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Adds --chunks-per-offset option to train_rdovae.py

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Jean-Marc Valin 2025-03-28 19:28:14 -04:00
parent 1ca6933ac4
commit af6dbd84a8
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2 changed files with 6 additions and 3 deletions
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5
dnn/torch/rdovae/train_rdovae.py
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@ -60,7 +60,8 @@ training_group = parser.add_argument_group(title="training parameters")
training_group.add_argument('--batch-size', type=int, help="batch size, default: 32", default=32)
training_group.add_argument('--lr', type=float, help='learning rate, default: 3e-4', default=3e-4)
training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 100', default=100)
training_group.add_argument('--sequence-length', type=int, help='sequence length, needs to be divisible by 4, default: 256', default=256)
training_group.add_argument('--sequence-length', type=int, help='sequence length, needs to be divisible by chunks_per_offset, default: 400', default=400)
training_group.add_argument('--chunks-per-offset', type=int, help='chunks per offset', default=4)
training_group.add_argument('--lr-decay-factor', type=float, help='learning rate decay factor, default: 2.5e-5', default=2.5e-5)
training_group.add_argument('--split-mode', type=str, choices=['split', 'random_split'], help='splitting mode for decoder input, default: split', default='split')
training_group.add_argument('--enable-first-frame-loss', action='store_true', default=False, help='enables dedicated distortion loss on first 4 decoder frames')
@ -120,7 +121,7 @@ feature_file = args.features
# model
checkpoint['model_args'] = (num_features, latent_dim, quant_levels, cond_size, cond_size2)
checkpoint['model_kwargs'] = {'state_dim': state_dim, 'split_mode' : split_mode, 'pvq_num_pulses': args.pvq_num_pulses, 'state_dropout_rate': args.state_dropout_rate, 'softquant': softquant}
checkpoint['model_kwargs'] = {'state_dim': state_dim, 'split_mode' : split_mode, 'pvq_num_pulses': args.pvq_num_pulses, 'state_dropout_rate': args.state_dropout_rate, 'softquant': softquant, 'chunks_per_offset': args.chunks_per_offset}
model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs'])
if type(args.initial_checkpoint) != type(None):