Merge pull request #1541 from zh794390558/egs

[speechx] move test to examples

.pre-commit-config.yaml

@@ -50,13 +50,13 @@ repos:
         entry: bash .pre-commit-hooks/clang-format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
-        exclude: (?=speechx/speechx/kaldi).*(\.cpp|\.cc|\.h|\.py)$
+        exclude: (?=speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$
     -   id: copyright_checker
         name: copyright_checker
         entry: python .pre-commit-hooks/copyright-check.hook
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
-        exclude: (?=third_party|pypinyin|speechx/speechx/kaldi).*(\.cpp|\.cc|\.h|\.py)$
+        exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$
 -   repo: https://github.com/asottile/reorder_python_imports
     rev: v2.4.0
     hooks:

paddlespeech/s2t/io/sampler.py

@@ -51,7 +51,7 @@ def _batch_shuffle(indices, batch_size, epoch, clipped=False):
     """
     rng = np.random.RandomState(epoch)
     shift_len = rng.randint(0, batch_size - 1)
-    batch_indices = list(zip(*[iter(indices[shift_len:])] * batch_size))
+    batch_indices = list(zip(* [iter(indices[shift_len:])] * batch_size))
     rng.shuffle(batch_indices)
     batch_indices = [item for batch in batch_indices for item in batch]
     assert clipped is False

paddlespeech/t2s/modules/transformer/repeat.py

@@ -36,4 +36,4 @@ def repeat(N, fn):
     Returns:
         MultiSequential: Repeated model instance.
     """
-    return MultiSequential(*[fn(n) for n in range(N)])
+    return MultiSequential(* [fn(n) for n in range(N)])

speechx/CMakeLists.txt

@@ -117,6 +117,7 @@ set(MKLDNN_PATH "${PADDLE_LIB_THIRD_PARTY_PATH}mkldnn")
 include_directories("${MKLDNN_PATH}/include")
 set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
 set(EXTERNAL_LIB "-lrt -ldl -lpthread")
+
 set(DEPS ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
 set(DEPS ${DEPS}
       ${MATH_LIB} ${MKLDNN_LIB}
@@ -137,4 +138,7 @@ set(DEPS ${DEPS}
 #target_link_libraries(lib_name item0 item1)
 #add_dependencies(lib_name depend-target)
 
+set(SPEECHX_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/speechx)
+
 add_subdirectory(speechx)
+add_subdirectory(examples)
\ No newline at end of file

speechx/build.sh

@@ -16,7 +16,7 @@ if [ ! -d ${boost_SOURCE_DIR} ]; then wget -c https://boostorg.jfrog.io/artifact
   echo -e "\n"
 fi
 
-rm -rf build
+#rm -rf build
 mkdir -p build
 cd build
 

speechx/cmake/external/openblas.cmake

@@ -18,6 +18,8 @@ ExternalProject_Add(
     SOURCE_DIR  ${OpenBLAS_SOURCE_DIR}
     CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=<INSTALL_DIR> 
     CMAKE_GENERATOR "Unix Makefiles")
+
+
 # https://cmake.org/cmake/help/latest/module/ExternalProject.html?highlight=externalproject_get_property#external-project-definition
 ExternalProject_Get_Property(OPENBLAS INSTALL_DIR)
 set(OpenBLAS_INSTALL_PREFIX ${INSTALL_DIR})

speechx/examples/CMakeLists.txt

+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_subdirectory(feat)
+add_subdirectory(nnet)
+add_subdirectory(decoder)

speechx/examples/README.md

+# Examples
+
+* decoder - offline decoder 
+* feat - mfcc, linear 
+* nnet - ds2 nn

speechx/examples/decoder/CMakeLists.txt

+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_executable(offline-decoder-main ${CMAKE_CURRENT_SOURCE_DIR}/offline-decoder-main.cc)
+target_include_directories(offline-decoder-main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(offline-decoder-main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
\ No newline at end of file

speechx/examples/decoder/offline-decoder-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 "decoder/ctc_beam_search_decoder.h"
+#include "kaldi/util/table-types.h"
+#include "nnet/decodable.h"
+#include "nnet/paddle_nnet.h"
+
+DEFINE_string(feature_respecifier, "", "test nnet prob");
+
+using kaldi::BaseFloat;
+using kaldi::Matrix;
+using std::vector;
+
+// void SplitFeature(kaldi::Matrix<BaseFloat> feature,
+//                  int32 chunk_size,
+//                  std::vector<kaldi::Matrix<BaseFloat>* feature_chunks) {
+
+//}
+
+int main(int argc, char* argv[]) {
+    gflags::ParseCommandLineFlags(&argc, &argv, false);
+    google::InitGoogleLogging(argv[0]);
+
+    kaldi::SequentialBaseFloatMatrixReader feature_reader(
+        FLAGS_feature_respecifier);
+
+    // test nnet_output --> decoder result
+    int32 num_done = 0, num_err = 0;
+
+    ppspeech::CTCBeamSearchOptions opts;
+    ppspeech::CTCBeamSearch decoder(opts);
+
+    ppspeech::ModelOptions model_opts;
+    std::shared_ptr<ppspeech::PaddleNnet> nnet(
+        new ppspeech::PaddleNnet(model_opts));
+
+    std::shared_ptr<ppspeech::Decodable> decodable(
+        new ppspeech::Decodable(nnet));
+
+    // int32 chunk_size = 35;
+    decoder.InitDecoder();
+    for (; !feature_reader.Done(); feature_reader.Next()) {
+        string utt = feature_reader.Key();
+        const kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
+        decodable->FeedFeatures(feature);
+        decoder.AdvanceDecode(decodable, 8);
+        decodable->InputFinished();
+        std::string result;
+        result = decoder.GetFinalBestPath();
+        KALDI_LOG << " the result of " << utt << " is " << result;
+        decodable->Reset();
+        ++num_done;
+    }
+
+    KALDI_LOG << "Done " << num_done << " utterances, " << num_err
+              << " with errors.";
+    return (num_done != 0 ? 0 : 1);
+}
\ No newline at end of file

speechx/examples/feat/CMakeLists.txt

+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+
+add_executable(mfcc-test ${CMAKE_CURRENT_SOURCE_DIR}/feature-mfcc-test.cc)
+target_include_directories(mfcc-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(mfcc-test kaldi-mfcc)
+
+add_executable(linear-spectrogram-main ${CMAKE_CURRENT_SOURCE_DIR}/linear-spectrogram-main.cc)
+target_include_directories(linear-spectrogram-main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(linear-spectrogram-main frontend kaldi-util kaldi-feat-common gflags glog)
\ No newline at end of file

speechx/examples/feat/linear-spectrogram-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 "frontend/feature_extractor_interface.h"
+#include "frontend/linear_spectrogram.h"
+#include "frontend/normalizer.h"
+#include "kaldi/feat/wave-reader.h"
+#include "kaldi/util/kaldi-io.h"
+#include "kaldi/util/table-types.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;
+    ppspeech::LinearSpectrogramOptions opt;
+    opt.frame_opts.frame_length_ms = 20;
+    opt.frame_opts.frame_shift_ms = 10;
+    ppspeech::DecibelNormalizerOptions db_norm_opt;
+    std::unique_ptr<ppspeech::FeatureExtractorInterface> base_feature_extractor(
+        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;
+
+    LOG(INFO) << mean_.size();
+    for (size_t i = 0; i < mean_.size(); i++) {
+        mean_[i] /= count_;
+        variance_[i] = variance_[i] / count_ - mean_[i] * mean_[i];
+        if (variance_[i] < 1.0e-20) {
+            variance_[i] = 1.0e-20;
+        }
+        variance_[i] = 1.0 / std::sqrt(variance_[i]);
+    }
+
+    for (; !wav_reader.Done(); wav_reader.Next()) {
+        std::string utt = wav_reader.Key();
+        const kaldi::WaveData& wave_data = wav_reader.Value();
+
+        int32 this_channel = 0;
+        kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
+                                                    this_channel);
+        int tot_samples = waveform.Dim();
+        int sample_offset = 0;
+        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_idx = 0;
+        kaldi::Matrix<kaldi::BaseFloat> features(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(cur_idx, col_idx) =
+                        (feat(row_idx, col_idx) - mean_[col_idx]) *
+                        variance_[col_idx];
+                }
+                ++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++;
+    }
+    KALDI_LOG << "Done " << num_done << " utterances, " << num_err
+              << " with errors.";
+    return (num_done != 0 ? 0 : 1);
+}

speechx/examples/nnet/CMakeLists.txt

+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_executable(pp-model-test ${CMAKE_CURRENT_SOURCE_DIR}/pp-model-test.cc)
+target_include_directories(pp-model-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(pp-model-test PUBLIC nnet gflags ${DEPS})
\ No newline at end of file

speechx/speechx/codelab/nnet_test/model_test.cc → speechx/examples/nnet/pp-model-test.cc

-#include "paddle_inference_api.h"
+// 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.
+
 #include <gflags/gflags.h>
-#include <iostream>
-#include <thread>
+#include <algorithm>
 #include <fstream>
+#include <functional>
+#include <iostream>
 #include <iterator>
-#include <algorithm>
 #include <numeric>
-#include <functional>
+#include <thread>
+#include "paddle_inference_api.h"
+
+using std::cout;
+using std::endl;
+
+DEFINE_string(model_path, "avg_1.jit.pdmodel", "xxx.pdmodel");
+DEFINE_string(param_path, "avg_1.jit.pdiparams", "xxx.pdiparams");
+
 
 void produce_data(std::vector<std::vector<float>>* data);
 void model_forward_test();
 
-int main(int argc, char* argv[]) {
-    gflags::ParseCommandLineFlags(&argc, &argv, true);
-    model_forward_test();
-    return 0;
+void produce_data(std::vector<std::vector<float>>* data) {
+    int chunk_size = 35;  // chunk_size in frame
+    int col_size = 161;   // feat dim
+    cout << "chunk size: " << chunk_size << endl;
+    cout << "feat dim: " << col_size << endl;
+
+    data->reserve(chunk_size);
+    data->back().reserve(col_size);
+    for (int row = 0; row < chunk_size; ++row) {
+        data->push_back(std::vector<float>());
+        for (int col_idx = 0; col_idx < col_size; ++col_idx) {
+            data->back().push_back(0.201);
+        }
+    }
 }
 
 void model_forward_test() {
@@ -22,19 +53,26 @@ void model_forward_test() {
     std::vector<std::vector<float>> feats;
     produce_data(&feats);
 
-    std::cout << "2. load the model" << std::endl;;
-    std::string model_graph = "../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdmodel";
-    std::string model_params = "../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdiparams";
+    std::cout << "2. load the model" << std::endl;
+    ;
+    std::string model_graph = FLAGS_model_path;
+    std::string model_params = FLAGS_param_path;
+    cout << "model path: " << model_graph << endl;
+    cout << "model param path : " << model_params << endl;
+
     paddle_infer::Config config;
     config.SetModel(model_graph, model_params);
     config.SwitchIrOptim(false);
+    cout << "SwitchIrOptim: " << false << endl;
     config.DisableFCPadding();
+    cout << "DisableFCPadding: " << endl;
     auto predictor = paddle_infer::CreatePredictor(config);
 
-    std::cout << "3. feat shape, row=" << feats.size() << ",col=" << feats[0].size() << std::endl;
-    std::vector<float> paddle_input_feature_matrix;
-    for(const auto& item : feats) {
-        paddle_input_feature_matrix.insert(paddle_input_feature_matrix.end(), item.begin(), item.end());
+    std::cout << "3. feat shape, row=" << feats.size()
+              << ",col=" << feats[0].size() << std::endl;
+    std::vector<float> pp_input_mat;
+    for (const auto& item : feats) {
+        pp_input_mat.insert(pp_input_mat.end(), item.begin(), item.end());
     }
 
     std::cout << "4. fead the data to model" << std::endl;
@@ -42,63 +80,89 @@ void model_forward_test() {
     int col = feats[0].size();
     std::vector<std::string> input_names = predictor->GetInputNames();
     std::vector<std::string> output_names = predictor->GetOutputNames();
+    for (auto name : input_names) {
+        cout << "model input names: " << name << endl;
+    }
+    for (auto name : output_names) {
+        cout << "model output names: " << name << endl;
+    }
 
+    // input
     std::unique_ptr<paddle_infer::Tensor> input_tensor =
         predictor->GetInputHandle(input_names[0]);
     std::vector<int> INPUT_SHAPE = {1, row, col};
     input_tensor->Reshape(INPUT_SHAPE);
-    input_tensor->CopyFromCpu(paddle_input_feature_matrix.data());
+    input_tensor->CopyFromCpu(pp_input_mat.data());
 
-    std::unique_ptr<paddle_infer::Tensor> input_len = predictor->GetInputHandle(input_names[1]);
+    // input length
+    std::unique_ptr<paddle_infer::Tensor> input_len =
+        predictor->GetInputHandle(input_names[1]);
     std::vector<int> input_len_size = {1};
     input_len->Reshape(input_len_size);
     std::vector<int64_t> audio_len;
     audio_len.push_back(row);
     input_len->CopyFromCpu(audio_len.data());
 
-    std::unique_ptr<paddle_infer::Tensor> chunk_state_h_box = predictor->GetInputHandle(input_names[2]);
+    // state_h
+    std::unique_ptr<paddle_infer::Tensor> chunk_state_h_box =
+        predictor->GetInputHandle(input_names[2]);
     std::vector<int> chunk_state_h_box_shape = {3, 1, 1024};
     chunk_state_h_box->Reshape(chunk_state_h_box_shape);
-    int chunk_state_h_box_size = std::accumulate(chunk_state_h_box_shape.begin(), chunk_state_h_box_shape.end(), 
-                                    1, std::multiplies<int>());
+    int chunk_state_h_box_size =
+        std::accumulate(chunk_state_h_box_shape.begin(),
+                        chunk_state_h_box_shape.end(),
+                        1,
+                        std::multiplies<int>());
     std::vector<float> chunk_state_h_box_data(chunk_state_h_box_size, 0.0f);
     chunk_state_h_box->CopyFromCpu(chunk_state_h_box_data.data());
 
-    std::unique_ptr<paddle_infer::Tensor> chunk_state_c_box = predictor->GetInputHandle(input_names[3]);
+    // state_c
+    std::unique_ptr<paddle_infer::Tensor> chunk_state_c_box =
+        predictor->GetInputHandle(input_names[3]);
     std::vector<int> chunk_state_c_box_shape = {3, 1, 1024};
     chunk_state_c_box->Reshape(chunk_state_c_box_shape);
-    int chunk_state_c_box_size = std::accumulate(chunk_state_c_box_shape.begin(), chunk_state_c_box_shape.end(), 
-                                    1, std::multiplies<int>());
+    int chunk_state_c_box_size =
+        std::accumulate(chunk_state_c_box_shape.begin(),
+                        chunk_state_c_box_shape.end(),
+                        1,
+                        std::multiplies<int>());
     std::vector<float> chunk_state_c_box_data(chunk_state_c_box_size, 0.0f);
     chunk_state_c_box->CopyFromCpu(chunk_state_c_box_data.data());
 
+    // run
     bool success = predictor->Run();
 
-    std::unique_ptr<paddle_infer::Tensor> h_out = predictor->GetOutputHandle(output_names[2]);
+    // state_h out
+    std::unique_ptr<paddle_infer::Tensor> h_out =
+        predictor->GetOutputHandle(output_names[2]);
     std::vector<int> h_out_shape = h_out->shape();
-    int h_out_size = std::accumulate(h_out_shape.begin(), h_out_shape.end(),
-                                      1, std::multiplies<int>());
+    int h_out_size = std::accumulate(
+        h_out_shape.begin(), h_out_shape.end(), 1, std::multiplies<int>());
     std::vector<float> h_out_data(h_out_size);
     h_out->CopyToCpu(h_out_data.data());
 
-    std::unique_ptr<paddle_infer::Tensor> c_out = predictor->GetOutputHandle(output_names[3]);
+    // stage_c out
+    std::unique_ptr<paddle_infer::Tensor> c_out =
+        predictor->GetOutputHandle(output_names[3]);
     std::vector<int> c_out_shape = c_out->shape();
-    int c_out_size = std::accumulate(c_out_shape.begin(), c_out_shape.end(),
-                                      1, std::multiplies<int>());
+    int c_out_size = std::accumulate(
+        c_out_shape.begin(), c_out_shape.end(), 1, std::multiplies<int>());
     std::vector<float> c_out_data(c_out_size);
     c_out->CopyToCpu(c_out_data.data());
 
+    // output tensor
     std::unique_ptr<paddle_infer::Tensor> output_tensor =
         predictor->GetOutputHandle(output_names[0]);
     std::vector<int> output_shape = output_tensor->shape();
     std::vector<float> output_probs;
-    int output_size = std::accumulate(output_shape.begin(), output_shape.end(),
-                                      1, std::multiplies<int>());
+    int output_size = std::accumulate(
+        output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
     output_probs.resize(output_size);
     output_tensor->CopyToCpu(output_probs.data());
     row = output_shape[1];
     col = output_shape[2];
 
+    // probs
     std::vector<std::vector<float>> probs;
     probs.reserve(row);
     for (int i = 0; i < row; i++) {
@@ -111,24 +175,19 @@ void model_forward_test() {
     }
 
     std::vector<std::vector<float>> log_feat = probs;
-    std::cout << "probs, row: " << log_feat.size() << " col: " << log_feat[0].size() << std::endl;
+    std::cout << "probs, row: " << log_feat.size()
+              << " col: " << log_feat[0].size() << std::endl;
     for (size_t row_idx = 0; row_idx < log_feat.size(); ++row_idx) {
-      for (size_t col_idx = 0; col_idx < log_feat[row_idx].size(); ++col_idx) {
+        for (size_t col_idx = 0; col_idx < log_feat[row_idx].size();
+             ++col_idx) {
             std::cout << log_feat[row_idx][col_idx] << " ";
         }
         std::cout << std::endl;
     }
 }
 
-void produce_data(std::vector<std::vector<float>>* data) {
-  int chunk_size = 35;
-  int col_size = 161;
-  data->reserve(chunk_size);
-  data->back().reserve(col_size);
-  for (int row = 0; row < chunk_size; ++row) {
-    data->push_back(std::vector<float>());
-    for (int col_idx = 0; col_idx < col_size; ++col_idx) {
-      data->back().push_back(0.201);
-    }
-  }
+int main(int argc, char* argv[]) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    model_forward_test();
+    return 0;
 }

speechx/speechx/CMakeLists.txt

@@ -31,15 +31,3 @@ ${CMAKE_CURRENT_SOURCE_DIR}
 ${CMAKE_CURRENT_SOURCE_DIR}/decoder
 )
 add_subdirectory(decoder)
-
-add_executable(mfcc-test codelab/feat_test/feature-mfcc-test.cc)
-target_link_libraries(mfcc-test kaldi-mfcc)
-
-add_executable(linear_spectrogram_main codelab/feat_test/linear_spectrogram_main.cc)
-target_link_libraries(linear_spectrogram_main frontend kaldi-util kaldi-feat-common gflags glog)
-
-add_executable(offline_decoder_main codelab/decoder_test/offline_decoder_main.cc)
-target_link_libraries(offline_decoder_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
-
-add_executable(model_test codelab/nnet_test/model_test.cc)
-target_link_libraries(model_test PUBLIC nnet gflags ${DEPS})

speechx/speechx/base/basic_types.h

@@ -43,18 +43,18 @@ typedef unsigned long long uint64;
 
 typedef signed int char32;
 
-const uint8  kuint8max  = (( uint8) 0xFF);
-const uint16 kuint16max = ((uint16) 0xFFFF);
-const uint32 kuint32max = ((uint32) 0xFFFFFFFF);
-const uint64 kuint64max = ((uint64) (0xFFFFFFFFFFFFFFFFLL));
-const  int8  kint8min   = ((  int8) 0x80);
-const  int8  kint8max   = ((  int8) 0x7F);
-const  int16 kint16min  = (( int16) 0x8000);
-const  int16 kint16max  = (( int16) 0x7FFF);
-const  int32 kint32min  = (( int32) 0x80000000);
-const  int32 kint32max  = (( int32) 0x7FFFFFFF);
-const  int64 kint64min  = (( int64) (0x8000000000000000LL));
-const  int64 kint64max  = (( int64) (0x7FFFFFFFFFFFFFFFLL));
+const uint8 kuint8max = ((uint8)0xFF);
+const uint16 kuint16max = ((uint16)0xFFFF);
+const uint32 kuint32max = ((uint32)0xFFFFFFFF);
+const uint64 kuint64max = ((uint64)(0xFFFFFFFFFFFFFFFFLL));
+const int8 kint8min = ((int8)0x80);
+const int8 kint8max = ((int8)0x7F);
+const int16 kint16min = ((int16)0x8000);
+const int16 kint16max = ((int16)0x7FFF);
+const int32 kint32min = ((int32)0x80000000);
+const int32 kint32max = ((int32)0x7FFFFFFF);
+const int64 kint64min = ((int64)(0x8000000000000000LL));
+const int64 kint64max = ((int64)(0x7FFFFFFFFFFFFFFFLL));
 
 const BaseFloat kBaseFloatMax = std::numeric_limits<BaseFloat>::max();
 const BaseFloat kBaseFloatMin = std::numeric_limits<BaseFloat>::min();

speechx/speechx/base/common.h

@@ -15,22 +15,22 @@
 #pragma once
 
 #include <deque>
+#include <fstream>
 #include <iostream>
 #include <istream>
-#include <fstream>
 #include <map>
 #include <memory>
+#include <mutex>
 #include <ostream>
 #include <set>
 #include <sstream>
 #include <stack>
 #include <string>
-#include <vector>
 #include <unordered_map>
 #include <unordered_set>
-#include <mutex>
+#include <vector>
 
-#include "base/log.h"
-#include "base/flags.h"
 #include "base/basic_types.h"
+#include "base/flags.h"
+#include "base/log.h"
 #include "base/macros.h"

speechx/speechx/base/thread_pool.h

@@ -23,28 +23,29 @@
 #ifndef BASE_THREAD_POOL_H
 #define BASE_THREAD_POOL_H
 
-#include <vector>
-#include <queue>
-#include <memory>
-#include <thread>
-#include <mutex>
 #include <condition_variable>
-#include <future>
 #include <functional>
+#include <future>
+#include <memory>
+#include <mutex>
+#include <queue>
 #include <stdexcept>
+#include <thread>
+#include <vector>
 
 class ThreadPool {
-public:
+  public:
     ThreadPool(size_t);
-    template<class F, class... Args>
+    template <class F, class... Args>
     auto enqueue(F&& f, Args&&... args)
         -> std::future<typename std::result_of<F(Args...)>::type>;
     ~ThreadPool();
-private:
+
+  private:
     // need to keep track of threads so we can join them
-    std::vector< std::thread > workers;
+    std::vector<std::thread> workers;
     // the task queue
-    std::queue< std::function<void()> > tasks;
+    std::queue<std::function<void()>> tasks;
 
     // synchronization
     std::mutex queue_mutex;
@@ -53,68 +54,57 @@ private:
 };
 
 // the constructor just launches some amount of workers
-inline ThreadPool::ThreadPool(size_t threads)
-    :   stop(false)
-{
-    for(size_t i = 0;i<threads;++i)
-        workers.emplace_back(
-            [this]
-            {
-                for(;;)
-                {
+inline ThreadPool::ThreadPool(size_t threads) : stop(false) {
+    for (size_t i = 0; i < threads; ++i)
+        workers.emplace_back([this] {
+            for (;;) {
                 std::function<void()> task;
 
                 {
                     std::unique_lock<std::mutex> lock(this->queue_mutex);
-                        this->condition.wait(lock,
-                            [this]{ return this->stop || !this->tasks.empty(); });
-                        if(this->stop && this->tasks.empty())
-                            return;
+                    this->condition.wait(lock, [this] {
+                        return this->stop || !this->tasks.empty();
+                    });
+                    if (this->stop && this->tasks.empty()) return;
                     task = std::move(this->tasks.front());
                     this->tasks.pop();
                 }
 
                 task();
             }
-            }
-        );
+        });
 }
 
 // add new work item to the pool
-template<class F, class... Args>
+template <class F, class... Args>
 auto ThreadPool::enqueue(F&& f, Args&&... args)
-    -> std::future<typename std::result_of<F(Args...)>::type>
-{
+    -> std::future<typename std::result_of<F(Args...)>::type> {
     using return_type = typename std::result_of<F(Args...)>::type;
 
-    auto task = std::make_shared< std::packaged_task<return_type()> >(
-            std::bind(std::forward<F>(f), std::forward<Args>(args)...)
-        );
+    auto task = std::make_shared<std::packaged_task<return_type()>>(
+        std::bind(std::forward<F>(f), std::forward<Args>(args)...));
 
     std::future<return_type> res = task->get_future();
     {
         std::unique_lock<std::mutex> lock(queue_mutex);
 
         // don't allow enqueueing after stopping the pool
-        if(stop)
-            throw std::runtime_error("enqueue on stopped ThreadPool");
+        if (stop) throw std::runtime_error("enqueue on stopped ThreadPool");
 
-        tasks.emplace([task](){ (*task)(); });
+        tasks.emplace([task]() { (*task)(); });
     }
     condition.notify_one();
     return res;
 }
 
 // the destructor joins all threads
-inline ThreadPool::~ThreadPool()
-{
+inline ThreadPool::~ThreadPool() {
     {
         std::unique_lock<std::mutex> lock(queue_mutex);
         stop = true;
     }
     condition.notify_all();
-    for(std::thread &worker: workers)
-        worker.join();
+    for (std::thread& worker : workers) worker.join();
 }
 
 #endif

speechx/speechx/codelab/README.md

-# codelab
-
-This directory is here for testing some funcitons temporaril.
-

speechx/speechx/codelab/decoder_test/offline_decoder_main.cc

-// todo refactor, repalce with gtest
-
-#include "decoder/ctc_beam_search_decoder.h"
-#include "kaldi/util/table-types.h"
-#include "base/log.h"
-#include "base/flags.h"
-#include "nnet/paddle_nnet.h"
-#include "nnet/decodable.h"
-
-DEFINE_string(feature_respecifier, "", "test nnet prob");
-
-using kaldi::BaseFloat;
-using kaldi::Matrix;
-using std::vector;
-
-//void SplitFeature(kaldi::Matrix<BaseFloat> feature, 
-//                  int32 chunk_size,
-//                  std::vector<kaldi::Matrix<BaseFloat>* feature_chunks) {
-
-//}
-
-int main(int argc, char* argv[]) {
-  gflags::ParseCommandLineFlags(&argc, &argv, false);
-  google::InitGoogleLogging(argv[0]);
-  
-  kaldi::SequentialBaseFloatMatrixReader feature_reader(FLAGS_feature_respecifier);
-
-  // test nnet_output --> decoder result
-  int32 num_done = 0, num_err = 0;
-  
-  ppspeech::CTCBeamSearchOptions opts;
-  ppspeech::CTCBeamSearch decoder(opts);
-  
-  ppspeech::ModelOptions model_opts;
-  std::shared_ptr<ppspeech::PaddleNnet> nnet(new ppspeech::PaddleNnet(model_opts));
-
-  std::shared_ptr<ppspeech::Decodable> decodable(new ppspeech::Decodable(nnet));
-
-  //int32 chunk_size = 35;
-  decoder.InitDecoder();
-  for (; !feature_reader.Done(); feature_reader.Next()) {
-    string utt = feature_reader.Key();
-    const kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
-    decodable->FeedFeatures(feature);
-    decoder.AdvanceDecode(decodable, 8);
-    decodable->InputFinished();
-    std::string result;
-    result = decoder.GetFinalBestPath();
-    KALDI_LOG << " the result of " << utt << " is " << result;
-    decodable->Reset();
-    ++num_done;
-  }
-
-  KALDI_LOG << "Done " << num_done << " utterances, " << num_err
-            << " with errors.";
-  return (num_done != 0 ? 0 : 1);
-}
\ No newline at end of file

speechx/speechx/codelab/feat_test/linear_spectrogram_main.cc

-// todo refactor, repalce with gtest
-
-#include "frontend/linear_spectrogram.h"
-#include "frontend/normalizer.h"
-#include "frontend/feature_extractor_interface.h"
-#include "kaldi/util/table-types.h"
-#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;
-  ppspeech::LinearSpectrogramOptions opt;
-  opt.frame_opts.frame_length_ms = 20;
-  opt.frame_opts.frame_shift_ms = 10;
-  ppspeech::DecibelNormalizerOptions db_norm_opt;
-  std::unique_ptr<ppspeech::FeatureExtractorInterface> base_feature_extractor(
-    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;
-
-  LOG(INFO) << mean_.size();
-  for (size_t i = 0; i < mean_.size(); i++) {
-    mean_[i] /= count_;
-    variance_[i] = variance_[i] / count_ - mean_[i] * mean_[i];
-    if (variance_[i] < 1.0e-20) {
-        variance_[i] = 1.0e-20;
-    }
-    variance_[i] = 1.0 / std::sqrt(variance_[i]);
-  }
-
-  for (; !wav_reader.Done(); wav_reader.Next()) {
-    std::string utt = wav_reader.Key();
-    const kaldi::WaveData &wave_data = wav_reader.Value();
-
-    int32 this_channel = 0;
-    kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(), this_channel);
-    int tot_samples = waveform.Dim(); 
-    int sample_offset = 0;
-    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_idx = 0;
-    kaldi::Matrix<kaldi::BaseFloat> features(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(cur_idx, col_idx) = (feat(row_idx, col_idx) - mean_[col_idx])*variance_[col_idx];
-          }
-          ++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++;
-  }
-    KALDI_LOG << "Done " << num_done << " utterances, " << num_err
-              << " with errors.";
-    return (num_done != 0 ? 0 : 1);
-}

speechx/speechx/decoder/common.h

+// 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.
+
 #include "base/basic_types.h"
 
 struct DecoderResult {

speechx/speechx/decoder/ctc_beam_search_decoder.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.
+
 #include "decoder/ctc_beam_search_decoder.h"
 
 #include "base/basic_types.h"
@@ -9,25 +23,23 @@ namespace ppspeech {
 using std::vector;
 using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
 
-CTCBeamSearch::CTCBeamSearch(const CTCBeamSearchOptions& opts) :
-    opts_(opts),
+CTCBeamSearch::CTCBeamSearch(const CTCBeamSearchOptions& opts)
+    : opts_(opts),
       init_ext_scorer_(nullptr),
       blank_id(-1),
       space_id(-1),
       num_frame_decoded_(0),
       root(nullptr) {
-
     LOG(INFO) << "dict path: " << opts_.dict_file;
     if (!ReadFileToVector(opts_.dict_file, &vocabulary_)) {
         LOG(INFO) << "load the dict failed";
     }
-    LOG(INFO) << "read the vocabulary success, dict size: " << vocabulary_.size();
+    LOG(INFO) << "read the vocabulary success, dict size: "
+              << vocabulary_.size();
 
     LOG(INFO) << "language model path: " << opts_.lm_path;
-    init_ext_scorer_ = std::make_shared<Scorer>(opts_.alpha, 
-                                                opts_.beta, 
-                                                opts_.lm_path, 
-                                                vocabulary_);
+    init_ext_scorer_ = std::make_shared<Scorer>(
+        opts_.alpha, opts_.beta, opts_.lm_path, vocabulary_);
 }
 
 void CTCBeamSearch::Reset() {
@@ -36,7 +48,6 @@ void CTCBeamSearch::Reset() {
 }
 
 void CTCBeamSearch::InitDecoder() {
-    
     blank_id = 0;
     auto it = std::find(vocabulary_.begin(), vocabulary_.end(), " ");
 
@@ -51,10 +62,11 @@ void CTCBeamSearch::InitDecoder() {
     root = std::make_shared<PathTrie>();
     root->score = root->log_prob_b_prev = 0.0;
     prefixes.push_back(root.get());
-    if (init_ext_scorer_ != nullptr && !init_ext_scorer_->is_character_based()) {
+    if (init_ext_scorer_ != nullptr &&
+        !init_ext_scorer_->is_character_based()) {
         auto fst_dict =
-            static_cast<fst::StdVectorFst *>(init_ext_scorer_->dictionary);
-        fst::StdVectorFst *dict_ptr = fst_dict->Copy(true);
+            static_cast<fst::StdVectorFst*>(init_ext_scorer_->dictionary);
+        fst::StdVectorFst* dict_ptr = fst_dict->Copy(true);
         root->set_dictionary(dict_ptr);
 
         auto matcher = std::make_shared<FSTMATCH>(*dict_ptr, fst::MATCH_INPUT);
@@ -62,23 +74,24 @@ void CTCBeamSearch::InitDecoder() {
     }
 }
 
-void CTCBeamSearch::Decode(std::shared_ptr<kaldi::DecodableInterface> decodable) {
+void CTCBeamSearch::Decode(
+    std::shared_ptr<kaldi::DecodableInterface> decodable) {
     return;
 }
 
-int32 CTCBeamSearch::NumFrameDecoded() {
-  return num_frame_decoded_;
-}
+int32 CTCBeamSearch::NumFrameDecoded() { return num_frame_decoded_; }
 
 // todo rename, refactor
-void CTCBeamSearch::AdvanceDecode(const std::shared_ptr<kaldi::DecodableInterface>& decodable,
+void CTCBeamSearch::AdvanceDecode(
+    const std::shared_ptr<kaldi::DecodableInterface>& decodable,
     int max_frames) {
     while (max_frames > 0) {
         vector<vector<BaseFloat>> likelihood;
         if (decodable->IsLastFrame(NumFrameDecoded() + 1)) {
             break;
         }
-      likelihood.push_back(decodable->FrameLogLikelihood(NumFrameDecoded() + 1));
+        likelihood.push_back(
+            decodable->FrameLogLikelihood(NumFrameDecoded() + 1));
         AdvanceDecoding(likelihood);
         max_frames--;
     }
@@ -93,12 +106,13 @@ void CTCBeamSearch::ResetPrefixes() {
     }
 }
 
-int CTCBeamSearch::DecodeLikelihoods(const vector<vector<float>>&probs, 
+int CTCBeamSearch::DecodeLikelihoods(const vector<vector<float>>& probs,
                                      vector<string>& nbest_words) {
     kaldi::Timer timer;
     timer.Reset();
     AdvanceDecoding(probs);
-  LOG(INFO) <<"ctc decoding elapsed time(s) " << static_cast<float>(timer.Elapsed()) / 1000.0f;
+    LOG(INFO) << "ctc decoding elapsed time(s) "
+              << static_cast<float>(timer.Elapsed()) / 1000.0f;
     return 0;
 }
 
@@ -124,12 +138,13 @@ void CTCBeamSearch::AdvanceDecoding(const vector<vector<BaseFloat>>& probs) {
     double cutoff_prob = opts_.cutoff_prob;
     size_t cutoff_top_n = opts_.cutoff_top_n;
 
-  vector<vector<double>> probs_seq(probs.size(), vector<double>(probs[0].size(), 0));
+    vector<vector<double>> probs_seq(probs.size(),
+                                     vector<double>(probs[0].size(), 0));
 
     int row = probs.size();
     int col = probs[0].size();
-  for(int i = 0; i < row; i++) {
-    for (int j = 0; j < col; j++){
+    for (int i = 0; i < row; i++) {
+        for (int j = 0; j < col; j++) {
             probs_seq[i][j] = static_cast<double>(probs[i][j]);
         }
     }
@@ -141,7 +156,8 @@ void CTCBeamSearch::AdvanceDecoding(const vector<vector<BaseFloat>>& probs) {
         bool full_beam = false;
         if (init_ext_scorer_ != nullptr) {
             size_t num_prefixes = std::min(prefixes.size(), beam_size);
-      std::sort(prefixes.begin(), prefixes.begin() + num_prefixes,
+            std::sort(prefixes.begin(),
+                      prefixes.begin() + num_prefixes,
                       prefix_compare);
 
             if (num_prefixes == 0) {
@@ -181,7 +197,8 @@ void CTCBeamSearch::AdvanceDecoding(const vector<vector<BaseFloat>>& probs) {
     }  // for probs_seq
 }
 
-int32 CTCBeamSearch::SearchOneChar(const bool& full_beam,
+int32 CTCBeamSearch::SearchOneChar(
+    const bool& full_beam,
     const std::pair<size_t, BaseFloat>& log_prob_idx,
     const BaseFloat& min_cutoff) {
     size_t beam_size = opts_.beam_size;
@@ -196,10 +213,8 @@ int32 CTCBeamSearch::SearchOneChar(const bool& full_beam,
         }
 
         if (c == blank_id) {
-      prefix->log_prob_b_cur = log_sum_exp(
-                              prefix->log_prob_b_cur, 
-                              log_prob_c +
-                              prefix->score);
+            prefix->log_prob_b_cur =
+                log_sum_exp(prefix->log_prob_b_cur, log_prob_c + prefix->score);
             continue;
         }
 
@@ -207,9 +222,7 @@ int32 CTCBeamSearch::SearchOneChar(const bool& full_beam,
         if (c == prefix->character) {
             // p_{nb}(l;x_{1:t}) = p(c;x_{t})p(l;x_{1:t-1})
             prefix->log_prob_nb_cur = log_sum_exp(
-                                  prefix->log_prob_nb_cur,
-                                  log_prob_c + 
-                                  prefix->log_prob_nb_prev);
+                prefix->log_prob_nb_cur, log_prob_c + prefix->log_prob_nb_prev);
         }
 
         // get new prefix
@@ -228,7 +241,7 @@ int32 CTCBeamSearch::SearchOneChar(const bool& full_beam,
             // language model scoring
             if (init_ext_scorer_ != nullptr &&
                 (c == space_id || init_ext_scorer_->is_character_based())) {
-        PathTrie *prefix_to_score = nullptr;
+                PathTrie* prefix_to_score = nullptr;
                 // skip scoring the space
                 if (init_ext_scorer_->is_character_based()) {
                     prefix_to_score = prefix_new;
@@ -247,8 +260,7 @@ int32 CTCBeamSearch::SearchOneChar(const bool& full_beam,
             }
             // p_{nb}(l;x_{1:t})
             prefix_new->log_prob_nb_cur =
-                      log_sum_exp(prefix_new->log_prob_nb_cur, 
-                                  log_p);
+                log_sum_exp(prefix_new->log_prob_nb_cur, log_p);
         }
     }  // end of loop over prefix
     return 0;
@@ -258,9 +270,7 @@ void CTCBeamSearch::CalculateApproxScore() {
     size_t beam_size = opts_.beam_size;
     size_t num_prefixes = std::min(prefixes.size(), beam_size);
     std::sort(
-      prefixes.begin(), 
-      prefixes.begin() + num_prefixes, 
-      prefix_compare);
+        prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
 
     // compute aproximate ctc score as the return score, without affecting the
     // return order of decoding result. To delete when decoder gets stable.
@@ -274,8 +284,8 @@ void CTCBeamSearch::CalculateApproxScore() {
             // remove word insert
             approx_ctc = approx_ctc - prefix_length * init_ext_scorer_->beta;
             // remove language model weight:
-      approx_ctc -=
-          (init_ext_scorer_->get_sent_log_prob(words)) * init_ext_scorer_->alpha;
+            approx_ctc -= (init_ext_scorer_->get_sent_log_prob(words)) *
+                          init_ext_scorer_->alpha;
         }
         prefixes[i]->approx_ctc = approx_ctc;
     }
@@ -283,13 +293,15 @@ void CTCBeamSearch::CalculateApproxScore() {
 
 void CTCBeamSearch::LMRescore() {
     size_t beam_size = opts_.beam_size;
-  if (init_ext_scorer_ != nullptr && !init_ext_scorer_->is_character_based()) {
+    if (init_ext_scorer_ != nullptr &&
+        !init_ext_scorer_->is_character_based()) {
         for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
             auto prefix = prefixes[i];
             if (!prefix->is_empty() && prefix->character != space_id) {
                 float score = 0.0;
                 vector<string> ngram = init_ext_scorer_->make_ngram(prefix);
-        score = init_ext_scorer_->get_log_cond_prob(ngram) * init_ext_scorer_->alpha;
+                score = init_ext_scorer_->get_log_cond_prob(ngram) *
+                        init_ext_scorer_->alpha;
                 score += init_ext_scorer_->beta;
                 prefix->score += score;
             }

speechx/speechx/decoder/ctc_beam_search_decoder.h

+// 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.
+
 #include "base/common.h"
+#include "decoder/ctc_decoders/path_trie.h"
+#include "decoder/ctc_decoders/scorer.h"
 #include "nnet/decodable-itf.h"
 #include "util/parse-options.h"
-#include "decoder/ctc_decoders/scorer.h"
-#include "decoder/ctc_decoders/path_trie.h"
 
 #pragma once
 
@@ -17,26 +31,27 @@ struct CTCBeamSearchOptions {
     int beam_size;
     int cutoff_top_n;
     int num_proc_bsearch;
-    CTCBeamSearchOptions() :
-        dict_file("./model/words.txt"),
+    CTCBeamSearchOptions()
+        : dict_file("./model/words.txt"),
           lm_path("./model/lm.arpa"),
           alpha(1.9f),
           beta(5.0),
           beam_size(300),
           cutoff_prob(0.99f),
           cutoff_top_n(40),
-        num_proc_bsearch(0) {
-    }
+          num_proc_bsearch(0) {}
 
     void Register(kaldi::OptionsItf* opts) {
         opts->Register("dict", &dict_file, "dict file ");
         opts->Register("lm-path", &lm_path, "language model file");
         opts->Register("alpha", &alpha, "alpha");
         opts->Register("beta", &beta, "beta");
-        opts->Register("beam-size", &beam_size, "beam size for beam search method");
+        opts->Register(
+            "beam-size", &beam_size, "beam size for beam search method");
         opts->Register("cutoff-prob", &cutoff_prob, "cutoff probs");
         opts->Register("cutoff-top-n", &cutoff_top_n, "cutoff top n");
-        opts->Register("num-proc-bsearch", &num_proc_bsearch, "num proc bsearch");
+        opts->Register(
+            "num-proc-bsearch", &num_proc_bsearch, "num proc bsearch");
     }
 };
 
@@ -50,11 +65,13 @@ class CTCBeamSearch {
     std::vector<std::pair<double, std::string>> GetNBestPath();
     std::string GetFinalBestPath();
     int NumFrameDecoded();
-    int DecodeLikelihoods(const std::vector<std::vector<BaseFloat>>&probs, 
+    int DecodeLikelihoods(const std::vector<std::vector<BaseFloat>>& probs,
                           std::vector<std::string>& nbest_words);
-    void AdvanceDecode(const std::shared_ptr<kaldi::DecodableInterface>& decodable,
+    void AdvanceDecode(
+        const std::shared_ptr<kaldi::DecodableInterface>& decodable,
         int max_frames);
     void Reset();
+
   private:
     void ResetPrefixes();
     int32 SearchOneChar(const bool& full_beam,
@@ -66,7 +83,7 @@ class CTCBeamSearch {
 
     CTCBeamSearchOptions opts_;
     std::shared_ptr<Scorer> init_ext_scorer_;  // todo separate later
-  //std::vector<DecodeResult> decoder_results_;
+    // std::vector<DecodeResult> decoder_results_;
     std::vector<std::string> vocabulary_;  // todo remove later
     size_t blank_id;
     int space_id;

speechx/speechx/frontend/fbank.h

@@ -24,7 +24,8 @@ class FbankExtractor : FeatureExtractorInterface {
   public:
     explicit FbankExtractor(const FbankOptions& opts,
                             share_ptr<FeatureExtractorInterface> pre_extractor);
-    virtual void AcceptWaveform(const kaldi::Vector<kaldi::BaseFloat>& input) = 0;
+    virtual void AcceptWaveform(
+        const kaldi::Vector<kaldi::BaseFloat>& input) = 0;
     virtual void Read(kaldi::Vector<kaldi::BaseFloat>* feat) = 0;
     virtual size_t Dim() const = 0;
 

speechx/speechx/frontend/feature_extractor_controller.h

+// 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.
+

speechx/speechx/frontend/feature_extractor_controller_impl.h

+// 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.
+

speechx/speechx/frontend/feature_extractor_interface.h

@@ -21,7 +21,8 @@ namespace ppspeech {
 
 class FeatureExtractorInterface {
   public:
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input) = 0;
+    virtual void AcceptWaveform(
+        const kaldi::VectorBase<kaldi::BaseFloat>& input) = 0;
     virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat) = 0;
     virtual size_t Dim() const = 0;
 };

speechx/speechx/frontend/linear_spectrogram.cc

@@ -25,7 +25,7 @@ using kaldi::VectorBase;
 using kaldi::Matrix;
 using std::vector;
 
-//todo remove later
+// todo remove later
 void CopyVector2StdVector_(const VectorBase<BaseFloat>& input,
                            vector<BaseFloat>* output) {
     if (input.Dim() == 0) return;

speechx/speechx/frontend/linear_spectrogram.h

+// 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.
+
 
 #pragma once
 
+#include "base/common.h"
 #include "frontend/feature_extractor_interface.h"
 #include "kaldi/feat/feature-window.h"
-#include "base/common.h"
 
 namespace ppspeech {
 
 struct LinearSpectrogramOptions {
     kaldi::FrameExtractionOptions frame_opts;
-    LinearSpectrogramOptions():
-        frame_opts() {}
+    LinearSpectrogramOptions() : frame_opts() {}
 
-    void Register(kaldi::OptionsItf* opts) {
-        frame_opts.Register(opts);
-    }
+    void Register(kaldi::OptionsItf* opts) { frame_opts.Register(opts); }
 };
 
 class LinearSpectrogram : public FeatureExtractorInterface {
   public:
-    explicit LinearSpectrogram(const LinearSpectrogramOptions& opts,
+    explicit LinearSpectrogram(
+        const LinearSpectrogramOptions& opts,
         std::unique_ptr<FeatureExtractorInterface> base_extractor);
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    virtual void AcceptWaveform(
+        const kaldi::VectorBase<kaldi::BaseFloat>& input);
     virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
     virtual size_t Dim() const { return dim_; }
     void ReadFeats(kaldi::Matrix<kaldi::BaseFloat>* feats);

speechx/speechx/frontend/normalizer.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.
+
 
 #include "frontend/normalizer.h"
 #include "kaldi/feat/cmvn.h"
@@ -16,7 +30,8 @@ DecibelNormalizer::DecibelNormalizer(const DecibelNormalizerOptions& opts) {
     dim_ = 0;
 }
 
-void DecibelNormalizer::AcceptWaveform(const kaldi::VectorBase<BaseFloat>& input) {
+void DecibelNormalizer::AcceptWaveform(
+    const kaldi::VectorBase<BaseFloat>& input) {
     dim_ = input.Dim();
     waveform_.Resize(input.Dim());
     waveform_.CopyFromVec(input);
@@ -27,7 +42,7 @@ void DecibelNormalizer::Read(kaldi::VectorBase<BaseFloat>* feat) {
     Compute(waveform_, feat);
 }
 
-//todo remove later
+// todo remove later
 void CopyVector2StdVector(const kaldi::VectorBase<BaseFloat>& input,
                           vector<BaseFloat>* output) {
     if (input.Dim() == 0) return;
@@ -61,7 +76,7 @@ bool DecibelNormalizer::Compute(const VectorBase<BaseFloat>& input,
     }
 
     // square
-  for (auto &d : samples) {
+    for (auto& d : samples) {
         if (opts_.convert_int_float) {
             d = d * wave_float_normlization;
         }
@@ -74,14 +89,15 @@ bool DecibelNormalizer::Compute(const VectorBase<BaseFloat>& input,
     gain = opts_.target_db - rms_db;
 
     if (gain > opts_.max_gain_db) {
-    LOG(ERROR) << "Unable to normalize segment to " << opts_.target_db << "dB,"
+        LOG(ERROR)
+            << "Unable to normalize segment to " << opts_.target_db << "dB,"
             << "because the the probable gain have exceeds opts_.max_gain_db"
             << opts_.max_gain_db << "dB.";
         return false;
     }
 
     // Note that this is an in-place transformation.
-  for (auto &item : samples) {
+    for (auto& item : samples) {
         // python item *= 10.0 ** (gain / 20.0)
         item *= std::pow(10.0, gain / 20.0);
     }
@@ -100,21 +116,20 @@ void CMVN::AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input) {
     return;
 }
 
-void CMVN::Read(kaldi::VectorBase<BaseFloat>* feat) {
-  return;
-}
+void CMVN::Read(kaldi::VectorBase<BaseFloat>* feat) { return; }
 
 // 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() > 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_ "
+        KALDI_ERR
+            << "You requested variance normalization but no variance stats_ "
             << "are supplied.";
     }
 
@@ -122,17 +137,20 @@ void CMVN::ApplyCMVN(bool var_norm, VectorBase<BaseFloat>* feats) {
     // 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: "
+        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;
+        // 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);
+            SubVector<double> stats_tmp(mean_stats_apply.Data() + dim * idx,
+                                        dim);
             stats_tmp.CopyFromVec(mean_stats);
         }
         offset.AddVec(-1.0 / count, mean_stats_apply);
@@ -144,18 +162,18 @@ void CMVN::ApplyCMVN(bool var_norm, VectorBase<BaseFloat>* feats) {
     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;
+        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);
+        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;

speechx/speechx/frontend/normalizer.h

+// 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.
+
 
 #pragma once
 
 #include "base/common.h"
 #include "frontend/feature_extractor_interface.h"
-#include "kaldi/util/options-itf.h"
 #include "kaldi/matrix/kaldi-matrix.h"
+#include "kaldi/util/options-itf.h"
 
 namespace ppspeech {
 
@@ -12,26 +26,30 @@ struct DecibelNormalizerOptions {
     float target_db;
     float max_gain_db;
     bool convert_int_float;
-  DecibelNormalizerOptions() :
-    target_db(-20),
-    max_gain_db(300.0),
-    convert_int_float(false) {}
+    DecibelNormalizerOptions()
+        : target_db(-20), max_gain_db(300.0), convert_int_float(false) {}
 
     void Register(kaldi::OptionsItf* opts) {
-      opts->Register("target-db", &target_db, "target db for db normalization");
-      opts->Register("max-gain-db", &max_gain_db, "max gain db for db normalization");
-      opts->Register("convert-int-float", &convert_int_float, "if convert int samples to float");
+        opts->Register(
+            "target-db", &target_db, "target db for db normalization");
+        opts->Register(
+            "max-gain-db", &max_gain_db, "max gain db for db normalization");
+        opts->Register("convert-int-float",
+                       &convert_int_float,
+                       "if convert int samples to float");
     }
 };
 
 class DecibelNormalizer : public FeatureExtractorInterface {
   public:
     explicit DecibelNormalizer(const DecibelNormalizerOptions& opts);
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    virtual void AcceptWaveform(
+        const kaldi::VectorBase<kaldi::BaseFloat>& input);
     virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
     virtual size_t Dim() const { return dim_; }
     bool Compute(const kaldi::VectorBase<kaldi::BaseFloat>& input,
                  kaldi::VectorBase<kaldi::BaseFloat>* feat) const;
+
   private:
     DecibelNormalizerOptions opts_;
     size_t dim_;
@@ -43,7 +61,8 @@ class DecibelNormalizer : public FeatureExtractorInterface {
 class CMVN : public FeatureExtractorInterface {
   public:
     explicit CMVN(std::string cmvn_file);
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    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,
@@ -51,6 +70,7 @@ class CMVN : public FeatureExtractorInterface {
     // for test
     void ApplyCMVN(bool var_norm, kaldi::VectorBase<BaseFloat>* feats);
     void ApplyCMVNMatrix(bool var_norm, kaldi::MatrixBase<BaseFloat>* feats);
+
   private:
     kaldi::Matrix<double> stats_;
     std::shared_ptr<FeatureExtractorInterface> base_extractor_;

speechx/speechx/frontend/window.h

@@ -13,4 +13,3 @@
 // limitations under the License.
 
 // extract the window of kaldi feat.
-

speechx/speechx/nnet/decodable-itf.h

+// 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.
+
 // itf/decodable-itf.h
 
 // Copyright 2009-2011  Microsoft Corporation;  Saarland University;
@@ -42,8 +56,10 @@ namespace kaldi {
 
     For online decoding, where the features are coming in in real time, it is
     important to understand the IsLastFrame() and NumFramesReady() functions.
-    There are two ways these are used: the old online-decoding code, in ../online/,
-    and the new online-decoding code, in ../online2/.  In the old online-decoding
+    There are two ways these are used: the old online-decoding code, in
+   ../online/,
+    and the new online-decoding code, in ../online2/.  In the old
+   online-decoding
     code, the decoder would do:
     \code{.cc}
     for (int frame = 0; !decodable.IsLastFrame(frame); frame++) {
@@ -52,13 +68,16 @@ namespace kaldi {
     \endcode
    and the call to IsLastFrame would block if the features had not arrived yet.
    The decodable object would have to know when to terminate the decoding.  This
-   online-decoding mode is still supported, it is what happens when you call, for
+   online-decoding mode is still supported, it is what happens when you call,
+   for
    example, LatticeFasterDecoder::Decode().
 
    We realized that this "blocking" mode of decoding is not very convenient
    because it forces the program to be multi-threaded and makes it complex to
-   control endpointing.  In the "new" decoding code, you don't call (for example)
-   LatticeFasterDecoder::Decode(), you call LatticeFasterDecoder::InitDecoding(),
+   control endpointing.  In the "new" decoding code, you don't call (for
+   example)
+   LatticeFasterDecoder::Decode(), you call
+   LatticeFasterDecoder::InitDecoding(),
    and then each time you get more features, you provide them to the decodable
    object, and you call LatticeFasterDecoder::AdvanceDecoding(), which does
    something like this:
@@ -68,7 +87,8 @@ namespace kaldi {
    }
    \endcode
    So the decodable object never has IsLastFrame() called.  For decoding where
-   you are starting with a matrix of features, the NumFramesReady() function will
+   you are starting with a matrix of features, the NumFramesReady() function
+   will
    always just return the number of frames in the file, and IsLastFrame() will
    return true for the last frame.
 
@@ -82,30 +102,39 @@ namespace kaldi {
 class DecodableInterface {
   public:
     /// Returns the log likelihood, which will be negated in the decoder.
-  /// The "frame" starts from zero.  You should verify that NumFramesReady() > frame
+    /// The "frame" starts from zero.  You should verify that NumFramesReady() >
+    /// frame
     /// before calling this.
     virtual BaseFloat LogLikelihood(int32 frame, int32 index) = 0;
 
     /// Returns true if this is the last frame.  Frames are zero-based, so the
     /// first frame is zero.  IsLastFrame(-1) will return false, unless the file
     /// is empty (which is a case that I'm not sure all the code will handle, so
-  /// be careful).  Caution: the behavior of this function in an online setting
+    /// be careful).  Caution: the behavior of this function in an online
+    /// setting
     /// is being changed somewhat.  In future it may return false in cases where
-  /// we haven't yet decided to terminate decoding, but later true if we decide
+    /// we haven't yet decided to terminate decoding, but later true if we
+    /// decide
     /// to terminate decoding.  The plan in future is to rely more on
     /// NumFramesReady(), and in future, IsLastFrame() would always return false
     /// in an online-decoding setting, and would only return true in a
-  /// decoding-from-matrix setting where we want to allow the last delta or LDA
+    /// decoding-from-matrix setting where we want to allow the last delta or
+    /// LDA
     /// features to be flushed out for compatibility with the baseline setup.
     virtual bool IsLastFrame(int32 frame) const = 0;
 
-  /// The call NumFramesReady() will return the number of frames currently available
-  /// for this decodable object.  This is for use in setups where you don't want the
-  /// decoder to block while waiting for input.  This is newly added as of Jan 2014,
-  /// and I hope, going forward, to rely on this mechanism more than IsLastFrame to
+    /// The call NumFramesReady() will return the number of frames currently
+    /// available
+    /// for this decodable object.  This is for use in setups where you don't
+    /// want the
+    /// decoder to block while waiting for input.  This is newly added as of Jan
+    /// 2014,
+    /// and I hope, going forward, to rely on this mechanism more than
+    /// IsLastFrame to
     /// know when to stop decoding.
     virtual int32 NumFramesReady() const {
-    KALDI_ERR << "NumFramesReady() not implemented for this decodable type.";
+        KALDI_ERR
+            << "NumFramesReady() not implemented for this decodable type.";
         return -1;
     }
 

speechx/speechx/nnet/decodable.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.
+
 #include "nnet/decodable.h"
 
 namespace ppspeech {
@@ -5,18 +19,14 @@ namespace ppspeech {
 using kaldi::BaseFloat;
 using kaldi::Matrix;
 
-Decodable::Decodable(const std::shared_ptr<NnetInterface>& nnet):
-  frontend_(NULL),
-  nnet_(nnet),
-  finished_(false),
-  frames_ready_(0) {
-}
+Decodable::Decodable(const std::shared_ptr<NnetInterface>& nnet)
+    : frontend_(NULL), nnet_(nnet), finished_(false), frames_ready_(0) {}
 
 void Decodable::Acceptlikelihood(const Matrix<BaseFloat>& likelihood) {
     frames_ready_ += likelihood.NumRows();
 }
 
-//Decodable::Init(DecodableConfig config) {
+// Decodable::Init(DecodableConfig config) {
 //}
 
 bool Decodable::IsLastFrame(int32 frame) const {
@@ -24,18 +34,14 @@ bool Decodable::IsLastFrame(int32 frame) const {
     return finished_ && (frame == frames_ready_ - 1);
 }
 
-int32 Decodable::NumIndices() const {
-  return 0;
-}
+int32 Decodable::NumIndices() const { return 0; }
 
-BaseFloat Decodable::LogLikelihood(int32 frame, int32 index) {
-  return 0;
-}
+BaseFloat Decodable::LogLikelihood(int32 frame, int32 index) { return 0; }
 
 void Decodable::FeedFeatures(const Matrix<kaldi::BaseFloat>& features) {
     nnet_->FeedForward(features, &nnet_cache_);
     frames_ready_ += nnet_cache_.NumRows();
-  return ;
+    return;
 }
 
 std::vector<BaseFloat> Decodable::FrameLogLikelihood(int32 frame) {

speechx/speechx/nnet/decodable.h

-#include "nnet/decodable-itf.h"
+// 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.
+
 #include "base/common.h"
-#include "kaldi/matrix/kaldi-matrix.h"
 #include "frontend/feature_extractor_interface.h"
+#include "kaldi/matrix/kaldi-matrix.h"
+#include "nnet/decodable-itf.h"
 #include "nnet/nnet_interface.h"
 
 namespace ppspeech {
@@ -11,15 +25,18 @@ struct DecodableOpts;
 class Decodable : public kaldi::DecodableInterface {
   public:
     explicit Decodable(const std::shared_ptr<NnetInterface>& nnet);
-    //void Init(DecodableOpts config);
+    // void Init(DecodableOpts config);
     virtual kaldi::BaseFloat LogLikelihood(int32 frame, int32 index);
     virtual bool IsLastFrame(int32 frame) const;
     virtual int32 NumIndices() const;
     virtual std::vector<BaseFloat> FrameLogLikelihood(int32 frame);
-    void Acceptlikelihood(const kaldi::Matrix<kaldi::BaseFloat>& likelihood); // remove later
-    void FeedFeatures(const kaldi::Matrix<kaldi::BaseFloat>& feature); // only for test, todo remove later
+    void Acceptlikelihood(
+        const kaldi::Matrix<kaldi::BaseFloat>& likelihood);  // remove later
+    void FeedFeatures(const kaldi::Matrix<kaldi::BaseFloat>&
+                          feature);  // only for test, todo remove later
     void Reset();
     void InputFinished() { finished_ = true; }
+
   private:
     std::shared_ptr<FeatureExtractorInterface> frontend_;
     std::shared_ptr<NnetInterface> nnet_;

speechx/speechx/nnet/nnet_interface.h

+// 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.
+
 
 #pragma once
 
@@ -10,10 +24,9 @@ namespace ppspeech {
 class NnetInterface {
   public:
     virtual void FeedForward(const kaldi::Matrix<kaldi::BaseFloat>& features,
-                             kaldi::Matrix<kaldi::BaseFloat>* inferences)= 0; 
+                             kaldi::Matrix<kaldi::BaseFloat>* inferences) = 0;
     virtual void Reset() = 0;
     virtual ~NnetInterface() {}
-
 };
 
 }  // namespace ppspeech

speechx/speechx/nnet/paddle_nnet.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.
+
 #include "nnet/paddle_nnet.h"
 #include "absl/strings/str_split.h"
 
@@ -21,18 +35,18 @@ void PaddleNnet::InitCacheEncouts(const ModelOptions& opts) {
         std::vector<std::string> tmp_shape;
         tmp_shape = absl::StrSplit(cache_shapes[i], "-");
         std::vector<int> cur_shape;
-    std::transform(tmp_shape.begin(), tmp_shape.end(),
+        std::transform(tmp_shape.begin(),
+                       tmp_shape.end(),
                        std::back_inserter(cur_shape),
-                    [](const std::string& s) {
-                        return atoi(s.c_str());
-                    });
+                       [](const std::string& s) { return atoi(s.c_str()); });
         cache_names_idx_[cache_names[i]] = i;
-    std::shared_ptr<Tensor<BaseFloat>> cache_eout = std::make_shared<Tensor<BaseFloat>>(cur_shape);
+        std::shared_ptr<Tensor<BaseFloat>> cache_eout =
+            std::make_shared<Tensor<BaseFloat>>(cur_shape);
         cache_encouts_.push_back(cache_eout);
     }
 }
 
-PaddleNnet::PaddleNnet(const ModelOptions& opts):opts_(opts) {
+PaddleNnet::PaddleNnet(const ModelOptions& opts) : opts_(opts) {
     paddle_infer::Config config;
     config.SetModel(opts.model_path, opts.params_path);
     if (opts.use_gpu) {
@@ -45,7 +59,8 @@ PaddleNnet::PaddleNnet(const ModelOptions& opts):opts_(opts) {
     if (opts.enable_profile) {
         config.EnableProfile();
     }
-    pool.reset(new paddle_infer::services::PredictorPool(config, opts.thread_num));
+    pool.reset(
+        new paddle_infer::services::PredictorPool(config, opts.thread_num));
     if (pool == nullptr) {
         LOG(ERROR) << "create the predictor pool failed";
     }
@@ -68,16 +83,14 @@ PaddleNnet::PaddleNnet(const ModelOptions& opts):opts_(opts) {
 
     std::vector<std::string> model_output_names = predictor->GetOutputNames();
     assert(output_names_vec.size() == model_output_names.size());
-    for (size_t i = 0;i < output_names_vec.size(); i++) {
+    for (size_t i = 0; i < output_names_vec.size(); i++) {
         assert(output_names_vec[i] == model_output_names[i]);
     }
     ReleasePredictor(predictor);
     InitCacheEncouts(opts);
 }
 
-void PaddleNnet::Reset() {
-  InitCacheEncouts(opts_);
-}
+void PaddleNnet::Reset() { InitCacheEncouts(opts_); }
 
 paddle_infer::Predictor* PaddleNnet::GetPredictor() {
     LOG(INFO) << "attempt to get a new predictor instance " << std::endl;
@@ -130,13 +143,14 @@ shared_ptr<Tensor<BaseFloat>> PaddleNnet::GetCacheEncoder(const string& name) {
     return cache_encouts_[iter->second];
 }
 
-void PaddleNnet::FeedForward(const Matrix<BaseFloat>& features, Matrix<BaseFloat>* inferences) {
+void PaddleNnet::FeedForward(const Matrix<BaseFloat>& features,
+                             Matrix<BaseFloat>* inferences) {
     paddle_infer::Predictor* predictor = GetPredictor();
     int row = features.NumRows();
     int col = features.NumCols();
     std::vector<BaseFloat> feed_feature;
     // todo refactor feed feature: SmileGoat
-  feed_feature.reserve(row*col);
+    feed_feature.reserve(row * col);
     for (size_t row_idx = 0; row_idx < features.NumRows(); ++row_idx) {
         for (size_t col_idx = 0; col_idx < features.NumCols(); ++col_idx) {
             feed_feature.push_back(features(row_idx, col_idx));
@@ -146,22 +160,26 @@ void PaddleNnet::FeedForward(const Matrix<BaseFloat>& features, Matrix<BaseFloat
     std::vector<std::string> output_names = predictor->GetOutputNames();
     LOG(INFO) << "feat info: row=" << row << ", col= " << col;
 
-  std::unique_ptr<paddle_infer::Tensor> input_tensor = predictor->GetInputHandle(input_names[0]);
+    std::unique_ptr<paddle_infer::Tensor> input_tensor =
+        predictor->GetInputHandle(input_names[0]);
     std::vector<int> INPUT_SHAPE = {1, row, col};
     input_tensor->Reshape(INPUT_SHAPE);
     input_tensor->CopyFromCpu(feed_feature.data());
-  std::unique_ptr<paddle_infer::Tensor> input_len = predictor->GetInputHandle(input_names[1]);
+    std::unique_ptr<paddle_infer::Tensor> input_len =
+        predictor->GetInputHandle(input_names[1]);
     std::vector<int> input_len_size = {1};
     input_len->Reshape(input_len_size);
     std::vector<int64_t> audio_len;
     audio_len.push_back(row);
     input_len->CopyFromCpu(audio_len.data());
 
-  std::unique_ptr<paddle_infer::Tensor> h_box = predictor->GetInputHandle(input_names[2]);
+    std::unique_ptr<paddle_infer::Tensor> h_box =
+        predictor->GetInputHandle(input_names[2]);
     shared_ptr<Tensor<BaseFloat>> h_cache = GetCacheEncoder(input_names[2]);
     h_box->Reshape(h_cache->get_shape());
     h_box->CopyFromCpu(h_cache->get_data().data());
-  std::unique_ptr<paddle_infer::Tensor> c_box = predictor->GetInputHandle(input_names[3]);
+    std::unique_ptr<paddle_infer::Tensor> c_box =
+        predictor->GetInputHandle(input_names[3]);
     shared_ptr<Tensor<float>> c_cache = GetCacheEncoder(input_names[3]);
     c_box->Reshape(c_cache->get_shape());
     c_box->CopyFromCpu(c_cache->get_data().data());
@@ -172,10 +190,12 @@ void PaddleNnet::FeedForward(const Matrix<BaseFloat>& features, Matrix<BaseFloat
     }
 
     LOG(INFO) << "get the model success";
-  std::unique_ptr<paddle_infer::Tensor> h_out = predictor->GetOutputHandle(output_names[2]);
+    std::unique_ptr<paddle_infer::Tensor> h_out =
+        predictor->GetOutputHandle(output_names[2]);
     assert(h_cache->get_shape() == h_out->shape());
     h_out->CopyToCpu(h_cache->get_data().data());
-  std::unique_ptr<paddle_infer::Tensor> c_out = predictor->GetOutputHandle(output_names[3]);
+    std::unique_ptr<paddle_infer::Tensor> c_out =
+        predictor->GetOutputHandle(output_names[3]);
     assert(c_cache->get_shape() == c_out->shape());
     c_out->CopyToCpu(c_cache->get_data().data());
 
@@ -187,13 +207,14 @@ void PaddleNnet::FeedForward(const Matrix<BaseFloat>& features, Matrix<BaseFloat
     col = output_shape[2];
     vector<float> inferences_result;
     inferences->Resize(row, col);
-  inferences_result.resize(row*col);
+    inferences_result.resize(row * col);
     output_tensor->CopyToCpu(inferences_result.data());
     ReleasePredictor(predictor);
 
     for (int row_idx = 0; row_idx < row; ++row_idx) {
         for (int col_idx = 0; col_idx < col; ++col_idx) {
-      (*inferences)(row_idx, col_idx) = inferences_result[col*row_idx + col_idx];
+            (*inferences)(row_idx, col_idx) =
+                inferences_result[col * row_idx + col_idx];
         }
     }
 }

speechx/speechx/nnet/paddle_nnet.h

+// 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.
+
 
 #pragma once
 
-#include "nnet/nnet_interface.h"
 #include "base/common.h"
+#include "nnet/nnet_interface.h"
 #include "paddle_inference_api.h"
 
 #include "kaldi/matrix/kaldi-matrix.h"
@@ -24,19 +38,27 @@ struct ModelOptions {
     std::string cache_shape;
     bool enable_fc_padding;
     bool enable_profile;
-  ModelOptions() : 
-      model_path("../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdmodel"),
-      params_path("../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdiparams"),
+    ModelOptions()
+        : model_path(
+              "../../../../model/paddle_online_deepspeech/model/"
+              "avg_1.jit.pdmodel"),
+          params_path(
+              "../../../../model/paddle_online_deepspeech/model/"
+              "avg_1.jit.pdiparams"),
           thread_num(2),
           use_gpu(false),
-      input_names("audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box"),
-      output_names("save_infer_model/scale_0.tmp_1,save_infer_model/scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/scale_3.tmp_1"),
+          input_names(
+              "audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_"
+              "box"),
+          output_names(
+              "save_infer_model/scale_0.tmp_1,save_infer_model/"
+              "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
+              "scale_3.tmp_1"),
           cache_names("chunk_state_h_box,chunk_state_c_box"),
           cache_shape("3-1-1024,3-1-1024"),
           switch_ir_optim(false),
           enable_fc_padding(false),
-      enable_profile(false) {
-  }
+          enable_profile(false) {}
 
     void Register(kaldi::OptionsItf* opts) {
         opts->Register("model-path", &model_path, "model file path");
@@ -47,37 +69,37 @@ struct ModelOptions {
         opts->Register("output-names", &output_names, "paddle output names");
         opts->Register("cache-names", &cache_names, "cache names");
         opts->Register("cache-shape", &cache_shape, "cache shape");
-    opts->Register("switch-ir-optiom", &switch_ir_optim, "paddle SwitchIrOptim option");
-    opts->Register("enable-fc-padding", &enable_fc_padding, "paddle EnableFCPadding option");
-    opts->Register("enable-profile", &enable_profile, "paddle EnableProfile option");
+        opts->Register("switch-ir-optiom",
+                       &switch_ir_optim,
+                       "paddle SwitchIrOptim option");
+        opts->Register("enable-fc-padding",
+                       &enable_fc_padding,
+                       "paddle EnableFCPadding option");
+        opts->Register(
+            "enable-profile", &enable_profile, "paddle EnableProfile option");
     }
 };
 
-template<typename T>
+template <typename T>
 class Tensor {
-public:
-    Tensor() {
-    }
-    Tensor(const std::vector<int>& shape) :
-        _shape(shape) {
-        int data_size = std::accumulate(_shape.begin(), _shape.end(),
-                                            1, std::multiplies<int>());
+  public:
+    Tensor() {}
+    Tensor(const std::vector<int>& shape) : _shape(shape) {
+        int data_size = std::accumulate(
+            _shape.begin(), _shape.end(), 1, std::multiplies<int>());
         LOG(INFO) << "data size: " << data_size;
         _data.resize(data_size, 0);
     }
     void reshape(const std::vector<int>& shape) {
         _shape = shape;
-        int data_size = std::accumulate(_shape.begin(), _shape.end(),
-                                        1, std::multiplies<int>());
+        int data_size = std::accumulate(
+            _shape.begin(), _shape.end(), 1, std::multiplies<int>());
         _data.resize(data_size, 0);
     }
-    const std::vector<int>& get_shape() const {
-        return _shape;
-    }
-    std::vector<T>& get_data() {
-        return _data;
-    }
-private:
+    const std::vector<int>& get_shape() const { return _shape; }
+    std::vector<T>& get_data() { return _data; }
+
+  private:
     std::vector<int> _shape;
     std::vector<T> _data;
 };
@@ -88,7 +110,8 @@ class PaddleNnet : public NnetInterface {
     virtual void FeedForward(const kaldi::Matrix<kaldi::BaseFloat>& features,
                              kaldi::Matrix<kaldi::BaseFloat>* inferences);
     virtual void Reset();
-    std::shared_ptr<Tensor<kaldi::BaseFloat>> GetCacheEncoder(const std::string& name);
+    std::shared_ptr<Tensor<kaldi::BaseFloat>> GetCacheEncoder(
+        const std::string& name);
     void InitCacheEncouts(const ModelOptions& opts);
 
   private:

speechx/speechx/utils/file_utils.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.
+
 #include "utils/file_utils.h"
 
 namespace ppspeech {
@@ -17,5 +31,4 @@ bool ReadFileToVector(const std::string& filename,
 
     return true;
 }
-
 }
\ No newline at end of file

speechx/speechx/utils/file_utils.h

+// 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.
+
 #include "base/common.h"
 
 namespace ppspeech {
 
 bool ReadFileToVector(const std::string& filename,
                       std::vector<std::string>* data);
-
 }