remove uesless code

speechx/speechx/nnet/u2_nnet.cc

@@ -25,65 +25,6 @@ using paddle::platform::TracerEventType;
 
 namespace ppspeech {
 
-int U2NnetBase::num_frames_for_chunk(bool start) const {
-    int num_needed_frames = 0;  // num feat frames
-    bool first = !start;        // start == false is first
-
-    if (chunk_size_ > 0) {
-        // streaming mode
-        if (first) {
-            // first chunk
-            // 1 decoder frame need `context` feat frames
-            int context = this->context();
-            num_needed_frames = (chunk_size_ - 1) * subsampling_rate_ + context;
-        } else {
-            // after first chunk, we need stride this num frames.
-            num_needed_frames = chunk_size_ * subsampling_rate_;
-        }
-    } else {
-        // non-streaming mode. feed all feats once.
-        num_needed_frames = std::numeric_limits<int>::max();
-    }
-
-    return num_needed_frames;
-}
-
-// cache feats for next chunk
-void U2NnetBase::CacheFeature(const std::vector<kaldi::BaseFloat>& chunk_feats,
-                              int32 feat_dim) {
-    // chunk_feats is nframes*feat_dim
-    const int chunk_size = chunk_feats.size() / feat_dim;
-    const int cached_feat_size = this->context() - subsampling_rate_;
-    if (chunk_size >= cached_feat_size) {
-        cached_feats_.resize(cached_feat_size);
-        for (int i = 0; i < cached_feat_size; ++i) {
-            auto start =
-                chunk_feats.begin() + chunk_size - cached_feat_size + i;
-            auto end = start + feat_dim;
-            cached_feats_[i] = std::vector<float>(start, end);
-        }
-    }
-}
-
-void U2NnetBase::ForwardEncoderChunk(
-    const std::vector<kaldi::BaseFloat>& chunk_feats,
-    const int32& feat_dim,
-    std::vector<kaldi::BaseFloat>* ctc_probs,
-    int32* vocab_dim) {
-    ctc_probs->clear();
-    // int num_frames = cached_feats_.size() + chunk_feats.size();
-    int num_frames = chunk_feats.size() / feat_dim;
-    VLOG(3) << "foward encoder chunk: " << num_frames << " frames";
-    VLOG(3) << "context: " << this->context() << " frames";
-
-    if (num_frames >= this->context()) {
-        this->ForwardEncoderChunkImpl(
-            chunk_feats, feat_dim, ctc_probs, vocab_dim);
-        VLOG(3) << "after forward chunk";
-        this->CacheFeature(chunk_feats, feat_dim);
-    }
-}
-
 
 void U2Nnet::LoadModel(const std::string& model_path_w_prefix) {
     paddle::jit::utils::InitKernelSignatureMap();
@@ -188,7 +129,7 @@ U2Nnet::U2Nnet(const U2Nnet& other) {
     forward_attention_decoder_ = other.forward_attention_decoder_;
     ctc_activation_ = other.ctc_activation_;
 
-    //   offset_ = other.offset_; // TODO: not used in nnets
+    offset_ = other.offset_; 
 
     // copy model ptr
     model_ = other.model_;
@@ -204,8 +145,7 @@ std::shared_ptr<NnetBase> U2Nnet::Copy() const {
 }
 
 void U2Nnet::Reset() {
-    //   offset_ = 0;
-    //   cached_feats_.clear(); // TODO: not used in nnets
+    offset_ = 0;
 
     att_cache_ =
         std::move(paddle::zeros({0, 0, 0, 0}, paddle::DataType::FLOAT32));
@@ -263,16 +203,6 @@ void U2Nnet::ForwardEncoderChunkImpl(
         paddle::zeros({1, num_frames, feat_dim}, paddle::DataType::FLOAT32);
     float* feats_ptr = feats.mutable_data<float>();
 
-    // for (size_t i = 0; i < cached_feats_.size(); ++i) {
-    //     float* row = feats_ptr + i * feat_dim;
-    //     std::memcpy(row, cached_feats_[i].data(), feat_dim * sizeof(float));
-    // }
-
-    // for (size_t i = 0; i < chunk_feats.size(); ++i) {
-    //     float* row = feats_ptr + (cached_feats_.size() + i) * feat_dim;
-    //     std::memcpy(row, chunk_feats[i].data(), feat_dim * sizeof(float));
-    // }
-
     // not cache feature in nnet
     CHECK(cached_feats_.size() == 0);
     // CHECK_EQ(std::is_same<float, kaldi::BaseFloat>::value, true);
@@ -427,15 +357,6 @@ void U2Nnet::ForwardEncoderChunkImpl(
 
     float* ctc_log_probs_ptr = ctc_log_probs.data<float>();
 
-    // // vector<vector<float>>
-    // out_prob->resize(T);
-    // for (int i = 0; i < T; i++) {
-    //     (*out_prob)[i].resize(D);
-    //     float* dst_ptr = (*out_prob)[i].data();
-    //     float* src_ptr = ctc_log_probs_ptr + (i * D);
-    //     std::memcpy(dst_ptr, src_ptr, D * sizeof(float));
-    // }
-    // CHECK(std::is_same<float, kaldi::BaseFloat>::value);
     out_prob->resize(T * D);
     std::memcpy(
         out_prob->data(), ctc_log_probs_ptr, T * D * sizeof(kaldi::BaseFloat));

speechx/speechx/nnet/u2_nnet.h

@@ -28,29 +28,21 @@ namespace ppspeech {
 
 class U2NnetBase : public NnetBase {
   public:
-    virtual int context() const { return right_context_ + 1; }
-    virtual int right_context() const { return right_context_; }
+    virtual int Context() const { return right_context_ + 1; }
+    virtual int RightContext() const { return right_context_; }
 
-    virtual int eos() const { return eos_; }
-    virtual int sos() const { return sos_; }
-    virtual int is_bidecoder() const { return is_bidecoder_; }
+    virtual int EOS() const { return eos_; }
+    virtual int SOS() const { return sos_; }
+    virtual int IsBidecoder() const { return is_bidecoder_; }
     // current offset in decoder frame
-    virtual int offset() const { return offset_; }
-    virtual void set_chunk_size(int chunk_size) { chunk_size_ = chunk_size; }
-    virtual void set_num_left_chunks(int num_left_chunks) {
+    virtual int Offset() const { return offset_; }
+    virtual void SetChunkSize(int chunk_size) { chunk_size_ = chunk_size; }
+    virtual void SetNumLeftChunks(int num_left_chunks) {
         num_left_chunks_ = num_left_chunks;
     }
-    // start: false, it is the start chunk of one sentence, else true
-    virtual int num_frames_for_chunk(bool start) const;
 
     virtual std::shared_ptr<NnetBase> Copy() const = 0;
 
-    virtual void ForwardEncoderChunk(
-        const std::vector<kaldi::BaseFloat>& chunk_feats,
-        const int32& feat_dim,
-        std::vector<kaldi::BaseFloat>* ctc_probs,
-        int32* vocab_dim);
-
   protected:
     virtual void ForwardEncoderChunkImpl(
         const std::vector<kaldi::BaseFloat>& chunk_feats,
@@ -58,9 +50,6 @@ class U2NnetBase : public NnetBase {
         std::vector<kaldi::BaseFloat>* ctc_probs,
         int32* vocab_dim) = 0;
 
-    virtual void CacheFeature(const std::vector<kaldi::BaseFloat>& chunk_feats,
-                              int32 feat_dim);
-
   protected:
     // model specification
     int right_context_{0};