Yang Zhou
3 years ago
parent
b78bc6375b
commit
1133540682
@ -0,0 +1,10 @@
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cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
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add_executable(websocket_server_main ${CMAKE_CURRENT_SOURCE_DIR}/websocket_server_main.cc)
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target_include_directories(websocket_server_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
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target_link_libraries(websocket_server_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder websocket ${DEPS})
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add_executable(websocket_client_main ${CMAKE_CURRENT_SOURCE_DIR}/websocket_client_main.cc)
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target_include_directories(websocket_client_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
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target_link_libraries(websocket_client_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder websocket ${DEPS})
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@ -0,0 +1,82 @@
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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 "websocket/websocket_client.h"
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#include "kaldi/feat/wave-reader.h"
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#include "kaldi/util/kaldi-io.h"
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#include "kaldi/util/table-types.h"
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DEFINE_string(host, "127.0.0.1", "host of websocket server");
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DEFINE_int32(port, 201314, "port of websocket server");
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DEFINE_string(wav_rspecifier, "", "test wav scp path");
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DEFINE_double(streaming_chunk, 0.1, "streaming feature chunk size");
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using kaldi::int16;
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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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ppspeech::WebSocketClient client(FLAGS_host, FLAGS_port);
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kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
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FLAGS_wav_rspecifier);
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const int sample_rate = 16000;
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const float streaming_chunk = FLAGS_streaming_chunk;
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const int chunk_sample_size = streaming_chunk * sample_rate;
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for (; !wav_reader.Done(); wav_reader.Next()) {
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client.SendStartSignal();
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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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CHECK_EQ(wave_data.SampFreq(), sample_rate);
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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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const int tot_samples = waveform.Dim();
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int sample_offset = 0;
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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<int16> 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] = static_cast<int16>(waveform(sample_offset + i));
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}
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client.SendBinaryData(wav_chunk.data(),
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wav_chunk.size() * sizeof(int16));
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sample_offset += cur_chunk_size;
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LOG(INFO) << "Send " << cur_chunk_size << " samples";
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std::this_thread::sleep_for(
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std::chrono::milliseconds(static_cast<int>(1 * 1000)));
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if (cur_chunk_size < chunk_sample_size) {
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client.SendEndSignal();
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}
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}
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while (!client.Done()) {
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}
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std::string result = client.GetResult();
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LOG(INFO) << "utt: " << utt << " " << result;
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client.Join();
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return 0;
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}
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return 0;
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}
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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 "websocket/websocket_server.h"
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#include "decoder/param.h"
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DEFINE_int32(port, 201314, "websocket listening port");
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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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ppspeech::RecognizerResource resource = ppspeech::InitRecognizerResoure();
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ppspeech::WebSocketServer server(FLAGS_port, resource);
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LOG(INFO) << "Listening at port " << FLAGS_port;
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server.Start();
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return 0;
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}
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@ -0,0 +1,94 @@
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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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#pragma once
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#include "base/common.h"
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#include "decoder/ctc_beam_search_decoder.h"
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#include "decoder/ctc_tlg_decoder.h"
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#include "frontend/audio/feature_pipeline.h"
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DEFINE_string(cmvn_file, "", "read cmvn");
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DEFINE_double(streaming_chunk, 0.1, "streaming feature chunk size");
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DEFINE_bool(convert2PCM32, true, "audio convert to pcm32");
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DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
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DEFINE_string(params_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, "max active");
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DEFINE_double(beam, 15.0, "decoder beam");
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DEFINE_double(lattice_beam, 7.5, "decoder beam");
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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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namespace ppspeech {
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// todo refactor later
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FeaturePipelineOptions InitFeaturePipelineOptions() {
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FeaturePipelineOptions opts;
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opts.cmvn_file = FLAGS_cmvn_file;
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opts.linear_spectrogram_opts.streaming_chunk = FLAGS_streaming_chunk;
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opts.convert2PCM32 = FLAGS_convert2PCM32;
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kaldi::FrameExtractionOptions frame_opts;
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frame_opts.frame_length_ms = 20;
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frame_opts.frame_shift_ms = 10;
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frame_opts.remove_dc_offset = false;
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frame_opts.window_type = "hanning";
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frame_opts.preemph_coeff = 0.0;
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frame_opts.dither = 0.0;
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opts.linear_spectrogram_opts.frame_opts = frame_opts;
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opts.feature_cache_opts.frame_chunk_size = FLAGS_receptive_field_length;
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opts.feature_cache_opts.frame_chunk_stride = FLAGS_downsampling_rate;
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return opts;
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}
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ModelOptions InitModelOptions() {
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ModelOptions model_opts;
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model_opts.model_path = FLAGS_model_path;
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model_opts.params_path = FLAGS_params_path;
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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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return model_opts;
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}
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TLGDecoderOptions InitDecoderOptions() {
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TLGDecoderOptions decoder_opts;
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decoder_opts.word_symbol_table = FLAGS_word_symbol_table;
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decoder_opts.fst_path = FLAGS_graph_path;
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decoder_opts.opts.max_active = FLAGS_max_active;
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decoder_opts.opts.beam = FLAGS_beam;
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decoder_opts.opts.lattice_beam = FLAGS_lattice_beam;
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return decoder_opts;
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}
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RecognizerResource InitRecognizerResoure() {
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RecognizerResource resource;
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resource.acoustic_scale = FLAGS_acoustic_scale;
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resource.feature_pipeline_opts = InitFeaturePipelineOptions();
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resource.model_opts = InitModelOptions();
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resource.tlg_opts = InitDecoderOptions();
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return resource;
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}
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}
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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 "decoder/recognizer.h"
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namespace ppspeech {
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using kaldi::Vector;
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using kaldi::VectorBase;
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using kaldi::BaseFloat;
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using std::vector;
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using kaldi::SubVector;
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using std::unique_ptr;
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Recognizer::Recognizer(const RecognizerResource& resource) {
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// resource_ = resource;
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const FeaturePipelineOptions& feature_opts = resource.feature_pipeline_opts;
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feature_pipeline_.reset(new FeaturePipeline(feature_opts));
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std::shared_ptr<PaddleNnet> nnet(new PaddleNnet(resource.model_opts));
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BaseFloat ac_scale = resource.acoustic_scale;
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decodable_.reset(new Decodable(nnet, feature_pipeline_, ac_scale));
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decoder_.reset(new TLGDecoder(resource.tlg_opts));
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input_finished_ = false;
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}
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void Recognizer::Accept(const Vector<BaseFloat>& waves) {
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feature_pipeline_->Accept(waves);
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}
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void Recognizer::Decode() { decoder_->AdvanceDecode(decodable_); }
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std::string Recognizer::GetFinalResult() {
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return decoder_->GetFinalBestPath();
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}
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void Recognizer::SetFinished() {
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feature_pipeline_->SetFinished();
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input_finished_ = true;
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}
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bool Recognizer::IsFinished() { return input_finished_; }
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void Recognizer::Reset() {
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feature_pipeline_->Reset();
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decodable_->Reset();
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decoder_->Reset();
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}
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} // namespace ppspeech
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@ -0,0 +1,59 @@
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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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// todo refactor later (SGoat)
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#pragma once
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#include "decoder/ctc_beam_search_decoder.h"
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#include "decoder/ctc_tlg_decoder.h"
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#include "frontend/audio/feature_pipeline.h"
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#include "nnet/decodable.h"
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#include "nnet/paddle_nnet.h"
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namespace ppspeech {
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struct RecognizerResource {
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FeaturePipelineOptions feature_pipeline_opts;
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ModelOptions model_opts;
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TLGDecoderOptions tlg_opts;
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// CTCBeamSearchOptions beam_search_opts;
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kaldi::BaseFloat acoustic_scale;
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RecognizerResource()
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: acoustic_scale(1.0),
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feature_pipeline_opts(),
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model_opts(),
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tlg_opts() {}
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};
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class Recognizer {
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public:
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explicit Recognizer(const RecognizerResource& resouce);
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void Accept(const kaldi::Vector<kaldi::BaseFloat>& waves);
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void Decode();
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std::string GetFinalResult();
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void SetFinished();
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bool IsFinished();
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void Reset();
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private:
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// std::shared_ptr<RecognizerResource> resource_;
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// RecognizerResource resource_;
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std::shared_ptr<FeaturePipeline> feature_pipeline_;
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std::shared_ptr<Decodable> decodable_;
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std::unique_ptr<TLGDecoder> decoder_;
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bool input_finished_;
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};
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} // namespace ppspeech
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@ -0,0 +1,36 @@
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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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// 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.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
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#include "frontend/audio/feature_pipeline.h"
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namespace ppspeech {
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using std::unique_ptr;
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FeaturePipeline::FeaturePipeline(const FeaturePipelineOptions& opts) {
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unique_ptr<FrontendInterface> data_source(
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new ppspeech::AudioCache(1000 * kint16max, opts.convert2PCM32));
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unique_ptr<FrontendInterface> linear_spectrogram(
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new ppspeech::LinearSpectrogram(opts.linear_spectrogram_opts,
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std::move(data_source)));
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unique_ptr<FrontendInterface> cmvn(
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new ppspeech::CMVN(opts.cmvn_file, std::move(linear_spectrogram)));
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base_extractor_.reset(
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new ppspeech::FeatureCache(opts.feature_cache_opts, std::move(cmvn)));
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}
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} // ppspeech
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@ -0,0 +1,57 @@
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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");
|
||||
// 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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// http://www.apache.org/licenses/LICENSE-2.0
|
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//
|
||||
// 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.
|
||||
|
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// todo refactor later (SGoat)
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#pragma once
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#include "frontend/audio/audio_cache.h"
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#include "frontend/audio/data_cache.h"
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#include "frontend/audio/feature_cache.h"
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#include "frontend/audio/frontend_itf.h"
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#include "frontend/audio/linear_spectrogram.h"
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#include "frontend/audio/normalizer.h"
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namespace ppspeech {
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struct FeaturePipelineOptions {
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std::string cmvn_file;
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bool convert2PCM32;
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LinearSpectrogramOptions linear_spectrogram_opts;
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FeatureCacheOptions feature_cache_opts;
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FeaturePipelineOptions()
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: cmvn_file(""),
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convert2PCM32(false),
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linear_spectrogram_opts(),
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feature_cache_opts() {}
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};
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class FeaturePipeline : public FrontendInterface {
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public:
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explicit FeaturePipeline(const FeaturePipelineOptions& opts);
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virtual void Accept(const kaldi::VectorBase<kaldi::BaseFloat>& waves) {
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base_extractor_->Accept(waves);
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}
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virtual bool Read(kaldi::Vector<kaldi::BaseFloat>* feats) {
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return base_extractor_->Read(feats);
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}
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virtual size_t Dim() const { return base_extractor_->Dim(); }
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virtual void SetFinished() { base_extractor_->SetFinished(); }
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virtual bool IsFinished() const { return base_extractor_->IsFinished(); }
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virtual void Reset() { base_extractor_->Reset(); }
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private:
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std::unique_ptr<FrontendInterface> base_extractor_;
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};
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}
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Loading…
Reference in new issue