offline-nemo-enc-dec-ctc-model.cc
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// sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.h"
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#include "sherpa-onnx/csrc/text-utils.h"
#include "sherpa-onnx/csrc/transpose.h"
namespace sherpa_onnx {
class OfflineNemoEncDecCtcModel::Impl {
public:
explicit Impl(const OfflineModelConfig &config)
: config_(config),
env_(ORT_LOGGING_LEVEL_ERROR),
sess_opts_{},
allocator_{} {
sess_opts_.SetIntraOpNumThreads(config_.num_threads);
sess_opts_.SetInterOpNumThreads(config_.num_threads);
Init();
}
std::pair<Ort::Value, Ort::Value> Forward(Ort::Value features,
Ort::Value features_length) {
std::vector<int64_t> shape =
features_length.GetTensorTypeAndShapeInfo().GetShape();
Ort::Value out_features_length = Ort::Value::CreateTensor<int64_t>(
allocator_, shape.data(), shape.size());
const int64_t *src = features_length.GetTensorData<int64_t>();
int64_t *dst = out_features_length.GetTensorMutableData<int64_t>();
for (int64_t i = 0; i != shape[0]; ++i) {
dst[i] = src[i] / subsampling_factor_;
}
// (B, T, C) -> (B, C, T)
features = Transpose12(allocator_, &features);
std::array<Ort::Value, 2> inputs = {std::move(features),
std::move(features_length)};
auto out =
sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
output_names_ptr_.data(), output_names_ptr_.size());
return {std::move(out[0]), std::move(out_features_length)};
}
int32_t VocabSize() const { return vocab_size_; }
int32_t SubsamplingFactor() const { return subsampling_factor_; }
OrtAllocator *Allocator() const { return allocator_; }
std::string FeatureNormalizationMethod() const { return normalize_type_; }
private:
void Init() {
auto buf = ReadFile(config_.nemo_ctc.model);
sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
sess_opts_);
GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
PrintModelMetadata(os, meta_data);
SHERPA_ONNX_LOGE("%s\n", os.str().c_str());
}
Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
SHERPA_ONNX_READ_META_DATA(subsampling_factor_, "subsampling_factor");
SHERPA_ONNX_READ_META_DATA_STR(normalize_type_, "normalize_type");
}
private:
OfflineModelConfig config_;
Ort::Env env_;
Ort::SessionOptions sess_opts_;
Ort::AllocatorWithDefaultOptions allocator_;
std::unique_ptr<Ort::Session> sess_;
std::vector<std::string> input_names_;
std::vector<const char *> input_names_ptr_;
std::vector<std::string> output_names_;
std::vector<const char *> output_names_ptr_;
int32_t vocab_size_ = 0;
int32_t subsampling_factor_ = 0;
std::string normalize_type_;
};
OfflineNemoEncDecCtcModel::OfflineNemoEncDecCtcModel(
const OfflineModelConfig &config)
: impl_(std::make_unique<Impl>(config)) {}
OfflineNemoEncDecCtcModel::~OfflineNemoEncDecCtcModel() = default;
std::pair<Ort::Value, Ort::Value> OfflineNemoEncDecCtcModel::Forward(
Ort::Value features, Ort::Value features_length) {
return impl_->Forward(std::move(features), std::move(features_length));
}
int32_t OfflineNemoEncDecCtcModel::VocabSize() const {
return impl_->VocabSize();
}
int32_t OfflineNemoEncDecCtcModel::SubsamplingFactor() const {
return impl_->SubsamplingFactor();
}
OrtAllocator *OfflineNemoEncDecCtcModel::Allocator() const {
return impl_->Allocator();
}
std::string OfflineNemoEncDecCtcModel::FeatureNormalizationMethod() const {
return impl_->FeatureNormalizationMethod();
}
} // namespace sherpa_onnx