-h] [--config CONFIG] [--checkpoint CHECKPOINT]
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[--test-metadata TEST_METADATA] [--output-dir OUTPUT_DIR]
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[--device DEVICE] [--verbose VERBOSE]
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synthesize with parallel wavegan.
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optional arguments:
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-h, --help show this help message and exit
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--config CONFIG config file to overwrite default config
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--checkpoint CHECKPOINT
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snapshot to load
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--test-metadata TEST_METADATA
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dev data
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--output-dir OUTPUT_DIR
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output dir
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--device DEVICE device to run
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--verbose VERBOSE verbose
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```
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1. `--config` is the extra configuration file to overwrite the default config. You should use the same config with which the model is trained.
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2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `/checkpoints` inside the training output directory. If you use the pretrained model, use the `pwg_snapshot_iter_400000.pdz`.
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3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
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4. `--output-dir` is the directory to save the synthesized audio files.
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5. `--device` is the type of device to run synthesis, 'cpu' and 'gpu' are supported.
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## Acknowledgement
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We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
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