Spaces:
Running
on
A10G
Running
on
A10G
File size: 5,920 Bytes
0883aa1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
# 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 os
from tqdm import tqdm
import torchaudio
from glob import glob
from collections import defaultdict
from utils.util import has_existed
from utils.io import save_audio
from utils.audio_slicer import Slicer
from preprocessors import GOLDEN_TEST_SAMPLES
def split_to_utterances(language_dir, output_dir):
print("Splitting to utterances for {}...".format(language_dir))
for wav_file in tqdm(glob("{}/*/*".format(language_dir))):
# Load waveform
singer_name, song_name = wav_file.split("/")[-2:]
song_name = song_name.split(".")[0]
waveform, fs = torchaudio.load(wav_file)
# Split
slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000)
chunks = slicer.slice(waveform)
for i, chunk in enumerate(chunks):
save_dir = os.path.join(output_dir, singer_name, song_name)
os.makedirs(save_dir, exist_ok=True)
output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
save_audio(output_file, chunk, fs)
def _main(dataset_path):
"""
Split to utterances
"""
utterance_dir = os.path.join(dataset_path, "utterances")
for lang in ["chinese", "western"]:
split_to_utterances(os.path.join(dataset_path, lang), utterance_dir)
def get_test_songs():
golden_samples = GOLDEN_TEST_SAMPLES["opera"]
# every item is a tuple (singer, song)
golden_songs = [s.split("#")[:2] for s in golden_samples]
# singer#song, eg:fem_01#neg_01
return golden_songs
def opera_statistics(data_dir):
singers = []
songs = []
singers2songs = defaultdict(lambda: defaultdict(list))
singer_infos = glob(data_dir + "/*")
for singer_info in singer_infos:
singer = singer_info.split("/")[-1]
song_infos = glob(singer_info + "/*")
for song_info in song_infos:
song = song_info.split("/")[-1]
singers.append(singer)
songs.append(song)
utts = glob(song_info + "/*.wav")
for utt in utts:
uid = utt.split("/")[-1].split(".")[0]
singers2songs[singer][song].append(uid)
unique_singers = list(set(singers))
unique_songs = list(set(songs))
unique_singers.sort()
unique_songs.sort()
print(
"opera: {} singers, {} utterances ({} unique songs)".format(
len(unique_singers), len(songs), len(unique_songs)
)
)
print("Singers: \n{}".format("\t".join(unique_singers)))
return singers2songs, unique_singers
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing test samples for opera...\n")
if not os.path.exists(os.path.join(dataset_path, "utterances")):
print("Spliting into utterances...\n")
_main(dataset_path)
save_dir = os.path.join(output_path, "opera")
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)
and has_existed(test_output_file)
and has_existed(singer_dict_file)
and has_existed(utt2singer_file)
):
return
utt2singer = open(utt2singer_file, "w")
# Load
opera_path = os.path.join(dataset_path, "utterances")
singers2songs, unique_singers = opera_statistics(opera_path)
test_songs = get_test_songs()
# We select songs of standard samples as test songs
train = []
test = []
train_index_count = 0
test_index_count = 0
train_total_duration = 0
test_total_duration = 0
for singer, songs in tqdm(singers2songs.items()):
song_names = list(songs.keys())
for chosen_song in song_names:
for chosen_uid in songs[chosen_song]:
res = {
"Dataset": "opera",
"Singer": singer,
"Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid),
}
res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid)
res["Path"] = os.path.join(opera_path, res["Path"])
assert os.path.exists(res["Path"])
waveform, sample_rate = torchaudio.load(res["Path"])
duration = waveform.size(-1) / sample_rate
res["Duration"] = duration
if duration <= 1e-8:
continue
if ([singer, chosen_song]) in test_songs:
res["index"] = test_index_count
test_total_duration += duration
test.append(res)
test_index_count += 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_singers)}
with open(singer_dict_file, "w") as f:
json.dump(singer_lut, f, indent=4, ensure_ascii=False)
|