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.
55 lines
1.5 KiB
C++
55 lines
1.5 KiB
C++
// sherpa-onnx/csrc/offline-lm-config.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/offline-lm-config.h"
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#include <string>
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#include "sherpa-onnx/csrc/file-utils.h"
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#include "sherpa-onnx/csrc/macros.h"
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namespace sherpa_onnx {
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void OfflineLMConfig::Register(ParseOptions *po) {
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po->Register("lm", &model, "Path to LM model.");
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po->Register("lm-scale", &scale, "LM scale.");
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po->Register("lm-num-threads", &lm_num_threads,
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"Number of threads to run the neural network of LM model");
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po->Register("lm-provider", &lm_provider,
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"Specify a provider to LM model use: cpu, cuda, coreml");
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po->Register("lodr-fst", &lodr_fst, "Path to LODR FST model.");
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po->Register("lodr-scale", &lodr_scale, "LODR scale.");
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po->Register("lodr-backoff-id", &lodr_backoff_id,
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"ID of the backoff in the LODR FST. -1 means autodetect");
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}
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bool OfflineLMConfig::Validate() const {
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if (!FileExists(model)) {
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SHERPA_ONNX_LOGE("'%s' does not exist", model.c_str());
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return false;
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}
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if (!lodr_fst.empty() && !FileExists(lodr_fst)) {
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SHERPA_ONNX_LOGE("'%s' does not exist", lodr_fst.c_str());
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return false;
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}
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return true;
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}
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std::string OfflineLMConfig::ToString() const {
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std::ostringstream os;
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os << "OfflineLMConfig(";
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os << "model=\"" << model << "\", ";
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os << "scale=" << scale << ", ";
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os << "lodr_scale=" << lodr_scale << ", ";
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os << "lodr_fst=\"" << lodr_fst << "\", ";
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os << "lodr_backoff_id=" << lodr_backoff_id << ")";
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return os.str();
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}
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} // namespace sherpa_onnx
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