def testBasic(self):
with self.session():
self.assertAllEqual(
decoder_utils.ComputeWer(hyps=["one"], refs=["one"]).eval(),
[[0, 1]])
self.assertAllEqual(
decoder_utils.ComputeWer(hyps=["one two"],
refs=["one two"]).eval(), [[0, 2]])
def testConsecutiveWhiteSpace(self):
with self.session():
wer = decoder_utils.ComputeWer(
hyps=["one two", "one two", "two pigs"],
refs=["one two", "one two ", "three pink pigs"])
self.assertAllEqual(wer.shape, [3, 2])
self.assertAllEqual(wer.eval(), [[0, 2], [0, 2], [2, 3]])
def _ComputeNormalizedWER(self, hyps, refs):
# Filter out all '<epsilon>' tokens for norm_wer computation.
hyps_no_epsilon = tf.regex_replace(hyps, '(<epsilon>)+', ' ')
# norm_wer is size [num_transcripts * hyps_per_beam, 2]
norm_wer = decoder_utils.ComputeWer(hyps_no_epsilon, refs)
# Split into two tensors of size [num_transcripts * hyps_per_beam, 1]
norm_wer_errors, norm_wer_words = tf.split(norm_wer, [1, 1], 1)
shape = [-1, self.params.decoder.beam_search.num_hyps_per_beam]
norm_wer_errors = tf.reshape(norm_wer_errors, shape)
norm_wer_words = tf.reshape(norm_wer_words, shape)
return norm_wer_errors, norm_wer_words
def _CalculateErrorRates(self, dec_outs_dict, input_batch):
return {'stuff': dec_outs_dict.sparse_ids.values}
gt_seq_lens = py_utils.LengthsFromBitMask(input_batch.tgt.paddings, 1)
gt_transcripts = py_utils.RunOnTpuHost(
self.input_generator.IdsToStrings, input_batch.tgt.labels,
gt_seq_lens)
# token error rate
char_dist = tf.edit_distance(tf.string_split(dec_outs_dict.transcripts,
sep=''),
tf.string_split(gt_transcripts, sep=''),
normalize=False)
ref_chars = tf.strings.length(gt_transcripts)
num_wrong_chars = tf.reduce_sum(char_dist)
num_ref_chars = tf.cast(tf.reduce_sum(ref_chars), tf.float32)
cer = num_wrong_chars / num_ref_chars
# word error rate
word_dist = decoder_utils.ComputeWer(dec_outs_dict.transcripts,
gt_transcripts) # (B, 2)
num_wrong_words = tf.reduce_sum(word_dist[:, 0])
num_ref_words = tf.reduce_sum(word_dist[:, 1])
wer = num_wrong_words / num_ref_words
ret_dict = {
'target_ids': input_batch.tgt.ids,
'target_labels': input_batch.tgt.labels,
'target_weights': input_batch.tgt.weights,
'target_paddings': input_batch.tgt.paddings,
'target_transcripts': gt_transcripts,
'decoded_transcripts': dec_outs_dict.transcripts,
'wer': wer,
'cer': cer,
'num_wrong_words': num_wrong_words,
'num_ref_words': num_ref_words,
'num_wrong_chars': num_wrong_chars,
'num_ref_chars': num_ref_chars
}
if not py_utils.use_tpu():
ret_dict['utt_id'] = input_batch.sample_ids
return ret_dict
def testDifferencesInCaseAreCountedAsErrors(self):
with self.session():
wer = decoder_utils.ComputeWer(hyps=["ONE two", "one two"],
refs=["one two", "ONE two"])
self.assertAllEqual(wer.shape, [2, 2])
self.assertAllEqual(wer.eval(), [[1, 2], [1, 2]])
def testEmptyRefsAndHyps(self):
with self.session():
wer = decoder_utils.ComputeWer(hyps=["", "one two", ""],
refs=["", "", "three four five"])
self.assertAllEqual(wer.shape, [3, 2])
self.assertAllEqual(wer.eval(), [[0, 0], [2, 0], [3, 3]])
def testMultiples(self):
with self.session():
wer = decoder_utils.ComputeWer(hyps=["one", "two pigs"],
refs=["one", "three pink pigs"])
self.assertAllEqual(wer.shape, [2, 2])
self.assertAllEqual(wer.eval(), [[0, 1], [2, 3]])
def testInvalidInputsWrongRank(self):
with self.session():
with self.assertRaises(Exception):
decoder_utils.ComputeWer(hyps=[["one"], ["two"]],
refs=[["one"], ["two"]]).eval()
def testInvalidInputsExtraRefs(self):
with self.session():
with self.assertRaises(Exception):
decoder_utils.ComputeWer(hyps=["one"], refs=["one",
"two"]).eval()
