135 lines
4.1 KiB
C++
135 lines
4.1 KiB
C++
// sherpa-onnx/csrc/offline-recognizer.h
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_H_
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#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_H_
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#include <memory>
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#include <string>
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#include <vector>
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#include "sherpa-onnx/csrc/features.h"
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#include "sherpa-onnx/csrc/homophone-replacer.h"
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#include "sherpa-onnx/csrc/offline-ctc-fst-decoder-config.h"
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#include "sherpa-onnx/csrc/offline-lm-config.h"
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#include "sherpa-onnx/csrc/offline-model-config.h"
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#include "sherpa-onnx/csrc/offline-stream.h"
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#include "sherpa-onnx/csrc/offline-transducer-model-config.h"
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#include "sherpa-onnx/csrc/parse-options.h"
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namespace sherpa_onnx {
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struct OfflineRecognitionResult;
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struct OfflineRecognizerConfig {
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FeatureExtractorConfig feat_config;
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OfflineModelConfig model_config;
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OfflineLMConfig lm_config;
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OfflineCtcFstDecoderConfig ctc_fst_decoder_config;
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std::string decoding_method = "greedy_search";
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int32_t max_active_paths = 4;
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std::string hotwords_file;
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float hotwords_score = 1.5;
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float blank_penalty = 0.0;
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// If there are multiple rules, they are applied from left to right.
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std::string rule_fsts;
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// If there are multiple FST archives, they are applied from left to right.
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std::string rule_fars;
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HomophoneReplacerConfig hr;
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// only greedy_search is implemented
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// TODO(fangjun): Implement modified_beam_search
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OfflineRecognizerConfig() = default;
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OfflineRecognizerConfig(
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const FeatureExtractorConfig &feat_config,
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const OfflineModelConfig &model_config, const OfflineLMConfig &lm_config,
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const OfflineCtcFstDecoderConfig &ctc_fst_decoder_config,
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const std::string &decoding_method, int32_t max_active_paths,
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const std::string &hotwords_file, float hotwords_score,
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float blank_penalty, const std::string &rule_fsts,
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const std::string &rule_fars, const HomophoneReplacerConfig &hr)
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: feat_config(feat_config),
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model_config(model_config),
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lm_config(lm_config),
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ctc_fst_decoder_config(ctc_fst_decoder_config),
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decoding_method(decoding_method),
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max_active_paths(max_active_paths),
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hotwords_file(hotwords_file),
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hotwords_score(hotwords_score),
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blank_penalty(blank_penalty),
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rule_fsts(rule_fsts),
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rule_fars(rule_fars),
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hr(hr) {}
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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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class OfflineRecognizerImpl;
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class OfflineRecognizer {
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public:
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~OfflineRecognizer();
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template <typename Manager>
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OfflineRecognizer(Manager *mgr, const OfflineRecognizerConfig &config);
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explicit OfflineRecognizer(const OfflineRecognizerConfig &config);
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/// Create a stream for decoding.
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std::unique_ptr<OfflineStream> CreateStream() const;
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/** Create a stream for decoding.
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*
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* @param The hotwords for this string, it might contain several hotwords,
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* the hotwords are separated by "/". In each of the hotwords, there
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* are cjkchars or bpes, the bpe/cjkchar are separated by space (" ").
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* For example, hotwords I LOVE YOU and HELLO WORLD, looks like:
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*
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* "▁I ▁LOVE ▁YOU/▁HE LL O ▁WORLD"
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*/
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std::unique_ptr<OfflineStream> CreateStream(
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const std::string &hotwords) const;
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/** Decode a single stream
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*
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* @param s The stream to decode.
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*/
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void DecodeStream(OfflineStream *s) const {
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OfflineStream *ss[1] = {s};
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DecodeStreams(ss, 1);
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}
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/** Decode a list of streams.
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*
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* @param ss Pointer to an array of streams.
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* @param n Size of the input array.
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*/
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void DecodeStreams(OfflineStream **ss, int32_t n) const;
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/** Onnxruntime Session objects are not affected by this method.
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* The exact behavior can be defined by a specific recognizer impl.
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* For instance, for the whisper recognizer, you can retrieve the language and
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* task from the config and ignore any remaining fields in `config`.
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*/
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void SetConfig(const OfflineRecognizerConfig &config);
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OfflineRecognizerConfig GetConfig() const;
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private:
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std::unique_ptr<OfflineRecognizerImpl> impl_;
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_H_
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