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README.md
Parallel WaveGAN with the LJSpeech-1.1
This example contains code used to train a parallel wavegan model with LJSpeech-1.1.
Dataset
Download and Extract the datasaet
Download LJSpeech-1.1 from the official website.
Get MFA results for silence trim
We use MFA results to cut silence in the edge of audio. You can download from here ljspeech_alignment.tar.gz, or train your own MFA model reference to use_mfa example of our repo.
Get Started
Assume the path to the dataset is ~/datasets/LJSpeech-1.1
.
Assume the path to the MFA result of LJSpeech-1.1 is ./ljspeech_alignment
.
Run the command below to
- source path.
- preprocess the dataset,
- train the model.
- synthesize wavs.
- synthesize waveform from
metadata.jsonl
.
- synthesize waveform from
./run.sh
Preprocess the dataset
./local/preprocess.sh ${conf_path}
When it is done. A dump
folder is created in the current directory. The structure of the dump folder is listed below.
dump
├── dev
│ ├── norm
│ └── raw
├── test
│ ├── norm
│ └── raw
└── train
├── norm
├── raw
└── feats_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/feats_stats.npy
.
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
./local/train.sh
calls ${BIN_DIR}/train.py
.
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
Here's the complete help message.
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--device DEVICE] [--nprocs NPROCS] [--verbose VERBOSE]
[--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
[--run-benchmark RUN_BENCHMARK]
[--profiler_options PROFILER_OPTIONS]
Train a ParallelWaveGAN model.
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.
benchmark:
arguments related to benchmark.
--batch-size BATCH_SIZE
batch size.
--max-iter MAX_ITER train max steps.
--run-benchmark RUN_BENCHMARK
runing benchmark or not, if True, use the --batch-size
and --max-iter.
--profiler_options PROFILER_OPTIONS
The option of profiler, which should be in format
"key1=value1;key2=value2;key3=value3".
--config
is a config file in yaml format to overwrite the default config, which can be found atconf/default.yaml
.--train-metadata
and--dev-metadata
should be the metadata file in the normalized subfolder oftrain
anddev
in thedump
folder.--output-dir
is the directory to save the results of the experiment. Checkpoints are save incheckpoints/
inside this directory.--device
is the type of the device to run the experiment, 'cpu' or 'gpu' are supported.--nprocs
is the number of processes to run in parallel, note that nprocs > 1 is only supported when--device
is 'gpu'.
Synthesize
./local/synthesize.sh
calls ${BIN_DIR}/synthesize.py
, which can synthesize waveform from metadata.jsonl
.
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
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 parallel wavegan config file.
--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.
--config
parallel wavegan config file. You should use the same config with which the model is trained.--checkpoint
is the checkpoint to load. Pick one of the checkpoints fromcheckpoints
inside the training output directory.--test-metadata
is the metadata of the test dataset. Use themetadata.jsonl
in thedev/norm
subfolder from the processed directory.--output-dir
is the directory to save the synthesized audio files.--device
is the type of device to run synthesis, 'cpu' and 'gpu' are supported.
Pretrained Models
Pretrained models can be downloaded here. pwg_ljspeech_ckpt_0.5.zip
Parallel WaveGAN checkpoint contains files listed below.
pwg_ljspeech_ckpt_0.5
├── 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
Acknowledgement
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.