Initial commit: FunASR Speech Recognition Toolkit
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Add complete FunASR codebase including models, runtime, and documentation.
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// lm/mikolov-rnnlm-lib.h
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// Copyright 2015 Guoguo Chen Hainan Xu
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// 2010-2012 Tomas Mikolov
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// See ../../COPYING for clarification regarding multiple authors
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//
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// This file is based on version 0.3e of the RNNLM language modeling
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// toolkit by Tomas Mikolov. Changes made by authors other than
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// Tomas Mikolov are licensed under the Apache License, the short form
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// os which is below. The original code by Tomas Mikolov is licensed
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// under the BSD 3-clause license, whose text is further below.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
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// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
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// MERCHANTABLITY OR NON-INFRINGEMENT.
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// See the Apache 2 License for the specific language governing permissions and
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// limitations under the License.
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//
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//
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// Original BSD 3-clause license text:
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// Copyright (c) 2010-2012 Tomas Mikolov
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//
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// All rights reserved. Redistribution and use in source and binary forms, with
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// or without modification, are permitted provided that the following conditions
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// are met: 1. Redistributions of source code must retain the above copyright
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// notice, this list of conditions and the following
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// disclaimer. 2. Redistributions in binary form must reproduce the above
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// copyright notice, this list of conditions and the following disclaimer in the
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// documentation and/or other materials provided with the
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// distribution. 3. Neither name of copyright holders nor the names of its
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// contributors may be used to endorse or promote products derived from this
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// software without specific prior written permission. THIS SOFTWARE IS PROVIDED
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// BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR
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// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
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// EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
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// OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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// EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#ifndef KALDI_LM_MIKOLOV_RNNLM_LIB_H_
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#define KALDI_LM_MIKOLOV_RNNLM_LIB_H_
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#include <string>
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#include <vector>
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#include "util/stl-utils.h"
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namespace rnnlm {
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#define MAX_STRING 100
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#define MAX_FILENAME_STRING 300
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typedef double real; // doubles for NN weights
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typedef double direct_t; // doubles for ME weights;
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struct neuron {
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real ac; // actual value stored in neuron
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real er; // error value in neuron, used by learning algorithm
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};
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struct synapse {
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real weight; // weight of synapse
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};
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struct vocab_word {
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int cn;
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char word[MAX_STRING];
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real prob;
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int class_index;
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};
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const unsigned int PRIMES[] = {108641969, 116049371, 125925907, 133333309,
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145678979, 175308587, 197530793, 234567803, 251851741, 264197411,
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330864029, 399999781,
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407407183, 459258997, 479012069, 545678687, 560493491, 607407037, 629629243,
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656789717, 716048933, 718518067, 725925469, 733332871, 753085943, 755555077,
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782715551, 790122953, 812345159, 814814293, 893826581, 923456189, 940740127,
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953085797, 985184539, 990122807};
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const unsigned int PRIMES_SIZE = sizeof(PRIMES) / sizeof(PRIMES[0]);
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const int MAX_NGRAM_ORDER = 20;
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enum FileTypeEnum {TEXT, BINARY, COMPRESSED}; // COMPRESSED not yet implemented
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class CRnnLM {
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protected:
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char train_file[MAX_FILENAME_STRING];
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char valid_file[MAX_FILENAME_STRING];
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char test_file[MAX_FILENAME_STRING];
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char rnnlm_file[MAX_FILENAME_STRING];
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char lmprob_file[MAX_FILENAME_STRING];
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int rand_seed;
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int version;
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int filetype;
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int use_lmprob;
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real gradient_cutoff;
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real dynamic;
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real alpha;
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real starting_alpha;
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int alpha_divide;
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double logp, llogp;
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float min_improvement;
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int iter;
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int vocab_max_size;
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int vocab_size;
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int train_words;
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int train_cur_pos;
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int counter;
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int anti_k;
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real beta;
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int class_size;
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int **class_words;
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int *class_cn;
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int *class_max_cn;
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int old_classes;
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struct vocab_word *vocab;
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void sortVocab();
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int *vocab_hash;
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int vocab_hash_size;
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int layer0_size;
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int layer1_size;
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int layerc_size;
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int layer2_size;
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long long direct_size;
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int direct_order;
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int history[MAX_NGRAM_ORDER];
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int bptt;
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int bptt_block;
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int *bptt_history;
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neuron *bptt_hidden;
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struct synapse *bptt_syn0;
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int gen;
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int independent;
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struct neuron *neu0; // neurons in input layer
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struct neuron *neu1; // neurons in hidden layer
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struct neuron *neuc; // neurons in hidden layer
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struct neuron *neu2; // neurons in output layer
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struct synapse *syn0; // weights between input and hidden layer
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struct synapse *syn1; // weights between hidden and output layer
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// (or hidden and compression if compression>0)
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struct synapse *sync; // weights between hidden and compression layer
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direct_t *syn_d; // direct parameters between input and output layer
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// (similar to Maximum Entropy model parameters)
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// backup used in training:
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struct neuron *neu0b;
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struct neuron *neu1b;
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struct neuron *neucb;
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struct neuron *neu2b;
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struct synapse *syn0b;
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struct synapse *syn1b;
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struct synapse *syncb;
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direct_t *syn_db;
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// backup used in n-bset rescoring:
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struct neuron *neu1b2;
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unordered_map<std::string, float> unk_penalty;
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std::string unk_sym;
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public:
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int alpha_set, train_file_set;
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CRnnLM();
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~CRnnLM();
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real random(real min, real max);
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void setRnnLMFile(const std::string &str);
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int getHiddenLayerSize() const { return layer1_size; }
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void setRandSeed(int newSeed);
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int getWordHash(const char *word);
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void readWord(char *word, FILE *fin);
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int searchVocab(const char *word);
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void saveWeights(); // saves current weights and unit activations
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void initNet();
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void goToDelimiter(int delim, FILE *fi);
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void restoreNet();
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void netReset(); // will erase just hidden layer state + bptt history
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// + maxent history (called at end of sentences in
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// the independent mode)
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void computeNet(int last_word, int word);
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void copyHiddenLayerToInput();
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void matrixXvector(struct neuron *dest, struct neuron *srcvec,
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struct synapse *srcmatrix, int matrix_width,
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int from, int to, int from2, int to2, int type);
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void restoreContextFromVector(const std::vector<float> &context_in);
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void saveContextToVector(std::vector<float> *context_out);
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float computeConditionalLogprob(
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std::string current_word,
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const std::vector<std::string> &history_words,
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const std::vector<float> &context_in,
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std::vector<float> *context_out);
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void setUnkSym(const std::string &unk);
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void setUnkPenalty(const std::string &filename);
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float getUnkPenalty(const std::string &word);
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bool isUnk(const std::string &word);
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};
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} // namespace rnnlm
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#endif // KALDI_LM_MIKOLOV_RNNLM_LIB_H_
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