Hugging Face's logo

Spaces:
raoyonghui
/
maskgct
Runtime error

App Files Community
maskgct
File size: 4,444 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
 
# 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 torchaudio
from tqdm import tqdm
from glob import glob
from collections import defaultdict

from utils.util import has_existed


def vocalist_statistics(data_dir):
    singers = []
    songs = []
    global2singer2songs = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))

    global_infos = glob(data_dir + "/*")

    for global_info in global_infos:
        global_split = global_info.split("/")[-1]

        singer_infos = glob(global_info + "/*")

        for singer_info in singer_infos:
            singer = singer_info.split("/")[-1]

            singers.append(singer)

            song_infos = glob(singer_info + "/*")
            for song_info in song_infos:
                song = song_info.split("/")[-1]

                songs.append(song)

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

                for utt in utts:
                    uid = utt.split("/")[-1].split(".")[0]
                    global2singer2songs[global_split][singer][song].append(uid)

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

    print(
        "vocalist: {} singers, {} songs ({} unique songs)".format(
            len(unique_singers), len(songs), len(unique_songs)
        )
    )
    print("Singers: \n{}".format("\t".join(unique_singers)))
    return global2singer2songs, unique_singers


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

    save_dir = os.path.join(output_path, "vocalist")
    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
    vocalist_path = dataset_path

    global2singer2songs, unique_singers = vocalist_statistics(vocalist_path)

    train = []
    test = []

    train_index_count = 0
    test_index_count = 0

    train_total_duration = 0
    test_total_duration = 0

    for global_info, singer2songs in tqdm(global2singer2songs.items()):
        for singer, songs in tqdm(singer2songs.items()):
            song_names = list(songs.keys())

            for chosen_song in song_names:
                for chosen_uid in songs[chosen_song]:
                    res = {
                        "Dataset": "opensinger",
                        "Singer": singer,
                        "Song": chosen_song,
                        "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
                    }
                    res["Path"] = "{}/{}/{}/{}.wav".format(
                        global_info, singer, chosen_song, chosen_uid
                    )
                    res["Path"] = os.path.join(vocalist_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

                    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)