enginex-mr_series-sherpa-onnx/sherpa-onnx/csrc/offline-recognizer.cc

// sherpa-onnx/csrc/offline-recognizer.cc
//
// Copyright (c)  2023  Xiaomi Corporation

#include "sherpa-onnx/csrc/offline-recognizer.h"

#include <memory>
#include <utility>

#include "sherpa-onnx/csrc/macros.h"
#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/pad-sequence.h"
#include "sherpa-onnx/csrc/symbol-table.h"

namespace sherpa_onnx {

static OfflineRecognitionResult Convert(
    const OfflineTransducerDecoderResult &src, const SymbolTable &sym_table,
    int32_t frame_shift_ms, int32_t subsampling_factor) {
  OfflineRecognitionResult r;
  r.tokens.reserve(src.tokens.size());
  r.timestamps.reserve(src.timestamps.size());

  std::string text;
  for (auto i : src.tokens) {
    auto sym = sym_table[i];
    text.append(sym);

    r.tokens.push_back(std::move(sym));
  }
  r.text = std::move(text);

  float frame_shift_s = frame_shift_ms / 1000. * subsampling_factor;
  for (auto t : src.timestamps) {
    float time = frame_shift_s * t;
    r.timestamps.push_back(time);
  }

  return r;
}

void OfflineRecognizerConfig::Register(ParseOptions *po) {
  feat_config.Register(po);
  model_config.Register(po);

  po->Register("decoding-method", &decoding_method,
               "decoding method,"
               "Valid values: greedy_search.");
}

bool OfflineRecognizerConfig::Validate() const {
  return model_config.Validate();
}

std::string OfflineRecognizerConfig::ToString() const {
  std::ostringstream os;

  os << "OfflineRecognizerConfig(";
  os << "feat_config=" << feat_config.ToString() << ", ";
  os << "model_config=" << model_config.ToString() << ", ";
  os << "decoding_method=\"" << decoding_method << "\")";

  return os.str();
}

class OfflineRecognizer::Impl {
 public:
  explicit Impl(const OfflineRecognizerConfig &config)
      : config_(config),
        symbol_table_(config_.model_config.tokens),
        model_(std::make_unique<OfflineTransducerModel>(config_.model_config)) {
    if (config_.decoding_method == "greedy_search") {
      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");
      exit(-1);
    } else {
      SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
                       config_.decoding_method.c_str());
      exit(-1);
    }
  }

  std::unique_ptr<OfflineStream> CreateStream() const {
    return std::make_unique<OfflineStream>(config_.feat_config);
  }

  void DecodeStreams(OfflineStream **ss, int32_t n) const {
    auto memory_info =
        Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);

    int32_t feat_dim = ss[0]->FeatureDim();

    std::vector<Ort::Value> features;

    features.reserve(n);

    std::vector<std::vector<float>> features_vec(n);
    std::vector<int64_t> features_length_vec(n);
    for (int32_t i = 0; i != n; ++i) {
      auto f = ss[i]->GetFrames();
      int32_t num_frames = f.size() / feat_dim;

      features_length_vec[i] = num_frames;
      features_vec[i] = std::move(f);

      std::array<int64_t, 2> shape = {num_frames, feat_dim};

      Ort::Value x = Ort::Value::CreateTensor(
          memory_info, features_vec[i].data(), features_vec[i].size(),
          shape.data(), shape.size());
      features.push_back(std::move(x));
    }

    std::vector<const Ort::Value *> features_pointer(n);
    for (int32_t i = 0; i != n; ++i) {
      features_pointer[i] = &features[i];
    }

    std::array<int64_t, 1> features_length_shape = {n};
    Ort::Value x_length = Ort::Value::CreateTensor(
        memory_info, features_length_vec.data(), n,
        features_length_shape.data(), features_length_shape.size());

    Ort::Value x = PadSequence(model_->Allocator(), features_pointer,
                               -23.025850929940457f);

    auto t = model_->RunEncoder(std::move(x), std::move(x_length));
    auto results = decoder_->Decode(std::move(t.first), std::move(t.second));

    int32_t frame_shift_ms = 10;
    for (int32_t i = 0; i != n; ++i) {
      auto r = Convert(results[i], symbol_table_, frame_shift_ms,
                       model_->SubsamplingFactor());

      ss[i]->SetResult(r);
    }
  }

 private:
  OfflineRecognizerConfig config_;
  SymbolTable symbol_table_;
  std::unique_ptr<OfflineTransducerModel> model_;
  std::unique_ptr<OfflineTransducerDecoder> decoder_;
};

OfflineRecognizer::OfflineRecognizer(const OfflineRecognizerConfig &config)
    : impl_(std::make_unique<Impl>(config)) {}

OfflineRecognizer::~OfflineRecognizer() = default;

std::unique_ptr<OfflineStream> OfflineRecognizer::CreateStream() const {
  return impl_->CreateStream();
}

void OfflineRecognizer::DecodeStreams(OfflineStream **ss, int32_t n) const {
  impl_->DecodeStreams(ss, n);
}

}  // namespace sherpa_onnx