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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. | |
| from glob import glob | |
| import os | |
| import json | |
| import torchaudio | |
| from tqdm import tqdm | |
| from collections import defaultdict | |
| from utils.util import has_existed, remove_and_create | |
| from utils.audio_slicer import split_utterances_from_audio | |
| def split_to_utterances(input_dir, output_dir): | |
| print("Splitting to utterances for {}...".format(input_dir)) | |
| files_list = glob("*", root_dir=input_dir) | |
| files_list.sort() | |
| for wav_file in tqdm(files_list): | |
| # # Load waveform | |
| # waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file)) | |
| # Singer name, Song name | |
| song_name, singer_name = wav_file.split("_")[2].split("-") | |
| save_dir = os.path.join(output_dir, singer_name, song_name) | |
| split_utterances_from_audio( | |
| os.path.join(input_dir, wav_file), save_dir, max_duration_of_utterance=10 | |
| ) | |
| # # Split | |
| # slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000, min_interval=1000) | |
| # chunks = slicer.slice(waveform) | |
| # for i, chunk in enumerate(chunks): | |
| # save_dir = os.path.join(output_dir, singer_name, 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") | |
| remove_and_create(utterance_dir) | |
| split_to_utterances(os.path.join(dataset_path, "vocal"), utterance_dir) | |
| def statistics(utterance_dir): | |
| singers = [] | |
| songs = [] | |
| singers2songs = defaultdict(lambda: defaultdict(list)) | |
| singer_infos = glob(utterance_dir + "/*") | |
| for singer_info in singer_infos: | |
| singer = singer_info.split("/")[-1] | |
| song_infos = glob(singer_info + "/*") | |
| for song_info in song_infos: | |
| song = song_info.split("/")[-1] | |
| singers.append(singer) | |
| songs.append(song) | |
| utts = glob(song_info + "/*.wav") | |
| for utt in utts: | |
| uid = utt.split("/")[-1].split(".")[0] | |
| singers2songs[singer][song].append(uid) | |
| unique_singers = list(set(singers)) | |
| unique_songs = list(set(songs)) | |
| unique_singers.sort() | |
| unique_songs.sort() | |
| print( | |
| "Statistics: {} singers, {} utterances ({} unique songs)".format( | |
| len(unique_singers), len(songs), len(unique_songs) | |
| ) | |
| ) | |
| print("Singers: \n{}".format("\t".join(unique_singers))) | |
| return singers2songs, unique_singers | |
| def main(output_path, dataset_path): | |
| print("-" * 10) | |
| print("Preparing samples for CD Music Eval...\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, "cdmusiceval") | |
| 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 | |
| utt_path = os.path.join(dataset_path, "utterances") | |
| singers2songs, unique_singers = statistics(utt_path) | |
| # 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 tqdm(singers2songs.items()): | |
| song_names = list(songs.keys()) | |
| for chosen_song in song_names: | |
| for chosen_uid in songs[chosen_song]: | |
| res = { | |
| "Dataset": "cdmusiceval", | |
| "Singer": singer, | |
| "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), | |
| } | |
| res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid) | |
| res["Path"] = os.path.join(utt_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 | |
| res["index"] = test_index_count | |
| test_total_duration += duration | |
| test.append(res) | |
| test_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) | |