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109 lines
4.0 KiB
109 lines
4.0 KiB
// 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/linear_spectrogram.h"
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#include "kaldi/base/kaldi-math.h"
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#include "kaldi/feat/feature-common.h"
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#include "kaldi/feat/feature-functions.h"
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#include "kaldi/matrix/matrix-functions.h"
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namespace ppspeech {
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using kaldi::int32;
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using kaldi::BaseFloat;
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using kaldi::Vector;
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using kaldi::SubVector;
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using kaldi::VectorBase;
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using kaldi::Matrix;
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using std::vector;
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LinearSpectrogram::LinearSpectrogram(
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const LinearSpectrogramOptions& opts,
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std::unique_ptr<FrontendInterface> base_extractor)
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: opts_(opts), feature_window_funtion_(opts.frame_opts) {
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base_extractor_ = std::move(base_extractor);
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int32 window_size = opts.frame_opts.WindowSize();
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int32 window_shift = opts.frame_opts.WindowShift();
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dim_ = window_size / 2 + 1;
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chunk_sample_size_ =
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static_cast<int32>(opts.streaming_chunk * opts.frame_opts.samp_freq);
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hanning_window_energy_ = kaldi::VecVec(feature_window_funtion_.window,
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feature_window_funtion_.window);
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}
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void LinearSpectrogram::Accept(const VectorBase<BaseFloat>& inputs) {
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base_extractor_->Accept(inputs);
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}
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bool LinearSpectrogram::Read(Vector<BaseFloat>* feats) {
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Vector<BaseFloat> input_feats(chunk_sample_size_);
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bool flag = base_extractor_->Read(&input_feats);
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if (flag == false || input_feats.Dim() == 0) return false;
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int32 feat_len = input_feats.Dim();
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int32 left_len = reminded_wav_.Dim();
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Vector<BaseFloat> waves(feat_len + left_len);
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waves.Range(0, left_len).CopyFromVec(reminded_wav_);
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waves.Range(left_len, feat_len).CopyFromVec(input_feats);
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Compute(waves, feats);
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int32 frame_shift = opts_.frame_opts.WindowShift();
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int32 num_frames = kaldi::NumFrames(waves.Dim(), opts_.frame_opts);
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int32 left_samples = waves.Dim() - frame_shift * num_frames;
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reminded_wav_.Resize(left_samples);
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reminded_wav_.CopyFromVec(
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waves.Range(frame_shift * num_frames, left_samples));
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return true;
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}
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// Compute spectrogram feat
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bool LinearSpectrogram::Compute(const Vector<BaseFloat>& waves,
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Vector<BaseFloat>* feats) {
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int32 num_samples = waves.Dim();
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int32 frame_length = opts_.frame_opts.WindowSize();
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int32 sample_rate = opts_.frame_opts.samp_freq;
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BaseFloat scale = 2.0 / (hanning_window_energy_ * sample_rate);
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if (num_samples < frame_length) {
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return true;
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}
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int32 num_frames = kaldi::NumFrames(num_samples, opts_.frame_opts);
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feats->Resize(num_frames * dim_);
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Vector<BaseFloat> window;
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for (int frame_idx = 0; frame_idx < num_frames; ++frame_idx) {
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kaldi::ExtractWindow(0,
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waves,
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frame_idx,
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opts_.frame_opts,
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feature_window_funtion_,
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&window,
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NULL);
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SubVector<BaseFloat> output_row(feats->Data() + frame_idx * dim_, dim_);
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window.Resize(frame_length, kaldi::kCopyData);
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RealFft(&window, true);
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kaldi::ComputePowerSpectrum(&window);
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SubVector<BaseFloat> power_spectrum(window, 0, dim_);
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power_spectrum.Scale(scale);
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power_spectrum(0) = power_spectrum(0) / 2;
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power_spectrum(dim_ - 1) = power_spectrum(dim_ - 1) / 2;
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power_spectrum.Add(1e-14);
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power_spectrum.ApplyLog();
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output_row.CopyFromVec(power_spectrum);
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}
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return true;
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}
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} // namespace ppspeech
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