65 lines
2.0 KiB
C++
65 lines
2.0 KiB
C++
// sherpa-onnx/csrc/online-lm.h
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_ONLINE_LM_H_
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#define SHERPA_ONNX_CSRC_ONLINE_LM_H_
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#include <memory>
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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/hypothesis.h"
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#include "sherpa-onnx/csrc/online-lm-config.h"
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namespace sherpa_onnx {
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class OnlineLM {
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public:
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virtual ~OnlineLM() = default;
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static std::unique_ptr<OnlineLM> Create(const OnlineLMConfig &config);
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virtual std::vector<Ort::Value> GetInitStates() = 0;
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/** Rescore a batch of sentences.
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*
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* @param x A 2-D tensor of shape (N, L) with data type int64.
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* @param y A 2-D tensor of shape (N, L) with data type int64.
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* @param states It contains the states for the LM model
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* @return Return a pair containingo
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* - negative loglike
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* - updated states
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*
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* Caution: It returns negative log likelihood (nll), not log likelihood
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*/
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std::pair<Ort::Value, std::vector<Ort::Value>> Ort::Value Rescore(
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Ort::Value x, Ort::Value y, std::vector<Ort::Value> states) = 0;
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// This function updates hyp.lm_lob_prob of hyps.
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//
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// @param scale LM score
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// @param context_size Context size of the transducer decoder model
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// @param hyps It is changed in-place.
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void ComputeLMScore(float scale, int32_t context_size,
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std::vector<Hypotheses> *hyps);
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/** TODO(fangjun):
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*
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* 1. Add two fields to Hypothesis
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* (a) int32_t lm_cur_pos = 0; number of scored tokens so far
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* (b) std::vector<Ort::Value> lm_states;
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* 2. When we want to score a hypothesis, we construct x and y as follows:
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*
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* std::vector x = {hyp.ys.begin() + context_size + lm_cur_pos,
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* hyp.ys.end() - 1};
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* std::vector y = {hyp.ys.begin() + context_size + lm_cur_pos + 1
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* hyp.ys.end()};
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* hyp.lm_cur_pos += hyp.ys.size() - context_size - lm_cur_pos;
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*/
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_ONLINE_LM_H_
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