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PaddleSpeech/speechx/examples/ds2_ol/decoder/wfst-decoder-ol.cc

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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_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, "5-1-1024,5-1-1024", "model cache names");
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.params_path = model_params;
model_opts.cache_shape = FLAGS_model_cache_names;
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();
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.";
return (num_done != 0 ? 0 : 1);
}