ivector-extractor-acc-stats.cc

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// ivectorbin/ivector-extractor-acc-stats.cc

// Copyright 2013  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.


#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "gmm/am-diag-gmm.h"
#include "ivector/ivector-extractor.h"
#include "util/kaldi-thread.h"


namespace kaldi {

// this class is used to run the command
//  stats.AccStatsForUtterance(extractor, mat, posterior);
// in parallel.
class IvectorTask {
 public:
  IvectorTask(const IvectorExtractor &extractor,
              const Matrix<BaseFloat> &features,
              const Posterior &posterior,
              IvectorExtractorStats *stats): extractor_(extractor),
                                    features_(features),
                                    posterior_(posterior),
                                    stats_(stats) { }

  void operator () () {
    stats_->AccStatsForUtterance(extractor_, features_, posterior_);
  }
  ~IvectorTask() { }  // the destructor doesn't have to do anything.
 private:
  const IvectorExtractor &extractor_;
  Matrix<BaseFloat> features_; // not a reference, since features come from a
                               // Table and the reference we get from that is
                               // not valid long-term.
  Posterior posterior_;  // as above.
  IvectorExtractorStats *stats_;
};



}



int main(int argc, char *argv[]) {
  using namespace kaldi;
  typedef kaldi::int32 int32;
  typedef kaldi::int64 int64;
  try {
    const char *usage =
        "Accumulate stats for iVector extractor training\n"
        "Reads in features and Gaussian-level posteriors (typically from a full GMM)\n"
        "Supports multiple threads, but won't be able to make use of too many at a time\n"
        "(e.g. more than about 4)\n"
        "Usage:  ivector-extractor-acc-stats [options] <model-in> <feature-rspecifier>"
        "<posteriors-rspecifier> <stats-out>\n"
        "e.g.: \n"
        " fgmm-global-gselect-to-post 1.fgmm '$feats' 'ark:gunzip -c gselect.1.gz|' ark:- | \\\n"
        "  ivector-extractor-acc-stats 2.ie '$feats' ark,s,cs:- 2.1.acc\n";

    ParseOptions po(usage);
    bool binary = true;
    IvectorExtractorStatsOptions stats_opts;
    TaskSequencerConfig sequencer_opts;
    po.Register("binary", &binary, "Write output in binary mode");
    stats_opts.Register(&po);
    sequencer_opts.Register(&po);

    po.Read(argc, argv);

    if (po.NumArgs() != 4) {
      po.PrintUsage();
      exit(1);
    }

    std::string ivector_extractor_rxfilename = po.GetArg(1),
        feature_rspecifier = po.GetArg(2),
        posteriors_rspecifier = po.GetArg(3),
        accs_wxfilename = po.GetArg(4);


    // Initialize these Reader objects before reading the IvectorExtractor,
    // because it uses up a lot of memory and any fork() after that will
    // be in danger of causing an allocation failure.
    SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
    RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);


    // This is a bit of a mess... the code that reads in the extractor calls
    // ComputeDerivedVars, and it can do this multi-threaded, controlled by
    // g_num_threads.  So if the user specified the --num-threads option, which
    // goes to sequencer_opts in this case, copy it to g_num_threads.
    g_num_threads = sequencer_opts.num_threads;

    IvectorExtractor extractor;
    ReadKaldiObject(ivector_extractor_rxfilename, &extractor);

    IvectorExtractorStats stats(extractor, stats_opts);


    int64 tot_t = 0;
    int32 num_done = 0, num_err = 0;

    {
      TaskSequencer<IvectorTask> sequencer(sequencer_opts);

      for (; !feature_reader.Done(); feature_reader.Next()) {
        std::string key = feature_reader.Key();
        if (!posteriors_reader.HasKey(key)) {
          KALDI_WARN << "No posteriors for utterance " << key;
          num_err++;
          continue;
        }
        const Matrix<BaseFloat> &mat = feature_reader.Value();
        const Posterior &posterior = posteriors_reader.Value(key);

        if (static_cast<int32>(posterior.size()) != mat.NumRows()) {
          KALDI_WARN << "Size mismatch between posterior " << (posterior.size())
                     << " and features " << (mat.NumRows()) << " for utterance "
                     << key;
          num_err++;
          continue;
        }

        sequencer.Run(new IvectorTask(extractor, mat, posterior, &stats));

        tot_t += posterior.size();
        num_done++;
      }
      // destructor of "sequencer" will wait for any remaining tasks that
      // have not yet completed.
    }

    KALDI_LOG << "Done " << num_done << " files, " << num_err
              << " with errors.  Total frames " << tot_t;

    {
      Output ko(accs_wxfilename, binary);
      stats.Write(ko.Stream(), binary);
    }

    KALDI_LOG << "Wrote stats to " << accs_wxfilename;

    return (num_done != 0 ? 0 : 1);
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}