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Running
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A10G
| # 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) | |