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import argparse
from concurrent.futures import ThreadPoolExecutor
import warnings
import numpy as np
import torch
from tqdm import tqdm
import utils
from common.log import logger
from common.stdout_wrapper import SAFE_STDOUT
from config import config
warnings.filterwarnings("ignore", category=UserWarning)
from pyannote.audio import Inference, Model
model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM")
inference = Inference(model, window="whole")
device = torch.device(config.style_gen_config.device)
inference.to(device)
class NaNValueError(ValueError):
"""カスタム例外クラス。NaN値が見つかった場合に使用されます。"""
pass
# 推論時にインポートするために短いが関数を書く
def get_style_vector(wav_path):
return inference(wav_path)
def save_style_vector(wav_path):
try:
style_vec = get_style_vector(wav_path)
except Exception as e:
print("\n")
logger.error(f"Error occurred with file: {wav_path}, Details:\n{e}\n")
raise
# 値にNaNが含まれていると悪影響なのでチェックする
if np.isnan(style_vec).any():
print("\n")
logger.warning(f"NaN value found in style vector: {wav_path}")
raise NaNValueError(f"NaN value found in style vector: {wav_path}")
np.save(f"{wav_path}.npy", style_vec) # `test.wav` -> `test.wav.npy`
def process_line(line):
wavname = line.split("|")[0]
try:
save_style_vector(wavname)
return line, None
except NaNValueError:
return line, "nan_error"
def save_average_style_vector(style_vectors, filename="style_vectors.npy"):
average_vector = np.mean(style_vectors, axis=0)
np.save(filename, average_vector)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-c", "--config", type=str, default=config.style_gen_config.config_path
)
parser.add_argument(
"--num_processes", type=int, default=config.style_gen_config.num_processes
)
args, _ = parser.parse_known_args()
config_path = args.config
num_processes = args.num_processes
hps = utils.get_hparams_from_file(config_path)
device = config.style_gen_config.device
training_lines = []
with open(hps.data.training_files, encoding="utf-8") as f:
training_lines.extend(f.readlines())
with ThreadPoolExecutor(max_workers=num_processes) as executor:
training_results = list(
tqdm(
executor.map(process_line, training_lines),
total=len(training_lines),
file=SAFE_STDOUT,
)
)
ok_training_lines = [line for line, error in training_results if error is None]
nan_training_lines = [
line for line, error in training_results if error == "nan_error"
]
if nan_training_lines:
nan_files = [line.split("|")[0] for line in nan_training_lines]
logger.warning(
f"Found NaN value in {len(nan_training_lines)} files: {nan_files}, so they will be deleted from training data."
)
val_lines = []
with open(hps.data.validation_files, encoding="utf-8") as f:
val_lines.extend(f.readlines())
with ThreadPoolExecutor(max_workers=num_processes) as executor:
val_results = list(
tqdm(
executor.map(process_line, val_lines),
total=len(val_lines),
file=SAFE_STDOUT,
)
)
ok_val_lines = [line for line, error in val_results if error is None]
nan_val_lines = [line for line, error in val_results if error == "nan_error"]
if nan_val_lines:
nan_files = [line.split("|")[0] for line in nan_val_lines]
logger.warning(
f"Found NaN value in {len(nan_val_lines)} files: {nan_files}, so they will be deleted from validation data."
)
with open(hps.data.training_files, "w", encoding="utf-8") as f:
f.writelines(ok_training_lines)
with open(hps.data.validation_files, "w", encoding="utf-8") as f:
f.writelines(ok_val_lines)
ok_num = len(ok_training_lines) + len(ok_val_lines)
logger.info(f"Finished generating style vectors! total: {ok_num} npy files.")
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