From d610540ce5e467932e05311dc46de1ecf44cd511 Mon Sep 17 00:00:00 2001 From: James Betker Date: Sat, 11 Dec 2021 08:16:50 -0700 Subject: [PATCH] mel norm computation script --- codes/scripts/audio/mel_bin_norm_compute.py | 34 +++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 codes/scripts/audio/mel_bin_norm_compute.py diff --git a/codes/scripts/audio/mel_bin_norm_compute.py b/codes/scripts/audio/mel_bin_norm_compute.py new file mode 100644 index 00000000..e2f942b7 --- /dev/null +++ b/codes/scripts/audio/mel_bin_norm_compute.py @@ -0,0 +1,34 @@ +import argparse + +import torch +import yaml +from tqdm import tqdm + +from data import create_dataset, create_dataloader +from trainer.injectors.base_injectors import TorchMelSpectrogramInjector +from utils.options import Loader + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='D:\\dlas\\options\\train_dvae_audio_clips.yml') + parser.add_argument('-key', type=str, help='Key where audio data is stored', default='clip') + parser.add_argument('-num_batches', type=str, help='Number of batches to collect to compute the norm', default=10) + args = parser.parse_args() + + with open(args.opt, mode='r') as f: + opt = yaml.load(f, Loader=Loader) + dopt = opt['datasets']['train'] + dopt['phase'] = 'train' + dataset, collate = create_dataset(dopt, return_collate=True) + dataloader = create_dataloader(dataset, dopt, collate_fn=collate, shuffle=True) + inj = TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel'},{}).cuda() + + mels = [] + for batch in tqdm(dataloader): + clip = batch[args.key].cuda() + mel = inj({'wav': clip})['mel'] + mels.append(mel.mean((0,2)).cpu()) + if len(mels) > args.num_batches: + break + mel_norms = torch.stack(mels).mean(0) + torch.save('mel_norms.pth', mel_norms) \ No newline at end of file