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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 os
from collections import defaultdict
from tqdm import tqdm
def get_uids_and_wav_paths(cfg, dataset, dataset_type):
assert dataset == "bigdata"
dataset_dir = os.path.join(
cfg.OUTPUT_PATH,
"preprocess/{}_version".format(cfg.PREPROCESS_VERSION),
"bigdata/{}".format(cfg.BIGDATA_VERSION),
)
dataset_file = os.path.join(
dataset_dir, "{}.json".format(dataset_type.split("_")[-1])
)
with open(dataset_file, "r") as f:
utterances = json.load(f)
# Uids
uids = [u["Uid"] for u in utterances]
# Wav paths
wav_paths = [u["Path"] for u in utterances]
return uids, wav_paths
def take_duration(utt):
return utt["Duration"]
def main(output_path, cfg):
datasets = cfg.dataset
print("-" * 10)
print("Preparing samples for bigdata...")
print("Including: \n{}\n".format("\n".join(datasets)))
datasets.sort()
bigdata_version = "_".join(datasets)
save_dir = os.path.join(output_path, bigdata_version)
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, cfg.preprocess.spk2id)
utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk)
utt2singer = open(utt2singer_file, "a+")
# We select songs of standard samples as test songs
train = []
test = []
train_total_duration = 0
test_total_duration = 0
# Singer unique names
singer_names = set()
for dataset in datasets:
dataset_path = os.path.join(output_path, dataset)
train_json = os.path.join(dataset_path, "train.json")
test_json = os.path.join(dataset_path, "test.json")
with open(train_json, "r", encoding="utf-8") as f:
train_utterances = json.load(f)
with open(test_json, "r", encoding="utf-8") as f:
test_utterances = json.load(f)
for utt in tqdm(train_utterances):
train.append(utt)
train_total_duration += utt["Duration"]
singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"]))
utt2singer.write(
"{}_{}\t{}_{}\n".format(
utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"]
)
)
for utt in test_utterances:
test.append(utt)
test_total_duration += utt["Duration"]
singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"]))
utt2singer.write(
"{}_{}\t{}_{}\n".format(
utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"]
)
)
utt2singer.close()
train.sort(key=take_duration)
test.sort(key=take_duration)
print("#Train = {}, #Test = {}".format(len(train), len(test)))
print(
"#Train hours= {}, #Test hours= {}".format(
train_total_duration / 3600, test_total_duration / 3600
)
)
# Singer Look Up Table
singer_names = list(singer_names)
singer_names.sort()
singer_lut = {name: i for i, name in enumerate(singer_names)}
print("#Singers: {}\n".format(len(singer_lut)))
# Save
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)
with open(singer_dict_file, "w") as f:
json.dump(singer_lut, f, indent=4, ensure_ascii=False)
# Save meta info
meta_info = {
"datasets": datasets,
"train": {"size": len(train), "hours": round(train_total_duration / 3600, 4)},
"test": {"size": len(test), "hours": round(test_total_duration / 3600, 4)},
"singers": {"size": len(singer_lut)},
}
singer2mins = defaultdict(float)
for utt in train:
dataset, singer, duration = utt["Dataset"], utt["Singer"], utt["Duration"]
singer2mins["{}_{}".format(dataset, singer)] += duration / 60
singer2mins = sorted(singer2mins.items(), key=lambda x: x[1], reverse=True)
singer2mins = dict(
zip([i[0] for i in singer2mins], [round(i[1], 2) for i in singer2mins])
)
meta_info["singers"]["training_minutes"] = singer2mins
with open(os.path.join(save_dir, "meta_info.json"), "w") as f:
json.dump(meta_info, f, indent=4, ensure_ascii=False)
for singer, min in singer2mins.items():
print("Singer {}: {} mins".format(singer, min))
print("-" * 10, "\n")
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