dev ing

speechx/examples/decoder/offline_wfst_decoder_main.cc

@@ -17,7 +17,7 @@
 #include "base/flags.h"
 #include "base/log.h"
 #include "decoder/ctc_tlg_decoder.h"
-#include "frontend/raw_audio.h"
+#include "frontend/audio/data_cache.h"
 #include "kaldi/util/table-types.h"
 #include "nnet/decodable.h"
 #include "nnet/paddle_nnet.h"
@@ -27,7 +27,7 @@ DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
 DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param");
 DEFINE_string(word_symbol_table, "vocab.txt", "word symbol table");
 DEFINE_string(graph_path, "TLG", "decoder graph");
-DEFINE_double(acoustic_scale, 1.0, "acoustic scale");
+DEFINE_double(acoustic_scale, 10.0, "acoustic scale");
 DEFINE_int32(max_active, 5000, "decoder graph");
 
 
@@ -52,6 +52,7 @@ int main(int argc, char* argv[]) {
     opts.word_symbol_table = word_symbol_table;
     opts.fst_path = graph_path;
     opts.opts.max_active = FLAGS_max_active;
+    opts.opts.beam = 
     ppspeech::TLGDecoder decoder(opts);
 
     ppspeech::ModelOptions model_opts;
@@ -60,8 +61,8 @@ int main(int argc, char* argv[]) {
     model_opts.cache_shape = "5-1-1024,5-1-1024";
     std::shared_ptr<ppspeech::PaddleNnet> nnet(
         new ppspeech::PaddleNnet(model_opts));
-    std::shared_ptr<ppspeech::RawDataCache> raw_data(
+    std::shared_ptr<ppspeech::DataCache> raw_data(
-        new ppspeech::RawDataCache());
+        new ppspeech::DataCache());
     std::shared_ptr<ppspeech::Decodable> decodable(
         new ppspeech::Decodable(nnet, raw_data));
 

speechx/speechx/nnet/decodable.cc

@@ -48,7 +48,7 @@ BaseFloat Decodable::LogLikelihood(int32 frame, int32 index) {
     CHECK_LE(index, nnet_cache_.NumCols());
     CHECK_LE(frame, frames_ready_);
     int32 frame_idx = frame - frame_offset_;
-    return nnet_cache_(frame_idx, index);
+    return std::log(nnet_cache_(frame_idx, index) + std::numeric_limits<float>::min());
 }
 
 bool Decodable::EnsureFrameHaveComputed(int32 frame) {
@@ -65,9 +65,20 @@ bool Decodable::AdvanceChunk() {
     }
     int32 nnet_dim = 0;
     Vector<BaseFloat> inferences;
+    Matrix<BaseFloat> nnet_cache_tmp;
     nnet_->FeedForward(features, frontend_->Dim(), &inferences, &nnet_dim);
-    nnet_cache_.Resize(inferences.Dim() / nnet_dim, nnet_dim);
+    nnet_cache_tmp.Resize(inferences.Dim() / nnet_dim, nnet_dim);
-    nnet_cache_.CopyRowsFromVec(inferences);
+    nnet_cache_tmp.CopyRowsFromVec(inferences);
+    // skip blank
+    vector<int> no_blank_record;
+    BaseFloat blank_threshold = 0.98;
+    for (int32 idx = 0; idx < nnet_cache_.NumRows(); ++idx) {
+      if (nnet_cache_(idx, 0) > blank_threshold) {
+             
+      }
+    }
+    
+    
     frame_offset_ = frames_ready_;
     frames_ready_ += nnet_cache_.NumRows();
     return true;