nnet-compute-discriminative.h

Go to the documentation of this file.

// nnet2/nnet-compute-discriminative.h

// Copyright 2012-2013  Johns Hopkins University (author: Daniel Povey)

// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//  http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.

#ifndef KALDI_NNET2_NNET_COMPUTE_DISCRIMINATIVE_H_
#define KALDI_NNET2_NNET_COMPUTE_DISCRIMINATIVE_H_

#include "nnet2/am-nnet.h"
#include "nnet2/nnet-example.h"
#include "hmm/transition-model.h"

namespace kaldi {
namespace nnet2 {

/* This header provides functionality for doing model updates, and computing
   gradients, using discriminative objective functions (MPFE, SMBR, MMI).
   We use the DiscriminativeNnetExample defined in nnet-example.h.
*/

struct NnetDiscriminativeUpdateOptions {
  std::string criterion; // "mmi" or "mpfe" or "smbr"
  BaseFloat acoustic_scale; // e.g. 0.1
  bool drop_frames; // for MMI, true if we ignore frames where alignment
                    // pdf-id is not in the lattice.
  bool one_silence_class;  // Affects MPE/SMBR>
  BaseFloat boost; // for MMI, boosting factor (would be Boosted MMI)... e.g. 0.1.

  std::string silence_phones_str; // colon-separated list of integer ids of silence phones,
                                  // for MPE/SMBR only.

  NnetDiscriminativeUpdateOptions(): criterion("smbr"), acoustic_scale(0.1),
                                     drop_frames(false),
                                     one_silence_class(false),
                                     boost(0.0) { }
  
  void Register(OptionsItf *opts) {
    opts->Register("criterion", &criterion, "Criterion, 'mmi'|'mpfe'|'smbr', "
                   "determines the objective function to use.  Should match "
                   "option used when we created the examples.");
    opts->Register("acoustic-scale", &acoustic_scale, "Weighting factor to "
                   "apply to acoustic likelihoods.");
    opts->Register("drop-frames", &drop_frames, "For MMI, if true we drop frames "
                   "with no overlap of num and den frames");
    opts->Register("boost", &boost, "Boosting factor for boosted MMI (e.g. 0.1)");
    opts->Register("one-silence-class", &one_silence_class, "If true, newer "
                   "behavior which will tend to reduce insertions.");
    opts->Register("silence-phones", &silence_phones_str,
                   "For MPFE or SMBR, colon-separated list of integer ids of "
                   "silence phones, e.g. 1:2:3");
    
  }
};


struct NnetDiscriminativeStats {
  double tot_t; // total number of frames
  double tot_t_weighted; // total number of frames times weight.
  double tot_num_count; // total count of numerator posterior (should be
                        // identical to denominator-posterior count, so we don't
                        // separately compute that).
  double tot_num_objf;  // for MMI, the (weighted) numerator likelihood; for
                        // SMBR/MPFE, 0.
  double tot_den_objf;  // for MMI, the (weighted) denominator likelihood; for
                        // SMBR/MPFE, the objective function.
  NnetDiscriminativeStats() { std::memset(this, 0, sizeof(*this)); }
  void Print(std::string criterion); // const NnetDiscriminativeUpdateOptions &opts);
  void Add(const NnetDiscriminativeStats &other);
};

void NnetDiscriminativeUpdate(const AmNnet &am_nnet,
                              const TransitionModel &tmodel,
                              const NnetDiscriminativeUpdateOptions &opts,
                              const DiscriminativeNnetExample &eg,
                              Nnet *nnet_to_update,
                              NnetDiscriminativeStats *stats);


} // namespace nnet2
} // namespace kaldi

#endif // KALDI_NNET2_NNET_COMPUTE_DISCRIMINATIVE_H_