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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 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["popcs"]
# every item is a string
golden_songs = [s.split("_")[:1] for s in golden_samples]
# song, eg: δΈζεΌε
return golden_songs
def popcs_statistics(data_dir):
songs = []
songs2utts = defaultdict(list)
song_infos = glob(data_dir + "/*")
for song_info in song_infos:
song_info_split = song_info.split("/")[-1].split("-")[-1]
songs.append(song_info_split)
utts = glob(song_info + "/*.wav")
for utt in utts:
uid = utt.split("/")[-1].split("_")[0]
songs2utts[song_info_split].append(uid)
unique_songs = list(set(songs))
unique_songs.sort()
print(
"popcs: {} utterances ({} unique songs)".format(len(songs), len(unique_songs))
)
print("Songs: \n{}".format("\t".join(unique_songs)))
return songs2utts
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing test samples for popcs...\n")
save_dir = os.path.join(output_path, "popcs")
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
popcs_dir = dataset_path
songs2utts = popcs_statistics(popcs_dir)
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
song_names = list(songs2utts.keys())
for chosen_song in song_names:
for chosen_uid in songs2utts[chosen_song]:
res = {
"Dataset": "popcs",
"Singer": "female1",
"Song": chosen_song,
"Uid": "{}_{}".format(chosen_song, chosen_uid),
}
res["Path"] = "popcs-{}/{}_wf0.wav".format(chosen_song, chosen_uid)
res["Path"] = os.path.join(popcs_dir, 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 ([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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