def map_fn(self, splited_logits):
_d = []
for value in splited_logits:
_d.append(
ctc_greedy_decoder(probs_seq=value,
vocabulary=self.vocab_array))
return _d
def _perform_greedy(
self,
probs: np.ndarray,
):
from ctc_decoders import ctc_greedy_decoder
decoded = ctc_greedy_decoder(
probs, vocabulary=self.text_featurizer.non_blank_tokens)
return tf.convert_to_tensor(decoded, dtype=tf.string)
def perform_greedy(self, probs: np.ndarray):
decoded = ctc_greedy_decoder(
probs, vocabulary=self.text_featurizer.vocab_array)
return tf.convert_to_tensor(decoded, dtype=tf.string)
0.04139363,
],
[
0.15882358,
0.1235788,
0.23376776,
0.20510435,
0.00279306,
0.05294827,
0.22298418,
],
]
greedy_result = ["ac'bdc", "b'da"]
beam_search_result = ['acdc', "b'a"]
ctc_greedy_decoder(np.array(probs_seq1), vocab_list) == greedy_result[0]
ctc_greedy_decoder(np.array(probs_seq2), vocab_list) == greedy_result[1]
ctc_beam_search_decoder(
probs_seq=np.array(probs_seq1),
beam_size=beam_size,
vocabulary=vocab_list,
)
ctc_beam_search_decoder(
probs_seq=np.array(probs_seq2),
beam_size=beam_size,
vocabulary=vocab_list,
)