219 lines
7.2 KiB
C++
219 lines
7.2 KiB
C++
// 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 <fstream>
|
|
#include <memory>
|
|
#include <regex> // NOLINT
|
|
#include <string>
|
|
#include <utility>
|
|
#include <vector>
|
|
|
|
#if __ANDROID_API__ >= 9
|
|
#include "android/asset_manager.h"
|
|
#include "android/asset_manager_jni.h"
|
|
#endif
|
|
|
|
#include "sherpa-onnx/csrc/context-graph.h"
|
|
#include "sherpa-onnx/csrc/log.h"
|
|
#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/offline-transducer-modified-beam-search-decoder.h"
|
|
#include "sherpa-onnx/csrc/pad-sequence.h"
|
|
#include "sherpa-onnx/csrc/symbol-table.h"
|
|
#include "sherpa-onnx/csrc/utils.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_.hotwords_file.empty()) {
|
|
InitHotwords();
|
|
}
|
|
if (config_.decoding_method == "greedy_search") {
|
|
decoder_ =
|
|
std::make_unique<OfflineTransducerGreedySearchDecoder>(model_.get());
|
|
} else if (config_.decoding_method == "modified_beam_search") {
|
|
if (!config_.lm_config.model.empty()) {
|
|
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());
|
|
exit(-1);
|
|
}
|
|
}
|
|
|
|
#if __ANDROID_API__ >= 9
|
|
explicit OfflineRecognizerTransducerImpl(
|
|
AAssetManager *mgr, const OfflineRecognizerConfig &config)
|
|
: config_(config),
|
|
symbol_table_(mgr, config_.model_config.tokens),
|
|
model_(std::make_unique<OfflineTransducerModel>(mgr,
|
|
config_.model_config)) {
|
|
if (config_.decoding_method == "greedy_search") {
|
|
decoder_ =
|
|
std::make_unique<OfflineTransducerGreedySearchDecoder>(model_.get());
|
|
} else if (config_.decoding_method == "modified_beam_search") {
|
|
if (!config_.lm_config.model.empty()) {
|
|
lm_ = OfflineLM::Create(mgr, 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());
|
|
exit(-1);
|
|
}
|
|
}
|
|
#endif
|
|
|
|
std::unique_ptr<OfflineStream> CreateStream(
|
|
const std::string &hotwords) const override {
|
|
auto hws = std::regex_replace(hotwords, std::regex("/"), "\n");
|
|
std::istringstream is(hws);
|
|
std::vector<std::vector<int32_t>> current;
|
|
if (!EncodeHotwords(is, symbol_table_, ¤t)) {
|
|
SHERPA_ONNX_LOGE("Encode hotwords failed, skipping, hotwords are : %s",
|
|
hotwords.c_str());
|
|
}
|
|
current.insert(current.end(), hotwords_.begin(), hotwords_.end());
|
|
|
|
auto context_graph =
|
|
std::make_shared<ContextGraph>(current, config_.hotwords_score);
|
|
return std::make_unique<OfflineStream>(config_.feat_config, context_graph);
|
|
}
|
|
|
|
std::unique_ptr<OfflineStream> CreateStream() const override {
|
|
return std::make_unique<OfflineStream>(config_.feat_config,
|
|
hotwords_graph_);
|
|
}
|
|
|
|
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), ss, n);
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
void InitHotwords() {
|
|
// each line in hotwords_file contains space-separated words
|
|
|
|
std::ifstream is(config_.hotwords_file);
|
|
if (!is) {
|
|
SHERPA_ONNX_LOGE("Open hotwords file failed: %s",
|
|
config_.hotwords_file.c_str());
|
|
exit(-1);
|
|
}
|
|
|
|
if (!EncodeHotwords(is, symbol_table_, &hotwords_)) {
|
|
SHERPA_ONNX_LOGE("Encode hotwords failed.");
|
|
exit(-1);
|
|
}
|
|
hotwords_graph_ =
|
|
std::make_shared<ContextGraph>(hotwords_, config_.hotwords_score);
|
|
}
|
|
|
|
private:
|
|
OfflineRecognizerConfig config_;
|
|
SymbolTable symbol_table_;
|
|
std::vector<std::vector<int32_t>> hotwords_;
|
|
ContextGraphPtr hotwords_graph_;
|
|
std::unique_ptr<OfflineTransducerModel> model_;
|
|
std::unique_ptr<OfflineTransducerDecoder> decoder_;
|
|
std::unique_ptr<OfflineLM> lm_;
|
|
};
|
|
|
|
} // namespace sherpa_onnx
|
|
|
|
#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_
|