add online-recognizer (#29)

sherpa-onnx/csrc/CMakeLists.txt

@@ -3,6 +3,7 @@ include_directories(${CMAKE_SOURCE_DIR})
 add_executable(sherpa-onnx
   features.cc
   online-lstm-transducer-model.cc
+  online-recognizer.cc
   online-stream.cc
   online-transducer-greedy-search-decoder.cc
   online-transducer-model-config.cc

sherpa-onnx/csrc/features.cc

@@ -7,12 +7,23 @@
 #include <algorithm>
 #include <memory>
 #include <mutex>  // NOLINT
+#include <sstream>
 #include <vector>
 
 #include "kaldi-native-fbank/csrc/online-feature.h"
 
 namespace sherpa_onnx {
 
+std::string FeatureExtractorConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "FeatureExtractorConfig(";
+  os << "sampling_rate=" << sampling_rate << ", ";
+  os << "feature_dim=" << feature_dim << ")";
+
+  return os.str();
+}
+
 class FeatureExtractor::Impl {
  public:
   explicit Impl(const FeatureExtractorConfig &config) {

sherpa-onnx/csrc/features.h

@@ -6,6 +6,7 @@
 #define SHERPA_ONNX_CSRC_FEATURES_H_
 
 #include <memory>
+#include <string>
 #include <vector>
 
 namespace sherpa_onnx {
@@ -13,6 +14,8 @@ namespace sherpa_onnx {
 struct FeatureExtractorConfig {
   float sampling_rate = 16000;
   int32_t feature_dim = 80;
+
+  std::string ToString() const;
 };
 
 class FeatureExtractor {

sherpa-onnx/csrc/online-recognizer.cc

@@ -0,0 +1,136 @@
+// sherpa-onnx/csrc/online-recognizer.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/online-recognizer.h"
+
+#include <assert.h>
+
+#include <memory>
+#include <sstream>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/online-transducer-decoder.h"
+#include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
+#include "sherpa-onnx/csrc/online-transducer-model.h"
+#include "sherpa-onnx/csrc/symbol-table.h"
+
+namespace sherpa_onnx {
+
+static OnlineRecognizerResult Convert(const OnlineTransducerDecoderResult &src,
+                                      const SymbolTable &sym_table) {
+  std::string text;
+  for (auto t : src.tokens) {
+    text += sym_table[t];
+  }
+
+  OnlineRecognizerResult ans;
+  ans.text = std::move(text);
+  return ans;
+}
+
+std::string OnlineRecognizerConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "OnlineRecognizerConfig(";
+  os << "feat_config=" << feat_config.ToString() << ", ";
+  os << "model_config=" << model_config.ToString() << ", ";
+  os << "tokens=\"" << tokens << "\")";
+
+  return os.str();
+}
+
+class OnlineRecognizer::Impl {
+ public:
+  explicit Impl(const OnlineRecognizerConfig &config)
+      : config_(config),
+        model_(OnlineTransducerModel::Create(config.model_config)),
+        sym_(config.tokens) {
+    decoder_ =
+        std::make_unique<OnlineTransducerGreedySearchDecoder>(model_.get());
+  }
+
+  std::unique_ptr<OnlineStream> CreateStream() const {
+    auto stream = std::make_unique<OnlineStream>(config_.feat_config);
+    stream->SetResult(decoder_->GetEmptyResult());
+    stream->SetStates(model_->GetEncoderInitStates());
+    return stream;
+  }
+
+  bool IsReady(OnlineStream *s) const {
+    return s->GetNumProcessedFrames() + model_->ChunkSize() <
+           s->NumFramesReady();
+  }
+
+  void DecodeStreams(OnlineStream **ss, int32_t n) {
+    if (n != 1) {
+      fprintf(stderr, "only n == 1 is implemented\n");
+      exit(-1);
+    }
+    OnlineStream *s = ss[0];
+    assert(IsReady(s));
+
+    int32_t chunk_size = model_->ChunkSize();
+    int32_t chunk_shift = model_->ChunkShift();
+
+    int32_t feature_dim = s->FeatureDim();
+
+    std::array<int64_t, 3> x_shape{1, chunk_size, feature_dim};
+
+    auto memory_info =
+        Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
+
+    std::vector<float> features =
+        s->GetFrames(s->GetNumProcessedFrames(), chunk_size);
+
+    s->GetNumProcessedFrames() += chunk_shift;
+
+    Ort::Value x =
+        Ort::Value::CreateTensor(memory_info, features.data(), features.size(),
+                                 x_shape.data(), x_shape.size());
+
+    auto pair = model_->RunEncoder(std::move(x), s->GetStates());
+
+    s->SetStates(std::move(pair.second));
+    std::vector<OnlineTransducerDecoderResult> results = {s->GetResult()};
+
+    decoder_->Decode(std::move(pair.first), &results);
+    s->SetResult(results[0]);
+  }
+
+  OnlineRecognizerResult GetResult(OnlineStream *s) {
+    OnlineTransducerDecoderResult decoder_result = s->GetResult();
+    decoder_->StripLeadingBlanks(&decoder_result);
+
+    return Convert(decoder_result, sym_);
+  }
+
+ private:
+  OnlineRecognizerConfig config_;
+  std::unique_ptr<OnlineTransducerModel> model_;
+  std::unique_ptr<OnlineTransducerDecoder> decoder_;
+  SymbolTable sym_;
+};
+
+OnlineRecognizer::OnlineRecognizer(const OnlineRecognizerConfig &config)
+    : impl_(std::make_unique<Impl>(config)) {}
+OnlineRecognizer::~OnlineRecognizer() = default;
+
+std::unique_ptr<OnlineStream> OnlineRecognizer::CreateStream() const {
+  return impl_->CreateStream();
+}
+
+bool OnlineRecognizer::IsReady(OnlineStream *s) const {
+  return impl_->IsReady(s);
+}
+
+void OnlineRecognizer::DecodeStreams(OnlineStream **ss, int32_t n) {
+  impl_->DecodeStreams(ss, n);
+}
+
+OnlineRecognizerResult OnlineRecognizer::GetResult(OnlineStream *s) {
+  return impl_->GetResult(s);
+}
+
+}  // namespace sherpa_onnx

sherpa-onnx/csrc/online-recognizer.h

@@ -0,0 +1,65 @@
+// sherpa-onnx/csrc/online-recognizer.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_ONLINE_RECOGNIZER_H_
+#define SHERPA_ONNX_CSRC_ONLINE_RECOGNIZER_H_
+
+#include <memory>
+#include <string>
+
+#include "sherpa-onnx/csrc/features.h"
+#include "sherpa-onnx/csrc/online-stream.h"
+#include "sherpa-onnx/csrc/online-transducer-model-config.h"
+
+namespace sherpa_onnx {
+
+struct OnlineRecognizerResult {
+  std::string text;
+};
+
+struct OnlineRecognizerConfig {
+  FeatureExtractorConfig feat_config;
+  OnlineTransducerModelConfig model_config;
+  std::string tokens;
+
+  std::string ToString() const;
+};
+
+class OnlineRecognizer {
+ public:
+  explicit OnlineRecognizer(const OnlineRecognizerConfig &config);
+  ~OnlineRecognizer();
+
+  /// Create a stream for decoding.
+  std::unique_ptr<OnlineStream> CreateStream() const;
+
+  /**
+   * Return true if the given stream has enough frames for decoding.
+   * Return false otherwise
+   */
+  bool IsReady(OnlineStream *s) const;
+
+  /** Decode a single stream. */
+  void DecodeStream(OnlineStream *s) {
+    OnlineStream *ss[1] = {s};
+    DecodeStreams(ss, 1);
+  }
+
+  /** Decode multiple streams in parallel
+   *
+   * @param ss Pointer array containing streams to be decoded.
+   * @param n Number of streams in `ss`.
+   */
+  void DecodeStreams(OnlineStream **ss, int32_t n);
+
+  OnlineRecognizerResult GetResult(OnlineStream *s);
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_ONLINE_RECOGNIZER_H_

sherpa-onnx/csrc/online-stream.cc

@@ -4,6 +4,7 @@
 #include "sherpa-onnx/csrc/online-stream.h"
 
 #include <memory>
+#include <utility>
 #include <vector>
 
 #include "sherpa-onnx/csrc/features.h"
@@ -41,10 +42,17 @@ class OnlineStream::Impl {
 
   int32_t FeatureDim() const { return feat_extractor_.FeatureDim(); }
 
+  void SetStates(std::vector<Ort::Value> states) {
+    states_ = std::move(states);
+  }
+
+  std::vector<Ort::Value> &GetStates() { return states_; }
+
  private:
   FeatureExtractor feat_extractor_;
   int32_t num_processed_frames_ = 0;  // before subsampling
   OnlineTransducerDecoderResult result_;
+  std::vector<Ort::Value> states_;
 };
 
 OnlineStream::OnlineStream(const FeatureExtractorConfig &config /*= {}*/)
@@ -86,4 +94,12 @@ const OnlineTransducerDecoderResult &OnlineStream::GetResult() const {
   return impl_->GetResult();
 }
 
+void OnlineStream::SetStates(std::vector<Ort::Value> states) {
+  impl_->SetStates(std::move(states));
+}
+
+std::vector<Ort::Value> &OnlineStream::GetStates() {
+  return impl_->GetStates();
+}
+
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/online-stream.h

@@ -8,6 +8,7 @@
 #include <memory>
 #include <vector>
 
+#include "onnxruntime_cxx_api.h"  // NOLINT
 #include "sherpa-onnx/csrc/features.h"
 #include "sherpa-onnx/csrc/online-transducer-decoder.h"
 
@@ -63,6 +64,9 @@ class OnlineStream {
   void SetResult(const OnlineTransducerDecoderResult &r);
   const OnlineTransducerDecoderResult &GetResult() const;
 
+  void SetStates(std::vector<Ort::Value> states);
+  std::vector<Ort::Value> &GetStates();
+
  private:
   class Impl;
   std::unique_ptr<Impl> impl_;

sherpa-onnx/csrc/online-transducer-decoder.h

@@ -26,13 +26,14 @@ class OnlineTransducerDecoder {
    * to the beginning of the decoding result, which will be
    * stripped by calling `StripPrecedingBlanks()`.
    */
-  virtual OnlineTransducerDecoderResult GetEmptyResult() = 0;
+  virtual OnlineTransducerDecoderResult GetEmptyResult() const = 0;
 
   /** Strip blanks added by `GetEmptyResult()`.
    *
    * @param r It is changed in-place.
    */
-  virtual void StripLeadingBlanks(OnlineTransducerDecoderResult * /*r*/) {}
+  virtual void StripLeadingBlanks(OnlineTransducerDecoderResult * /*r*/) const {
+  }
 
   /** Run transducer beam search given the output from the encoder model.
    *

sherpa-onnx/csrc/online-transducer-greedy-search-decoder.cc

@@ -33,7 +33,7 @@ static Ort::Value GetFrame(Ort::Value *encoder_out, int32_t t) {
 }
 
 OnlineTransducerDecoderResult
-OnlineTransducerGreedySearchDecoder::GetEmptyResult() {
+OnlineTransducerGreedySearchDecoder::GetEmptyResult() const {
   int32_t context_size = model_->ContextSize();
   int32_t blank_id = 0;  // always 0
   OnlineTransducerDecoderResult r;
@@ -43,7 +43,7 @@ OnlineTransducerGreedySearchDecoder::GetEmptyResult() {
 }
 
 void OnlineTransducerGreedySearchDecoder::StripLeadingBlanks(
-    OnlineTransducerDecoderResult *r) {
+    OnlineTransducerDecoderResult *r) const {
   int32_t context_size = model_->ContextSize();
 
   auto start = r->tokens.begin() + context_size;

sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h

@@ -17,9 +17,9 @@ class OnlineTransducerGreedySearchDecoder : public OnlineTransducerDecoder {
   explicit OnlineTransducerGreedySearchDecoder(OnlineTransducerModel *model)
       : model_(model) {}
 
-  OnlineTransducerDecoderResult GetEmptyResult() override;
+  OnlineTransducerDecoderResult GetEmptyResult() const override;
 
-  void StripLeadingBlanks(OnlineTransducerDecoderResult *r) override;
+  void StripLeadingBlanks(OnlineTransducerDecoderResult *r) const override;
 
   void Decode(Ort::Value encoder_out,
               std::vector<OnlineTransducerDecoderResult> *result) override;

sherpa-onnx/csrc/sherpa-onnx.cc

@@ -8,6 +8,7 @@
 #include <string>
 #include <vector>
 
+#include "sherpa-onnx/csrc/online-recognizer.h"
 #include "sherpa-onnx/csrc/online-stream.h"
 #include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
 #include "sherpa-onnx/csrc/online-transducer-model-config.h"
@@ -35,35 +36,26 @@ for a list of pre-trained models to download.
     return 0;
   }
 
-  std::string tokens = argv[1];
+  sherpa_onnx::OnlineRecognizerConfig config;
-  sherpa_onnx::OnlineTransducerModelConfig config;
+
-  config.debug = false;
+  config.tokens = argv[1];
-  config.encoder_filename = argv[2];
+
-  config.decoder_filename = argv[3];
+  config.model_config.debug = false;
-  config.joiner_filename = argv[4];
+  config.model_config.encoder_filename = argv[2];
+  config.model_config.decoder_filename = argv[3];
+  config.model_config.joiner_filename = argv[4];
+
   std::string wav_filename = argv[5];
 
-  config.num_threads = 2;
+  config.model_config.num_threads = 2;
   if (argc == 7) {
-    config.num_threads = atoi(argv[6]);
+    config.model_config.num_threads = atoi(argv[6]);
   }
   fprintf(stderr, "%s\n", config.ToString().c_str());
 
-  auto model = sherpa_onnx::OnlineTransducerModel::Create(config);
+  sherpa_onnx::OnlineRecognizer recognizer(config);
 
-  sherpa_onnx::SymbolTable sym(tokens);
+  float expected_sampling_rate = config.feat_config.sampling_rate;
-
-  Ort::AllocatorWithDefaultOptions allocator;
-
-  int32_t chunk_size = model->ChunkSize();
-  int32_t chunk_shift = model->ChunkShift();
-
-  auto memory_info =
-      Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
-
-  std::vector<Ort::Value> states = model->GetEncoderInitStates();
-
-  float expected_sampling_rate = 16000;
 
   bool is_ok = false;
   std::vector<float> samples =
@@ -82,44 +74,21 @@ for a list of pre-trained models to download.
   auto begin = std::chrono::steady_clock::now();
   fprintf(stderr, "Started\n");
 
-  sherpa_onnx::OnlineStream stream;
+  auto s = recognizer.CreateStream();
-  stream.AcceptWaveform(expected_sampling_rate, samples.data(), samples.size());
+  s->AcceptWaveform(expected_sampling_rate, samples.data(), samples.size());
 
   std::vector<float> tail_paddings(
       static_cast<int>(0.2 * expected_sampling_rate));
-  stream.AcceptWaveform(expected_sampling_rate, tail_paddings.data(),
+  s->AcceptWaveform(expected_sampling_rate, tail_paddings.data(),
-                        tail_paddings.size());
+                    tail_paddings.size());
-  stream.InputFinished();
+  s->InputFinished();
 
-  int32_t num_frames = stream.NumFramesReady();
+  while (recognizer.IsReady(s.get())) {
-  int32_t feature_dim = stream.FeatureDim();
+    recognizer.DecodeStream(s.get());
-
-  std::array<int64_t, 3> x_shape{1, chunk_size, feature_dim};
-
-  sherpa_onnx::OnlineTransducerGreedySearchDecoder decoder(model.get());
-  std::vector<sherpa_onnx::OnlineTransducerDecoderResult> result = {
-      decoder.GetEmptyResult()};
-  while (stream.NumFramesReady() - stream.GetNumProcessedFrames() >
-         chunk_size) {
-    std::vector<float> features =
-        stream.GetFrames(stream.GetNumProcessedFrames(), chunk_size);
-    stream.GetNumProcessedFrames() += chunk_shift;
-
-    Ort::Value x =
-        Ort::Value::CreateTensor(memory_info, features.data(), features.size(),
-                                 x_shape.data(), x_shape.size());
-
-    auto pair = model->RunEncoder(std::move(x), states);
-    states = std::move(pair.second);
-    decoder.Decode(std::move(pair.first), &result);
-  }
-  decoder.StripLeadingBlanks(&result[0]);
-  const auto &hyp = result[0].tokens;
-  std::string text;
-  for (auto t : hyp) {
-    text += sym[t];
   }
 
+  std::string text = recognizer.GetResult(s.get()).text;
+
   fprintf(stderr, "Done!\n");
 
   fprintf(stderr, "Recognition result for %s:\n%s\n", wav_filename.c_str(),
@@ -131,7 +100,7 @@ for a list of pre-trained models to download.
           .count() /
       1000.;
 
-  fprintf(stderr, "num threads: %d\n", config.num_threads);
+  fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
 
   fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
   float rtf = elapsed_seconds / duration;