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180 lines
6.2 KiB
180 lines
6.2 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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#include "absl/strings/str_split.h"
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#include "base/common.h"
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#include "decoder/ctc_prefix_beam_search_decoder.h"
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#include "frontend/audio/data_cache.h"
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#include "fst/symbol-table.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/u2_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(vocab_path, "", "vocab path");
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DEFINE_string(model_path, "", "paddle nnet model");
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DEFINE_int32(receptive_field_length,
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7,
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"receptive field of two CNN(kernel=3) downsampling module.");
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DEFINE_int32(subsampling_rate,
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4,
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"two CNN(kernel=3) module downsampling rate.");
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DEFINE_int32(nnet_decoder_chunk, 16, "paddle nnet forward chunk");
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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 ds2 online decoder by feeding speech feature
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int main(int argc, char* argv[]) {
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gflags::SetUsageMessage("Usage:");
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gflags::ParseCommandLineFlags(&argc, &argv, false);
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google::InitGoogleLogging(argv[0]);
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google::InstallFailureSignalHandler();
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FLAGS_logtostderr = 1;
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int32 num_done = 0, num_err = 0;
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CHECK_NE(FLAGS_result_wspecifier, "");
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CHECK_NE(FLAGS_feature_rspecifier, "");
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CHECK_NE(FLAGS_vocab_path, "");
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CHECK_NE(FLAGS_model_path, "");
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LOG(INFO) << "model path: " << FLAGS_model_path;
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LOG(INFO) << "Reading vocab table " << FLAGS_vocab_path;
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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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// nnet
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ppspeech::ModelOptions model_opts;
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model_opts.model_path = FLAGS_model_path;
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std::shared_ptr<ppspeech::U2Nnet> nnet =
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std::make_shared<ppspeech::U2Nnet>(model_opts);
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// decodeable
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std::shared_ptr<ppspeech::DataCache> raw_data =
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std::make_shared<ppspeech::DataCache>();
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std::shared_ptr<ppspeech::Decodable> decodable =
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std::make_shared<ppspeech::Decodable>(nnet, raw_data);
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// decoder
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ppspeech::CTCBeamSearchOptions opts;
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opts.blank = 0;
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opts.first_beam_size = 10;
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opts.second_beam_size = 10;
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ppspeech::CTCPrefixBeamSearch decoder(FLAGS_vocab_path, opts);
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int32 chunk_size = FLAGS_receptive_field_length +
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(FLAGS_nnet_decoder_chunk - 1) * FLAGS_subsampling_rate;
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int32 chunk_stride = FLAGS_subsampling_rate * FLAGS_nnet_decoder_chunk;
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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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kaldi::Timer timer;
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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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int nframes = feature.NumRows();
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int feat_dim = feature.NumCols();
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raw_data->SetDim(feat_dim);
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LOG(INFO) << "utt: " << utt;
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LOG(INFO) << "feat shape: " << nframes << ", " << feat_dim;
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raw_data->SetDim(feat_dim);
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int32 ori_feature_len = feature.NumRows();
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int32 num_chunks = feature.NumRows() / chunk_stride + 1;
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LOG(INFO) << "num_chunks: " << num_chunks;
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for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) {
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int32 this_chunk_size = 0;
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if (ori_feature_len > chunk_idx * chunk_stride) {
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this_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 (this_chunk_size < receptive_field_length) {
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LOG(WARNING)
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<< "utt: " << utt << " skip last " << this_chunk_size
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<< " frames, expect is " << receptive_field_length;
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break;
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}
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kaldi::Vector<kaldi::BaseFloat> feature_chunk(this_chunk_size *
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feat_dim);
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int32 start = chunk_idx * chunk_stride;
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for (int row_id = 0; row_id < this_chunk_size; ++row_id) {
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kaldi::SubVector<kaldi::BaseFloat> feat_row(feature, start);
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kaldi::SubVector<kaldi::BaseFloat> feature_chunk_row(
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feature_chunk.Data() + row_id * feat_dim, feat_dim);
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feature_chunk_row.CopyFromVec(feat_row);
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++start;
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}
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// feat to frontend pipeline cache
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raw_data->Accept(feature_chunk);
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// send data finish signal
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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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// forward nnet
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decoder.AdvanceDecode(decodable);
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LOG(INFO) << "Partial result: " << decoder.GetPartialResult();
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}
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decoder.FinalizeSearch();
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// get 1-best result
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std::string result = decoder.GetFinalBestPath();
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// after process one utt, then reset state.
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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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LOG(INFO) << " the result of " << utt << " is empty";
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continue;
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}
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LOG(INFO) << " 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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double elapsed = timer.Elapsed();
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LOG(INFO) << "Program cost:" << elapsed << " sec";
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LOG(INFO) << "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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