make wfst work & align frame

speechx/examples/decoder/offline_wfst_decoder_main.cc

@@ -27,14 +27,20 @@ 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, 10.0, "acoustic scale");
+DEFINE_double(acoustic_scale, 1.0, "acoustic scale");
-DEFINE_int32(max_active, 5000, "decoder graph");
+DEFINE_int32(max_active, 7500, "decoder graph");
-
+DEFINE_int32(receptive_field_length,
+             7,
+             "receptive field of two CNN(kernel=5) downsampling module.");
+DEFINE_int32(downsampling_rate,
+             4,
+             "two CNN(kernel=5) module downsampling rate.");
 
 using kaldi::BaseFloat;
 using kaldi::Matrix;
 using std::vector;
 
+// test clg decoder by feeding speech feature.
 int main(int argc, char* argv[]) {
     gflags::ParseCommandLineFlags(&argc, &argv, false);
     google::InitGoogleLogging(argv[0]);
@@ -52,7 +58,8 @@ 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 = 
+    opts.opts.beam = 15.0;
+    opts.opts.lattice_beam = 7.5;
     ppspeech::TLGDecoder decoder(opts);
 
     ppspeech::ModelOptions model_opts;
@@ -61,30 +68,55 @@ 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::DataCache> raw_data(
+    std::shared_ptr<ppspeech::DataCache> raw_data(new ppspeech::DataCache());
-        new ppspeech::DataCache());
     std::shared_ptr<ppspeech::Decodable> decodable(
-        new ppspeech::Decodable(nnet, raw_data));
+        new ppspeech::Decodable(nnet, raw_data, FLAGS_acoustic_scale));
 
-    int32 chunk_size = 35;
+    int32 chunk_size = FLAGS_receptive_field_length;
+    int32 chunk_stride = FLAGS_downsampling_rate;
+    int32 receptive_field_length = FLAGS_receptive_field_length;
+    LOG(INFO) << "chunk size (frame): " << chunk_size;
+    LOG(INFO) << "chunk stride (frame): " << chunk_stride;
+    LOG(INFO) << "receptive field (frame): " << receptive_field_length;
     decoder.InitDecoder();
 
     for (; !feature_reader.Done(); feature_reader.Next()) {
         string utt = feature_reader.Key();
-        const kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
+        kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
         raw_data->SetDim(feature.NumCols());
+        LOG(INFO) << "process utt: " << utt;
+        LOG(INFO) << "rows: " << feature.NumRows();
+        LOG(INFO) << "cols: " << feature.NumCols();
+
         int32 row_idx = 0;
-        int32 num_chunks = feature.NumRows() / chunk_size;
+        int32 padding_len = 0;
+        int32 ori_feature_len = feature.NumRows();
+        if ((feature.NumRows() - chunk_size) % chunk_stride != 0) {
+            padding_len =
+                chunk_stride - (feature.NumRows() - chunk_size) % chunk_stride;
+            feature.Resize(feature.NumRows() + padding_len,
+                           feature.NumCols(),
+                           kaldi::kCopyData);
+        }
+        int32 num_chunks = (feature.NumRows() - chunk_size) / chunk_stride + 1;
         for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) {
             kaldi::Vector<kaldi::BaseFloat> feature_chunk(chunk_size *
                                                           feature.NumCols());
+            int32 feature_chunk_size = 0;
+            if (ori_feature_len > chunk_idx * chunk_stride) {
+                feature_chunk_size = std::min(
+                    ori_feature_len - chunk_idx * chunk_stride, chunk_size);
+            }
+            if (feature_chunk_size < receptive_field_length) break;
+
+            int32 start = chunk_idx * chunk_stride;
             for (int row_id = 0; row_id < chunk_size; ++row_id) {
-                kaldi::SubVector<kaldi::BaseFloat> tmp(feature, row_idx);
+                kaldi::SubVector<kaldi::BaseFloat> tmp(feature, start);
                 kaldi::SubVector<kaldi::BaseFloat> f_chunk_tmp(
                     feature_chunk.Data() + row_id * feature.NumCols(),
                     feature.NumCols());
                 f_chunk_tmp.CopyFromVec(tmp);
-                row_idx++;
+                ++start;
             }
             raw_data->Accept(feature_chunk);
             if (chunk_idx == num_chunks - 1) {

speechx/speechx/decoder/ctc_tlg_decoder.cc

@@ -1,33 +1,50 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "decoder/ctc_tlg_decoder.h"
 namespace ppspeech {
 
 TLGDecoder::TLGDecoder(TLGDecoderOptions opts) {
     fst_.reset(fst::Fst<fst::StdArc>::Read(opts.fst_path));
     CHECK(fst_ != nullptr);
-    word_symbol_table_.reset(fst::SymbolTable::ReadText(opts.word_symbol_table));
+    word_symbol_table_.reset(
+        fst::SymbolTable::ReadText(opts.word_symbol_table));
     decoder_.reset(new kaldi::LatticeFasterOnlineDecoder(*fst_, opts.opts));
     decoder_->InitDecoding();
+    frame_decoded_size_ = 0;
 }
 
 void TLGDecoder::InitDecoder() {
     decoder_->InitDecoding();
+    frame_decoded_size_ = 0;
 }
 
-void TLGDecoder::AdvanceDecode(const std::shared_ptr<kaldi::DecodableInterface>& decodable) {
+void TLGDecoder::AdvanceDecode(
-    while (1) {
+    const std::shared_ptr<kaldi::DecodableInterface>& decodable) {
+    while (!decodable->IsLastFrame(frame_decoded_size_)) {
+        LOG(INFO) << "num frame decode: " << frame_decoded_size_;
         AdvanceDecoding(decodable.get());
-      if (decodable->IsLastFrame(num_frame_decoded_)) break;
     }
 }
 
 void TLGDecoder::AdvanceDecoding(kaldi::DecodableInterface* decodable) {
-  // skip blank frame?
     decoder_->AdvanceDecoding(decodable, 1);
-  num_frame_decoded_++;
+    frame_decoded_size_++;
 }
 
 void TLGDecoder::Reset() {
-  decoder_->InitDecoding();
+    InitDecoder();
     return;
 }
 
@@ -46,5 +63,4 @@ std::string TLGDecoder::GetFinalBestPath() {
     }
     return words;
 }
-
 }

speechx/speechx/decoder/ctc_tlg_decoder.h

@@ -1,9 +1,23 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #pragma once
 
-#include "kaldi/decoder/lattice-faster-online-decoder.h"
+#include "base/basic_types.h"
 #include "kaldi/decoder/decodable-itf.h"
+#include "kaldi/decoder/lattice-faster-online-decoder.h"
 #include "util/parse-options.h"
-#include "base/basic_types.h"
 
 namespace ppspeech {
 
@@ -13,9 +27,7 @@ struct TLGDecoderOptions {
     std::string word_symbol_table;
     std::string fst_path;
 
-   TLGDecoderOptions() 
+    TLGDecoderOptions() : word_symbol_table(""), fst_path("") {}
-       : word_symbol_table(""),
-         fst_path("") {}
 };
 
 class TLGDecoder {
@@ -39,9 +51,9 @@ class TLGDecoder {
     std::shared_ptr<kaldi::LatticeFasterOnlineDecoder> decoder_;
     std::shared_ptr<fst::Fst<fst::StdArc>> fst_;
     std::shared_ptr<fst::SymbolTable> word_symbol_table_;
-    int32 num_frame_decoded_;
+    // the frame size which have decoded starts from 0.
-  };
+    int32 frame_decoded_size_;
-
+};
 
 
 }  // namespace ppspeech

speechx/speechx/nnet/decodable.cc

@@ -22,8 +22,13 @@ using std::vector;
 using kaldi::Vector;
 
 Decodable::Decodable(const std::shared_ptr<NnetInterface>& nnet,
-                     const std::shared_ptr<FrontendInterface>& frontend)
+                     const std::shared_ptr<FrontendInterface>& frontend,
-    : frontend_(frontend), nnet_(nnet), frame_offset_(0), frames_ready_(0) {}
+                     kaldi::BaseFloat acoustic_scale)
+    : frontend_(frontend),
+      nnet_(nnet),
+      frame_offset_(0),
+      frames_ready_(0),
+      acoustic_scale_(acoustic_scale) {}
 
 void Decodable::Acceptlikelihood(const Matrix<BaseFloat>& likelihood) {
     nnet_cache_ = likelihood;
@@ -32,14 +37,14 @@ void Decodable::Acceptlikelihood(const Matrix<BaseFloat>& likelihood) {
 
 // Decodable::Init(DecodableConfig config) {
 //}
-int32 Decodable::NumFramesReady() const {
-    return frames_ready_;
-}
 
+// return the size of frame have computed.
+int32 Decodable::NumFramesReady() const { return frames_ready_; }
+
+// frame idx is from 0 to frame_ready_ -1;
 bool Decodable::IsLastFrame(int32 frame) {
     bool flag = EnsureFrameHaveComputed(frame);
-    //CHECK_LE(frame, frames_ready_);
+    return frame >= frames_ready_;
-    return (flag == false) && (frame == frames_ready_);
 }
 
 int32 Decodable::NumIndices() const { return 0; }
@@ -48,7 +53,8 @@ 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 std::log(nnet_cache_(frame_idx, index) + std::numeric_limits<float>::min());
+    return acoustic_scale_ * std::log(nnet_cache_(frame_idx, index - 1) +
+                                      std::numeric_limits<float>::min());
 }
 
 bool Decodable::EnsureFrameHaveComputed(int32 frame) {
@@ -67,20 +73,12 @@ bool Decodable::AdvanceChunk() {
     Vector<BaseFloat> inferences;
     Matrix<BaseFloat> nnet_cache_tmp;
     nnet_->FeedForward(features, frontend_->Dim(), &inferences, &nnet_dim);
-    nnet_cache_tmp.Resize(inferences.Dim() / nnet_dim, nnet_dim);
+    nnet_cache_.Resize(inferences.Dim() / nnet_dim, nnet_dim);
-    nnet_cache_tmp.CopyRowsFromVec(inferences);
+    nnet_cache_.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();
+    LOG(INFO) << "nnet size: " << nnet_cache_.NumRows();
     return true;
 }
 
@@ -89,7 +87,8 @@ bool Decodable::FrameLogLikelihood(int32 frame, vector<BaseFloat>* likelihood) {
     if (EnsureFrameHaveComputed(frame) == false) return false;
     likelihood->resize(nnet_cache_.NumCols());
     for (int32 idx = 0; idx < nnet_cache_.NumCols(); ++idx) {
-        (*likelihood)[idx] = nnet_cache_(frame - frame_offset_, idx);
+        (*likelihood)[idx] =
+            nnet_cache_(frame - frame_offset_, idx) * acoustic_scale_;
     }
     return true;
 }

speechx/speechx/nnet/decodable.h

@@ -14,8 +14,8 @@
 
 #include "base/common.h"
 #include "frontend/audio/frontend_itf.h"
-#include "kaldi/matrix/kaldi-matrix.h"
 #include "kaldi/decoder/decodable-itf.h"
+#include "kaldi/matrix/kaldi-matrix.h"
 #include "nnet/nnet_itf.h"
 
 namespace ppspeech {
@@ -25,7 +25,8 @@ struct DecodableOpts;
 class Decodable : public kaldi::DecodableInterface {
   public:
     explicit Decodable(const std::shared_ptr<NnetInterface>& nnet,
-                       const std::shared_ptr<FrontendInterface>& frontend);
+                       const std::shared_ptr<FrontendInterface>& frontend,
+                       kaldi::BaseFloat acoustic_scale = 1.0);
     // void Init(DecodableOpts config);
     virtual kaldi::BaseFloat LogLikelihood(int32 frame, int32 index);
     virtual bool IsLastFrame(int32 frame);
@@ -39,13 +40,11 @@ class Decodable : public kaldi::DecodableInterface {
     bool IsInputFinished() const { return frontend_->IsFinished(); }
     bool EnsureFrameHaveComputed(int32 frame);
 
-
   private:
     bool AdvanceChunk();
     std::shared_ptr<FrontendInterface> frontend_;
     std::shared_ptr<NnetInterface> nnet_;
     kaldi::Matrix<kaldi::BaseFloat> nnet_cache_;
-    // std::vector<std::vector<kaldi::BaseFloat>> nnet_cache_;
     int32 frame_offset_;
     int32 frames_ready_;
     // todo: feature frame mismatch with nnet inference frame
@@ -53,6 +52,7 @@ class Decodable : public kaldi::DecodableInterface {
     // so use subsampled_frame
     int32 current_log_post_subsampled_offset_;
     int32 num_chunk_computed_;
+    kaldi::BaseFloat acoustic_scale_;
 };
 
 }  // namespace ppspeech