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102 lines
3.7 KiB
102 lines
3.7 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_beam_search_decoder.h"
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#include "frontend/raw_audio.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_respecifier, "", "test feature rspecifier");
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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(dict_file, "vocab.txt", "vocabulary of lm");
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DEFINE_string(lm_path, "lm.klm", "language model");
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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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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_respecifier);
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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 dict_file = FLAGS_dict_file;
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std::string lm_path = FLAGS_lm_path;
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int32 num_done = 0, num_err = 0;
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ppspeech::CTCBeamSearchOptions opts;
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opts.dict_file = dict_file;
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opts.lm_path = lm_path;
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ppspeech::CTCBeamSearch 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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std::shared_ptr<ppspeech::PaddleNnet> nnet(
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new ppspeech::PaddleNnet(model_opts));
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std::shared_ptr<ppspeech::RawDataCache> raw_data(
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new ppspeech::RawDataCache());
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std::shared_ptr<ppspeech::Decodable> decodable(
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new ppspeech::Decodable(nnet, raw_data));
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int32 chunk_size = 35;
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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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const kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
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raw_data->SetDim(feature.NumCols());
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int32 row_idx = 0;
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int32 num_chunks = feature.NumRows() / chunk_size;
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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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for (int row_id = 0; row_id < chunk_size; ++row_id) {
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kaldi::SubVector<kaldi::BaseFloat> tmp(feature, row_idx);
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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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row_idx++;
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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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KALDI_LOG << " the result of " << utt << " is " << result;
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decodable->Reset();
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decoder.Reset();
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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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