import argparse from multiprocessing import Pool, cpu_count import torch import torch.multiprocessing as mp from tqdm import tqdm import utils from config import config from clap_wrapper import get_clap_audio_feature import librosa import os os.environ["OMP_NUM_THREADS"] = "1" os.environ["MKL_NUM_THREADS"] = "1" def process_line(line): device = config.bert_gen_config.device if config.bert_gen_config.use_multi_device: rank = mp.current_process()._identity rank = rank[0] if len(rank) > 0 else 0 if torch.cuda.is_available(): gpu_id = rank % torch.cuda.device_count() device = torch.device(f"cuda:{gpu_id}") else: device = torch.device("cpu") wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|") clap_path = wav_path.replace(".WAV", ".wav").replace(".wav", ".emo.npy") if os.path.isfile(clap_path): return audio = librosa.load(wav_path, 48000)[0] # audio = librosa.resample(audio, 44100, 48000) clap = get_clap_audio_feature(audio, device) torch.save(clap, clap_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "-c", "--config", type=str, default=config.bert_gen_config.config_path ) parser.add_argument( "--num_processes", type=int, default=config.bert_gen_config.num_processes ) args, _ = parser.parse_known_args() config_path = args.config hps = utils.get_hparams_from_file(config_path) lines = [] with open(hps.data.training_files, encoding="utf-8") as f: lines.extend(f.readlines()) with open(hps.data.validation_files, encoding="utf-8") as f: lines.extend(f.readlines()) if len(lines) != 0: num_processes = min(args.num_processes, cpu_count()) with Pool(processes=num_processes) as pool: for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)): pass print(f"clap生成完毕!, 共有{len(lines)}个emo.pt生成!")