def test_wer_4(self):
ref = 'the wood flamed up splendidly under the large brewing copper '\
'and it sighed so deeply'
hyp = 'the wood flame do splendidly under the large brewing copper '\
'and its side so deeply'
word_error_rate = error_rate.wer(ref, hyp)
self.assertTrue(abs(word_error_rate - 0.2666666667) < 1e-6)
def test_wer_2(self):
ref = 'as any in england i would say said gamewell proudly that is '\
'in his day'
hyp = 'as any in england i would say said came well proudly that is '\
'in his day'
word_error_rate = error_rate.wer(ref, hyp)
self.assertTrue(abs(word_error_rate - 0.1333333) < 1e-6)
def tune():
"""Tune parameters alpha and beta on one minibatch."""
if not args.num_alphas >= 0:
raise ValueError("num_alphas must be non-negative!")
if not args.num_betas >= 0:
raise ValueError("num_betas must be non-negative!")
data_generator = DataGenerator(
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=1)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.tune_manifest,
batch_size=args.num_samples,
sortagrad=False,
shuffle_method=None)
tune_data = batch_reader().next()
target_transcripts = [
''.join([data_generator.vocab_list[token] for token in transcript])
for _, transcript in tune_data
]
ds2_model = DeepSpeech2Model(
vocab_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
use_gru=args.use_gru,
pretrained_model_path=args.model_path,
share_rnn_weights=args.share_rnn_weights)
# create grid for search
cand_alphas = np.linspace(args.alpha_from, args.alpha_to, args.num_alphas)
cand_betas = np.linspace(args.beta_from, args.beta_to, args.num_betas)
params_grid = [(alpha, beta) for alpha in cand_alphas
for beta in cand_betas]
## tune parameters in loop
for alpha, beta in params_grid:
result_transcripts = ds2_model.infer_batch(
infer_data=tune_data,
decoding_method='ctc_beam_search',
beam_alpha=alpha,
beam_beta=beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.lang_model_path,
num_processes=args.num_proc_bsearch)
wer_sum, num_ins = 0.0, 0
for target, result in zip(target_transcripts, result_transcripts):
wer_sum += wer(target, result)
num_ins += 1
print("alpha = %f\tbeta = %f\tWER = %f" %
(alpha, beta, wer_sum / num_ins))
def test_wer_5(self):
ref = 'all the morning they trudged up the mountain path and at noon '\
'unc and ojo sat on a fallen tree trunk and ate the last of '\
'the bread which the old munchkin had placed in his pocket'
hyp = 'all the morning they trudged up the mountain path and at noon '\
'unc in ojo sat on a fallen tree trunk and ate the last of '\
'the bread which the old munchkin had placed in his pocket'
word_error_rate = error_rate.wer(ref, hyp)
self.assertTrue(abs(word_error_rate - 0.027027027) < 1e-6)
def test_wer_3(self):
ref = 'the lieutenant governor lilburn w boggs afterward governor '\
'was a pronounced mormon hater and throughout the period of '\
'the troubles he manifested sympathy with the persecutors'
hyp = 'the lieutenant governor little bit how bags afterward '\
'governor was a pronounced warman hater and throughout the '\
'period of th troubles he manifests sympathy with the '\
'persecutors'
word_error_rate = error_rate.wer(ref, hyp)
self.assertTrue(abs(word_error_rate - 0.2692307692) < 1e-6)
def test_wer_7(self):
ref = ' '
hyp = 'Hypothesis sentence'
with self.assertRaises(ValueError):
word_error_rate = error_rate.wer(ref, hyp)
def test_wer_6(self):
ref = 'i UM the PHONE IS i LEFT THE portable PHONE UPSTAIRS last night'
word_error_rate = error_rate.wer(ref, ref)
self.assertEqual(word_error_rate, 0.0)
def test_wer_1(self):
ref = 'i UM the PHONE IS i LEFT THE portable PHONE UPSTAIRS last night'
hyp = 'i GOT IT TO the FULLEST i LOVE TO portable FROM OF STORES last '\
'night'
word_error_rate = error_rate.wer(ref, hyp)
self.assertTrue(abs(word_error_rate - 0.769230769231) < 1e-6)
