add linear feature no db

speechx/examples/feat/linear_spectrogram_without_db_norm_main.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// todo refactor, repalce with gtest
+
+#include "base/flags.h"
+#include "base/log.h"
+#include "kaldi/feat/wave-reader.h"
+#include "kaldi/util/kaldi-io.h"
+#include "kaldi/util/table-types.h"
+
+#include "frontend/audio/audio_cache.h"
+#include "frontend/audio/data_cache.h"
+#include "frontend/audio/feature_cache.h"
+#include "frontend/audio/frontend_itf.h"
+#include "frontend/audio/linear_spectrogram.h"
+#include "frontend/audio/normalizer.h"
+
+DEFINE_string(wav_rspecifier, "", "test wav scp path");
+DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
+DEFINE_string(cmvn_file, "./cmvn.ark", "read cmvn");
+DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
+
+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);
+
+    int32 num_done = 0, num_err = 0;
+
+    // feature pipeline: wave cache --> hanning
+    // window -->linear_spectrogram --> global cmvn -> feat cache
+
+    std::unique_ptr<ppspeech::FrontendInterface> data_source(
+        new ppspeech::AudioCache(3600 * 1600, true));
+
+    ppspeech::LinearSpectrogramOptions opt;
+    opt.frame_opts.frame_length_ms = 20;
+    opt.frame_opts.frame_shift_ms = 10;
+    opt.streaming_chunk = FLAGS_streaming_chunk;
+    opt.frame_opts.dither = 0.0;
+    opt.frame_opts.remove_dc_offset = false;
+    opt.frame_opts.window_type = "hanning";
+    opt.frame_opts.preemph_coeff = 0.0;
+    LOG(INFO) << "frame length (ms): " << opt.frame_opts.frame_length_ms;
+    LOG(INFO) << "frame shift (ms): " << opt.frame_opts.frame_shift_ms;
+
+    std::unique_ptr<ppspeech::FrontendInterface> linear_spectrogram(
+        new ppspeech::LinearSpectrogram(opt, std::move(data_source)));
+
+    std::unique_ptr<ppspeech::FrontendInterface> cmvn(
+        new ppspeech::CMVN(FLAGS_cmvn_file, std::move(linear_spectrogram)));
+
+    ppspeech::FeatureCache feature_cache(kint16max, std::move(cmvn));
+    LOG(INFO) << "feat dim: " << feature_cache.Dim();
+
+    int sample_rate = 16000;
+    float streaming_chunk = FLAGS_streaming_chunk;
+    int chunk_sample_size = streaming_chunk * sample_rate;
+    LOG(INFO) << "sr: " << sample_rate;
+    LOG(INFO) << "chunk size (s): " << streaming_chunk;
+    LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
+
+
+    for (; !wav_reader.Done(); wav_reader.Next()) {
+        std::string utt = wav_reader.Key();
+        const kaldi::WaveData& wave_data = wav_reader.Value();
+        LOG(INFO) << "process utt: " << utt;
+
+        int32 this_channel = 0;
+        kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
+                                                    this_channel);
+        int tot_samples = waveform.Dim();
+        LOG(INFO) << "wav len (sample): " << tot_samples;
+
+        int sample_offset = 0;
+        std::vector<kaldi::Vector<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::Vector<BaseFloat> features;
+            feature_cache.Accept(wav_chunk);
+            if (cur_chunk_size < chunk_sample_size) {
+                feature_cache.SetFinished();
+            }
+            feature_cache.Read(&features);
+            if (features.Dim() == 0) break;
+
+            feats.push_back(features);
+            sample_offset += cur_chunk_size;
+            feature_rows += features.Dim() / feature_cache.Dim();
+        }
+
+        int cur_idx = 0;
+        kaldi::Matrix<kaldi::BaseFloat> features(feature_rows,
+                                                 feature_cache.Dim());
+        for (auto feat : feats) {
+            int num_rows = feat.Dim() / feature_cache.Dim();
+            for (int row_idx = 0; row_idx < num_rows; ++row_idx) {
+                for (size_t col_idx = 0; col_idx < feature_cache.Dim();
+                     ++col_idx) {
+                    features(cur_idx, col_idx) =
+                        feat(row_idx * feature_cache.Dim() + col_idx);
+                }
+                ++cur_idx;
+            }
+        }
+        feat_writer.Write(utt, features);
+        feature_cache.Reset();
+
+        if (num_done % 50 == 0 && num_done != 0)
+            KALDI_VLOG(2) << "Processed " << num_done << " utterances";
+        num_done++;
+    }
+    KALDI_LOG << "Done " << num_done << " utterances, " << num_err
+              << " with errors.";
+    return (num_done != 0 ? 0 : 1);
+}