Support split words

alphabet.py

@@ -0,0 +1,42 @@
+import codecs
+
+
+class Alphabet(object):
+    def __init__(self, config_file):
+        self._config_file = config_file
+        self._label_to_str = []
+        self._str_to_label = {}
+        self._size = 0
+        self.blank_token = 1
+        with codecs.open(config_file, 'r', 'utf-8') as fin:
+            for line in fin:
+                if line[0:2] == '\\#':
+                    line = '#\n'
+                elif line[0] == '#':
+                    continue
+                self._label_to_str += line[:-1] # remove the line ending
+                self._str_to_label[line[:-1]] = self._size
+                self._size += 1
+
+    def string_from_label(self, label):
+        return self._label_to_str[label]
+
+    def label_from_string(self, string):
+        try:
+            return self._str_to_label[string]
+        except KeyError as e:
+            raise KeyError(
+                '''ERROR: Your transcripts contain characters which do not occur in data/alphabet.txt! Use util/check_characters.py to see what characters are in your {train,dev,test}.csv transcripts, and then add all these to data/alphabet.txt.'''
+            ).with_traceback(e.__traceback__)
+
+    def decode(self, labels):
+        res = ''
+        for label in labels:
+            res += self.string_from_label(label)
+        return res
+
+    def size(self):
+        return self._size
+
+    def config_file(self):
+        return self._config_file

model_utils/model.py

@@ -1,4 +1,4 @@
-"""Contains DeepSpeech2 model."""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -12,9 +12,10 @@ import copy
 import inspect
 from distutils.dir_util import mkpath
 import paddle.v2 as paddle
-from decoders.swig_wrapper import Scorer
+
-from decoders.swig_wrapper import ctc_greedy_decoder
+from ds_ctcdecoder import Scorer, swigwrapper
-from decoders.swig_wrapper import ctc_beam_search_decoder_batch
+from alphabet import Alphabet
+from words import Words
 from model_utils.network import deep_speech_v2_network
 
 logging.basicConfig(
@@ -205,26 +206,8 @@ class DeepSpeech2Model(object):
         ]
         return probs_split
 
-    def decode_batch_greedy(self, probs_split, vocab_list):
+    def init_ext_scorer(self, beam_alpha, beam_beta,
-        """Decode by best path for a batch of probs matrix input.
+                        language_model_path, trie_path, alphabet):
-
-        :param probs_split: List of 2-D probability matrix, and each consists
-                            of prob vectors for one speech utterancce.
-        :param probs_split: List of matrix
-        :param vocab_list: List of tokens in the vocabulary, for decoding.
-        :type vocab_list: list
-        :return: List of transcription texts.
-        :rtype: List of basestring
-        """
-        results = []
-        for i, probs in enumerate(probs_split):
-            output_transcription = ctc_greedy_decoder(
-                probs_seq=probs, vocabulary=vocab_list)
-            results.append(output_transcription)
-        return results
-
-    def init_ext_scorer(self, beam_alpha, beam_beta, language_model_path,
-                        vocab_list):
         """Initialize the external scorer.
 
         :param beam_alpha: Parameter associated with language model.
@@ -236,39 +219,31 @@ class DeepSpeech2Model(object):
                                     None, and the decoding method will be pure
                                     beam search without scorer.
         :type language_model_path: basestring|None
-        :param vocab_list: List of tokens in the vocabulary, for decoding.
+        :param trie_path: Filepath for trie
-        :type vocab_list: list
+        :type trie_path: basestring\None
+        :param alphabet: Alphabet class for decoding.
+        :type alphabet: Alphabet
         """
         if language_model_path != '':
             self.logger.info("begin to initialize the external scorer "
                              "for decoding")
             self._ext_scorer = Scorer(beam_alpha, beam_beta,
-                                      language_model_path, vocab_list)
+                                      language_model_path, trie_path,
-            lm_char_based = self._ext_scorer.is_character_based()
+                                      alphabet)
-            lm_max_order = self._ext_scorer.get_max_order()
-            lm_dict_size = self._ext_scorer.get_dict_size()
-            self.logger.info("language model: "
-                             "is_character_based = %d," % lm_char_based +
-                             " max_order = %d," % lm_max_order +
-                             " dict_size = %d" % lm_dict_size)
             self.logger.info("end initializing scorer")
         else:
             self._ext_scorer = None
             self.logger.info("no language model provided, "
                              "decoding by pure beam search without scorer.")
 
-    def decode_batch_beam_search(self, probs_split, beam_alpha, beam_beta,
+    def decode_beam_search(self, probs_split, beam_size,
-                                 beam_size, cutoff_prob, cutoff_top_n,
+                           cutoff_prob, cutoff_top_n,
-                                 vocab_list, num_processes):
+                           alphabet):
         """Decode by beam search for a batch of probs matrix input.
 
         :param probs_split: List of 2-D probability matrix, and each consists
                             of prob vectors for one speech utterancce.
         :param probs_split: List of matrix
-        :param beam_alpha: Parameter associated with language model.
-        :type beam_alpha: float
-        :param beam_beta: Parameter associated with word count.
-        :type beam_beta: float
         :param beam_size: Width for Beam search.
         :type beam_size: int
         :param cutoff_prob: Cutoff probability in pruning,
@@ -278,28 +253,24 @@ class DeepSpeech2Model(object):
                         characters with highest probs in vocabulary will be
                         used in beam search, default 40.
         :type cutoff_top_n: int
-        :param vocab_list: List of tokens in the vocabulary, for decoding.
+        :param alphabet: alphabet used for decoding.
-        :type vocab_list: list
+        :type alphabet: Alphabet
-        :param num_processes: Number of processes (CPU) for decoder.
-        :type num_processes: int
         :return: List of transcription texts.
         :rtype: List of basestring
         """
-        if self._ext_scorer != None:
-            self._ext_scorer.reset_params(beam_alpha, beam_beta)
         # beam search decode
-        num_processes = min(num_processes, len(probs_split))
+
-        beam_search_results = ctc_beam_search_decoder_batch(
+        metadata = swigwrapper.ctc_beam_search_decoder(
-            probs_split=probs_split,
+            probs_split[0],
-            vocabulary=vocab_list,
+            alphabet.config_file(),
-            beam_size=beam_size,
+            beam_size,
-            num_processes=num_processes,
+            cutoff_prob,
-            ext_scoring_func=self._ext_scorer,
+            cutoff_top_n,
-            cutoff_prob=cutoff_prob,
+            self._ext_scorer)
-            cutoff_top_n=cutoff_top_n)
+
-
+        words = Words(probs_split[0], metadata[0], alphabet)
-        results = [result[0][1] for result in beam_search_results]
+
-        return results
+        return words
 
     def _adapt_feeding_dict(self, feeding_dict):
         """Adapt feeding dict according to network struct.

words.py

@@ -0,0 +1,53 @@
+import json
+
+
+class Words(object):
+
+    def __init__(self, prob_split, metadata, alphabet, frame_to_sec=.03):
+
+        self.raw_output = ''
+        self.extended_output = []
+
+        word = ''
+        start_step, confidence, num_char = 0, 1.0, 0
+        metadata_size = metadata.tokens.size()
+
+        for i in range(metadata_size):
+            token = metadata.tokens[i]
+            letter = alphabet.string_from_label(token)
+            time_step = metadata.timesteps[i]
+
+            # prepare raw output
+            self.raw_output += letter
+
+            # prepare extended output
+            if token != alphabet.blank_token:
+                word.append(letter)
+                confidence *= prob_split[time_step][token]
+                num_char += 1
+                if len(word) == 1:
+                    start_step = time_step
+
+            if token == alphabet.blank_token or i == metadata_size-1:
+                duration_step = time_step - start_step
+
+                if duration_step < 0:
+                    duration_step = 0
+
+                self.extended_output.append({"word": word,
+                                             "start_time": frame_to_sec * start_step,
+                                             "duration": frame_to_sec * duration_step,
+                                             "confidence": confidence**(1.0/num_char)})
+                # reset
+                word = ''
+                start_step, confidence, num_char = 0, 1.0, 0
+
+    def to_json(self):
+        return json.dumps({"raw_output": self.raw_output,
+                          "extended_output": self.extended_output})
+
+    def save_json(self, file_path):
+        with open(file_path, 'w') as outfile:
+            json.dump({"raw_output": self.raw_output,
+                       "extended_output": self.extended_output},
+                      outfile)