Spaces:
Running
on
A10G
Running
on
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 utils.audio_slicer import split_utterances_from_audio | |
| from preprocessors import GOLDEN_TEST_SAMPLES | |
| def _split_utts(): | |
| raw_dir = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/ζη/cocoeval/raw" | |
| output_root = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/ζη/cocoeval/utterances" | |
| if os.path.exists(output_root): | |
| os.system("rm -rf {}".format(output_root)) | |
| vocal_files = glob(os.path.join(raw_dir, "*/vocal.wav")) | |
| for vocal_f in tqdm(vocal_files): | |
| song_name = vocal_f.split("/")[-2] | |
| output_dir = os.path.join(output_root, song_name) | |
| os.makedirs(output_dir, exist_ok=True) | |
| split_utterances_from_audio(vocal_f, output_dir, min_interval=300) | |
| def cocoeval_statistics(data_dir): | |
| song2utts = defaultdict(list) | |
| song_infos = glob(data_dir + "/*") | |
| for song in song_infos: | |
| song_name = song.split("/")[-1] | |
| utts = glob(song + "/*.wav") | |
| for utt in utts: | |
| uid = utt.split("/")[-1].split(".")[0] | |
| song2utts[song_name].append(uid) | |
| print("Cocoeval: {} songs".format(len(song_infos))) | |
| return song2utts | |
| def main(output_path, dataset_path): | |
| print("-" * 10) | |
| print("Preparing datasets for Cocoeval...\n") | |
| save_dir = os.path.join(output_path, "cocoeval") | |
| test_output_file = os.path.join(save_dir, "test.json") | |
| if has_existed(test_output_file): | |
| return | |
| # Load | |
| song2utts = cocoeval_statistics(dataset_path) | |
| train, test = [], [] | |
| train_index_count, test_index_count = 0, 0 | |
| train_total_duration, test_total_duration = 0.0, 0.0 | |
| for song_name, uids in tqdm(song2utts.items()): | |
| for chosen_uid in uids: | |
| res = { | |
| "Dataset": "cocoeval", | |
| "Singer": "TBD", | |
| "Song": song_name, | |
| "Uid": "{}_{}".format(song_name, chosen_uid), | |
| } | |
| res["Path"] = "{}/{}.wav".format(song_name, chosen_uid) | |
| res["Path"] = os.path.join(dataset_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 | |
| res["index"] = test_index_count | |
| test_total_duration += duration | |
| test.append(res) | |
| test_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(test_output_file, "w") as f: | |
| json.dump(test, f, indent=4, ensure_ascii=False) | |