offline-recognizer-transducer-impl.h
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// sherpa-onnx/csrc/offline-recognizer-transducer-impl.h
//
// Copyright (c) 2022-2023 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
#include "sherpa-onnx/csrc/offline-recognizer.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;
}
class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
public:
explicit OfflineRecognizerTransducerImpl(
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 override {
return std::make_unique<OfflineStream>(config_.feat_config);
}
void DecodeStreams(OfflineStream **ss, int32_t n) const override {
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_;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_