Spaces:
Running
on
A10G
Running
on
A10G
File size: 5,625 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 |
# 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.
from glob import glob
import os
import json
import torchaudio
from tqdm import tqdm
from collections import defaultdict
from utils.util import has_existed, remove_and_create
from utils.audio_slicer import split_utterances_from_audio
def split_to_utterances(input_dir, output_dir):
print("Splitting to utterances for {}...".format(input_dir))
files_list = glob("*", root_dir=input_dir)
files_list.sort()
for wav_file in tqdm(files_list):
# # Load waveform
# waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file))
# Singer name, Song name
song_name, singer_name = wav_file.split("_")[2].split("-")
save_dir = os.path.join(output_dir, singer_name, song_name)
split_utterances_from_audio(
os.path.join(input_dir, wav_file), save_dir, max_duration_of_utterance=10
)
# # Split
# slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000, min_interval=1000)
# 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")
remove_and_create(utterance_dir)
split_to_utterances(os.path.join(dataset_path, "vocal"), utterance_dir)
def statistics(utterance_dir):
singers = []
songs = []
singers2songs = defaultdict(lambda: defaultdict(list))
singer_infos = glob(utterance_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(
"Statistics: {} 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 samples for CD Music Eval...\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, "cdmusiceval")
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
utt_path = os.path.join(dataset_path, "utterances")
singers2songs, unique_singers = statistics(utt_path)
# 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": "cdmusiceval",
"Singer": singer,
"Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
}
res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid)
res["Path"] = os.path.join(utt_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
res["index"] = test_index_count
test_total_duration += duration
test.append(res)
test_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)
|