def define_rnn(rnn_options, audio_conf):
rnn = DeepSpeech(rnn_hidden_size=800,
nb_layers=5,
labels=rnn_options['labels'],
rnn_type=rnn_options['rnn_type'],
audio_conf=audio_conf,
bidirectional=True)
parameters = rnn.parameters()
return (rnn, parameters)
def main():
global args, train_logger, test_logger
args = options.parse_args()
os.makedirs(args.log_dir)
test_logger = Logger(os.path.join(args.log_dir, 'test.log'))
with open(os.path.join(args.log_dir, 'config.log'), 'w') as f:
f.write(args.config_str)
if not args.evaluate:
os.makedirs(args.checkpoint_dir)
train_logger = Logger(os.path.join(args.log_dir, 'train.log'))
loss_results, cer_results = torch.FloatTensor(
args.epochs), torch.FloatTensor(args.epochs)
if args.visdom:
from visdom import Visdom
viz = Visdom()
opts = dict(title=args.experiment_id,
ylabel='',
xlabel='Epoch',
legend=['Loss', 'CER'])
viz_windows = None
epochs = torch.arange(0, args.epochs)
if args.resume:
print('Loading checkpoint model %s' % args.resume)
checkpoint = torch.load(args.resume)
model = DeepSpeech.load_model_checkpoint(checkpoint)
model = torch.nn.DataParallel(model,
device_ids=[i for i in range(args.nGPU)
]).cuda()
labels = DeepSpeech.get_labels(model)
audio_conf = DeepSpeech.get_audio_conf(model)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=args.lr,
momentum=args.momentum,
nesterov=True)
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = int(checkpoint.get('epoch',
0)) # Index start at 0 for training
loss_results, cer_results = checkpoint['loss_results'], checkpoint[
'cer_results']
if args.epochs > loss_results.numel():
loss_results.resize_(args.epochs)
cer_results.resize_(args.epochs)
loss_results[start_epoch:].zero_()
cer_results[start_epoch:].zero_()
# Add previous scores to visdom graph
if args.visdom and loss_results is not None:
x_axis = epochs[0:start_epoch]
y_axis = torch.stack(
(loss_results[0:start_epoch], cer_results[0:start_epoch]),
dim=1)
viz_window = viz.line(
X=x_axis,
Y=y_axis,
opts=opts,
)
else:
start_epoch = args.start_epoch
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))
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[args.rnn_type],
audio_conf=audio_conf,
bidirectional=not args.look_ahead)
model = torch.nn.DataParallel(model,
device_ids=[i for i in range(args.nGPU)
]).cuda()
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=args.lr,
momentum=args.momentum,
nesterov=True)
# define loss function (criterion) and decoder
best_cer = None
criterion = CTCLoss()
decoder = GreedyDecoder(labels)
# define dataloader
if not args.evaluate:
train_dataset = SpectrogramDataset(
audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
labels=labels,
normalize=True,
augment=args.augment)
train_sampler = BucketingSampler(train_dataset,
batch_size=args.batch_size)
train_loader = AudioDataLoader(train_dataset,
num_workers=args.num_workers,
batch_sampler=train_sampler)
if not args.in_order and start_epoch != 0:
print("Shuffling batches for the following epochs")
train_sampler.shuffle()
val_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.val_manifest,
labels=labels,
normalize=True,
augment=False)
val_loader = AudioDataLoader(val_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
if args.evaluate:
validate(val_loader, model, decoder, 0)
return
for epoch in range(start_epoch, args.epochs):
avg_loss = train(train_loader, train_sampler, model, criterion,
optimizer, epoch)
cer = validate(val_loader, model, decoder, epoch)
loss_results[epoch] = avg_loss
cer_results[epoch] = cer
adjust_learning_rate(optimizer)
is_best = False
if best_cer is None or best_cer > cer:
print('Found better validated model')
best_cer = cer
is_best = True
save_checkpoint(
DeepSpeech.serialize(model,
optimizer=optimizer,
epoch=epoch,
loss_results=loss_results,
cer_results=cer_results), is_best, epoch)
if not args.in_order:
print("Shuffling batches...")
train_sampler.shuffle()
if args.visdom:
x_axis = epochs[0:epoch + 1]
y_axis = torch.stack(
(loss_results[0:epoch + 1], cer_results[0:epoch + 1]), dim=1)
if viz_window is None:
viz_window = viz.line(
X=x_axis,
Y=y_axis,
opts=opts,
)
else:
viz.line(
X=x_axis.unsqueeze(0).expand(y_axis.size(1),
x_axis.size(0)).transpose(
0, 1), # Visdom fix
Y=y_axis,
win=viz_window,
update='replace',
)
def main():
args = parser.parse_args()
save_folder = args.save_folder
if args.visdom:
from visdom import Visdom
viz = Visdom()
opts = [
dict(title='Loss', ylabel='Loss', xlabel='Epoch'),
dict(title='WER', ylabel='WER', xlabel='Epoch'),
dict(title='CER', ylabel='CER', xlabel='Epoch')
]
viz_windows = [None, None, None]
loss_results, cer_results, wer_results = torch.Tensor(
args.epochs), torch.Tensor(args.epochs), torch.Tensor(args.epochs)
epochs = torch.range(1, args.epochs)
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
criterion = CTCLoss()
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)
train_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
labels=labels,
normalize=True)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.val_manifest,
labels=labels,
normalize=True)
train_loader = AudioDataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
test_loader = AudioDataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
num_classes=len(labels))
decoder = ArgMaxDecoder(labels)
if args.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=args.lr,
momentum=args.momentum,
nesterov=True)
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch in range(args.epochs):
model.train()
end = time.time()
avg_loss = 0
for i, (data) in enumerate(train_loader):
inputs, targets, input_percentages, target_sizes = data
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs)
target_sizes = Variable(target_sizes)
targets = Variable(targets)
if args.cuda:
inputs = inputs.cuda()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int())
loss = criterion(out, targets, sizes, target_sizes)
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.data.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss.data[0]
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), args.max_norm)
# SGD step
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if not args.silent:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1),
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses))
avg_loss /= len(train_loader)
print('Training Summary Epoch: [{0}]\t'
'Average Loss {loss:.3f}\t'.format(epoch + 1, loss=avg_loss))
total_cer, total_wer = 0, 0
for i, (data) in enumerate(test_loader): # test
inputs, targets, input_percentages, target_sizes = data
inputs = Variable(inputs)
# unflatten targets
split_targets = []
offset = 0
for size in target_sizes:
split_targets.append(targets[offset:offset + size])
offset += size
if args.cuda:
inputs = inputs.cuda()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int())
decoded_output = decoder.decode(out.data, sizes)
target_strings = decoder.process_strings(
decoder.convert_to_strings(split_targets))
wer, cer = 0, 0
for x in range(len(target_strings)):
wer += decoder.wer(decoded_output[x],
target_strings[x]) / float(
len(target_strings[x].split()))
cer += decoder.cer(decoded_output[x],
target_strings[x]) / float(
len(target_strings[x]))
total_cer += cer
total_wer += wer
wer = total_wer / len(test_loader.dataset)
cer = total_cer / len(test_loader.dataset)
wer *= 100
cer *= 100
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.0f}\t'
'Average CER {cer:.0f}\t'.format(epoch + 1, wer=wer, cer=cer))
if args.visdom:
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
epoch += 1
x_axis = epochs[0:epoch]
y_axis = [
loss_results[0:epoch], wer_results[0:epoch],
cer_results[0:epoch]
]
for x in range(len(viz_windows)):
if viz_windows[x] is None:
viz_windows[x] = viz.line(
X=x_axis,
Y=y_axis[x],
opts=opts[x],
)
else:
viz.line(
X=x_axis,
Y=y_axis[x],
win=viz_windows[x],
update='replace',
)
if args.epoch_save:
file_path = '%s/deepspeech_%d.pth.tar' % (save_folder, epoch)
torch.save(checkpoint(model, args, len(labels), epoch), file_path)
torch.save(checkpoint(model, args, len(labels)), args.final_model_path)
def train_main(args):
args.distributed = args.world_size > 1
main_proc = True
if args.distributed:
if args.gpu_rank:
torch.cuda.set_device(int(args.gpu_rank))
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
main_proc = args.rank == 0 # Only the first proc should save models
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(args.epochs), torch.Tensor(args.epochs), torch.Tensor(
args.epochs)
best_wer = None
if args.visdom and main_proc:
from visdom import Visdom
viz = Visdom()
opts = dict(title=args.id, ylabel='', xlabel='Epoch', legend=['Loss', 'WER', 'CER'])
viz_window = None
epochs = torch.arange(1, args.epochs + 1)
if args.tensorboard and main_proc:
os.makedirs(args.log_dir, exist_ok=True)
from tensorboardX import SummaryWriter
tensorboard_writer = SummaryWriter(args.log_dir)
os.makedirs(save_folder, exist_ok=True)
avg_loss, start_epoch, start_iter = 0, 0, 0
if args.continue_from: # Starting from previous model
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from, map_location=lambda storage, loc: storage)
model = DeepSpeech.load_model_package(package)
labels = DeepSpeech.get_labels(model)
audio_conf = DeepSpeech.get_audio_conf(model)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=args.lr,
momentum=args.momentum, nesterov=True)
if not args.finetune: # Don't want to restart training
if args.cuda:
model.cuda()
optimizer.load_state_dict(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 and \
package[
'loss_results'] is not None and start_epoch > 0: # Add previous scores to visdom graph
x_axis = epochs[0:start_epoch]
y_axis = torch.stack(
(loss_results[0:start_epoch], wer_results[0:start_epoch], cer_results[0:start_epoch]),
dim=1)
viz_window = viz.line(
X=x_axis,
Y=y_axis,
opts=opts,
)
if main_proc and args.tensorboard and \
package[
'loss_results'] is not None and start_epoch > 0: # Previous scores to tensorboard logs
for i in range(start_epoch):
values = {
'Avg Train Loss': loss_results[i],
'Avg WER': wer_results[i],
'Avg CER': cer_results[i]
}
tensorboard_writer.add_scalars(args.id, values, i + 1)
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()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=args.bidirectional)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=args.lr,
momentum=args.momentum, nesterov=True)
criterion = CTCLoss()
decoder = GreedyDecoder(labels)
train_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.train_manifest, labels=labels,
normalize=True, augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.val_manifest, labels=labels,
normalize=True, augment=False)
if not args.distributed:
train_sampler = BucketingSampler(train_dataset, batch_size=args.batch_size)
else:
train_sampler = DistributedBucketingSampler(train_dataset, batch_size=args.batch_size,
num_replicas=args.world_size, rank=args.rank)
train_loader = AudioDataLoader(train_dataset,
num_workers=args.num_workers, batch_sampler=train_sampler)
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)
if args.cuda:
model.cuda()
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=(int(args.gpu_rank),) if args.rank else None)
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch in range(start_epoch, args.epochs):
model.train()
end = time.time()
start_epoch_time = time.time()
for i, (data) in enumerate(train_loader, start=start_iter):
if i == len(train_sampler):
break
inputs, targets, input_percentages, target_sizes = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
# measure data loading time
data_time.update(time.time() - end)
if args.cuda:
inputs = inputs.cuda()
out, output_sizes = model(inputs, input_sizes)
out = out.transpose(0, 1) # TxNxH
loss = criterion(out, targets, output_sizes, target_sizes)
loss = loss / inputs.size(0) # average the loss by minibatch
inf = float("inf")
if args.distributed:
loss_value = reduce_tensor(loss, args.world_size)[0]
else:
loss_value = loss.item()
if loss_value == inf or loss_value == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
# SGD step
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if not args.silent:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1), len(train_sampler), batch_time=batch_time, data_time=data_time, loss=losses))
if args.checkpoint_per_batch > 0 and i > 0 and (i + 1) % args.checkpoint_per_batch == 0 and main_proc:
file_path = '%s/deepspeech_checkpoint_epoch_%d_iter_%d.pth' % (save_folder, epoch + 1, i + 1)
print("Saving checkpoint model to %s" % file_path)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, iteration=i,
loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results, avg_loss=avg_loss),
file_path)
del loss
del out
avg_loss /= len(train_sampler)
epoch_time = time.time() - start_epoch_time
print('Training Summary Epoch: [{0}]\t'
'Time taken (s): {epoch_time:.0f}\t'
'Average Loss {loss:.3f}\t'.format(epoch + 1, epoch_time=epoch_time, loss=avg_loss))
start_iter = 0 # Reset start iteration for next epoch
total_cer, total_wer = 0, 0
model.eval()
with torch.no_grad():
for i, (data) in tqdm(enumerate(test_loader), total=len(test_loader)):
inputs, targets, input_percentages, target_sizes = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
# unflatten targets
split_targets = []
offset = 0
for size in target_sizes:
split_targets.append(targets[offset:offset + size])
offset += size
if args.cuda:
inputs = inputs.cuda()
out, output_sizes = model(inputs, input_sizes)
decoded_output, _ = decoder.decode(out.data, output_sizes)
target_strings = decoder.convert_to_strings(split_targets)
wer, cer = 0, 0
for x in range(len(target_strings)):
transcript, reference = decoded_output[x][0], target_strings[x][0]
wer += decoder.wer(transcript, reference) / float(len(reference.split()))
cer += decoder.cer(transcript, reference) / float(len(reference))
total_cer += cer
total_wer += wer
del out
wer = total_wer / len(test_loader.dataset)
cer = total_cer / len(test_loader.dataset)
wer *= 100
cer *= 100
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(epoch + 1, wer=wer, cer=cer))
if args.visdom and main_proc:
x_axis = epochs[0:epoch + 1]
y_axis = torch.stack(
(loss_results[0:epoch + 1], wer_results[0:epoch + 1], cer_results[0:epoch + 1]), dim=1)
if viz_window is None:
viz_window = viz.line(
X=x_axis,
Y=y_axis,
opts=opts,
)
else:
viz.line(
X=x_axis.unsqueeze(0).expand(y_axis.size(1), x_axis.size(0)).transpose(0, 1), # Visdom fix
Y=y_axis,
win=viz_window,
update='replace',
)
if args.tensorboard and main_proc:
values = {
'Avg Train Loss': avg_loss,
'Avg WER': wer,
'Avg CER': cer
}
tensorboard_writer.add_scalars(args.id, values, epoch + 1)
if args.log_params:
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
tensorboard_writer.add_histogram(tag, to_np(value), epoch + 1)
tensorboard_writer.add_histogram(tag + '/grad', to_np(value.grad), epoch + 1)
if args.checkpoint and main_proc:
file_path = '%s/deepspeech_%d.pth' % (save_folder, epoch + 1)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results),
file_path)
# anneal lr
optim_state = optimizer.state_dict()
optim_state['param_groups'][0]['lr'] = optim_state['param_groups'][0]['lr'] / args.learning_anneal
optimizer.load_state_dict(optim_state)
print('Learning rate annealed to: {lr:.6f}'.format(lr=optim_state['param_groups'][0]['lr']))
if (best_wer is None or best_wer > wer) and main_proc:
print("Found better validated model, saving to %s" % args.model_path)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results), args.model_path)
best_wer = wer
avg_loss = 0
if not args.no_shuffle:
print("Shuffling batches...")
train_sampler.shuffle(epoch)
def main():
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if params.rnn_type == 'gru' and params.rnn_act_type != 'tanh':
print(
"ERROR: GRU does not currently support activations other than tanh"
)
sys.exit()
if params.rnn_type == 'rnn' and params.rnn_act_type != 'relu':
print("ERROR: We should be using ReLU RNNs")
sys.exit()
print("=======================================================")
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(
params.epochs), torch.Tensor(params.epochs), torch.Tensor(
params.epochs)
best_wer = None
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
criterion = CTCLoss()
with open(params.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
audio_conf = dict(sample_rate=params.sample_rate,
window_size=params.window_size,
window_stride=params.window_stride,
window=params.window,
noise_dir=params.noise_dir,
noise_prob=params.noise_prob,
noise_levels=(params.noise_min, params.noise_max))
train_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=params.train_manifest,
labels=labels,
normalize=True,
augment=params.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=params.val_manifest,
labels=labels,
normalize=True,
augment=False)
train_loader = AudioDataLoader(train_dataset,
batch_size=params.batch_size,
num_workers=1)
test_loader = AudioDataLoader(test_dataset,
batch_size=params.batch_size,
num_workers=1)
rnn_type = params.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=params.hidden_size,
nb_layers=params.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=False,
rnn_activation=params.rnn_act_type,
bias=params.bias)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=params.lr,
momentum=params.momentum,
nesterov=True,
weight_decay=params.l2)
decoder = GreedyDecoder(labels)
if args.continue_from:
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from)
model.load_state_dict(package['state_dict'])
optimizer.load_state_dict(package['optim_dict'])
start_epoch = int(package.get(
'epoch', 1)) - 1 # Python index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # Assume that we saved a model after an epoch finished, so start at the next epoch.
start_iter = 0
else:
start_iter += 1
avg_loss = int(package.get('avg_loss', 0))
if args.start_epoch != -1:
start_epoch = args.start_epoch
loss_results[:
start_epoch], cer_results[:start_epoch], wer_results[:start_epoch] = package[
'loss_results'][:start_epoch], package[
'cer_results'][:start_epoch], package[
'wer_results'][:start_epoch]
print(loss_results)
epoch = start_epoch
else:
avg_loss = 0
start_epoch = 0
start_iter = 0
avg_training_loss = 0
if params.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
ctc_time = AverageMeter()
for epoch in range(start_epoch, params.epochs):
model.train()
end = time.time()
for i, (data) in enumerate(train_loader, start=start_iter):
if i == len(train_loader):
break
inputs, targets, input_percentages, target_sizes = data
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs, requires_grad=False)
target_sizes = Variable(target_sizes, requires_grad=False)
targets = Variable(targets, requires_grad=False)
if params.cuda:
inputs = inputs.cuda()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int(),
requires_grad=False)
ctc_start_time = time.time()
loss = criterion(out, targets, sizes, target_sizes)
ctc_time.update(time.time() - ctc_start_time)
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.data.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss.data[0]
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), params.max_norm)
# SGD step
optimizer.step()
if params.cuda:
torch.cuda.synchronize()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'CTC Time {ctc_time.val:.3f} ({ctc_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1),
len(train_loader),
batch_time=batch_time,
data_time=data_time,
ctc_time=ctc_time,
loss=losses))
del loss
del out
avg_loss /= len(train_loader)
print('Training Summary Epoch: [{0}]\t'
'Average Loss {loss:.3f}\t'.format(
epoch + 1,
loss=avg_loss,
))
start_iter = 0 # Reset start iteration for next epoch
total_cer, total_wer = 0, 0
model.eval()
wer, cer = eval_model(model, test_loader, decoder)
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(epoch + 1, wer=wer, cer=cer))
if args.checkpoint:
file_path = '%s/deepspeech_%d.pth.tar' % (save_folder, epoch + 1)
torch.save(
DeepSpeech.serialize(model,
optimizer=optimizer,
epoch=epoch,
loss_results=loss_results,
wer_results=wer_results,
cer_results=cer_results), file_path)
# anneal lr
optim_state = optimizer.state_dict()
optim_state['param_groups'][0]['lr'] = optim_state['param_groups'][0][
'lr'] / params.learning_anneal
optimizer.load_state_dict(optim_state)
print('Learning rate annealed to: {lr:.6f}'.format(
lr=optim_state['param_groups'][0]['lr']))
if best_wer is None or best_wer > wer:
print("Found better validated model, saving to %s" %
args.model_path)
torch.save(
DeepSpeech.serialize(model,
optimizer=optimizer,
epoch=epoch,
loss_results=loss_results,
wer_results=wer_results,
cer_results=cer_results), args.model_path)
best_wer = wer
avg_loss = 0
#If set to exit at a given accuracy, exit
if params.exit_at_acc and (best_wer <= args.acc):
break
print("=======================================================")
print("***Best WER = ", best_wer)
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
def main():
args = parser.parse_args()
torch.set_printoptions(profile="full")
criterion = nn.CrossEntropyLoss()
class_accu_reg = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
class_accu_sum = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
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))
train_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
normalize=True,
augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.val_manifest,
normalize=True,
augment=False)
train_loader = AudioDataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
test_loader = AudioDataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
rnn_type = args.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
#print("FIRST LAYER TYPE:\t", args.first_layer_type)
#print("MFCC TRANSFORM:\t\t", args.mfcc)
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=True,
cnn_features=args.cnn_features,
kernel=args.kernel,
first_layer_type=args.first_layer_type,
stride=args.stride,
mfcc=args.mfcc)
########
#print(list(model.rnns.modules()))
#for rnn in model.rnns.modules():
# print(rnn)#.flatten_parameters()
#def flat_model(model):
# for m in model.modules():
# if isinstance(m, nn.LSTM):
# m.flatten_parameters()
########
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=args.lr,
momentum=args.momentum,
nesterov=True)
#scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.learning_rate_decay_epochs, gamma=args.learning_rate_decay_rate)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.99)
avg_loss = 0
start_epoch = 0
start_iter = 0
best_train_accu_reg = 0
best_train_accu_sum = 0
best_test_accu_reg = 0
best_test_accu_sum = 0
best_avg_loss = float("inf") # sys.float_info.max # 1000000
epoch_70 = None
epoch_90 = None
epoch_95 = None
epoch_99 = None
utterance_sequence_length = int(args.utterance_miliseconds / 10)
loss_begin = round(args.crop_begin / (10 * args.stride))
loss_end = -round(args.crop_end / (10 * args.stride)) or None
gap = loss_begin
print("LOSS BEGIN:", loss_begin)
print("LOSS END:", loss_end)
if args.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
print("Number of parameters: ", DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
#losses = AverageMeter()
print(args, "\n")
for epoch in range(start_epoch, args.epochs):
losses = AverageMeter()
scheduler.step()
optim_state_now = optimizer.state_dict()
print('\nLEARNING RATE: {lr:.6f}'.format(
lr=optim_state_now['param_groups'][0]['lr']))
class_accu_reg.reset()
class_accu_sum.reset()
model.train()
end = time.time()
for i, (data) in enumerate(train_loader, start=start_iter):
if i == len(train_loader):
break
inputs, input_percentages, speaker_labels, mfccs = data
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs, requires_grad=False)
########
mfccs = Variable(mfccs, requires_grad=False)
if args.mfcc == "true":
inputs = mfccs # <<-- This line makes us to use mfccs...
#print("INPUTS SIZE:", inputs.size())
#print("MFCCS SIZE:", mfccs.size())
########
speaker_labels = Variable(speaker_labels, requires_grad=False)
speaker_labels = speaker_labels.cuda(async=True).long()
if args.cuda:
inputs = inputs.cuda()
########
########
sizes = inputs.size()
inputs = inputs.view(sizes[0], sizes[1] * sizes[2],
sizes[3]) # Collapse feature dimension
#print("INPUTS SIZE: ====>>>>>\t", inputs.size())
#start = 0
#duration = 100
start = random.randint(
0, int((inputs.size(2) - 1) * (1 - args.sample_proportion)))
duration = int((inputs.size(2)) * (args.sample_proportion))
#start = random.randint(0, (inputs.size(3)-1)-utterance_sequence_length)
#duration = utterance_sequence_length
utterances = inputs[
..., start:start +
duration] # <<<<<<====== THIS IS THE MOST IMPORTANT CODE OF THE PROJECT
#print("UTTERS SIZE: ====>>>>>\t", utterances.size(), start, start+duration)
out = model(utterances)
#print("OUTPUT SIZE: ====>>>>>\t", out.size())
out = out.transpose(0, 1) # TxNxH
########
########
# Prints the output of the model in a sequence of probabilities of char for each audio...
#torch.set_printoptions(profile="full")
####print("OUT: " + str(out.size()), "SPEAKER LABELS:" + str(speaker_labels.size()), "INPUT PERCENTAGES MEAN: " + str(input_percentages.mean()))
#print(out[:,:,0])
#print("SPEAKER LABELS: " + str(speaker_labels))
#print(out[0][0])
#softmax_output = F.softmax(out).data # This DOES NOT what I want...
#softmax_output_alt = flex_softmax(out, axis=2).data # This is FINE!!! <<<===
#print(softmax_output[0][0])
#print(softmax_output_alt[0][0])
####new_out = torch.sum(out, 0)
####new_out = torch.sum(out[20:], 0)
#print(out.size())
#print(new_out.size())
#print(out[-1].size())
class_accu_reg.add(out[round(out.size(0) / 2)].data,
speaker_labels.data)
class_accu_sum.add(
torch.sum(out[loss_begin:loss_end], 0).data,
speaker_labels.data)
#class_accu_reg.add(processed_out.data, processed_speaker_labels.data)
if args.loss_type == "reg":
processed_out = out[round(out.size(0) / 2)]
processed_speaker_labels = speaker_labels
if args.loss_type == "mult":
#indices = torch.LongTensor([0,2])
mult = (round(out.size(0) / 4), round(out.size(0) / 2),
round(3 * out.size(0) / 4))
processed_out = out.contiguous()[mult, ...].view(-1, 48)
processed_speaker_labels = speaker_labels.repeat(
out.size(0), 1)[mult, ...].view(-1)
#processed_out = out.contiguous()[(round(out.size(0)/4),round(out.size(0)/2),round(3*out.size(0)/4)),...].view(-1,48)
#processed_speaker_labels = speaker_labels.repeat(out.size(0),1)[(round(out.size(0)/4),round(out.size(0)/2),round(3*out.size(0)/4)),...].view(-1)
#processed_out = out.contiguous()[(loss_begin,round(out.size(0)/2),loss_end),...].view(-1,48)
#processed_speaker_labels = speaker_labels.repeat(out.size(0),1)[(loss_begin,round(out.size(0)/2),loss_end),...].view(-1)
##speaker_labels = speaker_labels.expand(20, out.size(0))
elif args.loss_type == "sum":
sum_begin = round(out.size(0) / 2) - round(out.size(0) / 4)
sum_end = round(out.size(0) / 2) + round(out.size(0) / 4)
processed_out = torch.sum(out[sum_begin:sum_end], 0)
processed_speaker_labels = speaker_labels
#processed_out = torch.sum(out[loss_begin:loss_end], 0)
#processed_speaker_labels = speaker_labels
#processed_out = torch.sum(out, 0)
#processed_speaker_labels = speaker_labels
elif args.loss_type == "full":
full_begin = round(out.size(0) / 2) - round(out.size(0) / 4)
full_end = round(out.size(0) / 2) + round(out.size(0) / 4)
processed_out = out.contiguous()[full_begin:full_end].view(
-1, 48)
processed_speaker_labels = speaker_labels.repeat(
out.size(0), 1)[full_begin:full_end].view(-1)
##speaker_labels = speaker_labels.expand(20, out.size(0))
#processed_out = out.contiguous()[loss_begin:loss_end].view(-1,48)
#processed_speaker_labels = speaker_labels.repeat(out.size(0),1)[loss_begin:loss_end].view(-1)
##speaker_labels = speaker_labels.expand(20, out.size(0))
#processed_out = out.contiguous().view(-1, 48)
#processed_speaker_labels = speaker_labels.repeat(out.size(0),1).view(-1)
##speaker_labels = speaker_labels.expand(20, out.size(0))
#print("PROC OUTPUT: ====>>>>>\t" + str(processed_out.size()))
#print("PROC LABELS: ====>>>>>\t" + str(processed_speaker_labels.size()))
loss = criterion(processed_out, processed_speaker_labels)
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.data.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss.data[0]
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
#accu_out3 = torch.sum(flex_softmax(out[20:], axis=2), 0)
#print(classaccu.value()[0], classaccu.value()[1])
# Cross Entropy Loss for a Sequence (Time Series) of Output?
#output = output.view(-1,29)
#target = target.view(-1)
#criterion = nn.CrossEntropyLoss()
#loss = criterion(output,target)
# compute gradient
optimizer.zero_grad()
loss.backward()
#torch.nn.utils.clip_grad_norm(model.parameters(), args.max_norm)
# SGD step
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if not args.silent:
print('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.8f} ({loss.avg:.8f})\t'
'CARR {carr:.2f}\t'
'CARS {cars:.2f}\t'.format(
(epoch + 1), (i + 1),
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
carr=class_accu_reg.value()[0],
cars=class_accu_sum.value()[0]))
if args.cuda:
torch.cuda.synchronize()
del loss
del out
del processed_out
del speaker_labels
del processed_speaker_labels
avg_loss /= len(train_loader)
if (best_avg_loss > avg_loss): best_avg_loss = avg_loss
print("\nCURRENT EPOCH AVERAGE LOSS:\t", avg_loss)
print("\nCURRENT EPOCH TRAINING RESULTS:\t",
class_accu_reg.value()[0], "\t",
class_accu_sum.value()[0], "\n")
if (best_train_accu_reg < class_accu_reg.value()[0]):
best_train_accu_reg = class_accu_reg.value()[0]
if (best_train_accu_sum < class_accu_sum.value()[0]):
best_train_accu_sum = class_accu_sum.value()[0]
get_70 = (class_accu_reg.value()[0] > 70)
if ((epoch_70 is None) and (get_70 == True)): epoch_70 = epoch + 1
get_90 = (class_accu_reg.value()[0] > 90)
if ((epoch_90 is None) and (get_90 == True)): epoch_90 = epoch + 1
get_95 = (class_accu_reg.value()[0] > 95)
if ((epoch_95 is None) and (get_95 == True)): epoch_95 = epoch + 1
get_99 = (class_accu_reg.value()[0] > 99)
if ((epoch_99 is None) and (get_99 == True)): epoch_99 = epoch + 1
start_iter = 0 # Reset start iteration for next epoch
model.eval()
class_accu_reg.reset()
class_accu_sum.reset()
for i, (data) in enumerate(test_loader): # test
inputs, input_percentages, speaker_labels, mfccs = data
inputs = Variable(inputs, volatile=True)
########
mfccs = Variable(mfccs, requires_grad=False)
if args.mfcc == "true":
inputs = mfccs # <<-- This line makes us to use mfccs...
#print("INPUTS SIZE:", inputs.size())
#print("MFCCS SIZE:", mfccs.size())
########
speaker_labels = Variable(speaker_labels, requires_grad=False)
speaker_labels = speaker_labels.cuda(async=True).long()
if args.cuda:
inputs = inputs.cuda()
########
########
sizes = inputs.size()
inputs = inputs.view(sizes[0], sizes[1] * sizes[2],
sizes[3]) # Collapse feature dimension
#print("INPUTS SIZE: ====>>>>>\t", inputs.size())
#start = round(inputs.size(2)/2)-40
#duration = 80
#start = random.randint(0, int((inputs.size(3)-1)*(1-args.sample_proportion)))
#duration = int((inputs.size(3))*(args.sample_proportion))
#start = random.randint(0, (inputs.size(3)-1)-utterance_sequence_length)
#duration = utterance_sequence_length
utterances = inputs #[...,start:start+duration] # <<<<<<====== THIS IS THE MOST IMPORTANT CODE OF THE PROJECT
#print("UTTERS SIZE: ====>>>>>\t", utterances.size(), start, start+duration)
out = model(utterances)
#print("OUTPUT SIZE: ====>>>>>\t", out.size())
out = out.transpose(0, 1) # TxNxH
########
########
# Prints the output of the model in a sequence of probabilities of char for each audio...
#torch.set_printoptions(profile="full")
########print("OUT: " + str(out.size()), "NEW OUT:" + str(new_out.size()), "SPEAKER LABELS:" + str(speaker_labels.size()), "INPUT PERCENTAGES MEAN: " + str(input_percentages.mean()))
#print(out[:,:,0])
#print("SPEAKER LABELS: " + str(speaker_labels))
#print(out[0][0])
#softmax_output = F.softmax(out).data # This DOES NOT what I want...
#softmax_output_alt = flex_softmax(out, axis=2).data # This is FINE!!! <<<===
#print(softmax_output[0][0])
#print(softmax_output_alt[0][0])
########
#if args.loss_type == "reg":
# processed_out = out[round(out.size(0)/2)]; processed_speaker_labels = speaker_labels
#elif args.loss_type == "sum" or "full":
# #processed_out = torch.sum(out[loss_begin:loss_end], 0); processed_speaker_labels = speaker_labels
# processed_out = torch.sum(out, 0); processed_speaker_labels = speaker_labels
#elif args.loss_type == "full":
# #processed_out = out.contiguous()[loss_begin:loss_end].view(-1,48); processed_speaker_labels = speaker_labels.repeat(out.size(0),1)[loss_begin:loss_end].view(-1) #speaker_labels = speaker_labels.expand(20, out.size(0))
# processed_out = out.contiguous().view(-1, 48); processed_speaker_labels = speaker_labels.repeat(out.size(0),1).view(-1) # speaker_labels = speaker_labels.expand(20, out.size(0))
#print("OUT: " + str(out.size()), "SPEAKER LABELS:" + str(speaker_labels.size()))
#print("PROC OUTPUT: ====>>>>>\t" + str(processed_out.size()))
#print("PROC LABELS: ====>>>>>\t" + str(processed_speaker_labels.size()))
class_accu_reg.add(out[round(out.size(0) / 2)].data,
speaker_labels.data)
class_accu_sum.add(
torch.sum(out[loss_begin:loss_end], 0).data,
speaker_labels.data)
#class_accu_reg.add(processed_out.data, processed_speaker_labels.data)
print('Validation Summary Epoch: [{0}]\t'
'CARR {carr:.2f}\t'
'CARS {cars:.2f}\t'.format(epoch + 1,
carr=class_accu_reg.value()[0],
cars=class_accu_sum.value()[0]))
if args.cuda:
torch.cuda.synchronize()
del out
print("\nCURRENT EPOCH TEST RESULTS:\t",
class_accu_reg.value()[0], "\t",
class_accu_sum.value()[0], "\n")
if (best_test_accu_reg < class_accu_reg.value()[0]):
best_test_accu_reg = class_accu_reg.value()[0]
if (best_test_accu_sum < class_accu_sum.value()[0]):
best_test_accu_sum = class_accu_sum.value()[0]
print("\nBEST AVERAGE LOSS:\t\t", best_avg_loss)
print("\nBEST EPOCH TRAINING RESULTS:\t", best_train_accu_reg, "\t",
best_train_accu_sum)
print("\nBEST EPOCH TEST RESULTS:\t", best_test_accu_reg, "\t",
best_test_accu_sum)
print("\nEPOCHS 70%, 90%, 95%, 99%:\t", epoch_70, "\t", epoch_90, "\t",
epoch_95, "\t", epoch_99, "\n")
torch.save(
DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch),
args.model_path)
avg_loss = 0
if not args.no_bucketing and epoch == 0:
print("Switching to bucketing sampler for following epochs")
train_dataset = SpectrogramDatasetWithLength(
audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
normalize=True,
augment=args.augment)
sampler = BucketingSampler(train_dataset)
train_loader.sampler = sampler
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()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=args.bidirectional)
parameters = model.parameters()
# optimizer = torch.optim.SGD(parameters, lr=args.lr,
# momentum=args.momentum, nesterov=True)
optimizer = torch.optim.Adam(parameters, lr=args.lr)
decoder = GreedyDecoder(labels)
train_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
labels=labels,
normalize=True,
augment=args.augment,
pitch=args.pitch,
whitenoise=args.whitenoise)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.val_manifest,
labels=labels,
def main():
args = parser.parse_args()
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(
args.epochs), torch.Tensor(args.epochs), torch.Tensor(args.epochs)
if args.visdom:
from visdom import Visdom
viz = Visdom()
opts = [
dict(title='Loss', ylabel='Loss', xlabel='Epoch'),
dict(title='WER', ylabel='WER', xlabel='Epoch'),
dict(title='CER', ylabel='CER', xlabel='Epoch')
]
viz_windows = [None, None, None]
epochs = torch.arange(1, args.epochs + 1)
if args.tensorboard:
from logger import TensorBoardLogger
try:
os.makedirs(args.log_dir)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
for file in os.listdir(args.log_dir):
file_path = os.path.join(args.log_dir, file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception as e:
raise
else:
raise
logger = TensorBoardLogger(args.log_dir)
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
criterion = CTCLoss()
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))
train_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
labels=labels,
normalize=True,
augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.val_manifest,
labels=labels,
normalize=True,
augment=False)
train_loader = AudioDataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
test_loader = AudioDataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
rnn_type = args.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=True)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=args.lr,
momentum=args.momentum,
nesterov=True)
decoder = GreedyDecoder(labels)
if args.continue_from:
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from)
model.load_state_dict(package['state_dict'])
optimizer.load_state_dict(package['optim_dict'])
start_epoch = int(package.get(
'epoch', 1)) - 1 # Python index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # Assume that we saved a model after an epoch finished, so 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 args.visdom and \
package['loss_results'] is not None and start_epoch > 0: # Add previous scores to visdom graph
x_axis = epochs[0:start_epoch]
y_axis = [
loss_results[0:start_epoch], wer_results[0:start_epoch],
cer_results[0:start_epoch]
]
for x in range(len(viz_windows)):
viz_windows[x] = viz.line(
X=x_axis,
Y=y_axis[x],
opts=opts[x],
)
if args.tensorboard and \
package['loss_results'] is not None and start_epoch > 0: # Previous scores to tensorboard logs
for i in range(start_epoch):
info = {
'Avg Train Loss': loss_results[i],
'Avg WER': wer_results[i],
'Avg CER': cer_results[i]
}
for tag, val in info.items():
logger.scalar_summary(tag, val, i + 1)
if not args.no_bucketing:
print("Using bucketing sampler for the following epochs")
train_dataset = SpectrogramDatasetWithLength(
audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
labels=labels,
normalize=True,
augment=args.augment)
sampler = BucketingSampler(train_dataset)
train_loader.sampler = sampler
else:
avg_loss = 0
start_epoch = 0
start_iter = 0
if args.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch in range(start_epoch, args.epochs):
model.train()
end = time.time()
for i, (data) in enumerate(train_loader, start=start_iter):
if i == len(train_loader):
break
inputs, targets, input_percentages, target_sizes = data
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs, requires_grad=False)
target_sizes = Variable(target_sizes, requires_grad=False)
targets = Variable(targets, requires_grad=False)
if args.cuda:
inputs = inputs.cuda()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int(),
requires_grad=False)
loss = criterion(out, targets, sizes, target_sizes)
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.data.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss.data[0]
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), args.max_norm)
# SGD step
optimizer.step()
if args.cuda:
torch.cuda.synchronize()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if not args.silent:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1),
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses))
if args.checkpoint_per_batch > 0 and i > 0 and (
i + 1) % args.checkpoint_per_batch == 0:
file_path = '%s/deepspeech_checkpoint_epoch_%d_iter_%d.pth.tar' % (
save_folder, epoch + 1, i + 1)
print("Saving checkpoint model to %s" % file_path)
torch.save(
DeepSpeech.serialize(model,
optimizer=optimizer,
epoch=epoch,
iteration=i,
loss_results=loss_results,
wer_results=wer_results,
cer_results=cer_results,
avg_loss=avg_loss), file_path)
del loss
del out
avg_loss /= len(train_loader)
print('Training Summary Epoch: [{0}]\t'
'Average Loss {loss:.3f}\t'.format(epoch + 1, loss=avg_loss))
start_iter = 0 # Reset start iteration for next epoch
total_cer, total_wer = 0, 0
model.eval()
for i, (data) in enumerate(test_loader): # test
inputs, targets, input_percentages, target_sizes = data
inputs = Variable(inputs, volatile=True)
# unflatten targets
split_targets = []
offset = 0
for size in target_sizes:
split_targets.append(targets[offset:offset + size])
offset += size
if args.cuda:
inputs = inputs.cuda()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = input_percentages.mul_(int(seq_length)).int()
decoded_output = decoder.decode(out.data, sizes)
target_strings = decoder.process_strings(
decoder.convert_to_strings(split_targets))
wer, cer = 0, 0
for x in range(len(target_strings)):
wer += decoder.wer(decoded_output[x],
target_strings[x]) / float(
len(target_strings[x].split()))
cer += decoder.cer(decoded_output[x],
target_strings[x]) / float(
len(target_strings[x]))
total_cer += cer
total_wer += wer
if args.cuda:
torch.cuda.synchronize()
del out
wer = total_wer / len(test_loader.dataset)
cer = total_cer / len(test_loader.dataset)
wer *= 100
cer *= 100
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(epoch + 1, wer=wer, cer=cer))
if args.visdom:
# epoch += 1
x_axis = epochs[0:epoch + 1]
y_axis = [
loss_results[0:epoch + 1], wer_results[0:epoch + 1],
cer_results[0:epoch + 1]
]
for x in range(len(viz_windows)):
if viz_windows[x] is None:
viz_windows[x] = viz.line(
X=x_axis,
Y=y_axis[x],
opts=opts[x],
)
else:
viz.line(
X=x_axis,
Y=y_axis[x],
win=viz_windows[x],
update='replace',
)
if args.tensorboard:
info = {'Avg Train Loss': avg_loss, 'Avg WER': wer, 'Avg CER': cer}
for tag, val in info.items():
logger.scalar_summary(tag, val, epoch + 1)
if args.log_params:
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
logger.histo_summary(tag, to_np(value), epoch + 1)
logger.histo_summary(tag + '/grad', to_np(value.grad),
epoch + 1)
if args.checkpoint:
file_path = '%s/deepspeech_%d.pth.tar' % (save_folder, epoch + 1)
torch.save(
DeepSpeech.serialize(model,
optimizer=optimizer,
epoch=epoch,
loss_results=loss_results,
wer_results=wer_results,
cer_results=cer_results), file_path)
# anneal lr
optim_state = optimizer.state_dict()
optim_state['param_groups'][0][
'lr'] = optim_state['param_groups'][0]['lr'] / args.learning_anneal
optimizer.load_state_dict(optim_state)
print('Learning rate annealed to: {lr:.6f}'.format(
lr=optim_state['param_groups'][0]['lr']))
avg_loss = 0
if not args.no_bucketing and epoch == 0:
print("Switching to bucketing sampler for following epochs")
train_dataset = SpectrogramDatasetWithLength(
audio_conf=audio_conf,
manifest_filepath=args.train_manifest,
labels=labels,
normalize=True,
augment=args.augment)
sampler = BucketingSampler(train_dataset)
train_loader.sampler = sampler
torch.save(DeepSpeech.serialize(model, optimizer=optimizer),
args.final_model_path)
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()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=args.bidirectional)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=args.lr,
momentum=args.momentum, nesterov=True)
criterion = CTCLoss()
decoder = GreedyDecoder(labels)
train_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.train_manifest, labels=labels,
normalize=True, augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.val_manifest, labels=labels,
normalize=True, augment=False)
if not args.distributed:
train_sampler = BucketingSampler(train_dataset, batch_size=args.batch_size)
else:
train_sampler = DistributedBucketingSampler(train_dataset, batch_size=args.batch_size,
num_replicas=args.world_size, rank=args.rank)
train_loader = AudioDataLoader(train_dataset,
num_workers=args.num_workers, batch_sampler=train_sampler)
def main():
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if params.rnn_type == 'gru' and params.rnn_act_type != 'tanh':
print("ERROR: GRU does not currently support activations other than tanh")
sys.exit()
if params.rnn_type == 'rnn' and params.rnn_act_type != 'relu':
print("ERROR: We should be using ReLU RNNs")
sys.exit()
print("=======================================================")
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(params.epochs), torch.Tensor(params.epochs), torch.Tensor(params.epochs)
best_wer = None
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
with open(params.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
rnn_type = params.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size = params.hidden_size,
nb_layers = params.hidden_layers,
labels = labels,
rnn_type = supported_rnns[rnn_type],
audio_conf = None,
bidirectional = True,
rnn_activation = params.rnn_act_type,
bias = params.bias)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=params.lr,
momentum=params.momentum, nesterov=False,
weight_decay = params.l2)
cuda = torch.device('cuda')
criterion = torch.nn.CTCLoss(reduction='none').to(cuda)
avg_loss = 0
start_epoch = 0
start_iter = 0
avg_training_loss = 0
if params.cuda:
model.cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
ctc_time = AverageMeter()
forward_time = AverageMeter()
backward_time = AverageMeter()
filename = "/scratch-ml00/wang603/deepspeechData/deepspeech_train.pickle"
batchedData = user_defined_input.Batch(filename)
def train_one_epoch(epoch):
avg_loss = 0
for i in range(batchedData.numBatches):
# if i == 1: return
end = time.time()
inputs, targets, input_percentages, target_sizes = batchedData.batch(last=False)
# making all inputs Tensor
inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)
input_percentages = torch.from_numpy(input_percentages)
target_sizes = torch.from_numpy(target_sizes)
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs, requires_grad=False)
target_sizes = Variable(target_sizes, requires_grad=False)
targets = Variable(targets, requires_grad=False)
if params.cuda:
inputs = inputs.cuda()
# measure forward pass time
forward_start_time = time.time()
out = model(inputs)
# out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int(), requires_grad=False)
# measure ctc loss computing time
ctc_start_time = time.time()
out = out.log_softmax(2) #.detach().requires_grad_()
# print(sizes.shape)
# print(out.shape)
loss = criterion(out, targets, sizes, target_sizes)
ctc_time.update(time.time() - ctc_start_time)
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss_sum.data.item()
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
forward_time.update(time.time() - forward_start_time)
# measure backward pass time
backward_start_time = time.time()
# compute gradient
optimizer.zero_grad()
loss_sum.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), params.max_norm)
# SGD step
optimizer.step()
if params.cuda:
torch.cuda.synchronize()
backward_time.update(time.time() - backward_start_time)
# measure elapsed time
batch_time.update(time.time() - end)
if (i % 20 == 0):
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Forward {forward_time.val:.3f} ({forward_time.avg:.3f})\t'
'CTC Time {ctc_time.val:.3f} ({ctc_time.avg:.3f})\t'
'Backward {backward_time.val:.3f} ({backward_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1), batchedData.numBatches, batch_time=batch_time,
data_time=data_time, forward_time=forward_time, ctc_time=ctc_time,
backward_time=backward_time, loss=losses))
del loss
del out
avg_loss /= batchedData.numBatches # len(train_loader)
print('Training Summary Epoch: [{0}]\t'
'Average Loss {loss:.3f}\t'
.format(epoch + 1, loss=avg_loss, ))
return avg_loss
model.train()
loss_save = []
time_save = []
for epoch in range(start_epoch, args.epochs):
startTime = time.time()
loss_save.append(train_one_epoch(epoch))
endTime = time.time()
time_save.append(endTime - startTime)
print("epoch {} used {} seconds".format(epoch, endTime - startTime))
time_save.sort()
median_time = time_save[int(args.epochs / 2)]
with open(args.write_to, "w") as f:
f.write("unit: " + "1 epoch\n")
for loss in loss_save:
f.write("{}\n".format(loss))
f.write("run time: " + str(0.0) + " " + str(median_time) + "\n")
def main():
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if params.rnn_type == 'gru' and params.rnn_act_type != 'tanh':
print("ERROR: GRU does not currently support activations other than tanh")
sys.exit()
if params.rnn_type == 'rnn' and params.rnn_act_type != 'relu':
print("ERROR: We should be using ReLU RNNs")
sys.exit()
print("=======================================================")
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(params.epochs), torch.Tensor(params.epochs), torch.Tensor(params.epochs)
best_wer = None
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
#cuda = torch.device('cuda')
criterion = torch.nn.CTCLoss()#.to(cuda)
with open(params.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
# audio_conf = dict(sample_rate=params.sample_rate,
# window_size=params.window_size,
# window_stride=params.window_stride,
# window=params.window,
# noise_dir=params.noise_dir,
# noise_prob=params.noise_prob,
# noise_levels=(params.noise_min, params.noise_max))
# train_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=params.train_manifest, labels=labels,
# normalize=True, augment=params.augment)
# test_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=params.val_manifest, labels=labels,
# normalize=True, augment=False)
# train_loader = AudioDataLoader(train_dataset, batch_size=params.batch_size,
# num_workers=1)
# test_loader = AudioDataLoader(test_dataset, batch_size=params.batch_size,
# num_workers=1)
rnn_type = params.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size = params.hidden_size,
nb_layers = params.hidden_layers,
labels = labels,
rnn_type = supported_rnns[rnn_type],
audio_conf = None,
bidirectional = True,
rnn_activation = params.rnn_act_type,
bias = params.bias)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=params.lr,
momentum=params.momentum, nesterov=True,
weight_decay = params.l2)
# decoder = GreedyDecoder(labels)
if args.continue_from:
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from)
model.load_state_dict(package['state_dict'])
optimizer.load_state_dict(package['optim_dict'])
start_epoch = int(package.get('epoch', 1)) - 1 # Python index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # Assume that we saved a model after an epoch finished, so start at the next epoch.
start_iter = 0
else:
start_iter += 1
avg_loss = int(package.get('avg_loss', 0))
if args.start_epoch != -1:
start_epoch = args.start_epoch
loss_results[:start_epoch], cer_results[:start_epoch], wer_results[:start_epoch] = package['loss_results'][:start_epoch], package[ 'cer_results'][:start_epoch], package['wer_results'][:start_epoch]
print(loss_results)
epoch = start_epoch
else:
avg_loss = 0
start_epoch = 0
start_iter = 0
avg_training_loss = 0
if params.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
ctc_time = AverageMeter()
forward_time = AverageMeter()
backward_time = AverageMeter()
filename = "/scratch/wu636/Lantern/src/out/PLDI19evaluation/deepspeech2/ds2-pytorch/data/test/deepspeech_train.pickle"
# filename = "/scratch/wu636/training/speech_recognition/data/test/deep_speech_train.pickle"
batchedData = user_defined_input.Batch(filename)
for epoch in range(start_epoch, params.epochs):
model.train()
end = time.time()
for i in range(batchedData.numBatches):
inputs, targets, input_percentages, target_sizes = batchedData.batch()
inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)
input_percentages = torch.from_numpy(input_percentages)
target_sizes = torch.from_numpy(target_sizes)
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs, requires_grad=False)
target_sizes = Variable(target_sizes, requires_grad=False)
targets = Variable(targets, requires_grad=False)
if params.cuda:
inputs = inputs.cuda()
# measure forward pass time
forward_start_time = time.time()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int(), requires_grad=False)
# measure ctc loss computing time
ctc_start_time = time.time()
loss = criterion(out, targets, sizes, target_sizes)
ctc_time.update(time.time() - ctc_start_time)
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.data.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss.data.item()
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
forward_time.update(time.time() - forward_start_time)
# measure backward pass time
backward_start_time = time.time()
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), params.max_norm)
# SGD step
optimizer.step()
if params.cuda:
torch.cuda.synchronize()
backward_time.update(time.time() - backward_start_time)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if ((i+1) % 20 == 0):
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Forward {forward_time.val:.3f} ({forward_time.avg:.3f})\t'
'CTC Time {ctc_time.val:.3f} ({ctc_time.avg:.3f})\t'
'Backward {backward_time.val:.3f} ({backward_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1), batchedData.numBatches, batch_time=batch_time,
data_time=data_time, forward_time=forward_time, ctc_time=ctc_time,
backward_time=backward_time, loss=losses))
del loss
del out
avg_loss /= batchedData.numBatches # len(train_loader)
print('Training Summary Epoch: [{0}]\t'
'Average Loss {loss:.3f}\t'
.format( epoch + 1, loss=avg_loss, ))
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])
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)
model.cuda()
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model)
criterion = CTCLoss()
seconds = int(args.seconds)
batch_size = int(args.batch_size)
def iteration(inputs):
# targets, align half of the audio
targets = torch.ones(int(batch_size * ((seconds * 100) / 2)))
print(denoiser)
print("Number of parameters: %d" % DeepSpeech.get_param_size(denoiser))
criterion = CTCLoss()
criterion1 = torch.nn.L1Loss()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch in range(start_epoch, args.epochs):
model.eval()
for name, module in model.named_modules():
#print(module._get_name())
if module._get_name() in ['GRU']:
module.train()
for param in model.parameters():
param.requires_grad = False
denoiser.train()
end = time.time()
start_epoch_time = time.time()
for i, (data) in enumerate(zip(train_loader_clean, train_loader_adv),
start=start_iter):
if i == len(train_sampler_clean):
break
data_clean = data[0]
data_adv = data[1]
inputs, targets, input_percentages, target_sizes = data_clean
inputs_adv, targets_adv, input_percentages_adv, target_sizes_adv = data_adv
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
input_sizes_adv = input_percentages_adv.mul_(int(
'''
model_student = DeepSpeech.load_model_package(package)
parameters_student = model_student.parameters()
optimizer_student = torch.optim.SGD(parameters_student, lr=args.lr,
momentum=args.momentum, nesterov=True)
'''
# restart student model from scratch
rnn_type = args.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model_student = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=args.bidirectional)
parameters_student = model_student.parameters()
optimizer_student = torch.optim.SGD(parameters_student,
lr=args.lr,
momentum=args.momentum,
nesterov=True)
if not args.finetune: # Don't want to restart training
optimizer_teacher.load_state_dict(package['optim_dict'])
# Temporary fix for pytorch #2830 & #1442 while pull request #3658 in not incorporated in a release
# TODO : remove when a new release of pytorch include pull request #3658
if args.cuda:
for state in optimizer_teacher.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
def main():
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if params.rnn_type == 'gru' and params.rnn_act_type != 'tanh':
print("ERROR: GRU does not currently support activations other than tanh")
sys.exit()
if params.rnn_type == 'rnn' and params.rnn_act_type != 'relu':
print("ERROR: We should be using ReLU RNNs")
sys.exit()
print("=======================================================")
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(params.epochs), torch.Tensor(params.epochs), torch.Tensor(params.epochs)
best_wer = None
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
criterion = CTCLoss()
with open(params.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
audio_conf = dict(sample_rate=params.sample_rate,
window_size=params.window_size,
window_stride=params.window_stride,
window=params.window,
noise_dir=params.noise_dir,
noise_prob=params.noise_prob,
noise_levels=(params.noise_min, params.noise_max))
train_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=params.train_manifest, labels=labels,
normalize=True, augment=params.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=params.val_manifest, labels=labels,
normalize=True, augment=False)
train_loader = AudioDataLoader(train_dataset, batch_size=params.batch_size,
num_workers=1)
test_loader = AudioDataLoader(test_dataset, batch_size=params.batch_size,
num_workers=1)
rnn_type = params.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size = params.hidden_size,
nb_layers = params.hidden_layers,
labels = labels,
rnn_type = supported_rnns[rnn_type],
audio_conf = audio_conf,
bidirectional = True,
rnn_activation = params.rnn_act_type,
bias = params.bias)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=params.lr,
momentum=params.momentum, nesterov=True,
weight_decay = params.l2)
decoder = GreedyDecoder(labels)
if args.continue_from:
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from)
model.load_state_dict(package['state_dict'])
optimizer.load_state_dict(package['optim_dict'])
start_epoch = int(package.get('epoch', 1)) - 1 # Python index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # Assume that we saved a model after an epoch finished, so start at the next epoch.
start_iter = 0
else:
start_iter += 1
avg_loss = int(package.get('avg_loss', 0))
if args.start_epoch != -1:
start_epoch = args.start_epoch
loss_results[:start_epoch], cer_results[:start_epoch], wer_results[:start_epoch] = package['loss_results'][:start_epoch], package[ 'cer_results'][:start_epoch], package['wer_results'][:start_epoch]
print(loss_results)
epoch = start_epoch
else:
avg_loss = 0
start_epoch = 0
start_iter = 0
avg_training_loss = 0
if params.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
ctc_time = AverageMeter()
for epoch in range(start_epoch, params.epochs):
model.train()
end = time.time()
for i, (data) in enumerate(train_loader, start=start_iter):
if i == len(train_loader):
break
inputs, targets, input_percentages, target_sizes = data
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs, requires_grad=False)
target_sizes = Variable(target_sizes, requires_grad=False)
targets = Variable(targets, requires_grad=False)
if params.cuda:
inputs = inputs.cuda()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int(), requires_grad=False)
ctc_start_time = time.time()
loss = criterion(out, targets, sizes, target_sizes)
ctc_time.update(time.time() - ctc_start_time)
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.data.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss.data[0]
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), params.max_norm)
# SGD step
optimizer.step()
if params.cuda:
torch.cuda.synchronize()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'CTC Time {ctc_time.val:.3f} ({ctc_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1), len(train_loader), batch_time=batch_time,
data_time=data_time, ctc_time=ctc_time, loss=losses))
del loss
del out
avg_loss /= len(train_loader)
print('Training Summary Epoch: [{0}]\t'
'Average Loss {loss:.3f}\t'
.format( epoch + 1, loss=avg_loss, ))
start_iter = 0 # Reset start iteration for next epoch
total_cer, total_wer = 0, 0
model.eval()
wer, cer = eval_model( model, test_loader, decoder)
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(
epoch + 1, wer=wer, cer=cer))
if args.checkpoint:
file_path = '%s/deepspeech_%d.pth.tar' % (save_folder, epoch + 1)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results),
file_path)
# anneal lr
optim_state = optimizer.state_dict()
optim_state['param_groups'][0]['lr'] = optim_state['param_groups'][0]['lr'] / params.learning_anneal
optimizer.load_state_dict(optim_state)
print('Learning rate annealed to: {lr:.6f}'.format(lr=optim_state['param_groups'][0]['lr']))
if best_wer is None or best_wer > wer:
print("Found better validated model, saving to %s" % args.model_path)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results)
, args.model_path)
best_wer = wer
avg_loss = 0
#If set to exit at a given accuracy, exit
if params.exit_at_acc and (best_wer <= args.acc):
break
print("=======================================================")
print("***Best WER = ", best_wer)
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
def convert(parser):
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if params.rnn_type == 'gru' and params.rnn_act_type != 'tanh':
print("ERROR: GRU does not currently support activations other than tanh")
sys.exit()
if params.rnn_type == 'rnn' and params.rnn_act_type != 'relu':
print("ERROR: We should be using ReLU RNNs")
sys.exit()
print("=======================================================")
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
save_folder = args.save_folder
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
with open(params.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
audio_conf = dict(sample_rate=params.sample_rate,
window_size=params.window_size,
window_stride=params.window_stride,
window=params.window,
noise_dir=params.noise_dir,
noise_prob=params.noise_prob,
noise_levels=(params.noise_min, params.noise_max))
val_batch_size = min(8,params.batch_size_val)
print("Using bs={} for validation. Parameter found was {}".format(val_batch_size,params.batch_size_val))
train_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=params.train_manifest, labels=labels,
normalize=True, augment=params.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=params.val_manifest, labels=labels,
normalize=True, augment=False)
train_loader = AudioDataLoader(train_dataset, batch_size=params.batch_size,
num_workers=(1 if params.cuda else 1))
test_loader = AudioDataLoader(test_dataset, batch_size=val_batch_size,
num_workers=(1 if params.cuda else 1))
rnn_type = params.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size = params.hidden_size,
nb_layers = params.hidden_layers,
labels = labels,
rnn_type = supported_rnns[rnn_type],
audio_conf = audio_conf,
bidirectional = False,
rnn_activation = params.rnn_act_type,
bias = params.bias)
parameters = model.parameters()
if args.continue_from:
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from)
model.load_state_dict(package['state_dict'])
if params.cuda:
model = model.cuda()
if params.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
####################################################
# Begin ONNX conversion
####################################################
model.train(False)
# Input to the model
data = next(iter(train_loader))
inputs, targets, input_percentages, target_sizes = data
inputs = Variable(inputs, requires_grad=False)
target_sizes = Variable(target_sizes, requires_grad=False)
targets = Variable(targets, requires_grad=False)
if params.cuda:
inputs = inputs.cuda()
x = inputs
print(x.size())
# Export the model
onnx_file_path = osp.join(osp.dirname(args.continue_from),osp.basename(args.continue_from).split('.')[0]+".onnx")
print("Saving new ONNX model to: {}".format(onnx_file_path))
torch.onnx.export(model, # model being run
inputs, # model input (or a tuple for multiple inputs)
onnx_file_path, # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
verbose=False)
def main():
args = parser.parse_args()
save_folder = args.save_folder
########
"""
loss_results, cer_results, wer_results = torch.Tensor(args.epochs), torch.Tensor(args.epochs), torch.Tensor(
args.epochs)
best_wer = None
if args.visdom:
from visdom import Visdom
viz = Visdom()
opts = [dict(title=args.visdom_id + ' Loss', ylabel='Loss', xlabel='Epoch'),
dict(title=args.visdom_id + ' WER', ylabel='WER', xlabel='Epoch'),
dict(title=args.visdom_id + ' CER', ylabel='CER', xlabel='Epoch')]
viz_windows = [None, None, None]
epochs = torch.arange(1, args.epochs + 1)
if args.tensorboard:
from logger import TensorBoardLogger
try:
os.makedirs(args.log_dir)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
for file in os.listdir(args.log_dir):
file_path = os.path.join(args.log_dir, file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception as e:
raise
else:
raise
logger = TensorBoardLogger(args.log_dir)
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
"""
########
########
"""
criterion = CTCLoss()
"""
criterion = nn.CrossEntropyLoss()
class_accu = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
class_accu_sum = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
class_accu_sum_120 = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
class_accu_sum_240 = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
class_accu_sum_360 = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
class_accu_sum_480 = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True)
########
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))
train_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.train_manifest, labels=labels,
normalize=True, augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.val_manifest, labels=labels,
normalize=True, augment=False)
train_loader = AudioDataLoader(train_dataset, batch_size=args.batch_size,
num_workers=args.num_workers)
test_loader = AudioDataLoader(test_dataset, batch_size=args.batch_size,
num_workers=args.num_workers)
rnn_type = args.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
########
"""
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=True,
cnn_features=args.cnn_features)
"""
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=True,
cnn_features=args.cnn_features,
kernel=args.kernel,
stride=args.stride)
########
########
#print(list(model.rnns.modules()))
#for rnn in model.rnns.modules():
# print(rnn)#.flatten_parameters()
#def flat_model(model):
# for m in model.modules():
# if isinstance(m, nn.LSTM):
# m.flatten_parameters()
########
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=args.lr,
momentum=args.momentum, nesterov=True)
########
#scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.learning_rate_decay_epochs, gamma=args.learning_rate_decay_rate)
#scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.99)
########
########
"""
decoder = GreedyDecoder(labels)
"""
########
########
"""
if args.continue_from:
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from)
model.load_state_dict(package['state_dict'])
optimizer.load_state_dict(package['optim_dict'])
start_epoch = int(package.get('epoch', 1)) - 1 # Python index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # Assume that we saved a model after an epoch finished, so 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 args.visdom and \
package['loss_results'] is not None and start_epoch > 0: # Add previous scores to visdom graph
x_axis = epochs[0:start_epoch]
y_axis = [loss_results[0:start_epoch], wer_results[0:start_epoch], cer_results[0:start_epoch]]
for x in range(len(viz_windows)):
viz_windows[x] = viz.line(
X=x_axis,
Y=y_axis[x],
opts=opts[x],
)
if args.tensorboard and \
package['loss_results'] is not None and start_epoch > 0: # Previous scores to tensorboard logs
for i in range(start_epoch):
info = {
'Avg Train Loss': loss_results[i],
'Avg WER': wer_results[i],
'Avg CER': cer_results[i]
}
for tag, val in info.items():
logger.scalar_summary(tag, val, i + 1)
if not args.no_bucketing and epoch != 0:
print("Using bucketing sampler for the following epochs")
train_dataset = SpectrogramDatasetWithLength(audio_conf=audio_conf, manifest_filepath=args.train_manifest,
labels=labels,
normalize=True, augment=args.augment)
sampler = BucketingSampler(train_dataset)
train_loader.sampler = sampler
else:
avg_loss = 0
start_epoch = 0
start_iter = 0
"""
avg_loss = 0
start_epoch = 0
start_iter = 0
best_train_accu = 0
best_train_accu_sum = 0
best_train_accu_sum_120 = 0
best_train_accu_sum_240 = 0
best_train_accu_sum_360 = 0
best_train_accu_sum_480 = 0
best_test_accu = 0
best_test_accu_sum = 0
best_test_accu_sum_120 = 0
best_test_accu_sum_240 = 0
best_test_accu_sum_360 = 0
best_test_accu_sum_480 = 0
best_avg_loss = float("inf") # sys.float_info.max # 1000000
epoch_70 = None
epoch_90 = None
epoch_95 = None
epoch_99 = None
if args.stride == 1: multiplier = 6
if args.stride == 2: multiplier = 3
if args.stride == 3: multiplier = 2
if args.stride == 4: multiplier = 1 # (Should be 1.5...)
#sample_time_steps = int(args.sample_miliseconds / 10)
loss_begin = round(args.crop_begin / (10 * args.stride))
loss_end = -round(args.crop_end / (10 * args.stride)) or None
print("LOSS BEGIN:", loss_begin)
print("LOSS END:", loss_end)
########
if args.cuda:
model = torch.nn.DataParallel(model).cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch in range(start_epoch, args.epochs):
########
#scheduler.step()
optim_state_now = optimizer.state_dict()
print('\nLEARNING RATE: {lr:.6f}'.format(lr=optim_state_now['param_groups'][0]['lr']))
class_accu.reset()
class_accu_sum.reset()
class_accu_sum_120.reset()
class_accu_sum_240.reset()
class_accu_sum_360.reset()
class_accu_sum_480.reset()
########
model.train()
end = time.time()
for i, (data) in enumerate(train_loader, start=start_iter):
if i == len(train_loader):
break
########
"""
inputs, targets, input_percentages, target_sizes = data
"""
inputs, targets, input_percentages, target_sizes, speaker_labels = data
########
# measure data loading time
data_time.update(time.time() - end)
inputs = Variable(inputs, requires_grad=False)
########
"""
target_sizes = Variable(target_sizes, requires_grad=False)
targets = Variable(targets, requires_grad=False)
"""
speaker_labels = Variable(speaker_labels, requires_grad=False)
########
if args.cuda:
inputs = inputs.cuda()
########
"""
out = model(inputs)
"""
#temp_random = random.randint(0, (inputs.size(3)-1)-sample_time_steps)
#print("INPUT", inputs[...,temp_random:temp_random+sample_time_steps].size(),temp_random, temp_random+sample_time_steps)
#out = model(inputs[...,temp_random:temp_random+sample_time_steps])
#print("OUTPUT", out.size())
start = random.randint(0, int((inputs.size(3)-1)*(1-args.sample_proportion)))
print("INPUT", inputs.size(3), inputs[...,start:start+int((inputs.size(3))*(args.sample_proportion))].size(),start, start+int((inputs.size(3))*(args.sample_proportion)))
out = model(inputs[...,start:start+int((inputs.size(3))*(args.sample_proportion))])
print("OUTPUT", out.size())
########
out = out.transpose(0, 1) # TxNxH
########
speaker_labels = speaker_labels.cuda(async=True).long()
# Prints the output of the model in a sequence of probabilities of char for each audio...
torch.set_printoptions(profile="full")
####print("OUT: " + str(out.size()), "SPEAKER LABELS:" + str(speaker_labels.size()), "INPUT PERCENTAGES MEAN: " + str(input_percentages.mean()))
"""
seq_length = out.size(0)
sizes = Variable(input_percentages.mul_(int(seq_length)).int(), requires_grad=False)
loss = criterion(out, targets, sizes, target_sizes)
"""
#print(out[:,:,0])
#print("SPEAKER LABELS: " + str(speaker_labels))
#print(out[0][0])
#softmax_output = F.softmax(out).data # This DOES NOT what I want...
#softmax_output_alt = flex_softmax(out, axis=2).data # This is FINE!!! <<<===
#print(softmax_output[0][0])
#print(softmax_output_alt[0][0])
####new_out = torch.sum(out, 0)
####new_out = torch.sum(out[20:], 0)
#print(out.size())
#print(new_out.size())
#print(out[-1].size())
########
########
if args.loss_type == "reg":
#loss_out = out[-1]; loss_speaker_labels = speaker_labels
loss_out = out[round(out.size(0)/2)]; loss_speaker_labels = speaker_labels
#print("LOSS TYPE = REGULAR")
elif args.loss_type == "sum":
loss_out = torch.sum(out[loss_begin:loss_end], 0); loss_speaker_labels = speaker_labels
#print("LOSS TYPE = SUM")
elif args.loss_type == "full":
# Don't know if is ok!!! Don't use!!! => loss_out = out.contiguous().view(-1,48); loss_speaker_labels = speaker_labels.repeat(out.size(0)) #speaker_labels = speaker_labels.expand(20, out.size(0))
# Don't know if is ok!!! Don't use!!! => loss_out = out.contiguous().view(-1,48); loss_speaker_labels = speaker_labels.repeat(1, out.size(0)).squeeze() #speaker_labels = speaker_labels.expand(20, out.size(0))
loss_out = out.contiguous()[loss_begin:loss_end].view(-1,48); loss_speaker_labels = speaker_labels.repeat(out.size(0),1)[loss_begin:loss_end].view(-1) #speaker_labels = speaker_labels.expand(20, out.size(0))
#print("LOSS TYPE = FULL")
print("LOSS_OUT: " + str(loss_out.size()), "SPEAKER LABELS:" + str(loss_speaker_labels.size()))
loss = criterion(loss_out, loss_speaker_labels)
########
loss = loss / inputs.size(0) # average the loss by minibatch
loss_sum = loss.data.sum()
inf = float("inf")
if loss_sum == inf or loss_sum == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
else:
loss_value = loss.data[0]
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
########
#if args.stride == 1: multiplier = 6
#if args.stride == 2: multiplier = 3
#if args.stride == 3: multiplier = 2
#if args.stride == 4: multiplier = 1 #(Should be 1.5...)
#if args.stride == 5: multiplier = 1 #(Should be 1.25...)
class_accu.add(out[round(out.size(0)/2)].data, speaker_labels.data)
class_accu_sum.add(torch.sum(out, 0).data, speaker_labels.data)
#class_accu_sum_120.add(torch.sum(out[1*multiplier:-1*multiplier], 0).data, speaker_labels.data)
#class_accu_sum_240.add(torch.sum(out[2*multiplier:-2*multiplier], 0).data, speaker_labels.data)
#class_accu_sum_360.add(torch.sum(out[3*multiplier:-3*multiplier], 0).data, speaker_labels.data)
#class_accu_sum_480.add(torch.sum(out[4*multiplier:-4*multiplier], 0).data, speaker_labels.data)
####class_accu_sum_120.add(torch.sum(out[round(out.size(0)/2)-1*multiplier:round(out.size(0)/2)+1*multiplier], 0).data, speaker_labels.data)
####class_accu_sum_240.add(torch.sum(out[round(out.size(0)/2)-2*multiplier:round(out.size(0)/2)+2*multiplier], 0).data, speaker_labels.data)
####class_accu_sum_360.add(torch.sum(out[round(out.size(0)/2)-3*multiplier:round(out.size(0)/2)+3*multiplier], 0).data, speaker_labels.data)
####class_accu_sum_480.add(torch.sum(out[round(out.size(0)/2)-4*multiplier:round(out.size(0)/2)+4*multiplier], 0).data, speaker_labels.data)
#accu_out3 = torch.sum(flex_softmax(out[20:], axis=2), 0)
#print(classaccu.value()[0], classaccu.value()[1])
# Cross Entropy Loss for a Sequence (Time Series) of Output?
#output = output.view(-1,29)
#target = target.view(-1)
#criterion = nn.CrossEntropyLoss()
#loss = criterion(output,target)
########
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), args.max_norm)
# SGD step
optimizer.step()
if args.cuda:
torch.cuda.synchronize()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if not args.silent:
########
"""
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1), len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses))
"""
print('Epoch: [{0}][{1}/{2}]\t'
# 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
# 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'CAR {car:.3f}\t'
'CAR_SUM {car_sum:.3f}\t'
#'CAR_SUM_120 {car_sum_120:.3f}\t'
#'CAR_SUM_240 {car_sum_240:.3f}\t'
#'CAR_SUM_360 {car_sum_360:.3f}\t'
#'CAR_SUM_480 {car_sum_480:.3f}\t'
.format((epoch + 1), (i + 1), len(train_loader), batch_time=batch_time, data_time=data_time,
loss=losses, car=class_accu.value()[0], car_sum=class_accu_sum.value()[0],
# car_sum_240=class_accu_sum_240.value()[0], car_sum_120=class_accu_sum_120.value()[0],
# car_sum_360=class_accu_sum_360.value()[0], car_sum_480=class_accu_sum_480.value()[0]
)
)
########
########
"""
if args.checkpoint_per_batch > 0 and i > 0 and (i + 1) % args.checkpoint_per_batch == 0:
file_path = '%s/deepspeech_checkpoint_epoch_%d_iter_%d.pth.tar' % (save_folder, epoch + 1, i + 1)
print("Saving checkpoint model to %s" % file_path)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, iteration=i,
loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results, avg_loss=avg_loss),
file_path)
"""
########
del loss
del out
########
del loss_out
del speaker_labels
del loss_speaker_labels
########
avg_loss /= len(train_loader)
########
"""
print('Training Summary Epoch: [{0}]\t'
'Average Loss {loss:.3f}\t'.format(
epoch + 1, loss=avg_loss))
"""
if (best_avg_loss > avg_loss): best_avg_loss = avg_loss
print("\nCURRENT EPOCH TRAINING RESULTS:\t", class_accu.value()[0], "\t", class_accu_sum.value()[0],"\t",
#class_accu_sum_120.value()[0], class_accu_sum_240.value()[0], class_accu_sum_360.value()[0], "\t", class_accu_sum_480.value()[0], "\n"
)
if (best_train_accu < class_accu.value()[0]): best_train_accu = class_accu.value()[0]
if (best_train_accu_sum < class_accu_sum.value()[0]): best_train_accu_sum = class_accu_sum.value()[0]
#if (best_train_accu_sum_120 < class_accu_sum_120.value()[0]): best_train_accu_sum_120 = class_accu_sum_120.value()[0]
#if (best_train_accu_sum_240 < class_accu_sum_240.value()[0]): best_train_accu_sum_240 = class_accu_sum_240.value()[0]
#if (best_train_accu_sum_360 < class_accu_sum_360.value()[0]): best_train_accu_sum_360 = class_accu_sum_360.value()[0]
#if (best_train_accu_sum_480 < class_accu_sum_480.value()[0]): best_train_accu_sum_480 = class_accu_sum_480.value()[0]
get_70 = ((class_accu.value()[0] > 70) or (class_accu_sum.value()[0] > 70)
#or (class_accu_sum_120.value()[0] > 70) or (class_accu_sum_240.value()[0] > 70)
#or (class_accu_sum_360.value()[0] > 70) or (class_accu_sum_480.value()[0] > 70)
)
if ((epoch_70 is None) and (get_70 == True)): epoch_70 = epoch + 1
get_90 = ((class_accu.value()[0] > 90) or (class_accu_sum.value()[0] > 90)
#or (class_accu_sum_120.value()[0] > 90) or (class_accu_sum_240.value()[0] > 90)
#or (class_accu_sum_360.value()[0] > 90) or (class_accu_sum_480.value()[0] > 90)
)
if ((epoch_90 is None) and (get_90 == True)): epoch_90 = epoch + 1
get_95 = ((class_accu.value()[0] > 95) or (class_accu_sum.value()[0] > 95)
#or (class_accu_sum_120.value()[0] > 95) or (class_accu_sum_240.value()[0] > 95)
#or (class_accu_sum_360.value()[0] > 95) or (class_accu_sum_480.value()[0] > 95)
)
if ((epoch_95 is None) and (get_95 == True)): epoch_95 = epoch + 1
get_99 = ((class_accu.value()[0] > 99) or (class_accu_sum.value()[0] > 99)
#or (class_accu_sum_120.value()[0] > 99) or (class_accu_sum_240.value()[0] > 99)
#or (class_accu_sum_360.value()[0] > 99) or (class_accu_sum_480.value()[0] > 99)
)
if ((epoch_99 is None) and (get_99 == True)): epoch_99 = epoch + 1
########
start_iter = 0 # Reset start iteration for next epoch
total_cer, total_wer = 0, 0
model.eval()
########
class_accu.reset()
class_accu_sum.reset()
class_accu_sum_120.reset()
class_accu_sum_240.reset()
class_accu_sum_360.reset()
class_accu_sum_480.reset()
########
for i, (data) in enumerate(test_loader): # test
########
"""
inputs, targets, input_percentages, target_sizes = data
"""
inputs, targets, input_percentages, target_sizes, speaker_labels = data
########
inputs = Variable(inputs, volatile=True)
########
speaker_labels = Variable(speaker_labels, requires_grad=False)
speaker_labels = speaker_labels.cuda(async=True).long()
"""
# unflatten targets
split_targets = []
offset = 0
for size in target_sizes:
split_targets.append(targets[offset:offset + size])
offset += size
"""
########
if args.cuda:
inputs = inputs.cuda()
out = model(inputs)
out = out.transpose(0, 1) # TxNxH
########
speaker_labels = speaker_labels.cuda(async=True).long()
# Prints the output of the model in a sequence of probabilities of char for each audio...
torch.set_printoptions(profile="full")
########print("OUT: " + str(out.size()), "NEW OUT:" + str(new_out.size()), "SPEAKER LABELS:" + str(speaker_labels.size()), "INPUT PERCENTAGES MEAN: " + str(input_percentages.mean()))
#print(out[:,:,0])
#print("SPEAKER LABELS: " + str(speaker_labels))
#print(out[0][0])
#softmax_output = F.softmax(out).data # This DOES NOT what I want...
#softmax_output_alt = flex_softmax(out, axis=2).data # This is FINE!!! <<<===
#print(softmax_output[0][0])
#print(softmax_output_alt[0][0])
########
########
#if args.stride == 1: multiplier = 6
#if args.stride == 2: multiplier = 3
#if args.stride == 3: multiplier = 2
#if args.stride == 4: multiplier = 1 #(Should be 1.5...)
#if args.stride == 5: multiplier = 1 #(Should be 1.25...)
class_accu.add(out[round(out.size(0)/2)].data, speaker_labels.data)
class_accu_sum.add(torch.sum(out, 0).data, speaker_labels.data)
class_accu_sum_120.add(torch.sum(out[1*multiplier:-1*multiplier], 0).data, speaker_labels.data)
class_accu_sum_240.add(torch.sum(out[2*multiplier:-2*multiplier], 0).data, speaker_labels.data)
class_accu_sum_360.add(torch.sum(out[3*multiplier:-3*multiplier], 0).data, speaker_labels.data)
class_accu_sum_480.add(torch.sum(out[4*multiplier:-4*multiplier], 0).data, speaker_labels.data)
#class_accu_sum_120.add(torch.sum(out[round(out.size(0)/2)-1*multiplier:round(out.size(0)/2)+1*multiplier], 0).data, speaker_labels.data)
#class_accu_sum_240.add(torch.sum(out[round(out.size(0)/2)-2*multiplier:round(out.size(0)/2)+2*multiplier], 0).data, speaker_labels.data)
#class_accu_sum_360.add(torch.sum(out[round(out.size(0)/2)-3*multiplier:round(out.size(0)/2)+3*multiplier], 0).data, speaker_labels.data)
#class_accu_sum_480.add(torch.sum(out[round(out.size(0)/2)-4*multiplier:round(out.size(0)/2)+4*multiplier], 0).data, speaker_labels.data)
#accu_out3 = torch.sum(flex_softmax(out[20:], axis=2), 0)
#print(classaccu.value()[0], classaccu.value()[1])
# Cross Entropy Loss for a Sequence (Time Series) of Output?
#output = output.view(-1,29)
#target = target.view(-1)
#criterion = nn.CrossEntropyLoss()
#loss = criterion(output,target)
print('Validation Summary Epoch: [{0}]\t'
'CAR {car:.3f}\t'
'CAR_SUM {car_sum:.3f}\t'
'CAR_SUM_120 {car_sum_120:.3f}\t'
'CAR_SUM_240 {car_sum_240:.3f}\t'
'CAR_SUM_360 {car_sum_360:.3f}\t'
'CAR_SUM_480 {car_sum_480:.3f}\t'
.format(epoch + 1, car=class_accu.value()[0], car_sum=class_accu_sum.value()[0],
car_sum_240=class_accu_sum_240.value()[0], car_sum_120=class_accu_sum_120.value()[0],
car_sum_360=class_accu_sum_360.value()[0], car_sum_480=class_accu_sum_480.value()[0]
)
)
"""
seq_length = out.size(0)
sizes = input_percentages.mul_(int(seq_length)).int()
decoded_output = decoder.decode(out.data, sizes)
target_strings = decoder.process_strings(decoder.convert_to_strings(split_targets))
wer, cer = 0, 0
for x in range(len(target_strings)):
wer += decoder.wer(decoded_output[x], target_strings[x]) / float(len(target_strings[x].split()))
cer += decoder.cer(decoded_output[x], target_strings[x]) / float(len(target_strings[x]))
total_cer += cer
total_wer += wer
"""
########
if args.cuda:
torch.cuda.synchronize()
del out
########
"""
wer = total_wer / len(test_loader.dataset)
cer = total_cer / len(test_loader.dataset)
wer *= 100
cer *= 100
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(
epoch + 1, wer=wer, cer=cer))
"""
########
########
print("\nCURRENT EPOCH TEST RESULTS:\t", class_accu.value()[0], "\t", class_accu_sum.value()[0],
"\t", class_accu_sum_120.value()[0], "\t", class_accu_sum_240.value()[0],
"\t", class_accu_sum_360.value()[0], "\t", class_accu_sum_480.value()[0], "\n")
if (best_test_accu < class_accu.value()[0]): best_test_accu = class_accu.value()[0]
if (best_test_accu_sum < class_accu_sum.value()[0]): best_test_accu_sum = class_accu_sum.value()[0]
if (best_test_accu_sum_120 < class_accu_sum_120.value()[0]): best_test_accu_sum_120 = class_accu_sum_120.value()[0]
if (best_test_accu_sum_240 < class_accu_sum_240.value()[0]): best_test_accu_sum_240 = class_accu_sum_240.value()[0]
if (best_test_accu_sum_360 < class_accu_sum_360.value()[0]): best_test_accu_sum_360 = class_accu_sum_360.value()[0]
if (best_test_accu_sum_480 < class_accu_sum_480.value()[0]): best_test_accu_sum_480 = class_accu_sum_480.value()[0]
print("\nBEST EPOCH TRAINING RESULTS:\t", best_train_accu, "\t", best_train_accu_sum,
"\t", best_train_accu_sum_120, "\t", best_train_accu_sum_240,
"\t", best_train_accu_sum_360, "\t", best_train_accu_sum_480)
print("\nBEST EPOCH TEST RESULTS:\t", best_test_accu, "\t", best_test_accu_sum,
"\t", best_test_accu_sum_120, "\t", best_test_accu_sum_240,
"\t", best_test_accu_sum_360, "\t", best_test_accu_sum_480)
print("\nEPOCHS 70%, 90%, 95%, 99%:\t", epoch_70, "\t", epoch_90, "\t", epoch_95, "\t", epoch_99)
print("\nBEST AVERAGE LOSS:\t", best_avg_loss, "\n")
########
########
"""
if args.visdom:
# epoch += 1
x_axis = epochs[0:epoch + 1]
y_axis = [loss_results[0:epoch + 1], wer_results[0:epoch + 1], cer_results[0:epoch + 1]]
for x in range(len(viz_windows)):
if viz_windows[x] is None:
viz_windows[x] = viz.line(
X=x_axis,
Y=y_axis[x],
opts=opts[x],
)
else:
viz.line(
X=x_axis,
Y=y_axis[x],
win=viz_windows[x],
update='replace',
)
if args.tensorboard:
info = {
'Avg Train Loss': avg_loss,
'Avg WER': wer,
'Avg CER': cer
}
for tag, val in info.items():
logger.scalar_summary(tag, val, epoch + 1)
if args.log_params:
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
logger.histo_summary(tag, to_np(value), epoch + 1)
logger.histo_summary(tag + '/grad', to_np(value.grad), epoch + 1)
if args.checkpoint:
file_path = '%s/deepspeech_%d.pth.tar' % (save_folder, epoch + 1)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results),
file_path)
# anneal lr
optim_state = optimizer.state_dict()
optim_state['param_groups'][0]['lr'] = optim_state['param_groups'][0]['lr'] / args.learning_anneal
optimizer.load_state_dict(optim_state)
print('Learning rate annealed to: {lr:.6f}'.format(lr=optim_state['param_groups'][0]['lr']))
if best_wer is None or best_wer > wer:
print("Found better validated model, saving to %s" % args.model_path)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results)
, args.model_path)
best_wer = wer
"""
########
avg_loss = 0
if not args.no_bucketing and epoch == 0:
print("Switching to bucketing sampler for following epochs")
train_dataset = SpectrogramDatasetWithLength(audio_conf=audio_conf, manifest_filepath=args.train_manifest,
labels=labels,
normalize=True, augment=args.augment)
sampler = BucketingSampler(train_dataset)
train_loader.sampler = sampler
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)
def iteration(inputs):
def main(args):
# Set seeds for determinism
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
labels = LABELS
audio_conf = dict(sample_rate=args.sample_rate,
window_size=args.window_size,
window_stride=args.window_stride,
window=args.window)
rnn_type = args.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=args.rnn_hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=args.bidirectional)
# Data setup
evaluation_decoder = GreedyDecoder(
model.labels) # Decoder used for validation
train_df = pd.read_csv(args.train_path)
train_dataset = SpeechDataset(args=args, df=train_df)
test_df = pd.read_csv(args.test_path)
test_dataset = SpeechDataset(args=args, df=test_df)
train_loader = AudioDataLoader(dataset=train_dataset,
num_workers=args.num_workers,
batch_size=args.batch_size)
test_loader = AudioDataLoader(dataset=test_dataset,
num_workers=args.num_workers,
batch_size=args.batch_size)
model = model.to(args.device)
parameters = model.parameters()
optimizer = torch.optim.AdomW(parameters,
lr=args.learning_rate,
betas=args.betas,
eps=args.eps,
weight_decay=args.weight_decay)
criterion = CTCLoss()
best_score = 99999
for epoch in range(args.epochs):
train_loss = train_fn(args, train_loader, model, optimizer, criterion)
wer, cer, output_data = test_fn(args=args,
model=model,
decoder=evaluation_decoder,
target_decoder=evaluation_decoder)
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(epoch + 1, wer=wer, cer=cer))
if (wer + cer) / 2 < best_score:
print("**** Model Improved !!!! Saving Model")
torch.save(model.state_dict(), f"best_model.bin")
best_score = (wer + cer) / 2
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)
try:
model.load_state_dict(torch.load(args.weights)['state_dict'],
strict=False)
print('using weights')
except:
print('not using weighs')
model = model.to(device)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=args.lr,
momentum=args.momentum,
nesterov=True,
weight_decay=1e-5)
if optim_state is not None:
optimizer.load_state_dict(optim_state)
model, optimizer = amp.initialize(
model,
optimizer,
opt_level=args.opt_level,
keep_batchnorm_fp32=args.keep_batchnorm_fp32,
loss_scale=args.loss_scale)
if args.distributed:
def main():
args = parser.parse_args()
params.cuda = not bool(args.cpu)
print("Use cuda: {}".format(params.cuda))
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if params.rnn_type == 'gru' and params.rnn_act_type != 'tanh':
print(
"ERROR: GRU does not currently support activations other than tanh"
)
sys.exit()
if params.rnn_type == 'rnn' and params.rnn_act_type != 'relu':
print("ERROR: We should be using ReLU RNNs")
sys.exit()
print("=======================================================")
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(
params.epochs), torch.Tensor(params.epochs), torch.Tensor(
params.epochs)
best_wer = None
try:
os.makedirs(save_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory already exists.')
else:
raise
criterion = CTCLoss()
with open(params.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
audio_conf = dict(sample_rate=params.sample_rate,
window_size=params.window_size,
window_stride=params.window_stride,
window=params.window,
noise_dir=params.noise_dir,
noise_prob=params.noise_prob,
noise_levels=(params.noise_min, params.noise_max))
if args.use_set == 'libri':
testing_manifest = params.val_manifest + ("_held" if args.hold_idx >= 0
else "")
else:
testing_manifest = params.test_manifest
if args.batch_size_val > 0:
params.batch_size_val = args.batch_size_val
print("Testing on: {}".format(testing_manifest))
train_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=params.val_manifest,
labels=labels,
normalize=True,
augment=params.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=testing_manifest,
labels=labels,
normalize=True,
augment=False)
train_loader = AudioDataLoader(train_dataset,
batch_size=params.batch_size,
num_workers=1)
test_loader = AudioDataLoader(test_dataset,
batch_size=params.batch_size_val,
num_workers=1)
rnn_type = params.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=params.hidden_size,
nb_layers=params.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=False,
rnn_activation=params.rnn_act_type,
bias=params.bias)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters,
lr=params.lr,
momentum=params.momentum,
nesterov=True,
weight_decay=params.l2)
decoder = GreedyDecoder(labels)
if args.continue_from:
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from)
model.load_state_dict(package['state_dict'])
optimizer.load_state_dict(package['optim_dict'])
start_epoch = int(package.get(
'epoch', 1)) - 1 # Python index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # Assume that we saved a model after an epoch finished, so start at the next epoch.
start_iter = 0
else:
start_iter += 1
avg_loss = int(package.get('avg_loss', 0))
if args.start_epoch != -1:
start_epoch = args.start_epoch
avg_loss = 0
start_epoch = 0
start_iter = 0
avg_training_loss = 0
epoch = 1
else:
avg_loss = 0
start_epoch = 0
start_iter = 0
avg_training_loss = 0
if params.cuda:
model = torch.nn.DataParallel(model).cuda()
# model = torch.nn.parallel.DistributedDataParallel(model).cuda()
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
ctc_time = AverageMeter()
for epoch in range(start_epoch, params.epochs):
#################################################################################################################
# The test script only really cares about this section.
#################################################################################################################
model.eval()
wer, cer, trials = eval_model_verbose(model, test_loader, decoder,
params.cuda, args.n_trials)
root = os.getcwd()
outfile = osp.join(
root,
"inference_bs{}_i{}_gpu{}.csv".format(params.batch_size_val,
args.hold_idx, params.cuda))
print("Exporting inference to: {}".format(outfile))
make_file(outfile)
write_line(
outfile, "batch times pre normalized by hold_sec =,{}\n".format(
args.hold_sec))
write_line(outfile, "wer, {}\n".format(wer))
write_line(outfile, "cer, {}\n".format(cer))
write_line(outfile, "bs, {}\n".format(params.batch_size_val))
write_line(outfile, "hold_idx, {}\n".format(args.hold_idx))
write_line(outfile, "cuda, {}\n".format(params.cuda))
write_line(outfile,
"avg batch time, {}\n".format(trials.avg / args.hold_sec))
percentile_50 = np.percentile(
trials.array, 50) / params.batch_size_val / args.hold_sec
write_line(outfile, "50%-tile latency, {}\n".format(percentile_50))
percentile_99 = np.percentile(
trials.array, 99) / params.batch_size_val / args.hold_sec
write_line(outfile, "99%-tile latency, {}\n".format(percentile_99))
write_line(outfile, "through put, {}\n".format(1 / percentile_50))
write_line(outfile, "data\n")
for trial in trials.array:
write_line(outfile, "{}\n".format(trial / args.hold_sec))
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(epoch + 1, wer=wer, cer=cer))
# anneal lr
optim_state = optimizer.state_dict()
optim_state['param_groups'][0]['lr'] = optim_state['param_groups'][0][
'lr'] / params.learning_anneal
optimizer.load_state_dict(optim_state)
print('Learning rate annealed to: {lr:.6f}'.format(
lr=optim_state['param_groups'][0]['lr']))
break
print("=======================================================")
print("***Best WER = ", best_wer)
for arg in vars(args):
print("***%s = %s " % (arg.ljust(25), getattr(args, arg)))
print("=======================================================")
