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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 torchaudio
from tqdm import tqdm
from glob import glob
from collections import defaultdict
from utils.util import has_existed
def vocalist_statistics(data_dir):
singers = []
songs = []
global2singer2songs = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
global_infos = glob(data_dir + "/*")
for global_info in global_infos:
global_split = global_info.split("/")[-1]
singer_infos = glob(global_info + "/*")
for singer_info in singer_infos:
singer = singer_info.split("/")[-1]
singers.append(singer)
song_infos = glob(singer_info + "/*")
for song_info in song_infos:
song = song_info.split("/")[-1]
songs.append(song)
utts = glob(song_info + "/*.wav")
for utt in utts:
uid = utt.split("/")[-1].split(".")[0]
global2singer2songs[global_split][singer][song].append(uid)
unique_singers = list(set(singers))
unique_songs = list(set(songs))
unique_singers.sort()
unique_songs.sort()
print(
"vocalist: {} singers, {} songs ({} unique songs)".format(
len(unique_singers), len(songs), len(unique_songs)
)
)
print("Singers: \n{}".format("\t".join(unique_singers)))
return global2singer2songs, unique_singers
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing test samples for vocalist...\n")
save_dir = os.path.join(output_path, "vocalist")
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
vocalist_path = dataset_path
global2singer2songs, unique_singers = vocalist_statistics(vocalist_path)
train = []
test = []
train_index_count = 0
test_index_count = 0
train_total_duration = 0
test_total_duration = 0
for global_info, singer2songs in tqdm(global2singer2songs.items()):
for singer, songs in tqdm(singer2songs.items()):
song_names = list(songs.keys())
for chosen_song in song_names:
for chosen_uid in songs[chosen_song]:
res = {
"Dataset": "opensinger",
"Singer": singer,
"Song": chosen_song,
"Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
}
res["Path"] = "{}/{}/{}/{}.wav".format(
global_info, singer, chosen_song, chosen_uid
)
res["Path"] = os.path.join(vocalist_path, res["Path"])
assert os.path.exists(res["Path"])
waveform, sample_rate = torchaudio.load(res["Path"])
duration = waveform.size(-1) / sample_rate
res["Duration"] = duration
res["index"] = test_index_count
test_total_duration += duration
test.append(res)
test_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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