ctc decoder with blankid

deepspeech/decoders/swig/ctc_beam_search_decoder.cpp

@@ -35,7 +35,8 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
     size_t beam_size,
     double cutoff_prob,
     size_t cutoff_top_n,
-    Scorer *ext_scorer) {
+    Scorer *ext_scorer,
+    size_t blank_id) {
     // dimension check
     size_t num_time_steps = probs_seq.size();
     for (size_t i = 0; i < num_time_steps; ++i) {
@@ -45,19 +46,13 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
                        "The shape of probs_seq does not match with "
                        "the shape of the vocabulary");
     }
-
-    // assign blank id
-    // size_t blank_id = vocabulary.size();
-    size_t blank_id = 0;
-
     // assign space id
-    auto it = std::find(vocabulary.begin(), vocabulary.end(), " ");
+    auto it = std::find(vocabulary.begin(), vocabulary.end(), kSPACE);
     int space_id = it - vocabulary.begin();
     // if no space in vocabulary
     if ((size_t)space_id >= vocabulary.size()) {
         space_id = -2;
     }
-
     // init prefixes' root
     PathTrie root;
     root.score = root.log_prob_b_prev = 0.0;
@@ -218,7 +213,8 @@ ctc_beam_search_decoder_batch(
     size_t num_processes,
     double cutoff_prob,
     size_t cutoff_top_n,
-    Scorer *ext_scorer) {
+    Scorer *ext_scorer,
+    size_t blank_id) {
     VALID_CHECK_GT(num_processes, 0, "num_processes must be nonnegative!");
     // thread pool
     ThreadPool pool(num_processes);
@@ -234,7 +230,8 @@ ctc_beam_search_decoder_batch(
                                       beam_size,
                                       cutoff_prob,
                                       cutoff_top_n,
-                                      ext_scorer));
+                                      ext_scorer,
+                                      blank_id));
     }
 
     // get decoding results

deepspeech/decoders/swig/ctc_beam_search_decoder.h

@@ -43,7 +43,8 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
     size_t beam_size,
     double cutoff_prob = 1.0,
     size_t cutoff_top_n = 40,
-    Scorer *ext_scorer = nullptr);
+    Scorer *ext_scorer = nullptr,
+    size_t blank_id = 0);
 
 /* CTC Beam Search Decoder for batch data
 
@@ -70,6 +71,7 @@ ctc_beam_search_decoder_batch(
     size_t num_processes,
     double cutoff_prob = 1.0,
     size_t cutoff_top_n = 40,
-    Scorer *ext_scorer = nullptr);
+    Scorer *ext_scorer = nullptr,
+    size_t blank_id = 0);
 
 #endif  // CTC_BEAM_SEARCH_DECODER_H_

deepspeech/decoders/swig/ctc_greedy_decoder.cpp

@@ -17,17 +17,18 @@
 
 std::string ctc_greedy_decoder(
     const std::vector<std::vector<double>> &probs_seq,
-    const std::vector<std::string> &vocabulary) {
+    const std::vector<std::string> &vocabulary,
+    size_t blank_id) {
     // dimension check
     size_t num_time_steps = probs_seq.size();
     for (size_t i = 0; i < num_time_steps; ++i) {
         VALID_CHECK_EQ(probs_seq[i].size(),
-                       vocabulary.size() + 1,
+                       vocabulary.size(),
                        "The shape of probs_seq does not match with "
                        "the shape of the vocabulary");
     }
 
-    size_t blank_id = vocabulary.size();
+    // size_t blank_id = vocabulary.size();
 
     std::vector<size_t> max_idx_vec(num_time_steps, 0);
     std::vector<size_t> idx_vec;

deepspeech/decoders/swig/ctc_greedy_decoder.h

@@ -29,6 +29,7 @@
  */
 std::string ctc_greedy_decoder(
     const std::vector<std::vector<double>>& probs_seq,
-    const std::vector<std::string>& vocabulary);
+    const std::vector<std::string>& vocabulary,
+    size_t blank_id);
 
 #endif  // CTC_GREEDY_DECODER_H

deepspeech/decoders/swig/decoder_utils.h

@@ -15,10 +15,12 @@
 #ifndef DECODER_UTILS_H_
 #define DECODER_UTILS_H_
 
+#include <string>
 #include <utility>
 #include "fst/log.h"
 #include "path_trie.h"
 
+const std::string kSPACE = "<space>";
 const float NUM_FLT_INF = std::numeric_limits<float>::max();
 const float NUM_FLT_MIN = std::numeric_limits<float>::min();
 

deepspeech/decoders/swig/scorer.cpp

@@ -165,7 +165,7 @@ void Scorer::set_char_map(const std::vector<std::string>& char_list) {
 
     // Set the char map for the FST for spelling correction
     for (size_t i = 0; i < char_list_.size(); i++) {
-        if (char_list_[i] == " ") {
+        if (char_list_[i] == kSPACE) {
             SPACE_ID_ = i;
         }
         // The initial state of FST is state 0, hence the index of chars in

deepspeech/decoders/swig/setup.py

@@ -83,10 +83,12 @@ FILES = glob.glob('kenlm/util/*.cc') \
 
 FILES += glob.glob('openfst-1.6.3/src/lib/*.cc')
 
+# yapf: disable
 FILES = [
     fn for fn in FILES if not (fn.endswith('main.cc') or fn.endswith('test.cc')
                                or fn.endswith('unittest.cc'))
 ]
+# yapf: enable
 
 LIBS = ['stdc++']
 if platform.system() != 'Darwin':

deepspeech/decoders/swig_wrapper.py

@@ -32,7 +32,7 @@ class Scorer(swig_decoders.Scorer):
         swig_decoders.Scorer.__init__(self, alpha, beta, model_path, vocabulary)
 
 
-def ctc_greedy_decoder(probs_seq, vocabulary):
+def ctc_greedy_decoder(probs_seq, vocabulary, blank_id):
     """Wrapper for ctc best path decoder in swig.
 
     :param probs_seq: 2-D list of probability distributions over each time
@@ -44,7 +44,8 @@ def ctc_greedy_decoder(probs_seq, vocabulary):
     :return: Decoding result string.
     :rtype: str
     """
-    result = swig_decoders.ctc_greedy_decoder(probs_seq.tolist(), vocabulary)
+    result = swig_decoders.ctc_greedy_decoder(probs_seq.tolist(), vocabulary,
+                                              blank_id)
     return result
 
 
@@ -53,7 +54,8 @@ def ctc_beam_search_decoder(probs_seq,
                             beam_size,
                             cutoff_prob=1.0,
                             cutoff_top_n=40,
-                            ext_scoring_func=None):
+                            ext_scoring_func=None,
+                            blank_id=0):
     """Wrapper for the CTC Beam Search Decoder.
 
     :param probs_seq: 2-D list of probability distributions over each time
@@ -81,7 +83,7 @@ def ctc_beam_search_decoder(probs_seq,
     """
     beam_results = swig_decoders.ctc_beam_search_decoder(
         probs_seq.tolist(), vocabulary, beam_size, cutoff_prob, cutoff_top_n,
-        ext_scoring_func)
+        ext_scoring_func, blank_id)
     beam_results = [(res[0], res[1].decode('utf-8')) for res in beam_results]
     return beam_results
 
@@ -92,7 +94,8 @@ def ctc_beam_search_decoder_batch(probs_split,
                                   num_processes,
                                   cutoff_prob=1.0,
                                   cutoff_top_n=40,
-                                  ext_scoring_func=None):
+                                  ext_scoring_func=None,
+                                  blank_id=0):
     """Wrapper for the batched CTC beam search decoder.
 
     :param probs_seq: 3-D list with each element as an instance of 2-D list
@@ -125,7 +128,7 @@ def ctc_beam_search_decoder_batch(probs_split,
 
     batch_beam_results = swig_decoders.ctc_beam_search_decoder_batch(
         probs_split, vocabulary, beam_size, num_processes, cutoff_prob,
-        cutoff_top_n, ext_scoring_func)
+        cutoff_top_n, ext_scoring_func, blank_id)
     batch_beam_results = [[(res[0], res[1]) for res in beam_results]
                           for beam_results in batch_beam_results]
     return batch_beam_results