SmileGoat
3 years ago
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// transform/cmvn.cc
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// Copyright 2009-2013 Microsoft Corporation
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// Johns Hopkins University (author: Daniel Povey)
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// See ../../COPYING for clarification regarding multiple authors
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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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#include "feat/cmvn.h"
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namespace kaldi {
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void InitCmvnStats(int32 dim, Matrix<double> *stats) {
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KALDI_ASSERT(dim > 0);
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stats->Resize(2, dim+1);
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}
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void AccCmvnStats(const VectorBase<BaseFloat> &feats, BaseFloat weight, MatrixBase<double> *stats) {
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int32 dim = feats.Dim();
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KALDI_ASSERT(stats != NULL);
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KALDI_ASSERT(stats->NumRows() == 2 && stats->NumCols() == dim + 1);
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// Remove these __restrict__ modifiers if they cause compilation problems.
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// It's just an optimization.
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double *__restrict__ mean_ptr = stats->RowData(0),
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*__restrict__ var_ptr = stats->RowData(1),
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*__restrict__ count_ptr = mean_ptr + dim;
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const BaseFloat * __restrict__ feats_ptr = feats.Data();
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*count_ptr += weight;
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// Careful-- if we change the format of the matrix, the "mean_ptr < count_ptr"
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// statement below might become wrong.
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for (; mean_ptr < count_ptr; mean_ptr++, var_ptr++, feats_ptr++) {
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*mean_ptr += *feats_ptr * weight;
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*var_ptr += *feats_ptr * *feats_ptr * weight;
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}
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}
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void AccCmvnStats(const MatrixBase<BaseFloat> &feats,
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const VectorBase<BaseFloat> *weights,
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MatrixBase<double> *stats) {
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int32 num_frames = feats.NumRows();
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if (weights != NULL) {
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KALDI_ASSERT(weights->Dim() == num_frames);
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}
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for (int32 i = 0; i < num_frames; i++) {
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SubVector<BaseFloat> this_frame = feats.Row(i);
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BaseFloat weight = (weights == NULL ? 1.0 : (*weights)(i));
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if (weight != 0.0)
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AccCmvnStats(this_frame, weight, stats);
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}
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}
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void ApplyCmvn(const MatrixBase<double> &stats,
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bool var_norm,
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MatrixBase<BaseFloat> *feats) {
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KALDI_ASSERT(feats != NULL);
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int32 dim = stats.NumCols() - 1;
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if (stats.NumRows() > 2 || stats.NumRows() < 1 || feats->NumCols() != dim) {
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KALDI_ERR << "Dim mismatch: cmvn "
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<< stats.NumRows() << 'x' << stats.NumCols()
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<< ", feats " << feats->NumRows() << 'x' << feats->NumCols();
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}
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if (stats.NumRows() == 1 && var_norm)
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KALDI_ERR << "You requested variance normalization but no variance stats "
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<< "are supplied.";
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double count = stats(0, dim);
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// Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
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// computing an offset and representing it as stats, we use a count of one.
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if (count < 1.0)
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KALDI_ERR << "Insufficient stats for cepstral mean and variance normalization: "
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<< "count = " << count;
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if (!var_norm) {
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Vector<BaseFloat> offset(dim);
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SubVector<double> mean_stats(stats.RowData(0), dim);
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offset.AddVec(-1.0 / count, mean_stats);
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feats->AddVecToRows(1.0, offset);
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return;
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}
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// norm(0, d) = mean offset;
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// norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
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Matrix<BaseFloat> norm(2, dim);
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for (int32 d = 0; d < dim; d++) {
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double mean, offset, scale;
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mean = stats(0, d)/count;
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double var = (stats(1, d)/count) - mean*mean,
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floor = 1.0e-20;
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if (var < floor) {
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KALDI_WARN << "Flooring cepstral variance from " << var << " to "
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<< floor;
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var = floor;
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}
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scale = 1.0 / sqrt(var);
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if (scale != scale || 1/scale == 0.0)
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KALDI_ERR << "NaN or infinity in cepstral mean/variance computation";
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offset = -(mean*scale);
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norm(0, d) = offset;
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norm(1, d) = scale;
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}
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// Apply the normalization.
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feats->MulColsVec(norm.Row(1));
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feats->AddVecToRows(1.0, norm.Row(0));
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}
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void ApplyCmvnReverse(const MatrixBase<double> &stats,
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bool var_norm,
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MatrixBase<BaseFloat> *feats) {
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KALDI_ASSERT(feats != NULL);
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int32 dim = stats.NumCols() - 1;
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if (stats.NumRows() > 2 || stats.NumRows() < 1 || feats->NumCols() != dim) {
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KALDI_ERR << "Dim mismatch: cmvn "
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<< stats.NumRows() << 'x' << stats.NumCols()
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<< ", feats " << feats->NumRows() << 'x' << feats->NumCols();
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}
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if (stats.NumRows() == 1 && var_norm)
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KALDI_ERR << "You requested variance normalization but no variance stats "
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<< "are supplied.";
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double count = stats(0, dim);
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// Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
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// computing an offset and representing it as stats, we use a count of one.
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if (count < 1.0)
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KALDI_ERR << "Insufficient stats for cepstral mean and variance normalization: "
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<< "count = " << count;
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Matrix<BaseFloat> norm(2, dim); // norm(0, d) = mean offset
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// norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
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for (int32 d = 0; d < dim; d++) {
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double mean, offset, scale;
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mean = stats(0, d) / count;
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if (!var_norm) {
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scale = 1.0;
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offset = mean;
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} else {
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double var = (stats(1, d)/count) - mean*mean,
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floor = 1.0e-20;
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if (var < floor) {
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KALDI_WARN << "Flooring cepstral variance from " << var << " to "
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<< floor;
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var = floor;
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}
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// we aim to transform zero-mean, unit-variance input into data
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// with the given mean and variance.
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scale = sqrt(var);
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offset = mean;
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}
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norm(0, d) = offset;
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norm(1, d) = scale;
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}
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if (var_norm)
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feats->MulColsVec(norm.Row(1));
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feats->AddVecToRows(1.0, norm.Row(0));
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}
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void FakeStatsForSomeDims(const std::vector<int32> &dims,
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MatrixBase<double> *stats) {
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KALDI_ASSERT(stats->NumRows() == 2 && stats->NumCols() > 1);
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int32 dim = stats->NumCols() - 1;
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double count = (*stats)(0, dim);
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for (size_t i = 0; i < dims.size(); i++) {
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int32 d = dims[i];
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KALDI_ASSERT(d >= 0 && d < dim);
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(*stats)(0, d) = 0.0;
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(*stats)(1, d) = count;
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}
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}
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} // namespace kaldi
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@ -0,0 +1,75 @@
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// transform/cmvn.h
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// Copyright 2009-2013 Microsoft Corporation
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// Johns Hopkins University (author: Daniel Povey)
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// See ../../COPYING for clarification regarding multiple authors
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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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#ifndef KALDI_TRANSFORM_CMVN_H_
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#define KALDI_TRANSFORM_CMVN_H_
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#include "base/kaldi-common.h"
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#include "matrix/matrix-lib.h"
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namespace kaldi {
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/// This function initializes the matrix to dimension 2 by (dim+1);
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/// 1st "dim" elements of 1st row are mean stats, 1st "dim" elements
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/// of 2nd row are var stats, last element of 1st row is count,
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/// last element of 2nd row is zero.
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void InitCmvnStats(int32 dim, Matrix<double> *stats);
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/// Accumulation from a single frame (weighted).
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void AccCmvnStats(const VectorBase<BaseFloat> &feat,
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BaseFloat weight,
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MatrixBase<double> *stats);
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/// Accumulation from a feature file (possibly weighted-- useful in excluding silence).
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void AccCmvnStats(const MatrixBase<BaseFloat> &feats,
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const VectorBase<BaseFloat> *weights, // or NULL
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MatrixBase<double> *stats);
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/// Apply cepstral mean and variance normalization to a matrix of features.
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/// If norm_vars == true, expects stats to be of dimension 2 by (dim+1), but
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/// if norm_vars == false, will accept stats of dimension 1 by (dim+1); these
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/// are produced by the balanced-cmvn code when it computes an offset and
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/// represents it as "fake stats".
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void ApplyCmvn(const MatrixBase<double> &stats,
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bool norm_vars,
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MatrixBase<BaseFloat> *feats);
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/// This is as ApplyCmvn, but does so in the reverse sense, i.e. applies a transform
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/// that would take zero-mean, unit-variance input and turn it into output with the
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/// stats of "stats". This can be useful if you trained without CMVN but later want
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/// to correct a mismatch, so you would first apply CMVN and then do the "reverse"
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/// CMVN with the summed stats of your training data.
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void ApplyCmvnReverse(const MatrixBase<double> &stats,
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bool norm_vars,
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MatrixBase<BaseFloat> *feats);
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/// Modify the stats so that for some dimensions (specified in "dims"), we
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/// replace them with "fake" stats that have zero mean and unit variance; this
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/// is done to disable CMVN for those dimensions.
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void FakeStatsForSomeDims(const std::vector<int32> &dims,
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MatrixBase<double> *stats);
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} // namespace kaldi
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#endif // KALDI_TRANSFORM_CMVN_H_
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