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# 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
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