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train.py
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train.py
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import argparse
import errno
import json
import os
import time
import torch
from torch.autograd import Variable
from warpctc_pytorch import CTCLoss
from data.data_loader import AudioDataLoader, SpectrogramDataset
from decoder import ArgMaxDecoder
from model import DeepSpeech
parser = argparse.ArgumentParser(description='DeepSpeech training')
parser.add_argument('--train_manifest', metavar='DIR',
help='path to train manifest csv', default='data/train_manifest.csv')
parser.add_argument('--val_manifest', metavar='DIR',
help='path to validation manifest csv', default='data/val_manifest.csv')
parser.add_argument('--sample_rate', default=16000, type=int, help='Sample rate')
parser.add_argument('--batch_size', default=20, type=int, help='Batch size for training')
parser.add_argument('--num_workers', default=4, type=int, help='Number of workers used in dataloading')
parser.add_argument('--labels_path', default='labels.json', help='Contains all characters for prediction')
parser.add_argument('--window_size', default=.02, type=float, help='Window size for spectrogram in seconds')
parser.add_argument('--window_stride', default=.01, type=float, help='Window stride for spectrogram in seconds')
parser.add_argument('--window', default='hamming', help='Window type for spectrogram generation')
parser.add_argument('--hidden_size', default=400, type=int, help='Hidden size of RNNs')
parser.add_argument('--hidden_layers', default=4, type=int, help='Number of RNN layers')
parser.add_argument('--epochs', default=70, type=int, help='Number of training epochs')
parser.add_argument('--cuda', default=True, type=bool, help='Use cuda to train model')
parser.add_argument('--lr', '--learning-rate', default=3e-4, type=float, help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, help='momentum')
parser.add_argument('--max_norm', default=400, type=int, help='Norm cutoff to prevent explosion of gradients')
parser.add_argument('--learning_anneal', default=1.1, type=float, help='Annealing applied to learning rate every epoch')
parser.add_argument('--silent', default=False, type=bool, help='Turn off progress tracking per iteration')
parser.add_argument('--epoch_save', default=False, type=bool, help='Save model every epoch')
parser.add_argument('--visdom', default=False, type=bool, help='Turn on visdom graphing')
parser.add_argument('--save_folder', default='models/', help='Location to save epoch models')
parser.add_argument('--final_model_path', default='models/deepspeech_final.pth.tar',
help='Location to save final model')
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def checkpoint(model, args, nout, epoch=None):
package = {
'epoch': epoch if epoch else 'N/A',
'hidden_size': args.hidden_size,
'hidden_layers': args.hidden_layers,
'nout': nout,
'state_dict': model.state_dict(),
}
return package
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
model.eval()
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)
if __name__ == '__main__':
main()