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未验证 提交 6a32a0bf 编写于 作者: H Hui Zhang 提交者： GitHub
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Merge pull request #1537 from SmileGoat/add_cmvn 

[Speechx] add cmvn
上级 4cc3e43f c4a5be30
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Showing with 403 addition and 73 deletion
speechx/speechx/codelab/feat_test/linear_spectrogram_main.cc
浏览文件 @ 6a32a0bf
......@@ -7,20 +7,36 @@
#include "base/log.h"
#include "base/flags.h"
#include "kaldi/feat/wave-reader.h"
#include "kaldi/util/kaldi-io.h"
DEFINE_string(wav_rspecifier, "", "test wav path");
DEFINE_string(feature_wspecifier, "", "test wav ark");
DEFINE_string(feature_check_wspecifier, "", "test wav ark");
DEFINE_string(cmvn_write_path, "./cmvn.ark", "test wav ark");
std::vector<float> mean_{-13730251.531853663, -12982852.199316509, -13673844.299583456, -13089406.559646806, -12673095.524938712, -12823859.223276224, -13590267.158903603, -14257618.467152044, -14374605.116185192, -14490009.21822485, -14849827.158924166, -15354435.470563512, -15834149.206532761, -16172971.985514281, -16348740.496746974, -16423536.699409386, -16556246.263649225, -16744088.772748645, -16916184.08510357, -17054034.840031497, -17165612.509455364, -17255955.470915023, -17322572.527648456, -17408943.862033736, -17521554.799865916, -17620623.254924215, -17699792.395918526, -17723364.411134344, -17741483.4433254, -17747426.888704527, -17733315.928209435, -17748780.160905756, -17808336.883775543, -17895918.671983004, -18009812.59173023, -18098188.66548325, -18195798.958462656, -18293617.62980999, -18397432.92077201, -18505834.787318766, -18585451.8100908, -18652438.235649142, -18700960.306275308, -18734944.58792185, -18737426.313365128, -18735347.165987637, -18738813.444170244, -18737086.848890636, -18731576.2474336, -18717405.44095871, -18703089.25545657, -18691014.546456724, -18692460.568905357, -18702119.628629155, -18727710.621126678, -18761582.72034647, -18806745.835547544, -18850674.8692112, -18884431.510951452, -18919999.992506847, -18939303.799078144, -18952946.273760635, -18980289.22996379, -19011610.17803294, -19040948.61805145, -19061021.429847397, -19112055.53768819, -19149667.414264943, -19201127.05091321, -19270250.82564605, -19334606.883057203, -19390513.336589377, -19444176.259208687, -19502755.000038862, -19544333.014549147, -19612668.183176614, -19681902.19006569, -19771969.951249883, -19873329.723376893, -19996752.59235844, -20110031.131400537, -20231658.612529557, -20319378.894054495, -20378534.45718066, -20413332.089584175, -20438147.844177883, -20443710.248040095, -20465457.02238927, -20488610.969337028, -20516295.16424432, -20541423.795738827, -20553192.874953747, -20573605.50701977, -20577871.61936797, -20571807.008916274, -20556242.38912231, -20542199.30819195, -20521239.063551214, -20519150.80004532, -20527204.80248933, -20536933.769257784, -20543470.522332076, -20549700.089992985, -20551525.24958494, -20554873.406493705, -20564277.65794227, -20572211.740052115, -20574305.69550465, -20575494.450104576, -20567092.577932164, -20549302.929608088, -20545445.11878376, -20546625.326603737, -20549190.03499401, -20554824.947828256, -20568341.378989458, -20577582.331383612, -20577980.519402675, -20566603.03458152, -20560131.592262644, -20552166.469060015, -20549063.06763577, -20544490.562339947, -20539817.82346569, -20528747.715731595, -20518026.24576161, -20510977.844974525, -20506874.36087992, -20506731.11977665, -20510482.133420516, -20507760.92101862, -20494644.834457114, -20480107.89304893, -20461312.091867123, -20442941.75080173, -20426123.02834838, -20424607.675283, -20426810.369107097, -20434024.50097819, -20437404.75544205, -20447688.63916367, -20460893.335563846, -20482922.735127095, -20503610.119434915, -20527062.76448319, -20557830.035128627, -20593274.72068722, -20632528.452965066, -20673637.471334763, -20733106.97143075, -20842921.0447562, -21054357.83621519, -21416569.534189366, -21978460.272811692, -22753170.052172784, -23671344.10563395, -24613499.293358143, -25406477.12230188, -25884377.82156489, -26049040.62791664, -26996879.104431007};
std::vector<float> variance_{213747175.10846674, 188395815.34302503, 212706429.10966414, 199109025.81461075, 189235901.23864496, 194901336.53253657, 217481594.29306737, 238689869.12327808, 243977501.24115244, 248479623.6431067, 259766741.47116545, 275516766.7790273, 291271202.3691234, 302693239.8220509, 308627358.3997694, 311143911.38788426, 315446105.07731867, 321705430.9341829, 327458907.4659941, 332245072.43223983, 336251717.5935284, 339694069.7639722, 342188204.4322228, 345587110.31313115, 349903086.2875232, 353660214.20643026, 356700344.5270885, 357665362.3529641, 358493352.05658793, 358857951.620328, 358375239.52774596, 358899733.6342954, 361051818.3511561, 364361716.05025816, 368750322.3771452, 372047800.6462831, 375655861.1349018, 379358519.1980013, 383327605.3935181, 387458599.282341, 390434692.3406868, 392994486.35057056, 394874418.04603153, 396230525.79763395, 396365592.0414835, 396334819.8242737, 396488353.19250053, 396438877.00744957, 396197980.4459586, 395590921.6672991, 395001107.62072515, 394528291.7318225, 394593110.424006, 395018405.59353715, 396110577.5415993, 397506704.0371068, 399400197.4657644, 401243568.2468382, 402687134.7805103, 404136047.2872507, 404883170.001883, 405522253.219517, 406660365.3626476, 407919346.0991902, 409045348.5384909, 409759588.7889818, 411974821.8564483, 413489718.78201455, 415535392.56684107, 418466481.97674364, 421104678.35678065, 423405392.5200779, 425550570.40798235, 427929423.9579701, 429585274.253478, 432368493.55181056, 435193587.13513297, 438886855.20476013, 443058876.8633751, 448181232.5093362, 452883835.6332396, 458056721.77926534, 461816531.22735566, 464363620.1970998, 465886343.5057493, 466928872.0651, 467180536.42647296, 468111848.70714295, 469138695.3071312, 470378429.6930793, 471517958.7132626, 472109050.4262365, 473087417.0177867, 473381322.04648733, 473220195.85483915, 472666071.8998819, 472124669.87879956, 471298571.411737, 471251033.2902761, 471672676.43128747, 472177147.2193172, 472572361.7711908, 472968783.7751127, 473156295.4164052, 473398034.82676554, 473897703.5203811, 474328271.33112127, 474452670.98002136, 474549003.99284613, 474252887.13567275, 473557462.909069, 473483385.85193115, 473609738.04855174, 473746944.82085115, 474016729.91696435, 474617321.94138587, 475045097.237122, 475125402.586558, 474664112.9824912, 474426247.5800283, 474104075.42796475, 473978219.7273978, 473773171.7798875, 473578534.69508696, 473102924.16904145, 472651240.5232615, 472374383.1810912, 472209479.6956096, 472202298.8921673, 472370090.76781124, 472220933.99374026, 471625467.37106377, 470994646.51883453, 470182428.9637543, 469348211.5939578, 468570387.4467277, 468540442.7225135, 468672018.90414184, 468994346.9533251, 469138757.58201426, 469553915.95710236, 470134523.38582784, 471082421.62055486, 471962316.51804745, 472939745.1708408, 474250621.5944825, 475773933.43199486, 477465399.71087736, 479218782.61382693, 481752299.7930922, 486608947.8984568, 496119403.2067917, 512730085.5704984, 539048915.2641417, 576285298.3548826, 621610270.2240586, 669308196.4436442, 710656993.5957186, 736344437.3725077, 745481288.0241544, 801121432.9925804};
int count_ = 912592;
void WriteMatrix() {
kaldi::Matrix<double> cmvn_stats(2, mean_.size()+ 1);
for (size_t idx = 0; idx < mean_.size(); ++idx) {
cmvn_stats(0, idx) = mean_[idx];
cmvn_stats(1, idx) = variance_[idx];
}
cmvn_stats(0, mean_.size()) = count_;
kaldi::WriteKaldiObject(cmvn_stats, FLAGS_cmvn_write_path, true);
}
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(FLAGS_wav_rspecifier);
kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier);
kaldi::BaseFloatMatrixWriter feat_cmvn_check_writer(FLAGS_feature_check_wspecifier);
WriteMatrix();
// test feature linear_spectorgram: wave --> decibel_normalizer --> hanning window -->linear_spectrogram --> cmvn
int32 num_done = 0, num_err = 0;
......@@ -32,6 +48,8 @@ int main(int argc, char* argv[]) {
new ppspeech::DecibelNormalizer(db_norm_opt));
ppspeech::LinearSpectrogram linear_spectrogram(opt, std::move(base_feature_extractor));
ppspeech::CMVN cmvn(FLAGS_cmvn_write_path);
float streaming_chunk = 0.36;
int sample_rate = 16000;
int chunk_sample_size = streaming_chunk * sample_rate;
......@@ -57,18 +75,18 @@ int main(int argc, char* argv[]) {
std::vector<kaldi::Matrix<BaseFloat>> feats;
int feature_rows = 0;
while (sample_offset < tot_samples) {
int cur_chunk_size = std::min(chunk_sample_size, tot_samples - sample_offset);
kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
for (int i = 0; i < cur_chunk_size; ++i) {
wav_chunk(i) = waveform(sample_offset + i);
}
kaldi::Matrix<BaseFloat> features;
linear_spectrogram.AcceptWaveform(wav_chunk);
linear_spectrogram.ReadFeats(&features);
feats.push_back(features);
sample_offset += cur_chunk_size;
feature_rows += features.NumRows();
int cur_chunk_size = std::min(chunk_sample_size, tot_samples - sample_offset);
kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
for (int i = 0; i < cur_chunk_size; ++i) {
wav_chunk(i) = waveform(sample_offset + i);
}
kaldi::Matrix<BaseFloat> features;
linear_spectrogram.AcceptWaveform(wav_chunk);
linear_spectrogram.ReadFeats(&features);
feats.push_back(features);
sample_offset += cur_chunk_size;
feature_rows += features.NumRows();
}
int cur_idx = 0;
......@@ -81,8 +99,22 @@ int main(int argc, char* argv[]) {
++cur_idx;
}
}
feat_writer.Write(utt, features);
cur_idx = 0;
kaldi::Matrix<kaldi::BaseFloat> features_check(feature_rows, feats[0].NumCols());
for (auto feat : feats) {
for (int row_idx = 0; row_idx < feat.NumRows(); ++row_idx) {
for (int col_idx = 0; col_idx < feat.NumCols(); ++col_idx) {
features_check(cur_idx, col_idx) = feat(row_idx, col_idx);
}
kaldi::SubVector<BaseFloat> row_feat(features_check, cur_idx);
cmvn.ApplyCMVN(true, &row_feat);
++cur_idx;
}
}
feat_cmvn_check_writer.Write(utt, features_check);
if (num_done % 50 == 0 && num_done != 0)
KALDI_VLOG(2) << "Processed " << num_done << " utterances";
num_done++;
......@@ -90,4 +122,4 @@ int main(int argc, char* argv[]) {
KALDI_LOG << "Done " << num_done << " utterances, " << num_err
<< " with errors.";
return (num_done != 0 ? 0 : 1);
}
\ No newline at end of file
}
speechx/speechx/frontend/normalizer.cc
浏览文件 @ 6a32a0bf
#include "frontend/normalizer.h"
#include "kaldi/feat/cmvn.h"
#include "kaldi/util/kaldi-io.h"
namespace ppspeech {
......@@ -7,6 +9,7 @@ using kaldi::Vector;
using kaldi::VectorBase;
using kaldi::BaseFloat;
using std::vector;
using kaldi::SubVector;
DecibelNormalizer::DecibelNormalizer(const DecibelNormalizerOptions& opts) {
opts_ = opts;
......@@ -87,44 +90,91 @@ bool DecibelNormalizer::Compute(const VectorBase<BaseFloat>& input,
return true;
}
/*
PPNormalizer::PPNormalizer(
const PPNormalizerOptions& opts,
const std::unique_ptr<FeatureExtractorInterface>& pre_extractor) {
CMVN::CMVN(std::string cmvn_file) : var_norm_(true) {
bool binary;
kaldi::Input ki(cmvn_file, &binary);
stats_.Read(ki.Stream(), binary);
}
void CMVN::AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input) {
return;
}
void PPNormalizer::AcceptWavefrom(const Vector<BaseFloat>& input) {
void CMVN::Read(kaldi::VectorBase<BaseFloat>* feat) {
return;
}
void PPNormalizer::Read(Vector<BaseFloat>* feat) {
// feats contain num_frames feature.
void CMVN::ApplyCMVN(bool var_norm, VectorBase<BaseFloat>* feats) {
KALDI_ASSERT(feats != NULL);
int32 dim = stats_.NumCols() - 1;
if (stats_.NumRows() > 2 || stats_.NumRows() < 1 || feats->Dim() % dim != 0) {
KALDI_ERR << "Dim mismatch: cmvn "
<< stats_.NumRows() << 'x' << stats_.NumCols()
<< ", feats " << feats->Dim() << 'x';
}
if (stats_.NumRows() == 1 && var_norm) {
KALDI_ERR << "You requested variance normalization but no variance stats_ "
<< "are supplied.";
}
double count = stats_(0, dim);
// Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
// computing an offset and representing it as stats_, we use a count of one.
if (count < 1.0)
KALDI_ERR << "Insufficient stats_ for cepstral mean and variance normalization: "
<< "count = " << count;
if (!var_norm) {
Vector<BaseFloat> offset(feats->Dim());
SubVector<double> mean_stats(stats_.RowData(0), dim);
Vector<double> mean_stats_apply(feats->Dim());
//fill the datat of mean_stats in mean_stats_appy whose dim is equal with the dim of feature.
//the dim of feats = dim * num_frames;
for (int32 idx = 0; idx < feats->Dim() / dim; ++idx) {
SubVector<double> stats_tmp(mean_stats_apply.Data() + dim*idx, dim);
stats_tmp.CopyFromVec(mean_stats);
}
offset.AddVec(-1.0 / count, mean_stats_apply);
feats->AddVec(1.0, offset);
return;
}
// norm(0, d) = mean offset;
// norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
kaldi::Matrix<BaseFloat> norm(2, feats->Dim());
for (int32 d = 0; d < dim; d++) {
double mean, offset, scale;
mean = stats_(0, d)/count;
double var = (stats_(1, d)/count) - mean*mean,
floor = 1.0e-20;
if (var < floor) {
KALDI_WARN << "Flooring cepstral variance from " << var << " to "
<< floor;
var = floor;
}
scale = 1.0 / sqrt(var);
if (scale != scale || 1/scale == 0.0)
KALDI_ERR << "NaN or infinity in cepstral mean/variance computation";
offset = -(mean*scale);
for (int32 d_skip = d; d_skip < feats->Dim();) {
norm(0, d_skip) = offset;
norm(1, d_skip) = scale;
d_skip = d_skip + dim;
}
}
// Apply the normalization.
feats->MulElements(norm.Row(1));
feats->AddVec(1.0, norm.Row(0));
}
bool PPNormalizer::Compute(const Vector<BaseFloat>& input,
Vector<BaseFloat>>* feat) {
if ((input.Dim() % mean_.Dim()) == 0) {
LOG(ERROR) << "CMVN dimension is wrong!";
return false;
}
try {
int32 size = mean_.Dim();
feat->Resize(input.Dim());
for (int32 row_idx = 0; row_idx < j; ++row_idx) {
int32 base_idx = row_idx * size;
for (int32 idx = 0; idx < mean_.Dim(); ++idx) {
(*feat)(base_idx + idx) = (input(base_dix + idx) - mean_(idx))* variance_(idx);
}
}
} catch(const std::exception& e) {
std::cerr << e.what() << '\n';
return false;
}
void CMVN::ApplyCMVNMatrix(bool var_norm, kaldi::MatrixBase<BaseFloat>* feats) {
ApplyCmvn(stats_, var_norm, feats);
}
bool CMVN::Compute(const VectorBase<BaseFloat>& input,
VectorBase<BaseFloat>* feat) const {
return false;
}
return true;
}*/
} // namespace ppspeech
\ No newline at end of file
} // namespace ppspeech
speechx/speechx/frontend/normalizer.h
浏览文件 @ 6a32a0bf
......@@ -4,10 +4,10 @@
#include "base/common.h"
#include "frontend/feature_extractor_interface.h"
#include "kaldi/util/options-itf.h"
#include "kaldi/matrix/kaldi-matrix.h"
namespace ppspeech {
struct DecibelNormalizerOptions {
float target_db;
float max_gain_db;
......@@ -39,34 +39,23 @@ class DecibelNormalizer : public FeatureExtractorInterface {
kaldi::Vector<kaldi::BaseFloat> waveform_;
};
/*
struct NormalizerOptions {
std::string mean_std_path;
NormalizerOptions() :
mean_std_path("") {}
void Register(kaldi::OptionsItf* opts) {
opts->Register("mean-std", &mean_std_path, "mean std file");
}
};
// todo refactor later (SmileGoat)
class PPNormalizer : public FeatureExtractorInterface {
class CMVN : public FeatureExtractorInterface {
public:
explicit PPNormalizer(const NormalizerOptions& opts,
const std::unique_ptr<FeatureExtractorInterface>& pre_extractor);
~PPNormalizer() {}
virtual void AcceptWavefrom(const kaldi::Vector<kaldi::BaseFloat>& input);
virtual void Read(kaldi::Vector<kaldi::BaseFloat>* feat);
virtual size_t Dim() const;
bool Compute(const kaldi::Vector<kaldi::BaseFloat>& input,
kaldi::Vector<kaldi::BaseFloat>>& feat);
explicit CMVN(std::string cmvn_file);
virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
virtual size_t Dim() const { return stats_.NumCols() - 1; }
bool Compute(const kaldi::VectorBase<kaldi::BaseFloat>& input,
kaldi::VectorBase<kaldi::BaseFloat>* feat) const;
// for test
void ApplyCMVN(bool var_norm, kaldi::VectorBase<BaseFloat>* feats);
void ApplyCMVNMatrix(bool var_norm, kaldi::MatrixBase<BaseFloat>* feats);
private:
bool _initialized;
kaldi::Vector<float> mean_;
kaldi::Vector<float> variance_;
NormalizerOptions _opts;
kaldi::Matrix<double> stats_;
std::shared_ptr<FeatureExtractorInterface> base_extractor_;
size_t dim_;
bool var_norm_;
};
*/
} // namespace ppspeech
\ No newline at end of file
speechx/speechx/kaldi/feat/CMakeLists.txt
浏览文件 @ 6a32a0bf
......@@ -15,5 +15,6 @@ add_library(kaldi-feat-common
feature-window.cc
resample.cc
mel-computations.cc
cmvn.cc
)
target_link_libraries(kaldi-feat-common PUBLIC kaldi-base kaldi-matrix kaldi-util)
\ No newline at end of file
target_link_libraries(kaldi-feat-common PUBLIC kaldi-base kaldi-matrix kaldi-util)
speechx/speechx/kaldi/feat/cmvn.cc 0 → 100644
浏览文件 @ 6a32a0bf
// transform/cmvn.cc
// Copyright 2009-2013 Microsoft Corporation
// 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.
#include "feat/cmvn.h"
namespace kaldi {
void InitCmvnStats(int32 dim, Matrix<double> *stats) {
KALDI_ASSERT(dim > 0);
stats->Resize(2, dim+1);
}
void AccCmvnStats(const VectorBase<BaseFloat> &feats, BaseFloat weight, MatrixBase<double> *stats) {
int32 dim = feats.Dim();
KALDI_ASSERT(stats != NULL);
KALDI_ASSERT(stats->NumRows() == 2 && stats->NumCols() == dim + 1);
// Remove these __restrict__ modifiers if they cause compilation problems.
// It's just an optimization.
double *__restrict__ mean_ptr = stats->RowData(0),
*__restrict__ var_ptr = stats->RowData(1),
*__restrict__ count_ptr = mean_ptr + dim;
const BaseFloat * __restrict__ feats_ptr = feats.Data();
*count_ptr += weight;
// Careful-- if we change the format of the matrix, the "mean_ptr < count_ptr"
// statement below might become wrong.
for (; mean_ptr < count_ptr; mean_ptr++, var_ptr++, feats_ptr++) {
*mean_ptr += *feats_ptr * weight;
*var_ptr += *feats_ptr * *feats_ptr * weight;
}
}
void AccCmvnStats(const MatrixBase<BaseFloat> &feats,
const VectorBase<BaseFloat> *weights,
MatrixBase<double> *stats) {
int32 num_frames = feats.NumRows();
if (weights != NULL) {
KALDI_ASSERT(weights->Dim() == num_frames);
}
for (int32 i = 0; i < num_frames; i++) {
SubVector<BaseFloat> this_frame = feats.Row(i);
BaseFloat weight = (weights == NULL ? 1.0 : (*weights)(i));
if (weight != 0.0)
AccCmvnStats(this_frame, weight, stats);
}
}
void ApplyCmvn(const MatrixBase<double> &stats,
bool var_norm,
MatrixBase<BaseFloat> *feats) {
KALDI_ASSERT(feats != NULL);
int32 dim = stats.NumCols() - 1;
if (stats.NumRows() > 2 || stats.NumRows() < 1 || feats->NumCols() != dim) {
KALDI_ERR << "Dim mismatch: cmvn "
<< stats.NumRows() << 'x' << stats.NumCols()
<< ", feats " << feats->NumRows() << 'x' << feats->NumCols();
}
if (stats.NumRows() == 1 && var_norm)
KALDI_ERR << "You requested variance normalization but no variance stats "
<< "are supplied.";
double count = stats(0, dim);
// Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
// computing an offset and representing it as stats, we use a count of one.
if (count < 1.0)
KALDI_ERR << "Insufficient stats for cepstral mean and variance normalization: "
<< "count = " << count;
if (!var_norm) {
Vector<BaseFloat> offset(dim);
SubVector<double> mean_stats(stats.RowData(0), dim);
offset.AddVec(-1.0 / count, mean_stats);
feats->AddVecToRows(1.0, offset);
return;
}
// norm(0, d) = mean offset;
// norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
Matrix<BaseFloat> norm(2, dim);
for (int32 d = 0; d < dim; d++) {
double mean, offset, scale;
mean = stats(0, d)/count;
double var = (stats(1, d)/count) - mean*mean,
floor = 1.0e-20;
if (var < floor) {
KALDI_WARN << "Flooring cepstral variance from " << var << " to "
<< floor;
var = floor;
}
scale = 1.0 / sqrt(var);
if (scale != scale || 1/scale == 0.0)
KALDI_ERR << "NaN or infinity in cepstral mean/variance computation";
offset = -(mean*scale);
norm(0, d) = offset;
norm(1, d) = scale;
}
// Apply the normalization.
feats->MulColsVec(norm.Row(1));
feats->AddVecToRows(1.0, norm.Row(0));
}
void ApplyCmvnReverse(const MatrixBase<double> &stats,
bool var_norm,
MatrixBase<BaseFloat> *feats) {
KALDI_ASSERT(feats != NULL);
int32 dim = stats.NumCols() - 1;
if (stats.NumRows() > 2 || stats.NumRows() < 1 || feats->NumCols() != dim) {
KALDI_ERR << "Dim mismatch: cmvn "
<< stats.NumRows() << 'x' << stats.NumCols()
<< ", feats " << feats->NumRows() << 'x' << feats->NumCols();
}
if (stats.NumRows() == 1 && var_norm)
KALDI_ERR << "You requested variance normalization but no variance stats "
<< "are supplied.";
double count = stats(0, dim);
// Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
// computing an offset and representing it as stats, we use a count of one.
if (count < 1.0)
KALDI_ERR << "Insufficient stats for cepstral mean and variance normalization: "
<< "count = " << count;
Matrix<BaseFloat> norm(2, dim); // norm(0, d) = mean offset
// norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
for (int32 d = 0; d < dim; d++) {
double mean, offset, scale;
mean = stats(0, d) / count;
if (!var_norm) {
scale = 1.0;
offset = mean;
} else {
double var = (stats(1, d)/count) - mean*mean,
floor = 1.0e-20;
if (var < floor) {
KALDI_WARN << "Flooring cepstral variance from " << var << " to "
<< floor;
var = floor;
}
// we aim to transform zero-mean, unit-variance input into data
// with the given mean and variance.
scale = sqrt(var);
offset = mean;
}
norm(0, d) = offset;
norm(1, d) = scale;
}
if (var_norm)
feats->MulColsVec(norm.Row(1));
feats->AddVecToRows(1.0, norm.Row(0));
}
void FakeStatsForSomeDims(const std::vector<int32> &dims,
MatrixBase<double> *stats) {
KALDI_ASSERT(stats->NumRows() == 2 && stats->NumCols() > 1);
int32 dim = stats->NumCols() - 1;
double count = (*stats)(0, dim);
for (size_t i = 0; i < dims.size(); i++) {
int32 d = dims[i];
KALDI_ASSERT(d >= 0 && d < dim);
(*stats)(0, d) = 0.0;
(*stats)(1, d) = count;
}
}
} // namespace kaldi
speechx/speechx/kaldi/feat/cmvn.h 0 → 100644
浏览文件 @ 6a32a0bf
// transform/cmvn.h
// Copyright 2009-2013 Microsoft Corporation
// 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_TRANSFORM_CMVN_H_
#define KALDI_TRANSFORM_CMVN_H_
#include "base/kaldi-common.h"
#include "matrix/matrix-lib.h"
namespace kaldi {
/// This function initializes the matrix to dimension 2 by (dim+1);
/// 1st "dim" elements of 1st row are mean stats, 1st "dim" elements
/// of 2nd row are var stats, last element of 1st row is count,
/// last element of 2nd row is zero.
void InitCmvnStats(int32 dim, Matrix<double> *stats);
/// Accumulation from a single frame (weighted).
void AccCmvnStats(const VectorBase<BaseFloat> &feat,
BaseFloat weight,
MatrixBase<double> *stats);
/// Accumulation from a feature file (possibly weighted-- useful in excluding silence).
void AccCmvnStats(const MatrixBase<BaseFloat> &feats,
const VectorBase<BaseFloat> *weights, // or NULL
MatrixBase<double> *stats);
/// Apply cepstral mean and variance normalization to a matrix of features.
/// If norm_vars == true, expects stats to be of dimension 2 by (dim+1), but
/// if norm_vars == false, will accept stats of dimension 1 by (dim+1); these
/// are produced by the balanced-cmvn code when it computes an offset and
/// represents it as "fake stats".
void ApplyCmvn(const MatrixBase<double> &stats,
bool norm_vars,
MatrixBase<BaseFloat> *feats);
/// This is as ApplyCmvn, but does so in the reverse sense, i.e. applies a transform
/// that would take zero-mean, unit-variance input and turn it into output with the
/// stats of "stats". This can be useful if you trained without CMVN but later want
/// to correct a mismatch, so you would first apply CMVN and then do the "reverse"
/// CMVN with the summed stats of your training data.
void ApplyCmvnReverse(const MatrixBase<double> &stats,
bool norm_vars,
MatrixBase<BaseFloat> *feats);
/// Modify the stats so that for some dimensions (specified in "dims"), we
/// replace them with "fake" stats that have zero mean and unit variance; this
/// is done to disable CMVN for those dimensions.
void FakeStatsForSomeDims(const std::vector<int32> &dims,
MatrixBase<double> *stats);
} // namespace kaldi
#endif // KALDI_TRANSFORM_CMVN_H_
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