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159 lines
6.5 KiB
159 lines
6.5 KiB
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// todo refactor, repalce with gtest
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#include "base/flags.h"
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#include "base/log.h"
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#include "decoder/ctc_tlg_decoder.h"
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#include "frontend/audio/data_cache.h"
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#include "kaldi/util/table-types.h"
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#include "nnet/decodable.h"
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#include "nnet/paddle_nnet.h"
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DEFINE_string(feature_rspecifier, "", "test feature rspecifier");
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DEFINE_string(result_wspecifier, "", "test result wspecifier");
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DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
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DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param");
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DEFINE_string(word_symbol_table, "words.txt", "word symbol table");
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DEFINE_string(graph_path, "TLG", "decoder graph");
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DEFINE_double(acoustic_scale, 1.0, "acoustic scale");
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DEFINE_int32(max_active, 7500, "decoder graph");
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DEFINE_int32(receptive_field_length,
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7,
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"receptive field of two CNN(kernel=5) downsampling module.");
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DEFINE_int32(downsampling_rate,
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4,
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"two CNN(kernel=5) module downsampling rate.");
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DEFINE_string(model_output_names,
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"save_infer_model/scale_0.tmp_1,save_infer_model/"
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"scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
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"scale_3.tmp_1",
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"model output names");
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DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names");
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using kaldi::BaseFloat;
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using kaldi::Matrix;
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using std::vector;
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// test TLG decoder by feeding speech feature.
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int main(int argc, char* argv[]) {
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gflags::ParseCommandLineFlags(&argc, &argv, false);
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google::InitGoogleLogging(argv[0]);
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kaldi::SequentialBaseFloatMatrixReader feature_reader(
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FLAGS_feature_rspecifier);
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kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
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std::string model_graph = FLAGS_model_path;
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std::string model_params = FLAGS_param_path;
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std::string word_symbol_table = FLAGS_word_symbol_table;
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std::string graph_path = FLAGS_graph_path;
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LOG(INFO) << "model path: " << model_graph;
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LOG(INFO) << "model param: " << model_params;
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LOG(INFO) << "word symbol path: " << word_symbol_table;
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LOG(INFO) << "graph path: " << graph_path;
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int32 num_done = 0, num_err = 0;
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ppspeech::TLGDecoderOptions opts;
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opts.word_symbol_table = word_symbol_table;
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opts.fst_path = graph_path;
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opts.opts.max_active = FLAGS_max_active;
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opts.opts.beam = 15.0;
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opts.opts.lattice_beam = 7.5;
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ppspeech::TLGDecoder decoder(opts);
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ppspeech::ModelOptions model_opts;
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model_opts.model_path = model_graph;
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model_opts.params_path = model_params;
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model_opts.cache_shape = FLAGS_model_cache_names;
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model_opts.output_names = FLAGS_model_output_names;
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std::shared_ptr<ppspeech::PaddleNnet> nnet(
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new ppspeech::PaddleNnet(model_opts));
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std::shared_ptr<ppspeech::DataCache> raw_data(new ppspeech::DataCache());
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std::shared_ptr<ppspeech::Decodable> decodable(
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new ppspeech::Decodable(nnet, raw_data, FLAGS_acoustic_scale));
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int32 chunk_size = FLAGS_receptive_field_length;
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int32 chunk_stride = FLAGS_downsampling_rate;
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int32 receptive_field_length = FLAGS_receptive_field_length;
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LOG(INFO) << "chunk size (frame): " << chunk_size;
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LOG(INFO) << "chunk stride (frame): " << chunk_stride;
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LOG(INFO) << "receptive field (frame): " << receptive_field_length;
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decoder.InitDecoder();
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for (; !feature_reader.Done(); feature_reader.Next()) {
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string utt = feature_reader.Key();
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kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
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raw_data->SetDim(feature.NumCols());
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LOG(INFO) << "process utt: " << utt;
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LOG(INFO) << "rows: " << feature.NumRows();
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LOG(INFO) << "cols: " << feature.NumCols();
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int32 row_idx = 0;
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int32 padding_len = 0;
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int32 ori_feature_len = feature.NumRows();
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if ((feature.NumRows() - chunk_size) % chunk_stride != 0) {
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padding_len =
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chunk_stride - (feature.NumRows() - chunk_size) % chunk_stride;
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feature.Resize(feature.NumRows() + padding_len,
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feature.NumCols(),
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kaldi::kCopyData);
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}
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int32 num_chunks = (feature.NumRows() - chunk_size) / chunk_stride + 1;
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for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) {
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kaldi::Vector<kaldi::BaseFloat> feature_chunk(chunk_size *
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feature.NumCols());
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int32 feature_chunk_size = 0;
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if (ori_feature_len > chunk_idx * chunk_stride) {
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feature_chunk_size = std::min(
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ori_feature_len - chunk_idx * chunk_stride, chunk_size);
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}
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if (feature_chunk_size < receptive_field_length) break;
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int32 start = chunk_idx * chunk_stride;
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for (int row_id = 0; row_id < chunk_size; ++row_id) {
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kaldi::SubVector<kaldi::BaseFloat> tmp(feature, start);
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kaldi::SubVector<kaldi::BaseFloat> f_chunk_tmp(
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feature_chunk.Data() + row_id * feature.NumCols(),
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feature.NumCols());
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f_chunk_tmp.CopyFromVec(tmp);
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++start;
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}
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raw_data->Accept(feature_chunk);
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if (chunk_idx == num_chunks - 1) {
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raw_data->SetFinished();
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}
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decoder.AdvanceDecode(decodable);
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}
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std::string result;
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result = decoder.GetFinalBestPath();
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decodable->Reset();
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decoder.Reset();
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if (result.empty()) {
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// the TokenWriter can not write empty string.
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++num_err;
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KALDI_LOG << " the result of " << utt << " is empty";
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continue;
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}
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KALDI_LOG << " the result of " << utt << " is " << result;
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result_writer.Write(utt, result);
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++num_done;
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}
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KALDI_LOG << "Done " << num_done << " utterances, " << num_err
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<< " with errors.";
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return (num_done != 0 ? 0 : 1);
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}
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