File size: 5,593 Bytes
8c92a11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
# 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
def extract_acoustic_features(dataset, output_path, cfg, n_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"]
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))
acoustic_extractor.extract_utt_acoustic_features_serial(
metadata, dataset_output, cfg
)
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))
preprocess_dataset(
dataset,
cfg.dataset_path[dataset],
output_path,
cfg.preprocess,
cfg.task_type,
is_custom_dataset=dataset in 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)
# 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)
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()))
args = parser.parse_args()
cfg = load_config(args.config)
preprocess(cfg, args)
if __name__ == "__main__":
main()
|