nnet3-compute-from-egs.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "hmm/transition-model.h"
#include "nnet3/nnet-nnet.h"
#include "nnet3/nnet-example-utils.h"
#include "nnet3/nnet-optimize.h"
#include "transform/lda-estimate.h"

Include dependency graph for nnet3-compute-from-egs.cc:

Go to the source code of this file.

Classes
class	NnetComputerFromEg

Namespaces
	kaldi
	This code computes Goodness of Pronunciation (GOP) and extracts phone-level pronunciation feature for mispronunciations detection tasks, the reference:
	kaldi::nnet3

Functions
int	main (int argc, char *argv[])

Function Documentation

main()

int main	(	int	argc,
		char *	argv[]
	)

Definition at line 66 of file nnet3-compute-from-egs.cc.

References MatrixBase< Real >::ApplyExp(), NnetComputerFromEg::Compute(), SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), KALDI_ASSERT, KALDI_LOG, SequentialTableReader< Holder >::Key(), SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), MatrixBase< Real >::NumRows(), ParseOptions::PrintUsage(), ParseOptions::Read(), kaldi::ReadKaldiObject(), ParseOptions::Register(), SequentialTableReader< Holder >::Value(), and TableWriter< Holder >::Write().

                                 {
  try {
    using namespace kaldi;
    using namespace kaldi::nnet3;
    typedef kaldi::int32 int32;
    typedef kaldi::int64 int64;

    const char *usage =
        "Read input nnet training examples, and compute the output for each one.\n"
        "If --apply-exp=true, apply the Exp() function to the output before writing\n"
        "it out.\n"
        "\n"
        "Usage:  nnet3-compute-from-egs [options] <raw-nnet-in> <training-examples-in> <matrices-out>\n"
        "e.g.:\n"
        "nnet3-compute-from-egs --apply-exp=true 0.raw ark:1.egs ark:- | matrix-sum-rows ark:- ... \n"
        "See also: nnet3-compute\n";

    bool binary_write = true,
        apply_exp = false;
    std::string use_gpu = "yes";
    std::string output_name = "output";

    ParseOptions po(usage);
    po.Register("binary", &binary_write, "Write output in binary mode");
    po.Register("apply-exp", &apply_exp, "If true, apply exp function to "
                "output");
    po.Register("output-name", &output_name, "Do computation for "
                "specified output-node");
    po.Register("use-gpu", &use_gpu,
                "yes|no|optional|wait, only has effect if compiled with CUDA");

    po.Read(argc, argv);

    if (po.NumArgs() != 3) {
      po.PrintUsage();
      exit(1);
    }

#if HAVE_CUDA==1
    CuDevice::Instantiate().SelectGpuId(use_gpu);
#endif

    std::string nnet_rxfilename = po.GetArg(1),
        examples_rspecifier = po.GetArg(2),
        matrix_wspecifier = po.GetArg(3);

    Nnet nnet;
    ReadKaldiObject(nnet_rxfilename, &nnet);

    NnetComputerFromEg computer(nnet);

    int64 num_egs = 0;

    SequentialNnetExampleReader example_reader(examples_rspecifier);
    BaseFloatMatrixWriter matrix_writer(matrix_wspecifier);

    for (; !example_reader.Done(); example_reader.Next(), num_egs++) {
      Matrix<BaseFloat> output;
      computer.Compute(example_reader.Value(), output_name, &output);
      KALDI_ASSERT(output.NumRows() != 0);
      if (apply_exp)
        output.ApplyExp();
      matrix_writer.Write(example_reader.Key(), output);
    }
#if HAVE_CUDA==1
    CuDevice::Instantiate().PrintProfile();
#endif
    KALDI_LOG << "Processed " << num_egs << " examples.";
    return 0;
  } catch(const std::exception &e) {
    std::cerr << e.what() << '\n';
    return -1;
  }
}