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DeepSpeech

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提交 ae629e2f 编写于 作者: Y Yang Zhou
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rm ds2_ol test dir

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speechx/examples/README.md
浏览文件 @ ae629e2f
......@@ -22,14 +22,7 @@ netron exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel --port 8022 --host
## For Developer
> Warning: Only for developer, make sure you know what's it.
> Reminder: Only for developer, make sure you know what's it.
* dev - for speechx developer, using for test.
* codelab - for speechx developer, using for test.
## Build WFST
> Warning: Using below example when you know what's it.
* text_lm - process text for build lm
* ngram - using to build NGram ARPA lm.
* wfst - build wfst for TLG.
speechx/examples/ds2_ol/CMakeLists.txt 已删除 100644 → 0
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cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
add_subdirectory(feat)
add_subdirectory(nnet)
add_subdirectory(decoder)
add_subdirectory(websocket)
speechx/examples/ds2_ol/aishell/run_build_tlg.sh
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......@@ -118,7 +118,7 @@ export GLOG_logtostderr=1
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# TLG decoder
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/check_tlg.log \
recognizer_test_main \
recognizer_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--cmvn_file=$cmvn \
--model_path=$model_dir/avg_5.jit.pdmodel \
......
speechx/examples/ds2_ol/aishell/run_fbank.sh
浏览文件 @ ae629e2f
......@@ -148,7 +148,7 @@ fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/fbank_recognizer.log \
recognizer_test_main \
recognizer_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--cmvn_file=$cmvn \
--model_path=$model_dir/avg_5.jit.pdmodel \
......
speechx/examples/ds2_ol/decoder/CMakeLists.txt 已删除 100644 → 0
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cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
set(bin_name ctc-prefix-beam-search-decoder-ol)
add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
set(bin_name wfst-decoder-ol)
add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder ${DEPS})
set(bin_name nnet-logprob-decoder-test)
add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
add_executable(recognizer_test_main ${CMAKE_CURRENT_SOURCE_DIR}/recognizer_test_main.cc)
target_include_directories(recognizer_test_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(recognizer_test_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder ${DEPS})
speechx/examples/ds2_ol/decoder/README.md 已删除 100644 → 0
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# ASR Decoder
ASR Decoder test bins. We using theses bins to test CTC BeamSearch decoder and WFST decoder.
* decoder_test_main.cc
feed nnet output logprob, and only test decoder
* offline_decoder_sliding_chunk_main.cc
feed streaming audio feature, decode as streaming manner.
* offline_wfst_decoder_main.cc
feed streaming audio feature, decode using WFST as streaming manner.
speechx/examples/ds2_ol/decoder/ctc-prefix-beam-search-decoder-ol.cc 已删除 100644 → 0
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// 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 "frontend/audio/data_cache.h"
#include "kaldi/util/table-types.h"
#include "nnet/decodable.h"
#include "nnet/paddle_nnet.h"
DEFINE_string(feature_rspecifier, "", "test feature rspecifier");
DEFINE_string(result_wspecifier, "", "test result wspecifier");
DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param");
DEFINE_string(dict_file, "vocab.txt", "vocabulary of lm");
DEFINE_string(lm_path, "", "language model");
DEFINE_int32(receptive_field_length,
7,
"receptive field of two CNN(kernel=5) downsampling module.");
DEFINE_int32(downsampling_rate,
4,
"two CNN(kernel=5) module downsampling rate.");
DEFINE_string(
model_input_names,
"audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box",
"model input names");
DEFINE_string(model_output_names,
"softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0",
"model output names");
DEFINE_string(model_cache_names,
"chunk_state_h_box,chunk_state_c_box",
"model cache names");
DEFINE_string(model_cache_shapes, "5-1-1024,5-1-1024", "model cache shapes");
using kaldi::BaseFloat;
using kaldi::Matrix;
using std::vector;
// test ds2 online decoder by feeding speech feature
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
CHECK(FLAGS_result_wspecifier != "");
CHECK(FLAGS_feature_rspecifier != "");
kaldi::SequentialBaseFloatMatrixReader feature_reader(
FLAGS_feature_rspecifier);
kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
std::string model_path = FLAGS_model_path;
std::string model_params = FLAGS_param_path;
std::string dict_file = FLAGS_dict_file;
std::string lm_path = FLAGS_lm_path;
LOG(INFO) << "model path: " << model_path;
LOG(INFO) << "model param: " << model_params;
LOG(INFO) << "dict path: " << dict_file;
LOG(INFO) << "lm path: " << lm_path;
int32 num_done = 0, num_err = 0;
ppspeech::CTCBeamSearchOptions opts;
opts.dict_file = dict_file;
opts.lm_path = lm_path;
ppspeech::CTCBeamSearch decoder(opts);
ppspeech::ModelOptions model_opts;
model_opts.model_path = model_path;
model_opts.param_path = model_params;
model_opts.cache_names = FLAGS_model_cache_names;
model_opts.cache_shape = FLAGS_model_cache_shapes;
model_opts.input_names = FLAGS_model_input_names;
model_opts.output_names = FLAGS_model_output_names;
std::shared_ptr<ppspeech::PaddleNnet> nnet(
new ppspeech::PaddleNnet(model_opts));
std::shared_ptr<ppspeech::DataCache> raw_data(new ppspeech::DataCache());
std::shared_ptr<ppspeech::Decodable> decodable(
new ppspeech::Decodable(nnet, raw_data));
int32 chunk_size = FLAGS_receptive_field_length;
int32 chunk_stride = FLAGS_downsampling_rate;
int32 receptive_field_length = FLAGS_receptive_field_length;
LOG(INFO) << "chunk size (frame): " << chunk_size;
LOG(INFO) << "chunk stride (frame): " << chunk_stride;
LOG(INFO) << "receptive field (frame): " << receptive_field_length;
decoder.InitDecoder();
kaldi::Timer timer;
for (; !feature_reader.Done(); feature_reader.Next()) {
string utt = feature_reader.Key();
kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
raw_data->SetDim(feature.NumCols());
LOG(INFO) << "process utt: " << utt;
LOG(INFO) << "rows: " << feature.NumRows();
LOG(INFO) << "cols: " << feature.NumCols();
int32 row_idx = 0;
int32 padding_len = 0;
int32 ori_feature_len = feature.NumRows();
if ((feature.NumRows() - chunk_size) % chunk_stride != 0) {
padding_len =
chunk_stride - (feature.NumRows() - chunk_size) % chunk_stride;
feature.Resize(feature.NumRows() + padding_len,
feature.NumCols(),
kaldi::kCopyData);
}
int32 num_chunks = (feature.NumRows() - chunk_size) / chunk_stride + 1;
for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) {
kaldi::Vector<kaldi::BaseFloat> feature_chunk(chunk_size *
feature.NumCols());
int32 feature_chunk_size = 0;
if (ori_feature_len > chunk_idx * chunk_stride) {
feature_chunk_size = std::min(
ori_feature_len - chunk_idx * chunk_stride, chunk_size);
}
if (feature_chunk_size < receptive_field_length) break;
int32 start = chunk_idx * chunk_stride;
for (int row_id = 0; row_id < chunk_size; ++row_id) {
kaldi::SubVector<kaldi::BaseFloat> tmp(feature, start);
kaldi::SubVector<kaldi::BaseFloat> f_chunk_tmp(
feature_chunk.Data() + row_id * feature.NumCols(),
feature.NumCols());
f_chunk_tmp.CopyFromVec(tmp);
++start;
}
raw_data->Accept(feature_chunk);
if (chunk_idx == num_chunks - 1) {
raw_data->SetFinished();
}
decoder.AdvanceDecode(decodable);
}
std::string result;
result = decoder.GetFinalBestPath();
decodable->Reset();
decoder.Reset();
if (result.empty()) {
// the TokenWriter can not write empty string.
++num_err;
KALDI_LOG << " the result of " << utt << " is empty";
continue;
}
KALDI_LOG << " the result of " << utt << " is " << result;
result_writer.Write(utt, result);
++num_done;
}
KALDI_LOG << "Done " << num_done << " utterances, " << num_err
<< " with errors.";
double elapsed = timer.Elapsed();
KALDI_LOG << " cost:" << elapsed << " s";
return (num_done != 0 ? 0 : 1);
}
speechx/examples/ds2_ol/decoder/nnet-logprob-decoder-test.cc 已删除 100644 → 0
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// 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"
DEFINE_string(nnet_prob_respecifier, "", "test nnet prob rspecifier");
DEFINE_string(dict_file, "vocab.txt", "vocabulary of lm");
DEFINE_string(lm_path, "lm.klm", "language model");
using kaldi::BaseFloat;
using kaldi::Matrix;
using std::vector;
// test decoder by feeding nnet posterior probability
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
kaldi::SequentialBaseFloatMatrixReader likelihood_reader(
FLAGS_nnet_prob_respecifier);
std::string dict_file = FLAGS_dict_file;
std::string lm_path = FLAGS_lm_path;
LOG(INFO) << "dict path: " << dict_file;
LOG(INFO) << "lm path: " << lm_path;
int32 num_done = 0, num_err = 0;
ppspeech::CTCBeamSearchOptions opts;
opts.dict_file = dict_file;
opts.lm_path = lm_path;
ppspeech::CTCBeamSearch decoder(opts);
std::shared_ptr<ppspeech::Decodable> decodable(
new ppspeech::Decodable(nullptr, nullptr));
decoder.InitDecoder();
for (; !likelihood_reader.Done(); likelihood_reader.Next()) {
string utt = likelihood_reader.Key();
const kaldi::Matrix<BaseFloat> likelihood = likelihood_reader.Value();
LOG(INFO) << "process utt: " << utt;
LOG(INFO) << "rows: " << likelihood.NumRows();
LOG(INFO) << "cols: " << likelihood.NumCols();
decodable->Acceptlikelihood(likelihood);
decoder.AdvanceDecode(decodable);
std::string result;
result = decoder.GetFinalBestPath();
KALDI_LOG << " the result of " << utt << " is " << result;
decodable->Reset();
decoder.Reset();
++num_done;
}
KALDI_LOG << "Done " << num_done << " utterances, " << num_err
<< " with errors.";
return (num_done != 0 ? 0 : 1);
}
speechx/examples/ds2_ol/decoder/path.sh 已删除 100644 → 0
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# This contains the locations of binarys build required for running the examples.
SPEECHX_ROOT=$PWD/../../../
SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
SPEECHX_TOOLS=$SPEECHX_ROOT/tools
TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; }
export LC_AL=C
SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/decoder:$SPEECHX_EXAMPLES/ds2_ol/feat
export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN
speechx/examples/ds2_ol/decoder/recognizer_test_main.cc 已删除 100644 → 0
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// 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/recognizer.h"
#include "decoder/param.h"
#include "kaldi/feat/wave-reader.h"
#include "kaldi/util/table-types.h"
DEFINE_string(wav_rspecifier, "", "test feature rspecifier");
DEFINE_string(result_wspecifier, "", "test result wspecifier");
DEFINE_int32(sample_rate, 16000, "sample rate");
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
ppspeech::RecognizerResource resource = ppspeech::InitRecognizerResoure();
ppspeech::Recognizer recognizer(resource);
kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
FLAGS_wav_rspecifier);
kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
int sample_rate = FLAGS_sample_rate;
float streaming_chunk = FLAGS_streaming_chunk;
int chunk_sample_size = streaming_chunk * sample_rate;
LOG(INFO) << "sr: " << sample_rate;
LOG(INFO) << "chunk size (s): " << streaming_chunk;
LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
int32 num_done = 0, num_err = 0;
double tot_wav_duration = 0.0;
kaldi::Timer timer;
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();
tot_wav_duration += tot_samples * 1.0 / sample_rate;
LOG(INFO) << "wav len (sample): " << tot_samples;
int sample_offset = 0;
std::vector<kaldi::Vector<BaseFloat>> feats;
int feature_rows = 0;
while (sample_offset < tot_samples) {
int cur_chunk_size =
std::min(chunk_sample_size, tot_samples - sample_offset);
kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
for (int i = 0; i < cur_chunk_size; ++i) {
wav_chunk(i) = waveform(sample_offset + i);
}
// wav_chunk = waveform.Range(sample_offset + i, cur_chunk_size);
recognizer.Accept(wav_chunk);
if (cur_chunk_size < chunk_sample_size) {
recognizer.SetFinished();
}
recognizer.Decode();
// no overlap
sample_offset += cur_chunk_size;
}
std::string result;
result = recognizer.GetFinalResult();
recognizer.Reset();
if (result.empty()) {
// the TokenWriter can not write empty string.
++num_err;
KALDI_LOG << " the result of " << utt << " is empty";
continue;
}
KALDI_LOG << " the result of " << utt << " is " << result;
result_writer.Write(utt, result);
++num_done;
}
double elapsed = timer.Elapsed();
KALDI_LOG << "Done " << num_done << " out of " << (num_err + num_done);
KALDI_LOG << " cost:" << elapsed << " s";
KALDI_LOG << "total wav duration is: " << tot_wav_duration << " s";
KALDI_LOG << "the RTF is: " << elapsed / tot_wav_duration;
}
\ No newline at end of file
speechx/examples/ds2_ol/decoder/run.sh 已删除 100755 → 0
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#!/bin/bash
set +x
set -e
. path.sh
# 1. compile
if [ ! -d ${SPEECHX_EXAMPLES} ]; then
pushd ${SPEECHX_ROOT}
bash build.sh
popd
fi
# input
mkdir -p data
data=$PWD/data
ckpt_dir=$data/model
model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
vocb_dir=$ckpt_dir/data/lang_char/
lm=$data/zh_giga.no_cna_cmn.prune01244.klm
# output
exp_dir=./exp
mkdir -p $exp_dir
# 2. download model
if [[ ! -f data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]]; then
mkdir -p data/model
pushd data/model
wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
popd
fi
# produce wav scp
if [ ! -f data/wav.scp ]; then
pushd data
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
echo "utt1 " $PWD/zh.wav > wav.scp
popd
fi
# download lm
if [ ! -f $lm ]; then
pushd data
wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
popd
fi
feat_wspecifier=$exp_dir/feats.ark
cmvn=$exp_dir/cmvn.ark
export GLOG_logtostderr=1
# dump json cmvn to kaldi
cmvn-json2kaldi \
--json_file $ckpt_dir/data/mean_std.json \
--cmvn_write_path $cmvn \
--binary=false
echo "convert json cmvn to kaldi ark."
# generate linear feature as streaming
linear-spectrogram-wo-db-norm-ol \
--wav_rspecifier=scp:$data/wav.scp \
--feature_wspecifier=ark,t:$feat_wspecifier \
--cmvn_file=$cmvn
echo "compute linear spectrogram feature."
# run ctc beam search decoder as streaming
ctc-prefix-beam-search-decoder-ol \
--result_wspecifier=ark,t:$exp_dir/result.txt \
--feature_rspecifier=ark:$feat_wspecifier \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm
\ No newline at end of file
speechx/examples/ds2_ol/decoder/valgrind.sh 已删除 100755 → 0
浏览文件 @ 418cc37f
#!/bin/bash
# this script is for memory check, so please run ./run.sh first.
set +x
set -e
. ./path.sh
if [ ! -d ${SPEECHX_TOOLS}/valgrind/install ]; then
echo "please install valgrind in the speechx tools dir.\n"
exit 1
fi
model_dir=../paddle_asr_model
feat_wspecifier=./feats.ark
cmvn=./cmvn.ark
valgrind --tool=memcheck --track-origins=yes --leak-check=full --show-leak-kinds=all \
offline_decoder_main \
--feature_respecifier=ark:$feat_wspecifier \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdparams \
--dict_file=$model_dir/vocab.txt \
--lm_path=$model_dir/avg_1.jit.klm
speechx/examples/ds2_ol/decoder/wfst-decoder-ol.cc 已删除 100644 → 0
浏览文件 @ 418cc37f
// 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_tlg_decoder.h"
#include "frontend/audio/data_cache.h"
#include "kaldi/util/table-types.h"
#include "nnet/decodable.h"
#include "nnet/paddle_nnet.h"
DEFINE_string(feature_rspecifier, "", "test feature rspecifier");
DEFINE_string(result_wspecifier, "", "test result wspecifier");
DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param");
DEFINE_string(word_symbol_table, "words.txt", "word symbol table");
DEFINE_string(graph_path, "TLG", "decoder graph");
DEFINE_double(acoustic_scale, 1.0, "acoustic scale");
DEFINE_int32(max_active, 7500, "decoder graph");
DEFINE_int32(receptive_field_length,
7,
"receptive field of two CNN(kernel=5) downsampling module.");
DEFINE_int32(downsampling_rate,
4,
"two CNN(kernel=5) module downsampling rate.");
DEFINE_string(
model_input_names,
"audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box",
"model input names");
DEFINE_string(model_output_names,
"softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0",
"model output names");
DEFINE_string(model_cache_names,
"chunk_state_h_box,chunk_state_c_box",
"model cache names");
DEFINE_string(model_cache_shapes, "5-1-1024,5-1-1024", "model cache shapes");
using kaldi::BaseFloat;
using kaldi::Matrix;
using std::vector;
// test TLG decoder by feeding speech feature.
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
kaldi::SequentialBaseFloatMatrixReader feature_reader(
FLAGS_feature_rspecifier);
kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
std::string model_graph = FLAGS_model_path;
std::string model_params = FLAGS_param_path;
std::string word_symbol_table = FLAGS_word_symbol_table;
std::string graph_path = FLAGS_graph_path;
LOG(INFO) << "model path: " << model_graph;
LOG(INFO) << "model param: " << model_params;
LOG(INFO) << "word symbol path: " << word_symbol_table;
LOG(INFO) << "graph path: " << graph_path;
int32 num_done = 0, num_err = 0;
ppspeech::TLGDecoderOptions opts;
opts.word_symbol_table = word_symbol_table;
opts.fst_path = graph_path;
opts.opts.max_active = FLAGS_max_active;
opts.opts.beam = 15.0;
opts.opts.lattice_beam = 7.5;
ppspeech::TLGDecoder decoder(opts);
ppspeech::ModelOptions model_opts;
model_opts.model_path = model_graph;
model_opts.param_path = model_params;
model_opts.cache_names = FLAGS_model_cache_names;
model_opts.cache_shape = FLAGS_model_cache_shapes;
model_opts.input_names = FLAGS_model_input_names;
model_opts.output_names = FLAGS_model_output_names;
std::shared_ptr<ppspeech::PaddleNnet> nnet(
new ppspeech::PaddleNnet(model_opts));
std::shared_ptr<ppspeech::DataCache> raw_data(new ppspeech::DataCache());
std::shared_ptr<ppspeech::Decodable> decodable(
new ppspeech::Decodable(nnet, raw_data, FLAGS_acoustic_scale));
int32 chunk_size = FLAGS_receptive_field_length;
int32 chunk_stride = FLAGS_downsampling_rate;
int32 receptive_field_length = FLAGS_receptive_field_length;
LOG(INFO) << "chunk size (frame): " << chunk_size;
LOG(INFO) << "chunk stride (frame): " << chunk_stride;
LOG(INFO) << "receptive field (frame): " << receptive_field_length;
decoder.InitDecoder();
kaldi::Timer timer;
for (; !feature_reader.Done(); feature_reader.Next()) {
string utt = feature_reader.Key();
kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
raw_data->SetDim(feature.NumCols());
LOG(INFO) << "process utt: " << utt;
LOG(INFO) << "rows: " << feature.NumRows();
LOG(INFO) << "cols: " << feature.NumCols();
int32 row_idx = 0;
int32 padding_len = 0;
int32 ori_feature_len = feature.NumRows();
if ((feature.NumRows() - chunk_size) % chunk_stride != 0) {
padding_len =
chunk_stride - (feature.NumRows() - chunk_size) % chunk_stride;
feature.Resize(feature.NumRows() + padding_len,
feature.NumCols(),
kaldi::kCopyData);
}
int32 num_chunks = (feature.NumRows() - chunk_size) / chunk_stride + 1;
for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) {
kaldi::Vector<kaldi::BaseFloat> feature_chunk(chunk_size *
feature.NumCols());
int32 feature_chunk_size = 0;
if (ori_feature_len > chunk_idx * chunk_stride) {
feature_chunk_size = std::min(
ori_feature_len - chunk_idx * chunk_stride, chunk_size);
}
if (feature_chunk_size < receptive_field_length) break;
int32 start = chunk_idx * chunk_stride;
for (int row_id = 0; row_id < chunk_size; ++row_id) {
kaldi::SubVector<kaldi::BaseFloat> tmp(feature, start);
kaldi::SubVector<kaldi::BaseFloat> f_chunk_tmp(
feature_chunk.Data() + row_id * feature.NumCols(),
feature.NumCols());
f_chunk_tmp.CopyFromVec(tmp);
++start;
}
raw_data->Accept(feature_chunk);
if (chunk_idx == num_chunks - 1) {
raw_data->SetFinished();
}
decoder.AdvanceDecode(decodable);
}
std::string result;
result = decoder.GetFinalBestPath();
decodable->Reset();
decoder.Reset();
if (result.empty()) {
// the TokenWriter can not write empty string.
++num_err;
KALDI_LOG << " the result of " << utt << " is empty";
continue;
}
KALDI_LOG << " the result of " << utt << " is " << result;
result_writer.Write(utt, result);
++num_done;
}
double elapsed = timer.Elapsed();
KALDI_LOG << " cost:" << elapsed << " s";
KALDI_LOG << "Done " << num_done << " utterances, " << num_err
<< " with errors.";
return (num_done != 0 ? 0 : 1);
}
speechx/examples/ds2_ol/feat/CMakeLists.txt 已删除 100644 → 0
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cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
set(bin_name linear-spectrogram-wo-db-norm-ol)
add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} frontend kaldi-util kaldi-feat-common gflags glog)
set(bin_name compute_fbank_main)
add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} frontend kaldi-util kaldi-feat-common gflags glog)
set(bin_name cmvn-json2kaldi)
add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} utils kaldi-util kaldi-matrix gflags glog)
speechx/examples/ds2_ol/feat/README.md 已删除 100644 → 0
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# Deepspeech2 Straming Audio Feature
ASR audio feature test bins. We using theses bins to test linaer/fbank/mfcc asr feature as streaming manner.
* linear_spectrogram_without_db_norm_main.cc
compute linear spectrogram w/o db norm in streaming manner.
speechx/examples/ds2_ol/feat/cmvn-json2kaldi.cc 已删除 100644 → 0
浏览文件 @ 418cc37f
// 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.
// Note: Do not print/log ondemand object.
#include "base/common.h"
#include "base/flags.h"
#include "base/log.h"
#include "kaldi/matrix/kaldi-matrix.h"
#include "kaldi/util/kaldi-io.h"
#include "utils/file_utils.h"
// #include "boost/json.hpp"
#include <boost/json/src.hpp>
DEFINE_string(json_file, "", "cmvn json file");
DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn");
DEFINE_bool(binary, true, "write cmvn in binary (true) or text(false)");
using namespace boost::json; // from <boost/json.hpp>
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
LOG(INFO) << "cmvn josn path: " << FLAGS_json_file;
auto ifs = std::ifstream(FLAGS_json_file);
std::string json_str = ppspeech::ReadFile2String(FLAGS_json_file);
auto value = boost::json::parse(json_str);
if (!value.is_object()) {
LOG(ERROR) << "Input json file format error.";
}
for (auto obj : value.as_object()) {
if (obj.key() == "mean_stat") {
LOG(INFO) << "mean_stat:" << obj.value();
}
if (obj.key() == "var_stat") {
LOG(INFO) << "var_stat: " << obj.value();
}
if (obj.key() == "frame_num") {
LOG(INFO) << "frame_num: " << obj.value();
}
}
boost::json::array mean_stat = value.at("mean_stat").as_array();
std::vector<kaldi::BaseFloat> mean_stat_vec;
for (auto it = mean_stat.begin(); it != mean_stat.end(); it++) {
mean_stat_vec.push_back(it->as_double());
}
boost::json::array var_stat = value.at("var_stat").as_array();
std::vector<kaldi::BaseFloat> var_stat_vec;
for (auto it = var_stat.begin(); it != var_stat.end(); it++) {
var_stat_vec.push_back(it->as_double());
}
kaldi::int32 frame_num = uint64_t(value.at("frame_num").as_int64());
LOG(INFO) << "nframe: " << frame_num;
size_t mean_size = mean_stat_vec.size();
kaldi::Matrix<double> cmvn_stats(2, mean_size + 1);
for (size_t idx = 0; idx < mean_size; ++idx) {
cmvn_stats(0, idx) = mean_stat_vec[idx];
cmvn_stats(1, idx) = var_stat_vec[idx];
}
cmvn_stats(0, mean_size) = frame_num;
LOG(INFO) << cmvn_stats;
kaldi::WriteKaldiObject(cmvn_stats, FLAGS_cmvn_write_path, FLAGS_binary);
LOG(INFO) << "cmvn stats have write into: " << FLAGS_cmvn_write_path;
LOG(INFO) << "Binary: " << FLAGS_binary;
return 0;
}
speechx/examples/ds2_ol/feat/compute_fbank_main.cc 已删除 100644 → 0
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// todo refactor, repalce with gtest
#include "base/flags.h"
#include "base/log.h"
#include "kaldi/feat/wave-reader.h"
#include "kaldi/util/kaldi-io.h"
#include "kaldi/util/table-types.h"
#include "frontend/audio/audio_cache.h"
#include "frontend/audio/data_cache.h"
#include "frontend/audio/fbank.h"
#include "frontend/audio/feature_cache.h"
#include "frontend/audio/frontend_itf.h"
#include "frontend/audio/normalizer.h"
DEFINE_string(wav_rspecifier, "", "test wav scp path");
DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
DEFINE_string(cmvn_file, "", "read cmvn");
DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
DEFINE_int32(num_bins, 161, "fbank num bins");
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
FLAGS_wav_rspecifier);
kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier);
int32 num_done = 0, num_err = 0;
// feature pipeline: wave cache --> povey window
// -->fbank --> global cmvn -> feat cache
std::unique_ptr<ppspeech::FrontendInterface> data_source(
new ppspeech::AudioCache(3600 * 1600, false));
ppspeech::FbankOptions opt;
opt.fbank_opts.frame_opts.frame_length_ms = 25;
opt.fbank_opts.frame_opts.frame_shift_ms = 10;
opt.streaming_chunk = FLAGS_streaming_chunk;
opt.fbank_opts.mel_opts.num_bins = FLAGS_num_bins;
opt.fbank_opts.frame_opts.dither = 0.0;
std::unique_ptr<ppspeech::FrontendInterface> fbank(
new ppspeech::Fbank(opt, std::move(data_source)));
std::unique_ptr<ppspeech::FrontendInterface> cmvn(
new ppspeech::CMVN(FLAGS_cmvn_file, std::move(fbank)));
ppspeech::FeatureCacheOptions feat_cache_opts;
// the feature cache output feature chunk by chunk.
// frame_chunk_size : num frame of a chunk.
// frame_chunk_stride: chunk sliding window stride.
feat_cache_opts.frame_chunk_stride = 1;
feat_cache_opts.frame_chunk_size = 1;
ppspeech::FeatureCache feature_cache(feat_cache_opts, std::move(cmvn));
LOG(INFO) << "fbank: " << true;
LOG(INFO) << "feat dim: " << feature_cache.Dim();
int sample_rate = 16000;
float streaming_chunk = FLAGS_streaming_chunk;
int chunk_sample_size = streaming_chunk * sample_rate;
LOG(INFO) << "sr: " << sample_rate;
LOG(INFO) << "chunk size (s): " << streaming_chunk;
LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
for (; !wav_reader.Done(); wav_reader.Next()) {
std::string utt = wav_reader.Key();
const kaldi::WaveData& wave_data = wav_reader.Value();
LOG(INFO) << "process utt: " << utt;
int32 this_channel = 0;
kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
this_channel);
int tot_samples = waveform.Dim();
LOG(INFO) << "wav len (sample): " << tot_samples;
int sample_offset = 0;
std::vector<kaldi::Vector<BaseFloat>> feats;
int feature_rows = 0;
while (sample_offset < tot_samples) {
int cur_chunk_size =
std::min(chunk_sample_size, tot_samples - sample_offset);
kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
for (int i = 0; i < cur_chunk_size; ++i) {
wav_chunk(i) = waveform(sample_offset + i);
}
kaldi::Vector<BaseFloat> features;
feature_cache.Accept(wav_chunk);
if (cur_chunk_size < chunk_sample_size) {
feature_cache.SetFinished();
}
bool flag = true;
do {
flag = feature_cache.Read(&features);
feats.push_back(features);
feature_rows += features.Dim() / feature_cache.Dim();
} while (flag == true && features.Dim() != 0);
sample_offset += cur_chunk_size;
}
int cur_idx = 0;
kaldi::Matrix<kaldi::BaseFloat> features(feature_rows,
feature_cache.Dim());
for (auto feat : feats) {
int num_rows = feat.Dim() / feature_cache.Dim();
for (int row_idx = 0; row_idx < num_rows; ++row_idx) {
for (size_t col_idx = 0; col_idx < feature_cache.Dim();
++col_idx) {
features(cur_idx, col_idx) =
feat(row_idx * feature_cache.Dim() + col_idx);
}
++cur_idx;
}
}
feat_writer.Write(utt, features);
feature_cache.Reset();
if (num_done % 50 == 0 && num_done != 0)
KALDI_VLOG(2) << "Processed " << num_done << " utterances";
num_done++;
}
KALDI_LOG << "Done " << num_done << " utterances, " << num_err
<< " with errors.";
return (num_done != 0 ? 0 : 1);
}
speechx/examples/ds2_ol/feat/linear-spectrogram-wo-db-norm-ol.cc 已删除 100644 → 0
浏览文件 @ 418cc37f
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// todo refactor, repalce with gtest
#include "base/flags.h"
#include "base/log.h"
#include "kaldi/feat/wave-reader.h"
#include "kaldi/util/kaldi-io.h"
#include "kaldi/util/table-types.h"
#include "frontend/audio/audio_cache.h"
#include "frontend/audio/data_cache.h"
#include "frontend/audio/feature_cache.h"
#include "frontend/audio/frontend_itf.h"
#include "frontend/audio/linear_spectrogram.h"
#include "frontend/audio/normalizer.h"
DEFINE_string(wav_rspecifier, "", "test wav scp path");
DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
DEFINE_string(cmvn_file, "./cmvn.ark", "read cmvn");
DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
FLAGS_wav_rspecifier);
kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier);
int32 num_done = 0, num_err = 0;
// feature pipeline: wave cache --> hanning window
// -->linear_spectrogram --> global cmvn -> feat cache
std::unique_ptr<ppspeech::FrontendInterface> data_source(
new ppspeech::AudioCache(3600 * 1600, true));
ppspeech::LinearSpectrogramOptions opt;
opt.frame_opts.frame_length_ms = 20;
opt.frame_opts.frame_shift_ms = 10;
opt.streaming_chunk = FLAGS_streaming_chunk;
opt.frame_opts.dither = 0.0;
opt.frame_opts.remove_dc_offset = false;
opt.frame_opts.window_type = "hanning";
opt.frame_opts.preemph_coeff = 0.0;
LOG(INFO) << "linear feature: " << true;
LOG(INFO) << "frame length (ms): " << opt.frame_opts.frame_length_ms;
LOG(INFO) << "frame shift (ms): " << opt.frame_opts.frame_shift_ms;
std::unique_ptr<ppspeech::FrontendInterface> linear_spectrogram(
new ppspeech::LinearSpectrogram(opt, std::move(data_source)));
std::unique_ptr<ppspeech::FrontendInterface> cmvn(
new ppspeech::CMVN(FLAGS_cmvn_file, std::move(linear_spectrogram)));
ppspeech::FeatureCacheOptions feat_cache_opts;
// the feature cache output feature chunk by chunk.
// frame_chunk_size : num frame of a chunk.
// frame_chunk_stride: chunk sliding window stride.
feat_cache_opts.frame_chunk_stride = 1;
feat_cache_opts.frame_chunk_size = 1;
ppspeech::FeatureCache feature_cache(feat_cache_opts, std::move(cmvn));
LOG(INFO) << "feat dim: " << feature_cache.Dim();
int sample_rate = 16000;
float streaming_chunk = FLAGS_streaming_chunk;
int chunk_sample_size = streaming_chunk * sample_rate;
LOG(INFO) << "sample rate: " << sample_rate;
LOG(INFO) << "chunk size (s): " << streaming_chunk;
LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
for (; !wav_reader.Done(); wav_reader.Next()) {
std::string utt = wav_reader.Key();
const kaldi::WaveData& wave_data = wav_reader.Value();
LOG(INFO) << "process utt: " << utt;
int32 this_channel = 0;
kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
this_channel);
int tot_samples = waveform.Dim();
LOG(INFO) << "wav len (sample): " << tot_samples;
int sample_offset = 0;
std::vector<kaldi::Vector<BaseFloat>> feats;
int feature_rows = 0;
while (sample_offset < tot_samples) {
int cur_chunk_size =
std::min(chunk_sample_size, tot_samples - sample_offset);
kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
for (int i = 0; i < cur_chunk_size; ++i) {
wav_chunk(i) = waveform(sample_offset + i);
}
kaldi::Vector<BaseFloat> features;
feature_cache.Accept(wav_chunk);
if (cur_chunk_size < chunk_sample_size) {
feature_cache.SetFinished();
}
bool flag = true;
do {
flag = feature_cache.Read(&features);
feats.push_back(features);
feature_rows += features.Dim() / feature_cache.Dim();
} while (flag == true && features.Dim() != 0);
sample_offset += cur_chunk_size;
}
int cur_idx = 0;
kaldi::Matrix<kaldi::BaseFloat> features(feature_rows,
feature_cache.Dim());
for (auto feat : feats) {
int num_rows = feat.Dim() / feature_cache.Dim();
for (int row_idx = 0; row_idx < num_rows; ++row_idx) {
for (size_t col_idx = 0; col_idx < feature_cache.Dim();
++col_idx) {
features(cur_idx, col_idx) =
feat(row_idx * feature_cache.Dim() + col_idx);
}
++cur_idx;
}
}
feat_writer.Write(utt, features);
feature_cache.Reset();
if (num_done % 50 == 0 && num_done != 0)
KALDI_VLOG(2) << "Processed " << num_done << " utterances";
num_done++;
}
KALDI_LOG << "Done " << num_done << " utterances, " << num_err
<< " with errors.";
return (num_done != 0 ? 0 : 1);
}
speechx/examples/ds2_ol/feat/path.sh 已删除 100644 → 0
浏览文件 @ 418cc37f
# This contains the locations of binarys build required for running the examples.
SPEECHX_ROOT=$PWD/../../../
SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
SPEECHX_TOOLS=$SPEECHX_ROOT/tools
TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; }
export LC_AL=C
SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/feat
export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN
speechx/examples/ds2_ol/feat/run.sh 已删除 100755 → 0
浏览文件 @ 418cc37f
#!/bin/bash
set +x
set -e
. ./path.sh
# 1. compile
if [ ! -d ${SPEECHX_EXAMPLES} ]; then
pushd ${SPEECHX_ROOT}
bash build.sh
popd
fi
# 2. download model
if [ ! -e data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]; then
mkdir -p data/model
pushd data/model
wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
popd
fi
# produce wav scp
if [ ! -f data/wav.scp ]; then
mkdir -p data
pushd data
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
echo "utt1 " $PWD/zh.wav > wav.scp
popd
fi
# input
data_dir=./data
exp_dir=./exp
model_dir=$data_dir/model/
mkdir -p $exp_dir
# 3. run feat
export GLOG_logtostderr=1
cmvn-json2kaldi \
--json_file $model_dir/data/mean_std.json \
--cmvn_write_path $exp_dir/cmvn.ark \
--binary=false
echo "convert json cmvn to kaldi ark."
linear-spectrogram-wo-db-norm-ol \
--wav_rspecifier=scp:$data_dir/wav.scp \
--feature_wspecifier=ark,t:$exp_dir/feats.ark \
--cmvn_file=$exp_dir/cmvn.ark
echo "compute linear spectrogram feature."
speechx/examples/ds2_ol/feat/valgrind.sh 已删除 100755 → 0
浏览文件 @ 418cc37f
#!/bin/bash
# this script is for memory check, so please run ./run.sh first.
set +x
set -e
. ./path.sh
if [ ! -d ${SPEECHX_TOOLS}/valgrind/install ]; then
echo "please install valgrind in the speechx tools dir.\n"
exit 1
fi
model_dir=../paddle_asr_model
feat_wspecifier=./feats.ark
cmvn=./cmvn.ark
valgrind --tool=memcheck --track-origins=yes --leak-check=full --show-leak-kinds=all \
linear_spectrogram_main \
--wav_rspecifier=scp:$model_dir/wav.scp \
--feature_wspecifier=ark,t:$feat_wspecifier \
--cmvn_write_path=$cmvn
speechx/examples/ds2_ol/nnet/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ 418cc37f
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
set(bin_name ds2-model-ol-test)
add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} PUBLIC nnet gflags glog ${DEPS})
\ No newline at end of file
speechx/examples/ds2_ol/nnet/README.md 已删除 100644 → 0
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# Deepspeech2 Streaming NNet Test
Using for ds2 streaming nnet inference test.
speechx/examples/ds2_ol/nnet/ds2-model-ol-test.cc 已删除 100644 → 0
浏览文件 @ 418cc37f
// 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.
// deepspeech2 online model info
#include <algorithm>
#include <fstream>
#include <functional>
#include <iostream>
#include <iterator>
#include <numeric>
#include <thread>
#include "base/flags.h"
#include "base/log.h"
#include "paddle_inference_api.h"
using std::cout;
using std::endl;
DEFINE_string(model_path, "", "xxx.pdmodel");
DEFINE_string(param_path, "", "xxx.pdiparams");
DEFINE_int32(chunk_size, 35, "feature chunk size, unit:frame");
DEFINE_int32(feat_dim, 161, "feature dim");
void produce_data(std::vector<std::vector<float>>* data);
void model_forward_test();
void produce_data(std::vector<std::vector<float>>* data) {
int chunk_size = FLAGS_chunk_size; // chunk_size in frame
int col_size = FLAGS_feat_dim; // 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() {
std::cout << "1. read the data" << std::endl;
std::vector<std::vector<float>> feats;
produce_data(&feats);
std::cout << "2. load the model" << std::endl;
;
std::string model_graph = FLAGS_model_path;
std::string model_params = FLAGS_param_path;
CHECK(model_graph != "");
CHECK(model_params != "");
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> 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;
int row = feats.size();
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(pp_input_mat.data());
// 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());
// 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 = {5, 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>());
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());
// 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 = {5, 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>());
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();
// 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>());
std::vector<float> h_out_data(h_out_size);
h_out->CopyToCpu(h_out_data.data());
// 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>());
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>());
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++) {
probs.push_back(std::vector<float>());
probs.back().reserve(col);
for (int j = 0; j < col; j++) {
probs.back().push_back(output_probs[i * col + j]);
}
}
std::vector<std::vector<float>> log_feat = probs;
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) {
std::cout << log_feat[row_idx][col_idx] << " ";
}
std::cout << std::endl;
}
}
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
model_forward_test();
return 0;
}
speechx/examples/ds2_ol/nnet/path.sh 已删除 100644 → 0
浏览文件 @ 418cc37f
# This contains the locations of binarys build required for running the examples.
SPEECHX_ROOT=$PWD/../../../
SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
SPEECHX_TOOLS=$SPEECHX_ROOT/tools
TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; }
export LC_AL=C
SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/nnet
export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN
speechx/examples/ds2_ol/nnet/run.sh 已删除 100755 → 0
浏览文件 @ 418cc37f
#!/bin/bash
set +x
set -e
. path.sh
# 1. compile
if [ ! -d ${SPEECHX_EXAMPLES} ]; then
pushd ${SPEECHX_ROOT}
bash build.sh
popd
fi
# 2. download model
if [ ! -f data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]; then
mkdir -p data/model
pushd data/model
wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
popd
fi
# produce wav scp
if [ ! -f data/wav.scp ]; then
mkdir -p data
pushd data
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
echo "utt1 " $PWD/zh.wav > wav.scp
popd
fi
ckpt_dir=./data/model
model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
ds2-model-ol-test \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams
speechx/examples/ds2_ol/nnet/valgrind.sh 已删除 100755 → 0
浏览文件 @ 418cc37f
#!/bin/bash
# this script is for memory check, so please run ./run.sh first.
set +x
set -e
. ./path.sh
if [ ! -d ${SPEECHX_TOOLS}/valgrind/install ]; then
echo "please install valgrind in the speechx tools dir.\n"
exit 1
fi
model_dir=../paddle_asr_model
valgrind --tool=memcheck --track-origins=yes --leak-check=full --show-leak-kinds=all \
pp-model-test \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdparams
\ No newline at end of file
utils/README.md
浏览文件 @ ae629e2f
# Utils
* [kaldi utils](https://github.com/kaldi-asr/kaldi/blob/cbed4ff688/egs/wsj/s5/utils)
* [espnet utils)(https://github.com/espnet/espnet/tree/master/utils)
* [espnet utils](https://github.com/espnet/espnet/tree/master/utils)
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