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add README for examples/parallel_wavegan

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examples/parallelwave_gan/baker/README.md
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# Parallel WaveGAN with the Baker dataset
This example contains code used to train a parallel wavegan model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
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).
## Preprocess the dataset
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 `~/datasets/BZNSYP`.
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`.
Run the script for preprocessing.
@ -28,26 +28,26 @@ dump
└── stats.npy
```
The dataset is split into 3 parts, namely train, dev and test, each of which contains a `norm` and `raw` sub folder. 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`.
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`.
Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains ids and paths to spectrogam of each utterance.
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.
## Train the model
To train the model use the `run.sh`.
To train the model use the `run.sh`. It is an example script to run `train.py`.
```bash
bash run.sh
```
Or you can use the `train.py` script. Here's the complete help message to run it.
Or you can use the `train.py` directly. Here's the complete help message to run it.
```text
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--device DEVICE] [--nprocs NPROCS] [--verbose VERBOSE]
Train a Speedyspeech model with Baker Mandrin TTS dataset.
Train a Parallel WaveGAN model with Baker Mandrin TTS dataset.
optional arguments:
-h, --help show this help message and exit
@ -65,7 +65,7 @@ optional arguments:
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
3. `--output-dir` is the directory to save the results of the experiment.
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
4. `--device` is the type of the device to run the experiment, 'cpu' or 'gpu' are supported.
5. `--nprocs` is the number of processes to run in parallel, note that nprocs > 1 is only supported when `--device` is 'gpu'.
@ -112,3 +112,7 @@ optional arguments:
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
4. `--output-dir` is the directory to save the synthesized audio files.
5. `--device` is the type of device to run synthesis, 'cpu' and 'gpu' are supported.
## Acknowledgement
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.

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examples/speedyspeech/baker/README.md
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# Speedyspeech with the Baker dataset
This example contains code used to train a speedyspeech model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html). NOTE that we only implement the student part of the Speedyspeech model. The ground truth alignment used to train the model is extracted from the dataset.
This example contains code used to train a [Speedyspeech](http://arxiv.org/abs/2008.03802) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html). NOTE that we only implement the student part of the Speedyspeech model. The ground truth alignment used to train the model is extracted from the dataset.
## Preprocess the dataset
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 `~/datasets/BZNSYP`.
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`.
Run the script for preprocessing.
@ -30,17 +30,17 @@ dump
The dataset is split into 3 parts, namely train, dev and test, each of which contains a `norm` and `raw` sub folder. 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`.
Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, tones, durations and ids of each utterance.
Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, tones, durations, path of spectrogram, and id of each utterance.
## Train the model
To train the model use the `run.sh`.
To train the model use the `run.sh`. It is an example script to run `train.py`.
```bash
bash run.sh
```
Or you can change some arguments. Here's the complete help message.
Or you can use `train.py` directly. Here's the complete help message.
```text
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
@ -65,7 +65,7 @@ optional arguments:
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
3. `--output-dir` is the directory to save the results of the experiment.
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
4. `--device` is the type of the device to run the experiment, 'cpu' or 'gpu' are supported.
5. `--nprocs` is the number of processes to run in parallel, note that nprocs > 1 is only supported when `--device` is 'gpu'.
@ -95,7 +95,7 @@ pwg_baker_ckpt_0.4
## Synthesize End to End
When training is done or pretrained models are downloaded. You can run `synthesize_e2e.py` to synthsize. An example shell script `synthesize_e2e.sh` is provided.
When training is done or pretrained models are downloaded. You can run `synthesize_e2e.py` to synthsize.
```text
usage: synthesize_e2e.py [-h] [--speedyspeech-config SPEEDYSPEECH_CONFIG]