def add_evaluation(self, utt_id, ref, pred, bpe_symbol=None):
if not isinstance(utt_id, str):
raise TypeError('utt_id must be a string(got {})'.format(
type(utt_id)))
if not isinstance(ref, str):
raise TypeError('ref must be a string (got {})'.format(type(ref)))
if not isinstance(pred, str):
raise TypeError('pred must be a string(got {})'.format(type(pred)))
# filter out any non_lang_syms from ref and pred
non_lang_syms = getattr(self.dictionary, 'non_lang_syms', None)
assert non_lang_syms is None or isinstance(non_lang_syms, list)
if non_lang_syms is not None and len(non_lang_syms) > 0:
ref_list, pred_list = ref.strip().split(), pred.strip().split()
ref = ' '.join([x for x in ref_list if x not in non_lang_syms])
pred = ' '.join([x for x in pred_list if x not in non_lang_syms])
# char level counts
_, _, counter = speech_utils.edit_distance(
ref.strip().split(),
pred.strip().split(),
)
self.char_counter += counter
# word level counts
ref_words = self.dictionary.tokens_to_sentence(ref,
use_unk_sym=False,
bpe_symbol=bpe_symbol)
pred_words = self.dictionary.tokens_to_sentence(pred,
bpe_symbol=bpe_symbol)
# filter words according to self.word_filters (support re.sub only)
for pattern, repl in self.word_filters:
ref_words = re.sub(pattern, repl, ref_words)
pred_words = re.sub(pattern, repl, pred_words)
ref_word_list, pred_word_list = ref_words.split(), pred_words.split()
_, steps, counter = speech_utils.edit_distance(
ref_word_list,
pred_word_list,
)
self.word_counter += counter
assert utt_id not in self.aligned_results, \
'Duplicated utterance id detected: {}'.format(utt_id)
self.aligned_results[utt_id] = speech_utils.aligned_print(
ref_word_list,
pred_word_list,
steps,
)
def add_evaluation(self, utt_id, ref, pred):
if not isinstance(utt_id, str):
raise TypeError("utt_id must be a string(got {})".format(
type(utt_id)))
if not isinstance(ref, str):
raise TypeError("ref must be a string (got {})".format(type(ref)))
if not isinstance(pred, str):
raise TypeError("pred must be a string(got {})".format(type(pred)))
# filter out any non_lang_syms from ref and pred
non_lang_syms = getattr(self.dictionary, "non_lang_syms", None)
assert non_lang_syms is None or isinstance(non_lang_syms, list)
if non_lang_syms is not None and len(non_lang_syms) > 0:
ref_list, pred_list = ref.strip().split(), pred.strip().split()
ref = " ".join([x for x in ref_list if x not in non_lang_syms])
pred = " ".join([x for x in pred_list if x not in non_lang_syms])
# char level counts
_, _, counter = speech_utils.edit_distance(
ref.strip().split(),
pred.strip().split(),
)
self.char_counter += counter
# word level counts
ref_words = self.dictionary.wordpiece_decode(ref)
pred_words = self.dictionary.wordpiece_decode(pred)
# filter words according to self.word_filters (support re.sub only)
for pattern, repl in self.word_filters:
ref_words = re.sub(pattern, repl, ref_words)
pred_words = re.sub(pattern, repl, pred_words)
ref_word_list, pred_word_list = ref_words.split(), pred_words.split()
_, steps, counter = speech_utils.edit_distance(
ref_word_list,
pred_word_list,
)
self.word_counter += counter
assert (utt_id not in self.aligned_results
), "Duplicated utterance id detected: {}".format(utt_id)
self.aligned_results[utt_id] = speech_utils.aligned_print(
ref_word_list,
pred_word_list,
steps,
)