Yibing Liu
8 years ago
parent
e016778e20
commit
c943ca79ac
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## This is a prototype of ctc beam search decoder
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import copy
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import random
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import numpy as np
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# vocab = blank + space + English characters
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#vocab = ['-', ' '] + [chr(i) for i in range(97, 123)]
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vocab = ['-', '_', 'a']
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def ids_str2list(ids_str):
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ids_str = ids_str.split(' ')
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ids_list = [int(elem) for elem in ids_str]
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return ids_list
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def ids_list2str(ids_list):
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ids_str = [str(elem) for elem in ids_list]
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ids_str = ' '.join(ids_str)
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return ids_str
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def ids_id2token(ids_list):
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ids_str = ''
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for ids in ids_list:
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ids_str += vocab[ids]
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return ids_str
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def ctc_beam_search_decoder(input_probs_matrix,
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beam_size,
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max_time_steps=None,
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lang_model=None,
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alpha=1.0,
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beta=1.0,
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blank_id=0,
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space_id=1,
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num_results_per_sample=None):
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'''
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beam search decoder for CTC-trained network, called outside of the recurrent group.
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adapted from Algorithm 1 in https://arxiv.org/abs/1408.2873.
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param input_probs_matrix: probs matrix for input sequence, row major
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type input_probs_matrix: 2D matrix.
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param beam_size: width for beam search
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type beam_size: int
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max_time_steps: maximum steps' number for input sequence, <=len(input_probs_matrix)
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type max_time_steps: int
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lang_model: language model for scoring
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type lang_model: function
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......
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'''
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if num_results_per_sample is None:
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num_results_per_sample = beam_size
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assert num_results_per_sample <= beam_size
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if max_time_steps is None:
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max_time_steps = len(input_probs_matrix)
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else:
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max_time_steps = min(max_time_steps, len(input_probs_matrix))
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assert max_time_steps > 0
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vocab_dim = len(input_probs_matrix[0])
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assert blank_id < vocab_dim
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assert space_id < vocab_dim
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## initialize
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start_id = -1
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# the set containing selected prefixes
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prefix_set_prev = {str(start_id): 1.0}
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probs_b, probs_nb = {str(start_id): 1.0}, {str(start_id): 0.0}
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## extend prefix in loop
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for time_step in range(max_time_steps):
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# the set containing candidate prefixes
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prefix_set_next = {}
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probs_b_cur, probs_nb_cur = {}, {}
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for l in prefix_set_prev:
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prob = input_probs_matrix[time_step]
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# convert ids in string to list
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ids_list = ids_str2list(l)
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end_id = ids_list[-1]
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if not prefix_set_next.has_key(l):
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probs_b_cur[l], probs_nb_cur[l] = 0.0, 0.0
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# extend prefix by travering vocabulary
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for c in range(0, vocab_dim):
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if c == blank_id:
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probs_b_cur[l] += prob[c] * (probs_b[l] + probs_nb[l])
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else:
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l_plus = l + ' ' + str(c)
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if not prefix_set_next.has_key(l_plus):
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probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0
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if c == end_id:
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probs_nb_cur[l_plus] += prob[c] * probs_b[l]
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probs_nb_cur[l] += prob[c] * probs_nb[l]
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elif c == space_id:
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lm = 1.0 if lang_model is None \
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else np.power(lang_model(ids_list), alpha)
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probs_nb_cur[l_plus] += lm * prob[c] * (
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probs_b[l] + probs_nb[l])
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else:
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probs_nb_cur[l_plus] += prob[c] * (
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probs_b[l] + probs_nb[l])
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# add l_plus into prefix_set_next
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prefix_set_next[l_plus] = probs_nb_cur[
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l_plus] + probs_b_cur[l_plus]
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# add l into prefix_set_next
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prefix_set_next[l] = probs_b_cur[l] + probs_nb_cur[l]
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# update probs
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probs_b, probs_nb = copy.deepcopy(probs_b_cur), copy.deepcopy(
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probs_nb_cur)
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## store top beam_size prefixes
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prefix_set_prev = sorted(
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prefix_set_next.iteritems(), key=lambda asd: asd[1], reverse=True)
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if beam_size < len(prefix_set_prev):
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prefix_set_prev = prefix_set_prev[:beam_size]
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prefix_set_prev = dict(prefix_set_prev)
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beam_result = []
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for (seq, prob) in prefix_set_prev.items():
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if prob > 0.0:
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ids_list = ids_str2list(seq)
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log_prob = np.log(prob)
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beam_result.append([log_prob, ids_list[1:]])
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## output top beam_size decoding results
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beam_result = sorted(beam_result, key=lambda asd: asd[0], reverse=True)
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if num_results_per_sample < beam_size:
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beam_result = beam_result[:num_results_per_sample]
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return beam_result
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def language_model(input):
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# TODO
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return random.uniform(0, 1)
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def simple_test():
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input_probs_matrix = [[0.1, 0.3, 0.6], [0.2, 0.1, 0.7], [0.5, 0.2, 0.3]]
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beam_result = ctc_beam_search_decoder(
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input_probs_matrix=input_probs_matrix,
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beam_size=20,
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blank_id=0,
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space_id=1, )
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print "\nbeam search output:"
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for result in beam_result:
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print("%6f\t%s" % (result[0], ids_id2token(result[1])))
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if __name__ == '__main__':
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simple_test()
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@ -0,0 +1,69 @@
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from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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import ctc_beam_search_decoder as tested_decoder
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def test_beam_search_decoder():
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max_time_steps = 6
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beam_size = 20
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num_results_per_sample = 20
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input_prob_matrix_0 = np.asarray(
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[
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[0.30999, 0.309938, 0.0679938, 0.0673362, 0.0708352, 0.173908],
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[0.215136, 0.439699, 0.0370931, 0.0393967, 0.0381581, 0.230517],
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[0.199959, 0.489485, 0.0233221, 0.0251417, 0.0233289, 0.238763],
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[0.279611, 0.452966, 0.0204795, 0.0209126, 0.0194803, 0.20655],
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[0.51286, 0.288951, 0.0243026, 0.0220788, 0.0219297, 0.129878],
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# Random entry added in at time=5
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[0.155251, 0.164444, 0.173517, 0.176138, 0.169979, 0.160671]
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],
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dtype=np.float32)
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# Add arbitrary offset - this is fine
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input_log_prob_matrix_0 = np.log(input_prob_matrix_0) #+ 2.0
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# len max_time_steps array of batch_size x depth matrices
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inputs = ([
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input_log_prob_matrix_0[t, :][np.newaxis, :]
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for t in range(max_time_steps)
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])
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inputs_t = [ops.convert_to_tensor(x) for x in inputs]
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inputs_t = array_ops.stack(inputs_t)
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# run CTC beam search decoder in tensorflow
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with tf.Session() as sess:
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decoded, log_probabilities = tf.nn.ctc_beam_search_decoder(
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inputs_t, [max_time_steps],
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beam_width=beam_size,
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top_paths=num_results_per_sample,
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merge_repeated=False)
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tf_decoded = sess.run(decoded)
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tf_log_probs = sess.run(log_probabilities)
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# run tested CTC beam search decoder
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beam_result = tested_decoder.ctc_beam_search_decoder(
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input_probs_matrix=input_prob_matrix_0,
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beam_size=beam_size,
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blank_id=5, # default blank_id in tensorflow decoder is (num classes-1)
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space_id=4, # doesn't matter
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max_time_steps=max_time_steps,
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num_results_per_sample=num_results_per_sample)
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# compare decoding result
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print(
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"{tf_decoder log probs} \t {tested_decoder log probs}: {tf_decoder result} {tested_decoder result}"
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)
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for index in range(len(beam_result)):
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print(('%6f\t%6f: ') % (tf_log_probs[0][index], beam_result[index][0]),
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tf_decoded[index].values, ' ', beam_result[index][1])
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if __name__ == '__main__':
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test_beam_search_decoder()
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