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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 os | |
| import json | |
| import torchaudio | |
| from glob import glob | |
| from collections import defaultdict | |
| from utils.util import has_existed | |
| from preprocessors import GOLDEN_TEST_SAMPLES | |
| def get_test_songs(): | |
| golden_samples = GOLDEN_TEST_SAMPLES["popcs"] | |
| # every item is a string | |
| golden_songs = [s.split("_")[:1] for s in golden_samples] | |
| # song, eg: 万有引力 | |
| return golden_songs | |
| def popcs_statistics(data_dir): | |
| songs = [] | |
| songs2utts = defaultdict(list) | |
| song_infos = glob(data_dir + "/*") | |
| for song_info in song_infos: | |
| song_info_split = song_info.split("/")[-1].split("-")[-1] | |
| songs.append(song_info_split) | |
| utts = glob(song_info + "/*.wav") | |
| for utt in utts: | |
| uid = utt.split("/")[-1].split("_")[0] | |
| songs2utts[song_info_split].append(uid) | |
| unique_songs = list(set(songs)) | |
| unique_songs.sort() | |
| print( | |
| "popcs: {} utterances ({} unique songs)".format(len(songs), len(unique_songs)) | |
| ) | |
| print("Songs: \n{}".format("\t".join(unique_songs))) | |
| return songs2utts | |
| def main(output_path, dataset_path): | |
| print("-" * 10) | |
| print("Preparing test samples for popcs...\n") | |
| save_dir = os.path.join(output_path, "popcs") | |
| 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 | |
| popcs_dir = dataset_path | |
| songs2utts = popcs_statistics(popcs_dir) | |
| 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 | |
| song_names = list(songs2utts.keys()) | |
| for chosen_song in song_names: | |
| for chosen_uid in songs2utts[chosen_song]: | |
| res = { | |
| "Dataset": "popcs", | |
| "Singer": "female1", | |
| "Song": chosen_song, | |
| "Uid": "{}_{}".format(chosen_song, chosen_uid), | |
| } | |
| res["Path"] = "popcs-{}/{}_wf0.wav".format(chosen_song, chosen_uid) | |
| res["Path"] = os.path.join(popcs_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_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 | |
| 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) | |