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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 | |
| from tqdm import tqdm | |
| def cal_metadata(cfg): | |
| """ | |
| Dump metadata (singers.json, meta_info.json, utt2singer) for singer dataset or multi-datasets. | |
| """ | |
| from collections import Counter | |
| datasets = cfg.dataset | |
| print("-" * 10) | |
| print("Preparing metadata...") | |
| print("Including: \n{}\n".format("\n".join(datasets))) | |
| datasets.sort() | |
| for dataset in tqdm(datasets): | |
| save_dir = os.path.join(cfg.preprocess.processed_dir, dataset) | |
| assert os.path.exists(save_dir) | |
| # 'train.json' and 'test.json' of target dataset | |
| train_metadata = os.path.join(save_dir, "train.json") | |
| test_metadata = os.path.join(save_dir, "test.json") | |
| # Sort the metadata as the duration order | |
| with open(train_metadata, "r", encoding="utf-8") as f: | |
| train_utterances = json.load(f) | |
| with open(test_metadata, "r", encoding="utf-8") as f: | |
| test_utterances = json.load(f) | |
| train_utterances = sorted(train_utterances, key=lambda x: x["Duration"]) | |
| test_utterances = sorted(test_utterances, key=lambda x: x["Duration"]) | |
| # Write back the sorted metadata | |
| with open(train_metadata, "w") as f: | |
| json.dump(train_utterances, f, indent=4, ensure_ascii=False) | |
| with open(test_metadata, "w") as f: | |
| json.dump(test_utterances, f, indent=4, ensure_ascii=False) | |
| # Paths of metadata needed to be generated | |
| singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id) | |
| utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk) | |
| # Get the total duration and singer names for train and test utterances | |
| train_total_duration = sum(utt["Duration"] for utt in train_utterances) | |
| test_total_duration = sum(utt["Duration"] for utt in test_utterances) | |
| singer_names = set( | |
| f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" | |
| for utt in train_utterances + test_utterances | |
| ) | |
| # Write the utt2singer file and sort the singer names | |
| with open(utt2singer_file, "w", encoding="utf-8") as f: | |
| for utt in train_utterances + test_utterances: | |
| f.write( | |
| f"{utt['Dataset']}_{utt['Uid']}\t{replace_augment_name(utt['Dataset'])}_{utt['Singer']}\n" | |
| ) | |
| singer_names = sorted(singer_names) | |
| singer_lut = {name: i for i, name in enumerate(singer_names)} | |
| # dump singers.json | |
| with open(singer_dict_file, "w", encoding="utf-8") as f: | |
| json.dump(singer_lut, f, indent=4, ensure_ascii=False) | |
| meta_info = { | |
| "dataset": dataset, | |
| "statistics": { | |
| "size": len(train_utterances) + len(test_utterances), | |
| "hours": round(train_total_duration / 3600, 4) | |
| + round(test_total_duration / 3600, 4), | |
| }, | |
| "train": { | |
| "size": len(train_utterances), | |
| "hours": round(train_total_duration / 3600, 4), | |
| }, | |
| "test": { | |
| "size": len(test_utterances), | |
| "hours": round(test_total_duration / 3600, 4), | |
| }, | |
| "singers": {"size": len(singer_lut)}, | |
| } | |
| # Use Counter to count the minutes for each singer | |
| total_singer2mins = Counter() | |
| training_singer2mins = Counter() | |
| for utt in train_utterances: | |
| k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" | |
| training_singer2mins[k] += utt["Duration"] / 60 | |
| total_singer2mins[k] += utt["Duration"] / 60 | |
| for utt in test_utterances: | |
| k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" | |
| total_singer2mins[k] += utt["Duration"] / 60 | |
| training_singer2mins = dict( | |
| sorted(training_singer2mins.items(), key=lambda x: x[1], reverse=True) | |
| ) | |
| training_singer2mins = {k: round(v, 2) for k, v in training_singer2mins.items()} | |
| meta_info["singers"]["training_minutes"] = training_singer2mins | |
| total_singer2mins = dict( | |
| sorted(total_singer2mins.items(), key=lambda x: x[1], reverse=True) | |
| ) | |
| total_singer2mins = {k: round(v, 2) for k, v in total_singer2mins.items()} | |
| meta_info["singers"]["minutes"] = total_singer2mins | |
| with open(os.path.join(save_dir, "meta_info.json"), "w") as f: | |
| json.dump(meta_info, f, indent=4, ensure_ascii=False) | |
| for singer, min in training_singer2mins.items(): | |
| print(f"Singer {singer}: {min} mins for training") | |
| print("-" * 10, "\n") | |
| def replace_augment_name(dataset: str) -> str: | |
| """Replace the augmented dataset name with the original dataset name. | |
| >>> print(replace_augment_name("dataset_equalizer")) | |
| dataset | |
| """ | |
| if "equalizer" in dataset: | |
| dataset = dataset.replace("_equalizer", "") | |
| elif "formant_shift" in dataset: | |
| dataset = dataset.replace("_formant_shift", "") | |
| elif "pitch_shift" in dataset: | |
| dataset = dataset.replace("_pitch_shift", "") | |
| elif "time_stretch" in dataset: | |
| dataset = dataset.replace("_time_stretch", "") | |
| else: | |
| pass | |
| return dataset | |