Hui Zhang
3 years ago
committed by
GitHub
commit
2e94e0f699
@ -1,10 +1,2 @@
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project(frontend)
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add_library(frontend STATIC
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normalizer.cc
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linear_spectrogram.cc
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audio_cache.cc
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feature_cache.cc
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)
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target_link_libraries(frontend PUBLIC kaldi-matrix)
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add_subdirectory(audio)
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@ -0,0 +1,11 @@
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project(frontend)
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add_library(frontend STATIC
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cmvn.cc
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db_norm.cc
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linear_spectrogram.cc
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audio_cache.cc
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feature_cache.cc
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)
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target_link_libraries(frontend PUBLIC kaldi-matrix)
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@ -0,0 +1,48 @@
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "base/common.h"
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#include "frontend/audio/frontend_itf.h"
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#include "kaldi/matrix/kaldi-matrix.h"
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#include "kaldi/util/options-itf.h"
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namespace ppspeech {
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class CMVN : public FrontendInterface {
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public:
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explicit CMVN(std::string cmvn_file,
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std::unique_ptr<FrontendInterface> base_extractor);
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virtual void Accept(const kaldi::VectorBase<kaldi::BaseFloat>& inputs);
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// the length of feats = feature_row * feature_dim,
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// the Matrix is squashed into Vector
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virtual bool Read(kaldi::Vector<kaldi::BaseFloat>* feats);
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// the dim_ is the feautre dim.
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virtual size_t Dim() const { return dim_; }
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virtual void SetFinished() { base_extractor_->SetFinished(); }
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virtual bool IsFinished() const { return base_extractor_->IsFinished(); }
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virtual void Reset() { base_extractor_->Reset(); }
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private:
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void Compute(kaldi::VectorBase<kaldi::BaseFloat>* feats) const;
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void ApplyCMVN(kaldi::MatrixBase<BaseFloat>* feats);
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kaldi::Matrix<double> stats_;
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std::unique_ptr<FrontendInterface> base_extractor_;
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size_t dim_;
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bool var_norm_;
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};
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} // namespace ppspeech
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@ -0,0 +1,95 @@
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "frontend/audio/db_norm.h"
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#include "kaldi/feat/cmvn.h"
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#include "kaldi/util/kaldi-io.h"
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namespace ppspeech {
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using kaldi::Vector;
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using kaldi::VectorBase;
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using kaldi::BaseFloat;
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using std::vector;
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using kaldi::SubVector;
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using std::unique_ptr;
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DecibelNormalizer::DecibelNormalizer(
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const DecibelNormalizerOptions& opts,
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std::unique_ptr<FrontendInterface> base_extractor) {
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base_extractor_ = std::move(base_extractor);
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opts_ = opts;
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dim_ = 1;
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}
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void DecibelNormalizer::Accept(const kaldi::VectorBase<BaseFloat>& waves) {
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base_extractor_->Accept(waves);
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}
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bool DecibelNormalizer::Read(kaldi::Vector<BaseFloat>* waves) {
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if (base_extractor_->Read(waves) == false || waves->Dim() == 0) {
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return false;
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}
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Compute(waves);
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return true;
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}
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bool DecibelNormalizer::Compute(VectorBase<BaseFloat>* waves) const {
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// calculate db rms
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BaseFloat rms_db = 0.0;
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BaseFloat mean_square = 0.0;
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BaseFloat gain = 0.0;
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BaseFloat wave_float_normlization = 1.0f / (std::pow(2, 16 - 1));
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vector<BaseFloat> samples;
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samples.resize(waves->Dim());
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for (size_t i = 0; i < samples.size(); ++i) {
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samples[i] = (*waves)(i);
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}
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// square
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for (auto& d : samples) {
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if (opts_.convert_int_float) {
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d = d * wave_float_normlization;
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}
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mean_square += d * d;
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}
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// mean
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mean_square /= samples.size();
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rms_db = 10 * std::log10(mean_square);
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gain = opts_.target_db - rms_db;
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if (gain > opts_.max_gain_db) {
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LOG(ERROR)
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<< "Unable to normalize segment to " << opts_.target_db << "dB,"
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<< "because the the probable gain have exceeds opts_.max_gain_db"
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<< opts_.max_gain_db << "dB.";
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return false;
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}
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// Note that this is an in-place transformation.
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for (auto& item : samples) {
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// python item *= 10.0 ** (gain / 20.0)
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item *= std::pow(10.0, gain / 20.0);
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}
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std::memcpy(
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waves->Data(), samples.data(), sizeof(BaseFloat) * samples.size());
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return true;
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}
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} // namespace ppspeech
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@ -1,13 +0,0 @@
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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@ -1,13 +0,0 @@
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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