65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
import argparse
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from multiprocessing import Pool, cpu_count
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import torch
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import torch.multiprocessing as mp
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from tqdm import tqdm
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import utils
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from config import config
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from oldVersion.V220.clap_wrapper import get_clap_audio_feature
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import librosa
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import os
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os.environ["OMP_NUM_THREADS"] = "1"
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os.environ["MKL_NUM_THREADS"] = "1"
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def process_line(line):
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device = config.emo_gen_config.device
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if config.emo_gen_config.use_multi_device:
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rank = mp.current_process()._identity
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rank = rank[0] if len(rank) > 0 else 0
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if torch.cuda.is_available():
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gpu_id = rank % torch.cuda.device_count()
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device = torch.device(f"cuda:{gpu_id}")
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else:
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device = torch.device("cpu")
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wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|")
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clap_path = wav_path.replace(".WAV", ".wav").replace(".wav", ".emo.npy")
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if os.path.isfile(clap_path):
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return
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audio = librosa.load(wav_path, 48000)[0]
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# audio = librosa.resample(audio, 44100, 48000)
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clap = get_clap_audio_feature(audio, device)
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torch.save(clap, clap_path)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-c", "--config", type=str, default=config.emo_gen_config.config_path
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)
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parser.add_argument(
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"--num_processes", type=int, default=config.emo_gen_config.num_processes
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)
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args, _ = parser.parse_known_args()
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config_path = args.config
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hps = utils.get_hparams_from_file(config_path)
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lines = []
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with open(hps.data.training_files, encoding="utf-8") as f:
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lines.extend(f.readlines())
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with open(hps.data.validation_files, encoding="utf-8") as f:
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lines.extend(f.readlines())
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if len(lines) != 0:
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num_processes = min(args.num_processes, cpu_count())
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with Pool(processes=num_processes) as pool:
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for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)):
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pass
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print(f"clap生成完毕!, 共有{len(lines)}个emo.pt生成!")
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