def main(args):
with open(args.pk_file1, 'rb') as fin:
hyps1 = pickle.load(fin)
refs = pickle.load(fin)
pickle.load(fin) # hypscores
pickle.load(fin) # refscores
numphones = pickle.load(fin)
with open(args.pk_file2, 'rb') as fin:
hyps2 = pickle.load(fin)
assert len(hyps1) == len(hyps2), 'hyps have different lengths'
differ = difflib.Differ()
num_diff = 0
hyp1_better = 0
hyp2_better = 0
for (hyp1, hyp2, ref) in zip(hyps1, hyps2, refs):
if hyp1 == hyp2:
continue
num_diff += 1
label1 = 'hyp1:'
label2 = 'hyp2:'
if args.score:
dist1, _, _, _, _ = ed.edit_distance(ref, hyp1)
dist2, _, _, _, _ = ed.edit_distance(ref, hyp2)
if dist1 < dist2:
hyp1_better += 1
label1 = blue(label1)
label2 = red(label2)
else:
hyp2_better += 1
label1 = red(label1)
label2 = blue(label2)
print label1, collapse_seq(hyp1)
print label2, collapse_seq(hyp2)
pprint(list(differ.compare([collapse_seq(hyp1)],
[collapse_seq(hyp2)])))
print green(' ref:'), collapse_seq(ref)
print '-' * 80
if args.score:
print 'hyp1 better: %d' % hyp1_better
print 'hyp2 better: %d' % hyp2_better
print 'Differ on %d/%d utts' % (num_diff, len(refs))
def main(args):
with open(args.pk_file1, 'rb') as fin:
hyps1 = pickle.load(fin)
refs = pickle.load(fin)
pickle.load(fin) # hypscores
pickle.load(fin) # refscores
numphones = pickle.load(fin)
with open(args.pk_file2, 'rb') as fin:
hyps2 = pickle.load(fin)
assert len(hyps1) == len(hyps2), 'hyps have different lengths'
differ = difflib.Differ()
num_diff = 0
hyp1_better = 0
hyp2_better = 0
for (hyp1, hyp2, ref) in zip(hyps1, hyps2, refs):
if hyp1 == hyp2:
continue
num_diff += 1
label1 = 'hyp1:'
label2 = 'hyp2:'
if args.score:
dist1, _, _, _, _ = ed.edit_distance(ref, hyp1)
dist2, _, _, _, _ = ed.edit_distance(ref, hyp2)
if dist1 < dist2:
hyp1_better += 1
label1 = blue(label1)
label2 = red(label2)
else:
hyp2_better += 1
label1 = red(label1)
label2 = blue(label2)
print label1, collapse_seq(hyp1)
print label2, collapse_seq(hyp2)
pprint(list(differ.compare([collapse_seq(hyp1)], [collapse_seq(hyp2)])))
print green(' ref:'), collapse_seq(ref)
print '-' * 80
if args.score:
print 'hyp1 better: %d' % hyp1_better
print 'hyp2 better: %d' % hyp2_better
print 'Differ on %d/%d utts' % (num_diff, len(refs))
import numpy as np
from decoder_utils import load_data, load_nnet, decode, int_to_char,\
collapse_seq, load_char_map
if __name__ == '__main__':
print 'Loading data'
fnum = 1
data_dict, alis, keys = load_data(fnum)
print 'Loading neural net'
rnn = load_nnet()
data, labels = data_dict[keys[0]], np.array(alis[keys[0]], dtype=np.int32)
probs = rnn.costAndGrad(data, labels)
probs = np.log(probs.astype(np.float64) + 1e-30)
hyp, hypScore, refScore = decode(probs,
alpha=1.0,
beta=0.0,
beam=200,
method='bg')
char_map = load_char_map()
if labels is not None:
print ' labels:', collapse_seq(int_to_char(labels, char_map))
print ' top hyp:', collapse_seq(int_to_char(hyp, char_map))
print 'score:', hypScore
#print 'ref score:', refScore
import numpy as np
from decoder_utils import load_data, load_nnet, decode, int_to_char, collapse_seq, load_char_map
if __name__ == "__main__":
print "Loading data"
fnum = 1
data_dict, alis, keys = load_data(fnum)
print "Loading neural net"
rnn = load_nnet()
data, labels = data_dict[keys[0]], np.array(alis[keys[0]], dtype=np.int32)
probs = rnn.costAndGrad(data, labels)
probs = np.log(probs.astype(np.float64) + 1e-30)
hyp, hypScore, refScore = decode(probs, alpha=1.0, beta=0.0, beam=200, method="bg")
char_map = load_char_map()
if labels is not None:
print " labels:", collapse_seq(int_to_char(labels, char_map))
print " top hyp:", collapse_seq(int_to_char(hyp, char_map))
print "score:", hypScore
# print 'ref score:', refScore
import numpy as np
from decoder_utils import load_data, load_nnet, int_to_char,\
collapse_seq, decode, load_char_map
if __name__ == '__main__':
print 'Loading data'
fnum = 1
data_dict, alis, keys = load_data(fnum)
print 'Loading neural net'
rnn = load_nnet()
for k in range(0, 10):
data, labels = data_dict[keys[k]], np.array(alis[keys[k]],
dtype=np.int32)
probs = rnn.costAndGrad(data, labels)
probs = np.log(probs.astype(np.float64) + 1e-30)
hyp, hypScore, refscore = decode(probs,
alpha=0.0, beta=0.0, beam=40, method='clm2')
hyp_pmax, _, _ = decode(probs,
alpha=1.0, beta=0.0, method='pmax')
char_map = load_char_map()
if labels is not None:
print ' labels:', collapse_seq(int_to_char(labels, char_map))
print ' top hyp:', collapse_seq(int_to_char(hyp, char_map))
print 'pmax hyp:', collapse_seq(int_to_char(hyp_pmax, char_map))
print 'score:', hypScore
#print 'ref score:', refScore
