Yang Zhou
3 years ago
parent
eea56a4af2
commit
bbcb1ea297
@ -0,0 +1,162 @@
|
||||
// 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.
|
||||
|
||||
#include "base/flags.h"
|
||||
#include "base/log.h"
|
||||
#include "frontend/audio/data_cache.h"
|
||||
#include "frontend/audio/assembler.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(nnet_prob_wspecifier, "", "nnet porb 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_int32(nnet_decoder_chunk, 1, "paddle nnet forward chunk");
|
||||
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,
|
||||
"chunk_state_h_box,chunk_state_c_box",
|
||||
"model cache names");
|
||||
DEFINE_string(model_cache_shapes, "5-1-1024,5-1-1024", "model cache shapes");
|
||||
DEFINE_double(acoustic_scale, 1.0, "acoustic scale");
|
||||
|
||||
using kaldi::BaseFloat;
|
||||
using kaldi::Matrix;
|
||||
using std::vector;
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
gflags::ParseCommandLineFlags(&argc, &argv, false);
|
||||
google::InitGoogleLogging(argv[0]);
|
||||
|
||||
kaldi::SequentialBaseFloatMatrixReader feature_reader(
|
||||
FLAGS_feature_rspecifier);
|
||||
kaldi::BaseFloatMatrixWriter nnet_writer(FLAGS_nnet_prob_wspecifier);
|
||||
std::string model_graph = FLAGS_model_path;
|
||||
std::string model_params = FLAGS_param_path;
|
||||
LOG(INFO) << "model path: " << model_graph;
|
||||
LOG(INFO) << "model param: " << model_params;
|
||||
|
||||
int32 num_done = 0, num_err = 0;
|
||||
|
||||
ppspeech::ModelOptions model_opts;
|
||||
model_opts.model_path = model_graph;
|
||||
model_opts.param_path = model_params;
|
||||
model_opts.cache_names = FLAGS_model_cache_names;
|
||||
model_opts.cache_shape = FLAGS_model_cache_shapes;
|
||||
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
|
||||
+ (FLAGS_nnet_decoder_chunk - 1) * FLAGS_downsampling_rate;
|
||||
int32 chunk_stride = FLAGS_downsampling_rate * FLAGS_nnet_decoder_chunk;
|
||||
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;
|
||||
kaldi::Timer timer;
|
||||
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;
|
||||
int32 frame_idx = 0;
|
||||
std::vector<kaldi::Vector<kaldi::BaseFloat>> prob_vec;
|
||||
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();
|
||||
}
|
||||
vector<kaldi::BaseFloat> prob;
|
||||
while (decodable->FrameLikelihood(frame_idx, &prob)) {
|
||||
kaldi::Vector<kaldi::BaseFloat> vec_tmp(prob.size());
|
||||
std::memcpy(vec_tmp.Data(), prob.data(), sizeof(kaldi::BaseFloat)*prob.size());
|
||||
prob_vec.push_back(vec_tmp);
|
||||
frame_idx++;
|
||||
}
|
||||
}
|
||||
decodable->Reset();
|
||||
if (prob_vec.size() == 0) {
|
||||
// the TokenWriter can not write empty string.
|
||||
++num_err;
|
||||
KALDI_LOG << " the nnet prob of " << utt << " is empty";
|
||||
continue;
|
||||
}
|
||||
kaldi::Matrix<kaldi::BaseFloat> result(prob_vec.size(),prob_vec[0].Dim());
|
||||
for (int32 row_idx = 0; row_idx < prob_vec.size(); ++row_idx) {
|
||||
for (int32 col_idx = 0; col_idx < prob_vec[0].Dim(); ++col_idx) {
|
||||
result(row_idx, col_idx) = prob_vec[row_idx](col_idx);
|
||||
}
|
||||
}
|
||||
|
||||
nnet_writer.Write(utt, result);
|
||||
++num_done;
|
||||
}
|
||||
|
||||
double elapsed = timer.Elapsed();
|
||||
KALDI_LOG << " cost:" << elapsed << " s";
|
||||
|
||||
KALDI_LOG << "Done " << num_done << " utterances, " << num_err
|
||||
<< " with errors.";
|
||||
return (num_done != 0 ? 0 : 1);
|
||||
}
|
||||
Loading…
Reference in new issue