offline-recognizer-transducer-nemo-impl.h
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// sherpa-onnx/csrc/offline-recognizer-transducer-nemo-impl.h
//
// Copyright (c) 2022-2024 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_NEMO_IMPL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_NEMO_IMPL_H_
#include <fstream>
#include <ios>
#include <memory>
#include <regex> // NOLINT
#include <sstream>
#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/macros.h"
#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
#include "sherpa-onnx/csrc/offline-recognizer.h"
#include "sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.h"
#include "sherpa-onnx/csrc/offline-transducer-nemo-model.h"
#include "sherpa-onnx/csrc/pad-sequence.h"
#include "sherpa-onnx/csrc/symbol-table.h"
#include "sherpa-onnx/csrc/transpose.h"
#include "sherpa-onnx/csrc/utils.h"
namespace sherpa_onnx {
// defined in ./offline-recognizer-transducer-impl.h
OfflineRecognitionResult Convert(const OfflineTransducerDecoderResult &src,
const SymbolTable &sym_table,
int32_t frame_shift_ms,
int32_t subsampling_factor);
class OfflineRecognizerTransducerNeMoImpl : public OfflineRecognizerImpl {
public:
explicit OfflineRecognizerTransducerNeMoImpl(
const OfflineRecognizerConfig &config)
: config_(config),
symbol_table_(config_.model_config.tokens),
model_(std::make_unique<OfflineTransducerNeMoModel>(
config_.model_config)) {
if (config_.decoding_method == "greedy_search") {
decoder_ = std::make_unique<OfflineTransducerGreedySearchNeMoDecoder>(
model_.get(), config_.blank_penalty);
} else {
SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
config_.decoding_method.c_str());
exit(-1);
}
PostInit();
}
#if __ANDROID_API__ >= 9
explicit OfflineRecognizerTransducerNeMoImpl(
AAssetManager *mgr, const OfflineRecognizerConfig &config)
: config_(config),
symbol_table_(mgr, config_.model_config.tokens),
model_(std::make_unique<OfflineTransducerNeMoModel>(
mgr, config_.model_config)) {
if (config_.decoding_method == "greedy_search") {
decoder_ = std::make_unique<OfflineTransducerGreedySearchNeMoDecoder>(
model_.get(), config_.blank_penalty);
} else {
SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
config_.decoding_method.c_str());
exit(-1);
}
PostInit();
}
#endif
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, 0);
auto t = model_->RunEncoder(std::move(x), std::move(x_length));
// t[0] encoder_out, float tensor, (batch_size, dim, T)
// t[1] encoder_out_length, int64 tensor, (batch_size,)
Ort::Value encoder_out = Transpose12(model_->Allocator(), &t[0]);
auto results = decoder_->Decode(std::move(encoder_out), std::move(t[1]));
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:
void PostInit() {
config_.feat_config.nemo_normalize_type =
model_->FeatureNormalizationMethod();
config_.feat_config.low_freq = 0;
// config_.feat_config.high_freq = 8000;
config_.feat_config.is_librosa = true;
config_.feat_config.remove_dc_offset = false;
// config_.feat_config.window_type = "hann";
config_.feat_config.dither = 0;
config_.feat_config.nemo_normalize_type =
model_->FeatureNormalizationMethod();
int32_t vocab_size = model_->VocabSize();
// check the blank ID
if (!symbol_table_.Contains("<blk>")) {
SHERPA_ONNX_LOGE("tokens.txt does not include the blank token <blk>");
exit(-1);
}
if (symbol_table_["<blk>"] != vocab_size - 1) {
SHERPA_ONNX_LOGE("<blk> is not the last token!");
exit(-1);
}
if (symbol_table_.NumSymbols() != vocab_size) {
SHERPA_ONNX_LOGE("number of lines in tokens.txt %d != %d (vocab_size)",
symbol_table_.NumSymbols(), vocab_size);
exit(-1);
}
}
private:
OfflineRecognizerConfig config_;
SymbolTable symbol_table_;
std::unique_ptr<OfflineTransducerNeMoModel> model_;
std::unique_ptr<OfflineTransducerDecoder> decoder_;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_NEMO_IMPL_H_