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[TTS] add opencpop PWGAN example (#3031)

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* add opencpop voc, test=tts

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139
examples/opencpop/voc1/README.md
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# Parallel WaveGAN with Opencpop
This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [Mandarin singing corpus](https://wenet.org.cn/opencpop/).
## Dataset
### Download and Extract
Download Opencpop from it's [Official Website](https://wenet.org.cn/opencpop/download/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/Opencpop`.
## Get Started
Assume the path to the dataset is `~/datasets/Opencpop`.
Run the command below to
1. **source path**.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
- synthesize waveform from `metadata.jsonl`.
```bash
./run.sh
```
You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
```bash
./run.sh --stage 0 --stop-stage 0
```
### Data Preprocessing
```bash
./local/preprocess.sh ${conf_path}
```
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
```text
dump
├── dev
│ ├── norm
│ └── raw
├── test
│ ├── norm
│ └── raw
└── train
├── norm
├── raw
└── feats_stats.npy
```
The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
### Model Training
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
```
`./local/train.sh` calls `${BIN_DIR}/train.py`.
Here's the complete help message.
```text
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU] [--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 ParallelWaveGAN config file.
--train-metadata TRAIN_METADATA
training data.
--dev-metadata DEV_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
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".
```
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. Checkpoints are saved in `checkpoints/` inside this directory.
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
### Synthesizing
`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
```
```text
usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
Synthesize with GANVocoder.
optional arguments:
-h, --help show this help message and exit
--generator-type GENERATOR_TYPE
type of GANVocoder, should in {pwgan, mb_melgan,
style_melgan, } now
--config CONFIG GANVocoder config file.
--checkpoint CHECKPOINT
snapshot to load.
--test-metadata TEST_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
```
1. `--config` parallel wavegan config file. 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.
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. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
## Pretrained Models
The pretrained model can be downloaded here:
- [pwgan_opencpop_ckpt_1.4.0](https://paddlespeech.bj.bcebos.com/t2s/svs/opencpop/pwgan_opencpop_ckpt_1.4.0.zip)
Parallel WaveGAN checkpoint contains files listed below.
```text
pwgan_opencpop_ckpt_1.4.0
├── default.yaml # default config used to train parallel wavegan
├── snapshot_iter_100000.pdz # generator parameters of parallel wavegan
└── feats_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
```
## Acknowledgement
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.

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examples/opencpop/voc1/conf/default.yaml
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# This is the hyperparameter configuration file for Parallel WaveGAN.
# Please make sure this is adjusted for the CSMSC dataset. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This configuration requires 12 GB GPU memory and takes ~3 days on RTX TITAN.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 24000 # Sampling rate.
n_fft: 512 # FFT size (samples).
n_shift: 128 # Hop size (samples). 12.5ms
win_length: 512 # Window length (samples). 50ms
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
n_mels: 80 # Number of mel basis.
fmin: 30 # Minimum freq in mel basis calculation. (Hz)
fmax: 12000 # Maximum frequency in mel basis calculation. (Hz)
###########################################################
# GENERATOR NETWORK ARCHITECTURE SETTING #
###########################################################
generator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_size: 3 # Kernel size of dilated convolution.
layers: 30 # Number of residual block layers.
stacks: 3 # Number of stacks i.e., dilation cycles.
residual_channels: 64 # Number of channels in residual conv.
gate_channels: 128 # Number of channels in gated conv.
skip_channels: 64 # Number of channels in skip conv.
aux_channels: 80 # Number of channels for auxiliary feature conv.
# Must be the same as num_mels.
aux_context_window: 2 # Context window size for auxiliary feature.
# If set to 2, previous 2 and future 2 frames will be considered.
dropout: 0.0 # Dropout rate. 0.0 means no dropout applied.
bias: True # use bias in residual blocks
use_weight_norm: True # Whether to use weight norm.
# If set to true, it will be applied to all of the conv layers.
use_causal_conv: False # use causal conv in residual blocks and upsample layers
upsample_scales: [8, 4, 2, 2] # Upsampling scales. Prodcut of these must be the same as hop size.
interpolate_mode: "nearest" # upsample net interpolate mode
freq_axis_kernel_size: 1 # upsamling net: convolution kernel size in frequencey axis
nonlinear_activation: null
nonlinear_activation_params: {}
###########################################################
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
###########################################################
discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_size: 3 # Number of output channels.
layers: 10 # Number of conv layers.
conv_channels: 64 # Number of chnn layers.
bias: True # Whether to use bias parameter in conv.
use_weight_norm: True # Whether to use weight norm.
# If set to true, it will be applied to all of the conv layers.
nonlinear_activation: "leakyrelu" # Nonlinear function after each conv.
nonlinear_activation_params: # Nonlinear function parameters
negative_slope: 0.2 # Alpha in leakyrelu.
###########################################################
# STFT LOSS SETTING #
###########################################################
stft_loss_params:
fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss.
hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss
win_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
window: "hann" # Window function for STFT-based loss
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_adv: 4.0 # Loss balancing coefficient.
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 8 # Batch size.
batch_max_steps: 25500 # Length of each audio in batch. Make sure dividable by n_shift.
num_workers: 1 # Number of workers in DataLoader.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
generator_optimizer_params:
epsilon: 1.0e-6 # Generator's epsilon.
weight_decay: 0.0 # Generator's weight decay coefficient.
generator_scheduler_params:
learning_rate: 0.0001 # Generator's learning rate.
step_size: 200000 # Generator's scheduler step size.
gamma: 0.5 # Generator's scheduler gamma.
# At each step size, lr will be multiplied by this parameter.
generator_grad_norm: 10 # Generator's gradient norm.
discriminator_optimizer_params:
epsilon: 1.0e-6 # Discriminator's epsilon.
weight_decay: 0.0 # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
learning_rate: 0.00005 # Discriminator's learning rate.
step_size: 200000 # Discriminator's scheduler step size.
gamma: 0.5 # Discriminator's scheduler gamma.
# At each step size, lr will be multiplied by this parameter.
discriminator_grad_norm: 1 # Discriminator's gradient norm.
###########################################################
# INTERVAL SETTING #
###########################################################
discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
train_max_steps: 400000 # Number of training steps.
save_interval_steps: 5000 # Interval steps to save checkpoint.
eval_interval_steps: 1000 # Interval steps to evaluate the network.
###########################################################
# OTHER SETTING #
###########################################################
num_save_intermediate_results: 4 # Number of results to be saved as intermediate results.
num_snapshots: 10 # max number of snapshots to keep while training
seed: 42 # random seed for paddle, random, and np.random

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examples/opencpop/voc1/local/PTQ_static.sh
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../../../csmsc/voc1/local/PTQ_static.sh

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examples/opencpop/voc1/local/dygraph_to_static.sh
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#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../../dygraph_to_static.py \
--type=voc \
--voc=pwgan_opencpop \
--voc_config=${config_path} \
--voc_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--voc_stat=dump/train/feats_stats.npy \
--inference_dir=exp/default/inference/

47
examples/opencpop/voc1/local/preprocess.sh
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#!/bin/bash
stage=0
stop_stage=100
config_path=$1
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# extract features
echo "Extract features ..."
python3 ${BIN_DIR}/../preprocess.py \
--rootdir=~/datasets/Opencpop/segments/ \
--dataset=opencpop \
--dumpdir=dump \
--dur-file=~/datasets/Opencpop/segments/transcriptions.txt \
--config=${config_path} \
--cut-sil=False \
--num-cpu=20
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# get features' stats(mean and std)
echo "Get features' stats ..."
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="feats"
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# normalize, dev and test should use train's stats
echo "Normalize ..."
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/train/raw/metadata.jsonl \
--dumpdir=dump/train/norm \
--stats=dump/train/feats_stats.npy
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/dev/raw/metadata.jsonl \
--dumpdir=dump/dev/norm \
--stats=dump/train/feats_stats.npy
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/test/raw/metadata.jsonl \
--dumpdir=dump/test/norm \
--stats=dump/train/feats_stats.npy
fi

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examples/opencpop/voc1/local/synthesize.sh
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../../../csmsc/voc1/local/synthesize.sh

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examples/opencpop/voc1/local/train.sh
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../../../csmsc/voc1/local/train.sh

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examples/opencpop/voc1/path.sh
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../../csmsc/voc1/path.sh

42
examples/opencpop/voc1/run.sh
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#!/bin/bash
set -e
source path.sh
gpus=0
stage=0
stop_stage=100
conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_100000.pdz
# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
./local/preprocess.sh ${conf_path} || exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# synthesize
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
# dygraph to static
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/dygraph_to_static.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
# PTQ_static
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/PTQ_static.sh ${train_output_path} pwgan_opencpop || exit -1
fi

1
paddlespeech/t2s/exps/PTQ_static.py
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@ -42,6 +42,7 @@ def parse_args():
'hifigan_aishell3',
'hifigan_ljspeech',
'hifigan_vctk',
'pwgan_opencpop',
],
help='Choose model type of tts task.')

169
paddlespeech/t2s/exps/dygraph_to_static.py
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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import yaml
from yacs.config import CfgNode
from paddlespeech.t2s.exps.syn_utils import am_to_static
from paddlespeech.t2s.exps.syn_utils import get_am_inference
from paddlespeech.t2s.exps.syn_utils import get_voc_inference
from paddlespeech.t2s.exps.syn_utils import voc_to_static
def am_dygraph_to_static(args):
with open(args.am_config) as f:
am_config = CfgNode(yaml.safe_load(f))
am_inference = get_am_inference(
am=args.am,
am_config=am_config,
am_ckpt=args.am_ckpt,
am_stat=args.am_stat,
phones_dict=args.phones_dict,
tones_dict=args.tones_dict,
speaker_dict=args.speaker_dict)
print("acoustic model done!")
# dygraph to static
am_inference = am_to_static(
am_inference=am_inference,
am=args.am,
inference_dir=args.inference_dir,
speaker_dict=args.speaker_dict)
print("finish to convert dygraph acoustic model to static!")
def voc_dygraph_to_static(args):
with open(args.voc_config) as f:
voc_config = CfgNode(yaml.safe_load(f))
voc_inference = get_voc_inference(
voc=args.voc,
voc_config=voc_config,
voc_ckpt=args.voc_ckpt,
voc_stat=args.voc_stat)
print("voc done!")
# dygraph to static
voc_inference = voc_to_static(
voc_inference=voc_inference,
voc=args.voc,
inference_dir=args.inference_dir)
print("finish to convert dygraph vocoder model to static!")
def parse_args():
# parse args and config
parser = argparse.ArgumentParser(
description="Synthesize with acoustic model & vocoder")
parser.add_argument(
'--type',
type=str,
required=True,
choices=["am", "voc"],
help='Choose the model type of dynamic to static, am or voc')
# acoustic model
parser.add_argument(
'--am',
type=str,
default='fastspeech2_csmsc',
choices=[
'speedyspeech_csmsc',
'speedyspeech_aishell3',
'fastspeech2_csmsc',
'fastspeech2_ljspeech',
'fastspeech2_aishell3',
'fastspeech2_vctk',
'tacotron2_csmsc',
'tacotron2_ljspeech',
'fastspeech2_mix',
'fastspeech2_canton',
'fastspeech2_male-zh',
'fastspeech2_male-en',
'fastspeech2_male-mix',
],
help='Choose acoustic model type of tts task.')
parser.add_argument(
'--am_config', type=str, default=None, help='Config of acoustic model.')
parser.add_argument(
'--am_ckpt',
type=str,
default=None,
help='Checkpoint file of acoustic model.')
parser.add_argument(
"--am_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
)
parser.add_argument(
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
parser.add_argument(
"--tones_dict", type=str, default=None, help="tone vocabulary file.")
parser.add_argument(
"--speaker_dict", type=str, default=None, help="speaker id map file.")
# vocoder
parser.add_argument(
'--voc',
type=str,
default='pwgan_csmsc',
choices=[
'pwgan_csmsc',
'pwgan_ljspeech',
'pwgan_aishell3',
'pwgan_vctk',
'mb_melgan_csmsc',
'style_melgan_csmsc',
'hifigan_csmsc',
'hifigan_ljspeech',
'hifigan_aishell3',
'hifigan_vctk',
'wavernn_csmsc',
'pwgan_male',
'hifigan_male',
'pwgan_opencpop',
],
help='Choose vocoder type of tts task.')
parser.add_argument(
'--voc_config', type=str, default=None, help='Config of voc.')
parser.add_argument(
'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
parser.add_argument(
"--voc_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training voc."
)
# other
parser.add_argument(
"--inference_dir",
type=str,
default=None,
help="dir to save inference models")
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.type == "am":
am_dygraph_to_static(args)
elif args.type == "voc":
voc_dygraph_to_static(args)
else:
print("type should be in ['am', 'voc'] !")
if __name__ == "__main__":
main()

39
paddlespeech/t2s/exps/gan_vocoder/preprocess.py
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@ -29,6 +29,7 @@ from yacs.config import CfgNode
from paddlespeech.t2s.datasets.get_feats import LogMelFBank
from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
from paddlespeech.t2s.datasets.preprocess_utils import get_sentences_svs
from paddlespeech.t2s.datasets.preprocess_utils import merge_silence
from paddlespeech.t2s.utils import str2bool
@ -192,8 +193,15 @@ def main():
with open(args.config, 'rt') as f:
config = CfgNode(yaml.safe_load(f))
sentences, speaker_set = get_phn_dur(dur_file)
merge_silence(sentences)
if args.dataset == "opencpop":
sentences, speaker_set = get_sentences_svs(
dur_file,
dataset=args.dataset,
sample_rate=config.fs,
n_shift=config.n_shift, )
else:
sentences, speaker_set = get_phn_dur(dur_file)
merge_silence(sentences)
# split data into 3 sections
if args.dataset == "baker":
@ -240,6 +248,33 @@ def main():
test_wav_files += wav_files[-sub_num_dev:]
else:
train_wav_files += wav_files
elif args.dataset == "opencpop":
wavdir = rootdir / "wavs"
# split data into 3 sections
train_file = rootdir / "train.txt"
train_wav_files = []
with open(train_file, "r") as f_train:
for line in f_train.readlines():
utt = line.split("|")[0]
wav_name = utt + ".wav"
wav_path = wavdir / wav_name
train_wav_files.append(wav_path)
test_file = rootdir / "test.txt"
dev_wav_files = []
test_wav_files = []
num_dev = 106
count = 0
with open(test_file, "r") as f_test:
for line in f_test.readlines():
count += 1
utt = line.split("|")[0]
wav_name = utt + ".wav"
wav_path = wavdir / wav_name
if count > num_dev:
test_wav_files.append(wav_path)
else:
dev_wav_files.append(wav_path)
else:
print("dataset should in {baker, ljspeech, vctk, aishell3} now!")

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