Re-implement LM rescore for online transducer (#1231)

Co-authored-by: Martins Kronis <[email protected]>

sherpa-onnx/csrc/hypothesis.h

@@ -51,8 +51,13 @@ struct Hypothesis {
   // LM log prob if any.
   double lm_log_prob = 0;
 
-  // the nn lm score for next token given the current ys
+  // the nn lm score for next token given the current ys,
+  // when using shallow fusion
   CopyableOrtValue nn_lm_scores;
+
+  // cur scored tokens by RNN LM, when rescoring
+  int32_t cur_scored_pos = 0;
+
   // the nn lm states
   std::vector<CopyableOrtValue> nn_lm_states;
 

sherpa-onnx/csrc/online-lm-config.cc

@@ -18,6 +18,8 @@ void OnlineLMConfig::Register(ParseOptions *po) {
                "Number of threads to run the neural network of LM model");
   po->Register("lm-provider", &lm_provider,
                "Specify a provider to LM model use: cpu, cuda, coreml");
+  po->Register("lm-shallow-fusion", &shallow_fusion,
+               "Boolean whether to use shallow fusion or rescore.");
 }
 
 bool OnlineLMConfig::Validate() const {
@@ -34,7 +36,8 @@ std::string OnlineLMConfig::ToString() const {
 
   os << "OnlineLMConfig(";
   os << "model=\"" << model << "\", ";
-  os << "scale=" << scale << ")";
+  os << "scale=" << scale << ", ";
+  os << "shallow_fusion=" << (shallow_fusion ? "True" : "False") << ")";
 
   return os.str();
 }

sherpa-onnx/csrc/online-lm-config.h

@@ -18,15 +18,18 @@ struct OnlineLMConfig {
   float scale = 0.5;
   int32_t lm_num_threads = 1;
   std::string lm_provider = "cpu";
+  // enable shallow fusion
+  bool shallow_fusion = true;
 
   OnlineLMConfig() = default;
 
   OnlineLMConfig(const std::string &model, float scale, int32_t lm_num_threads,
-                 const std::string &lm_provider)
+                 const std::string &lm_provider, bool shallow_fusion)
       : model(model),
         scale(scale),
         lm_num_threads(lm_num_threads),
-        lm_provider(lm_provider) {}
+        lm_provider(lm_provider),
+        shallow_fusion(shallow_fusion) {}
 
   void Register(ParseOptions *po);
   bool Validate() const;

sherpa-onnx/csrc/online-lm.h

@@ -21,27 +21,41 @@ class OnlineLM {
 
   static std::unique_ptr<OnlineLM> Create(const OnlineLMConfig &config);
 
-  virtual std::pair<Ort::Value, std::vector<Ort::Value>> GetInitStates() = 0;
+  // init states for classic rescore
+  virtual std::vector<Ort::Value> GetInitStates() = 0;
 
-  /** ScoreToken a batch of sentences.
+  // init states for shallow fusion
+  virtual std::pair<Ort::Value, std::vector<Ort::Value>> GetInitStatesSF() = 0;
+
+   /** ScoreToken a batch of sentences (shallow fusion).
    *
    * @param x A 2-D tensor of shape (N, 1) with data type int64.
    * @param states It contains the states for the LM model
-   * @return Return a pair containingo
+   * @return Return a pair containing
    *          - log_prob of NN LM
    *          - updated states
    *
    */
   virtual std::pair<Ort::Value, std::vector<Ort::Value>> ScoreToken(
       Ort::Value x, std::vector<Ort::Value> states) = 0;
 
-  /** This function updates lm_lob_prob and nn_lm_scores of hyp
+  /** This function updates hyp.lm_log_prob of hyps (classic rescore).
+   *
+   * @param scale LM score
+   * @param context_size Context size of the transducer decoder model
+   * @param hyps It is changed in-place.
+   *
+   */
+  virtual void ComputeLMScore(float scale, int32_t context_size,
+                      std::vector<Hypotheses> *hyps) = 0;
+
+  /** This function updates lm_log_prob and nn_lm_scores of hyp (shallow fusion).
    *
    * @param scale LM score
    * @param hyps It is changed in-place.
    *
    */
-  virtual void ComputeLMScore(float scale, Hypothesis *hyp) = 0;
+  virtual void ComputeLMScoreSF(float scale, Hypothesis *hyp) = 0;
 };
 
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/online-recognizer-transducer-impl.h

@@ -107,7 +107,8 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
 
       decoder_ = std::make_unique<OnlineTransducerModifiedBeamSearchDecoder>(
           model_.get(), lm_.get(), config_.max_active_paths,
-          config_.lm_config.scale, unk_id_, config_.blank_penalty,
+          config_.lm_config.scale, config_.lm_config.shallow_fusion, unk_id_,
+          config_.blank_penalty,
           config_.temperature_scale);
 
     } else if (config.decoding_method == "greedy_search") {
@@ -156,7 +157,8 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
 
       decoder_ = std::make_unique<OnlineTransducerModifiedBeamSearchDecoder>(
           model_.get(), lm_.get(), config_.max_active_paths,
-          config_.lm_config.scale, unk_id_, config_.blank_penalty,
+          config_.lm_config.scale, config_.lm_config.shallow_fusion, unk_id_,
+          config_.blank_penalty,
           config_.temperature_scale);
 
     } else if (config.decoding_method == "greedy_search") {

sherpa-onnx/csrc/online-rnn-lm.cc

@@ -8,6 +8,7 @@
 #include <string>
 #include <utility>
 #include <vector>
+#include <algorithm>
 
 #include "onnxruntime_cxx_api.h"  // NOLINT
 #include "sherpa-onnx/csrc/macros.h"
@@ -27,9 +28,10 @@ class OnlineRnnLM::Impl {
     Init(config);
   }
 
-  void ComputeLMScore(float scale, Hypothesis *hyp) {
+  // shallow fusion scoring function
+  void ComputeLMScoreSF(float scale, Hypothesis *hyp) {
     if (hyp->nn_lm_states.empty()) {
-      auto init_states = GetInitStates();
+      auto init_states = GetInitStatesSF();
       hyp->nn_lm_scores.value = std::move(init_states.first);
       hyp->nn_lm_states = Convert(std::move(init_states.second));
     }
@@ -49,6 +51,52 @@ class OnlineRnnLM::Impl {
     hyp->nn_lm_states = Convert(std::move(lm_out.second));
   }
 
+  // classic rescore function
+  void ComputeLMScore(float scale, int32_t context_size,
+                              std::vector<Hypotheses> *hyps) {
+      Ort::AllocatorWithDefaultOptions allocator;
+
+      for (auto &hyp : *hyps) {
+        for (auto &h_m : hyp) {
+          auto &h = h_m.second;
+          auto &ys = h.ys;
+          const int32_t token_num_in_chunk =
+              ys.size() - context_size - h.cur_scored_pos - 1;
+
+          if (token_num_in_chunk < 1) {
+            continue;
+          }
+
+          if (h.nn_lm_states.empty()) {
+            h.nn_lm_states = Convert(GetInitStates());
+          }
+
+          if (token_num_in_chunk >= h.lm_rescore_min_chunk) {
+            std::array<int64_t, 2> x_shape{1, token_num_in_chunk};
+
+            Ort::Value x = Ort::Value::CreateTensor<int64_t>(
+                allocator, x_shape.data(), x_shape.size());
+            int64_t *p_x = x.GetTensorMutableData<int64_t>();
+            std::copy(ys.begin() + context_size + h.cur_scored_pos,
+                               ys.end() - 1, p_x);
+
+            // streaming forward by NN LM
+            auto out = ScoreToken(std::move(x),
+                               Convert(std::move(h.nn_lm_states)));
+
+            // update NN LM score in hyp
+            const float *p_nll = out.first.GetTensorData<float>();
+            h.lm_log_prob = -scale * (*p_nll);
+
+            // update NN LM states in hyp
+            h.nn_lm_states = Convert(std::move(out.second));
+
+            h.cur_scored_pos += token_num_in_chunk;
+          }
+        }
+      }
+    }
+
   std::pair<Ort::Value, std::vector<Ort::Value>> ScoreToken(
       Ort::Value x, std::vector<Ort::Value> states) {
     std::array<Ort::Value, 3> inputs = {std::move(x), std::move(states[0]),
@@ -66,7 +114,8 @@ class OnlineRnnLM::Impl {
     return {std::move(out[0]), std::move(next_states)};
   }
 
-  std::pair<Ort::Value, std::vector<Ort::Value>> GetInitStates() {
+  // get init states for shallow fusion
+  std::pair<Ort::Value, std::vector<Ort::Value>> GetInitStatesSF() {
     std::vector<Ort::Value> ans;
     ans.reserve(init_states_.size());
     for (auto &s : init_states_) {
@@ -75,6 +124,18 @@ class OnlineRnnLM::Impl {
     return {View(&init_scores_.value), std::move(ans)};
   }
 
+  // get init states for classic rescore
+  std::vector<Ort::Value> GetInitStates() const {
+    std::vector<Ort::Value> ans;
+    ans.reserve(init_states_.size());
+
+    for (const auto &s : init_states_) {
+      ans.emplace_back(Clone(allocator_, &s));
+    }
+
+    return ans;
+  }
+
  private:
   void Init(const OnlineLMConfig &config) {
     auto buf = ReadFile(config_.model);
@@ -116,7 +177,8 @@ class OnlineRnnLM::Impl {
     states.push_back(std::move(c));
     auto pair = ScoreToken(std::move(x), std::move(states));
 
-    init_scores_.value = std::move(pair.first);
+    init_scores_.value = std::move(pair.first);  // only used during
+                                                 // shallow fusion
     init_states_ = std::move(pair.second);
   }
 
@@ -147,17 +209,31 @@ OnlineRnnLM::OnlineRnnLM(const OnlineLMConfig &config)
 
 OnlineRnnLM::~OnlineRnnLM() = default;
 
-std::pair<Ort::Value, std::vector<Ort::Value>> OnlineRnnLM::GetInitStates() {
+// classic rescore state init
+std::vector<Ort::Value> OnlineRnnLM::GetInitStates() {
   return impl_->GetInitStates();
 }
 
+// shallow fusion state init
+std::pair<Ort::Value, std::vector<Ort::Value>> OnlineRnnLM::GetInitStatesSF() {
+  return impl_->GetInitStatesSF();
+}
+
 std::pair<Ort::Value, std::vector<Ort::Value>> OnlineRnnLM::ScoreToken(
     Ort::Value x, std::vector<Ort::Value> states) {
   return impl_->ScoreToken(std::move(x), std::move(states));
 }
 
-void OnlineRnnLM::ComputeLMScore(float scale, Hypothesis *hyp) {
-  return impl_->ComputeLMScore(scale, hyp);
+// classic rescore scores
+void OnlineRnnLM::ComputeLMScore(float scale, int32_t context_size,
+                              std::vector<Hypotheses> *hyps) {
+  return impl_->ComputeLMScore(scale, context_size, hyps);
 }
 
+// shallow fusion scores
+void OnlineRnnLM::ComputeLMScoreSF(float scale, Hypothesis *hyp) {
+  return impl_->ComputeLMScoreSF(scale, hyp);
+}
+
+
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/online-rnn-lm.h

@@ -22,27 +22,41 @@ class OnlineRnnLM : public OnlineLM {
 
   explicit OnlineRnnLM(const OnlineLMConfig &config);
 
-  std::pair<Ort::Value, std::vector<Ort::Value>> GetInitStates() override;
+  // init scores for classic rescore
+  std::vector<Ort::Value> GetInitStates() override;
 
-  /** ScoreToken a batch of sentences.
+  // init scores for shallow fusion
+  std::pair<Ort::Value, std::vector<Ort::Value>> GetInitStatesSF() override;
+
+   /** ScoreToken a batch of sentences (shallow fusion).
    *
    * @param x A 2-D tensor of shape (N, L) with data type int64.
    * @param states It contains the states for the LM model
-   * @return Return a pair containingo
+   * @return Return a pair containing
    *          - log_prob of NN LM
    *          - updated states
    *
    */
   std::pair<Ort::Value, std::vector<Ort::Value>> ScoreToken(
       Ort::Value x, std::vector<Ort::Value> states) override;
 
-  /** This function updates lm_lob_prob and nn_lm_scores of hyp
+   /** This function updates hyp.lm_lob_prob of hyps (classic rescore).
+   *
+   * @param scale LM score
+   * @param context_size Context size of the transducer decoder model
+   * @param hyps It is changed in-place.
+   *
+   */
+  void ComputeLMScore(float scale, int32_t context_size,
+                              std::vector<Hypotheses> *hyps) override;
+
+   /** This function updates lm_lob_prob and nn_lm_scores of hyp (shallow fusion).
    *
    * @param scale LM score
    * @param hyps It is changed in-place.
    *
    */
-  void ComputeLMScore(float scale, Hypothesis *hyp) override;
+  void ComputeLMScoreSF(float scale, Hypothesis *hyp) override;
 
  private:
   class Impl;

sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.cc

@@ -156,7 +156,11 @@ void OnlineTransducerModifiedBeamSearchDecoder::Decode(
 
     // add log_prob of each hypothesis to p_logprob before taking top_k
     for (int32_t i = 0; i != num_hyps; ++i) {
-      float log_prob = prev[i].log_prob + prev[i].lm_log_prob;
+      float log_prob = prev[i].log_prob;
+      if (lm_ && shallow_fusion_) {
+         log_prob += prev[i].lm_log_prob;
+      }
+
       for (int32_t k = 0; k != vocab_size; ++k, ++p_logprob) {
         *p_logprob += log_prob;
       }
@@ -192,22 +196,31 @@ void OnlineTransducerModifiedBeamSearchDecoder::Decode(
             context_score = std::get<0>(context_res);
             new_hyp.context_state = std::get<1>(context_res);
           }
-          if (lm_) {
-            lm_->ComputeLMScore(lm_scale_, &new_hyp);
+          if (lm_ && shallow_fusion_) {
+            lm_->ComputeLMScoreSF(lm_scale_, &new_hyp);
           }
         } else {
           ++new_hyp.num_trailing_blanks;
         }
-        new_hyp.log_prob = p_logprob[k] + context_score -
+        if (lm_ && shallow_fusion_) {
+           new_hyp.log_prob = p_logprob[k] + context_score -
                            prev_lm_log_prob;  // log_prob only includes the
                                               // score of the transducer
+        } else {
+           new_hyp.log_prob = p_logprob[k] + context_score;  // rescore or no LM
+                                                             // previous token
+                                                             // score is ignored
+        }
+
         // export the per-token log scores
         if (new_token != 0 && new_token != unk_id_) {
           float y_prob = logit_with_temperature[start * vocab_size + k];
           new_hyp.ys_probs.push_back(y_prob);
 
-          if (lm_) {  // export only when LM is used
+          if (lm_ && shallow_fusion_) {  // export only if
+                                         // LM shallow fusion is used
             float lm_prob = new_hyp.lm_log_prob - prev_lm_log_prob;
+
             if (lm_scale_ != 0.0) {
               lm_prob /= lm_scale_;  // remove lm-scale
             }
@@ -227,6 +240,11 @@ void OnlineTransducerModifiedBeamSearchDecoder::Decode(
     }  // for (int32_t b = 0; b != batch_size; ++b)
   }    // for (int32_t t = 0; t != num_frames; ++t)
 
+  // classic lm rescore
+  if (lm_ && !shallow_fusion_) {
+    lm_->ComputeLMScore(lm_scale_, model_->ContextSize(), &cur);
+  }
+
   for (int32_t b = 0; b != batch_size; ++b) {
     auto &hyps = cur[b];
     auto best_hyp = hyps.GetMostProbable(true);