code clean & add external scorer

deep_speech_2/ctc_beam_search_decoder.py

-## This is a prototype of ctc beam search decoder
-
-import copy
-import random
-import numpy as np
-
-# vocab = blank + space + English characters
-#vocab = ['-', ' '] + [chr(i) for i in range(97, 123)]
-
-vocab = ['-', '_', 'a']
-
-
-def ids_list2str(ids_list):
-    ids_str = [str(elem) for elem in ids_list]
-    ids_str = ' '.join(ids_str)
-    return ids_str
-
-
-def ids_id2token(ids_list):
-    ids_str = ''
-    for ids in ids_list:
-        ids_str += vocab[ids]
-    return ids_str
-
-
-def language_model(ids_list, vocabulary):
-    # lookup ptb vocabulary
-    ptb_vocab_path = "./data/ptb_vocab.txt"
-    sentence = ''.join([vocabulary[ids] for ids in ids_list])
-    words = sentence.split(' ')
-    last_word = words[-1]
-    with open(ptb_vocab_path, 'r') as ptb_vocab:
-        f = ptb_vocab.readline()
-        while f:
-            if f == last_word:
-                return 1.0
-            f = ptb_vocab.readline()
-    return 0.0
-
-
-def ctc_beam_search_decoder(input_probs_matrix,
-                            beam_size,
-                            vocabulary,
-                            max_time_steps=None,
-                            lang_model=language_model,
-                            alpha=1.0,
-                            beta=1.0,
-                            blank_id=0,
-                            space_id=1,
-                            num_results_per_sample=None):
-    '''
-    Beam search decoder for CTC-trained network,  adapted from Algorithm 1 
-    in https://arxiv.org/abs/1408.2873.
-
-    :param input_probs_matrix: probs matrix for input sequence, row major
-    :type input_probs_matrix: 2D matrix. 
-    :param beam_size: width for beam search
-    :type beam_size: int
-    :max_time_steps: maximum steps' number for input sequence, 
-                     <=len(input_probs_matrix)
-    :type max_time_steps: int
-    :lang_model: language model for scoring
-    :type lang_model: function
-    :param alpha: parameter associated with language model.
-    :type alpha: float 
-    :param beta: parameter associated with word count
-    :type beta: float
-    :param blank_id: id of blank, default 0.
-    :type blank_id: int
-    :param space_id: id of space, default 1.
-    :type space_id: int
-    :param num_result_per_sample: the number of output decoding results 
-                                 per given sample, <=beam_size.
-    :type num_result_per_sample: int
-    '''
-
-    # function to convert ids in string to list
-    def ids_str2list(ids_str):
-        ids_str = ids_str.split(' ')
-        ids_list = [int(elem) for elem in ids_str]
-        return ids_list
-
-    # counting words in a character list
-    def word_count(ids_list):
-        cnt = 0
-        for elem in ids_list:
-            if elem == space_id:
-                cnt += 1
-        return cnt
-
-    if num_results_per_sample is None:
-        num_results_per_sample = beam_size
-    assert num_results_per_sample <= beam_size
-
-    if max_time_steps is None:
-        max_time_steps = len(input_probs_matrix)
-    else:
-        max_time_steps = min(max_time_steps, len(input_probs_matrix))
-    assert max_time_steps > 0
-
-    vocab_dim = len(input_probs_matrix[0])
-    assert blank_id < vocab_dim
-    assert space_id < vocab_dim
-
-    ## initialize 
-    start_id = -1
-    # the set containing selected prefixes 
-    prefix_set_prev = {str(start_id): 1.0}
-    probs_b, probs_nb = {str(start_id): 1.0}, {str(start_id): 0.0}
-
-    ## extend prefix in loop 
-    for time_step in range(max_time_steps):
-        # the set containing candidate prefixes
-        prefix_set_next = {}
-        probs_b_cur, probs_nb_cur = {}, {}
-        for l in prefix_set_prev:
-            prob = input_probs_matrix[time_step]
-
-            # convert ids in string to list
-            ids_list = ids_str2list(l)
-            end_id = ids_list[-1]
-            if not prefix_set_next.has_key(l):
-                probs_b_cur[l], probs_nb_cur[l] = 0.0, 0.0
-
-            # extend prefix by travering vocabulary
-            for c in range(0, vocab_dim):
-                if c == blank_id:
-                    probs_b_cur[l] += prob[c] * (probs_b[l] + probs_nb[l])
-                else:
-                    l_plus = l + ' ' + str(c)
-                    if not prefix_set_next.has_key(l_plus):
-                        probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0
-
-                    if c == end_id:
-                        probs_nb_cur[l_plus] += prob[c] * probs_b[l]
-                        probs_nb_cur[l] += prob[c] * probs_nb[l]
-                    elif c == space_id:
-                        lm = 1.0 if lang_model is None \
-                               else np.power(lang_model(ids_list, vocabulary), alpha)
-                        probs_nb_cur[l_plus] += lm * prob[c] * (
-                            probs_b[l] + probs_nb[l])
-                    else:
-                        probs_nb_cur[l_plus] += prob[c] * (
-                            probs_b[l] + probs_nb[l])
-                # add l_plus into prefix_set_next
-                    prefix_set_next[l_plus] = probs_nb_cur[
-                        l_plus] + probs_b_cur[l_plus]
-            # add l into prefix_set_next
-            prefix_set_next[l] = probs_b_cur[l] + probs_nb_cur[l]
-        # update probs
-        probs_b, probs_nb = copy.deepcopy(probs_b_cur), copy.deepcopy(
-            probs_nb_cur)
-
-        ## store top beam_size prefixes 
-        prefix_set_prev = sorted(
-            prefix_set_next.iteritems(), key=lambda asd: asd[1], reverse=True)
-        if beam_size < len(prefix_set_prev):
-            prefix_set_prev = prefix_set_prev[:beam_size]
-        prefix_set_prev = dict(prefix_set_prev)
-
-    beam_result = []
-    for (seq, prob) in prefix_set_prev.items():
-        if prob > 0.0:
-            ids_list = ids_str2list(seq)[1:]
-            result = ''.join([vocabulary[ids] for ids in ids_list])
-            log_prob = np.log(prob)
-            beam_result.append([log_prob, result])
-
-    ## output top beam_size decoding results
-    beam_result = sorted(beam_result, key=lambda asd: asd[0], reverse=True)
-    if num_results_per_sample < beam_size:
-        beam_result = beam_result[:num_results_per_sample]
-    return beam_result
-
-
-def simple_test():
-
-    input_probs_matrix = [[0.1, 0.3, 0.6], [0.2, 0.1, 0.7], [0.5, 0.2, 0.3]]
-
-    beam_result = ctc_beam_search_decoder(
-        input_probs_matrix=input_probs_matrix,
-        beam_size=20,
-        blank_id=0,
-        space_id=1, )
-
-    print "\nbeam search output:"
-    for result in beam_result:
-        print("%6f\t%s" % (result[0], ids_id2token(result[1])))
-
-
-if __name__ == '__main__':
-    simple_test()

deep_speech_2/decoder.py

@@ -4,7 +4,8 @@
 
 from itertools import groupby
 import numpy as np
-from ctc_beam_search_decoder import *
+import copy
+import kenlm
 
 
 def ctc_best_path_decode(probs_seq, vocabulary):
@@ -37,36 +38,165 @@ def ctc_best_path_decode(probs_seq, vocabulary):
     return ''.join([vocabulary[index] for index in index_list])
 
 
-def ctc_decode(probs_seq,
-               vocabulary,
-               method,
-               beam_size=None,
-               num_results_per_sample=None):
+class Scorer(object):
     """
-    CTC-like sequence decoding from a sequence of likelihood probablilites. 
+    External defined scorer to evaluate a sentence in beam search
+               decoding, consisting of language model and word count.
 
-    :param probs_seq: 2-D list of probabilities over the vocabulary for each
-                      character. Each element is a list of float probabilities
-                      for one character.
-    :type probs_seq: list
+    :param alpha: Parameter associated with language model.
+    :type alpha: float
+    :param beta: Parameter associated with word count.
+    :type beta: float
+    :model_path: Path to load language model.
+    :type model_path: basestring
+    """
+
+    def __init__(self, alpha, beta, model_path):
+
+        self._alpha = alpha
+        self._beta = beta
+        self._language_model = kenlm.LanguageModel(model_path)
+
+    def language_model_score(self, sentence, bos=True, eos=False):
+        log_prob = self._language_model.score(sentence, bos, eos)
+        return np.power(10, log_prob)
+
+    def word_count(self, sentence):
+        words = sentence.strip().split(' ')
+        return len(words)
+
+    # execute evaluation
+    def evaluate(self, sentence, bos=True, eos=False):
+        lm = self.language_model_score(sentence, bos, eos)
+        word_count = self.word_count(sentence)
+        score = np.power(lm, self._alpha) \
+                * np.power(word_count, self._beta)
+        return score
+
+
+def ctc_beam_search_decoder(probs_seq,
+                            beam_size,
+                            vocabulary,
+                            ext_scoring_func=None,
+                            blank_id=0):
+    '''
+    Beam search decoder for CTC-trained network, using beam search with width
+    beam_size to find many paths to one label, return  beam_size labels in
+    the order of probabilities. The implementation is based on Prefix Beam
+    Search(https://arxiv.org/abs/1408.2873), and the unclear part is
+    redesigned, need to be verified.
+
+    :param probs_seq: 2-D list with length max_time_steps, each element
+                      is a list of normalized probabilities over vocabulary
+                      and blank for one time step.
+    :type probs_seq: 2-D list
+    :param beam_size: Width for beam search.
+    :type beam_size: int
     :param vocabulary: Vocabulary list.
     :type vocabulary: list
-    :param method: Decoding method name, with options: "best_path".
-    :type method: basestring
-    :return: Decoding result string.
-    :rtype: baseline
-    """
+    :param ext_scoring_func: External defined scoring function for
+                            partially decoded sentence, e.g. word count
+                            and language model.
+    :type external_scoring_function: function
+    :param blank_id: id of blank, default 0.
+    :type blank_id: int
+    :return: Decoding log probability and result string.
+    :rtype: list
+
+    '''
+
     for prob_list in probs_seq:
         if not len(prob_list) == len(vocabulary) + 1:
             raise ValueError("probs dimension mismatchedd with vocabulary")
-    if method == "best_path":
-        return ctc_best_path_decode(probs_seq, vocabulary)
-    elif method == "beam_search":
-        return ctc_beam_search_decoder(
-            input_probs_matrix=probs_seq,
-            vocabulary=vocabulary,
-            beam_size=beam_size,
-            blank_id=len(vocabulary),
-            num_results_per_sample=num_results_per_sample)
-    else:
-        raise ValueError("Decoding method [%s] is not supported." % method)
+
+    max_time_steps = len(probs_seq)
+    if not max_time_steps > 0:
+        raise ValueError("probs_seq shouldn't be empty")
+
+    probs_dim = len(probs_seq[0])
+    if not blank_id < probs_dim:
+        raise ValueError("blank_id shouldn't be greater than probs dimension")
+
+    if ' ' not in vocabulary:
+        raise ValueError("space doesn't exist in vocabulary")
+    space_id = vocabulary.index(' ')
+
+    # function to convert ids in string to list
+    def ids_str2list(ids_str):
+        ids_str = ids_str.split(' ')
+        ids_list = [int(elem) for elem in ids_str]
+        return ids_list
+
+    # function to convert ids list to sentence
+    def ids2sentence(ids_list, vocab):
+        return ''.join([vocab[ids] for ids in ids_list])
+
+    ## initialize
+    # the set containing selected prefixes
+    prefix_set_prev = {'-1': 1.0}
+    probs_b, probs_nb = {'-1': 1.0}, {'-1': 0.0}
+
+    ## extend prefix in loop
+    for time_step in range(max_time_steps):
+        # the set containing candidate prefixes
+        prefix_set_next = {}
+        probs_b_cur, probs_nb_cur = {}, {}
+        for l in prefix_set_prev:
+            prob = probs_seq[time_step]
+
+            # convert ids in string to list
+            ids_list = ids_str2list(l)
+            end_id = ids_list[-1]
+            if not prefix_set_next.has_key(l):
+                probs_b_cur[l], probs_nb_cur[l] = 0.0, 0.0
+
+            # extend prefix by travering vocabulary
+            for c in range(0, probs_dim):
+                if c == blank_id:
+                    probs_b_cur[l] += prob[c] * (probs_b[l] + probs_nb[l])
+                else:
+                    l_plus = l + ' ' + str(c)
+                    if not prefix_set_next.has_key(l_plus):
+                        probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0
+
+                    if c == end_id:
+                        probs_nb_cur[l_plus] += prob[c] * probs_b[l]
+                        probs_nb_cur[l] += prob[c] * probs_nb[l]
+                    elif c == space_id:
+                        if ext_scoring_func is None:
+                            score = 1.0
+                        else:
+                            prefix_sent = ids2sentence(ids_list, vocabulary)
+                            score = ext_scoring_func(prefix_sent)
+                        probs_nb_cur[l_plus] += score * prob[c] * (
+                            probs_b[l] + probs_nb[l])
+                    else:
+                        probs_nb_cur[l_plus] += prob[c] * (
+                            probs_b[l] + probs_nb[l])
+                    # add l_plus into prefix_set_next
+                    prefix_set_next[l_plus] = probs_nb_cur[
+                        l_plus] + probs_b_cur[l_plus]
+            # add l into prefix_set_next
+            prefix_set_next[l] = probs_b_cur[l] + probs_nb_cur[l]
+        # update probs
+        probs_b, probs_nb = copy.deepcopy(probs_b_cur), copy.deepcopy(
+            probs_nb_cur)
+
+        ## store top beam_size prefixes
+        prefix_set_prev = sorted(
+            prefix_set_next.iteritems(), key=lambda asd: asd[1], reverse=True)
+        if beam_size < len(prefix_set_prev):
+            prefix_set_prev = prefix_set_prev[:beam_size]
+        prefix_set_prev = dict(prefix_set_prev)
+
+    beam_result = []
+    for (seq, prob) in prefix_set_prev.items():
+        if prob > 0.0:
+            ids_list = ids_str2list(seq)[1:]
+            result = ids2sentence(ids_list, vocabulary)
+            log_prob = np.log(prob)
+            beam_result.append([log_prob, result])
+
+    ## output top beam_size decoding results
+    beam_result = sorted(beam_result, key=lambda asd: asd[0], reverse=True)
+    return beam_result

deep_speech_2/infer.py

@@ -8,7 +8,7 @@ import argparse
 import gzip
 from audio_data_utils import DataGenerator
 from model import deep_speech2
-from decoder import ctc_decode
+from decoder import *
 
 parser = argparse.ArgumentParser(
     description='Simplified version of DeepSpeech2 inference.')
@@ -59,7 +59,7 @@ parser.add_argument(
     help="Vocabulary filepath. (default: %(default)s)")
 parser.add_argument(
     "--decode_method",
-    default='best_path',
+    default='beam_search',
     type=str,
     help="Method for ctc decoding, best_path or beam_search. (default: %(default)s)"
 )
@@ -69,11 +69,25 @@ parser.add_argument(
     type=int,
     help="Width for beam search decoding. (default: %(default)d)")
 parser.add_argument(
-    "--num_result_per_sample",
-    default=2,
+    "--num_results_per_sample",
+    default=1,
     type=int,
-    help="Number of results per given sample in beam search. (default: %(default)d)"
-)
+    help="Number of output per sample in beam search. (default: %(default)d)")
+parser.add_argument(
+    "--language_model_path",
+    default="./data/1Billion.klm",
+    type=str,
+    help="Path for language model. (default: %(default)d)")
+parser.add_argument(
+    "--alpha",
+    default=0.0,
+    type=float,
+    help="Parameter associated with language model. (default: %(default)f)")
+parser.add_argument(
+    "--beta",
+    default=0.0,
+    type=float,
+    help="Parameter associated with word count. (default: %(default)f)")
 args = parser.parse_args()
 
 
@@ -135,24 +149,34 @@ def infer():
         for i in xrange(0, len(infer_data))
     ]
 
-    # decode and print
-    for i, probs in enumerate(probs_split):
-        best_path_transcription = ctc_decode(
-            probs_seq=probs, vocabulary=vocab_list, method="best_path")
-        target_transcription = ''.join(
-            [vocab_list[index] for index in infer_data[i][1]])
-        print("\nTarget   Transcription: %s \nBst_path Transcription: %s" %
-              (target_transcription, best_path_transcription))
-        beam_search_transcription = ctc_decode(
-            probs_seq=probs,
-            vocabulary=vocab_list,
-            method="beam_search",
-            beam_size=args.beam_size,
-            num_results_per_sample=args.num_result_per_sample)
-        for index in range(len(beam_search_transcription)):
-            print("LM No, %d - %4f: %s " %
-                  (index, beam_search_transcription[index][0],
-                   beam_search_transcription[index][1]))
+    ## decode and print
+    # best path decode
+    if args.decode_method == "best_path":
+        for i, probs in enumerate(probs_split):
+            target_transcription = ''.join(
+                [vocab_list[index] for index in infer_data[i][1]])
+            best_path_transcription = ctc_best_path_decode(
+                probs_seq=probs, vocabulary=vocab_list)
+            print("\nTarget Transcription: %s\nOutput Transcription: %s" %
+                  (target_transcription, best_path_transcription))
+    # beam search decode
+    elif args.decode_method == "beam_search":
+        for i, probs in enumerate(probs_split):
+            target_transcription = ''.join(
+                [vocab_list[index] for index in infer_data[i][1]])
+            ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
+            beam_search_result = ctc_beam_search_decoder(
+                probs_seq=probs,
+                vocabulary=vocab_list,
+                beam_size=args.beam_size,
+                ext_scoring_func=ext_scorer.evaluate,
+                blank_id=len(vocab_list))
+            print("\nTarget Transcription:\t%s" % target_transcription)
+            for index in range(args.num_results_per_sample):
+                result = beam_search_result[index]
+                print("Beam %d: %f \t%s" % (index, result[0], result[1]))
+    else:
+        raise ValueError("Decoding method [%s] is not supported." % method)
 
 
 def main():

deep_speech_2/test_ctc_beam_search_decoder.py

-from __future__ import absolute_import
-from __future__ import print_function
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-import ctc_beam_search_decoder as tested_decoder
-
-
-def test_beam_search_decoder():
-    max_time_steps = 6
-    beam_size = 20
-    num_results_per_sample = 20
-
-    input_prob_matrix_0 = np.asarray(
-        [
-            [0.30999, 0.309938, 0.0679938, 0.0673362, 0.0708352, 0.173908],
-            [0.215136, 0.439699, 0.0370931, 0.0393967, 0.0381581, 0.230517],
-            [0.199959, 0.489485, 0.0233221, 0.0251417, 0.0233289, 0.238763],
-            [0.279611, 0.452966, 0.0204795, 0.0209126, 0.0194803, 0.20655],
-            [0.51286, 0.288951, 0.0243026, 0.0220788, 0.0219297, 0.129878],
-            # Random entry added in at time=5
-            [0.155251, 0.164444, 0.173517, 0.176138, 0.169979, 0.160671]
-        ],
-        dtype=np.float32)
-
-    # Add arbitrary offset - this is fine
-    input_log_prob_matrix_0 = np.log(input_prob_matrix_0)  #+ 2.0
-
-    # len max_time_steps array of batch_size x depth matrices
-    inputs = ([
-        input_log_prob_matrix_0[t, :][np.newaxis, :]
-        for t in range(max_time_steps)
-    ])
-
-    inputs_t = [ops.convert_to_tensor(x) for x in inputs]
-    inputs_t = array_ops.stack(inputs_t)
-
-    # run CTC beam search decoder in tensorflow
-    with tf.Session() as sess:
-        decoded, log_probabilities = tf.nn.ctc_beam_search_decoder(
-            inputs_t, [max_time_steps],
-            beam_width=beam_size,
-            top_paths=num_results_per_sample,
-            merge_repeated=False)
-        tf_decoded = sess.run(decoded)
-        tf_log_probs = sess.run(log_probabilities)
-
-    # run tested CTC beam search decoder     
-    beam_result = tested_decoder.ctc_beam_search_decoder(
-        input_probs_matrix=input_prob_matrix_0,
-        beam_size=beam_size,
-        blank_id=5,  # default blank_id in tensorflow decoder is (num classes-1)
-        space_id=4,  # doesn't matter
-        max_time_steps=max_time_steps,
-        num_results_per_sample=num_results_per_sample)
-
-    # compare decoding result
-    print(
-        "{tf_decoder log probs} \t {tested_decoder log probs}:  {tf_decoder result}  {tested_decoder result}"
-    )
-    for index in range(len(beam_result)):
-        print(('%6f\t%6f: ') % (tf_log_probs[0][index], beam_result[index][0]),
-              tf_decoded[index].values, '  ', beam_result[index][1])
-
-
-if __name__ == '__main__':
-    test_beam_search_decoder()