expose param cutoff_top_n

data_utils/featurizer/text_featurizer.py

@@ -22,8 +22,6 @@ class TextFeaturizer(object):
     def __init__(self, vocab_filepath):
         self._vocab_dict, self._vocab_list = self._load_vocabulary_from_file(
             vocab_filepath)
-        # from unicode to string
-        self._vocab_list = [chars.encode("utf-8") for chars in self._vocab_list]
 
     def featurize(self, text):
         """Convert text string to a list of token indices in char-level.Note

decoders/decoder_deprecated.py

@@ -42,8 +42,8 @@ def ctc_greedy_decoder(probs_seq, vocabulary):
 def ctc_beam_search_decoder(probs_seq,
                             beam_size,
                             vocabulary,
-                            blank_id,
                             cutoff_prob=1.0,
+                            cutoff_top_n=40,
                             ext_scoring_func=None,
                             nproc=False):
     """CTC Beam search decoder.
@@ -66,8 +66,6 @@ def ctc_beam_search_decoder(probs_seq,
     :type beam_size: int
     :param vocabulary: Vocabulary list.
     :type vocabulary: list
-    :param blank_id: ID of blank.
-    :type blank_id: int
     :param cutoff_prob: Cutoff probability in pruning,
                         default 1.0, no pruning.
     :type cutoff_prob: float
@@ -87,9 +85,8 @@ def ctc_beam_search_decoder(probs_seq,
             raise ValueError("The shape of prob_seq does not match with the "
                              "shape of the vocabulary.")
 
-    # blank_id check
-    if not blank_id < len(probs_seq[0]):
-        raise ValueError("blank_id shouldn't be greater than probs dimension")
+    # blank_id assign
+    blank_id = len(vocabulary)
 
     # If the decoder called in the multiprocesses, then use the global scorer
     # instantiated in ctc_beam_search_decoder_batch().
@@ -114,7 +111,7 @@ def ctc_beam_search_decoder(probs_seq,
         prob_idx = list(enumerate(probs_seq[time_step]))
         cutoff_len = len(prob_idx)
         #If pruning is enabled
-        if cutoff_prob < 1.0:
+        if cutoff_prob < 1.0 or cutoff_top_n < cutoff_len:
             prob_idx = sorted(prob_idx, key=lambda asd: asd[1], reverse=True)
             cutoff_len, cum_prob = 0, 0.0
             for i in xrange(len(prob_idx)):
@@ -122,6 +119,7 @@ def ctc_beam_search_decoder(probs_seq,
                 cutoff_len += 1
                 if cum_prob >= cutoff_prob:
                     break
+            cutoff_len = min(cutoff_top_n, cutoff_top_n)
             prob_idx = prob_idx[0:cutoff_len]
 
         for l in prefix_set_prev:
@@ -191,9 +189,9 @@ def ctc_beam_search_decoder(probs_seq,
 def ctc_beam_search_decoder_batch(probs_split,
                                   beam_size,
                                   vocabulary,
-                                  blank_id,
                                   num_processes,
                                   cutoff_prob=1.0,
+                                  cutoff_top_n=40,
                                   ext_scoring_func=None):
     """CTC beam search decoder using multiple processes.
 
@@ -204,8 +202,6 @@ def ctc_beam_search_decoder_batch(probs_split,
     :type beam_size: int
     :param vocabulary: Vocabulary list.
     :type vocabulary: list
-    :param blank_id: ID of blank.
-    :type blank_id: int
     :param num_processes: Number of parallel processes.
     :type num_processes: int
     :param cutoff_prob: Cutoff probability in pruning,
@@ -232,8 +228,8 @@ def ctc_beam_search_decoder_batch(probs_split,
     pool = multiprocessing.Pool(processes=num_processes)
     results = []
     for i, probs_list in enumerate(probs_split):
-        args = (probs_list, beam_size, vocabulary, blank_id, cutoff_prob, None,
-                nproc)
+        args = (probs_list, beam_size, vocabulary, blank_id, cutoff_prob,
+                cutoff_top_n, None, nproc)
         results.append(pool.apply_async(ctc_beam_search_decoder, args))
 
     pool.close()

decoders/lm_scorer_deprecated.py

@@ -8,7 +8,7 @@ import kenlm
 import numpy as np
 
 
-class LmScorer(object):
+class Scorer(object):
     """External scorer to evaluate a prefix or whole sentence in
        beam search decoding, including the score from n-gram language
        model and word count.

decoders/swig/ctc_decoders.cpp

@@ -128,7 +128,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
 
     // pruning of vacobulary
     size_t cutoff_len = prob.size();
-    if (cutoff_prob < 1.0 || cutoff_top_n < prob.size()) {
+    if (cutoff_prob < 1.0 || cutoff_top_n < cutoff_len) {
       std::sort(
           prob_idx.begin(), prob_idx.end(), pair_comp_second_rev<int, double>);
       if (cutoff_prob < 1.0) {

examples/librispeech/run_infer.sh

@@ -24,6 +24,7 @@ python -u infer.py \
 --alpha=2.15 \
 --beta=0.35 \
 --cutoff_prob=1.0 \
+--cutoff_top_n=40 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

examples/librispeech/run_infer_golden.sh

@@ -33,6 +33,7 @@ python -u infer.py \
 --alpha=2.15 \
 --beta=0.35 \
 --cutoff_prob=1.0 \
+--cutoff_top_n=40 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

examples/librispeech/run_test_golden.sh

@@ -34,6 +34,7 @@ python -u test.py \
 --alpha=2.15 \
 --beta=0.35 \
 --cutoff_prob=1.0 \
+--cutoff_top_n=40 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

infer.py

@@ -24,6 +24,7 @@ add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
 add_arg('alpha',            float,  2.15,   "Coef of LM for beam search.")
 add_arg('beta',             float,  0.35,   "Coef of WC for beam search.")
 add_arg('cutoff_prob',      float,  1.0,    "Cutoff probability for pruning.")
+add_arg('cutoff_top_n',     int,    40,     "Cutoff number for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of simple RNNs.")
 add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
@@ -85,6 +86,9 @@ def infer():
         pretrained_model_path=args.model_path,
         share_rnn_weights=args.share_rnn_weights)
 
+    # decoders only accept string encoded in utf-8
+    vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
+
     result_transcripts = ds2_model.infer_batch(
         infer_data=infer_data,
         decoding_method=args.decoding_method,
@@ -92,7 +96,8 @@ def infer():
         beam_beta=args.beta,
         beam_size=args.beam_size,
         cutoff_prob=args.cutoff_prob,
-        vocab_list=data_generator.vocab_list,
+        cutoff_top_n=args.cutoff_top_n,
+        vocab_list=vocab_list,
         language_model_path=args.lang_model_path,
         num_processes=args.num_proc_bsearch)
 

model_utils/model.py

@@ -148,8 +148,8 @@ class DeepSpeech2Model(object):
         return self._loss_inferer.infer(input=infer_data)
 
     def infer_batch(self, infer_data, decoding_method, beam_alpha, beam_beta,
-                    beam_size, cutoff_prob, vocab_list, language_model_path,
-                    num_processes):
+                    beam_size, cutoff_prob, cutoff_top_n, vocab_list,
+                    language_model_path, num_processes):
         """Model inference. Infer the transcription for a batch of speech
         utterances.
 
@@ -169,6 +169,10 @@ class DeepSpeech2Model(object):
         :param cutoff_prob: Cutoff probability in pruning,
                             default 1.0, no pruning.
         :type cutoff_prob: float
+        :param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
+                        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.
         :type vocab_list: list
         :param language_model_path: Filepath for language model.
@@ -216,7 +220,8 @@ class DeepSpeech2Model(object):
                 beam_size=beam_size,
                 num_processes=num_processes,
                 ext_scoring_func=self._ext_scorer,
-                cutoff_prob=cutoff_prob)
+                cutoff_prob=cutoff_prob,
+                cutoff_top_n=cutoff_top_n)
 
             results = [result[0][1] for result in beam_search_results]
         else:

test.py

@@ -25,6 +25,7 @@ add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
 add_arg('alpha',            float,  2.15,   "Coef of LM for beam search.")
 add_arg('beta',             float,  0.35,   "Coef of WC for beam search.")
 add_arg('cutoff_prob',      float,  1.0,    "Cutoff probability for pruning.")
+add_arg('cutoff_top_n',     int,    40,     "Cutoff number for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of simple RNNs.")
 add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
@@ -85,6 +86,9 @@ def evaluate():
         pretrained_model_path=args.model_path,
         share_rnn_weights=args.share_rnn_weights)
 
+    # decoders only accept string encoded in utf-8
+    vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
+
     error_rate_func = cer if args.error_rate_type == 'cer' else wer
     error_sum, num_ins = 0.0, 0
     for infer_data in batch_reader():
@@ -95,7 +99,8 @@ def evaluate():
             beam_beta=args.beta,
             beam_size=args.beam_size,
             cutoff_prob=args.cutoff_prob,
-            vocab_list=data_generator.vocab_list,
+            cutoff_top_n=args.cutoff_top_n,
+            vocab_list=vocab_list,
             language_model_path=args.lang_model_path,
             num_processes=args.num_proc_bsearch)
         target_transcripts = [