1eb155ed93
* enable hot-word boosting * more consistent ordering of CLI arguments * progress on review * use map instead of set for hot-words, move string logic to client.cc * typo bug * pointer things? * use map for hotwords, better string splitting * add the boost, not multiply * cleaning up * cleaning whitespace * remove <set> inclusion * change typo set-->map * rename boost_coefficient to boost X-DeepSpeech: NOBUILD * add hot_words to python bindings * missing hot_words * include map in swigwrapper.i * add Map template to swigwrapper.i * emacs intermediate file * map things * map-->unordered_map * typu * typu * use dict() not None * error out if hot_words without scorer * two new functions: remove hot-word and clear all hot-words * starting to work on better error messages X-DeepSpeech: NOBUILD * better error handling + .Net ERR codes * allow for negative boosts:) * adding TC test for hot-words * add hot-words to python client, make TC test hot-words everywhere * only run TC tests for C++ and Python * fully expose API in python bindings * expose API in Java (thanks spectie!) * expose API in dotnet (thanks spectie!) * expose API in javascript (thanks spectie!) * java lol * typo in javascript * commenting * java error codes from swig * java docs from SWIG * java and dotnet issues * add hotword test to android tests * dotnet fixes from carlos * add DS_BINARY_PREFIX to tc-asserts.sh for hotwords command * make sure lm is on android for hotword test * path to android model + nit * path * path
81 lines
2.4 KiB
C++
81 lines
2.4 KiB
C++
#ifndef MODELSTATE_H
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#define MODELSTATE_H
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#include <vector>
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#include "deepspeech.h"
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#include "alphabet.h"
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#include "ctcdecode/scorer.h"
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#include "ctcdecode/output.h"
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class DecoderState;
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struct ModelState {
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//TODO: infer batch size from model/use dynamic batch size
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static constexpr unsigned int BATCH_SIZE = 1;
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Alphabet alphabet_;
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std::shared_ptr<Scorer> scorer_;
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std::unordered_map<std::string, float> hot_words_;
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unsigned int beam_width_;
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unsigned int n_steps_;
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unsigned int n_context_;
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unsigned int n_features_;
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unsigned int mfcc_feats_per_timestep_;
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unsigned int sample_rate_;
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unsigned int audio_win_len_;
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unsigned int audio_win_step_;
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unsigned int state_size_;
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ModelState();
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virtual ~ModelState();
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virtual int init(const char* model_path);
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virtual void compute_mfcc(const std::vector<float>& audio_buffer, std::vector<float>& mfcc_output) = 0;
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/**
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* @brief Do a single inference step in the acoustic model, with:
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* input=mfcc
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* input_lengths=[n_frames]
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*
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* @param mfcc batch input data
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* @param n_frames number of timesteps in the data
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*
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* @param[out] output_logits Where to store computed logits.
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*/
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virtual void infer(const std::vector<float>& mfcc,
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unsigned int n_frames,
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const std::vector<float>& previous_state_c,
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const std::vector<float>& previous_state_h,
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std::vector<float>& logits_output,
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std::vector<float>& state_c_output,
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std::vector<float>& state_h_output) = 0;
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/**
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* @brief Perform decoding of the logits, using basic CTC decoder or
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* CTC decoder with KenLM enabled
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*
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* @param state Decoder state to use when decoding.
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*
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* @return String representing the decoded text.
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*/
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virtual char* decode(const DecoderState& state) const;
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/**
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* @brief Return character-level metadata including letter timings.
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*
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* @param state Decoder state to use when decoding.
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* @param num_results Maximum number of candidate results to return.
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*
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* @return A Metadata struct containing CandidateTranscript structs.
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* Each represents an candidate transcript, with the first ranked most probable.
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* The user is responsible for freeing Result by calling DS_FreeMetadata().
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*/
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virtual Metadata* decode_metadata(const DecoderState& state,
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size_t num_results);
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};
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#endif // MODELSTATE_H
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