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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.io import save_audio | |
| from utils.util import has_existed | |
| from utils.audio_slicer import Slicer | |
| from preprocessors import GOLDEN_TEST_SAMPLES | |
| def split_to_utterances(dataset_path, singer, style, output_dir): | |
| data_dir = os.path.join(dataset_path, singer, style) | |
| print("Splitting to utterances for {}...".format(data_dir)) | |
| wave_files = glob(data_dir + "/*.wav") | |
| for wav_file in tqdm(wave_files): | |
| # Load waveform | |
| song_name = wav_file.split("/")[-1].split(".")[0] | |
| waveform, fs = torchaudio.load(wav_file) | |
| # Split | |
| slicer = Slicer(sr=fs, threshold=-40.0, max_sil_kept=4000) | |
| chunks = slicer.slice(waveform) | |
| for i, chunk in enumerate(chunks): | |
| save_dir = os.path.join(output_dir, singer, style, song_name) | |
| os.makedirs(save_dir, exist_ok=True) | |
| 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") | |
| singer_infos = glob(dataset_path + "/*") | |
| for singer_info in singer_infos: | |
| singer = singer_info.split("/")[-1] | |
| for style in ["read", "sing"]: | |
| split_to_utterances(dataset_path, singer, style, utterance_dir) | |
| def get_test_songs(): | |
| golden_samples = GOLDEN_TEST_SAMPLES["nus48e"] | |
| # every item is a tuple (singer, song) | |
| golden_songs = [s.split("#")[:2] for s in golden_samples] | |
| # singer_song, eg: Female1#Almost_lover_Amateur | |
| return golden_songs | |
| def nus48e_statistics(data_dir): | |
| singers = [] | |
| songs = [] | |
| singer2songs = defaultdict(lambda: defaultdict(list)) | |
| singer_infos = glob(data_dir + "/*") | |
| for singer_info in singer_infos: | |
| singer_info_split = singer_info.split("/")[-1] | |
| style_infos = glob(singer_info + "/*") | |
| for style_info in style_infos: | |
| style_info_split = style_info.split("/")[-1] | |
| singer = singer_info_split + "_" + style_info_split | |
| singers.append(singer) | |
| song_infos = glob(style_info + "/*") | |
| for song_info in song_infos: | |
| song = song_info.split("/")[-1] | |
| songs.append(song) | |
| utts = glob(song_info + "/*.wav") | |
| for utt in utts: | |
| uid = utt.split("/")[-1].split(".")[0] | |
| singer2songs[singer][song].append(uid) | |
| unique_singers = list(set(singers)) | |
| unique_songs = list(set(songs)) | |
| unique_singers.sort() | |
| unique_songs.sort() | |
| print( | |
| "nus_48_e: {} singers, {} utterances ({} unique songs)".format( | |
| len(unique_singers), len(songs), len(unique_songs) | |
| ) | |
| ) | |
| print("Singers: \n{}".format("\t".join(unique_singers))) | |
| return singer2songs, unique_singers | |
| def main(output_path, dataset_path): | |
| print("-" * 10) | |
| print("Preparing test samples for nus48e...\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, "nus48e") | |
| os.makedirs(save_dir, exist_ok=True) | |
| train_output_file = os.path.join(save_dir, "train.json") | |
| test_output_file = os.path.join(save_dir, "test.json") | |
| singer_dict_file = os.path.join(save_dir, "singers.json") | |
| utt2singer_file = os.path.join(save_dir, "utt2singer") | |
| if ( | |
| has_existed(train_output_file) | |
| and has_existed(test_output_file) | |
| and has_existed(singer_dict_file) | |
| and has_existed(utt2singer_file) | |
| ): | |
| return | |
| utt2singer = open(utt2singer_file, "w") | |
| # Load | |
| nus48e_path = os.path.join(dataset_path, "utterances") | |
| singer2songs, unique_singers = nus48e_statistics(nus48e_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 singer, songs in singer2songs.items(): | |
| song_names = list(songs.keys()) | |
| for chosen_song in song_names: | |
| for chosen_uid in songs[chosen_song]: | |
| res = { | |
| "Dataset": "nus48e", | |
| "Singer": singer, | |
| "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid), | |
| } | |
| res["Path"] = "{}/{}/{}/{}.wav".format( | |
| singer.split("_")[0], singer.split("_")[-1], chosen_song, chosen_uid | |
| ) | |
| res["Path"] = os.path.join(nus48e_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 ([singer, 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 | |
| utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) | |
| print("#Train = {}, #Test = {}".format(len(train), len(test))) | |
| print( | |
| "#Train hours= {}, #Test hours= {}".format( | |
| train_total_duration / 3600, test_total_duration / 3600 | |
| ) | |
| ) | |
| # Save train.json and test.json | |
| 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) | |
| # Save singers.json | |
| singer_lut = {name: i for i, name in enumerate(unique_singers)} | |
| with open(singer_dict_file, "w") as f: | |
| json.dump(singer_lut, f, indent=4, ensure_ascii=False) | |