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import os
import json
from torch.utils.data import DataLoader
import soundfile as sf
import tqdm
from dataloader import DelimitValidDataset
def main():
# Parameters
data_path = "/path/to/musdb18hq"
save_path = (
"/path/to/musdb18hq_custom_limiter_fixed_attack"
)
batch_size = 1
num_workers = 1
sr = 44100
# Dataset
dataset = DelimitValidDataset(
root=data_path, use_custom_limiter=True, custom_limiter_attack_range=[2.0, 2.0]
)
data_loader = DataLoader(
dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False
)
dict_valid_loudness = {}
dict_limiter_params = {}
# Preprocessing
for (
limited_audio,
orig_audio,
audio_name,
loudness,
custom_attack,
custom_release,
) in tqdm.tqdm(data_loader):
audio_name = audio_name[0]
limited_audio = limited_audio[0].numpy()
loudness = float(loudness[0].numpy())
dict_valid_loudness[audio_name] = loudness
dict_limiter_params[audio_name] = {
"attack_ms": float(custom_attack[0].numpy()),
"release_ms": float(custom_release[0].numpy()),
}
# Save audio
os.makedirs(os.path.join(save_path, "valid"), exist_ok=True)
audio_path = os.path.join(save_path, "valid", audio_name)
sf.write(f"{audio_path}.wav", limited_audio.T, sr)
# write json write code
with open(os.path.join(save_path, "valid_loudness.json"), "w") as f:
json.dump(dict_valid_loudness, f, indent=4)
with open(os.path.join(save_path, "valid_limiter_params.json"), "w") as f:
json.dump(dict_limiter_params, f, indent=4)
if __name__ == "__main__":
main()
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