labels = model.labels
audio_conf = model.audio_conf
if not args.finetune: # Don't want to restart training
optim_state = package['optim_dict']
start_epoch = int(package.get('epoch', 1)) - 1 # Index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # We saved model after epoch finished, start at the next epoch.
start_iter = 0
else:
start_iter += 1
avg_loss = int(package.get('avg_loss', 0))
loss_results, cer_results, wer_results = package['loss_results'], package['cer_results'], \
package['wer_results']
if main_proc and args.visdom: # Add previous scores to visdom graph
visdom_logger.load_previous_values(start_epoch, package)
if main_proc and args.tensorboard: # Previous scores to tensorboard logs
tensorboard_logger.load_previous_values(start_epoch, package)
else:
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,
window_stride=args.window_stride,
window=args.window,
noise_dir=args.noise_dir,
noise_prob=args.noise_prob,
noise_levels=(args.noise_min, args.noise_max))
rnn_type = args.rnn_type.lower()
if main_proc and args.tensorboard:
tensorboard_logger = TensorBoardLogger(args.id + "-" + str(int(time.time())), args.log_dir, args.log_params)
if args.load_auto_checkpoint:
latest_checkpoint = checkpoint_handler.find_latest_checkpoint()
if latest_checkpoint:
args.continue_from = latest_checkpoint
if args.continue_from: # Starting from previous model
state = TrainingState.load_state(state_path=args.continue_from)
model = state.model
if args.finetune:
state.init_finetune_states(args.epochs)
if main_proc and args.visdom: # Add previous scores to visdom graph
visdom_logger.load_previous_values(state.epoch, state.results)
if main_proc and args.tensorboard: # Previous scores to tensorboard logs
tensorboard_logger.load_previous_values(state.epoch, state.results)
else:
# Initialise new model training
with open(args.labels_path) as label_file:
labels = json.load(label_file)
audio_conf = dict(sample_rate=args.sample_rate,
window_size=args.window_size,
window_stride=args.window_stride,
window=args.window,
noise_dir=args.noise_dir,
noise_prob=args.noise_prob,
noise_levels=(args.noise_min, args.noise_max),
num_channels=args.num_channels)
