Add RNN LM rescore for offline ASR with modified_beam_search (#125)

.gitignore

@@ -56,3 +56,4 @@ run-offline-decode-files.sh
 sherpa-onnx-nemo-ctc-en-citrinet-512
 run-offline-decode-files-nemo-ctc.sh
 *.jar
+sherpa-onnx-nemo-ctc-*

CMakeLists.txt

@@ -51,6 +51,11 @@ if(DEFINED ANDROID_ABI)
   set(SHERPA_ONNX_ENABLE_JNI ON CACHE BOOL "" FORCE)
 endif()
 
+if(SHERPA_ONNX_ENABLE_PYTHON AND NOT BUILD_SHARED_LIBS)
+  message(STATUS "Set BUILD_SHARED_LIBS to ON since SHERPA_ONNX_ENABLE_PYTHON is ON")
+  set(BUILD_SHARED_LIBS ON CACHE BOOL "" FORCE)
+endif()
+
 if(SHERPA_ONNX_ENABLE_JNI AND NOT BUILD_SHARED_LIBS)
   message(STATUS "Set BUILD_SHARED_LIBS to ON since SHERPA_ONNX_ENABLE_JNI is ON")
   set(BUILD_SHARED_LIBS ON CACHE BOOL "" FORCE)

sherpa-onnx/csrc/CMakeLists.txt

@@ -18,6 +18,8 @@ set(sources
   hypothesis.cc
   offline-ctc-greedy-search-decoder.cc
   offline-ctc-model.cc
+  offline-lm-config.cc
+  offline-lm.cc
   offline-model-config.cc
   offline-nemo-enc-dec-ctc-model-config.cc
   offline-nemo-enc-dec-ctc-model.cc
@@ -26,10 +28,13 @@ set(sources
   offline-paraformer-model.cc
   offline-recognizer-impl.cc
   offline-recognizer.cc
+  offline-rnn-lm.cc
   offline-stream.cc
   offline-transducer-greedy-search-decoder.cc
   offline-transducer-model-config.cc
   offline-transducer-model.cc
+  offline-transducer-modified-beam-search-decoder.cc
+  online-lm-config.cc
   online-lstm-transducer-model.cc
   online-recognizer.cc
   online-stream.cc

sherpa-onnx/csrc/hypothesis.cc

@@ -17,6 +17,9 @@ void Hypotheses::Add(Hypothesis hyp) {
     hyps_dict_[key] = std::move(hyp);
   } else {
     it->second.log_prob = LogAdd<double>()(it->second.log_prob, hyp.log_prob);
+
+    it->second.lm_log_prob =
+        LogAdd<double>()(it->second.lm_log_prob, hyp.lm_log_prob);
   }
 }
 
@@ -24,8 +27,8 @@ Hypothesis Hypotheses::GetMostProbable(bool length_norm) const {
   if (length_norm == false) {
     return std::max_element(hyps_dict_.begin(), hyps_dict_.end(),
                             [](const auto &left, auto &right) -> bool {
-                              return left.second.log_prob <
+                              return left.second.TotalLogProb() <
-                                     right.second.log_prob;
+                                     right.second.TotalLogProb();
                             })
         ->second;
   } else {
@@ -33,8 +36,8 @@ Hypothesis Hypotheses::GetMostProbable(bool length_norm) const {
     return std::max_element(
                hyps_dict_.begin(), hyps_dict_.end(),
                [](const auto &left, const auto &right) -> bool {
-                 return left.second.log_prob / left.second.ys.size() <
+                 return left.second.TotalLogProb() / left.second.ys.size() <
-                        right.second.log_prob / right.second.ys.size();
+                        right.second.TotalLogProb() / right.second.ys.size();
                })
         ->second;
   }
@@ -47,15 +50,16 @@ std::vector<Hypothesis> Hypotheses::GetTopK(int32_t k, bool length_norm) const {
   std::vector<Hypothesis> all_hyps = Vec();
 
   if (length_norm == false) {
-    std::partial_sort(
+    std::partial_sort(all_hyps.begin(), all_hyps.begin() + k, all_hyps.end(),
-        all_hyps.begin(), all_hyps.begin() + k, all_hyps.end(),
+                      [](const auto &a, const auto &b) {
-        [](const auto &a, const auto &b) { return a.log_prob > b.log_prob; });
+                        return a.TotalLogProb() > b.TotalLogProb();
+                      });
   } else {
     // for length_norm is true
     std::partial_sort(all_hyps.begin(), all_hyps.begin() + k, all_hyps.end(),
                       [](const auto &a, const auto &b) {
-                        return a.log_prob / a.ys.size() >
+                        return a.TotalLogProb() / a.ys.size() >
-                               b.log_prob / b.ys.size();
+                               b.TotalLogProb() / b.ys.size();
                       });
   }
 

sherpa-onnx/csrc/hypothesis.h

@@ -25,14 +25,20 @@ struct Hypothesis {
   std::vector<int32_t> timestamps;
 
   // The total score of ys in log space.
+  // It contains only acoustic scores
   double log_prob = 0;
 
+  // LM log prob if any.
+  double lm_log_prob = 0;
+
   int32_t num_trailing_blanks = 0;
 
   Hypothesis() = default;
   Hypothesis(const std::vector<int64_t> &ys, double log_prob)
       : ys(ys), log_prob(log_prob) {}
 
+  double TotalLogProb() const { return log_prob + lm_log_prob; }
+
   // If two Hypotheses have the same `Key`, then they contain
   // the same token sequence.
   std::string Key() const {
@@ -94,6 +100,9 @@ class Hypotheses {
   const auto begin() const { return hyps_dict_.begin(); }
   const auto end() const { return hyps_dict_.end(); }
 
+  auto begin() { return hyps_dict_.begin(); }
+  auto end() { return hyps_dict_.end(); }
+
   void Clear() { hyps_dict_.clear(); }
 
  private:

sherpa-onnx/csrc/math.h

@@ -88,6 +88,16 @@ void LogSoftmax(T *input, int32_t input_len) {
   }
 }
 
+template <typename T>
+void LogSoftmax(T *in, int32_t w, int32_t h) {
+  for (int32_t i = 0; i != h; ++i) {
+    LogSoftmax(in, w);
+    in += w;
+  }
+}
+
+// TODO(fangjun): use std::partial_sort to replace std::sort.
+// Remember also to fix sherpa-ncnn
 template <class T>
 std::vector<int32_t> TopkIndex(const T *vec, int32_t size, int32_t topk) {
   std::vector<int32_t> vec_index(size);

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

@@ -0,0 +1,38 @@
+// sherpa-onnx/csrc/offline-lm-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-lm-config.h"
+
+#include <string>
+
+#include "sherpa-onnx/csrc/file-utils.h"
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+void OfflineLMConfig::Register(ParseOptions *po) {
+  po->Register("lm", &model, "Path to LM model.");
+  po->Register("lm-scale", &scale, "LM scale.");
+}
+
+bool OfflineLMConfig::Validate() const {
+  if (!FileExists(model)) {
+    SHERPA_ONNX_LOGE("%s does not exist", model.c_str());
+    return false;
+  }
+
+  return true;
+}
+
+std::string OfflineLMConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "OfflineLMConfig(";
+  os << "model=\"" << model << "\", ";
+  os << "scale=" << scale << ")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx

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

@@ -0,0 +1,33 @@
+// sherpa-onnx/csrc/offline-lm-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_LM_CONFIG_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_LM_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/parse-options.h"
+
+namespace sherpa_onnx {
+
+struct OfflineLMConfig {
+  // path to the onnx model
+  std::string model;
+
+  // LM scale
+  float scale = 1.0;
+
+  OfflineLMConfig() = default;
+
+  OfflineLMConfig(const std::string &model, float scale)
+      : model(model), scale(scale) {}
+
+  void Register(ParseOptions *po);
+  bool Validate() const;
+
+  std::string ToString() const;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_LM_CONFIG_H_

sherpa-onnx/csrc/offline-lm.cc

@@ -0,0 +1,71 @@
+// sherpa-onnx/csrc/offline-lm.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-lm.h"
+
+#include <algorithm>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-rnn-lm.h"
+
+namespace sherpa_onnx {
+
+std::unique_ptr<OfflineLM> OfflineLM::Create(const OfflineLMConfig &config) {
+  return std::make_unique<OfflineRnnLM>(config);
+}
+
+void OfflineLM::ComputeLMScore(float scale, int32_t context_size,
+                               std::vector<Hypotheses> *hyps) {
+  // compute the max token seq so that we know how much space to allocate
+  int32_t max_token_seq = 0;
+  int32_t num_hyps = 0;
+
+  // we subtract context_size below since each token sequence is prepended
+  // with context_size blanks
+  for (const auto &h : *hyps) {
+    num_hyps += h.Size();
+    for (const auto &t : h) {
+      max_token_seq =
+          std::max<int32_t>(max_token_seq, t.second.ys.size() - context_size);
+    }
+  }
+
+  Ort::AllocatorWithDefaultOptions allocator;
+  std::array<int64_t, 2> x_shape{num_hyps, max_token_seq};
+  Ort::Value x = Ort::Value::CreateTensor<int64_t>(allocator, x_shape.data(),
+                                                   x_shape.size());
+
+  std::array<int64_t, 1> x_lens_shape{num_hyps};
+  Ort::Value x_lens = Ort::Value::CreateTensor<int64_t>(
+      allocator, x_lens_shape.data(), x_lens_shape.size());
+
+  int64_t *p = x.GetTensorMutableData<int64_t>();
+  std::fill(p, p + num_hyps * max_token_seq, 0);
+
+  int64_t *p_lens = x_lens.GetTensorMutableData<int64_t>();
+
+  for (const auto &h : *hyps) {
+    for (const auto &t : h) {
+      const auto &ys = t.second.ys;
+      int32_t len = ys.size() - context_size;
+      std::copy(ys.begin() + context_size, ys.end(), p);
+      *p_lens = len;
+
+      p += max_token_seq;
+      ++p_lens;
+    }
+  }
+  auto negative_loglike = Rescore(std::move(x), std::move(x_lens));
+  const float *p_nll = negative_loglike.GetTensorData<float>();
+  for (auto &h : *hyps) {
+    for (auto &t : h) {
+      // Use -scale here since we want to change negative loglike to loglike.
+      t.second.lm_log_prob = -scale * (*p_nll);
+      ++p_nll;
+    }
+  }
+}
+
+}  // namespace sherpa_onnx

sherpa-onnx/csrc/offline-lm.h

@@ -0,0 +1,46 @@
+// sherpa-onnx/csrc/offline-lm.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_LM_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_LM_H_
+
+#include <memory>
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/hypothesis.h"
+#include "sherpa-onnx/csrc/offline-lm-config.h"
+
+namespace sherpa_onnx {
+
+class OfflineLM {
+ public:
+  virtual ~OfflineLM() = default;
+
+  static std::unique_ptr<OfflineLM> Create(const OfflineLMConfig &config);
+
+  /** Rescore a batch of sentences.
+   *
+   * @param x A 2-D tensor of shape (N, L) with data type int64.
+   * @param x_lens A 1-D tensor of shape (N,) with data type int64.
+   *               It contains number of valid tokens in x before padding.
+   * @return Return a 1-D tensor of shape (N,) containing the negative log
+   *         likelihood of each utterance. Its data type is float32.
+   *
+   * Caution: It returns negative log likelihood (nll), not log likelihood
+   */
+  virtual Ort::Value Rescore(Ort::Value x, Ort::Value x_lens) = 0;
+
+  // This function updates hyp.lm_lob_prob of hyps.
+  //
+  // @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);
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_LM_H_

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

@@ -16,6 +16,7 @@
 #include "sherpa-onnx/csrc/offline-transducer-decoder.h"
 #include "sherpa-onnx/csrc/offline-transducer-greedy-search-decoder.h"
 #include "sherpa-onnx/csrc/offline-transducer-model.h"
+#include "sherpa-onnx/csrc/offline-transducer-modified-beam-search-decoder.h"
 #include "sherpa-onnx/csrc/pad-sequence.h"
 #include "sherpa-onnx/csrc/symbol-table.h"
 
@@ -57,8 +58,13 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
       decoder_ =
           std::make_unique<OfflineTransducerGreedySearchDecoder>(model_.get());
     } else if (config_.decoding_method == "modified_beam_search") {
-      SHERPA_ONNX_LOGE("TODO: modified_beam_search is to be implemented");
+      if (!config_.lm_config.model.empty()) {
-      exit(-1);
+        lm_ = OfflineLM::Create(config.lm_config);
+      }
+
+      decoder_ = std::make_unique<OfflineTransducerModifiedBeamSearchDecoder>(
+          model_.get(), lm_.get(), config_.max_active_paths,
+          config_.lm_config.scale);
     } else {
       SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
                        config_.decoding_method.c_str());
@@ -127,6 +133,7 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
   SymbolTable symbol_table_;
   std::unique_ptr<OfflineTransducerModel> model_;
   std::unique_ptr<OfflineTransducerDecoder> decoder_;
+  std::unique_ptr<OfflineLM> lm_;
 };
 
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/offline-recognizer.cc

@@ -8,6 +8,7 @@
 
 #include "sherpa-onnx/csrc/file-utils.h"
 #include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/offline-lm-config.h"
 #include "sherpa-onnx/csrc/offline-recognizer-impl.h"
 
 namespace sherpa_onnx {
@@ -15,13 +16,28 @@ namespace sherpa_onnx {
 void OfflineRecognizerConfig::Register(ParseOptions *po) {
   feat_config.Register(po);
   model_config.Register(po);
+  lm_config.Register(po);
 
-  po->Register("decoding-method", &decoding_method,
+  po->Register(
+      "decoding-method", &decoding_method,
       "decoding method,"
-               "Valid values: greedy_search.");
+      "Valid values: greedy_search, modified_beam_search. "
+      "modified_beam_search is applicable only for transducer models.");
+
+  po->Register("max-active-paths", &max_active_paths,
+               "Used only when decoding_method is modified_beam_search");
 }
 
 bool OfflineRecognizerConfig::Validate() const {
+  if (decoding_method == "modified_beam_search" && !lm_config.model.empty()) {
+    if (max_active_paths <= 0) {
+      SHERPA_ONNX_LOGE("max_active_paths is less than 0! Given: %d",
+                       max_active_paths);
+      return false;
+    }
+    if (!lm_config.Validate()) return false;
+  }
+
   return model_config.Validate();
 }
 
@@ -31,7 +47,9 @@ std::string OfflineRecognizerConfig::ToString() const {
   os << "OfflineRecognizerConfig(";
   os << "feat_config=" << feat_config.ToString() << ", ";
   os << "model_config=" << model_config.ToString() << ", ";
-  os << "decoding_method=\"" << decoding_method << "\")";
+  os << "lm_config=" << lm_config.ToString() << ", ";
+  os << "decoding_method=\"" << decoding_method << "\", ";
+  os << "max_active_paths=" << max_active_paths << ")";
 
   return os.str();
 }

sherpa-onnx/csrc/offline-recognizer.h

@@ -9,6 +9,7 @@
 #include <string>
 #include <vector>
 
+#include "sherpa-onnx/csrc/offline-lm-config.h"
 #include "sherpa-onnx/csrc/offline-model-config.h"
 #include "sherpa-onnx/csrc/offline-stream.h"
 #include "sherpa-onnx/csrc/offline-transducer-model-config.h"
@@ -21,18 +22,24 @@ struct OfflineRecognitionResult;
 struct OfflineRecognizerConfig {
   OfflineFeatureExtractorConfig feat_config;
   OfflineModelConfig model_config;
+  OfflineLMConfig lm_config;
 
   std::string decoding_method = "greedy_search";
+  int32_t max_active_paths = 4;
   // only greedy_search is implemented
   // TODO(fangjun): Implement modified_beam_search
 
   OfflineRecognizerConfig() = default;
   OfflineRecognizerConfig(const OfflineFeatureExtractorConfig &feat_config,
                           const OfflineModelConfig &model_config,
-                          const std::string &decoding_method)
+                          const OfflineLMConfig &lm_config,
+                          const std::string &decoding_method,
+                          int32_t max_active_paths)
       : feat_config(feat_config),
         model_config(model_config),
-        decoding_method(decoding_method) {}
+        lm_config(lm_config),
+        decoding_method(decoding_method),
+        max_active_paths(max_active_paths) {}
 
   void Register(ParseOptions *po);
   bool Validate() const;

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

@@ -0,0 +1,74 @@
+// sherpa-onnx/csrc/offline-rnn-lm.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-rnn-lm.h"
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
+#include "sherpa-onnx/csrc/text-utils.h"
+
+namespace sherpa_onnx {
+
+class OfflineRnnLM::Impl {
+ public:
+  explicit Impl(const OfflineLMConfig &config)
+      : config_(config),
+        env_(ORT_LOGGING_LEVEL_ERROR),
+        sess_opts_{},
+        allocator_{} {
+    Init(config);
+  }
+
+  Ort::Value Rescore(Ort::Value x, Ort::Value x_lens) {
+    std::array<Ort::Value, 2> inputs = {std::move(x), std::move(x_lens)};
+
+    auto out =
+        sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
+                   output_names_ptr_.data(), output_names_ptr_.size());
+
+    return std::move(out[0]);
+  }
+
+ private:
+  void Init(const OfflineLMConfig &config) {
+    auto buf = ReadFile(config_.model);
+
+    sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
+                                           sess_opts_);
+
+    GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
+
+    GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
+  }
+
+ private:
+  OfflineLMConfig config_;
+  Ort::Env env_;
+  Ort::SessionOptions sess_opts_;
+  Ort::AllocatorWithDefaultOptions allocator_;
+
+  std::unique_ptr<Ort::Session> sess_;
+
+  std::vector<std::string> input_names_;
+  std::vector<const char *> input_names_ptr_;
+
+  std::vector<std::string> output_names_;
+  std::vector<const char *> output_names_ptr_;
+};
+
+OfflineRnnLM::OfflineRnnLM(const OfflineLMConfig &config)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+OfflineRnnLM::~OfflineRnnLM() = default;
+
+Ort::Value OfflineRnnLM::Rescore(Ort::Value x, Ort::Value x_lens) {
+  return impl_->Rescore(std::move(x), std::move(x_lens));
+}
+
+}  // namespace sherpa_onnx

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

@@ -0,0 +1,41 @@
+// sherpa-onnx/csrc/offline-rnn-lm.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_RNN_LM_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_RNN_LM_H_
+
+#include <memory>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/offline-lm-config.h"
+#include "sherpa-onnx/csrc/offline-lm.h"
+
+namespace sherpa_onnx {
+
+class OfflineRnnLM : public OfflineLM {
+ public:
+  ~OfflineRnnLM() override;
+
+  explicit OfflineRnnLM(const OfflineLMConfig &config);
+
+  /** Rescore a batch of sentences.
+   *
+   * @param x A 2-D tensor of shape (N, L) with data type int64.
+   * @param x_lens A 1-D tensor of shape (N,) with data type int64.
+   *               It contains number of valid tokens in x before padding.
+   * @return Return a 1-D tensor of shape (N,) containing the log likelihood
+   *         of each utterance. Its data type is float32.
+   *
+   * Caution: It returns log likelihood, not negative log likelihood (nll).
+   */
+  Ort::Value Rescore(Ort::Value x, Ort::Value x_lens) override;
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_RNN_LM_H_

sherpa-onnx/csrc/offline-transducer-model.cc

@@ -95,6 +95,30 @@ class OfflineTransducerModel::Impl {
       std::copy(begin, end, p);
       p += context_size;
     }
+
+    return decoder_input;
+  }
+
+  Ort::Value BuildDecoderInput(const std::vector<Hypothesis> &results,
+                               int32_t end_index) const {
+    assert(end_index <= results.size());
+
+    int32_t batch_size = end_index;
+    int32_t context_size = ContextSize();
+    std::array<int64_t, 2> shape{batch_size, context_size};
+
+    Ort::Value decoder_input = Ort::Value::CreateTensor<int64_t>(
+        Allocator(), shape.data(), shape.size());
+    int64_t *p = decoder_input.GetTensorMutableData<int64_t>();
+
+    for (int32_t i = 0; i != batch_size; ++i) {
+      const auto &r = results[i];
+      const int64_t *begin = r.ys.data() + r.ys.size() - context_size;
+      const int64_t *end = r.ys.data() + r.ys.size();
+      std::copy(begin, end, p);
+      p += context_size;
+    }
+
     return decoder_input;
   }
 
@@ -234,4 +258,9 @@ Ort::Value OfflineTransducerModel::BuildDecoderInput(
   return impl_->BuildDecoderInput(results, end_index);
 }
 
+Ort::Value OfflineTransducerModel::BuildDecoderInput(
+    const std::vector<Hypothesis> &results, int32_t end_index) const {
+  return impl_->BuildDecoderInput(results, end_index);
+}
+
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/offline-transducer-model.h

@@ -9,6 +9,7 @@
 #include <vector>
 
 #include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/hypothesis.h"
 #include "sherpa-onnx/csrc/offline-model-config.h"
 
 namespace sherpa_onnx {
@@ -79,13 +80,16 @@ class OfflineTransducerModel {
    *
    * @param results Current decoded results.
    * @param end_index We only use results[0:end_index] to build
-   *                  the decoder_input.
+   *                  the decoder_input. results[end_index] is not used.
    * @return Return a tensor of shape (results.size(), ContextSize())
    */
   Ort::Value BuildDecoderInput(
       const std::vector<OfflineTransducerDecoderResult> &results,
       int32_t end_index) const;
 
+  Ort::Value BuildDecoderInput(const std::vector<Hypothesis> &results,
+                               int32_t end_index) const;
+
  private:
   class Impl;
   std::unique_ptr<Impl> impl_;

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

@@ -0,0 +1,163 @@
+// sherpa-onnx/csrc/offline-transducer-modified-beam-search-decoder.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-transducer-modified-beam-search-decoder.h"
+
+#include <deque>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/hypothesis.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
+#include "sherpa-onnx/csrc/packed-sequence.h"
+#include "sherpa-onnx/csrc/slice.h"
+
+namespace sherpa_onnx {
+
+static std::vector<int32_t> GetHypsRowSplits(
+    const std::vector<Hypotheses> &hyps) {
+  std::vector<int32_t> row_splits;
+  row_splits.reserve(hyps.size() + 1);
+
+  row_splits.push_back(0);
+  int32_t s = 0;
+  for (const auto &h : hyps) {
+    s += h.Size();
+    row_splits.push_back(s);
+  }
+
+  return row_splits;
+}
+
+std::vector<OfflineTransducerDecoderResult>
+OfflineTransducerModifiedBeamSearchDecoder::Decode(
+    Ort::Value encoder_out, Ort::Value encoder_out_length) {
+  PackedSequence packed_encoder_out = PackPaddedSequence(
+      model_->Allocator(), &encoder_out, &encoder_out_length);
+
+  int32_t batch_size =
+      static_cast<int32_t>(packed_encoder_out.sorted_indexes.size());
+
+  int32_t vocab_size = model_->VocabSize();
+  int32_t context_size = model_->ContextSize();
+
+  std::vector<int64_t> blanks(context_size, 0);
+  Hypotheses blank_hyp({{blanks, 0}});
+
+  std::deque<Hypotheses> finalized;
+  std::vector<Hypotheses> cur(batch_size, blank_hyp);
+  std::vector<Hypothesis> prev;
+
+  int32_t start = 0;
+  int32_t t = 0;
+  for (auto n : packed_encoder_out.batch_sizes) {
+    Ort::Value cur_encoder_out = packed_encoder_out.Get(start, n);
+    start += n;
+
+    if (n < static_cast<int32_t>(cur.size())) {
+      for (int32_t k = static_cast<int32_t>(cur.size()) - 1; k >= n; --k) {
+        finalized.push_front(std::move(cur[k]));
+      }
+
+      cur.erase(cur.begin() + n, cur.end());
+    }  // if (n < static_cast<int32_t>(cur.size()))
+
+    // Due to merging paths with identical token sequences,
+    // not all utterances have "max_active_paths" paths.
+    auto hyps_row_splits = GetHypsRowSplits(cur);
+    int32_t num_hyps = hyps_row_splits.back();
+
+    prev.clear();
+    prev.reserve(num_hyps);
+
+    for (auto &hyps : cur) {
+      for (auto &h : hyps) {
+        prev.push_back(std::move(h.second));
+      }
+    }
+    cur.clear();
+    cur.reserve(n);
+
+    auto decoder_input = model_->BuildDecoderInput(prev, num_hyps);
+    // decoder_input shape: (num_hyps, context_size)
+
+    auto decoder_out = model_->RunDecoder(std::move(decoder_input));
+    // decoder_out is (num_hyps, joiner_dim)
+
+    cur_encoder_out =
+        Repeat(model_->Allocator(), &cur_encoder_out, hyps_row_splits);
+    // now cur_encoder_out is of shape (num_hyps, joiner_dim)
+
+    Ort::Value logit = model_->RunJoiner(
+        std::move(cur_encoder_out), Clone(model_->Allocator(), &decoder_out));
+
+    float *p_logit = logit.GetTensorMutableData<float>();
+    LogSoftmax(p_logit, vocab_size, num_hyps);
+
+    // now p_logit contains log_softmax output, we rename it to p_logprob
+    // to match what it actually contains
+    float *p_logprob = p_logit;
+
+    // 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;
+      for (int32_t k = 0; k != vocab_size; ++k, ++p_logprob) {
+        *p_logprob += log_prob;
+      }
+    }
+    p_logprob = p_logit;  // we changed p_logprob in the above for loop
+
+    // Now compute top_k for each utterance
+    for (int32_t i = 0; i != n; ++i) {
+      int32_t start = hyps_row_splits[i];
+      int32_t end = hyps_row_splits[i + 1];
+      auto topk =
+          TopkIndex(p_logprob, vocab_size * (end - start), max_active_paths_);
+
+      Hypotheses hyps;
+      for (auto k : topk) {
+        int32_t hyp_index = k / vocab_size + start;
+        int32_t new_token = k % vocab_size;
+        Hypothesis new_hyp = prev[hyp_index];
+
+        if (new_token != 0) {
+          // blank id is fixed to 0
+          new_hyp.ys.push_back(new_token);
+          new_hyp.timestamps.push_back(t);
+        }
+
+        new_hyp.log_prob = p_logprob[k];
+        hyps.Add(std::move(new_hyp));
+      }  // for (auto k : topk)
+      p_logprob += (end - start) * vocab_size;
+      cur.push_back(std::move(hyps));
+    }  // for (int32_t i = 0; i != n; ++i)
+
+    ++t;
+  }  // for (auto n : packed_encoder_out.batch_sizes)
+
+  for (auto &h : finalized) {
+    cur.push_back(std::move(h));
+  }
+
+  if (lm_) {
+    // use LM for rescoring
+    lm_->ComputeLMScore(lm_scale_, context_size, &cur);
+  }
+
+  std::vector<OfflineTransducerDecoderResult> unsorted_ans(batch_size);
+  for (int32_t i = 0; i != batch_size; ++i) {
+    Hypothesis hyp = cur[i].GetMostProbable(true);
+
+    auto &r = unsorted_ans[packed_encoder_out.sorted_indexes[i]];
+
+    // strip leading blanks
+    r.tokens = {hyp.ys.begin() + context_size, hyp.ys.end()};
+    r.timestamps = std::move(hyp.timestamps);
+  }
+
+  return unsorted_ans;
+}
+
+}  // namespace sherpa_onnx

sherpa-onnx/csrc/offline-transducer-modified-beam-search-decoder.h

@@ -0,0 +1,41 @@
+// sherpa-onnx/csrc/offline-transducer-modified-beam-search-decoder.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_TRANSDUCER_MODIFIED_BEAM_SEARCH_DECODER_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_TRANSDUCER_MODIFIED_BEAM_SEARCH_DECODER_H_
+
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-lm.h"
+#include "sherpa-onnx/csrc/offline-transducer-decoder.h"
+#include "sherpa-onnx/csrc/offline-transducer-model.h"
+
+namespace sherpa_onnx {
+
+class OfflineTransducerModifiedBeamSearchDecoder
+    : public OfflineTransducerDecoder {
+ public:
+  OfflineTransducerModifiedBeamSearchDecoder(OfflineTransducerModel *model,
+                                             OfflineLM *lm,
+                                             int32_t max_active_paths,
+                                             float lm_scale)
+      : model_(model),
+        lm_(lm),
+        max_active_paths_(max_active_paths),
+        lm_scale_(lm_scale) {}
+
+  std::vector<OfflineTransducerDecoderResult> Decode(
+      Ort::Value encoder_out, Ort::Value encoder_out_length) override;
+
+ private:
+  OfflineTransducerModel *model_;  // Not owned
+  OfflineLM *lm_;                  // Not owned; may be nullptr
+
+  int32_t max_active_paths_;
+  float lm_scale_;  // used only when lm_ is not nullptr
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_TRANSDUCER_MODIFIED_BEAM_SEARCH_DECODER_H_

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

@@ -0,0 +1,38 @@
+// sherpa-onnx/csrc/online-lm-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/online-lm-config.h"
+
+#include <string>
+
+#include "sherpa-onnx/csrc/file-utils.h"
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+void OnlineLMConfig::Register(ParseOptions *po) {
+  po->Register("lm", &model, "Path to LM model.");
+  po->Register("lm-scale", &scale, "LM scale.");
+}
+
+bool OnlineLMConfig::Validate() const {
+  if (!FileExists(model)) {
+    SHERPA_ONNX_LOGE("%s does not exist", model.c_str());
+    return false;
+  }
+
+  return true;
+}
+
+std::string OnlineLMConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "OnlineLMConfig(";
+  os << "model=\"" << model << "\", ";
+  os << "scale=" << scale << ")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx

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

@@ -0,0 +1,33 @@
+// sherpa-onnx/csrc/online-lm-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_ONLINE_LM_CONFIG_H_
+#define SHERPA_ONNX_CSRC_ONLINE_LM_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/parse-options.h"
+
+namespace sherpa_onnx {
+
+struct OnlineLMConfig {
+  // path to the onnx model
+  std::string model;
+
+  // LM scale
+  float scale = 1.0;
+
+  OnlineLMConfig() = default;
+
+  OnlineLMConfig(const std::string &model, float scale)
+      : model(model), scale(scale) {}
+
+  void Register(ParseOptions *po);
+  bool Validate() const;
+
+  std::string ToString() const;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_ONLINE_LM_CONFIG_H_

sherpa-onnx/csrc/online-lm.h

@@ -0,0 +1,64 @@
+// sherpa-onnx/csrc/online-lm.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_ONLINE_LM_H_
+#define SHERPA_ONNX_CSRC_ONLINE_LM_H_
+
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/hypothesis.h"
+#include "sherpa-onnx/csrc/online-lm-config.h"
+
+namespace sherpa_onnx {
+
+class OnlineLM {
+ public:
+  virtual ~OnlineLM() = default;
+
+  static std::unique_ptr<OnlineLM> Create(const OnlineLMConfig &config);
+
+  virtual std::vector<Ort::Value> GetInitStates() = 0;
+
+  /** Rescore a batch of sentences.
+   *
+   * @param x A 2-D tensor of shape (N, L) with data type int64.
+   * @param y 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
+   *          - negative loglike
+   *          - updated states
+   *
+   * Caution: It returns negative log likelihood (nll), not log likelihood
+   */
+  std::pair<Ort::Value, std::vector<Ort::Value>> Ort::Value Rescore(
+      Ort::Value x, Ort::Value y, std::vector<Ort::Value> states) = 0;
+
+  // This function updates hyp.lm_lob_prob of hyps.
+  //
+  // @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);
+  /** TODO(fangjun):
+   *
+   * 1. Add two fields to Hypothesis
+   *      (a) int32_t lm_cur_pos = 0; number of scored tokens so far
+   *      (b) std::vector<Ort::Value> lm_states;
+   * 2. When we want to score a hypothesis, we construct x and y as follows:
+   *
+   *      std::vector x = {hyp.ys.begin() + context_size + lm_cur_pos,
+   *                       hyp.ys.end() - 1};
+   *      std::vector y = {hyp.ys.begin() + context_size + lm_cur_pos + 1
+   *                       hyp.ys.end()};
+   *       hyp.lm_cur_pos += hyp.ys.size() - context_size - lm_cur_pos;
+   */
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_ONLINE_LM_H_

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

@@ -36,38 +36,6 @@ static void UseCachedDecoderOut(
   }
 }
 
-static Ort::Value Repeat(OrtAllocator *allocator, Ort::Value *cur_encoder_out,
-                         const std::vector<int32_t> &hyps_num_split) {
-  std::vector<int64_t> cur_encoder_out_shape =
-      cur_encoder_out->GetTensorTypeAndShapeInfo().GetShape();
-
-  std::array<int64_t, 2> ans_shape{hyps_num_split.back(),
-                                   cur_encoder_out_shape[1]};
-
-  Ort::Value ans = Ort::Value::CreateTensor<float>(allocator, ans_shape.data(),
-                                                   ans_shape.size());
-
-  const float *src = cur_encoder_out->GetTensorData<float>();
-  float *dst = ans.GetTensorMutableData<float>();
-  int32_t batch_size = static_cast<int32_t>(hyps_num_split.size()) - 1;
-  for (int32_t b = 0; b != batch_size; ++b) {
-    int32_t cur_stream_hyps_num = hyps_num_split[b + 1] - hyps_num_split[b];
-    for (int32_t i = 0; i != cur_stream_hyps_num; ++i) {
-      std::copy(src, src + cur_encoder_out_shape[1], dst);
-      dst += cur_encoder_out_shape[1];
-    }
-    src += cur_encoder_out_shape[1];
-  }
-  return ans;
-}
-
-static void LogSoftmax(float *in, int32_t w, int32_t h) {
-  for (int32_t i = 0; i != h; ++i) {
-    LogSoftmax(in, w);
-    in += w;
-  }
-}
-
 OnlineTransducerDecoderResult
 OnlineTransducerModifiedBeamSearchDecoder::GetEmptyResult() const {
   int32_t context_size = model_->ContextSize();

sherpa-onnx/csrc/onnx-utils.cc

@@ -193,4 +193,29 @@ std::vector<char> ReadFile(AAssetManager *mgr, const std::string &filename) {
 }
 #endif
 
+Ort::Value Repeat(OrtAllocator *allocator, Ort::Value *cur_encoder_out,
+                  const std::vector<int32_t> &hyps_num_split) {
+  std::vector<int64_t> cur_encoder_out_shape =
+      cur_encoder_out->GetTensorTypeAndShapeInfo().GetShape();
+
+  std::array<int64_t, 2> ans_shape{hyps_num_split.back(),
+                                   cur_encoder_out_shape[1]};
+
+  Ort::Value ans = Ort::Value::CreateTensor<float>(allocator, ans_shape.data(),
+                                                   ans_shape.size());
+
+  const float *src = cur_encoder_out->GetTensorData<float>();
+  float *dst = ans.GetTensorMutableData<float>();
+  int32_t batch_size = static_cast<int32_t>(hyps_num_split.size()) - 1;
+  for (int32_t b = 0; b != batch_size; ++b) {
+    int32_t cur_stream_hyps_num = hyps_num_split[b + 1] - hyps_num_split[b];
+    for (int32_t i = 0; i != cur_stream_hyps_num; ++i) {
+      std::copy(src, src + cur_encoder_out_shape[1], dst);
+      dst += cur_encoder_out_shape[1];
+    }
+    src += cur_encoder_out_shape[1];
+  }
+  return ans;
+}
+
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/onnx-utils.h

@@ -86,6 +86,9 @@ std::vector<char> ReadFile(const std::string &filename);
 std::vector<char> ReadFile(AAssetManager *mgr, const std::string &filename);
 #endif
 
+// TODO(fangjun): Document it
+Ort::Value Repeat(OrtAllocator *allocator, Ort::Value *cur_encoder_out,
+                  const std::vector<int32_t> &hyps_num_split);
 }  // namespace sherpa_onnx
 
 #endif  // SHERPA_ONNX_CSRC_ONNX_UTILS_H_

sherpa-onnx/csrc/sherpa-onnx-offline.cc

@@ -111,6 +111,9 @@ for a list of pre-trained models to download.
 
   fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
   fprintf(stderr, "decoding method: %s\n", config.decoding_method.c_str());
+  if (config.decoding_method == "modified_beam_search") {
+    fprintf(stderr, "max active paths: %d\n", config.max_active_paths);
+  }
 
   fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
   float rtf = elapsed_seconds / duration;

sherpa-onnx/csrc/sherpa-onnx.cc

@@ -117,6 +117,9 @@ for a list of pre-trained models to download.
 
   fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
   fprintf(stderr, "decoding method: %s\n", config.decoding_method.c_str());
+  if (config.decoding_method == "modified_beam_search") {
+    fprintf(stderr, "max active paths: %d\n", config.max_active_paths);
+  }
 
   fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
   float rtf = elapsed_seconds / duration;

sherpa-onnx/python/csrc/CMakeLists.txt

@@ -4,6 +4,7 @@ pybind11_add_module(_sherpa_onnx
   display.cc
   endpoint.cc
   features.cc
+  offline-lm-config.cc
   offline-model-config.cc
   offline-nemo-enc-dec-ctc-model-config.cc
   offline-paraformer-model-config.cc

sherpa-onnx/python/csrc/offline-lm-config.cc

@@ -0,0 +1,23 @@
+// sherpa-onnx/python/csrc/offline-lm-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/offline-lm-config.h"
+
+#include <string>
+
+#include "sherpa-onnx//csrc/offline-lm-config.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineLMConfig(py::module *m) {
+  using PyClass = OfflineLMConfig;
+  py::class_<PyClass>(*m, "OfflineLMConfig")
+      .def(py::init<const std::string &, float>(), py::arg("model"),
+           py::arg("scale"))
+      .def_readwrite("model", &PyClass::model)
+      .def_readwrite("scale", &PyClass::scale)
+      .def("__str__", &PyClass::ToString);
+}
+
+}  // namespace sherpa_onnx

sherpa-onnx/python/csrc/offline-lm-config.h

@@ -0,0 +1,16 @@
+// sherpa-onnx/python/csrc/offline-lm-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_LM_CONFIG_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_LM_CONFIG_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineLMConfig(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_LM_CONFIG_H_

sherpa-onnx/python/csrc/offline-recognizer.cc

@@ -15,12 +15,17 @@ static void PybindOfflineRecognizerConfig(py::module *m) {
   using PyClass = OfflineRecognizerConfig;
   py::class_<PyClass>(*m, "OfflineRecognizerConfig")
       .def(py::init<const OfflineFeatureExtractorConfig &,
-                    const OfflineModelConfig &, const std::string &>(),
+                    const OfflineModelConfig &, const OfflineLMConfig &,
+                    const std::string &, int32_t>(),
            py::arg("feat_config"), py::arg("model_config"),
-           py::arg("decoding_method"))
+           py::arg("lm_config") = OfflineLMConfig(),
+           py::arg("decoding_method") = "greedy_search",
+           py::arg("max_active_paths") = 4)
       .def_readwrite("feat_config", &PyClass::feat_config)
       .def_readwrite("model_config", &PyClass::model_config)
+      .def_readwrite("lm_config", &PyClass::lm_config)
       .def_readwrite("decoding_method", &PyClass::decoding_method)
+      .def_readwrite("max_active_paths", &PyClass::max_active_paths)
       .def("__str__", &PyClass::ToString);
 }
 

sherpa-onnx/python/csrc/sherpa-onnx.cc

@@ -7,6 +7,7 @@
 #include "sherpa-onnx/python/csrc/display.h"
 #include "sherpa-onnx/python/csrc/endpoint.h"
 #include "sherpa-onnx/python/csrc/features.h"
+#include "sherpa-onnx/python/csrc/offline-lm-config.h"
 #include "sherpa-onnx/python/csrc/offline-model-config.h"
 #include "sherpa-onnx/python/csrc/offline-recognizer.h"
 #include "sherpa-onnx/python/csrc/offline-stream.h"
@@ -28,6 +29,7 @@ PYBIND11_MODULE(_sherpa_onnx, m) {
   PybindDisplay(&m);
 
   PybindOfflineStream(&m);
+  PybindOfflineLMConfig(&m);
   PybindOfflineModelConfig(&m);
   PybindOfflineRecognizer(&m);
 }