append some changes

deep_speech_2/decoders/swig/ctc_decoders.cpp

@@ -18,7 +18,7 @@ std::string ctc_greedy_decoder(
     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++) {
+  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 "
@@ -28,7 +28,7 @@ std::string ctc_greedy_decoder(
   size_t blank_id = vocabulary.size();
 
   std::vector<size_t> max_idx_vec;
-  for (size_t i = 0; i < num_time_steps; i++) {
+  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++) {
@@ -41,14 +41,14 @@ std::string ctc_greedy_decoder(
   }
 
   std::vector<size_t> idx_vec;
-  for (size_t i = 0; i < max_idx_vec.size(); i++) {
+  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++) {
+  for (size_t i = 0; i < idx_vec.size(); ++i) {
     if (idx_vec[i] != blank_id) {
       best_path_result += vocabulary[idx_vec[i]];
     }
@@ -65,7 +65,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
     Scorer *ext_scorer) {
   // dimension check
   size_t num_time_steps = probs_seq.size();
-  for (size_t i = 0; i < num_time_steps; i++) {
+  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 "
@@ -111,7 +111,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
   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++) {
+    for (size_t i = 0; i < prob.size(); ++i) {
       prob_idx.push_back(std::pair<int, double>(i, prob[i]));
     }
 
@@ -134,7 +134,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
       if (cutoff_prob < 1.0) {
         double cum_prob = 0.0;
         cutoff_len = 0;
-        for (size_t i = 0; i < prob_idx.size(); i++) {
+        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;
@@ -145,7 +145,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
           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++) {
+    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)));
     }
@@ -155,7 +155,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
       auto c = log_prob_idx[index].first;
       float log_prob_c = log_prob_idx[index].second;
 
-      for (size_t i = 0; i < prefixes.size() && i < beam_size; i++) {
+      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) {
@@ -222,14 +222,14 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
                        prefixes.end(),
                        prefix_compare);
 
-      for (size_t i = beam_size; i < prefixes.size(); i++) {
+      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
-  for (size_t i = 0; i < beam_size && i < prefixes.size(); i++) {
+  for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
     double approx_ctc = prefixes[i]->score;
 
     if (ext_scorer != nullptr) {
@@ -249,14 +249,14 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
   // 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++) {
+    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++) {
+  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
@@ -301,7 +301,7 @@ ctc_beam_search_decoder_batch(
 
   // 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++) {
+  for (size_t i = 0; i < batch_size; ++i) {
     res.emplace_back(pool.enqueue(ctc_beam_search_decoder,
                                   probs_split[i],
                                   beam_size,
@@ -313,7 +313,7 @@ ctc_beam_search_decoder_batch(
 
   // get decoding results
   std::vector<std::vector<std::pair<double, std::string>>> batch_results;
-  for (size_t i = 0; i < batch_size; i++) {
+  for (size_t i = 0; i < batch_size; ++i) {
     batch_results.emplace_back(res[i].get());
   }
   return batch_results;

deep_speech_2/models/aishell/download_model.sh

+#! /usr/bin/bash
+
+source ../../utils/utility.sh
+
+URL='http://cloud.dlnel.org/filepub/?uuid=6c83b9d8-3255-4adf-9726-0fe0be3d0274'
+MD5=28521a58552885a81cf92a1e9b133a71
+TARGET=./aishell_model.tar.gz
+
+
+echo "Download Aishell model ..."
+download $URL $MD5 $TARGET
+if [ $? -ne 0 ]; then
+    echo "Fail to download Aishell model!"
+    exit 1
+fi
+tar -zxvf $TARGET
+
+
+exit 0

deep_speech_2/models/librispeech/download_model.sh

@@ -2,9 +2,8 @@
 
 source ../../utils/utility.sh
 
-# TODO: add urls
-URL='to-be-added'
-MD5=5b4af224b26c1dc4dd972b7d32f2f52a
+URL='http://cloud.dlnel.org/filepub/?uuid=17404caf-cf19-492f-9707-1fad07c19aae'
+MD5=ea5024a457a91179472f6dfee60e053d
 TARGET=./librispeech_model.tar.gz
 
 

deep_speech_2/models/lm/download_lm_ch.sh

+#! /usr/bin/bash
+
+source ../../utils/utility.sh
+
+URL=http://cloud.dlnel.org/filepub/?uuid=d21861e4-4ed6-45bb-ad8e-ae417a43195e
+MD5="29e02312deb2e59b3c8686c7966d4fe3"
+TARGET=./zh_giga.no_cna_cmn.prune01244.klm
+
+
+echo "Download language model ..."
+download $URL $MD5 $TARGET
+if [ $? -ne 0 ]; then
+    echo "Fail to download the language model!"
+    exit 1
+fi
+
+
+exit 0

deep_speech_2/requirements.txt

@@ -2,4 +2,3 @@ scipy==0.13.1
 resampy==0.1.5
 SoundFile==0.9.0.post1
 python_speech_features
-https://github.com/luotao1/kenlm/archive/master.zip

deep_speech_2/utils/utility.sh

@@ -11,10 +11,9 @@ download() {
         fi
     fi
 
-    wget -c $URL -P `dirname "$TARGET"`
+    wget -c $URL -O "$TARGET"
     md5_result=`md5sum $TARGET | awk -F[' '] '{print $1}'`
     if [ ! $MD5 == $md5_result ]; then
-        echo "Fail to download the language model!"
         return 1
     fi
 }

mt_with_external_memory/external_memory.py

@@ -35,6 +35,8 @@ class ExternalMemory(object):
                        sequence layer has sequence length indicating the number
                        of memory slots, and size as memory slot size.
     :type boot_layer: LayerOutput
+    :param initial_weight: Initializer for addressing weights.
+    :type initial_weight: LayerOutput
     :param readonly: If true, the memory is read-only, and write function cannot
                      be called. Default is false.
     :type readonly: bool
@@ -49,6 +51,7 @@ class ExternalMemory(object):
                  name,
                  mem_slot_size,
                  boot_layer,
+                 initial_weight,
                  readonly=False,
                  enable_interpolation=True):
         self.name = name
@@ -57,11 +60,7 @@ class ExternalMemory(object):
         self.enable_interpolation = enable_interpolation
         self.external_memory = paddle.layer.memory(
             name=self.name, size=self.mem_slot_size, boot_layer=boot_layer)
-        # prepare a constant (zero) intializer for addressing weights 
-        self.zero_addressing_init = paddle.layer.slope_intercept(
-            input=paddle.layer.fc(input=boot_layer, size=1),
-            slope=0.0,
-            intercept=0.0)
+        self.initial_weight = initial_weight
         # set memory to constant when readonly=True
         if self.readonly:
             self.updated_external_memory = paddle.layer.mixed(
@@ -111,7 +110,7 @@ class ExternalMemory(object):
         last_addressing_weight = paddle.layer.memory(
             name=self.name + "_addressing_weight_" + head_name,
             size=1,
-            boot_layer=self.zero_addressing_init)
+            boot_layer=self.initial_weight)
         interpolated_weight = paddle.layer.interpolation(
             name=self.name + "_addressing_weight_" + head_name,
             input=[addressing_weight, addressing_weight],

mt_with_external_memory/model.py

@@ -125,7 +125,15 @@ def memory_enhanced_decoder(input, target, initial_state, source_context, size,
             bounded_memory_perturbation
         ],
         act=paddle.activation.Linear())
+    bounded_memory_weight_init = paddle.layer.slope_intercept(
+        input=paddle.layer.fc(input=bounded_memory_init, size=1),
+        slope=0.0,
+        intercept=0.0)
     unbounded_memory_init = source_context
+    unbounded_memory_weight_init = paddle.layer.slope_intercept(
+        input=paddle.layer.fc(input=unbounded_memory_init, size=1),
+        slope=0.0,
+        intercept=0.0)
 
     # prepare step function for reccurent group
     def recurrent_decoder_step(cur_embedding):
@@ -136,12 +144,14 @@ def memory_enhanced_decoder(input, target, initial_state, source_context, size,
             name="bounded_memory",
             mem_slot_size=size,
             boot_layer=bounded_memory_init,
+            initial_weight=bounded_memory_weight_init,
             readonly=False,
             enable_interpolation=True)
         unbounded_memory = ExternalMemory(
             name="unbounded_memory",
             mem_slot_size=size * 2,
             boot_layer=unbounded_memory_init,
+            initial_weight=unbounded_memory_weight_init,
             readonly=True,
             enable_interpolation=False)
         # write bounded memory
@@ -154,7 +164,7 @@ def memory_enhanced_decoder(input, target, initial_state, source_context, size,
             size=size,
             act=paddle.activation.Tanh(),
             bias_attr=False)
-        # read unbounded memory (i.e. attention mechanism) 
+        # read unbounded memory (i.e. attention mechanism)
         context = unbounded_memory.read(key_for_unbounded_memory)
         # gated recurrent unit
         gru_inputs = paddle.layer.fc(