adjust scorer's init & add logging for scorer & separate long functions

deep_speech_2/README.md

@@ -176,7 +176,6 @@ Data augmentation has often been a highly effective technique to boost the deep
 
 Six optional augmentation components are provided to be selected, configured and inserted into the processing pipeline.
 
-### Inference
   - Volume Perturbation
   - Speed Perturbation
   - Shifting Perturbation

deep_speech_2/decoders/decoder_deprecated.py → deep_speech_2/decoders/decoders_deprecated.py

@@ -119,7 +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)
+            cutoff_len = min(cutoff_len, cutoff_top_n)
             prob_idx = prob_idx[0:cutoff_len]
 
         for l in prefix_set_prev:
@@ -228,8 +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,
-                cutoff_top_n, None, nproc)
+        args = (probs_list, beam_size, vocabulary, cutoff_prob, cutoff_top_n,
+                None, nproc)
         results.append(pool.apply_async(ctc_beam_search_decoder, args))
 
     pool.close()

deep_speech_2/decoders/swig/ctc_decoders.cpp → deep_speech_2/decoders/swig/ctc_beam_search_decoder.cpp

-#include "ctc_decoders.h"
+#include "ctc_beam_search_decoder.h"
 
 #include <algorithm>
 #include <cmath>
@@ -9,59 +9,19 @@
 
 #include "ThreadPool.h"
 #include "fst/fstlib.h"
+#include "fst/log.h"
 
 #include "decoder_utils.h"
 #include "path_trie.h"
 
-std::string ctc_greedy_decoder(
-    const std::vector<std::vector<double>> &probs_seq,
-    const std::vector<std::string> &vocabulary) {
-  // 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,
-                   "The shape of probs_seq does not match with "
-                   "the shape of the vocabulary");
-  }
-
-  size_t blank_id = vocabulary.size();
-
-  std::vector<size_t> max_idx_vec;
-  for (size_t i = 0; i < num_time_steps; ++i) {
-    double max_prob = 0.0;
-    size_t max_idx = 0;
-    for (size_t j = 0; j < probs_seq[i].size(); j++) {
-      if (max_prob < probs_seq[i][j]) {
-        max_idx = j;
-        max_prob = probs_seq[i][j];
-      }
-    }
-    max_idx_vec.push_back(max_idx);
-  }
-
-  std::vector<size_t> idx_vec;
-  for (size_t i = 0; i < max_idx_vec.size(); ++i) {
-    if ((i == 0) || ((i > 0) && max_idx_vec[i] != max_idx_vec[i - 1])) {
-      idx_vec.push_back(max_idx_vec[i]);
-    }
-  }
-
-  std::string best_path_result;
-  for (size_t i = 0; i < idx_vec.size(); ++i) {
-    if (idx_vec[i] != blank_id) {
-      best_path_result += vocabulary[idx_vec[i]];
-    }
-  }
-  return best_path_result;
-}
+using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
 
 std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
     const std::vector<std::vector<double>> &probs_seq,
-    const size_t beam_size,
+    size_t beam_size,
     std::vector<std::string> vocabulary,
-    const double cutoff_prob,
-    const size_t cutoff_top_n,
+    double cutoff_prob,
+    size_t cutoff_top_n,
     Scorer *ext_scorer) {
   // dimension check
   size_t num_time_steps = probs_seq.size();
@@ -80,7 +40,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
       std::find(vocabulary.begin(), vocabulary.end(), " ");
   int space_id = it - vocabulary.begin();
   // if no space in vocabulary
-  if (space_id >= vocabulary.size()) {
+  if ((size_t)space_id >= vocabulary.size()) {
     space_id = -2;
   }
 
@@ -90,30 +50,17 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
   std::vector<PathTrie *> prefixes;
   prefixes.push_back(&root);
 
-  if (ext_scorer != nullptr) {
-    if (ext_scorer->is_char_map_empty()) {
-      ext_scorer->set_char_map(vocabulary);
-    }
-    if (!ext_scorer->is_character_based()) {
-      if (ext_scorer->dictionary == nullptr) {
-        // fill dictionary for fst with space
-        ext_scorer->fill_dictionary(true);
-      }
-      auto fst_dict = static_cast<fst::StdVectorFst *>(ext_scorer->dictionary);
-      fst::StdVectorFst *dict_ptr = fst_dict->Copy(true);
-      root.set_dictionary(dict_ptr);
-      auto matcher = std::make_shared<FSTMATCH>(*dict_ptr, fst::MATCH_INPUT);
-      root.set_matcher(matcher);
-    }
+  if (ext_scorer != nullptr && !ext_scorer->is_character_based()) {
+    auto fst_dict = static_cast<fst::StdVectorFst *>(ext_scorer->dictionary);
+    fst::StdVectorFst *dict_ptr = fst_dict->Copy(true);
+    root.set_dictionary(dict_ptr);
+    auto matcher = std::make_shared<FSTMATCH>(*dict_ptr, fst::MATCH_INPUT);
+    root.set_matcher(matcher);
   }
 
   // prefix search over time
-  for (size_t time_step = 0; time_step < num_time_steps; time_step++) {
-    std::vector<double> prob = probs_seq[time_step];
-    std::vector<std::pair<int, double>> prob_idx;
-    for (size_t i = 0; i < prob.size(); ++i) {
-      prob_idx.push_back(std::pair<int, double>(i, prob[i]));
-    }
+  for (size_t time_step = 0; time_step < num_time_steps; ++time_step) {
+    auto &prob = probs_seq[time_step];
 
     float min_cutoff = -NUM_FLT_INF;
     bool full_beam = false;
@@ -121,43 +68,20 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
       size_t num_prefixes = std::min(prefixes.size(), beam_size);
       std::sort(
           prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
-      min_cutoff = prefixes[num_prefixes - 1]->score + log(prob[blank_id]) -
-                   std::max(0.0, ext_scorer->beta);
+      min_cutoff = prefixes[num_prefixes - 1]->score +
+                   std::log(prob[blank_id]) - std::max(0.0, ext_scorer->beta);
       full_beam = (num_prefixes == beam_size);
     }
 
-    // pruning of vacobulary
-    size_t cutoff_len = 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) {
-        double cum_prob = 0.0;
-        cutoff_len = 0;
-        for (size_t i = 0; i < prob_idx.size(); ++i) {
-          cum_prob += prob_idx[i].second;
-          cutoff_len += 1;
-          if (cum_prob >= cutoff_prob) break;
-        }
-      }
-      cutoff_len = std::min(cutoff_len, cutoff_top_n);
-      prob_idx = std::vector<std::pair<int, double>>(
-          prob_idx.begin(), prob_idx.begin() + cutoff_len);
-    }
-    std::vector<std::pair<size_t, float>> log_prob_idx;
-    for (size_t i = 0; i < cutoff_len; ++i) {
-      log_prob_idx.push_back(std::pair<int, float>(
-          prob_idx[i].first, log(prob_idx[i].second + NUM_FLT_MIN)));
-    }
-
+    std::vector<std::pair<size_t, float>> log_prob_idx =
+        get_pruned_log_probs(prob, cutoff_prob, cutoff_top_n);
     // loop over chars
     for (size_t index = 0; index < log_prob_idx.size(); index++) {
       auto c = log_prob_idx[index].first;
-      float log_prob_c = log_prob_idx[index].second;
+      auto log_prob_c = log_prob_idx[index].second;
 
       for (size_t i = 0; i < prefixes.size() && i < beam_size; ++i) {
         auto prefix = prefixes[i];
-
         if (full_beam && log_prob_c + prefix->score < min_cutoff) {
           break;
         }
@@ -189,7 +113,6 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
           if (ext_scorer != nullptr &&
               (c == space_id || ext_scorer->is_character_based())) {
             PathTrie *prefix_toscore = nullptr;
-
             // skip scoring the space
             if (ext_scorer->is_character_based()) {
               prefix_toscore = prefix_new;
@@ -201,7 +124,6 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
             std::vector<std::string> ngram;
             ngram = ext_scorer->make_ngram(prefix_toscore);
             score = ext_scorer->get_log_cond_prob(ngram) * ext_scorer->alpha;
-
             log_p += score;
             log_p += ext_scorer->beta;
           }
@@ -221,57 +143,33 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
                        prefixes.begin() + beam_size,
                        prefixes.end(),
                        prefix_compare);
-
       for (size_t i = beam_size; i < prefixes.size(); ++i) {
         prefixes[i]->remove();
       }
     }
   }  // end of loop over time
 
-  // compute aproximate ctc score as the return score
+  // compute aproximate ctc score as the return score, without affecting the
+  // return order of decoding result. To delete when decoder gets stable.
   for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
     double approx_ctc = prefixes[i]->score;
-
     if (ext_scorer != nullptr) {
       std::vector<int> output;
       prefixes[i]->get_path_vec(output);
-      size_t prefix_length = output.size();
+      auto prefix_length = output.size();
       auto words = ext_scorer->split_labels(output);
       // remove word insert
       approx_ctc = approx_ctc - prefix_length * ext_scorer->beta;
       // remove language model weight:
       approx_ctc -= (ext_scorer->get_sent_log_prob(words)) * ext_scorer->alpha;
     }
-
     prefixes[i]->approx_ctc = approx_ctc;
   }
 
-  // allow for the post processing
-  std::vector<PathTrie *> space_prefixes;
-  if (space_prefixes.empty()) {
-    for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
-      space_prefixes.push_back(prefixes[i]);
-    }
-  }
-
-  std::sort(space_prefixes.begin(), space_prefixes.end(), prefix_compare);
-  std::vector<std::pair<double, std::string>> output_vecs;
-  for (size_t i = 0; i < beam_size && i < space_prefixes.size(); ++i) {
-    std::vector<int> output;
-    space_prefixes[i]->get_path_vec(output);
-    // convert index to string
-    std::string output_str;
-    for (size_t j = 0; j < output.size(); j++) {
-      output_str += vocabulary[output[j]];
-    }
-    std::pair<double, std::string> output_pair(-space_prefixes[i]->approx_ctc,
-                                               output_str);
-    output_vecs.emplace_back(output_pair);
-  }
-
-  return output_vecs;
+  return get_beam_search_result(prefixes, vocabulary, beam_size);
 }
 
+
 std::vector<std::vector<std::pair<double, std::string>>>
 ctc_beam_search_decoder_batch(
     const std::vector<std::vector<std::vector<double>>> &probs_split,
@@ -287,18 +185,6 @@ ctc_beam_search_decoder_batch(
   // number of samples
   size_t batch_size = probs_split.size();
 
-  // scorer filling up
-  if (ext_scorer != nullptr) {
-    if (ext_scorer->is_char_map_empty()) {
-      ext_scorer->set_char_map(vocabulary);
-    }
-    if (!ext_scorer->is_character_based() &&
-        ext_scorer->dictionary == nullptr) {
-      // init dictionary
-      ext_scorer->fill_dictionary(true);
-    }
-  }
-
   // enqueue the tasks of decoding
   std::vector<std::future<std::vector<std::pair<double, std::string>>>> res;
   for (size_t i = 0; i < batch_size; ++i) {

deep_speech_2/decoders/swig/ctc_decoders.h → deep_speech_2/decoders/swig/ctc_beam_search_decoder.h

@@ -7,19 +7,6 @@
 
 #include "scorer.h"
 
-/* CTC Best Path Decoder
- *
- * Parameters:
- *     probs_seq: 2-D vector that each element is a vector of probabilities
- *               over vocabulary of one time step.
- *     vocabulary: A vector of vocabulary.
- * Return:
- *     The decoding result in string
- */
-std::string ctc_greedy_decoder(
-    const std::vector<std::vector<double>> &probs_seq,
-    const std::vector<std::string> &vocabulary);
-
 /* CTC Beam Search Decoder
 
  * Parameters:
@@ -38,11 +25,11 @@ std::string ctc_greedy_decoder(
 */
 std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
     const std::vector<std::vector<double>> &probs_seq,
-    const size_t beam_size,
+    size_t beam_size,
     std::vector<std::string> vocabulary,
-    const double cutoff_prob = 1.0,
-    const size_t cutoff_top_n = 40,
-    Scorer *ext_scorer = NULL);
+    double cutoff_prob = 1.0,
+    size_t cutoff_top_n = 40,
+    Scorer *ext_scorer = nullptr);
 
 /* CTC Beam Search Decoder for batch data
 
@@ -65,11 +52,11 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
 std::vector<std::vector<std::pair<double, std::string>>>
 ctc_beam_search_decoder_batch(
     const std::vector<std::vector<std::vector<double>>> &probs_split,
-    const size_t beam_size,
+    size_t beam_size,
     const std::vector<std::string> &vocabulary,
-    const size_t num_processes,
+    size_t num_processes,
     double cutoff_prob = 1.0,
-    const size_t cutoff_top_n = 40,
-    Scorer *ext_scorer = NULL);
+    size_t cutoff_top_n = 40,
+    Scorer *ext_scorer = nullptr);
 
 #endif  // CTC_BEAM_SEARCH_DECODER_H_

deep_speech_2/decoders/swig/ctc_greedy_decoder.cpp

+#include "ctc_greedy_decoder.h"
+#include "decoder_utils.h"
+
+std::string ctc_greedy_decoder(
+    const std::vector<std::vector<double>> &probs_seq,
+    const std::vector<std::string> &vocabulary) {
+  // 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,
+                   "The shape of probs_seq does not match with "
+                   "the shape of the vocabulary");
+  }
+
+  size_t blank_id = vocabulary.size();
+
+  std::vector<size_t> max_idx_vec(num_time_steps, 0);
+  std::vector<size_t> idx_vec;
+  for (size_t i = 0; i < num_time_steps; ++i) {
+    double max_prob = 0.0;
+    size_t max_idx = 0;
+    const std::vector<double> &probs_step = probs_seq[i];
+    for (size_t j = 0; j < probs_step.size(); ++j) {
+      if (max_prob < probs_step[j]) {
+        max_idx = j;
+        max_prob = probs_step[j];
+      }
+    }
+    // id with maximum probability in current step
+    max_idx_vec[i] = max_idx;
+    // deduplicate
+    if ((i == 0) || ((i > 0) && max_idx_vec[i] != max_idx_vec[i - 1])) {
+      idx_vec.push_back(max_idx_vec[i]);
+    }
+  }
+
+  std::string best_path_result;
+  for (size_t i = 0; i < idx_vec.size(); ++i) {
+    if (idx_vec[i] != blank_id) {
+      best_path_result += vocabulary[idx_vec[i]];
+    }
+  }
+  return best_path_result;
+}

deep_speech_2/decoders/swig/ctc_greedy_decoder.h

+#ifndef CTC_GREEDY_DECODER_H
+#define CTC_GREEDY_DECODER_H
+
+#include <string>
+#include <vector>
+
+/* CTC Greedy (Best Path) Decoder
+ *
+ * Parameters:
+ *     probs_seq: 2-D vector that each element is a vector of probabilities
+ *               over vocabulary of one time step.
+ *     vocabulary: A vector of vocabulary.
+ * Return:
+ *     The decoding result in string
+ */
+std::string ctc_greedy_decoder(
+    const std::vector<std::vector<double>> &probs_seq,
+    const std::vector<std::string> &vocabulary);
+
+#endif  // CTC_GREEDY_DECODER_H

deep_speech_2/decoders/swig/decoder_utils.cpp

@@ -4,6 +4,71 @@
 #include <cmath>
 #include <limits>
 
+std::vector<std::pair<size_t, float>> get_pruned_log_probs(
+    const std::vector<double> &prob_step,
+    double cutoff_prob,
+    size_t cutoff_top_n) {
+  std::vector<std::pair<int, double>> prob_idx;
+  for (size_t i = 0; i < prob_step.size(); ++i) {
+    prob_idx.push_back(std::pair<int, double>(i, prob_step[i]));
+  }
+  // pruning of vacobulary
+  size_t cutoff_len = prob_step.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) {
+      double cum_prob = 0.0;
+      cutoff_len = 0;
+      for (size_t i = 0; i < prob_idx.size(); ++i) {
+        cum_prob += prob_idx[i].second;
+        cutoff_len += 1;
+        if (cum_prob >= cutoff_prob) break;
+      }
+    }
+    cutoff_len = std::min(cutoff_len, cutoff_top_n);
+    prob_idx = std::vector<std::pair<int, double>>(
+        prob_idx.begin(), prob_idx.begin() + cutoff_len);
+  }
+  std::vector<std::pair<size_t, float>> log_prob_idx;
+  for (size_t i = 0; i < cutoff_len; ++i) {
+    log_prob_idx.push_back(std::pair<int, float>(
+        prob_idx[i].first, log(prob_idx[i].second + NUM_FLT_MIN)));
+  }
+  return log_prob_idx;
+}
+
+
+std::vector<std::pair<double, std::string>> get_beam_search_result(
+    const std::vector<PathTrie *> &prefixes,
+    const std::vector<std::string> &vocabulary,
+    size_t beam_size) {
+  // allow for the post processing
+  std::vector<PathTrie *> space_prefixes;
+  if (space_prefixes.empty()) {
+    for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
+      space_prefixes.push_back(prefixes[i]);
+    }
+  }
+
+  std::sort(space_prefixes.begin(), space_prefixes.end(), prefix_compare);
+  std::vector<std::pair<double, std::string>> output_vecs;
+  for (size_t i = 0; i < beam_size && i < space_prefixes.size(); ++i) {
+    std::vector<int> output;
+    space_prefixes[i]->get_path_vec(output);
+    // convert index to string
+    std::string output_str;
+    for (size_t j = 0; j < output.size(); j++) {
+      output_str += vocabulary[output[j]];
+    }
+    std::pair<double, std::string> output_pair(-space_prefixes[i]->approx_ctc,
+                                               output_str);
+    output_vecs.emplace_back(output_pair);
+  }
+
+  return output_vecs;
+}
+
 size_t get_utf8_str_len(const std::string &str) {
   size_t str_len = 0;
   for (char c : str) {

deep_speech_2/decoders/swig/decoder_utils.h

@@ -3,25 +3,26 @@
 
 #include <utility>
 #include "path_trie.h"
+#include "fst/log.h"
 
 const float NUM_FLT_INF = std::numeric_limits<float>::max();
 const float NUM_FLT_MIN = std::numeric_limits<float>::min();
 
-// check if __A == _B
-#define VALID_CHECK_EQ(__A, __B, __ERR)          \
-  if ((__A) != (__B)) {                          \
-    std::ostringstream str;                      \
-    str << (__A) << " != " << (__B) << ", ";     \
-    throw std::runtime_error(str.str() + __ERR); \
+// inline function for validation check
+inline void check(
+    bool x, const char *expr, const char *file, int line, const char *err) {
+  if (!x) {
+    std::cout << "[" << file << ":" << line << "] ";
+    LOG(FATAL) << "\"" << expr << "\" check failed. " << err;
   }
+}
+
+#define VALID_CHECK(x, info) \
+  check(static_cast<bool>(x), #x, __FILE__, __LINE__, info)
+#define VALID_CHECK_EQ(x, y, info) VALID_CHECK((x) == (y), info)
+#define VALID_CHECK_GT(x, y, info) VALID_CHECK((x) > (y), info)
+#define VALID_CHECK_LT(x, y, info) VALID_CHECK((x) < (y), info)
 
-// check if __A > __B
-#define VALID_CHECK_GT(__A, __B, __ERR)          \
-  if ((__A) <= (__B)) {                          \
-    std::ostringstream str;                      \
-    str << (__A) << " <= " << (__B) << ", ";     \
-    throw std::runtime_error(str.str() + __ERR); \
-  }
 
 // Function template for comparing two pairs
 template <typename T1, typename T2>
@@ -47,6 +48,18 @@ T log_sum_exp(const T &x, const T &y) {
   return std::log(std::exp(x - xmax) + std::exp(y - xmax)) + xmax;
 }
 
+// Get pruned probability vector for each time step's beam search
+std::vector<std::pair<size_t, float>> get_pruned_log_probs(
+    const std::vector<double> &prob_step,
+    double cutoff_prob,
+    size_t cutoff_top_n);
+
+// Get beam search result from prefixes in trie tree
+std::vector<std::pair<double, std::string>> get_beam_search_result(
+    const std::vector<PathTrie *> &prefixes,
+    const std::vector<std::string> &vocabulary,
+    size_t beam_size);
+
 // Functor for prefix comparsion
 bool prefix_compare(const PathTrie *x, const PathTrie *y);
 

deep_speech_2/decoders/swig/decoders.i

 %module swig_decoders
 %{
 #include "scorer.h"
-#include "ctc_decoders.h"
+#include "ctc_greedy_decoder.h"
+#include "ctc_beam_search_decoder.h"
 #include "decoder_utils.h"
 %}
 
@@ -28,4 +29,5 @@ namespace std {
 %template(DoubleStringPairCompFirstRev) pair_comp_first_rev<double, std::string>;
 
 %include "scorer.h"
-%include "ctc_decoders.h"
+%include "ctc_greedy_decoder.h"
+%include "ctc_beam_search_decoder.h"

deep_speech_2/decoders/swig/path_trie.h

 #ifndef PATH_TRIE_H
 #define PATH_TRIE_H
-#pragma once
-#include <fst/fstlib.h>
+
 #include <algorithm>
 #include <limits>
 #include <memory>
 #include <utility>
 #include <vector>
 
-using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
+#include "fst/fstlib.h"
 
 /* Trie tree for prefix storing and manipulating, with a dictionary in
  * finite-state transducer for spelling correction.
@@ -35,7 +34,7 @@ public:
   // set dictionary for FST
   void set_dictionary(fst::StdVectorFst* dictionary);
 
-  void set_matcher(std::shared_ptr<FSTMATCH> matcher);
+  void set_matcher(std::shared_ptr<fst::SortedMatcher<fst::StdVectorFst>>);
 
   bool is_empty() { return _ROOT == character; }
 
@@ -62,7 +61,7 @@ private:
   fst::StdVectorFst* _dictionary;
   fst::StdVectorFst::StateId _dictionary_state;
   // true if finding ars in FST
-  std::shared_ptr<FSTMATCH> _matcher;
+  std::shared_ptr<fst::SortedMatcher<fst::StdVectorFst>> _matcher;
 };
 
 #endif  // PATH_TRIE_H

deep_speech_2/decoders/swig/scorer.cpp

@@ -13,29 +13,47 @@
 
 using namespace lm::ngram;
 
-Scorer::Scorer(double alpha, double beta, const std::string& lm_path) {
+Scorer::Scorer(double alpha,
+               double beta,
+               const std::string& lm_path,
+               const std::vector<std::string>& vocab_list) {
   this->alpha = alpha;
   this->beta = beta;
   _is_character_based = true;
   _language_model = nullptr;
   dictionary = nullptr;
   _max_order = 0;
+  _dict_size = 0;
   _SPACE_ID = -1;
-  // load language model
-  load_LM(lm_path.c_str());
+
+  setup(lm_path, vocab_list);
 }
 
 Scorer::~Scorer() {
-  if (_language_model != nullptr)
+  if (_language_model != nullptr) {
     delete static_cast<lm::base::Model*>(_language_model);
-  if (dictionary != nullptr) delete static_cast<fst::StdVectorFst*>(dictionary);
+  }
+  if (dictionary != nullptr) {
+    delete static_cast<fst::StdVectorFst*>(dictionary);
+  }
 }
 
-void Scorer::load_LM(const char* filename) {
-  if (access(filename, F_OK) != 0) {
-    std::cerr << "Invalid language model file !!!" << std::endl;
-    exit(1);
+void Scorer::setup(const std::string& lm_path,
+                   const std::vector<std::string>& vocab_list) {
+  // load language model
+  load_lm(lm_path);
+  // set char map for scorer
+  set_char_map(vocab_list);
+  // fill the dictionary for FST
+  if (!is_character_based()) {
+    fill_dictionary(true);
   }
+}
+
+void Scorer::load_lm(const std::string& lm_path) {
+  const char* filename = lm_path.c_str();
+  VALID_CHECK_EQ(access(filename, F_OK), 0, "Invalid language model path");
+
   RetriveStrEnumerateVocab enumerate;
   lm::ngram::Config config;
   config.enumerate_vocab = &enumerate;
@@ -180,14 +198,14 @@ void Scorer::fill_dictionary(bool add_space) {
   }
 
   // For each unigram convert to ints and put in trie
-  int vocab_size = 0;
+  int dict_size = 0;
   for (const auto& word : _vocabulary) {
     bool added = add_word_to_dictionary(
         word, char_map, add_space, _SPACE_ID, &dictionary);
-    vocab_size += added ? 1 : 0;
+    dict_size += added ? 1 : 0;
   }
 
-  std::cerr << "Vocab Size " << vocab_size << std::endl;
+  _dict_size = dict_size;
 
   /* Simplify FST
 

deep_speech_2/decoders/swig/scorer.h

@@ -40,31 +40,32 @@ public:
  */
 class Scorer {
 public:
-  Scorer(double alpha, double beta, const std::string &lm_path);
+  Scorer(double alpha,
+         double beta,
+         const std::string &lm_path,
+         const std::vector<std::string> &vocabulary);
   ~Scorer();
 
   double get_log_cond_prob(const std::vector<std::string> &words);
 
   double get_sent_log_prob(const std::vector<std::string> &words);
 
-  size_t get_max_order() { return _max_order; }
+  size_t get_max_order() const { return _max_order; }
 
-  bool is_char_map_empty() { return _char_map.size() == 0; }
+  size_t get_dict_size() const { return _dict_size; }
 
-  bool is_character_based() { return _is_character_based; }
+  bool is_char_map_empty() const { return _char_map.size() == 0; }
+
+  bool is_character_based() const { return _is_character_based; }
 
   // reset params alpha & beta
   void reset_params(float alpha, float beta);
 
-  // make ngram
+  // make ngram for a given prefix
   std::vector<std::string> make_ngram(PathTrie *prefix);
 
-  // fill dictionary for fst
-  void fill_dictionary(bool add_space);
-
-  // set char map
-  void set_char_map(const std::vector<std::string> &char_list);
-
+  // trransform the labels in index to the vector of words (word based lm) or
+  // the vector of characters (character based lm)
   std::vector<std::string> split_labels(const std::vector<int> &labels);
 
   // expose to decoder
@@ -75,7 +76,16 @@ public:
   void *dictionary;
 
 protected:
-  void load_LM(const char *filename);
+  void setup(const std::string &lm_path,
+             const std::vector<std::string> &vocab_list);
+
+  void load_lm(const std::string &lm_path);
+
+  // fill dictionary for fst
+  void fill_dictionary(bool add_space);
+
+  // set char map
+  void set_char_map(const std::vector<std::string> &char_list);
 
   double get_log_prob(const std::vector<std::string> &words);
 
@@ -85,6 +95,7 @@ private:
   void *_language_model;
   bool _is_character_based;
   size_t _max_order;
+  size_t _dict_size;
 
   int _SPACE_ID;
   std::vector<std::string> _char_list;

deep_speech_2/decoders/swig/setup.py

@@ -70,8 +70,11 @@ FILES = glob.glob('kenlm/util/*.cc') \
 
 FILES += glob.glob('openfst-1.6.3/src/lib/*.cc')
 
+# FILES + glob.glob('glog/src/*.cc')
 FILES = [
-    fn for fn in FILES if not (fn.endswith('main.cc') or fn.endswith('test.cc'))
+    fn for fn in FILES
+    if not (fn.endswith('main.cc') or fn.endswith('test.cc') or fn.endswith(
+        'unittest.cc'))
 ]
 
 LIBS = ['stdc++']
@@ -99,7 +102,13 @@ decoders_module = [
         name='_swig_decoders',
         sources=FILES + glob.glob('*.cxx') + glob.glob('*.cpp'),
         language='c++',
-        include_dirs=['.', 'kenlm', 'openfst-1.6.3/src/include', 'ThreadPool'],
+        include_dirs=[
+            '.',
+            'kenlm',
+            'openfst-1.6.3/src/include',
+            'ThreadPool',
+            #'glog/src'
+        ],
         libraries=LIBS,
         extra_compile_args=ARGS)
 ]

deep_speech_2/decoders/swig/setup.sh

-#!/bin/bash
+#!/usr/bin/env bash
 
 if [ ! -d kenlm ]; then
     git clone https://github.com/luotao1/kenlm.git

deep_speech_2/decoders/swig_wrapper.py

@@ -13,14 +13,14 @@ class Scorer(swig_decoders.Scorer):
                   language model when alpha = 0.
     :type alpha: float
     :param beta: Parameter associated with word count. Don't use word
-                count when beta = 0.
+                 count when beta = 0.
     :type beta: float
     :model_path: Path to load language model.
     :type model_path: basestring
     """
 
-    def __init__(self, alpha, beta, model_path):
-        swig_decoders.Scorer.__init__(self, alpha, beta, model_path)
+    def __init__(self, alpha, beta, model_path, vocabulary):
+        swig_decoders.Scorer.__init__(self, alpha, beta, model_path, vocabulary)
 
 
 def ctc_greedy_decoder(probs_seq, vocabulary):
@@ -58,12 +58,12 @@ def ctc_beam_search_decoder(probs_seq,
                         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.
+                         characters with highest probs in vocabulary will be
+                         used in beam search, default 40.
     :type cutoff_top_n: int
     :param ext_scoring_func: External scoring function for
-                            partially decoded sentence, e.g. word count
-                            or language model.
+                             partially decoded sentence, e.g. word count
+                             or language model.
     :type external_scoring_func: callable
     :return: List of tuples of log probability and sentence as decoding
              results, in descending order of the probability.
@@ -96,14 +96,14 @@ def ctc_beam_search_decoder_batch(probs_split,
                         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.
+                         characters with highest probs in vocabulary will be
+                         used in beam search, default 40.
     :type cutoff_top_n: int
     :param num_processes: Number of parallel processes.
     :type num_processes: int
     :param ext_scoring_func: External scoring function for
-                            partially decoded sentence, e.g. word count
-                            or language model.
+                             partially decoded sentence, e.g. word count
+                             or language model.
     :type external_scoring_function: callable
     :return: List of tuples of log probability and sentence as decoding
              results, in descending order of the probability.

deep_speech_2/examples/tiny/run_infer.sh

@@ -21,9 +21,9 @@ python -u infer.py \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---alpha=0.36 \
---beta=0.25 \
---cutoff_prob=0.99 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

deep_speech_2/examples/tiny/run_infer_golden.sh

@@ -30,9 +30,9 @@ python -u infer.py \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---alpha=0.36 \
---beta=0.25 \
---cutoff_prob=0.99 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

deep_speech_2/examples/tiny/run_test.sh

@@ -22,9 +22,9 @@ python -u test.py \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---alpha=0.36 \
---beta=0.25 \
---cutoff_prob=0.99 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

deep_speech_2/examples/tiny/run_test_golden.sh

@@ -31,9 +31,9 @@ python -u test.py \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---alpha=0.36 \
---beta=0.25 \
---cutoff_prob=0.99 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

deep_speech_2/infer.py

@@ -112,6 +112,7 @@ def infer():
         print("Current error rate [%s] = %f" %
               (args.error_rate_type, error_rate_func(target, result)))
 
+    ds2_model.logger.info("finish inference")
 
 def main():
     print_arguments(args)

deep_speech_2/model_utils/model.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import sys
 import os
 import time
+import logging
 import gzip
 import paddle.v2 as paddle
 from decoders.swig_wrapper import Scorer
@@ -13,6 +14,9 @@ from decoders.swig_wrapper import ctc_greedy_decoder
 from decoders.swig_wrapper import ctc_beam_search_decoder_batch
 from model_utils.network import deep_speech_v2_network
 
+logging.basicConfig(
+    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s')
+
 
 class DeepSpeech2Model(object):
     """DeepSpeech2Model class.
@@ -43,6 +47,8 @@ class DeepSpeech2Model(object):
         self._inferer = None
         self._loss_inferer = None
         self._ext_scorer = None
+        self.logger = logging.getLogger("")
+        self.logger.setLevel(level=logging.INFO)
 
     def train(self,
               train_batch_reader,
@@ -204,16 +210,25 @@ class DeepSpeech2Model(object):
         elif decoding_method == "ctc_beam_search":
             # initialize external scorer
             if self._ext_scorer == None:
-                self._ext_scorer = Scorer(beam_alpha, beam_beta,
-                                          language_model_path)
                 self._loaded_lm_path = language_model_path
-                self._ext_scorer.set_char_map(vocab_list)
-                if (not self._ext_scorer.is_character_based()):
-                    self._ext_scorer.fill_dictionary(True)
+                self.logger.info("begin to initialize the external scorer "
+                                 "for decoding")
+                self._ext_scorer = Scorer(beam_alpha, beam_beta,
+                                          language_model_path, vocab_list)
+
+                lm_char_based = self._ext_scorer.is_character_based()
+                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. Start decoding ...")
             else:
                 self._ext_scorer.reset_params(beam_alpha, beam_beta)
                 assert self._loaded_lm_path == language_model_path
             # beam search decode
+            num_processes = min(num_processes, len(probs_split))
             beam_search_results = ctc_beam_search_decoder_batch(
                 probs_split=probs_split,
                 vocabulary=vocab_list,

deep_speech_2/test.py

@@ -115,6 +115,7 @@ def evaluate():
     print("Final error rate [%s] (%d/%d) = %f" %
           (args.error_rate_type, num_ins, num_ins, error_sum / num_ins))
 
+    ds2_model.logger.info("finish evaluation")
 
 def main():
     print_arguments(args)