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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.
import os
import json
import pickle
import glob
from collections import defaultdict
from tqdm import tqdm
from preprocessors import get_golden_samples_indexes
TRAIN_MAX_NUM_EVERY_PERSON = 250
TEST_MAX_NUM_EVERY_PERSON = 25
def select_sample_idxs():
# =========== Train ===========
with open(os.path.join(vctk_dir, "train.json"), "r") as f:
raw_train = json.load(f)
train_idxs = []
train_nums = defaultdict(int)
for utt in tqdm(raw_train):
idx = utt["index"]
singer = utt["Singer"]
if train_nums[singer] < TRAIN_MAX_NUM_EVERY_PERSON:
train_idxs.append(idx)
train_nums[singer] += 1
# =========== Test ===========
with open(os.path.join(vctk_dir, "test.json"), "r") as f:
raw_test = json.load(f)
# golden test
test_idxs = get_golden_samples_indexes(
dataset_name="vctk", split="test", dataset_dir=vctk_dir
)
test_nums = defaultdict(int)
for idx in test_idxs:
singer = raw_test[idx]["Singer"]
test_nums[singer] += 1
for utt in tqdm(raw_test):
idx = utt["index"]
singer = utt["Singer"]
if test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON:
test_idxs.append(idx)
test_nums[singer] += 1
train_idxs.sort()
test_idxs.sort()
return train_idxs, test_idxs, raw_train, raw_test
if __name__ == "__main__":
root_path = ""
vctk_dir = os.path.join(root_path, "vctk")
sample_dir = os.path.join(root_path, "vctksample")
os.makedirs(sample_dir, exist_ok=True)
train_idxs, test_idxs, raw_train, raw_test = select_sample_idxs()
print("#Train = {}, #Test = {}".format(len(train_idxs), len(test_idxs)))
for split, chosen_idxs, utterances in zip(
["train", "test"], [train_idxs, test_idxs], [raw_train, raw_test]
):
print(
"#{} = {}, #chosen idx = {}\n".format(
split, len(utterances), len(chosen_idxs)
)
)
# Select features
feat_files = glob.glob(
"**/{}.pkl".format(split), root_dir=vctk_dir, recursive=True
)
for file in tqdm(feat_files):
raw_file = os.path.join(vctk_dir, file)
new_file = os.path.join(sample_dir, file)
new_dir = "/".join(new_file.split("/")[:-1])
os.makedirs(new_dir, exist_ok=True)
if "mel_min" in file or "mel_max" in file:
os.system("cp {} {}".format(raw_file, new_file))
continue
with open(raw_file, "rb") as f:
raw_feats = pickle.load(f)
print("file: {}, #raw_feats = {}".format(file, len(raw_feats)))
new_feats = [raw_feats[idx] for idx in chosen_idxs]
with open(new_file, "wb") as f:
pickle.dump(new_feats, f)
# Utterance re-index
news_utts = [utterances[idx] for idx in chosen_idxs]
for i, utt in enumerate(news_utts):
utt["Dataset"] = "vctksample"
utt["index"] = i
with open(os.path.join(sample_dir, "{}.json".format(split)), "w") as f:
json.dump(news_utts, f, indent=4)
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