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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.io import save_audio
from utils.util import has_existed
from utils.audio_slicer import Slicer
from preprocessors import GOLDEN_TEST_SAMPLES
def split_to_utterances(dataset_path, singer, style, output_dir):
data_dir = os.path.join(dataset_path, singer, style)
print("Splitting to utterances for {}...".format(data_dir))
wave_files = glob(data_dir + "/*.wav")
for wav_file in tqdm(wave_files):
# Load waveform
song_name = wav_file.split("/")[-1].split(".")[0]
waveform, fs = torchaudio.load(wav_file)
# Split
slicer = Slicer(sr=fs, threshold=-40.0, max_sil_kept=4000)
chunks = slicer.slice(waveform)
for i, chunk in enumerate(chunks):
save_dir = os.path.join(output_dir, singer, style, song_name)
os.makedirs(save_dir, exist_ok=True)
output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
save_audio(output_file, chunk, fs)
def _main(dataset_path):
"""
Split to utterances
"""
utterance_dir = os.path.join(dataset_path, "utterances")
singer_infos = glob(dataset_path + "/*")
for singer_info in singer_infos:
singer = singer_info.split("/")[-1]
for style in ["read", "sing"]:
split_to_utterances(dataset_path, singer, style, utterance_dir)
def get_test_songs():
golden_samples = GOLDEN_TEST_SAMPLES["nus48e"]
# every item is a tuple (singer, song)
golden_songs = [s.split("#")[:2] for s in golden_samples]
# singer_song, eg: Female1#Almost_lover_Amateur
return golden_songs
def nus48e_statistics(data_dir):
singers = []
songs = []
singer2songs = defaultdict(lambda: defaultdict(list))
singer_infos = glob(data_dir + "/*")
for singer_info in singer_infos:
singer_info_split = singer_info.split("/")[-1]
style_infos = glob(singer_info + "/*")
for style_info in style_infos:
style_info_split = style_info.split("/")[-1]
singer = singer_info_split + "_" + style_info_split
singers.append(singer)
song_infos = glob(style_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]
singer2songs[singer][song].append(uid)
unique_singers = list(set(singers))
unique_songs = list(set(songs))
unique_singers.sort()
unique_songs.sort()
print(
"nus_48_e: {} singers, {} utterances ({} 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 nus48e...\n")
if not os.path.exists(os.path.join(dataset_path, "utterances")):
print("Spliting into utterances...\n")
_main(dataset_path)
save_dir = os.path.join(output_path, "nus48e")
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
nus48e_path = os.path.join(dataset_path, "utterances")
singer2songs, unique_singers = nus48e_statistics(nus48e_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 singer, songs in singer2songs.items():
song_names = list(songs.keys())
for chosen_song in song_names:
for chosen_uid in songs[chosen_song]:
res = {
"Dataset": "nus48e",
"Singer": singer,
"Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid),
}
res["Path"] = "{}/{}/{}/{}.wav".format(
singer.split("_")[0], singer.split("_")[-1], chosen_song, chosen_uid
)
res["Path"] = os.path.join(nus48e_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
if duration <= 1e-8:
continue
if ([singer, 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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