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add README for examples/parallel_wavegan

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chenfeiyu 2021-08-16 15:21:47 +08:00
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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).
## 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`.
Run the script for preprocessing.
```bash
bash preprocess.sh
```
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
```text
dump
├── dev
│   ├── norm
│   └── raw
├── test
│   ├── norm
│   └── raw
└── train
├── norm
├── raw
└── 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`.
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.
## Train the model
To train the model use the `run.sh`.
```bash
bash run.sh
```
Or you can use the `train.py` script. 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.
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file to overwrite default config
--train-metadata TRAIN_METADATA
training data
--dev-metadata DEV_METADATA
dev data
--output-dir OUTPUT_DIR
output dir
--device DEVICE device type to use
--nprocs NPROCS number of processes
--verbose VERBOSE verbose
```
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.
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'.
## Pretrained Models
Pretrained models can be downloaded here:
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.
Parallel WaveGAN checkpoint contains files listed below.
```text
pwg_baker_ckpt_0.4
├── pwg_default.yaml # default config used to train parallel wavegan
├── pwg_snapshot_iter_400000.pdz # generator parameters of parallel wavegan
└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
```
## Synthesize
When training is done or pretrained models are downloaded. You can run `synthesize.py` to synthsize.
```text
usage: synthesize.py [-h] [--config CONFIG] [--checkpoint CHECKPOINT]
[--test-metadata TEST_METADATA] [--output-dir OUTPUT_DIR]
[--device DEVICE] [--verbose VERBOSE]
synthesize with parallel wavegan.
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file to overwrite default config
--checkpoint CHECKPOINT
snapshot to load
--test-metadata TEST_METADATA
dev data
--output-dir OUTPUT_DIR
output dir
--device DEVICE device to run
--verbose VERBOSE verbose
```
1. `--config` is the extra configuration file to overwrite the default config. You should use the same config with which the model is trained.
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`.
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.

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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 utterances.
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.
## Train the model
@ -88,9 +88,9 @@ Parallel WaveGAN checkpoint contains files listed below.
```text
pwg_baker_ckpt_0.4
├── pwg_default.yaml # default config used to train parallel wavegan
├── pwg_generator.pdparams # generator parameters of parallel wavegan
└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
├── pwg_default.yaml # default config used to train parallel wavegan
├── pwg_snapshot_iter_400000.pdz # model parameters and optimizer states of parallel wavegan
└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
```
## Synthesize End to End
@ -120,8 +120,8 @@ optional arguments:
spectrogram when training speedyspeech.
--pwg-config PWG_CONFIG
config file for parallelwavegan.
--pwg-params PWG_PARAMS
parallel wavegan generator parameters to load.
--pwg-checkpoint PWG_PARAMS
parallel wavegan checkpoint to load.
--pwg-stat PWG_STAT mean and standard deviation used to normalize
spectrogram when training speedyspeech.
--text TEXT text to synthesize, a 'utt_id sentence' pair per line
@ -134,7 +134,7 @@ optional arguments:
```
1. `--speedyspeech-config`, `--speedyspeech-checkpoint`, `--speedyspeech-stat` are arguments for speedyspeech, which correspond to the 3 files in the speedyspeech pretrained model.
2. `--pwg-config`, `--pwg-params`, `--pwg-stat` are arguments for speedyspeech, which correspond to the 3 files in the parallel wavegan pretrained model.
2. `--pwg-config`, `--pwg-checkpoint`, `--pwg-stat` are arguments for speedyspeech, which correspond to the 3 files in the parallel wavegan pretrained model.
3. `--text` is the text file, which contains sentences to synthesize.
4. `--output-dir` is the directory to save synthesized audio files.
5. `--inference-dir` is the directory to save exported model, which can be used with paddle infernece.

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examples/speedyspeech/baker/synthesize_e2e.py
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@ -57,7 +57,8 @@ def evaluate(args, speedyspeech_config, pwg_config):
model.eval()
vocoder = PWGGenerator(**pwg_config["generator_params"])
vocoder.set_state_dict(paddle.load(args.pwg_params))
vocoder.set_state_dict(
paddle.load(args.pwg_checkpoint)["generator_params"])
vocoder.remove_weight_norm()
vocoder.eval()
print("model done!")
@ -133,9 +134,9 @@ def main():
parser.add_argument(
"--pwg-config", type=str, help="config file for parallelwavegan.")
parser.add_argument(
"--pwg-params",
"--pwg-checkpoint",
type=str,
help="parallel wavegan generator parameters to load.")
help="parallel wavegan checkpoint to load.")
parser.add_argument(
"--pwg-stat",
type=str,