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add backend inference and inferface output
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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 random
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_folders():
golden_samples = GOLDEN_TEST_SAMPLES["kising"]
# every item is a string
golden_folders = [s.split("_")[:1] for s in golden_samples]
# folder, eg: 422
return golden_folders
def KiSing_statistics(data_dir):
folders = []
folders2utts = defaultdict(list)
folder_infos = glob(data_dir + "/*")
for folder_info in folder_infos:
folder = folder_info.split("/")[-1]
folders.append(folder)
utts = glob(folder_info + "/*.wav")
for utt in utts:
uid = utt.split("/")[-1].split(".")[0]
folders2utts[folder].append(uid)
unique_folders = list(set(folders))
unique_folders.sort()
print("KiSing: {} unique songs".format(len(unique_folders)))
return folders2utts
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing test samples for KiSing...\n")
save_dir = os.path.join(output_path, "kising")
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
KiSing_dir = dataset_path
folders2utts = KiSing_statistics(KiSing_dir)
test_folders = get_test_folders()
# 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
folder_names = list(folders2utts.keys())
for chosen_folder in folder_names:
for chosen_uid in folders2utts[chosen_folder]:
res = {
"Dataset": "kising",
"Singer": "female1",
"Uid": "{}_{}".format(chosen_folder, chosen_uid),
}
res["Path"] = "{}/{}.wav".format(chosen_folder, chosen_uid)
res["Path"] = os.path.join(KiSing_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_folder]) in test_folders:
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)