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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.emo_gen_config.device
if config.emo_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.pt")
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.emo_gen_config.config_path
)
parser.add_argument(
"--num_processes", type=int, default=config.emo_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!")
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