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Simple Kaldi websocket server

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Nickolay Shmyrev 2019-05-07 19:21:36 +02:00
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#ATLASLIBS = /usr/lib/libatlas.so.3 /usr/lib/libf77blas.so.3 /usr/lib/libcblas.so.3 /usr/lib/liblapack_atlas.so.3
ATLASLIBS = /usr/lib/x86_64-linux-gnu/libatlas.so.3 /usr/lib/x86_64-linux-gnu/libf77blas.so.3 /usr/lib/x86_64-linux-gnu/libcblas.so.3 /usr/lib/x86_64-linux-gnu/liblapack_atlas.so.3 -Wl,-rpath=/usr/lib/x86_64-linux-gnu
KALDI_ROOT=$(HOME)/voicechat/kaldi
CXX := g++
KALDI_FLAGS := -DKALDI_DOUBLEPRECISION=0 -DHAVE_POSIX_MEMALIGN \
-Wno-sign-compare -Wno-unused-local-typedefs -Winit-self \
-DHAVE_EXECINFO_H=1 -rdynamic -DHAVE_CXXABI_H -DHAVE_ATLAS \
-I$(KALDI_ROOT)/tools/ATLAS/include \
-I$(KALDI_ROOT)/tools/openfst/include -I$(KALDI_ROOT)/src
CXXFLAGS := -std=c++11 -g -Wall -DPIC -fPIC $(KALDI_FLAGS) `pkg-config --cflags python3`
KALDI_LIBS = \
-rdynamic -Wl,-rpath=$(KALDI_ROOT)/tools/openfst/lib \
$(KALDI_ROOT)/src/online2/kaldi-online2.a \
$(KALDI_ROOT)/src/decoder/kaldi-decoder.a \
$(KALDI_ROOT)/src/ivector/kaldi-ivector.a \
$(KALDI_ROOT)/src/gmm/kaldi-gmm.a \
$(KALDI_ROOT)/src/nnet3/kaldi-nnet3.a \
$(KALDI_ROOT)/src/tree/kaldi-tree.a \
$(KALDI_ROOT)/src/feat/kaldi-feat.a \
$(KALDI_ROOT)/src/lat/kaldi-lat.a \
$(KALDI_ROOT)/src/hmm/kaldi-hmm.a \
$(KALDI_ROOT)/src/transform/kaldi-transform.a \
$(KALDI_ROOT)/src/cudamatrix/kaldi-cudamatrix.a \
$(KALDI_ROOT)/src/matrix/kaldi-matrix.a \
$(KALDI_ROOT)/src/fstext/kaldi-fstext.a \
$(KALDI_ROOT)/src/util/kaldi-util.a \
$(KALDI_ROOT)/src/base/kaldi-base.a \
-L $(KALDI_ROOT)/tools/openfst/lib -lfst \
$(ATLASLIBS) \
`pkg-config --libs python3` \
-lm -lpthread
all: _kaldi_websocket.so
_kaldi_websocket.so: kaldi_websocket_wrap.cc kaldi_recognizer.cc model.cc
$(CXX) $(CXXFLAGS) -shared -o $@ kaldi_recognizer.cc model.cc kaldi_websocket_wrap.cc $(KALDI_LIBS)
kaldi_websocket_wrap.cc: kaldi_websocket.i
swig -python -c++ -o kaldi_websocket_wrap.cc kaldi_websocket.i
clean:
$(RM) *.so kaldi_websocket_wrap.cc *.o *.pyc kaldi_websocket.py

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A very simple websocket server based on Kaldi

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#!/usr/bin/env python3
import asyncio
import pathlib
import websockets
from kaldi_websocket import Model, KaldiRecognizer
model = Model()
async def recognize(websocket, path):
rec = KaldiRecognizer(model);
while True:
message = await websocket.recv()
if message == '{"eof" : 1}':
await websocket.send(rec.Result())
break
if rec.AcceptWaveform(message):
await websocket.send(rec.Result())
else:
await websocket.send(rec.PartialResult())
start_server = websockets.serve(
recognize, '127.0.0.1', 2601)
asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()

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#include "kaldi_recognizer.h"
#include "fstext/fstext-utils.h"
#include "lat/sausages.h"
using namespace fst;
using namespace kaldi::nnet3;
KaldiRecognizer::KaldiRecognizer(Model &model) : model_(model) {
feature_info_ = new kaldi::OnlineNnet2FeaturePipelineInfo(model_.feature_config_);
feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (*feature_info_);
silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_.trans_model_, feature_info_->silence_weighting_config, 3);
decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_.nnet3_decoding_config_,
*model_.trans_model_,
*model_.decodable_info_,
*model_.decode_fst_,
feature_pipeline_);
input_finalized_ = false;
}
KaldiRecognizer::~KaldiRecognizer() {
delete feature_pipeline_;
delete feature_info_;
delete silence_weighting_;
delete decoder_;
}
void KaldiRecognizer::CleanUp()
{
delete decoder_;
OnlineIvectorExtractorAdaptationState state(feature_info_->ivector_extractor_info);
feature_pipeline_->GetAdaptationState(&state);
delete feature_pipeline_;
feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (*feature_info_);
feature_pipeline_->SetAdaptationState(state);
delete silence_weighting_;
silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_.trans_model_, feature_info_->silence_weighting_config, 3);
decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_.nnet3_decoding_config_,
*model_.trans_model_,
*model_.decodable_info_,
*model_.decode_fst_,
feature_pipeline_);
}
bool KaldiRecognizer::AcceptWaveform(const char *data, int len) {
if (input_finalized_) {
CleanUp();
input_finalized_ = false;
}
Vector<BaseFloat> wave;
wave.Resize(len / 2, kUndefined);
for (int i = 0; i < len / 2; i++)
wave(i) = *(((short *)data) + i);
feature_pipeline_->AcceptWaveform(8000, wave);
if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0 &&
feature_pipeline_->IvectorFeature() != NULL) {
std::vector<std::pair<int32, BaseFloat> > delta_weights;
silence_weighting_->ComputeCurrentTraceback(decoder_->Decoder());
silence_weighting_->GetDeltaWeights(feature_pipeline_->NumFramesReady(),
&delta_weights);
feature_pipeline_->IvectorFeature()->UpdateFrameWeights(delta_weights);
}
decoder_->AdvanceDecoding();
if (decoder_->EndpointDetected(model_.endpoint_config_)) {
return true;
}
return false;
}
std::string KaldiRecognizer::Result()
{
if (!input_finalized_) {
feature_pipeline_->InputFinished();
if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0 &&
feature_pipeline_->IvectorFeature() != NULL) {
std::vector<std::pair<int32, BaseFloat> > delta_weights;
silence_weighting_->ComputeCurrentTraceback(decoder_->Decoder());
silence_weighting_->GetDeltaWeights(feature_pipeline_->NumFramesReady(),
&delta_weights);
feature_pipeline_->IvectorFeature()->UpdateFrameWeights(delta_weights);
}
decoder_->AdvanceDecoding();
decoder_->FinalizeDecoding();
input_finalized_ = true;
}
kaldi::CompactLattice clat;
decoder_->GetLattice(true, &clat);
CompactLattice aligned_lat;
WordAlignLattice(clat, *model_.trans_model_, *model_.winfo_, 0, &aligned_lat);
MinimumBayesRisk mbr(aligned_lat);
const std::vector<BaseFloat> &conf = mbr.GetOneBestConfidences();
const std::vector<int32> &words = mbr.GetOneBest();
const std::vector<std::pair<BaseFloat, BaseFloat> > &times =
mbr.GetOneBestTimes();
int size = words.size();
std::stringstream ss;
// Create JSON object
ss << "{\"result\" : [ ";
for (int i = 0; i < size; i++) {
ss << "{\"word\": \"" << model_.word_syms_->Find(words[i]) << "\", \"start\" : " << times[i].first << "," <<
" \"end\" : " << times[i].second << ", \"conf\" : " << conf[i] << "}";
if (i != size - 1)
ss << ",\n";
else
ss << "\n";
}
ss << " ], \"text\" : \"";
for (int i = 0; i < size; i++) {
ss << model_.word_syms_->Find(words[i]);
if (i != size - 1)
ss << " ";
}
ss << "\" }";
return ss.str();
}
std::string KaldiRecognizer::PartialResult()
{
decoder_->AdvanceDecoding();
if (decoder_->NumFramesDecoded() < 50) {
return "{\"partial\" : \"\"}";
}
kaldi::Lattice lat;
decoder_->GetBestPath(false, &lat);
std::vector<kaldi::int32> alignment, words;
LatticeWeight weight;
GetLinearSymbolSequence(lat, &alignment, &words, &weight);
std::ostringstream outss;
outss << "{\"partial\" : \"";
for (size_t i = 0; i < words.size(); i++) {
if (i) {
outss << " ";
}
outss << model_.word_syms_->Find(words[i]);
}
outss << "\"}";
return outss.str();
}

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#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "fstext/fstext-lib.h"
#include "fstext/fstext-utils.h"
#include "decoder/lattice-faster-decoder.h"
#include "feat/feature-mfcc.h"
#include "lat/kaldi-lattice.h"
#include "lat/word-align-lattice.h"
#include "nnet3/am-nnet-simple.h"
#include "nnet3/nnet-am-decodable-simple.h"
#include "nnet3/nnet-utils.h"
#include "model.h"
using namespace kaldi;
class KaldiRecognizer {
public:
KaldiRecognizer(Model &model);
~KaldiRecognizer();
void CleanUp();
bool AcceptWaveform(const char *data, int len);
std::string Result();
std::string PartialResult();
private:
Model &model_;
SingleUtteranceNnet3Decoder *decoder_;
OnlineNnet2FeaturePipelineInfo *feature_info_;
OnlineNnet2FeaturePipeline *feature_pipeline_;
OnlineSilenceWeighting *silence_weighting_;
bool input_finalized_;
};

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%module kaldi_websocket
%include <typemaps.i>
%include <std_string.i>
%include <pybuffer.i>
namespace kaldi {
}
%pybuffer_binary(const char *data, int len)
%{
#include "kaldi_recognizer.h"
#include "model.h"
%}
%include "kaldi_recognizer.h"
%include "model.h"

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#include "model.h"
Model::Model() {
const char *usage = "Read the docs";
const char *extra_args[] = {
"--feature-type=mfcc",
"--mfcc-config=model/mfcc.conf",
"--min-active=200",
"--max-active=6000",
"--beam=13.0",
"--lattice-beam=6.0",
"--acoustic-scale=1.0",
"--frames-per-chunk=51",
"--frame-subsampling-factor=3",
"--endpoint.silence-phones=1:2:3:4:5:6:7:8:9:10",
"--endpoint.rule2.min-trailing-silence=0.5",
"--endpoint.rule3.min-trailing-silence=1.0",
"--endpoint.rule4.min-trailing-silence=2.0",
"--ivector-silence-weighting.silence-weight=0.001",
"--ivector-silence-weighting.silence-phones=1:2:3:4:5:6:7:8:9:10",
"--ivector-extraction-config=model/ivector/ivector.conf",
};
kaldi::ParseOptions po(usage);
feature_config_.Register(&po);
nnet3_decoding_config_.Register(&po);
endpoint_config_.Register(&po);
decodable_opts_.Register(&po);
MfccOptions mfcc_opts;
ReadConfigFromFile("model/mfcc.conf", &mfcc_opts);
sample_frequency = mfcc_opts.frame_opts.samp_freq;
KALDI_LOG << "Sample rate is " << sample_frequency;
std::vector<const char*> args;
args.push_back("server");
args.insert(args.end(), extra_args, extra_args + sizeof(extra_args) / sizeof(extra_args[0]));
po.Read(args.size(), args.data());
nnet3_rxfilename_ = "model/final.mdl";
word_syms_rxfilename_ = "model/words.txt";
fst_rxfilename_ = "model/HCLG.fst";
trans_model_ = new kaldi::TransitionModel();
nnet_ = new kaldi::nnet3::AmNnetSimple();
{
bool binary;
kaldi::Input ki(nnet3_rxfilename_, &binary);
trans_model_->Read(ki.Stream(), binary);
nnet_->Read(ki.Stream(), binary);
SetBatchnormTestMode(true, &(nnet_->GetNnet()));
SetDropoutTestMode(true, &(nnet_->GetNnet()));
nnet3::CollapseModel(nnet3::CollapseModelConfig(), &(nnet_->GetNnet()));
}
decodable_info_ = new nnet3::DecodableNnetSimpleLoopedInfo(decodable_opts_,
nnet_);
decode_fst_ = fst::ReadFstKaldiGeneric(fst_rxfilename_);
word_syms_ = NULL;
if (!(word_syms_ = fst::SymbolTable::ReadText(word_syms_rxfilename_)))
KALDI_ERR << "Could not read symbol table from file " << word_syms_rxfilename_;
kaldi::WordBoundaryInfoNewOpts opts;
winfo_ = new kaldi::WordBoundaryInfo(opts, "model/word_boundary.int");
}
Model::~Model() {
delete decodable_info_;
delete decode_fst_;
delete trans_model_;
delete nnet_;
delete word_syms_;
delete winfo_;
}

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// model.h
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#ifndef model_H_
#define model_H_
#include "base/kaldi-common.h"
#include "fstext/fstext-lib.h"
#include "fstext/fstext-utils.h"
#include "online2/onlinebin-util.h"
#include "online2/online-timing.h"
#include "online2/online-endpoint.h"
#include "online2/online-nnet3-decoding.h"
#include "online2/online-feature-pipeline.h"
#include "lat/lattice-functions.h"
#include "lat/sausages.h"
#include "lat/word-align-lattice.h"
#include "lm/const-arpa-lm.h"
#include "util/parse-options.h"
#include "nnet3/nnet-utils.h"
#include "rnnlm/rnnlm-utils.h"
using namespace kaldi;
class KaldiRecognizer;
class Model {
public:
Model();
~Model();
protected:
friend class KaldiRecognizer;
std::string nnet3_rxfilename_;
std::string fst_rxfilename_;
std::string word_syms_rxfilename_;
kaldi::OnlineEndpointConfig endpoint_config_;
kaldi::LatticeFasterDecoderConfig nnet3_decoding_config_;
kaldi::nnet3::NnetSimpleLoopedComputationOptions decodable_opts_;
kaldi::OnlineNnet2FeaturePipelineConfig feature_config_;
kaldi::BaseFloat sample_frequency;
kaldi::nnet3::DecodableNnetSimpleLoopedInfo *decodable_info_;
fst::Fst<fst::StdArc> *decode_fst_;
kaldi::TransitionModel *trans_model_;
kaldi::nnet3::AmNnetSimple *nnet_;
fst::SymbolTable *word_syms_;
kaldi::WordBoundaryInfo *winfo_;
};
#endif /* model_H_ */