YangZhou
3 years ago
parent
08e8aafdbf
commit
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// 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 "base/common.h"
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#include "decoder/param.h"
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#include "frontend/wave-reader.h"
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#include "frontend/feature_pipeline.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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#include "nnet/nnet_producer.h"
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DEFINE_string(wav_rspecifier, "", "test wav rspecifier");
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DEFINE_string(nnet_prob_wspecifier, "", "nnet porb wspecifier");
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DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
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DEFINE_int32(sample_rate, 16000, "sample rate");
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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::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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int sample_rate = FLAGS_sample_rate;
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float streaming_chunk = FLAGS_streaming_chunk;
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int chunk_sample_size = streaming_chunk * sample_rate;
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CHECK_GT(FLAGS_wav_rspecifier.size(), 0);
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CHECK_GT(FLAGS_nnet_prob_wspecifier.size(), 0);
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CHECK_GT(FLAGS_model_path.size(), 0);
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LOG(INFO) << "input rspecifier: " << FLAGS_wav_rspecifier;
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LOG(INFO) << "output wspecifier: " << FLAGS_nnet_prob_wspecifier;
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LOG(INFO) << "model path: " << FLAGS_model_path;
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kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
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FLAGS_wav_rspecifier);
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kaldi::BaseFloatMatrixWriter nnet_out_writer(FLAGS_nnet_prob_wspecifier);
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ppspeech::ModelOptions model_opts = ppspeech::ModelOptions::InitFromFlags();
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ppspeech::FeaturePipelineOptions feature_opts =
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ppspeech::FeaturePipelineOptions::InitFromFlags();
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feature_opts.assembler_opts.fill_zero = false;
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std::shared_ptr<ppspeech::U2Nnet> nnet(new ppspeech::U2Nnet(model_opts));
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std::shared_ptr<ppspeech::FeaturePipeline> raw_data(new ppspeech::FeaturePipeline(feature_opts));
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std::shared_ptr<ppspeech::NnetProducer> nnet_producer(new ppspeech::NnetProducer(nnet, raw_data));
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kaldi::Timer timer;
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float tot_wav_duration = 0;
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for (; !wav_reader.Done(); wav_reader.Next()) {
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std::string utt = wav_reader.Key();
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const kaldi::WaveData& wave_data = wav_reader.Value();
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LOG(INFO) << "utt: " << utt;
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LOG(INFO) << "wav dur: " << wave_data.Duration() << " sec.";
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double dur = wave_data.Duration();
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tot_wav_duration += dur;
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int32 this_channel = 0;
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kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
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this_channel);
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int tot_samples = waveform.Dim();
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LOG(INFO) << "wav len (sample): " << tot_samples;
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int sample_offset = 0;
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kaldi::Timer timer;
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while (sample_offset < tot_samples) {
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int cur_chunk_size =
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std::min(chunk_sample_size, tot_samples - sample_offset);
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std::vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
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for (int i = 0; i < cur_chunk_size; ++i) {
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wav_chunk[i] = waveform(sample_offset + i);
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}
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// wav_chunk = waveform.Range(sample_offset + i, cur_chunk_size);
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nnet_producer->Accept(wav_chunk);
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if (cur_chunk_size < chunk_sample_size) {
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nnet_producer->SetInputFinished();
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}
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// no overlap
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sample_offset += cur_chunk_size;
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}
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CHECK(sample_offset == tot_samples);
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std::vector<kaldi::Vector<kaldi::BaseFloat>> prob_vec;
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while(1) {
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std::vector<kaldi::BaseFloat> logprobs;
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//nnet_producer->ReadandCompute(&logprobs);
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bool isok = nnet_producer->Read(&logprobs);
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//bool isok = nnet_producer->IsFinished();
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int vocab_dim = logprobs.size();
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if (nnet_producer->IsFinished()) break;
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//for (int row_idx = 0; row_idx < logprobs.Dim() / vocab_dim;
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//row_idx++) {
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//kaldi::Vector<kaldi::BaseFloat> vec_tmp(vocab_dim);
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//std::memcpy(vec_tmp.Data(),
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//logprobs.Data() + row_idx * vocab_dim,
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//sizeof(kaldi::BaseFloat) * vocab_dim);
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//prob_vec.push_back(vec_tmp);
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//}
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}
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nnet_producer->Reset();
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}
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nnet_producer->Wait();
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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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