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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 random
import os
import json
import librosa
from tqdm import tqdm
from glob import glob
from collections import defaultdict
from utils.util import has_existed
from preprocessors import GOLDEN_TEST_SAMPLES
def get_test_songs():
golden_samples = GOLDEN_TEST_SAMPLES["opensinger"]
# every item is a tuple (singer, song)
golden_songs = [s.split("_")[:3] for s in golden_samples]
# singer_song, eg: Female1#Almost_lover_Amateur
return golden_songs
def opensinger_statistics(data_dir):
singers = []
songs = []
singer2songs = defaultdict(lambda: defaultdict(list))
gender_infos = glob(data_dir + "/*")
for gender_info in gender_infos:
gender_info_split = gender_info.split("/")[-1][:-3]
singer_and_song_infos = glob(gender_info + "/*")
for singer_and_song_info in singer_and_song_infos:
singer_and_song_info_split = singer_and_song_info.split("/")[-1].split("_")
singer_id, song = (
singer_and_song_info_split[0],
singer_and_song_info_split[1],
)
singer = gender_info_split + "_" + singer_id
singers.append(singer)
songs.append(song)
utts = glob(singer_and_song_info + "/*.wav")
for utt in utts:
uid = utt.split("/")[-1].split("_")[-1].split(".")[0]
singer2songs[singer][song].append(uid)
unique_singers = list(set(singers))
unique_songs = list(set(songs))
unique_singers.sort()
unique_songs.sort()
print(
"opensinger: {} singers, {} songs ({} unique songs)".format(
len(unique_singers), len(songs), len(unique_songs)
)
)
print("Singers: \n{}".format("\t".join(unique_singers)))
return singer2songs, unique_singers
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing test samples for opensinger...\n")
save_dir = os.path.join(output_path, "opensinger")
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, "singers.json")
utt2singer_file = os.path.join(save_dir, "utt2singer")
if (
has_existed(train_output_file)
and has_existed(test_output_file)
and has_existed(singer_dict_file)
and has_existed(utt2singer_file)
):
return
utt2singer = open(utt2singer_file, "w")
# Load
opensinger_path = dataset_path
singer2songs, unique_singers = opensinger_statistics(opensinger_path)
test_songs = get_test_songs()
# We select songs of standard samples as test songs
train = []
test = []
train_index_count = 0
test_index_count = 0
train_total_duration = 0
test_total_duration = 0
for i, (singer, songs) in enumerate(singer2songs.items()):
song_names = list(songs.keys())
for chosen_song in tqdm(
song_names, desc="Singer {}/{}".format(i, len(singer2songs))
):
for chosen_uid in songs[chosen_song]:
res = {
"Dataset": "opensinger",
"Singer": singer,
"Song": chosen_song,
"Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
}
res["Path"] = "{}Raw/{}_{}/{}_{}_{}.wav".format(
singer.split("_")[0],
singer.split("_")[1],
chosen_song,
singer.split("_")[1],
chosen_song,
chosen_uid,
)
res["Path"] = os.path.join(opensinger_path, res["Path"])
assert os.path.exists(res["Path"])
duration = librosa.get_duration(filename=res["Path"])
res["Duration"] = duration
if duration > 30:
print(
"Wav file: {}, the duration = {:.2f}s > 30s, which has been abandoned.".format(
res["Path"], duration
)
)
continue
if (
[singer.split("_")[0], singer.split("_")[1], chosen_song]
) in test_songs:
res["index"] = test_index_count
test_total_duration += duration
test.append(res)
test_index_count += 1
else:
res["index"] = train_index_count
train_total_duration += duration
train.append(res)
train_index_count += 1
utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
print("#Train = {}, #Test = {}".format(len(train), len(test)))
print(
"#Train hours= {}, #Test hours= {}".format(
train_total_duration / 3600, test_total_duration / 3600
)
)
# Save train.json and test.json
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)
# Save singers.json
singer_lut = {name: i for i, name in enumerate(unique_singers)}
with open(singer_dict_file, "w") as f:
json.dump(singer_lut, f, indent=4, ensure_ascii=False)
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