Hugging Face's logo

Spaces:
amphion
/
maskgct
Running on Zero

App Files Community
4
maskgct
File size: 4,786 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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
 
# 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.

"""
For source datasets' standard samples
"""

from collections import defaultdict
import os
import json

SPEECH_DATASETS = ["vctk", "vctksample"]

GOLDEN_TEST_SAMPLES = defaultdict(list)
GOLDEN_TEST_SAMPLES["m4singer"] = [
    "Alto-1_美错_0014",
    "Bass-1_十年_0008",
    "Soprano-2_同桌的你_0018",
    "Tenor-5_爱笑的眼睛_0010",
]
GOLDEN_TEST_SAMPLES["svcc"] = [
    # IDF1
    "IDF1_10030",
    "IDF1_10120",
    "IDF1_10140",
    # IDM1
    "IDM1_10001",
    "IDM1_10030",
    "IDM1_10120",
    # CDF1
    "CDF1_10030",
    "CDF1_10120",
    "CDF1_10140",
    # CDM1
    "CDM1_10001",
    "CDM1_10030",
    "CDM1_10120",
]
GOLDEN_TEST_SAMPLES["svcceval"] = [
    # SF1
    "SF1_30001",
    "SF1_30002",
    "SF1_30003",
    # SM1
    "SM1_30001",
    "SM1_30002",
    "SM1_30003",
]
GOLDEN_TEST_SAMPLES["popbutfy"] = [
    "Female1#you_are_my_sunshine_Professional#0",
    "Female4#Someone_Like_You_Professional#10",
    "Male2#Lemon_Tree_Professional#12",
    "Male5#can_you_feel_the_love_tonight_Professional#20",
]
GOLDEN_TEST_SAMPLES["opensinger"] = [
    "Man_0_大鱼_10",
    "Man_21_丑八怪_14",
    "Woman_39_mojito_22",
    "Woman_40_易燃易爆炸_12",
]
GOLDEN_TEST_SAMPLES["nus48e"] = [
    "ADIZ_read#01#0000",
    "MCUR_sing#10#0000",
    "JLEE_read#08#0001",
    "SAMF_sing#18#0001",
]
GOLDEN_TEST_SAMPLES["popcs"] = [
    "明天会更好_0004",
    "欧若拉_0005",
    "虫儿飞_0006",
    "隐形的翅膀_0008",
]
GOLDEN_TEST_SAMPLES["kising"] = [
    "421_0040",
    "424_0013",
    "431_0026",
]
GOLDEN_TEST_SAMPLES["csd"] = [
    "en_004a_0001",
    "en_042b_0006",
    "kr_013a_0006",
    "kr_045b_0004",
]
GOLDEN_TEST_SAMPLES["opera"] = [
    "fem_01#neg_1#0000",
    "fem_12#pos_3#0003",
    "male_02#neg_1#0002",
    "male_11#pos_2#0001",
]
GOLDEN_TEST_SAMPLES["lijian"] = [
    "058矜持_0000",
    "079绒花_0000",
    "120遥远的天空底下_0000",
]
GOLDEN_TEST_SAMPLES["cdmusiceval"] = ["陶喆_普通朋友", "蔡琴_给电影人的情书"]

GOLDEN_TRAIN_SAMPLES = defaultdict(list)


def get_golden_samples_indexes(
    dataset_name,
    dataset_dir=None,
    cfg=None,
    split=None,
    min_samples=5,
):
    """
    # Get Standard samples' indexes
    """
    if dataset_dir is None:
        assert cfg is not None
        dataset_dir = os.path.join(
            cfg.OUTPUT_PATH,
            "preprocess/{}_version".format(cfg.PREPROCESS_VERSION),
            dataset_name,
        )

    assert split is not None
    utt_file = os.path.join(dataset_dir, "{}.json".format(split))
    with open(utt_file, "r", encoding="utf-8") as f:
        samples = json.load(f)

    if "train" in split:
        golden_samples = GOLDEN_TRAIN_SAMPLES[dataset_name]
    if "test" in split:
        golden_samples = GOLDEN_TEST_SAMPLES[dataset_name]

    res = []
    for idx, utt in enumerate(samples):
        if utt["Uid"] in golden_samples:
            res.append(idx)

        if dataset_name == "cdmusiceval":
            if "_".join(utt["Uid"].split("_")[:2]) in golden_samples:
                res.append(idx)

    if len(res) == 0:
        res = [i for i in range(min_samples)]

    return res


def get_specific_singer_indexes(dataset_dir, singer_name, split):
    utt_file = os.path.join(dataset_dir, "{}.json".format(split))
    with open(utt_file, "r", encoding="utf-8") as f:
        samples = json.load(f)

    res = []
    for idx, utt in enumerate(samples):
        if utt["Singer"] == singer_name:
            res.append(idx)

    assert len(res) != 0
    return res


def get_uids_and_wav_paths(
    cfg, dataset, dataset_type="train", only_specific_singer=None, return_singers=False
):
    dataset_dir = os.path.join(
        cfg.OUTPUT_PATH, "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), dataset
    )
    dataset_file = os.path.join(
        dataset_dir, "{}.json".format(dataset_type.split("_")[-1])
    )
    with open(dataset_file, "r") as f:
        utterances = json.load(f)

    indexes = range(len(utterances))
    if "golden" in dataset_type:
        # golden_train or golden_test
        indexes = get_golden_samples_indexes(
            dataset, dataset_dir, split=dataset_type.split("_")[-1]
        )
    if only_specific_singer is not None:
        indexes = get_specific_singer_indexes(
            dataset_dir, only_specific_singer, dataset_type
        )

    uids = [utterances[i]["Uid"] for i in indexes]
    wav_paths = [utterances[i]["Path"] for i in indexes]
    singers = [utterances[i]["Singer"] for i in indexes]

    if not return_singers:
        return uids, wav_paths
    else:
        return uids, wav_paths, singers