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