File size: 4,883 Bytes
c968fc3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
# 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 os
import json
import librosa
from tqdm import tqdm
from glob import glob
from collections import defaultdict
from utils.util import has_existed
def get_lines(file):
with open(file, "r") as f:
lines = f.readlines()
lines = [l.strip() for l in lines]
return lines
def vctk_statistics(data_dir):
speakers = []
speakers2utts = defaultdict(list)
speaker_infos = glob(data_dir + "/wav48_silence_trimmed" + "/*")
for speaker_info in speaker_infos:
speaker = speaker_info.split("/")[-1]
if speaker == "log.txt":
continue
speakers.append(speaker)
utts = glob(speaker_info + "/*")
for utt in utts:
uid = (
utt.split("/")[-1].split("_")[1]
+ "_"
+ utt.split("/")[-1].split("_")[2].split(".")[0]
)
speakers2utts[speaker].append(uid)
unique_speakers = list(set(speakers))
unique_speakers.sort()
print("Speakers: \n{}".format("\t".join(unique_speakers)))
return speakers2utts, unique_speakers
def vctk_speaker_infos(data_dir):
file = os.path.join(data_dir, "speaker-info.txt")
lines = get_lines(file)
ID2speakers = defaultdict()
for l in tqdm(lines):
items = l.replace(" ", "")
if items[:2] == "ID":
# The header line
continue
if items[0] == "p":
id = items[:4]
gender = items[6]
elif items[0] == "s":
id = items[:2]
gender = items[4]
if gender == "F":
speaker = "female_{}".format(id)
elif gender == "M":
speaker = "male_{}".format(id)
ID2speakers[id] = speaker
return ID2speakers
def main(output_path, dataset_path, TEST_NUM_OF_EVERY_SPEAKER=3):
print("-" * 10)
print("Preparing test samples for vctk...")
save_dir = os.path.join(output_path, "vctk")
os.makedirs(save_dir, exist_ok=True)
train_output_file = os.path.join(save_dir, "train.json")
test_output_file = os.path.join(save_dir, "test.json")
singer_dict_file = os.path.join(save_dir, "singers.json")
utt2singer_file = os.path.join(save_dir, "utt2singer")
if has_existed(train_output_file):
return
utt2singer = open(utt2singer_file, "w")
# Load
vctk_dir = dataset_path
ID2speakers = vctk_speaker_infos(vctk_dir)
speaker2utts, unique_speakers = vctk_statistics(vctk_dir)
# We select speakers of standard samples as test utts
train = []
test = []
train_index_count = 0
test_index_count = 0
test_speaker_count = defaultdict(int)
train_total_duration = 0
test_total_duration = 0
for i, speaker in enumerate(speaker2utts.keys()):
for chosen_uid in tqdm(
speaker2utts[speaker],
desc="Speaker {}/{}, #Train = {}, #Test = {}".format(
i + 1, len(speaker2utts), train_index_count, test_index_count
),
):
res = {
"Dataset": "vctk",
"Singer": ID2speakers[speaker],
"Uid": "{}#{}".format(ID2speakers[speaker], chosen_uid),
}
res["Path"] = "{}/{}_{}.flac".format(speaker, speaker, chosen_uid)
res["Path"] = os.path.join(vctk_dir, "wav48_silence_trimmed", res["Path"])
assert os.path.exists(res["Path"])
duration = librosa.get_duration(filename=res["Path"])
res["Duration"] = duration
if test_speaker_count[speaker] < TEST_NUM_OF_EVERY_SPEAKER:
res["index"] = test_index_count
test_total_duration += duration
test.append(res)
test_index_count += 1
test_speaker_count[speaker] += 1
else:
res["index"] = train_index_count
train_total_duration += duration
train.append(res)
train_index_count += 1
utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
print("#Train = {}, #Test = {}".format(len(train), len(test)))
print(
"#Train hours= {}, #Test hours= {}".format(
train_total_duration / 3600, test_total_duration / 3600
)
)
# Save train.json and test.json
with open(train_output_file, "w") as f:
json.dump(train, f, indent=4, ensure_ascii=False)
with open(test_output_file, "w") as f:
json.dump(test, f, indent=4, ensure_ascii=False)
# Save singers.json
singer_lut = {name: i for i, name in enumerate(unique_speakers)}
with open(singer_dict_file, "w") as f:
json.dump(singer_lut, f, indent=4, ensure_ascii=False)
|