Simple Kaldi websocket server

COPYING

@@ -0,0 +1,176 @@
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Makefile

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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

README.md

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+A very simple websocket server based on Kaldi

asr_server.py

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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()

kaldi_recognizer.cc

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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();
+}

kaldi_recognizer.h

@@ -0,0 +1,35 @@
+#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_;
+};

kaldi_websocket.i

@@ -0,0 +1,19 @@
+%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"
+

model.cc

@@ -0,0 +1,79 @@
+#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_;
+}

model.h

@@ -0,0 +1,67 @@
+// 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_ */