with open(args.labels_path) as label_file: labels = str(''.join(json.load(label_file))) audio_conf = dict(sample_rate=args.sample_rate, window_size=args.window_size) model = DeepSpeech(rnn_hidden_size=args.hidden_size, nb_layers=args.hidden_layers, audio_conf=audio_conf, labels=labels, rnn_type=supported_rnns[rnn_type], mixed_precision=args.mixed_precision) model = model.to(device) if args.mixed_precision: model = convert_model_to_half(model) print("Number of parameters: %d" % DeepSpeech.get_param_size(model)) parameters = model.parameters() optimizer = torch.optim.SGD(parameters, lr=3e-4, momentum=0.9, nesterov=True, weight_decay=1e-5) if args.distributed: model = DistributedDataParallel(model) if args.mixed_precision: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.static_loss_scale, dynamic_loss_scale=args.dynamic_loss_scale) criterion = CTCLoss() seconds = int(args.seconds) batch_size = int(args.batch_size)
batch_sampler=train_sampler_clean) train_loader_adv = AudioDataLoader(train_dataset_adv, num_workers=args.num_workers, batch_sampler=train_sampler_adv) test_loader = AudioDataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers) ''' if (not args.no_shuffle and start_epoch != 0) or args.no_sorta_grad: print("Shuffling batches for the following epochs") train_sampler.shuffle(start_epoch) ''' model = model.to(device) denoiser = denoiser.to(device) if args.mixed_precision: model = convert_model_to_half(model) denoiser = convert_model_to_half(denoiser) parameters = denoiser.parameters() optimizer = torch.optim.Adam(parameters, lr=args.lr) #, #momentum=args.momentum, nesterov=True, weight_decay=1e-5) if args.distributed: model = DistributedDataParallel(model) denoiser = DistributedDataParallel(denoiser) if args.mixed_precision: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.static_loss_scale, dynamic_loss_scale=args.dynamic_loss_scale) if optim_state is not None: optimizer.load_state_dict(optim_state) print(model) print("Number of parameters: %d" % DeepSpeech.get_param_size(model))