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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
from tqdm import tqdm
def cal_metadata(cfg, dataset_types=["train", "test"]):
"""
Dump metadata (singers.json, meta_info.json, utt2singer) for singer dataset or multi-datasets.
"""
from collections import Counter
datasets = cfg.dataset
print("-" * 10)
print("Preparing metadata...")
print("Including: \n{}\n".format("\n".join(datasets)))
datasets.sort()
for dataset in tqdm(datasets):
save_dir = os.path.join(cfg.preprocess.processed_dir, dataset)
assert os.path.exists(save_dir)
# 'train.json' and 'test.json' and 'valid.json' of target dataset
meta_info = dict()
utterances_dict = dict()
all_utterances = list()
duration = dict()
total_duration = 0.0
for dataset_type in dataset_types:
metadata = os.path.join(save_dir, "{}.json".format(dataset_type))
# Sort the metadata as the duration order
with open(metadata, "r", encoding="utf-8") as f:
utterances = json.load(f)
utterances = sorted(utterances, key=lambda x: x["Duration"])
utterances_dict[dataset_type] = utterances
all_utterances.extend(utterances)
# Write back the sorted metadata
with open(metadata, "w") as f:
json.dump(utterances, f, indent=4, ensure_ascii=False)
# Get the total duration and singer names for train and test utterances
duration[dataset_type] = sum(utt["Duration"] for utt in utterances)
total_duration += duration[dataset_type]
# Paths of metadata needed to be generated
singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id)
utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk)
singer_names = set(
f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}"
for utt in all_utterances
)
# Write the utt2singer file and sort the singer names
with open(utt2singer_file, "w", encoding="utf-8") as f:
for utt in all_utterances:
f.write(
f"{utt['Dataset']}_{utt['Uid']}\t{replace_augment_name(utt['Dataset'])}_{utt['Singer']}\n"
)
singer_names = sorted(singer_names)
singer_lut = {name: i for i, name in enumerate(singer_names)}
# dump singers.json
with open(singer_dict_file, "w", encoding="utf-8") as f:
json.dump(singer_lut, f, indent=4, ensure_ascii=False)
meta_info = {
"dataset": dataset,
"statistics": {
"size": len(all_utterances),
"hours": round(total_duration / 3600, 4),
},
}
for dataset_type in dataset_types:
meta_info[dataset_type] = {
"size": len(utterances_dict[dataset_type]),
"hours": round(duration[dataset_type] / 3600, 4),
}
meta_info["singers"] = {"size": len(singer_lut)}
# Use Counter to count the minutes for each singer
total_singer2mins = Counter()
training_singer2mins = Counter()
for dataset_type in dataset_types:
for utt in utterances_dict[dataset_type]:
k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}"
if dataset_type == "train":
training_singer2mins[k] += utt["Duration"] / 60
total_singer2mins[k] += utt["Duration"] / 60
training_singer2mins = dict(
sorted(training_singer2mins.items(), key=lambda x: x[1], reverse=True)
)
training_singer2mins = {k: round(v, 2) for k, v in training_singer2mins.items()}
meta_info["singers"]["training_minutes"] = training_singer2mins
total_singer2mins = dict(
sorted(total_singer2mins.items(), key=lambda x: x[1], reverse=True)
)
total_singer2mins = {k: round(v, 2) for k, v in total_singer2mins.items()}
meta_info["singers"]["minutes"] = total_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 training_singer2mins.items():
print(f"Speaker/Singer {singer}: {min} mins for training")
print("-" * 10, "\n")
def replace_augment_name(dataset: str) -> str:
"""Replace the augmented dataset name with the original dataset name.
>>> print(replace_augment_name("dataset_equalizer"))
dataset
"""
if "equalizer" in dataset:
dataset = dataset.replace("_equalizer", "")
elif "formant_shift" in dataset:
dataset = dataset.replace("_formant_shift", "")
elif "pitch_shift" in dataset:
dataset = dataset.replace("_pitch_shift", "")
elif "time_stretch" in dataset:
dataset = dataset.replace("_time_stretch", "")
else:
pass
return dataset
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