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on
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Running
on
Zero
File size: 2,927 Bytes
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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
from preprocessors import GOLDEN_TEST_SAMPLES
def get_test_songs():
return ["007Di Da Di"]
def coco_statistics(data_dir):
song2utts = defaultdict(list)
song_infos = glob(data_dir + "/*")
for song in song_infos:
song_name = song.split("/")[-1]
utts = glob(song + "/*.wav")
for utt in utts:
uid = utt.split("/")[-1].split(".")[0]
song2utts[song_name].append(uid)
print("Coco: {} songs".format(len(song_infos)))
return song2utts
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing datasets for Coco...\n")
save_dir = os.path.join(output_path, "coco")
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
song2utts = coco_statistics(dataset_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 song_name, uids in tqdm(song2utts.items()):
for chosen_uid in uids:
res = {
"Dataset": "coco",
"Singer": "coco",
"Song": song_name,
"Uid": "{}_{}".format(song_name, chosen_uid),
}
res["Path"] = "{}/{}.wav".format(song_name, chosen_uid)
res["Path"] = os.path.join(dataset_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 song_name 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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