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main.py
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main.py
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import time
import torch
from aeon import DataLoader, gen_backend
import numpy as np
from torch.autograd import Variable
import argparse
import progressbar
from CTCLoss import ctc_loss
from decoder import ArgMaxDecoder
from model import DeepSpeech
parser = argparse.ArgumentParser(description='DeepSpeech pytorch params')
parser.add_argument('--noise_manifest', metavar='DIR',
help='path to noise manifest csv', default='noise_manifest.csv')
parser.add_argument('--train_manifest', metavar='DIR',
help='path to train manifest csv', default='train_manifest.csv')
parser.add_argument('--test_manifest', metavar='DIR',
help='path to test manifest csv', default='test_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('--max_transcript_length', default=1300, type=int, help='Maximum size of transcript in training')
parser.add_argument('--frame_length', default=.02, type=float, help='Window size for spectrogram in seconds')
parser.add_argument('--frame_stride', default=.01, type=float, help='Window stride for spectrogram in seconds')
parser.add_argument('--max_duration', default=6.4, type=float,
help='The maximum duration of the training data in seconds')
parser.add_argument('--window', default='hamming', help='Window type for spectrogram generation')
parser.add_argument('--noise_probability', default=0.4, type=float, help='Window type for spectrogram generation')
parser.add_argument('--noise_min', default=0.5, type=float, help='Minimum noise to add')
parser.add_argument('--noise_max', default=1, type=float, help='Maximum noise to add (1 is an SNR of 0 (pure noise)')
parser.add_argument('--hidden_size', default=512, 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')
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 main():
args = parser.parse_args()
minibatch_size = args.batch_size
alphabet = "_'ABCDEFGHIJKLMNOPQRSTUVWXYZ "
nout = len(alphabet)
spect_size = (args.frame_length * args.sample_rate / 2) + 1
be = gen_backend()
audio_config = dict(sample_freq_hz=args.sample_rate,
max_duration="%f seconds" % args.max_duration,
frame_length="%f seconds" % args.frame_length,
frame_stride="%f seconds" % args.frame_stride,
window_type=args.window,
noise_index_file=args.noise_manifest,
add_noise_probability=args.noise_probability,
noise_level=(args.noise_min, args.noise_max)
)
transcription_config = dict(alphabet=alphabet,
max_length=args.max_transcript_length,
pack_for_ctc=True)
train_dataloader_config = dict(type="audio,transcription",
audio=audio_config,
transcription=transcription_config,
manifest_filename=args.train_manifest,
macrobatch_size=minibatch_size,
minibatch_size=minibatch_size)
transcription_config = dict(alphabet=alphabet,
max_length=args.max_transcript_length,
pack_for_ctc=False)
test_dataloader_config = dict(type="audio,transcription",
audio=audio_config,
transcription=transcription_config,
manifest_filename=args.test_manifest,
macrobatch_size=minibatch_size,
minibatch_size=minibatch_size)
train_loader = DataLoader(train_dataloader_config, be)
test_loader = DataLoader(test_dataloader_config, be)
model = DeepSpeech(rnn_hidden_size=args.hidden_size, nb_layers=args.hidden_layers, num_classes=nout)
decoder = ArgMaxDecoder(alphabet=alphabet)
if args.cuda:
model = model.cuda()
print(model)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, args.lr,
momentum=args.momentum)
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch in xrange(args.epochs - 1):
model.train()
end = time.time()
avg_loss = 0
for i, (data) in enumerate(train_loader): # train
# measure data loading time
data_time.update(time.time() - end)
input = data[0].reshape(minibatch_size, 1, spect_size,
-1) # batch x channels x freq x time
label_lengths = Variable(torch.FloatTensor(data[2].astype(dtype=np.float32)).view(-1))
input = Variable(torch.FloatTensor(input.astype(dtype=np.float32)))
target = Variable(torch.FloatTensor(data[1].astype(dtype=np.float32)).view(-1))
if args.cuda:
input = input.cuda()
target = target.cuda()
out = model(input)
max_seq_length = out.size(0)
seq_percentage = torch.FloatTensor(data[3].astype(dtype=np.float32)).view(-1)
sizes = Variable(seq_percentage.mul_(int(max_seq_length) / 100))
loss = ctc_loss(out, target, sizes, label_lengths)
loss = loss / input.size(0) # average the loss by minibatch
avg_loss = avg_loss + loss.data[0]
losses.update(loss.data[0], input.size(0))
# compute gradient
optimizer.zero_grad()
loss.backward()
# rescale gradients if necessary
total_norm = torch.FloatTensor([0])
for param in model.parameters():
param = Variable(param.data).cpu()
total_norm.add_(param.norm().pow(2).data)
total_norm = total_norm.sqrt()
if total_norm[0] > args.max_norm:
for param in model.parameters():
param.grad.mul_(args.max_norm / total_norm[0])
# SGD step
optimizer.step()
# 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'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1), train_loader.nbatches, batch_time=batch_time,
data_time=data_time, loss=losses))
avg_loss = avg_loss / train_loader.nbatches
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
input = data[0].reshape(minibatch_size, 1, spect_size,
-1) # batch x channels x freq x time
input = Variable(torch.FloatTensor(input.astype(dtype=np.float32)))
target = Variable(torch.FloatTensor(
data[1].astype(dtype=np.float32).reshape(args.max_transcript_length, minibatch_size, order='F').T))
if args.cuda:
input = input.cuda()
target = target.cuda()
out = model(input)
decoded_output = decoder.decode(out.data)
target_strings = decoder.process_string(decoder.convert_to_string(target.data))
wer, cer = 0, 0
for x in xrange(len(target_strings)):
wer += decoder.wer(decoded_output[x], target_strings[x])
cer += decoder.cer(decoded_output[x], target_strings[x])
total_cer += cer
total_wer += wer
batch_size = input.size(0)
wer = wer / batch_size
cer = cer / batch_size
print('Validation Epoch: [{0}][{1}/{2}]\t'
'Average WER {wer:.0f}\t'
'Average CER {cer:.0f}\t'.format(
(epoch + 1), (i + 1), test_loader.nbatches, wer=wer, cer=cer))
wer = total_wer / test_loader.ndata
cer = total_cer / test_loader.ndata
# We need to format the targets into actual sentences
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.0f}\t'
'Average CER {cer:.0f}\t'.format(
(epoch + 1), wer=wer, cer=cer))
if __name__ == '__main__':
main()