f0960342ad
This PR integrates LODR (Level-Ordered Deterministic Rescoring) support from Icefall into both online and offline recognizers, enabling LODR for LM shallow fusion and LM rescore. - Extended OnlineLMConfig and OfflineLMConfig to include lodr_fst, lodr_scale, and lodr_backoff_id. - Implemented LodrFst and LodrStateCost classes and wired them into RNN LM scoring in both online and offline code paths. - Updated Python bindings, CLI entry points, examples, and CI test scripts to accept and exercise the new LODR options.
51 lines
1.3 KiB
C++
51 lines
1.3 KiB
C++
// sherpa-onnx/csrc/online-lm-config.h
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_ONLINE_LM_CONFIG_H_
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#define SHERPA_ONNX_CSRC_ONLINE_LM_CONFIG_H_
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#include <string>
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#include "sherpa-onnx/csrc/parse-options.h"
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namespace sherpa_onnx {
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struct OnlineLMConfig {
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// path to the onnx model
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std::string model;
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// LM scale
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float scale = 0.5;
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int32_t lm_num_threads = 1;
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std::string lm_provider = "cpu";
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std::string lodr_fst;
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float lodr_scale = 0.01;
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int32_t lodr_backoff_id = -1; // -1 means not set
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// enable shallow fusion
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bool shallow_fusion = true;
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OnlineLMConfig() = default;
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OnlineLMConfig(const std::string &model, float scale, int32_t lm_num_threads,
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const std::string &lm_provider, bool shallow_fusion,
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const std::string &lodr_fst, float lodr_scale,
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int32_t lodr_backoff_id)
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: model(model),
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scale(scale),
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lm_num_threads(lm_num_threads),
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lm_provider(lm_provider),
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shallow_fusion(shallow_fusion),
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lodr_fst(lodr_fst),
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lodr_scale(lodr_scale),
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lodr_backoff_id(lodr_backoff_id) {}
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void Register(ParseOptions *po);
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bool Validate() const;
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std::string ToString() const;
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_ONLINE_LM_CONFIG_H_
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