1a1b9fd236
* rnnlm model inference supports num_threads setting * rnnlm params decouple num_thread and provider with Transducer. * fix python csrc bug which offline-lm-config.cc and online-lm-config.cc arguments problem * lm_num_threads and lm_provider set default values --------- Co-authored-by: cuidongcai1035 <cuidongcai1035@wezhuiyi.com>
76 lines
2.0 KiB
C++
76 lines
2.0 KiB
C++
// sherpa-onnx/csrc/offline-rnn-lm.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/offline-rnn-lm.h"
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#include <string>
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#include <utility>
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#include <vector>
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#include "onnxruntime_cxx_api.h" // NOLINT
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#include "sherpa-onnx/csrc/macros.h"
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#include "sherpa-onnx/csrc/onnx-utils.h"
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#include "sherpa-onnx/csrc/text-utils.h"
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#include "sherpa-onnx/csrc/session.h"
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namespace sherpa_onnx {
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class OfflineRnnLM::Impl {
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public:
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explicit Impl(const OfflineLMConfig &config)
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: config_(config),
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env_(ORT_LOGGING_LEVEL_ERROR),
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sess_opts_{GetSessionOptions(config)},
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allocator_{} {
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Init(config);
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}
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Ort::Value Rescore(Ort::Value x, Ort::Value x_lens) {
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std::array<Ort::Value, 2> inputs = {std::move(x), std::move(x_lens)};
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auto out =
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sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
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output_names_ptr_.data(), output_names_ptr_.size());
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return std::move(out[0]);
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}
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private:
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void Init(const OfflineLMConfig &config) {
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auto buf = ReadFile(config_.model);
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sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
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sess_opts_);
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GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
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GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
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}
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private:
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OfflineLMConfig config_;
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Ort::Env env_;
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Ort::SessionOptions sess_opts_;
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Ort::AllocatorWithDefaultOptions allocator_;
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std::unique_ptr<Ort::Session> sess_;
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std::vector<std::string> input_names_;
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std::vector<const char *> input_names_ptr_;
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std::vector<std::string> output_names_;
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std::vector<const char *> output_names_ptr_;
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};
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OfflineRnnLM::OfflineRnnLM(const OfflineLMConfig &config)
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: impl_(std::make_unique<Impl>(config)) {}
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OfflineRnnLM::~OfflineRnnLM() = default;
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Ort::Value OfflineRnnLM::Rescore(Ort::Value x, Ort::Value x_lens) {
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return impl_->Rescore(std::move(x), std::move(x_lens));
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}
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} // namespace sherpa_onnx
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