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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 glob
import os
import json
import torchaudio
from tqdm import tqdm
from collections import defaultdict
from utils.io import save_audio
from utils.util import has_existed, remove_and_create
from utils.audio_slicer import Slicer
from preprocessors import GOLDEN_TEST_SAMPLES
def split_to_utterances(input_dir, output_dir):
print("Splitting to utterances for {}...".format(input_dir))
files_list = glob.glob("*.flac", root_dir=input_dir)
files_list.sort()
for wav_file in tqdm(files_list):
# Load waveform
waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file))
# Song name
filename = wav_file.replace(" ", "")
filename = filename.replace("(Live)", "")
song_id, filename = filename.split("李健-")
song_id = song_id.split("_")[0]
song_name = "{:03d}".format(int(song_id)) + filename.split("_")[0].split("-")[0]
# Split
slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000)
chunks = slicer.slice(waveform)
save_dir = os.path.join(output_dir, song_name)
remove_and_create(save_dir)
for i, chunk in enumerate(chunks):
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")
split_to_utterances(os.path.join(dataset_path, "vocal_v2"), utterance_dir)
def get_test_songs():
golden_samples = GOLDEN_TEST_SAMPLES["lijian"]
golden_songs = [s.split("_")[0] for s in golden_samples]
return golden_songs
def statistics(utt_dir):
song2utts = defaultdict(list)
song_infos = glob.glob(utt_dir + "/*")
song_infos.sort()
for song in song_infos:
song_name = song.split("/")[-1]
utt_infos = glob.glob(song + "/*.wav")
utt_infos.sort()
for utt in utt_infos:
uid = utt.split("/")[-1].split(".")[0]
song2utts[song_name].append(uid)
utt_sum = sum([len(utts) for utts in song2utts.values()])
print("Li Jian: {} unique songs, {} utterances".format(len(song2utts), utt_sum))
return song2utts
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing test samples for Li Jian...\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, "lijian")
train_output_file = os.path.join(save_dir, "train.json")
test_output_file = os.path.join(save_dir, "test.json")
if has_existed(test_output_file):
return
# Load
lijian_path = os.path.join(dataset_path, "utterances")
song2utts = statistics(lijian_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 chosen_song, utts in tqdm(song2utts.items()):
for chosen_uid in song2utts[chosen_song]:
res = {
"Dataset": "lijian",
"Singer": "lijian",
"Uid": "{}_{}".format(chosen_song, chosen_uid),
}
res["Path"] = "{}/{}.wav".format(chosen_song, chosen_uid)
res["Path"] = os.path.join(lijian_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 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
print("#Train = {}, #Test = {}".format(len(train), len(test)))
print(
"#Train hours= {}, #Test hours= {}".format(
train_total_duration / 3600, test_total_duration / 3600
)
)
# Save
os.makedirs(save_dir, exist_ok=True)
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)
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