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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import faulthandler
faulthandler.enable()
import os
import argparse
import json
import pyworld as pw
from multiprocessing import cpu_count
from utils.util import load_config
from preprocessors.processor import preprocess_dataset, prepare_align
from preprocessors.metadata import cal_metadata
from processors import acoustic_extractor, content_extractor, data_augment, phone_extractor
def extract_acoustic_features(dataset, output_path, cfg, num_workers=1):
"""Extract acoustic features of utterances in the dataset
Args:
dataset (str): name of dataset, e.g. opencpop
output_path (str): directory that stores train, test and feature files of datasets
cfg (dict): dictionary that stores configurations
n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1.
"""
# types = ["train", "test"] if "eval" not in dataset else ["test"]
types = list()
types.append((cfg.preprocess.train_file).split('.')[0])
types.append((cfg.preprocess.valid_file).split('.')[0])
if 'test' not in types:
types.append('test')
if "eval" in dataset:
types = ["test"]
print('types: ', types)
metadata = []
for dataset_type in types:
dataset_output = os.path.join(output_path, dataset)
dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type))
with open(dataset_file, "r") as f:
metadata.extend(json.load(f))
if num_workers > 1:
acoustic_extractor.extract_utt_acoustic_features_parallel(
metadata, dataset_output, cfg, num_workers=num_workers
)
else:
acoustic_extractor.extract_utt_acoustic_features_serial(
metadata, dataset_output, cfg
)
def extract_content_features(dataset, output_path, cfg, num_workers=1):
"""Extract content features of utterances in the dataset
Args:
dataset (str): name of dataset, e.g. opencpop
output_path (str): directory that stores train, test and feature files of datasets
cfg (dict): dictionary that stores configurations
"""
# types = ["train", "test"] if "eval" not in dataset else ["test"]
types = list()
types.append((cfg.preprocess.train_file).split('.')[0])
types.append((cfg.preprocess.valid_file).split('.')[0])
if 'test' not in types:
types.append('test')
if "eval" in dataset:
types = ["test"]
metadata = []
for dataset_type in types:
dataset_output = os.path.join(output_path, dataset)
# dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type))
dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type))
with open(dataset_file, "r") as f:
metadata.extend(json.load(f))
content_extractor.extract_utt_content_features_dataloader(
cfg, metadata, num_workers
)
def extract_phonme_sequences(dataset, output_path, cfg):
"""Extract phoneme features of utterances in the dataset
Args:
dataset (str): name of dataset, e.g. opencpop
output_path (str): directory that stores train, test and feature files of datasets
cfg (dict): dictionary that stores configurations
"""
# types = ["train", "test"] if "eval" not in dataset else ["test"]
types = list()
types.append((cfg.preprocess.train_file).split('.')[0])
types.append((cfg.preprocess.valid_file).split('.')[0])
if 'test' not in types:
types.append('test')
if "eval" in dataset:
types = ["test"]
metadata = []
for dataset_type in types:
dataset_output = os.path.join(output_path, dataset)
dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type))
with open(dataset_file, "r") as f:
metadata.extend(json.load(f))
phone_extractor.extract_utt_phone_sequence(
cfg, metadata
)
def preprocess(cfg, args):
"""Proprocess raw data of single or multiple datasets (in cfg.dataset)
Args:
cfg (dict): dictionary that stores configurations
args (ArgumentParser): specify the configuration file and num_workers
"""
# Specify the output root path to save the processed data
output_path = cfg.preprocess.processed_dir
os.makedirs(output_path, exist_ok=True)
'''
## Split train and test sets
for dataset in cfg.dataset:
print("Preprocess {}...".format(dataset))
if args.prepare_alignment:
## Prepare alignment with MFA
print("Prepare alignment {}...".format(dataset))
prepare_align(
dataset, cfg.dataset_path[dataset], cfg.preprocess, output_path
)
preprocess_dataset(
dataset,
cfg.dataset_path[dataset],
output_path,
cfg.preprocess,
is_custom_dataset=cfg.use_custom_dataset,
)
# Data augmentation: create new wav files with pitch shift, formant shift, equalizer, time stretch
try:
assert isinstance(
cfg.preprocess.data_augment, list
), "Please provide a list of datasets need to be augmented."
if len(cfg.preprocess.data_augment) > 0:
new_datasets_list = []
for dataset in cfg.preprocess.data_augment:
new_datasets = data_augment.augment_dataset(cfg, dataset)
new_datasets_list.extend(new_datasets)
cfg.dataset.extend(new_datasets_list)
print("Augmentation datasets: ", cfg.dataset)
except:
print("No Data Augmentation.")
# Dump metadata of datasets (singers, train/test durations, etc.)
cal_metadata(cfg)
'''
## Prepare the acoustic features
for dataset in cfg.dataset:
# Skip augmented datasets which do not need to extract acoustic features
# We will copy acoustic features from the original dataset later
if (
"pitch_shift" in dataset
or "formant_shift" in dataset
or "equalizer" in dataset in dataset
):
continue
print(
"Extracting acoustic features for {} using {} workers ...".format(
dataset, args.num_workers
)
)
extract_acoustic_features(dataset, output_path, cfg, args.num_workers)
# Calculate the statistics of acoustic features
if cfg.preprocess.mel_min_max_norm:
acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg)
if cfg.preprocess.extract_pitch:
acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg)
if cfg.preprocess.extract_energy:
acoustic_extractor.cal_energy_statistics(dataset, output_path, cfg)
if cfg.preprocess.pitch_norm:
acoustic_extractor.normalize(dataset, cfg.preprocess.pitch_dir, cfg)
if cfg.preprocess.energy_norm:
acoustic_extractor.normalize(dataset, cfg.preprocess.energy_dir, cfg)
# Copy acoustic features for augmented datasets by creating soft-links
for dataset in cfg.dataset:
if "pitch_shift" in dataset:
src_dataset = dataset.replace("_pitch_shift", "")
src_dataset_dir = os.path.join(output_path, src_dataset)
elif "formant_shift" in dataset:
src_dataset = dataset.replace("_formant_shift", "")
src_dataset_dir = os.path.join(output_path, src_dataset)
elif "equalizer" in dataset:
src_dataset = dataset.replace("_equalizer", "")
src_dataset_dir = os.path.join(output_path, src_dataset)
else:
continue
dataset_dir = os.path.join(output_path, dataset)
metadata = []
for split in ["train", "test"] if not "eval" in dataset else ["test"]:
metadata_file_path = os.path.join(src_dataset_dir, "{}.json".format(split))
with open(metadata_file_path, "r") as f:
metadata.extend(json.load(f))
print("Copying acoustic features for {}...".format(dataset))
acoustic_extractor.copy_acoustic_features(
metadata, dataset_dir, src_dataset_dir, cfg
)
if cfg.preprocess.mel_min_max_norm:
acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg)
if cfg.preprocess.extract_pitch:
acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg)
# Prepare the content features
for dataset in cfg.dataset:
print("Extracting content features for {}...".format(dataset))
extract_content_features(dataset, output_path, cfg, args.num_workers)
# Prepare the phenome squences
if cfg.preprocess.extract_phone:
for dataset in cfg.dataset:
print("Extracting phoneme sequence for {}...".format(dataset))
extract_phonme_sequences(dataset, output_path, cfg)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--config", default="config.json", help="json files for configurations."
)
parser.add_argument("--num_workers", type=int, default=int(cpu_count()))
parser.add_argument("--prepare_alignment", type=bool, default=False)
args = parser.parse_args()
cfg = load_config(args.config)
preprocess(cfg, args)
if __name__ == "__main__":
main()
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