File size: 5,626 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
165
166
167
168
169
170
# 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 random
import os
import json
import librosa
from tqdm import tqdm
from glob import glob
from collections import defaultdict

from utils.util import has_existed
from preprocessors import GOLDEN_TEST_SAMPLES


def get_test_songs():
    golden_samples = GOLDEN_TEST_SAMPLES["opensinger"]
    # every item is a tuple (singer, song)
    golden_songs = [s.split("_")[:3] for s in golden_samples]
    # singer_song, eg: Female1#Almost_lover_Amateur
    return golden_songs


def opensinger_statistics(data_dir):
    singers = []
    songs = []
    singer2songs = defaultdict(lambda: defaultdict(list))

    gender_infos = glob(data_dir + "/*")

    for gender_info in gender_infos:
        gender_info_split = gender_info.split("/")[-1][:-3]

        singer_and_song_infos = glob(gender_info + "/*")

        for singer_and_song_info in singer_and_song_infos:
            singer_and_song_info_split = singer_and_song_info.split("/")[-1].split("_")
            singer_id, song = (
                singer_and_song_info_split[0],
                singer_and_song_info_split[1],
            )
            singer = gender_info_split + "_" + singer_id
            singers.append(singer)
            songs.append(song)

            utts = glob(singer_and_song_info + "/*.wav")

            for utt in utts:
                uid = utt.split("/")[-1].split("_")[-1].split(".")[0]
                singer2songs[singer][song].append(uid)

    unique_singers = list(set(singers))
    unique_songs = list(set(songs))
    unique_singers.sort()
    unique_songs.sort()

    print(
        "opensinger: {} singers, {} songs ({} unique songs)".format(
            len(unique_singers), len(songs), len(unique_songs)
        )
    )
    print("Singers: \n{}".format("\t".join(unique_singers)))
    return singer2songs, unique_singers


def main(output_path, dataset_path):
    print("-" * 10)
    print("Preparing test samples for opensinger...\n")

    save_dir = os.path.join(output_path, "opensinger")
    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
    opensinger_path = dataset_path

    singer2songs, unique_singers = opensinger_statistics(opensinger_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 i, (singer, songs) in enumerate(singer2songs.items()):
        song_names = list(songs.keys())

        for chosen_song in tqdm(
            song_names, desc="Singer {}/{}".format(i, len(singer2songs))
        ):
            for chosen_uid in songs[chosen_song]:
                res = {
                    "Dataset": "opensinger",
                    "Singer": singer,
                    "Song": chosen_song,
                    "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
                }
                res["Path"] = "{}Raw/{}_{}/{}_{}_{}.wav".format(
                    singer.split("_")[0],
                    singer.split("_")[1],
                    chosen_song,
                    singer.split("_")[1],
                    chosen_song,
                    chosen_uid,
                )
                res["Path"] = os.path.join(opensinger_path, res["Path"])
                assert os.path.exists(res["Path"])

                duration = librosa.get_duration(filename=res["Path"])
                res["Duration"] = duration

                if duration > 30:
                    print(
                        "Wav file: {}, the duration = {:.2f}s > 30s, which has been abandoned.".format(
                            res["Path"], duration
                        )
                    )
                    continue

                if (
                    [singer.split("_")[0], singer.split("_")[1], 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)