Critical ivector bugfix and version 0.3.15

Add srt example
Revert compile flags
2020-10-13 01:28:46 +02:00 · 2020-10-07 13:43:09 +02:00 · 2020-10-05 01:24:32 +02:00 · 2020-10-05 00:16:15 +02:00 · 2020-10-05 00:13:25 +02:00 · 2020-10-04 23:45:34 +02:00
57 changed files with 2207 additions and 765 deletions
@@ -5,6 +5,9 @@
 # Java class files
 *.class

+# Object files
+*.o
+
 # Gradle files
 .gradle/
 build/
@@ -24,14 +27,37 @@ wheelhouse
 __pycache__
 *.egg-info
 python/dist
+python/vosk/*.cc
+python/vosk/*.c
+python/vosk/*.h
+python/vosk/*.i
+python/vosk/vosk.py
+python/vosk/vosk_wrap.cpp
+python/test/db
+python/test/hyp
+python/test/model
+python/test/ref
+python/test/result.txt
+python/test/wav.scp

 # Java
 *.so
 java/org
-java/model-en
 java/*.cc
+java/model-spk/
+java/model/

 # CSharp
 csharp/gen
 csharp/*.exe
 csharp/*.c
+csharp/model/
+csharp/test.wav
+
+# Node
+nodejs/vosk_wrap.cc
+nodejs/example/model
+nodejs/example/test.wav
+nodejs/node_modules
+nodejs/package-lock.json
+nodejs/build
@@ -1,134 +1,25 @@
-[![Build Status](https://travis-ci.com/alphacep/vosk-api.svg?branch=master)](https://travis-ci.com/alphacep/vosk-api)
+# About

-Language bindings for Vosk and Kaldi to access speech recognition from various languages and on various platforms
+Vosk is an offline open source speech recognition toolkit. It enables
+speech recognition models for 16 languages and dialects - English, Indian
+English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish,
+Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi.

-  * Python on Linux, Windows and RPi
-  * Node
-  * Android
-  * iOS
+Vosk models are small (50 Mb) but provide continuous large vocabulary
+transcription, zero-latency response with streaming API, reconfigurable
+vocabulary and speaker identification.

-## Android build
+Speech recognition bindings implemented for various programming languages
+like Python, Java, Node.JS, C#, C++ and others.

-```
-cd android
-gradle build
-```
+Vosk supplies speech recognition for chatbots, smart home appliances,
+virtual assistants. It can also create subtitles for movies,
+transcription for lectures and interviews.

-Please note that medium blog post about 64-bit is not relevant anymore, the script builds x86, arm64 and armv7 libraries automatically without any modifications.
+Vosk scales from small devices like Raspberry Pi or Android smartphone to
+big clusters.

-For example of Android application using Vosk-API check https://github.com/alphacep/kaldi-android-demo project
+# Documentation

-## iOS build
-
-Available on request. Drop as a mail at [contact@alphacephei.com](mailto:contact@alphacephei.com).
-
-## Python installation from Pypi
-
-The easiest way to install vosk api is with pip. You do not have to compile anything. We currently support only Linux on x86_64 and Raspberry Pi. Other systems (windows, mac) will come soon.
-
-Make sure you have newer pip and python:
-
-  * Python version >= 3.4
-  * pip version >= 19.0
-
-Uprade python and pip if needed. Then install vosk on Linux with a simple command
-
-```
-pip3 install vosk
-```
-
-## Websocket Server and GRPC server
-
-We also provide a websocket server and grpc server which can be used in telephony and other applications. With bigger models adapted for 8khz audio it provides more accuracy.
-
-The server is installed with docker and can run with a single command:
-
-```
-docker run -d -p 2700:2700 alphacep/kaldi-en:latest
-```
-
-For details see https://github.com/alphacep/vosk-server
-
-
-## Compilation from source
-
-If you still want to build from scratch, you can compile Kaldi and Vosk yourself. The compilation is straightforward but might be a little confusing for newbie. In case you want to follow this, please watch the errors.
-
-#### Kaldi compilation for local python, node and java modules
-
-```
-git clone -b lookahead --single-branch https://github.com/alphacep/kaldi
-cd kaldi/tools
-make
-```
-
-install all dependencies and repeat `make` if needed
-
-```
-extras/install_openblas.sh
-cd ../src
-./configure --mathlib=OPENBLAS --shared --use-cuda=no
-make -j 10
-```
-
-#### Python module build
-
-Then build the python module
-
-```
-export KALDI_ROOT=<KALDI_ROOT>
-cd python
-python3 setup.py install
-```
-
-#### Java example API build
-
-Or Java
-
-```
-cd java && KALDI_ROOT=<KALDI_ROOT> make
-wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz
-tar xf alphacep-model-android-en-us-0.3.tar.gz 
-mv alphacep-model-android-en-us-0.3 model
-make run
-```
-
-#### C# build
-
-Or C#
-
-```
-cd csharp && KALDI_ROOT=<KALDI_ROOT> make
-wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz
-tar xf alphacep-model-android-en-us-0.3.tar.gz 
-mv alphacep-model-android-en-us-0.3 model
-mono test.exe
-```
-
-## Running the example code with python
-
-Run like this:
-
-```
-cd vosk-api/python/example
-wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz
-tar xf alphacep-model-android-en-us-0.3.tar.gz 
-mv alphacep-model-android-en-us-0.3 model-en
-python3 ./test_simple.py test.wav
-```
-
-To run with your audio file make sure it has proper format - PCM 16khz 16bit mono, otherwise decoding will not work.
-
-You can find other examples of using a microphone, decoding with a fixed small vocabulary or speaker identification setup in  [python/example subfolder](https://github.com/alphacep/vosk-api/tree/master/python/example)
-
-## Models for different languages
-
-For information about models see [the documentation on available models](https://github.com/alphacep/vosk-api/blob/master/doc/models.md).
-
-## Contact Us
-
-If you have any questions, feel free to
-
-   * Post an issue here on github
-   * Send us an e-mail at [contact@alphacephei.com](mailto:contact@alphacephei.com)
-   * Join our group dedicated to speech recognition on Telegram [@speech_recognition](https://t.me/speech_recognition)
+For installation instructions, examples and documentation visit [Vosk
+Website](https://alphacephei.com/vosk).
@@ -8,6 +8,9 @@ set(KALDI_SUFFIX "arm_32")
 elseif ("x${ANDROID_ABI}" STREQUAL "xarm64-v8a")
 set(OPENBLAS_ARCH "armv8")
 set(KALDI_SUFFIX "arm_64")
+elseif ("x${ANDROID_ABI}" STREQUAL "xx86")
+set(OPENBLAS_ARCH "atom")
+set(KALDI_SUFFIX "x86")
 else ("x${ANDROID_ABI}" STREQUAL "xarmeabi-v7a")
 set(OPENBLAS_ARCH "atom")
 set(KALDI_SUFFIX "x86_64")
@@ -19,6 +22,8 @@ set(LIB_ROOT "${PROJECT_SOURCE_DIR}/build/kaldi_${KALDI_SUFFIX}/local")
 set(API_SOURCES
 "${PROJECT_SOURCE_DIR}/../src/kaldi_recognizer.cc"
 "${PROJECT_SOURCE_DIR}/../src/kaldi_recognizer.h"
+"${PROJECT_SOURCE_DIR}/../src/language_model.cc"
+"${PROJECT_SOURCE_DIR}/../src/language_model.h"
 "${PROJECT_SOURCE_DIR}/../src/model.cc"
 "${PROJECT_SOURCE_DIR}/../src/model.h"
 "${PROJECT_SOURCE_DIR}/../src/spk_model.cc"
@@ -29,14 +34,14 @@ set(API_SOURCES

 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -DFST_NO_DYNAMIC_LINKING")

-add_library( kaldi_jni SHARED
+add_library( vosk_jni SHARED
             build/generated-src/cpp/vosk_wrap.cc
             ${API_SOURCES}
 )

 include_directories("${PROJECT_SOURCE_DIR}/../src" "build/kaldi_${KALDI_SUFFIX}/kaldi/src" "build/kaldi_${KALDI_SUFFIX}/local/include")

-target_link_libraries( kaldi_jni
+target_link_libraries( vosk_jni
             ${KALDI_ROOT}/src/online2/kaldi-online2.a
             ${KALDI_ROOT}/src/decoder/kaldi-decoder.a
             ${KALDI_ROOT}/src/ivector/kaldi-ivector.a
@@ -45,6 +50,7 @@ target_link_libraries( kaldi_jni
             ${KALDI_ROOT}/src/tree/kaldi-tree.a
             ${KALDI_ROOT}/src/feat/kaldi-feat.a
             ${KALDI_ROOT}/src/lat/kaldi-lat.a
+             ${KALDI_ROOT}/src/lm/kaldi-lm.a
             ${KALDI_ROOT}/src/hmm/kaldi-hmm.a
             ${KALDI_ROOT}/src/transform/kaldi-transform.a
             ${KALDI_ROOT}/src/cudamatrix/kaldi-cudamatrix.a
@@ -31,36 +31,38 @@ fi

 set -x

+OS_NAME=`echo $(uname -s) | tr '[:upper:]' '[:lower:]'`
 ANDROID_NDK_HOME=$ANDROID_SDK_HOME/ndk-bundle
-ANDROID_TOOLCHAIN_PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64
+ANDROID_TOOLCHAIN_PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/${OS_NAME}-x86_64
+WORKDIR_X86=`pwd`/build/kaldi_x86
 WORKDIR_X86_64=`pwd`/build/kaldi_x86_64
 WORKDIR_ARM32=`pwd`/build/kaldi_arm_32
 WORKDIR_ARM64=`pwd`/build/kaldi_arm_64
-PATH=$PATH:$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64/bin
+PATH=$PATH:$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/${OS_NAME}-x86_64/bin
 OPENFST_VERSION=1.6.7

-mkdir -p $WORKDIR_ARM64/local/lib $WORKDIR_ARM32/local/lib $WORKDIR_X86_64/local/lib
+mkdir -p $WORKDIR_ARM64/local/lib $WORKDIR_ARM32/local/lib $WORKDIR_X86_64/local/lib $WORKDIR_X86/local/lib

 # Build standalone CLAPACK since gfortran is missing
 cd build
 git clone https://github.com/simonlynen/android_libs
 cd android_libs/lapack
-sed -i 's/APP_STL := gnustl_static/APP_STL := c++_static/g' jni/Application.mk && \
-sed -i 's/android-10/android-21/g' project.properties && \
-sed -i 's/APP_ABI := armeabi armeabi-v7a/APP_ABI := armeabi-v7a arm64-v8a x86_64/g' jni/Application.mk && \
-sed -i 's/LOCAL_MODULE:= testlapack/#LOCAL_MODULE:= testlapack/g' jni/Android.mk && \
-sed -i 's/LOCAL_SRC_FILES:= testclapack.cpp/#LOCAL_SRC_FILES:= testclapack.cpp/g' jni/Android.mk && \
-sed -i 's/LOCAL_STATIC_LIBRARIES := lapack/#LOCAL_STATIC_LIBRARIES := lapack/g' jni/Android.mk && \
-sed -i 's/include $(BUILD_SHARED_LIBRARY)/#include $(BUILD_SHARED_LIBRARY)/g' jni/Android.mk && \
+sed -i.bak -e 's/APP_STL := gnustl_static/APP_STL := c++_static/g' jni/Application.mk && \
+sed -i.bak -e 's/android-10/android-21/g' project.properties && \
+sed -i.bak -e 's/APP_ABI := armeabi armeabi-v7a/APP_ABI := armeabi-v7a arm64-v8a x86_64 x86/g' jni/Application.mk && \
+sed -i.bak -e 's/LOCAL_MODULE:= testlapack/#LOCAL_MODULE:= testlapack/g' jni/Android.mk && \
+sed -i.bak -e 's/LOCAL_SRC_FILES:= testclapack.cpp/#LOCAL_SRC_FILES:= testclapack.cpp/g' jni/Android.mk && \
+sed -i.bak -e 's/LOCAL_STATIC_LIBRARIES := lapack/#LOCAL_STATIC_LIBRARIES := lapack/g' jni/Android.mk && \
+sed -i.bak -e 's/include $(BUILD_SHARED_LIBRARY)/#include $(BUILD_SHARED_LIBRARY)/g' jni/Android.mk && \
 ${ANDROID_NDK_HOME}/ndk-build && \
 cp obj/local/armeabi-v7a/*.a ${WORKDIR_ARM32}/local/lib && \
 cp obj/local/arm64-v8a/*.a ${WORKDIR_ARM64}/local/lib
 cp obj/local/x86_64/*.a ${WORKDIR_X86_64}/local/lib
+cp obj/local/x86/*.a ${WORKDIR_X86}/local/lib

 # Architecture-specific part

-
-for arch in arm32 arm64 x86_64; do
+for arch in arm32 arm64 x86_64 x86; do
 #for arch in x86_64; do

 case $arch in
@@ -91,6 +93,15 @@ case $arch in
          CXX=x86_64-linux-android21-clang++
          ARCHFLAGS=""
          ;;
+    x86)
+          BLAS_ARCH=ATOM
+          WORKDIR=$WORKDIR_X86
+          HOST=i686-linux-android
+          AR=i686-linux-android-ar
+          CC=i686-linux-android21-clang
+          CXX=i686-linux-android21-clang++
+          ARCHFLAGS=""
+          ;;
 esac

 # openblas first
@@ -101,12 +112,9 @@ make -C OpenBLAS install PREFIX=$WORKDIR/local

 # tools directory --> we'll only compile OpenFST
 cd $WORKDIR
-wget -c -T 10 -t 1 http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-${OPENFST_VERSION}.tar.gz || \
-wget -c -T 10 -t 3 http://www.openslr.org/resources/2/openfst-${OPENFST_VERSION}.tar.gz
-
-tar -zxvf openfst-${OPENFST_VERSION}.tar.gz
-cd openfst-${OPENFST_VERSION}
-
+git clone https://github.com/alphacep/openfst
+cd openfst
+autoreconf -i
 CXX=$CXX CXXFLAGS="$ARCHFLAGS -O3 -DFST_NO_DYNAMIC_LINKING" ./configure --prefix=${WORKDIR}/local \
    --enable-shared --enable-static --with-pic --disable-bin \
    --enable-lookahead-fsts --enable-ngram-fsts --host=$HOST --build=x86-linux-gnu
@@ -117,6 +125,9 @@ make install
 cd $WORKDIR
 git clone -b android-mix --single-branch https://github.com/alphacep/kaldi
 cd $WORKDIR/kaldi/src
+if [ "`uname`" == "Darwin"  ]; then
+  sed -i.bak -e 's/libfst.dylib/libfst.a/' configure
+fi

 CXX=$CXX CXXFLAGS="$ARCHFLAGS -O3 -DFST_NO_DYNAMIC_LINKING" ./configure --use-cuda=no \
    --mathlib=OPENBLAS --shared \
@@ -5,9 +5,17 @@ buildscript {
    }
    dependencies {
        classpath 'com.android.tools.build:gradle:3.5.3'
+        classpath 'com.jfrog.bintray.gradle:gradle-bintray-plugin:1.8.5'
    }
 }

+plugins {
+    id "com.jfrog.bintray" version "1.8.5"
+}
+
+def archiveName = "vosk-android"
+def libVersion = "0.3.15"
+
 allprojects {
  repositories {
      google()
@@ -16,22 +24,23 @@ allprojects {
 }

 apply plugin: 'com.android.library'
+apply plugin: 'maven-publish'

 android {
    compileSdkVersion 29
    defaultConfig {
        minSdkVersion 21
        targetSdkVersion 29
-        versionCode 5
-        versionName "5.2"
-        setProperty("archivesBaseName", "kaldi-android-$versionName")
+        versionCode 6
+        versionName = libVersion
+        archivesBaseName = archiveName
        externalNativeBuild {
            cmake {
                arguments "-DCMAKE_VERBOSE_MAKEFILE=ON", "-DANDROID_ARM_NEON=TRUE", "-DCMAKE_CXX_FLAGS_RELEASE=-O3"
            }
        }
        ndk {
-            abiFilters 'armeabi-v7a', 'arm64-v8a', 'x86_64'
+            abiFilters 'armeabi-v7a', 'arm64-v8a', 'x86_64', 'x86'
        }
    }
    sourceSets {
@@ -46,6 +55,39 @@ android {
    }
 }

+Properties properties = new Properties()
+properties.load(project.rootProject.file('local.properties').newDataInputStream())
+
+bintray {
+    user = properties.getProperty("bintray.user")
+    key = properties.getProperty("bintray.apikey")
+    pkg {
+        repo = 'vosk'
+        name = 'vosk-android'
+        userOrg = "alphacep"
+        licenses = ['Apache2.0']
+        websiteUrl = 'https://github.com/alphacep/vosk-api'
+        issueTrackerUrl = 'https://github.com/alphacep/vosk-api/issues'
+        vcsUrl = 'https://github.com/alphacep/vosk-api'
+        version {
+            name = libVersion
+            vcsTag = libVersion
+        }
+    }
+    publications = ['aar']
+}
+
+publishing {
+    publications {
+        aar(MavenPublication) {
+            groupId 'com.alphacep'
+            artifactId archiveName
+            version libVersion
+            artifact("$buildDir/outputs/aar/$archiveName-release.aar")
+        }
+    }
+}
+
 task swig {
    doLast {
        mkdir 'build/generated-src/java'
@@ -241,10 +241,6 @@ public class Assets {
            if (!items.get(path).equals(externalItems.get(path))
                    || !(new File(externalDir, path).exists()))
                newItems.add(path);
-            else
-                Log.i(TAG,
-                        String.format("Skipping asset %s: checksums are equal", path));
-
        }

        unusedItems.addAll(externalItems.keySet());
@@ -252,13 +248,11 @@ public class Assets {

        for (String path : newItems) {
            File file = copy(path);
-            Log.i(TAG, String.format("Copying asset %s to %s", path, file));
        }

        for (String path : unusedItems) {
            File file = new File(externalDir, path);
            file.delete();
-            Log.i(TAG, String.format("Removing asset %s", file));
        }

        updateItemList(items);
@@ -29,19 +29,18 @@ import android.os.Looper;
 import android.util.Log;

 /**
- * Main class to access recognizer functions. After configuration this class
- * starts a listener thread which records the data and recognizes it using
- * VOSK engine. Recognition events are passed to a client using
+ * Service that records audio in a thread, passes it to a recognizer and emits 
+ * recognition results. Recognition events are passed to a client using
 * {@link RecognitionListener}
- * 
+ *
 */
-public class SpeechRecognizer {
+public class SpeechService {

-    protected static final String TAG = SpeechRecognizer.class.getSimpleName();
+    protected static final String TAG = SpeechService.class.getSimpleName();

    private final KaldiRecognizer recognizer;

-    private final int sampleRate;        
+    private final int sampleRate;
    private final static float BUFFER_SIZE_SECONDS = 0.4f;
    private int bufferSize;
    private final AudioRecord recorder;
@@ -53,17 +52,18 @@ public class SpeechRecognizer {
    private final Collection<RecognitionListener> listeners = new HashSet<RecognitionListener>();
    
    /**
-     * Creates speech recognizer. Recognizer holds the AudioRecord object, so you 
+     * Creates speech service. Service holds the AudioRecord object, so you 
     * need to call {@link release} in order to properly finalize it.
     * 
     * @throws IOException thrown if audio recorder can not be created for some reason.
     */
-    public SpeechRecognizer(Model model) throws IOException {
-        recognizer = new KaldiRecognizer(model, 16000.0f);
-        sampleRate = 16000;
-        bufferSize = Math.round(sampleRate * BUFFER_SIZE_SECONDS);
+    public SpeechService(KaldiRecognizer recognizer, float sampleRate) throws IOException {
+        this.recognizer = recognizer;
+        this.sampleRate = (int)sampleRate;
+
+        bufferSize = Math.round(this.sampleRate * BUFFER_SIZE_SECONDS);
        recorder = new AudioRecord(
-                AudioSource.VOICE_RECOGNITION, sampleRate,
+                AudioSource.VOICE_RECOGNITION, this.sampleRate,
                AudioFormat.CHANNEL_IN_MONO,
                AudioFormat.ENCODING_PCM_16BIT, bufferSize * 2);

@@ -148,8 +148,7 @@ public class SpeechRecognizer {
    public boolean stop() {
        boolean result = stopRecognizerThread();
        if (result) {
-            Log.i(TAG, "Stop recognition");
-            mainHandler.post(new ResultEvent(recognizer.FinalResult(), true));
+            mainHandler.post(new ResultEvent(recognizer.Result(), true));
        }
        return result;
    }
@@ -162,10 +161,7 @@ public class SpeechRecognizer {
     */
    public boolean cancel() {
        boolean result = stopRecognizerThread();
-        if (result) {
-            Log.i(TAG, "Cancel recognition");
-        }
-
+        recognizer.Result(); // Reset recognizer state
        return result;
    }
    
@@ -175,9 +171,9 @@ public class SpeechRecognizer {
    public void shutdown() {
        recorder.release();
    }
-    
+
    private final class RecognizerThread extends Thread {
-        
+
        private int remainingSamples;
        private int timeoutSamples;
        private final static int NO_TIMEOUT = -1;
@@ -206,8 +202,6 @@ public class SpeechRecognizer {
                return;
            }

-            Log.d(TAG, "Starting decoding");
-
            short[] buffer = new short[bufferSize];

            while (!interrupted()
@@ -0,0 +1,50 @@
+KALDI_ROOT=$(HOME)/travis/kaldi
+
+VOSK_SOURCES= \
+    ../src/kaldi_recognizer.cc \
+    ../src/language_model.cc \
+    ../src/model.cc \
+    ../src/spk_model.cc \
+    ../src/vosk_api.cc
+
+CFLAGS=-g -O2 -DFST_NO_DYNAMIC_LINKING -I../src -I$(KALDI_ROOT)/src -I$(KALDI_ROOT)/tools/openfst/include
+LIBS= \
+	$(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/lm/kaldi-lm.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 \
+	$(KALDI_ROOT)/tools/OpenBLAS/libopenblas.a \
+	$(KALDI_ROOT)/tools/openfst/lib/libfst.a \
+	$(KALDI_ROOT)/tools/openfst/lib/libfstngram.a
+
+all: test_vosk test_vosk_speaker
+
+test_vosk: test_vosk.o libvosk.a
+	g++ $^ -o $@ $(LIBS) -lgfortran -lpthread
+
+test_vosk_speaker: test_vosk_speaker.o libvosk.a
+	g++ $^ -o $@ $(LIBS) -lgfortran -lpthread
+
+libvosk.a: $(VOSK_SOURCES:.cc=.o)
+	ar rcs $@ $^
+
+%.o: %.c
+	g++ $(CFLAGS) -c -o $@ $<
+
+%.o: %.cc
+	g++ -std=c++11 $(CFLAGS) -c -o $@ $<
+
+clean:
+	rm -f *.o *.a test_vosk
@@ -0,0 +1,28 @@
+#include <vosk_api.h>
+#include <stdio.h>
+
+int main() {
+    FILE *wavin;
+    char buf[3200];
+    int nread, final;
+
+    VoskModel *model = vosk_model_new("model");
+    VoskRecognizer *recognizer = vosk_recognizer_new(model, 16000.0);
+
+    wavin = fopen("test.wav", "rb");
+    fseek(wavin, 44, SEEK_SET);
+    while (!feof(wavin)) {
+         nread = fread(buf, 1, sizeof(buf), wavin);
+         final = vosk_recognizer_accept_waveform(recognizer, buf, nread);
+         if (final) {
+             printf("%s\n", vosk_recognizer_result(recognizer));
+         } else {
+             printf("%s\n", vosk_recognizer_partial_result(recognizer));
+         }
+    }
+    printf("%s\n", vosk_recognizer_final_result(recognizer));
+
+    vosk_recognizer_free(recognizer);
+    vosk_model_free(model);
+    return 0;
+}
@@ -0,0 +1,30 @@
+#include <vosk_api.h>
+#include <stdio.h>
+
+int main() {
+    FILE *wavin;
+    char buf[3200];
+    int nread, final;
+
+    VoskModel *model = vosk_model_new("model");
+    VoskSpkModel *spk_model = vosk_spk_model_new("spk-model");
+    VoskRecognizer *recognizer = vosk_recognizer_new_spk(model, spk_model, 16000.0);
+
+    wavin = fopen("test.wav", "rb");
+    fseek(wavin, 44, SEEK_SET);
+    while (!feof(wavin)) {
+         nread = fread(buf, 1, sizeof(buf), wavin);
+         final = vosk_recognizer_accept_waveform(recognizer, buf, nread);
+         if (final) {
+             printf("%s\n", vosk_recognizer_result(recognizer));
+         } else {
+             printf("%s\n", vosk_recognizer_partial_result(recognizer));
+         }
+    }
+    printf("%s\n", vosk_recognizer_final_result(recognizer));
+
+    vosk_recognizer_free(recognizer);
+    vosk_spk_model_free(spk_model);
+    vosk_model_free(model);
+    return 0;
+}
@@ -11,6 +11,7 @@ KALDI_LIBS = \
             ${KALDI_ROOT}/src/tree/kaldi-tree.a \
             ${KALDI_ROOT}/src/feat/kaldi-feat.a \
             ${KALDI_ROOT}/src/lat/kaldi-lat.a \
+             ${KALDI_ROOT}/src/lm/kaldi-lm.a \
             ${KALDI_ROOT}/src/hmm/kaldi-hmm.a \
             ${KALDI_ROOT}/src/transform/kaldi-transform.a \
             ${KALDI_ROOT}/src/cudamatrix/kaldi-cudamatrix.a \
@@ -19,9 +20,12 @@ KALDI_LIBS = \
             ${KALDI_ROOT}/src/util/kaldi-util.a \
             ${KALDI_ROOT}/src/base/kaldi-base.a \
             ${KALDI_ROOT}/tools/openfst/lib/libfst.a \
-             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a \
-             ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a \
-             -lgfortran -lstdc++
+             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a
+
+# On Linux
+MATH_LIBS = ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a -lgfortran
+# On OSX
+# MATH_LIBS = -framework Accelerate

 all: test.exe

@@ -31,22 +35,27 @@ test.exe: libkaldiwrap.so test.cs
 VOSK_SOURCES = \
 	vosk_wrap.c \
 	../src/kaldi_recognizer.cc \
-	../src/kaldi_recognizer.h \
+	../src/language_model.cc \
 	../src/model.cc \
-	../src/model.h \
 	../src/spk_model.cc \
-	../src/spk_model.h \
-	../src/vosk_api.cc \
-	../src/vosk_api.h
+	../src/vosk_api.cc

-libkaldiwrap.so: $(VOSK_SOURCES)
-	$(CXX) -fpermissive $(CFLAGS) $(CPPFLAGS) -shared -o $@ $(VOSK_SOURCES) $(KALDI_LIBS)
+VOSK_HEADERS = \
+	../src/kaldi_recognizer.h \
+	../src/language_model.h \
+	../src/model.h \
+	../src/vosk_api.h \
+	../src/spk_model.h
+
+libkaldiwrap.so: $(VOSK_SOURCES) $(VOSK_HEADERS)
+	$(CXX) -fpermissive $(CFLAGS) $(CPPFLAGS) -shared -o $@ $(VOSK_SOURCES) $(KALDI_LIBS) $(MATH_LIBS)

 vosk_wrap.c: ../src/vosk.i
-	swig -csharp -DSWIG_CSHARP_NO_EXCEPTION_HELPER -dllimport "libkaldiwrap.so" \
+	mkdir -p gen
+	swig -csharp -DSWIG_CSHARP_NO_EXCEPTION_HELPER -dllimport "libkaldiwrap" \
 	    -namespace "Kaldi" -outdir gen -o vosk_wrap.c ../src/vosk.i

-run:
+run: test.exe
 	mono test.exe

 clean:
@@ -7,6 +7,7 @@ public class Test
   public static void Main()
   {

+        Vosk.SetLogLevel(0);
        Model model = new Model("model");
        KaldiRecognizer rec = new KaldiRecognizer(model, 16000.0f);

@@ -1,21 +1 @@
-## Accuracy issues
-
-Accuracy of modern systems is still unstable, that means sometimes you can have a very good accuracy and sometimes it could be bad.
-It is hard to make a system that will work good. And there could be many reasons for that:
-
-  * Audio has very bad quality
-  * Vocabulary of the system doesn't match (yes, we still use fixed vocabulary)
-  * Audio conditions like accent were not really the ones that were used in training
-  * Some unpredictable audio issues like frame drop or frame coding bugs
-  * Software bugs
-
-It is hard to guess what is going on under the hood without getting your hands dirty. For that reason in case of any accuracy
-issues you must provide the following for analysis:
-
-  * Who are you, where are you from and why are you doing that. We don't like dealing with anonymous
-  * The complete and exact description of the system you want to build - what is it going to do, what do you want to build
-  * The precise description of hardware you are trying to run the system on
-  * The detailed list of software versions you are using
-  * Audio samples to demonstrate the problem together with the reference transcription for those samples
-
-Remember, the more information you provide the faster you get a solution.
+See https://alphacephei.com/vosk/accuracy
@@ -1,42 +1 @@
-## Updating the language model
-
-The Kaldi model used in Vosk is compiled from 3 data sources:
-
-  * dictionary
-  * acoustic model
-  * language model
-
-You can rebuild all three with different level of effort, but sometimes you just
-need to adjust the probability of the words to improve the recognition. For
-that it is enough to recompile the language model from the text. To do that
-
-1) Take a text that reflects the speech you want to recognize
-2) Remove punctuation, convert everything to the lowercase, you can do it with a python script
-3) Build openfst and opengrm inside kaldi
-
-```
-export KALDI_ROOT=`pwd`/kaldi
-git clone https://github.com/kaldi-asr/kaldi
-cd kaldi/tools
-make
-# install all required dependencies and repeat `make` if needed
-extras/install_opengrm.sh
-```
-
-4) Now lets build a grammar
-
-```
-export PATH=$KALDI_ROOT/tools/openfst/bin:$PATH
-export LD_LIBRARY_PATH=$KALDI_ROOT/tools/openfst/lib/fst
-cd model
-fstsymbols --save_osymbols=words.txt Gr.fst > /dev/null
-farcompilestrings --fst_type=compact --symbols=words.txt --keep_symbols text.txt | \
-    ngramcount | ngrammake | \
-    fstconvert --fst_type=ngram > Gr.fst
-```
-
-Use created Gr.fst instead of standard one in your model.
-
-For more details see OpenGRM documentation http://www.opengrm.org/twiki/bin/view/GRM/NGramLibrary
-
-You can not introduce new words this way, that is something we will cover later.
+See https://alphacephei.com/vosk/adaptation
@@ -1,67 +1 @@
-# Models
-
-This is the list of models compatible with Vosk-API.
-
-To add a new model here create an issue on Github.
-
-### English
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [kaldi-en-us-aspire-0.1](http://alphacephei.com/kaldi/kaldi-en-us-aspire-0.1.tar.gz)                      |  363M |   TBD      | Trained on Fisher + more or less recent LM. Pretty outdated but still ok even even for calls |
-| [alphacep-model-android-en-us-0.3](http://alphacephei.com/kaldi/alphacep-model-android-en-us-0.3.tar.gz)  |  36M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-
-### Chinese
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [kaldi-cn-0.1.tar.gz](http://alphacephei.com/kaldi/kaldi-cn-0.1.tar.gz)                                   |  195M |   TBD      | Big narrowband Chinese model for server processing                                           |
-| [alphacep-model-android-cn-0.3](http://alphacephei.com/kaldi/alphacep-model-android-cn-0.3.tar.gz)        |  32M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-
-### Russian
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [kaldi-ru-0.9.tar.gz](http://alphacephei.com/kaldi/kaldi-ru-0.9.tar.gz)                                   |  2.5G |   TBD      | Big narrowband Russian model for server processing                                           |
-| [alphacep-model-android-ru-0.3](http://alphacephei.com/kaldi/alphacep-model-android-ru-0.3.tar.gz)        |  39M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-
-### French
-
-| Model                                                                                                                   | Size  | Accuracy   | Notes                                                                                        |
-|-------------------------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [alphacep-model-android-fr-pguyot-0.3](http://alphacephei.com/kaldi/alphacep-model-android-fr-pguyot-0.3.tar.gz)        |  39M  |   TBD      | Lightweight wideband model for Android and RPi trained by [Paul Guyot](https://github.com/pguyot/zamia-speech/releases) |
-
-### German
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                                |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|------------------------------------------------------------------------------------------------------|
-| [tuda-de](http://ltdata1.informatik.uni-hamburg.de/kaldi_tuda_de/de_400k_nnet3chain_tdnn1f_2048_sp_bi.tar.bz2)        |  566M |   TBD      | Wideband server model from [tuda-de](https://github.com/uhh-lt/kaldi-tuda-de)            |
-| [alphacep-model-android-de-zamia-0.3](http://alphacephei.com/kaldi/alphacep-model-android-de-zamia-0.3.tar.gz)        |  49M  |   TBD      | Lightweight wideband model for Android and RPi                                           |
-
-### Spanish
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [alphacep-model-android-es-0.3](http://alphacephei.com/kaldi/alphacep-model-android-es-0.3.tar.gz)        |  33M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-
-### Portuguese
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [alphacep-model-android-pt-0.3](http://alphacephei.com/kaldi/alphacep-model-android-pt-0.3.tar.gz)        |  31M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-
-### Dutch
-
-https://github.com/opensource-spraakherkenning-nl/Kaldi_NL
-
-### Greek
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [kaldi-el-gr-0.6.tar.gz](http://alphacephei.com/kaldi/kaldi-el-gr-0.6.tar.gz)                             |  1.1G |   TBD      | Big narrowband Greek model for server processing, not extremely accurate though        |
-
-### Vietnamese
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-----------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| [alphacep-model-android-vn-0.3](http://alphacephei.com/kaldi/alphacep-model-android-vn-0.3.tar.gz)        |  32M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
+See https://alphacephei.com/vosk/models
@@ -12,37 +12,199 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

+/* This header contains the C API for Vosk speech recognition system */

-#ifndef _VOSK_API_H_
-#define _VOSK_API_H_
+#ifndef VOSK_API_H
+#define VOSK_API_H

 #ifdef __cplusplus
 extern "C" {
 #endif

+/** Model stores all the data required for recognition
+ *  it contains static data and can be shared across processing
+ *  threads. */
 typedef struct VoskModel VoskModel;
+
+
+/** Speaker model is the same as model but contains the data
+ *  for speaker identification. */
 typedef struct VoskSpkModel VoskSpkModel;
+
+
+/** Recognizer object is the main object which processes data.
+ *  Each recognizer usually runs in own thread and takes audio as input.
+ *  Once audio is processed recognizer returns JSON object as a string
+ *  which represent decoded information - words, confidences, times, n-best lists,
+ *  speaker information and so on */
 typedef struct VoskRecognizer VoskRecognizer;

+
+/** Loads model data from the file and returns the model object
+ *
+ * @param model_path: the path of the model on the filesystem
+ @ @returns model object */
 VoskModel *vosk_model_new(const char *model_path);
+
+
+/** Releases the model memory
+ *
+ *  The model object is reference-counted so if some recognizer
+ *  depends on this model, model might still stay alive. When
+ *  last recognizer is released, model will be released too. */
 void vosk_model_free(VoskModel *model);

+
+/** Loads speaker model data from the file and returns the model object
+ *
+ * @param model_path: the path of the model on the filesystem
+ * @returns model object */
 VoskSpkModel *vosk_spk_model_new(const char *model_path);
+
+
+/** Releases the model memory
+ *
+ *  The model object is reference-counted so if some recognizer
+ *  depends on this model, model might still stay alive. When
+ *  last recognizer is released, model will be released too. */
 void vosk_spk_model_free(VoskSpkModel *model);

+/** Creates the recognizer object
+ *
+ *  The recognizers process the speech and return text using shared model data 
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
+ *  @returns recognizer object */
 VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
+
+
+/** Creates the recognizer object with speaker recognition
+ *
+ *  With the speaker recognition mode the recognizer not just recognize
+ *  text but also return speaker vectors one can use for speaker identification
+ *
+ *  @param spk_model speaker model for speaker identification
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
+ *  @returns recognizer object */
 VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, VoskSpkModel *spk_model, float sample_rate);
+
+
+/** Creates the recognizer object with the grammar
+ *
+ *  Sometimes when you want to improve recognition accuracy and when you don't need
+ *  to recognize large vocabulary you can specify a list of words to recognize. This
+ *  will improve recognizer speed and accuracy but might return [unk] if user said
+ *  something different.
+ *
+ *  Only recognizers with lookahead models support this type of quick configuration.
+ *  Precompiled HCLG graph models are not supported.
+ *
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
+ *  @param grammar The string with the list of words to recognize, for example "one two three four five [unk]"
+ *
+ *  @returns recognizer object */
 VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar);
+
+
+/** Accept voice data
+ *
+ *  accept and process new chunk of voice data
+ *
+ *  @param data - audio data in PCM 16-bit mono format
+ *  @param length - length of the audio data
+ *  @returns true if silence is occured and you can retrieve a new utterance with result method */
 int vosk_recognizer_accept_waveform(VoskRecognizer *recognizer, const char *data, int length);
+
+
+/** Same as above but the version with the short data for language bindings where you have
+ *  audio as array of shorts */
 int vosk_recognizer_accept_waveform_s(VoskRecognizer *recognizer, const short *data, int length);
+
+
+/** Same as above but the version with the float data for language bindings where you have
+ *  audio as array of floats */
 int vosk_recognizer_accept_waveform_f(VoskRecognizer *recognizer, const float *data, int length);
+
+
+/** Returns speech recognition result
+ *
+ * @returns the result in JSON format which contains decoded line, decoded
+ *          words, times in seconds and confidences. You can parse this result
+ *          with any json parser
+ *
+ * <pre>
+ * {
+ *   "result" : [{
+ *       "conf" : 1.000000,
+ *       "end" : 1.110000,
+ *       "start" : 0.870000,
+ *       "word" : "what"
+ *     }, {
+ *       "conf" : 1.000000,
+ *       "end" : 1.530000,
+ *       "start" : 1.110000,
+ *       "word" : "zero"
+ *     }, {
+ *       "conf" : 1.000000,
+ *       "end" : 1.950000,
+ *       "start" : 1.530000,
+ *       "word" : "zero"
+ *     }, {
+ *       "conf" : 1.000000,
+ *       "end" : 2.340000,
+ *       "start" : 1.950000,
+ *       "word" : "zero"
+ *     }, {
+ *       "conf" : 1.000000,
+ *      "end" : 2.610000,
+ *       "start" : 2.340000,
+ *       "word" : "one"
+ *     }],
+ *   "text" : "what zero zero zero one"
+ *  }
+ * </pre>
+ */
 const char *vosk_recognizer_result(VoskRecognizer *recognizer);
+
+
+/** Returns partial speech recognition
+ *
+ * @returns partial speech recognition text which is not yet finalized.
+ *          result may change as recognizer process more data.
+ *
+ * <pre>
+ * {
+ *  "partial" : "cyril one eight zero"
+ * }
+ * </pre>
+ */
 const char *vosk_recognizer_partial_result(VoskRecognizer *recognizer);
+
+
+/** Returns speech recognition result. Same as result, but doesn't wait for silence
+ *  You usually call it in the end of the stream to get final bits of audio. It
+ *  flushes the feature pipeline, so all remaining audio chunks got processed.
+ *
+ *  @returns speech result in JSON format.
+ */
 const char *vosk_recognizer_final_result(VoskRecognizer *recognizer);
+
+
+/** Releases recognizer object
+ *
+ *  Underlying model is also unreferenced and if needed released */
 void vosk_recognizer_free(VoskRecognizer *recognizer);

+/** Set log level for Kaldi messages
+ *
+ *  @param log_level the level
+ *     0 - default value to print info and error messages but no debug
+ *     less than 0 - don't print info messages
+ *     greather than 0 - more verbose mode
+ */
+void vosk_set_log_level(int log_level);
+
 #ifdef __cplusplus
 }
 #endif

-#endif /* _VOSK_API_H_ */
+#endif /* VOSK_API_H */
@@ -11,6 +11,7 @@ KALDI_LIBS = \
             ${KALDI_ROOT}/src/tree/kaldi-tree.a \
             ${KALDI_ROOT}/src/feat/kaldi-feat.a \
             ${KALDI_ROOT}/src/lat/kaldi-lat.a \
+             ${KALDI_ROOT}/src/lm/kaldi-lm.a \
             ${KALDI_ROOT}/src/hmm/kaldi-hmm.a \
             ${KALDI_ROOT}/src/transform/kaldi-transform.a \
             ${KALDI_ROOT}/src/cudamatrix/kaldi-cudamatrix.a \
@@ -29,6 +30,8 @@ VOSK_SOURCES = \
 	vosk_wrap.cc \
 	../src/kaldi_recognizer.cc \
 	../src/kaldi_recognizer.h \
+	../src/language_model.cc \
+	../src/language_model.h \
 	../src/model.cc \
 	../src/model.h \
 	../src/spk_model.cc \
@@ -49,16 +52,16 @@ clean:
 	$(RM) *.so *_wrap.cc *_wrap.o test/*.class
 	$(RM) -r org model-en

-model-en:
-	wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz
-	tar xf alphacep-model-android-en-us-0.3.tar.gz && rm alphacep-model-android-en-us-0.3.tar.gz
-	mv alphacep-model-android-en-us-0.3 model-en
+model:
+	wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
+	unzip vosk-model-small-en-us-0.3.zip && rm vosk-model-small-en-us-0.3.zip
+	mv vosk-model-small-en-us-0.3 model

 model-spk:
-	wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-spk-model-0.3.tar.gz
-	tar xf alphacep-spk-model-0.3.tar.gz && rm alphacep-spk-model-0.3.tar.gz
-	mv alphacep-spk-model-0.3 model-spk
+	wget https://alphacephei.com/kaldi/models/vosk-model-spk-0.3.zip
+	unzip vosk-model-spk-0.3.zip && rm vosk-model-spk-0.3.zip
+	mv vosk-model-spk-0.3 model-spk

-run: model-en model-spk
+run: model model-spk
 	javac test/*.java org/kaldi/*.java
 	java -Djava.library.path=. -cp . test.DecoderTest
@@ -11,15 +11,15 @@ import java.nio.*;
 import org.kaldi.KaldiRecognizer;
 import org.kaldi.Model;
 import org.kaldi.SpkModel;
+import org.kaldi.Vosk;

 public class DecoderTest {
-    static {
-        System.loadLibrary("vosk_jni");
-    }

    public static void main(String args[]) throws IOException {
+        Vosk.SetLogLevel(-10);
+
        FileInputStream ais = new FileInputStream(new File("../python/example/test.wav"));
-        Model model = new Model("model-en");
+        Model model = new Model("model");
        SpkModel spkModel = new SpkModel("model-spk");
        KaldiRecognizer rec = new KaldiRecognizer(model, spkModel, 16000.0f);

@@ -0,0 +1,3 @@
+build
+node_modules
+vosk_wrap.cc
@@ -0,0 +1,20 @@
+Installation requires vosk-api checkout, it doesn't yet work with `npm
+install vosk`. We have to figure out how to properly distribute native
+modules for Vosk.
+
+The build tested with node-0.10.15, node-0.12 is not yet supported by swig.
+
+Still, you need swig of newest version 4.0.1
+
+Build like this
+
+```
+npm install --kaldi_root=/home/user/kaldi
+```
+
+Then test with
+
+```
+cd example
+node test.js
+```
@@ -0,0 +1,80 @@
+{
+  'targets': [
+    {
+      'target_name': 'vosk',
+      'sources': [
+         '../src/kaldi_recognizer.cc',
+         '../src/model.cc',
+         '../src/language_model.cc',
+         '../src/spk_model.cc',
+         '../src/vosk_api.cc',
+         'vosk_wrap.cc',
+      ],
+      'cflags': [
+          '-std=c++11',
+          '-DFST_NO_DYNAMIC_LINKING',
+          '-Wno-deprecated-declarations',
+          '-Wno-sign-compare',
+          '-Wno-unused-local-typedefs',
+          '-Wno-ignored-quaifiers',
+          '-Wno-extra',
+      ],
+      'cflags_cc!' : [
+          '-fno-rtti',
+          '-fno-exceptions',
+      ],
+      'conditions': [
+          ['OS == "mac"', {
+              'xcode_settings': {
+                  'GCC_ENABLE_CPP_EXCEPTIONS': 'YES',
+                  'GCC_ENABLE_CPP_RTTI': 'YES',
+                  'CLANG_CXX_LANGUAGE_STANDARD': 'c++11'
+              }
+          }]
+      ],
+      'actions': [
+        {
+          'action_name': 'swig',
+          'inputs': [
+            '../src/vosk.i',
+          ],
+          'outputs': [
+            'vosk_wrap.cc',
+          ],
+          'action': ['swig', '-c++', '-javascript', '-o', 'vosk_wrap.cc', '-v8', '-DV8_MAJOR_VERSION=10', '../src/vosk.i']
+        },
+      ],
+      'include_dirs': [
+         '<@(kaldi_root)/src',
+         '<@(kaldi_root)/tools/openfst/include',
+         '../src',
+      ],
+      'link_settings': {
+         'libraries': [
+             '<@(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/lm/kaldi-lm.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',
+             '<@(kaldi_root)/tools/openfst/lib/libfst.a',
+             '<@(kaldi_root)/tools/openfst/lib/libfstngram.a',
+             '<@(kaldi_root)/tools/OpenBLAS/libopenblas.a',
+          ],
+         'library_dirs': [
+               '/usr/lib',
+          ],
+      },
+    }
+  ]
+}
@@ -0,0 +1,90 @@
+const wav = require('wav')
+const fs = require('fs')
+
+const {Readable} = require('stream')
+const {Model, KaldiRecognizer, SpkModel} = require('..')
+
+try {
+    fs.accessSync('model', fs.constants.R_OK)
+} catch (err) {
+    console.error("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model' in the current folder.")
+    process.exit(1)
+}
+
+try {
+    fs.accessSync('model-spk', fs.constants.R_OK)
+} catch (err) {
+    console.error("Please download the speaker model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model-spk' in the current folder.")
+    process.exit(1)
+}
+
+const wfStream = fs.createReadStream('test.wav', { highWaterMark: 4096 })
+const wfReader = new wav.Reader()
+
+const model = new Model('model')
+const spkModel = new SpkModel('model-spk')
+
+const spk_sig = [-0.56648, 0.030579, 1.730239, 0.239899, -1.194183,
+0.251954, 0.540388, -0.971872, -0.020963, 0.085036, 0.563973, -0.019682,
+-0.597381, 1.094719, -0.566738, 0.29819, 0.171165, 0.370341, -0.033539,
+-0.09757, 1.228286, 0.485949, 0.427826, 0.147762, -0.015112, 0.599513,
+-2.040655, -0.490882, 0.440161, -0.072991, 0.835955, -0.496124, 0.952978,
+0.85356, -1.096116, 0.107764, -0.385486, 1.410305, 0.609147, -0.457014,
+-1.542864, 0.343669, 0.171913, -0.627281, -1.281781, -1.134276,
+-0.639895, 1.190183, -0.700537, 1.063457, 0.206946, 0.342198, -1.165625,
+1.475955, -0.089007, -2.555155, 0.551438, -0.212736, 1.025625, -1.631965,
+-0.716256, -1.295995, 1.554956, -1.866009, -1.010782, -1.43231, 0.109027,
+2.123925, 1.703283, -0.784997, 2.730568, 0.755113, 0.0617, 0.128955,
+-0.054047, 1.359119, -0.611666, -1.105754, -0.631353, 0.052109, 0.729386,
+-0.769876, 1.250235, -1.463298, 0.648176, -0.73239, -0.385239,
+-1.661856, 0.602106, -0.45567, -1.438431, -0.836673, 0.033557, 0.373597,
+-1.343341, -0.181095, 0.237287, -0.522005, -1.722836, 0.932333,
+-0.092861, -0.219254, 0.476182, 1.033803, -1.633563, -0.874341, 1.039064,
+-1.758573, -0.838422, -0.324336, -0.924634, 1.962594, 2.152814, 1.2521,
+-0.46172, -1.50271, 1.685691, 0.403097, -0.819042, 0.866403, -0.591716,
+-0.578645, -0.553839, 0.381861, -1.051647, -1.477578, 0.524005, 0.925245]
+
+function dotp(x, y) {
+    function dotp_sum(a, b) {
+        return a + b
+    }
+    function dotp_times(a, i) {
+        return x[i] * y[i]
+    }
+    return x.map(dotp_times).reduce(dotp_sum, 0)
+}
+
+function cosineSimilarity(A, B) {
+    var similarity =
+        dotp(A, B) / (Math.sqrt(dotp(A, A)) * Math.sqrt(dotp(B, B)))
+        return similarity
+}
+
+function cosine_dist(x, y) {
+    return 1 - cosineSimilarity(x, y)
+}
+
+wfReader.on('format', async ({ audioFormat, sampleRate, channels }) => {
+    if (audioFormat != 1 || channels != 1) {
+        console.error('Audio file must be WAV format mono PCM.')
+        process.exit(1)
+    }
+    const rec = new KaldiRecognizer(model, spkModel, sampleRate)
+    for await (const data of new Readable().wrap(wfReader)) {
+        const endOfSpeech = await rec.AcceptWaveform(data)
+        if (endOfSpeech) {
+            res = await JSON.parse(rec.Result());
+            console.log(res)
+            console.log('X-vector:', JSON.stringify(res['spk']))
+            console.log('Speaker distance:', cosine_dist(spk_sig, res['spk']))
+        } else {
+            console.log(await rec.PartialResult())
+        }
+    }
+    res = await JSON.parse(rec.FinalResult());
+    console.log(res)
+    console.log('X-vector:', JSON.stringify(res['spk']))
+    console.log('Speaker distance:', cosine_dist(spk_sig, res['spk']))
+})
+
+wfStream.pipe(wfReader)
@@ -0,0 +1,38 @@
+#!/usr/bin/env node
+
+const fs = require("fs");
+const { Readable } = require("stream");
+const wav = require("wav");
+
+const { Model, KaldiRecognizer } = require("..");
+
+try {
+    fs.accessSync("model", fs.constants.R_OK);
+} catch(err) {
+    console.error("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model' in the current folder.");
+    process.exit(1);
+}
+
+const wfStream = fs.createReadStream("test.wav", {'highWaterMark': 4096});
+const wfReader = new wav.Reader();
+
+const model = new Model("model");
+
+wfReader.on('format', async ({ audioFormat, sampleRate, channels }) => {
+    if (audioFormat != 1 || channels != 1) {
+        console.error("Audio file must be WAV format mono PCM.");
+        process.exit(1);
+    }
+    const rec = new KaldiRecognizer(model, sampleRate);
+    for await (const data of new Readable().wrap(wfReader)) {
+        const result = await rec.AcceptWaveform(data);
+        if (result != 0) {
+           console.log(await rec.Result());
+        } else {
+           console.log(await rec.PartialResult());
+        }
+    }
+    console.log(await rec.FinalResult());
+});
+
+wfStream.pipe(wfReader);
@@ -1,3 +1,3 @@
-exports.printMsg = function() {
-  console.log("This is a message from the Vosk package");
-}
+const voskNativeModule = require('./build/Release/vosk.node');
+
+module.exports = voskNativeModule;
@@ -1,7 +1,7 @@
 {
  "name": "vosk",
-  "version": "0.1.0",
-  "description": "Node binding for continuous voice recoginition through pocketsphinx.",
+  "version": "0.3.15",
+  "description": "Node binding for continuous offline voice recoginition with Vosk library.",
  "repository": {
    "type": "git",
    "url": "git://github.com/alphacep/vosk-api.git"
@@ -13,6 +13,12 @@
    "voice"
  ],
  "author": "Alpha Cephei Inc.",
-  "license": "Apache 2.0",
-  "engines": { "node" : ">= 12.x.x" }
+  "license": "Apache-2.0",
+  "engines": {
+    "node": ">= 10.x.x"
+  },
+  "dependencies": {
+    "node-gyp": "^5.1.1",
+    "wav": "^1.0.2"
+  }
 }
@@ -1,50 +0,0 @@
-cmake_minimum_required(VERSION 3.12.0)
-project(vosk)
-
-set(TOP_SRCDIR "${CMAKE_SOURCE_DIR}/..")
-if("x$ENV{WHEEL_FLAGS}" STREQUAL "x")
-    find_package (Python COMPONENTS Interpreter Development)
-else()
-    # docker case
-    set(Python_INCLUDE_DIRS "")
-    set(TOP_SRCDIR "/io")
-endif()
-
-set(KALDI_ROOT "$ENV{KALDI_ROOT}")
-set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -O3 -DFST_NO_DYNAMIC_LINKING")
-set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} $ENV{WHEEL_FLAGS}")
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${CMAKE_C_FLAGS} -std=c++11")
-include_directories("${TOP_SRCDIR}/src" "${KALDI_ROOT}/src" "${KALDI_ROOT}/tools/openfst/include" ${Python_INCLUDE_DIRS})
-
-find_package(SWIG REQUIRED)
-include(${SWIG_USE_FILE})
-
-swig_add_library(vosk TYPE SHARED LANGUAGE Python OUTPUT_DIR "${CMAKE_LIBRARY_OUTPUT_DIRECTORY}" OUTFILE_DIR "."
-    SOURCES "${TOP_SRCDIR}/src/kaldi_recognizer.cc"
-            "${TOP_SRCDIR}/src/spk_model.cc"
-            "${TOP_SRCDIR}/src/model.cc"
-            "${TOP_SRCDIR}/src/vosk_api.cc"
-            "${TOP_SRCDIR}/src/vosk.i")
-
-swig_link_libraries(vosk
-             ${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
-             ${KALDI_ROOT}/tools/openfst/lib/libfst.a
-             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a
-             ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a
-             -lgfortran -lstdc++)
-
-set_target_properties(_vosk PROPERTIES LINK_FLAGS_RELEASE -s)
@@ -1,3 +1,23 @@
-Python module for vosk-api
+This is a Python module for Vosk.

-See for details https://github.com/alphacep/vosk-api
+Vosk is an offline open source speech recognition toolkit. It enables
+speech recognition models for 16 languages and dialects - English, Indian
+English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish,
+Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi.
+
+Vosk models are small (50 Mb) but provide continuous large vocabulary
+transcription, zero-latency response with streaming API, reconfigurable
+vocabulary and speaker identification.
+
+Vosk supplies speech recognition for chatbots, smart home appliances,
+virtual assistants. It can also create subtitles for movies,
+transcription for lectures and interviews.
+
+Vosk scales from small devices like Raspberry Pi or Android smartphone to
+big clusters.
+
+# Documentation
+
+For installation instructions, examples and documentation visit [Vosk
+Website](https://alphacephei.com/vosk). See also our project on
+[Github](https://github.com/alphacep/vosk-api).
@@ -1,76 +0,0 @@
-# From https://github.com/raydouglass/cmake_setuptools
-
-import os
-import subprocess
-import shutil
-import sys
-from setuptools import Extension
-from setuptools.command.build_ext import build_ext
-from setuptools.command.build_py import build_py
-
-CMAKE_EXE = os.environ.get('CMAKE_EXE', shutil.which('cmake'))
-
-
-def check_for_cmake():
-    if not CMAKE_EXE:
-        print('cmake executable not found. '
-              'Set CMAKE_EXE environment or update your path')
-        sys.exit(1)
-
-
-class CMakeExtension(Extension):
-    """
-    setuptools.Extension for cmake
-    """
-
-    def __init__(self, name, pkg_name, sourcedir=''):
-        check_for_cmake()
-        Extension.__init__(self, name, sources=[])
-        self.sourcedir = os.path.abspath(sourcedir)
-        self.pkg_name = pkg_name
-
-
-class CMakeBuildExt(build_ext):
-    """
-    setuptools build_exit which builds using cmake & make
-    You can add cmake args with the CMAKE_COMMON_VARIABLES environment variable
-    """
-
-    def build_extension(self, ext):
-        check_for_cmake()
-        if isinstance(ext, CMakeExtension):
-            output_dir = os.path.abspath(
-                os.path.dirname(self.get_ext_fullpath(ext.pkg_name + "/" + ext.name)))
-
-            build_type = 'Debug' if self.debug else 'Release'
-            cmake_args = [CMAKE_EXE,
-                          ext.sourcedir,
-                          '-Wno-dev',
-                          '-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + output_dir,
-                          '-DCMAKE_BUILD_TYPE=' + build_type]
-            cmake_args.extend(
-                [x for x in
-                 os.environ.get('CMAKE_COMMON_VARIABLES', '').split(' ')
-                 if x])
-
-            env = os.environ.copy()
-            if not os.path.exists(self.build_temp):
-                os.makedirs(self.build_temp)
-            subprocess.check_call(cmake_args,
-                                  cwd=self.build_temp,
-                                  env=env)
-            subprocess.check_call(['make', 'VERBOSE=1', ext.name],
-                                  cwd=self.build_temp,
-                                  env=env)
-            print()
-        else:
-            super().build_extension(ext)
-
-
-
-class CMakeBuildExtFirst(build_py):
-    def run(self):
-        self.run_command("build_ext")
-        return super().run()
-
-__all__ = ['CMakeBuildExt', 'CMakeExtension', 'CMakeBuildExtFirst']
@@ -1,10 +1,10 @@
-#!/usr/bin/python3
+#!/usr/bin/env python3

 from vosk import Model, KaldiRecognizer
 import sys
 import json

-model = Model("model-en")
+model = Model("model")
 rec = KaldiRecognizer(model, 8000)

 res = json.loads(rec.FinalResult())
@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+import sys
+import os
+import wave
+import subprocess
+
+SetLogLevel(0)
+
+if not os.path.exists("model"):
+    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
+    exit (1)
+
+sample_rate=16000
+model = Model("model")
+rec = KaldiRecognizer(model, sample_rate)
+
+process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
+                            sys.argv[1],
+                            '-ar', str(sample_rate) , '-ac', '1', '-f', 's16le', '-'],
+                            stdout=subprocess.PIPE)
+
+while True:
+    data = process.stdout.read(4000)
+    if len(data) == 0:
+        break
+    if rec.AcceptWaveform(data):
+        print(rec.Result())
+    else:
+        print(rec.PartialResult())
+
+print(rec.FinalResult())
@@ -1,23 +1,23 @@
-#!/usr/bin/python3
+#!/usr/bin/env python3

 from vosk import Model, KaldiRecognizer
 import os

-if not os.path.exists("model-en"):
-    print ("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model-en' in the current folder.")
+if not os.path.exists("model"):
+    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
    exit (1)

 import pyaudio

+model = Model("model")
+rec = KaldiRecognizer(model, 16000)
+
 p = pyaudio.PyAudio()
 stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=8000)
 stream.start_stream()

-model = Model("model-en")
-rec = KaldiRecognizer(model, 16000)
-
 while True:
-    data = stream.read(2000)
+    data = stream.read(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
@@ -1,12 +1,14 @@
-#!/usr/bin/python3
+#!/usr/bin/env python3

-from vosk import Model, KaldiRecognizer
+from vosk import Model, KaldiRecognizer, SetLogLevel
 import sys
 import os
 import wave

-if not os.path.exists("model-en"):
-    print ("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model-en' in the current folder.")
+SetLogLevel(0)
+
+if not os.path.exists("model"):
+    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
    exit (1)

 wf = wave.open(sys.argv[1], "rb")
@@ -14,11 +16,11 @@ if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE
    print ("Audio file must be WAV format mono PCM.")
    exit (1)

-model = Model("model-en")
+model = Model("model")
 rec = KaldiRecognizer(model, wf.getframerate())

 while True:
-    data = wf.readframes(1000)
+    data = wf.readframes(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
@@ -1,4 +1,4 @@
-#!/usr/bin/python3
+#!/usr/bin/env python3

 from vosk import Model, KaldiRecognizer, SpkModel
 import sys
@@ -7,15 +7,15 @@ import json
 import os
 import numpy as np

-model_path = "model-en"
+model_path = "model"
 spk_model_path = "model-spk"

 if not os.path.exists(model_path):
-    print ("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as {} in the current folder.".format(model_path))
+    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as {} in the current folder.".format(model_path))
    exit (1)

 if not os.path.exists(spk_model_path):
-    print ("Please download the speaker model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as {} in the current folder.".format(spk_model_path))
+    print ("Please download the speaker model from https://alphacephei.com/vosk/models and unpack as {} in the current folder.".format(spk_model_path))
    exit (1)

 wf = wave.open(sys.argv[1], "rb")
@@ -30,7 +30,7 @@ rec = KaldiRecognizer(model, spk_model, wf.getframerate())

 # We compare speakers with cosine distance. We can keep one or several fingerprints for the speaker in a database
 # to distingusih among users.
-spk_sig = [5.64308, 4.23898, 1.119433, -0.810904, 2.115443, 2.328436, 6.135152, 1.348195, 2.60771, 1.020717, 4.324225, -0.873012, 6.123375, 4.903791, 0.064803, 4.66212, 3.502724, 2.535861, 5.452417, 7.081769, -0.823969, -5.167974, 8.568919, 4.159035, 5.314441, 3.688272, 5.730379, 4.463213, 7.227232, 3.538961, 3.316218, 1.269628, -1.902378, 3.512679, -1.947611, -1.520158, 3.80928, -2.721601, 5.359588, 2.942463, -7.474174, 3.788054, 0.303426, 4.951366, 1.72281, -1.867125, -3.574615, 3.622509, 4.803109, 2.829714, 1.528521, 6.408293, 0.820131, 5.066522, 2.836125, 2.867029, 3.725267, 0.505927, 1.462984, 5.001863, -3.838309, -2.45902, 3.992581, 4.451616, 2.865211, -1.148313, 4.996399, -3.473454, 2.876967, 3.940124, 7.553079, 0.373356, 1.396561, 2.686691, 2.094895, 0.913796, -0.286909, 3.540179, 4.904687, 0.84554, 7.585956, 1.017081, 0.168355, 6.672327, 4.092033, -4.240158, -2.017081, -0.813043, 6.468298, 4.115041, 2.231936, 2.370055, 4.972295, 5.58382, 6.022872, 2.706988, 5.248096, -1.918003, 8.259204, -0.900911, 1.961962, 2.349709, 3.290093, 3.344172, 3.307027, 4.203372, -0.315103, 5.61919, -3.229496, 3.777309, 4.328595, 1.461014, 2.622894, 0.315525, 5.447259, 5.407609, 5.339016, 1.604555, 5.359932, 0.090242, 0.535306, 4.724705, 4.692502, 0.5783, -5.436688, -4.915511, 1.959807, 2.825248]
+spk_sig = [-1.110417,0.09703002,1.35658,0.7798632,-0.305457,-0.339204,0.6186931,-0.4521213,0.3982236,-0.004530723,0.7651616,0.6500852,-0.6664245,0.1361499,0.1358056,-0.2887807,-0.1280468,-0.8208137,-1.620276,-0.4628615,0.7870904,-0.105754,0.9739769,-0.3258137,-0.7322628,-0.6212429,-0.5531687,-0.7796484,0.7035915,1.056094,-0.4941756,-0.6521456,-0.2238328,-0.003737517,0.2165709,1.200186,-0.7737719,0.492015,1.16058,0.6135428,-0.7183084,0.3153541,0.3458071,-1.418189,-0.9624157,0.4168292,-1.627305,0.2742135,-0.6166027,0.1962581,-0.6406527,0.4372789,-0.4296024,0.4898657,-0.9531326,-0.2945702,0.7879696,-1.517101,-0.9344181,-0.5049928,-0.005040941,-0.4637912,0.8223695,-1.079849,0.8871287,-0.9732434,-0.5548235,1.879138,-1.452064,-0.1975368,1.55047,0.5941782,-0.52897,1.368219,0.6782904,1.202505,-0.9256122,-0.9718158,-0.9570228,-0.5563112,-1.19049,-1.167985,2.606804,-2.261825,0.01340385,0.2526799,-1.125458,-1.575991,-0.363153,0.3270262,1.485984,-1.769565,1.541829,0.7293826,0.1743717,-0.4759418,1.523451,-2.487134,-1.824067,-0.626367,0.7448186,-1.425648,0.3524166,-0.9903384,3.339342,0.4563958,-0.2876643,1.521635,0.9508078,-0.1398541,0.3867955,-0.7550205,0.6568405,0.09419366,-1.583935,1.306094,-0.3501927,0.1794427,-0.3768163,0.9683866,-0.2442541,-1.696921,-1.8056,-0.6803037,-1.842043,0.3069353,0.9070363,-0.486526]

 def cosine_dist(x, y):
    nx = np.array(x)
@@ -38,15 +38,19 @@ def cosine_dist(x, y):
    return 1 - np.dot(nx, ny) / np.linalg.norm(nx) / np.linalg.norm(ny)

 while True:
-    data = wf.readframes(1000)
+    data = wf.readframes(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
        res = json.loads(rec.Result())
        print ("Text:", res['text'])
-        print ("Speaker distance:", cosine_dist(spk_sig, res['spk']))
+        print ("X-vector:", res['spk'])
+        print ("Speaker distance:", cosine_dist(spk_sig, res['spk']), "based on", res['spk_frames'], "frames")
+
+print ("Note that second distance is not very reliable because utterance is too short. Utterances longer than 4 seconds give better xvector")

 res = json.loads(rec.FinalResult())
 print ("Text:", res['text'])
-print ("Speaker distance:", cosine_dist(spk_sig, res['spk']))
+print ("X-vector:", res['spk'])
+print ("Speaker distance:", cosine_dist(spk_sig, res['spk']), "based on", res['spk_frames'], "frames")

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+import sys
+import os
+import wave
+import subprocess
+import srt
+import json
+import datetime
+
+SetLogLevel(-1)
+
+if not os.path.exists("model"):
+    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
+    exit (1)
+
+sample_rate=16000
+model = Model("model")
+rec = KaldiRecognizer(model, sample_rate)
+
+process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
+                            sys.argv[1],
+                            '-ar', str(sample_rate) , '-ac', '1', '-f', 's16le', '-'],
+                            stdout=subprocess.PIPE)
+
+
+def transcribe():
+    results = []
+    subs = []
+    while True:
+       data = process.stdout.read(4000)
+       if len(data) == 0:
+           break
+       if rec.AcceptWaveform(data):
+           results.append(rec.Result())
+    results.append(rec.FinalResult())
+
+    for i, res in enumerate(results):
+       jres = json.loads(res)
+       s = srt.Subtitle(index=i, 
+                   content=jres['text'], 
+                   start=datetime.timedelta(seconds=jres['result'][0]['start']), 
+                   end=datetime.timedelta(seconds=jres['result'][-1]['end']))
+       subs.append(s)
+    return subs
+
+print (srt.compose(transcribe()))
+
@@ -1,16 +1,16 @@
-#!/usr/bin/python3
+#!/usr/bin/env python3

 from vosk import Model, KaldiRecognizer
 import sys
 import json
 import os

-if not os.path.exists("model-en"):
-    print ("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model-en' in the current folder.")
+if not os.path.exists("model"):
+    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
    exit (1)


-model = Model("model-en")
+model = Model("model")

 # Large vocabulary free form recognition
 rec = KaldiRecognizer(model, 16000)
@@ -22,7 +22,7 @@ wf = open(sys.argv[1], "rb")
 wf.read(44) # skip header

 while True:
-    data = wf.read(2000)
+    data = wf.read(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
@@ -1,12 +1,12 @@
-#!/usr/bin/python3
+#!/usr/bin/env python3

 from vosk import Model, KaldiRecognizer
 import sys
 import os
 import wave

-if not os.path.exists("model-en"):
-    print ("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model-en' in the current folder.")
+if not os.path.exists("model"):
+    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
    exit (1)

 wf = wave.open(sys.argv[1], "rb")
@@ -14,12 +14,13 @@ if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE
    print ("Audio file must be WAV format mono PCM.")
    exit (1)

-model = Model("model-en")
-# You can also specify the possible word list
-rec = KaldiRecognizer(model, wf.getframerate(), "zero oh one two three four five six seven eight nine")
+model = Model("model")
+
+# You can also specify the possible word or phrase list as JSON list, the order doesn't have to be strict
+rec = KaldiRecognizer(model, wf.getframerate(), '["oh one two three four five six seven eight nine zero", "[unk]"]')

 while True:
-    data = wf.readframes(1000)
+    data = wf.readframes(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
@@ -1,22 +1,88 @@
 import os
+import sys
 import setuptools
-from cmake import *
+from setuptools import Extension
+from setuptools.command.build_py import build_py as _build_py
+import distutils.dir_util
+import distutils.log
+
+class build_py(_build_py):
+    def run(self):
+        self.run_command("build_ext")
+        return super().run()
+
+kaldi_root = os.getenv('KALDI_ROOT')
+kaldi_mkl = os.getenv('KALDI_MKL')
+source_path = os.getenv("VOSK_SOURCE", os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(__file__)), "../src")))
+
+if kaldi_root == None:
+    print("Define KALDI_ROOT")
+    exit(1)
+
+distutils.log.set_verbosity(distutils.log.DEBUG)
+distutils.dir_util.copy_tree(
+    source_path,
+    "vosk",
+    update=1,
+    verbose=1)

 with open("README.md", "r") as fh:
    long_description = fh.read()

+kaldi_static_libs = ['src/online2/kaldi-online2.a',
+             'src/decoder/kaldi-decoder.a',
+             'src/ivector/kaldi-ivector.a',
+             'src/gmm/kaldi-gmm.a',
+             'src/nnet3/kaldi-nnet3.a',
+             'src/tree/kaldi-tree.a',
+             'src/feat/kaldi-feat.a',
+             'src/lat/kaldi-lat.a',
+             'src/lm/kaldi-lm.a',
+             'src/hmm/kaldi-hmm.a',
+             'src/transform/kaldi-transform.a',
+             'src/cudamatrix/kaldi-cudamatrix.a',
+             'src/matrix/kaldi-matrix.a',
+             'src/fstext/kaldi-fstext.a',
+             'src/util/kaldi-util.a',
+             'src/base/kaldi-base.a',
+             'tools/openfst/lib/libfst.a',
+             'tools/openfst/lib/libfstngram.a']
+kaldi_link_args = ['-s']
+kaldi_libraries = []
+
+if sys.platform.startswith('darwin'):
+    kaldi_link_args.extend(['-Wl,-undefined,dynamic_lookup', '-framework', 'Accelerate'])
+elif kaldi_mkl == "1":
+    kaldi_link_args.extend(['-L/opt/intel/mkl/lib/intel64', '-Wl,-rpath=/opt/intel/mkl/lib/intel64'])
+    kaldi_libraries.extend(['mkl_rt', 'mkl_intel_lp64', 'mkl_core', 'mkl_sequential'])
+else:
+    kaldi_static_libs.append('tools/OpenBLAS/libopenblas.a')
+    kaldi_libraries.append('gfortran')
+
+sources = ['kaldi_recognizer.cc', 'model.cc', 'spk_model.cc', 'vosk_api.cc', 'language_model.cc', 'vosk.i']
+
+vosk_ext = Extension('vosk._vosk',
+                    define_macros = [('FST_NO_DYNAMIC_LINKING', '1')],
+                    include_dirs = [kaldi_root + '/src', kaldi_root + '/tools/openfst/include', 'vosk'],
+                    swig_opts=['-outdir', 'vosk', '-c++'],
+                    libraries = kaldi_libraries,
+                    extra_objects = [kaldi_root + '/' + x for x in kaldi_static_libs],
+                    sources = ['vosk/' + x for x in sources],
+                    extra_link_args = kaldi_link_args,
+                    extra_compile_args = ['-std=c++11', '-Wno-sign-compare', '-Wno-unused-variable', '-Wno-unused-local-typedefs'])
+
 setuptools.setup(
-    name="vosk", # Replace with your own username
-    version="0.3.4",
+    name="vosk",
+    version="0.3.15",
    author="Alpha Cephei Inc",
    author_email="contact@alphacephei.com",
-    description="API for Kaldi and Vosk",
+    description="Offline open source speech recognition API based on Kaldi and Vosk",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/alphacep/vosk-api",
    packages=setuptools.find_packages(),
-    ext_modules=[CMakeExtension('_vosk', 'vosk')],
-    cmdclass={'build_ext': CMakeBuildExt, 'build_py' : CMakeBuildExtFirst},
+    ext_modules=[vosk_ext],
+    cmdclass = {'build_py' : build_py},
    classifiers=[
        'Programming Language :: Python :: 3',
        'License :: OSI Approved :: Apache Software License',
@@ -25,5 +91,5 @@ setuptools.setup(
        'Operating System :: MacOS :: MacOS X',
        'Topic :: Software Development :: Libraries :: Python Modules'
    ],
-    python_requires='>=3.4',
+    python_requires='>=3.5',
 )
@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+
+from multiprocessing.dummy import Pool
+from vosk import Model, KaldiRecognizer
+
+import sys
+import os
+import wave
+import json
+
+model = Model("model")
+
+def recognize(line):
+    uid, fn = line.split()
+    wf = wave.open(fn, "rb")
+    rec = KaldiRecognizer(model, wf.getframerate())
+
+    text = ""
+    while True:
+        data = wf.readframes(1000)
+        if len(data) == 0:
+            break
+        if rec.AcceptWaveform(data):
+            jres = json.loads(rec.Result())
+            text = text + " " + jres['text']
+    jres = json.loads(rec.FinalResult())
+    text = text + " " + jres['text']
+    return (uid + text)
+
+def main():
+    p = Pool(8)
+    texts = p.map(recognize, open(sys.argv[1]).readlines())
+    print ("\n".join(texts))
+
+main()
@@ -1 +1,2 @@
-from .vosk import KaldiRecognizer, Model, SpkModel
+from .vosk import KaldiRecognizer, Model, SpkModel, SetLogLevel
+
@@ -1,4 +1,4 @@
-// Copyright 2019 Alpha Cephei Inc.
+// Copyright 2019-2020 Alpha Cephei Inc.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
@@ -16,128 +16,214 @@
 #include "json.h"
 #include "fstext/fstext-utils.h"
 #include "lat/sausages.h"
+#include "language_model.h"

 using namespace fst;
 using namespace kaldi::nnet3;

-KaldiRecognizer::KaldiRecognizer(Model &model, float sample_frequency) : model_(model), spk_model_(0), sample_frequency_(sample_frequency) {
+KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency) : model_(model), spk_model_(0), sample_frequency_(sample_frequency) {

-    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_.feature_info_);
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_.trans_model_, model_.feature_info_.silence_weighting_config, 3);
+    model_->Ref();
+
+    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
+    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);

    g_fst_ = NULL;
    decode_fst_ = NULL;

-    if (!model_.hclg_fst_) {
-        if (model_.hcl_fst_ && model_.g_fst_) {
-            decode_fst_ = LookaheadComposeFst(*model_.hcl_fst_, *model_.g_fst_, model_.disambig_);
+    if (!model_->hclg_fst_) {
+        if (model_->hcl_fst_ && model_->g_fst_) {
+            decode_fst_ = LookaheadComposeFst(*model_->hcl_fst_, *model_->g_fst_, model_->disambig_);
        } else {
            KALDI_ERR << "Can't create decoding graph";
        }
    }

-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_.nnet3_decoding_config_,
-            *model_.trans_model_,
-            *model_.decodable_info_,
-            model_.hclg_fst_ ? *model.hclg_fst_ : *decode_fst_,
+    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+            *model_->trans_model_,
+            *model_->decodable_info_,
+            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
            feature_pipeline_);

-    frame_offset_ = 0;
-    input_finalized_ = false;
    spk_feature_ = NULL;
+
+    InitState();
+    InitRescoring();
 }

-KaldiRecognizer::KaldiRecognizer(Model &model, float sample_frequency, char const *grammar) : model_(model), spk_model_(0), sample_frequency_(sample_frequency)
+KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, char const *grammar) : model_(model), spk_model_(0), sample_frequency_(sample_frequency)
 {
-    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_.feature_info_);
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_.trans_model_, model_.feature_info_.silence_weighting_config, 3);
+    model_->Ref();

-    g_fst_ = new StdVectorFst();
-    if (model_.hcl_fst_) {
-        g_fst_->AddState();
-        g_fst_->SetStart(0);
-        g_fst_->AddState();
-        g_fst_->SetFinal(1, fst::TropicalWeight::One());
-        g_fst_->AddArc(1, StdArc(0, 0, fst::TropicalWeight::One(), 0));
+    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
+    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);

-        // Create simple word loop FST
-        std::stringstream ss(grammar);
-        std::string token;
+    if (model_->hcl_fst_) {
+        json::JSON obj;
+        obj = json::JSON::Load(grammar);

-        while (std::getline(ss, token, ' ')) {
-            int32 id = model_.word_syms_->Find(token);
-            g_fst_->AddArc(0, StdArc(id, id, fst::TropicalWeight::One(), 1));
+        if (obj.length() <= 0) {
+            KALDI_WARN << "Expecting array of strings, got: '" << grammar << "'";
+        } else {
+            KALDI_LOG << obj;
+
+            LanguageModelOptions opts;
+
+            opts.ngram_order = 2;
+            opts.discount = 0.5;
+
+            LanguageModelEstimator estimator(opts);
+            for (int i = 0; i < obj.length(); i++) {
+                bool ok;
+                string line = obj[i].ToString(ok);
+                if (!ok) {
+                    KALDI_ERR << "Expecting array of strings, got: '" << obj << "'";
+                }
+
+                std::vector<int32> sentence;
+                stringstream ss(line);
+                string token;
+                while (getline(ss, token, ' ')) {
+                    int32 id = model_->word_syms_->Find(token);
+                    if (id == kNoSymbol) {
+                        KALDI_WARN << "Ignoring word missing in vocabulary: '" << token << "'";
+                    } else {
+                        sentence.push_back(id);
+                    }
+                }
+                estimator.AddCounts(sentence);
+            }
+            g_fst_ = new StdVectorFst();
+            estimator.Estimate(g_fst_);
+
+            decode_fst_ = LookaheadComposeFst(*model_->hcl_fst_, *g_fst_, model_->disambig_);
        }
-        ArcSort(g_fst_, ILabelCompare<StdArc>());
-
-        decode_fst_ = LookaheadComposeFst(*model_.hcl_fst_, *g_fst_, model_.disambig_);
    } else {
        decode_fst_ = NULL;
-        KALDI_ERR << "Can't create decoding graph";
+        KALDI_WARN << "Runtime graphs are not supported by this model";
    }

-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_.nnet3_decoding_config_,
-            *model_.trans_model_,
-            *model_.decodable_info_,
-            model_.hclg_fst_ ? *model.hclg_fst_ : *decode_fst_,
+    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+            *model_->trans_model_,
+            *model_->decodable_info_,
+            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
            feature_pipeline_);

-    frame_offset_ = 0;
-    input_finalized_ = false;
    spk_feature_ = NULL;
+
+    InitState();
+    InitRescoring();
 }

+KaldiRecognizer::KaldiRecognizer(Model *model, SpkModel *spk_model, float sample_frequency) : model_(model), spk_model_(spk_model), sample_frequency_(sample_frequency) {

-KaldiRecognizer::KaldiRecognizer(Model &model, SpkModel *spk_model, float sample_frequency) : model_(model), spk_model_(spk_model), sample_frequency_(sample_frequency) {
-    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_.feature_info_);
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_.trans_model_, model_.feature_info_.silence_weighting_config, 3);
+    model_->Ref();
+    spk_model->Ref();
+
+    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
+    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);

    decode_fst_ = NULL;
    g_fst_ = NULL;

-    if (!model_.hclg_fst_) {
-        if (model_.hcl_fst_ && model_.g_fst_) {
-            decode_fst_ = LookaheadComposeFst(*model_.hcl_fst_, *model_.g_fst_, model_.disambig_);
+    if (!model_->hclg_fst_) {
+        if (model_->hcl_fst_ && model_->g_fst_) {
+            decode_fst_ = LookaheadComposeFst(*model_->hcl_fst_, *model_->g_fst_, model_->disambig_);
        } else {
            KALDI_ERR << "Can't create decoding graph";
        }
    }

-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_.nnet3_decoding_config_,
-            *model_.trans_model_,
-            *model_.decodable_info_,
-            model_.hclg_fst_ ? *model.hclg_fst_ : *decode_fst_,
+    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+            *model_->trans_model_,
+            *model_->decodable_info_,
+            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
            feature_pipeline_);

-    frame_offset_ = 0;
-    input_finalized_ = false;
-
    spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
+
+    InitState();
+    InitRescoring();
 }

 KaldiRecognizer::~KaldiRecognizer() {
+    delete decoder_;
    delete feature_pipeline_;
    delete silence_weighting_;
-    delete decoder_;
    delete g_fst_;
    delete decode_fst_;
    delete spk_feature_;
+    delete lm_fst_;
+
+    model_->Unref();
+    if (spk_model_)
+         spk_model_->Unref();
+}
+
+void KaldiRecognizer::InitState()
+{
+    frame_offset_ = 0;
+    samples_processed_ = 0;
+    samples_round_start_ = 0;
+
+    state_ = RECOGNIZER_INITIALIZED;
+}
+
+void KaldiRecognizer::InitRescoring()
+{
+    if (model_->std_lm_fst_) {
+        fst::CacheOptions cache_opts(true, 50000);
+        fst::MapFstOptions mapfst_opts(cache_opts);
+        fst::StdToLatticeMapper<kaldi::BaseFloat> mapper;
+        lm_fst_ = new fst::MapFst<fst::StdArc, kaldi::LatticeArc, fst::StdToLatticeMapper<kaldi::BaseFloat> >(*model_->std_lm_fst_, mapper, mapfst_opts);
+    } else {
+        lm_fst_ = NULL;
+    }
 }

 void KaldiRecognizer::CleanUp()
 {
    delete silence_weighting_;
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_.trans_model_, model_.feature_info_.silence_weighting_config, 3);
+    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);

-    frame_offset_ += decoder_->NumFramesDecoded();
-    decoder_->InitDecoding(frame_offset_);
+    if (decoder_)
+       frame_offset_ += decoder_->NumFramesDecoded();
+
+    // Each 10 minutes we drop the pipeline to save frontend memory in continuous processing
+    // here we drop few frames remaining in the feature pipeline but hope it will not
+    // cause a huge accuracy drop since it happens not very frequently.
+
+    // Also restart if we retrieved final result already
+
+    if (decoder_ == NULL || state_ == RECOGNIZER_FINALIZED || frame_offset_ > 20000) {
+        samples_round_start_ += samples_processed_;
+        samples_processed_ = 0;
+        frame_offset_ = 0;
+
+        delete decoder_;
+        delete feature_pipeline_;
+
+        feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
+        decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+            *model_->trans_model_,
+            *model_->decodable_info_,
+            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
+            feature_pipeline_);
+
+        if (spk_model_) {
+            delete spk_feature_;
+            spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
+        }
+    } else {
+        decoder_->InitDecoding(frame_offset_);
+    }
 }

 void KaldiRecognizer::UpdateSilenceWeights()
 {
    if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0 &&
        feature_pipeline_->IvectorFeature() != NULL) {
-        std::vector<std::pair<int32, BaseFloat> > delta_weights;
+        vector<pair<int32, BaseFloat> > delta_weights;
        silence_weighting_->ComputeCurrentTraceback(decoder_->Decoder());
        silence_weighting_->GetDeltaWeights(feature_pipeline_->NumFramesReady(),
                                          frame_offset_ * 3,
@@ -175,27 +261,32 @@ bool KaldiRecognizer::AcceptWaveform(const float *fdata, int len)

 bool KaldiRecognizer::AcceptWaveform(Vector<BaseFloat> &wdata)
 {
-    if (input_finalized_) {
+    // Cleanup if we finalized previous utterance or the whole feature pipeline
+    if (!(state_ == RECOGNIZER_RUNNING || state_ == RECOGNIZER_INITIALIZED)) {
        CleanUp();
-        input_finalized_ = false;
    }
+    state_ = RECOGNIZER_RUNNING;

-    feature_pipeline_->AcceptWaveform(sample_frequency_, wdata);
-    UpdateSilenceWeights();
-    decoder_->AdvanceDecoding();
+    int step = static_cast<int>(sample_frequency_ * 0.2);
+    for (int i = 0; i < wdata.Dim(); i+= step) {
+        SubVector<BaseFloat> r = wdata.Range(i, std::min(step, wdata.Dim() - i));
+        feature_pipeline_->AcceptWaveform(sample_frequency_, r);
+        UpdateSilenceWeights();
+        decoder_->AdvanceDecoding();
+    }
+    samples_processed_ += wdata.Dim();

    if (spk_feature_) {
        spk_feature_->AcceptWaveform(sample_frequency_, wdata);
    }

-    if (decoder_->EndpointDetected(model_.endpoint_config_)) {
+    if (decoder_->EndpointDetected(model_->endpoint_config_)) {
        return true;
    }

    return false;
 }

-
 // Computes an xvector from a chunk of speech features.
 static void RunNnetComputation(const MatrixBase<BaseFloat> &features,
    const nnet3::Nnet &nnet, nnet3::CachingOptimizingCompiler *compiler,
@@ -212,7 +303,7 @@ static void RunNnetComputation(const MatrixBase<BaseFloat> &features,
    output_spec.indexes.resize(1);
    request.outputs.resize(1);
    request.outputs[0].Swap(&output_spec);
-    std::shared_ptr<const nnet3::NnetComputation> computation = compiler->Compile(request);
+    shared_ptr<const nnet3::NnetComputation> computation = compiler->Compile(request);
    nnet3::Nnet *nnet_to_update = NULL;  // we're not doing any update.
    nnet3::NnetComputer computer(nnet3::NnetComputeOptions(), *computation,
                    nnet, nnet_to_update);
@@ -225,17 +316,48 @@ static void RunNnetComputation(const MatrixBase<BaseFloat> &features,
    xvector->CopyFromVec(cu_output.Row(0));
 }

+#define MIN_SPK_FEATS 50

-void KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &xvector)
+bool KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &out_xvector, int *num_spk_frames)
 {
+    vector<int32> nonsilence_frames;
+    if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0) {
+        silence_weighting_->ComputeCurrentTraceback(decoder_->Decoder(), true);
+        silence_weighting_->GetNonsilenceFrames(feature_pipeline_->NumFramesReady(),
+                                          frame_offset_ * 3,
+                                          &nonsilence_frames);
+    }
+
    int num_frames = spk_feature_->NumFramesReady() - frame_offset_ * 3;
    Matrix<BaseFloat> mfcc(num_frames, spk_feature_->Dim());
+
+    // Not very efficient, would be nice to have faster search
+    int num_nonsilence_frames = 0;
+    Vector<BaseFloat> feat(spk_feature_->Dim());
+
    for (int i = 0; i < num_frames; ++i) {
-       Vector<BaseFloat> feat(spk_feature_->Dim());
+       if (std::find(nonsilence_frames.begin(),
+                     nonsilence_frames.end(), i / 3) == nonsilence_frames.end()) {
+           continue;
+       }
+
       spk_feature_->GetFrame(i + frame_offset_ * 3, &feat);
-       mfcc.CopyRowFromVec(feat, i);
+       mfcc.CopyRowFromVec(feat, num_nonsilence_frames);
+       num_nonsilence_frames++;
    }
+
+    *num_spk_frames = num_nonsilence_frames;
+
+    // Don't extract vector if not enough data
+    if (num_nonsilence_frames < MIN_SPK_FEATS) {
+        return false;
+    }
+
+    mfcc.Resize(num_nonsilence_frames, spk_feature_->Dim(), kCopyData);
+
    SlidingWindowCmnOptions cmvn_opts;
+    cmvn_opts.center = true;
+    cmvn_opts.cmn_window = 300;
    Matrix<BaseFloat> features(mfcc.NumRows(), mfcc.NumCols(), kUndefined);
    SlidingWindowCmn(cmvn_opts, mfcc, &features);

@@ -243,112 +365,178 @@ void KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &xvector)
    nnet3::CachingOptimizingCompilerOptions compiler_config;
    nnet3::CachingOptimizingCompiler compiler(spk_model_->speaker_nnet, opts.optimize_config, compiler_config);

+    Vector<BaseFloat> xvector;
    RunNnetComputation(features, spk_model_->speaker_nnet, &compiler, &xvector);
+
+    // Whiten the vector with global mean and transform and normalize mean
+    xvector.AddVec(-1.0, spk_model_->mean);
+
+    out_xvector.Resize(spk_model_->transform.NumRows(), kSetZero);
+    out_xvector.AddMatVec(1.0, spk_model_->transform, kNoTrans, xvector, 0.0);
+
+    BaseFloat norm = out_xvector.Norm(2.0);
+    BaseFloat ratio = norm / sqrt(out_xvector.Dim()); // how much larger it is
+                                                  // than it would be, in
+                                                  // expectation, if normally
+    out_xvector.Scale(1.0 / ratio);
+
+    return true;
 }

-
-const char* KaldiRecognizer::Result()
+const char* KaldiRecognizer::GetResult()
 {
-
-    if (!input_finalized_) {
-        decoder_->FinalizeDecoding();
-        input_finalized_ = true;
-    }
-
    if (decoder_->NumFramesDecoded() == 0) {
-        last_result_ = "{\"text\": \"\"}";
-        return last_result_.c_str();
+        return StoreReturn("{\"text\": \"\"}");
    }

    kaldi::CompactLattice clat;
    decoder_->GetLattice(true, &clat);
-    fst::ScaleLattice(fst::LatticeScale(8.0, 10.0), &clat);

+    if (model_->std_lm_fst_) {
+        Lattice lat1;
+
+        ConvertLattice(clat, &lat1);
+        fst::ScaleLattice(fst::GraphLatticeScale(-1.0), &lat1);
+        fst::ArcSort(&lat1, fst::OLabelCompare<kaldi::LatticeArc>());
+        kaldi::Lattice composed_lat;
+        fst::Compose(lat1, *lm_fst_, &composed_lat);
+        fst::Invert(&composed_lat);
+        kaldi::CompactLattice determinized_lat;
+        DeterminizeLattice(composed_lat, &determinized_lat);
+        fst::ScaleLattice(fst::GraphLatticeScale(-1), &determinized_lat);
+        fst::ArcSort(&determinized_lat, fst::OLabelCompare<kaldi::CompactLatticeArc>());
+
+        kaldi::ConstArpaLmDeterministicFst const_arpa_fst(model_->const_arpa_);
+        kaldi::CompactLattice composed_clat;
+        kaldi::ComposeCompactLatticeDeterministic(determinized_lat, &const_arpa_fst, &composed_clat);
+        kaldi::Lattice composed_lat1;
+        ConvertLattice(composed_clat, &composed_lat1);
+        fst::Invert(&composed_lat1);
+        DeterminizeLattice(composed_lat1, &clat);
+    }
+
+    fst::ScaleLattice(fst::GraphLatticeScale(0.9), &clat); // Apply rescoring weight
    CompactLattice aligned_lat;
-    if (model_.winfo_) {
-        WordAlignLattice(clat, *model_.trans_model_, *model_.winfo_, 0, &aligned_lat);
+    if (model_->winfo_) {
+        WordAlignLattice(clat, *model_->trans_model_, *model_->winfo_, 0, &aligned_lat);
    } else {
        aligned_lat = clat;
    }

    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 =
+    const vector<BaseFloat> &conf = mbr.GetOneBestConfidences();
+    const vector<int32> &words = mbr.GetOneBest();
+    const vector<pair<BaseFloat, BaseFloat> > &times =
          mbr.GetOneBestTimes();

    int size = words.size();

    json::JSON obj;
-    std::stringstream text;
+    stringstream text;

    // Create JSON object
    for (int i = 0; i < size; i++) {
        json::JSON word;
-        word["word"] = model_.word_syms_->Find(words[i]);
-        word["start"] = (frame_offset_ + times[i].first) * 0.03;
-        word["end"] = (frame_offset_ + times[i].second) * 0.03;
+        word["word"] = model_->word_syms_->Find(words[i]);
+        word["start"] = samples_round_start_ / sample_frequency_ + (frame_offset_ + times[i].first) * 0.03;
+        word["end"] = samples_round_start_ / sample_frequency_ + (frame_offset_ + times[i].second) * 0.03;
        word["conf"] = conf[i];
        obj["result"].append(word);

        if (i) {
            text << " ";
        }
-        text << model_.word_syms_->Find(words[i]);
+        text << model_->word_syms_->Find(words[i]);
    }
    obj["text"] = text.str();

    if (spk_model_) {
        Vector<BaseFloat> xvector;
-        GetSpkVector(xvector);
-        for (int i = 0; i < xvector.Dim(); i++) {
-            obj["spk"].append(xvector(i));
+        int num_spk_frames;
+        if (GetSpkVector(xvector, &num_spk_frames)) {
+            for (int i = 0; i < xvector.Dim(); i++) {
+                obj["spk"].append(xvector(i));
+            }
+            obj["spk_frames"] = num_spk_frames;
        }
    }

-    last_result_ = obj.dump();
-    return last_result_.c_str();
+    return StoreReturn(obj.dump());
 }

+
 const char* KaldiRecognizer::PartialResult()
 {
+    if (state_ != RECOGNIZER_RUNNING) {
+        return StoreReturn("{\"text\": \"\"}");
+    }
+
    json::JSON res;
+
    if (decoder_->NumFramesDecoded() == 0) {
        res["partial"] = "";
-        last_result_ = res.dump();
-        return last_result_.c_str();
+        return StoreReturn(res.dump());
    }

    kaldi::Lattice lat;
    decoder_->GetBestPath(false, &lat);
-    std::vector<kaldi::int32> alignment, words;
+    vector<kaldi::int32> alignment, words;
    LatticeWeight weight;
    GetLinearSymbolSequence(lat, &alignment, &words, &weight);

-    std::ostringstream text;
+    ostringstream text;
    for (size_t i = 0; i < words.size(); i++) {
        if (i) {
            text << " ";
        }
-        text << model_.word_syms_->Find(words[i]);
+        text << model_->word_syms_->Find(words[i]);
    }
    res["partial"] = text.str();

-    last_result_ = res.dump();
-    return last_result_.c_str();
+    return StoreReturn(res.dump());
+}
+
+const char* KaldiRecognizer::Result()
+{
+    if (state_ != RECOGNIZER_RUNNING) {
+        return StoreReturn("{\"text\": \"\"}");
+    }
+    decoder_->FinalizeDecoding();
+    state_ = RECOGNIZER_ENDPOINT;
+    return GetResult();
 }

 const char* KaldiRecognizer::FinalResult()
 {
-    if (!input_finalized_) {
-        feature_pipeline_->InputFinished();
-        UpdateSilenceWeights();
-        decoder_->AdvanceDecoding();
-        decoder_->FinalizeDecoding();
-        input_finalized_ = true;
-        return Result();
-    } else {
-        last_result_ = "{\"text\": \"\"}";
-        return last_result_.c_str();
+    if (state_ != RECOGNIZER_RUNNING) {
+        return StoreReturn("{\"text\": \"\"}");
    }
+
+    feature_pipeline_->InputFinished();
+    UpdateSilenceWeights();
+    decoder_->AdvanceDecoding();
+    decoder_->FinalizeDecoding();
+    state_ = RECOGNIZER_FINALIZED;
+    GetResult();
+
+    // Free some memory while we are finalized, next
+    // iteration will reinitialize them anyway
+    delete decoder_;
+    delete feature_pipeline_;
+    delete silence_weighting_;
+    delete spk_feature_;
+
+    feature_pipeline_ = NULL;
+    silence_weighting_ = NULL;
+    decoder_ = NULL;
+    spk_feature_ = NULL;
+
+    return last_result_.c_str();
+}
+
+// Store result in recognizer and return as const string
+const char *KaldiRecognizer::StoreReturn(const string &res)
+{
+    last_result_ = res;
+    return last_result_.c_str();
 }
@@ -12,6 +12,9 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

+#ifndef VOSK_KALDI_RECOGNIZER_H
+#define VOSK_KALDI_RECOGNIZER_H
+
 #include "base/kaldi-common.h"
 #include "util/common-utils.h"
 #include "fstext/fstext-lib.h"
@@ -29,11 +32,18 @@

 using namespace kaldi;

+enum KaldiRecognizerState {
+    RECOGNIZER_INITIALIZED,
+    RECOGNIZER_RUNNING,
+    RECOGNIZER_ENDPOINT,
+    RECOGNIZER_FINALIZED
+};
+
 class KaldiRecognizer {
    public:
-        KaldiRecognizer(Model &model, float sample_frequency);
-        KaldiRecognizer(Model &model, SpkModel *spk_model, float sample_frequency);
-        KaldiRecognizer(Model &model, float sample_frequency, char const *grammar);
+        KaldiRecognizer(Model *model, float sample_frequency);
+        KaldiRecognizer(Model *model, SpkModel *spk_model, float sample_frequency);
+        KaldiRecognizer(Model *model, float sample_frequency, char const *grammar);
        ~KaldiRecognizer();
        bool AcceptWaveform(const char *data, int len);
        bool AcceptWaveform(const short *sdata, int len);
@@ -43,12 +53,16 @@ class KaldiRecognizer {
        const char* PartialResult();

    private:
+        void InitState();
+        void InitRescoring();
        void CleanUp();
        void UpdateSilenceWeights();
        bool AcceptWaveform(Vector<BaseFloat> &wdata);
-        void GetSpkVector(Vector<BaseFloat> &xvector);
+        bool GetSpkVector(Vector<BaseFloat> &out_xvector, int *frames);
+        const char *GetResult();
+        const char *StoreReturn(const string &res);

-        Model &model_;
+        Model *model_;
        SingleUtteranceNnet3Decoder *decoder_;
        fst::LookaheadFst<fst::StdArc, int32> *decode_fst_;
        fst::StdVectorFst *g_fst_; // dynamically constructed grammar
@@ -58,8 +72,16 @@ class KaldiRecognizer {
        SpkModel *spk_model_;
        OnlineBaseFeature *spk_feature_;

+        fst::MapFst<fst::StdArc, kaldi::LatticeArc, fst::StdToLatticeMapper<kaldi::BaseFloat> > *lm_fst_;
+
        float sample_frequency_;
        int32 frame_offset_;
-        bool input_finalized_;
-        std::string last_result_;
+
+        int64 samples_processed_;
+        int64 samples_round_start_;
+
+        KaldiRecognizerState state_;
+        string last_result_;
 };
+
+#endif /* VOSK_KALDI_RECOGNIZER_H */
@@ -0,0 +1,211 @@
+// Copyright      2015   Johns Hopkins University (author: Daniel Povey)
+
+// See ../../COPYING for clarification regarding multiple authors
+//
+// 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.
+
+// A modified version from chain/language-model.cc for static backoff
+
+#include <algorithm>
+#include <numeric>
+
+#include "language_model.h"
+
+using namespace kaldi;
+
+void LanguageModelEstimator::AddCounts(const std::vector<int32> &sentence) {
+  KALDI_ASSERT(opts_.ngram_order >= 2 && "--ngram-order must be >= 2");
+  int32 order = opts_.ngram_order;
+  // 0 is used for left-context at the beginning of the file.. treat it as BOS.
+  std::vector<int32> history(0);
+  std::vector<int32>::const_iterator iter = sentence.begin(),
+      end = sentence.end();
+  for (; iter != end; ++iter) {
+    KALDI_ASSERT(*iter != 0);
+    IncrementCount(history, *iter);
+    history.push_back(*iter);
+    if (history.size() >= order)
+      history.erase(history.begin());
+  }
+  // Probability of end of sentence.  This will end up getting ignored later, but
+  // it still makes a difference for probability-normalization reasons.
+  IncrementCount(history, 0);
+}
+
+void LanguageModelEstimator::IncrementCount(const std::vector<int32> &history,
+                                            int32 next_phone) {
+  int32 lm_state_index = FindOrCreateLmStateIndexForHistory(history);
+  if (lm_states_[lm_state_index].tot_count == 0) {
+    num_active_lm_states_++;
+  }
+  lm_states_[lm_state_index].AddCount(next_phone, 1);
+}
+
+void LanguageModelEstimator::SetParentCounts() {
+  int32 num_lm_states = lm_states_.size();
+  for (int32 l = 0; l < num_lm_states; l++) {
+    int32 l_iter = lm_states_[l].backoff_lmstate_index;
+    while (l_iter != -1) {
+      lm_states_[l_iter].Add(lm_states_[l]);
+      l_iter = lm_states_[l_iter].backoff_lmstate_index;
+    }
+  }
+}
+
+int32 LanguageModelEstimator::FindLmStateIndexForHistory(
+    const std::vector<int32> &hist) const {
+  MapType::const_iterator iter = hist_to_lmstate_index_.find(hist);
+  if (iter == hist_to_lmstate_index_.end())
+    return -1;
+  else
+    return iter->second;
+}
+
+int32 LanguageModelEstimator::FindNonzeroLmStateIndexForHistory(
+    std::vector<int32> hist) const {
+  while (1) {
+    int32 l = FindLmStateIndexForHistory(hist);
+    if (l == -1 || lm_states_[l].tot_count == 0) {
+      // no such state or state has zero count.
+      if (hist.empty())
+        KALDI_ERR << "Error looking up LM state index for history "
+                  << "(likely code bug)";
+      hist.erase(hist.begin());  // back off.
+    } else {
+      return l;
+    }
+  }
+}
+
+int32 LanguageModelEstimator::FindOrCreateLmStateIndexForHistory(
+    const std::vector<int32> &hist) {
+  MapType::const_iterator iter = hist_to_lmstate_index_.find(hist);
+  if (iter != hist_to_lmstate_index_.end())
+    return iter->second;
+  int32 ans = lm_states_.size();  // index of next element
+  // next statement relies on default construct of LmState.
+  lm_states_.resize(lm_states_.size() + 1);
+  lm_states_.back().history = hist;
+  hist_to_lmstate_index_[hist] = ans;
+
+  // make sure backoff_lmstate_index is set
+  if (hist.size() > 0) {
+    std::vector<int32> backoff_hist(hist.begin() + 1,
+                                    hist.end());
+    int32 backoff_lm_state = FindOrCreateLmStateIndexForHistory(backoff_hist);
+    lm_states_[ans].backoff_lmstate_index = backoff_lm_state;
+  }
+  return ans;
+}
+
+void LanguageModelEstimator::LmState::AddCount(int32 phone, int32 count) {
+  std::map<int32, int32>::iterator iter = phone_to_count.find(phone);
+  if (iter == phone_to_count.end())
+    phone_to_count[phone] = count;
+  else
+    iter->second += count;
+  tot_count += count;
+}
+
+void LanguageModelEstimator::LmState::Add(const LmState &other) {
+  KALDI_ASSERT(&other != this);
+  std::map<int32, int32>::const_iterator iter = other.phone_to_count.begin(),
+      end = other.phone_to_count.end();
+  for (; iter != end; ++iter)
+    AddCount(iter->first, iter->second);
+}
+
+int32 LanguageModelEstimator::AssignFstStates() {
+  int32 num_lm_states = lm_states_.size();
+  int32 current_fst_state = 0;
+  for (int32 l = 0; l < num_lm_states; l++) {
+    if (lm_states_[l].tot_count != 0) {
+      lm_states_[l].fst_state = current_fst_state++;
+    }
+  }
+  KALDI_ASSERT(current_fst_state == num_active_lm_states_);
+  return current_fst_state;
+}
+
+void LanguageModelEstimator::Estimate(fst::StdVectorFst *fst) {
+  KALDI_LOG << "Estimating language model with ngram-order="
+            << opts_.ngram_order << ", discount="
+            << opts_.discount;
+  SetParentCounts();
+  int32 num_fst_states = AssignFstStates();
+  OutputToFst(num_fst_states, fst);
+}
+
+int32 LanguageModelEstimator::FindInitialFstState() const {
+  std::vector<int32> history(0);
+  int32 l = FindNonzeroLmStateIndexForHistory(history);
+  KALDI_ASSERT(l != -1 && lm_states_[l].fst_state != -1);
+  return lm_states_[l].fst_state;
+}
+
+void LanguageModelEstimator::OutputToFst(
+    int32 num_states,
+    fst::StdVectorFst *fst) const {
+  KALDI_ASSERT(num_states == num_active_lm_states_);
+  fst->DeleteStates();
+  for (int32 i = 0; i < num_states; i++)
+    fst->AddState();
+  fst->SetStart(FindInitialFstState());
+
+  int64 tot_count = 0;
+  double tot_logprob = 0.0;
+
+  int32 num_lm_states = lm_states_.size();
+  // note: not all lm-states end up being 'active'.
+  for (int32 l = 0; l < num_lm_states; l++) {
+    const LmState &lm_state = lm_states_[l];
+    if (lm_state.fst_state == -1) {
+      continue;
+    }
+    int32 state_count = lm_state.tot_count;
+    KALDI_ASSERT(state_count != 0);
+    std::map<int32, int32>::const_iterator
+        iter = lm_state.phone_to_count.begin(),
+        end = lm_state.phone_to_count.end();
+    for (; iter != end; ++iter) {
+      int32 phone = iter->first, count = iter->second;
+      BaseFloat logprob = log(count * opts_.discount / state_count);
+      tot_count += count;
+      tot_logprob += logprob * count;
+      if (phone == 0) {  // Go to final state
+        fst->SetFinal(lm_state.fst_state, fst::TropicalWeight(-logprob));
+      } else {  // It becomes a transition.
+        std::vector<int32> next_history(lm_state.history);
+        next_history.push_back(phone);
+        int32 dest_lm_state = FindNonzeroLmStateIndexForHistory(next_history),
+            dest_fst_state = lm_states_[dest_lm_state].fst_state;
+        KALDI_ASSERT(dest_fst_state != -1);
+        fst->AddArc(lm_state.fst_state,
+                    fst::StdArc(phone, phone, fst::TropicalWeight(-logprob),
+                                dest_fst_state));
+      }
+    }
+    if (lm_state.backoff_lmstate_index >= 0) {
+      fst->AddArc(lm_state.fst_state, fst::StdArc(0, 0, fst::TropicalWeight(-log(1 - opts_.discount)), lm_states_[lm_state.backoff_lmstate_index].fst_state));
+    }
+  }
+  fst::Connect(fst);
+  // Make sure that Connect does not delete any states.
+  int32 num_states_connected = fst->NumStates();
+  KALDI_ASSERT(num_states_connected == num_states);
+  // arc-sort.  ilabel or olabel doesn't matter, it's an acceptor.
+  fst::ArcSort(fst, fst::ILabelCompare<fst::StdArc>());
+  KALDI_LOG << "Created language model with " << num_states
+            << " states and " << fst::NumArcs(*fst) << " arcs.";
+}
@@ -0,0 +1,150 @@
+// Copyright      2015  Johns Hopkins University (Author: Daniel Povey)
+
+// See ../../COPYING for clarification regarding multiple authors
+//
+// 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 VOSK_LANGUAGE_MODEL_H
+#define VOSK_LANGUAGE_MODEL_H
+
+#include <vector>
+#include <map>
+
+#include "base/kaldi-common.h"
+#include "util/common-utils.h"
+#include "fstext/fstext-lib.h"
+#include "lat/kaldi-lattice.h"
+
+using namespace kaldi;
+
+// Very simply lm construction with absolute discounting
+
+struct LanguageModelOptions {
+  int32 ngram_order;  // you might want to tune this
+  BaseFloat discount; // discount for backoff
+
+  LanguageModelOptions():
+      ngram_order(3),
+      discount(0.5)
+      { }
+
+  void Register(OptionsItf *opts) {
+    opts->Register("ngram-order", &ngram_order, "n-gram order for the phone "
+                   "language model used for the 'denominator model'");
+    opts->Register("discount", &discount, "Discount for backoff");
+  }
+};
+
+class LanguageModelEstimator {
+ public:
+  LanguageModelEstimator(LanguageModelOptions &opts): opts_(opts),
+                                                      num_active_lm_states_(0) {
+    KALDI_ASSERT(opts.ngram_order >= 1);
+  }
+
+  // Adds counts for this sentence.  Basically does: for each n-gram in the
+  // sentence, count[n-gram] += 1.  The only constraint on 'sentence' is that it
+  // should contain no zeros.
+  void AddCounts(const std::vector<int32> &sentence);
+
+  // Estimates the LM and outputs it as an FST.  Note: there is
+  // no concept here of backoff arcs.
+  void Estimate(fst::StdVectorFst *fst);
+
+ protected:
+  struct LmState {
+    // the phone history associated with this state (length can vary).
+    std::vector<int32> history;
+
+    // maps from
+    std::map<int32, int32> phone_to_count;
+
+    // total count of this state.  As we back off states to lower-order states
+    // (and note that this is a hard backoff where we completely remove un-needed
+    // states) this tot_count may become zero.
+    int32 tot_count;
+
+    // LM-state index of the backoff LM state (if it exists, else -1)...
+    // provided for convenience.
+    int32 backoff_lmstate_index;
+
+    // this is only set after we decide on the FST state numbering (at the end).
+    // If not set, it's -1.
+    int32 fst_state;
+
+    void AddCount(int32 phone, int32 count);
+
+    // Add the contents of another LmState.
+    void Add(const LmState &other);
+
+    LmState(): tot_count(0), backoff_lmstate_index(-1),
+               fst_state(-1) { }
+    LmState(const LmState &other):
+        history(other.history), phone_to_count(other.phone_to_count),
+        tot_count(other.tot_count),
+        backoff_lmstate_index(other.backoff_lmstate_index),
+        fst_state(other.fst_state) { }
+  };
+
+  // maps from history to int32
+  typedef unordered_map<std::vector<int32>, int32, VectorHasher<int32> > MapType;
+
+  LanguageModelOptions opts_;
+
+  MapType hist_to_lmstate_index_;
+  std::vector<LmState> lm_states_;  // indexed by lmstate_index, the LmStates.
+
+  // Keeps track of the number of lm states that have nonzero counts.
+  int32 num_active_lm_states_;
+
+
+  // adds the counts for this ngram (called from AddCounts()).
+  inline void IncrementCount(const std::vector<int32> &history,
+                             int32 next_phone);
+
+  // sets up tot_count_with_parents in all the lm-states
+  void SetParentCounts();
+
+  // Finds and returns an LM-state index for a history -- or -1 if it doesn't
+  // exist.  No backoff is done.
+  int32 FindLmStateIndexForHistory(const std::vector<int32> &hist) const;
+
+  // Finds and returns an LM-state index for a history -- and creates one if
+  // it doesn't exist -- and also creates any backoff states needed, down
+  // to history-length no_prune_ngram_order - 1.
+  int32 FindOrCreateLmStateIndexForHistory(const std::vector<int32> &hist);
+
+  // Finds and returns the most specific LM-state index for a history or
+  // backed-off versions of it, that exists and has nonzero count.  Will die if
+  // there is no such history.  [e.g. if there is no unigram backoff state,
+  // which generally speaking there won't be.]
+  int32 FindNonzeroLmStateIndexForHistory(std::vector<int32> hist) const;
+
+  // after all backoff has been done, assigns FST state indexes to all states
+  // that exist and have nonzero count.  Returns the number of states.
+  int32 AssignFstStates();
+
+  // find the FST index of the initial-state, and returns it.
+  int32 FindInitialFstState() const;
+
+  // Write to an FST
+  void OutputToFst(
+      int32 num_fst_states,
+      fst::StdVectorFst *fst) const;
+
+};
+
+#endif
+
@@ -14,19 +14,7 @@


 //
-// Possible model layout:
-//
-// * Default kaldi model with HCLG.fst
-//
-// * Lookahead model with const G.fst
-//
-// * Lookahead model with ngram G.fst
-//
-// * File disambig_tid.int required only for lookadhead models
-//
-// * File word_boundary.int is required if we want to have precise word timing information
-//   otherwise we don't do any word alignment. Optionally lexicon alignment can be done
-//   with corresponding C++ code inside kaldi recognizer.
+// For details of possible model layout see doc/models.md section model-structure

 #include "model.h"

@@ -45,24 +33,101 @@ static FstRegisterer<NGramFst<StdArc>> NGramFst_StdArc_registerer;

 #ifdef __ANDROID__
 #include <android/log.h>
-static void AndroidLogHandler(const LogMessageEnvelope &env, const char *message)
+static void KaldiLogHandler(const LogMessageEnvelope &env, const char *message)
 {
-    __android_log_print(ANDROID_LOG_VERBOSE, "KaldiDemo", message, 1);
+  int priority;
+  if (env.severity > GetVerboseLevel())
+      return;
+
+  if (env.severity > LogMessageEnvelope::kInfo) {
+    priority = ANDROID_LOG_VERBOSE;
+  } else {
+    switch (env.severity) {
+    case LogMessageEnvelope::kInfo:
+      priority = ANDROID_LOG_INFO;
+      break;
+    case LogMessageEnvelope::kWarning:
+      priority = ANDROID_LOG_WARN;
+      break;
+    case LogMessageEnvelope::kAssertFailed:
+      priority = ANDROID_LOG_FATAL;
+      break;
+    case LogMessageEnvelope::kError:
+    default: // If not the ERROR, it still an error!
+      priority = ANDROID_LOG_ERROR;
+      break;
+    }
+  }
+
+  std::stringstream full_message;
+  full_message << env.func << "():" << env.file << ':'
+               << env.line << ") " << message;
+
+  __android_log_print(priority, "VoskAPI", "%s", full_message.str().c_str());
+}
+#else
+static void KaldiLogHandler(const LogMessageEnvelope &env, const char *message)
+{
+  if (env.severity > GetVerboseLevel())
+      return;
+
+  // Modified default Kaldi logging so we can disable LOG messages.
+  std::stringstream full_message;
+  if (env.severity > LogMessageEnvelope::kInfo) {
+    full_message << "VLOG[" << env.severity << "] (";
+  } else {
+    switch (env.severity) {
+    case LogMessageEnvelope::kInfo:
+      full_message << "LOG (";
+      break;
+    case LogMessageEnvelope::kWarning:
+      full_message << "WARNING (";
+      break;
+    case LogMessageEnvelope::kAssertFailed:
+      full_message << "ASSERTION_FAILED (";
+      break;
+    case LogMessageEnvelope::kError:
+    default: // If not the ERROR, it still an error!
+      full_message << "ERROR (";
+      break;
+    }
+  }
+  // Add other info from the envelope and the message text.
+  full_message << "VoskAPI" << ':'
+               << env.func << "():" << env.file << ':'
+               << env.line << ") " << message;
+
+  // Print the complete message to stderr.
+  full_message << "\n";
+  std::cerr << full_message.str();
 }
 #endif

-Model::Model(const char *model_path) {
+Model::Model(const char *model_path) : model_path_str_(model_path) {

-#ifdef __ANDROID__
-    SetLogHandler(AndroidLogHandler);
-#endif
+    SetLogHandler(KaldiLogHandler);

-    const char *usage = "Read the docs";
+    struct stat buffer;
+    string am_path = model_path_str_ + "/am/final.mdl";
+    if (stat(am_path.c_str(), &buffer) == 0) {
+         ConfigureV2();
+    } else {
+         ConfigureV1();
+    }
+
+    ReadDataFiles();
+
+    ref_cnt_ = 1;
+}
+
+// Old model layout without model configuration file
+
+void Model::ConfigureV1()
+{
    const char *extra_args[] = {
-        "--min-active=200",
-        "--max-active=3000",
-        "--beam=10.0",
-        "--lattice-beam=2.0",
+        "--max-active=7000",
+        "--beam=13.0",
+        "--lattice-beam=6.0",
        "--acoustic-scale=1.0",

        "--frame-subsampling-factor=3",
@@ -71,55 +136,76 @@ Model::Model(const char *model_path) {
        "--endpoint.rule2.min-trailing-silence=0.5",
        "--endpoint.rule3.min-trailing-silence=1.0",
        "--endpoint.rule4.min-trailing-silence=2.0",
-    };
-    std::string model_path_str(model_path);

-    kaldi::ParseOptions po(usage);
+        "--print-args=false",
+    };
+
+    kaldi::ParseOptions po("");
    nnet3_decoding_config_.Register(&po);
    endpoint_config_.Register(&po);
    decodable_opts_.Register(&po);

-    std::vector<const char*> args;
+    vector<const char*> args;
    args.push_back("vosk");
    args.insert(args.end(), extra_args, extra_args + sizeof(extra_args) / sizeof(extra_args[0]));
    po.Read(args.size(), args.data());

+    nnet3_rxfilename_ = model_path_str_ + "/final.mdl";
+    hclg_fst_rxfilename_ = model_path_str_ + "/HCLG.fst";
+    hcl_fst_rxfilename_ = model_path_str_ + "/HCLr.fst";
+    g_fst_rxfilename_ = model_path_str_ + "/Gr.fst";
+    disambig_rxfilename_ = model_path_str_ + "/disambig_tid.int";
+    word_syms_rxfilename_ = model_path_str_ + "/words.txt";
+    winfo_rxfilename_ = model_path_str_ + "/word_boundary.int";
+    carpa_rxfilename_ = model_path_str_ + "/rescore/G.carpa";
+    std_fst_rxfilename_ = model_path_str_ + "/rescore/G.fst";
+    final_ie_rxfilename_ = model_path_str_ + "/ivector/final.ie";
+    mfcc_conf_rxfilename_ = model_path_str_ + "/mfcc.conf";
+}
+
+void Model::ConfigureV2()
+{
+    kaldi::ParseOptions po("something");
+    nnet3_decoding_config_.Register(&po);
+    endpoint_config_.Register(&po);
+    decodable_opts_.Register(&po);
+    po.ReadConfigFile(model_path_str_ + "/conf/model.conf");
+
+
+    nnet3_rxfilename_ = model_path_str_ + "/am/final.mdl";
+    hclg_fst_rxfilename_ = model_path_str_ + "/graph/HCLG.fst";
+    hcl_fst_rxfilename_ = model_path_str_ + "/graph/HCLr.fst";
+    g_fst_rxfilename_ = model_path_str_ + "/graph/Gr.fst";
+    disambig_rxfilename_ = model_path_str_ + "/graph/disambig_tid.int";
+    word_syms_rxfilename_ = model_path_str_ + "/graph/words.txt";
+    winfo_rxfilename_ = model_path_str_ + "/graph/phones/word_boundary.int";
+    carpa_rxfilename_ = model_path_str_ + "/rescore/G.carpa";
+    std_fst_rxfilename_ = model_path_str_ + "/rescore/G.fst";
+    final_ie_rxfilename_ = model_path_str_ + "/ivector/final.ie";
+    mfcc_conf_rxfilename_ = model_path_str_ + "/conf/mfcc.conf";
+}
+
+void Model::ReadDataFiles()
+{
+    struct stat buffer;
+
+    KALDI_LOG << "Decoding params beam=" << nnet3_decoding_config_.beam <<
+         " max-active=" << nnet3_decoding_config_.max_active <<
+         " lattice-beam=" << nnet3_decoding_config_.lattice_beam;
+    KALDI_LOG << "Silence phones " << endpoint_config_.silence_phones;
+
    feature_info_.feature_type = "mfcc";
-    ReadConfigFromFile(model_path_str + "/mfcc.conf", &feature_info_.mfcc_opts);
+    ReadConfigFromFile(mfcc_conf_rxfilename_, &feature_info_.mfcc_opts);
    feature_info_.mfcc_opts.frame_opts.allow_downsample = true; // It is safe to downsample

    feature_info_.silence_weighting_config.silence_weight = 1e-3;
-    feature_info_.silence_weighting_config.silence_phones_str = "1:2:3:4:5:6:7:8:9:10";
-
-    OnlineIvectorExtractionConfig ivector_extraction_opts;
-    ivector_extraction_opts.splice_config_rxfilename = model_path_str + "/ivector/splice.conf";
-    ivector_extraction_opts.cmvn_config_rxfilename = model_path_str + "/ivector/online_cmvn.conf";
-    ivector_extraction_opts.lda_mat_rxfilename = model_path_str + "/ivector/final.mat";
-    ivector_extraction_opts.global_cmvn_stats_rxfilename = model_path_str + "/ivector/global_cmvn.stats";
-    ivector_extraction_opts.diag_ubm_rxfilename = model_path_str + "/ivector/final.dubm";
-    ivector_extraction_opts.ivector_extractor_rxfilename = model_path_str + "/ivector/final.ie";
-    ivector_extraction_opts.num_gselect = 5;
-    ivector_extraction_opts.min_post = 0.025;
-    ivector_extraction_opts.posterior_scale = 0.1;
-    ivector_extraction_opts.max_remembered_frames = 1000;
-    ivector_extraction_opts.max_count = 100;
-    ivector_extraction_opts.ivector_period = 200;
-    feature_info_.use_ivectors = true;
-    feature_info_.ivector_extractor_info.Init(ivector_extraction_opts);
-
-    std::string nnet3_rxfilename = model_path_str + "/final.mdl";
-    std::string hclg_fst_rxfilename = model_path_str + "/HCLG.fst";
-    std::string hcl_fst_rxfilename = model_path_str + "/HCLr.fst";
-    std::string g_fst_rxfilename = model_path_str + "/Gr.fst";
-    std::string disambig_rxfilename = model_path_str + "/disambig_tid.int";
-    std::string word_syms_rxfilename = model_path_str + "/words.txt";
-    std::string winfo_rxfilename = model_path_str + "/word_boundary.int";
+    feature_info_.silence_weighting_config.silence_phones_str = endpoint_config_.silence_phones;

    trans_model_ = new kaldi::TransitionModel();
    nnet_ = new kaldi::nnet3::AmNnetSimple();
    {
        bool binary;
-        kaldi::Input ki(nnet3_rxfilename, &binary);
+        kaldi::Input ki(nnet3_rxfilename_, &binary);
        trans_model_->Read(ki.Stream(), binary);
        nnet_->Read(ki.Stream(), binary);
        SetBatchnormTestMode(true, &(nnet_->GetNnet()));
@@ -129,16 +215,35 @@ Model::Model(const char *model_path) {
    decodable_info_ = new nnet3::DecodableNnetSimpleLoopedInfo(decodable_opts_,
                                                               nnet_);

-    struct stat buffer;
-    if (stat(hclg_fst_rxfilename.c_str(), &buffer) == 0) {
-        hclg_fst_ = fst::ReadFstKaldiGeneric(hclg_fst_rxfilename);
+    if (stat(final_ie_rxfilename_.c_str(), &buffer) == 0) {
+        KALDI_LOG << "Loading i-vector extractor from " << final_ie_rxfilename_;
+
+        OnlineIvectorExtractionConfig ivector_extraction_opts;
+        ivector_extraction_opts.splice_config_rxfilename = model_path_str_ + "/ivector/splice.conf";
+        ivector_extraction_opts.cmvn_config_rxfilename = model_path_str_ + "/ivector/online_cmvn.conf";
+        ivector_extraction_opts.lda_mat_rxfilename = model_path_str_ + "/ivector/final.mat";
+        ivector_extraction_opts.global_cmvn_stats_rxfilename = model_path_str_ + "/ivector/global_cmvn.stats";
+        ivector_extraction_opts.diag_ubm_rxfilename = model_path_str_ + "/ivector/final.dubm";
+        ivector_extraction_opts.ivector_extractor_rxfilename = model_path_str_ + "/ivector/final.ie";
+        ivector_extraction_opts.max_count = 100;
+
+        feature_info_.use_ivectors = true;
+        feature_info_.ivector_extractor_info.Init(ivector_extraction_opts);
+    } else {
+        feature_info_.use_ivectors = false;
+    }
+
+    if (stat(hclg_fst_rxfilename_.c_str(), &buffer) == 0) {
+        KALDI_LOG << "Loading HCLG from " << hclg_fst_rxfilename_;
+        hclg_fst_ = fst::ReadFstKaldiGeneric(hclg_fst_rxfilename_);
        hcl_fst_ = NULL;
        g_fst_ = NULL;
    } else {
+        KALDI_LOG << "Loading HCL and G from " << hcl_fst_rxfilename_ << " " << g_fst_rxfilename_;
        hclg_fst_ = NULL;
-        hcl_fst_ = fst::StdFst::Read(hcl_fst_rxfilename);
-        g_fst_ = fst::StdFst::Read(g_fst_rxfilename);
-        ReadIntegerVectorSimple(disambig_rxfilename, &disambig_);
+        hcl_fst_ = fst::StdFst::Read(hcl_fst_rxfilename_);
+        g_fst_ = fst::StdFst::Read(g_fst_rxfilename_);
+        ReadIntegerVectorSimple(disambig_rxfilename_, &disambig_);
    }

    word_syms_ = NULL;
@@ -148,18 +253,46 @@ Model::Model(const char *model_path) {
        word_syms_ = g_fst_->OutputSymbols();
    }
    if (!word_syms_) {
-        if (!(word_syms_ = fst::SymbolTable::ReadText(word_syms_rxfilename)))
+        KALDI_LOG << "Loading words from " << word_syms_rxfilename_;
+        if (!(word_syms_ = fst::SymbolTable::ReadText(word_syms_rxfilename_)))
            KALDI_ERR << "Could not read symbol table from file "
-                      << word_syms_rxfilename;
+                      << word_syms_rxfilename_;
    }
    KALDI_ASSERT(word_syms_);

-    if (stat(winfo_rxfilename.c_str(), &buffer) == 0) {
+    if (stat(winfo_rxfilename_.c_str(), &buffer) == 0) {
+        KALDI_LOG << "Loading winfo " << winfo_rxfilename_;
        kaldi::WordBoundaryInfoNewOpts opts;
-        winfo_ = new kaldi::WordBoundaryInfo(opts, winfo_rxfilename);
+        winfo_ = new kaldi::WordBoundaryInfo(opts, winfo_rxfilename_);
    } else {
        winfo_ = NULL;
    }
+
+    if (stat(carpa_rxfilename_.c_str(), &buffer) == 0) {
+        KALDI_LOG << "Loading CARPA model from " << carpa_rxfilename_;
+        std_lm_fst_ = fst::ReadFstKaldi(std_fst_rxfilename_);
+        fst::Project(std_lm_fst_, fst::PROJECT_OUTPUT);
+        if (std_lm_fst_->Properties(fst::kILabelSorted, true) == 0) {
+            fst::ILabelCompare<fst::StdArc> ilabel_comp;
+            fst::ArcSort(std_lm_fst_, ilabel_comp);
+        }
+        ReadKaldiObject(carpa_rxfilename_, &const_arpa_);
+    } else {
+        std_lm_fst_ = NULL;
+    }
+}
+
+void Model::Ref() 
+{
+    ref_cnt_++;
+}
+
+void Model::Unref() 
+{
+    ref_cnt_--;
+    if (ref_cnt_ == 0) {
+        delete this;
+    }
 }

 Model::~Model() {
@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#ifndef MODEL_H_
-#define MODEL_H_
+#ifndef VOSK_MODEL_H
+#define VOSK_MODEL_H

 #include "base/kaldi-common.h"
 #include "fstext/fstext-lib.h"
@@ -32,6 +32,7 @@
 #include "rnnlm/rnnlm-utils.h"

 using namespace kaldi;
+using namespace std;

 class KaldiRecognizer;

@@ -39,11 +40,30 @@ class Model {

 public:
    Model(const char *model_path);
-    ~Model();
+    void Ref();
+    void Unref();

 protected:
+    ~Model();
+    void ConfigureV1();
+    void ConfigureV2();
+    void ReadDataFiles();
+
    friend class KaldiRecognizer;

+    string model_path_str_;
+    string nnet3_rxfilename_;
+    string hclg_fst_rxfilename_;
+    string hcl_fst_rxfilename_;
+    string g_fst_rxfilename_;
+    string disambig_rxfilename_;
+    string word_syms_rxfilename_;
+    string winfo_rxfilename_;
+    string carpa_rxfilename_;
+    string std_fst_rxfilename_;
+    string final_ie_rxfilename_;
+    string mfcc_conf_rxfilename_;
+
    kaldi::OnlineEndpointConfig endpoint_config_;
    kaldi::LatticeFasterDecoderConfig nnet3_decoding_config_;
    kaldi::nnet3::NnetSimpleLoopedComputationOptions decodable_opts_;
@@ -54,11 +74,16 @@ protected:
    kaldi::nnet3::AmNnetSimple *nnet_;
    const fst::SymbolTable *word_syms_;
    kaldi::WordBoundaryInfo *winfo_;
-    std::vector<int32> disambig_;
+    vector<int32> disambig_;

    fst::Fst<fst::StdArc> *hclg_fst_;
    fst::Fst<fst::StdArc> *hcl_fst_;
    fst::Fst<fst::StdArc> *g_fst_;
+
+    fst::VectorFst<fst::StdArc> *std_lm_fst_;
+    kaldi::ConstArpaLm const_arpa_;
+
+    int ref_cnt_;
 };

-#endif /* MODEL_H_ */
+#endif /* VOSK_MODEL_H */
@@ -24,4 +24,22 @@ SpkModel::SpkModel(const char *speaker_path) {
    SetBatchnormTestMode(true, &speaker_nnet);
    SetDropoutTestMode(true, &speaker_nnet);
    CollapseModel(nnet3::CollapseModelConfig(), &speaker_nnet);
+
+    ReadKaldiObject(speaker_path_str + "/mean.vec", &mean);
+    ReadKaldiObject(speaker_path_str + "/transform.mat", &transform);
+
+    ref_cnt_ = 1;
+}
+
+void SpkModel::Ref() 
+{
+    ref_cnt_++;
+}
+
+void SpkModel::Unref() 
+{
+    ref_cnt_--;
+    if (ref_cnt_ == 0) {
+        delete this;
+    }
 }
@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#ifndef SPK_MODEL_H_
-#define SPK_MODEL_H_
+#ifndef VOSK_SPK_MODEL_H
+#define VOSK_SPK_MODEL_H

 #include "base/kaldi-common.h"
 #include "online2/online-feature-pipeline.h"
@@ -27,12 +27,20 @@ class SpkModel {

 public:
    SpkModel(const char *spk_path);
+    void Ref();
+    void Unref();

 protected:
    friend class KaldiRecognizer;
+    ~SpkModel() {};

    kaldi::nnet3::Nnet speaker_nnet;
+    kaldi::Vector<BaseFloat> mean;
+    kaldi::Matrix<BaseFloat> transform;
+
    MfccOptions spkvector_mfcc_opts;
+
+    int ref_cnt_;
 };

-#endif /* SPK_MODEL_H_ */
+#endif /* VOSK_SPK_MODEL_H */
@@ -1,4 +1,8 @@
-%module(package="vosk") vosk
+#if SWIGPYTHON
+%module(package="vosk", "threads"=1) vosk
+#else
+%module Vosk
+#endif

 %include <typemaps.i>

@@ -10,8 +14,6 @@
 %include <arrays_csharp.i>
 #endif

-
-
 #if SWIGPYTHON
 %pybuffer_binary(const char *data, int len);
 #endif
@@ -32,6 +34,11 @@ import java.nio.ByteOrder;
    return AcceptWaveform(bdata, bdata.length);
  }
 %}
+%pragma(java) jniclasscode=%{
+    static {
+        System.loadLibrary("vosk_jni");
+    }
+%}
 #endif

 #if SWIGCSHARP
@@ -42,6 +49,14 @@ CSHARP_ARRAYS(char, byte)
 #endif


+#if SWIGJAVASCRIPT
+%begin %{
+#include <v8.h>
+#include <node.h>
+#include <node_buffer.h>
+%}
+#endif
+
 %{
 #include "vosk_api.h"
 typedef struct VoskModel Model;
@@ -99,6 +114,12 @@ typedef struct {} KaldiRecognizer;
    bool AcceptWaveform(const char *data, int len) {
        return vosk_recognizer_accept_waveform($self, data, len);
    }
+#elif SWIGJAVASCRIPT
+    bool AcceptWaveform(SWIG_Object ptr) {
+        char* data = (char*) node::Buffer::Data(ptr);
+        size_t length = node::Buffer::Length(ptr);
+        return vosk_recognizer_accept_waveform($self, data, length);
+    }
 #else
    int AcceptWaveform(const char *data, int len) {
        return vosk_recognizer_accept_waveform($self, data, len);
@@ -115,3 +136,6 @@ typedef struct {} KaldiRecognizer;
        return vosk_recognizer_final_result($self);
    }
 }
+
+%rename(SetLogLevel) vosk_set_log_level;
+void vosk_set_log_level(int level);
@@ -28,7 +28,7 @@ VoskModel *vosk_model_new(const char *model_path)

 void vosk_model_free(VoskModel *model)
 {
-    delete (Model *)model;
+    ((Model *)model)->Unref();
 }

 VoskSpkModel *vosk_spk_model_new(const char *model_path)
@@ -38,22 +38,22 @@ VoskSpkModel *vosk_spk_model_new(const char *model_path)

 void vosk_spk_model_free(VoskSpkModel *model)
 {
-    delete (SpkModel *)model;
+    ((SpkModel *)model)->Unref();
 }

 VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate)
 {
-    return (VoskRecognizer *)new KaldiRecognizer(*(Model *)model, sample_rate);
+    return (VoskRecognizer *)new KaldiRecognizer((Model *)model, sample_rate);
 }

 VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, VoskSpkModel *spk_model, float sample_rate)
 {
-    return (VoskRecognizer *)new KaldiRecognizer(*(Model *)model, (SpkModel *)spk_model, sample_rate);
+    return (VoskRecognizer *)new KaldiRecognizer((Model *)model, (SpkModel *)spk_model, sample_rate);
 }

 VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar)
 {
-    return (VoskRecognizer *)new KaldiRecognizer(*(Model *)model, sample_rate, grammar);
+    return (VoskRecognizer *)new KaldiRecognizer((Model *)model, sample_rate, grammar);
 }

 int vosk_recognizer_accept_waveform(VoskRecognizer *recognizer, const char *data, int length)
@@ -90,3 +90,8 @@ void vosk_recognizer_free(VoskRecognizer *recognizer)
 {
    delete (KaldiRecognizer *)(recognizer);
 }
+
+void vosk_set_log_level(int log_level)
+{
+    SetVerboseLevel(log_level);
+}
@@ -12,37 +12,200 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

+/* This header contains the C API for Vosk speech recognition system */

-#ifndef _VOSK_API_H_
-#define _VOSK_API_H_
+#ifndef VOSK_API_H
+#define VOSK_API_H

 #ifdef __cplusplus
 extern "C" {
 #endif

+/** Model stores all the data required for recognition
+ *  it contains static data and can be shared across processing
+ *  threads. */
 typedef struct VoskModel VoskModel;
+
+
+/** Speaker model is the same as model but contains the data
+ *  for speaker identification. */
 typedef struct VoskSpkModel VoskSpkModel;
+
+
+/** Recognizer object is the main object which processes data.
+ *  Each recognizer usually runs in own thread and takes audio as input.
+ *  Once audio is processed recognizer returns JSON object as a string
+ *  which represent decoded information - words, confidences, times, n-best lists,
+ *  speaker information and so on */
 typedef struct VoskRecognizer VoskRecognizer;

+
+/** Loads model data from the file and returns the model object
+ *
+ * @param model_path: the path of the model on the filesystem
+ @ @returns model object */
 VoskModel *vosk_model_new(const char *model_path);
+
+
+/** Releases the model memory
+ *
+ *  The model object is reference-counted so if some recognizer
+ *  depends on this model, model might still stay alive. When
+ *  last recognizer is released, model will be released too. */
 void vosk_model_free(VoskModel *model);

+
+/** Loads speaker model data from the file and returns the model object
+ *
+ * @param model_path: the path of the model on the filesystem
+ * @returns model object */
 VoskSpkModel *vosk_spk_model_new(const char *model_path);
+
+
+/** Releases the model memory
+ *
+ *  The model object is reference-counted so if some recognizer
+ *  depends on this model, model might still stay alive. When
+ *  last recognizer is released, model will be released too. */
 void vosk_spk_model_free(VoskSpkModel *model);

+/** Creates the recognizer object
+ *
+ *  The recognizers process the speech and return text using shared model data 
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
+ *  @returns recognizer object */
 VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
+
+
+/** Creates the recognizer object with speaker recognition
+ *
+ *  With the speaker recognition mode the recognizer not just recognize
+ *  text but also return speaker vectors one can use for speaker identification
+ *
+ *  @param spk_model speaker model for speaker identification
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
+ *  @returns recognizer object */
 VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, VoskSpkModel *spk_model, float sample_rate);
+
+
+/** Creates the recognizer object with the phrase list
+ *
+ *  Sometimes when you want to improve recognition accuracy and when you don't need
+ *  to recognize large vocabulary you can specify a list of phrases to recognize. This
+ *  will improve recognizer speed and accuracy but might return [unk] if user said
+ *  something different.
+ *
+ *  Only recognizers with lookahead models support this type of quick configuration.
+ *  Precompiled HCLG graph models are not supported.
+ *
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
+ *  @param grammar The string with the list of phrases to recognize as JSON array of strings,
+ *                 for example "["one two three four five", "[unk]"]".
+ *
+ *  @returns recognizer object */
 VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar);
+
+
+/** Accept voice data
+ *
+ *  accept and process new chunk of voice data
+ *
+ *  @param data - audio data in PCM 16-bit mono format
+ *  @param length - length of the audio data
+ *  @returns true if silence is occured and you can retrieve a new utterance with result method */
 int vosk_recognizer_accept_waveform(VoskRecognizer *recognizer, const char *data, int length);
+
+
+/** Same as above but the version with the short data for language bindings where you have
+ *  audio as array of shorts */
 int vosk_recognizer_accept_waveform_s(VoskRecognizer *recognizer, const short *data, int length);
+
+
+/** Same as above but the version with the float data for language bindings where you have
+ *  audio as array of floats */
 int vosk_recognizer_accept_waveform_f(VoskRecognizer *recognizer, const float *data, int length);
+
+
+/** Returns speech recognition result
+ *
+ * @returns the result in JSON format which contains decoded line, decoded
+ *          words, times in seconds and confidences. You can parse this result
+ *          with any json parser
+ *
+ * <pre>
+ * {
+ *   "result" : [{
+ *       "conf" : 1.000000,
+ *       "end" : 1.110000,
+ *       "start" : 0.870000,
+ *       "word" : "what"
+ *     }, {
+ *       "conf" : 1.000000,
+ *       "end" : 1.530000,
+ *       "start" : 1.110000,
+ *       "word" : "zero"
+ *     }, {
+ *       "conf" : 1.000000,
+ *       "end" : 1.950000,
+ *       "start" : 1.530000,
+ *       "word" : "zero"
+ *     }, {
+ *       "conf" : 1.000000,
+ *       "end" : 2.340000,
+ *       "start" : 1.950000,
+ *       "word" : "zero"
+ *     }, {
+ *       "conf" : 1.000000,
+ *      "end" : 2.610000,
+ *       "start" : 2.340000,
+ *       "word" : "one"
+ *     }],
+ *   "text" : "what zero zero zero one"
+ *  }
+ * </pre>
+ */
 const char *vosk_recognizer_result(VoskRecognizer *recognizer);
+
+
+/** Returns partial speech recognition
+ *
+ * @returns partial speech recognition text which is not yet finalized.
+ *          result may change as recognizer process more data.
+ *
+ * <pre>
+ * {
+ *  "partial" : "cyril one eight zero"
+ * }
+ * </pre>
+ */
 const char *vosk_recognizer_partial_result(VoskRecognizer *recognizer);
+
+
+/** Returns speech recognition result. Same as result, but doesn't wait for silence
+ *  You usually call it in the end of the stream to get final bits of audio. It
+ *  flushes the feature pipeline, so all remaining audio chunks got processed.
+ *
+ *  @returns speech result in JSON format.
+ */
 const char *vosk_recognizer_final_result(VoskRecognizer *recognizer);
+
+
+/** Releases recognizer object
+ *
+ *  Underlying model is also unreferenced and if needed released */
 void vosk_recognizer_free(VoskRecognizer *recognizer);

+/** Set log level for Kaldi messages
+ *
+ *  @param log_level the level
+ *     0 - default value to print info and error messages but no debug
+ *     less than 0 - don't print info messages
+ *     greather than 0 - more verbose mode
+ */
+void vosk_set_log_level(int log_level);
+
 #ifdef __cplusplus
 }
 #endif

-#endif /* _VOSK_API_H_ */
+#endif /* VOSK_API_H */
@@ -10,6 +10,9 @@ RUN apt-get update && \
        libffi-dev \
        libpcre3-dev \
        zlib1g-dev \
+        automake \
+        autoconf \
+        libtool \
    && rm -rf /var/lib/apt/lists/*

 RUN cd /opt \
@@ -20,24 +23,6 @@ RUN cd /opt \
    && cd .. \
    && rm -rf swig-4.0.1.tar.gz swig-4.0.1

-ARG OPENBLAS_ARCH=ARMV7
-ARG ARM_HARDWARE_OPTS="-mfloat-abi=hard -mfpu=neon"
-RUN cd /opt \
-    && export OPENFST_CONFIGURE="--enable-static --enable-shared --enable-far --enable-ngram-fsts --enable-lookahead-fsts --with-pic --disable-bin --host=${CROSS_TRIPLE} --build=x86-linux-gnu" \
-    && git clone -b lookahead --single-branch https://github.com/alphacep/kaldi \
-    && cd kaldi/tools \
-    && git clone -b v0.3.7 --single-branch https://github.com/xianyi/OpenBLAS \
-    && make PREFIX=$(pwd)/OpenBLAS/install TARGET="${OPENBLAS_ARCH}" HOSTCC=gcc USE_LOCKING=1 USE_THREAD=0 -C OpenBLAS all install \
-    && sed -i 's:status=0:exit 0:g' extras/check_dependencies.sh \
-    && make -j 10 openfst \
-    && cd /opt/kaldi/src \
-    && sed -i "s:TARGET_ARCH=\"\`uname -m\`\":TARGET_ARCH=$(echo $CROSS_TRIPLE|cut -d - -f 1):g" configure \
-    && sed -i "s:-mfloat-abi=hard -mfpu=neon:${ARM_HARDWARE_OPTS}:g" makefiles/linux_openblas_arm.mk \
-    && sed -i "s: -O1 : -O3 :g" makefiles/linux_openblas_arm.mk \
-    && ./configure --mathlib=OPENBLAS --shared --use-cuda=no \
-    && make -j 10 online2 \
-    && find /opt/kaldi -name "*.o" -exec rm {} \;
-
 RUN cd /opt \
    && wget -q https://github.com/openssl/openssl/archive/OpenSSL_1_0_2u.tar.gz \
    && tar xf OpenSSL_1_0_2u.tar.gz \
@@ -80,3 +65,40 @@ RUN cd /opt \
    && make -j $(nproc) \
    && make install \
    && rm -rf /opt/cpython-3.6.10 /opt/cpython-3.6.10-cross /opt/v3.6.10.tar.gz
+
+RUN cd /opt \
+    && wget -q https://github.com/python/cpython/archive/v3.8.3.tar.gz \
+    && tar xf v3.8.3.tar.gz \
+    && cp -r cpython-3.8.3 cpython-3.8.3-cross \
+    && cd /opt/cpython-3.8.3 \
+    && AR=/usr/bin/ar RANLIB=/usr/bin/ranlib CPP=/usr/bin/cpp CXX=/usr/bin/g++ CC=/usr/bin/gcc  ./configure --prefix="/opt/python/cp3.8-cp3.8m" \
+    && make -j $(nproc) \
+    && make install \
+    && /opt/python/cp3.8-cp3.8m/bin/pip3 install -U pip \
+    && /opt/python/cp3.8-cp3.8m/bin/pip3 install -U wheel \
+    && cd /opt/cpython-3.8.3-cross \
+    && export PATH=/opt/python/cp3.8-cp3.8m/bin:$PATH \
+    && ./configure --prefix=$CROSS_ROOT --with-openssl=$CROSS_ROOT --host=${CROSS_TRIPLE} --build=x86-linux-gnu --disable-ipv6 ac_cv_file__dev_ptmx=no ac_cv_file__dev_ptc=no ac_cv_have_long_long_format=yes \
+    && make -j $(nproc) \
+    && make install \
+    && rm -rf /opt/cpython-3.8.3 /opt/cpython-3.8.3-cross /opt/v3.8.3.tar.gz
+
+ARG OPENBLAS_ARCH=ARMV7
+ARG ARM_HARDWARE_OPTS="-mfloat-abi=hard -mfpu=neon"
+RUN cd /opt \
+    && git clone -b lookahead --single-branch https://github.com/alphacep/kaldi \
+    && cd kaldi/tools \
+    && git clone -b v0.3.7 --single-branch https://github.com/xianyi/OpenBLAS \
+    && make PREFIX=$(pwd)/OpenBLAS/install TARGET="${OPENBLAS_ARCH}" HOSTCC=gcc USE_LOCKING=1 USE_THREAD=0 -C OpenBLAS all install \
+    && git clone -b old-gcc --single-branch https://github.com/alphacep/openfst openfst \
+    && cd openfst \
+    && autoreconf -i \
+    && ./configure --prefix=/opt/kaldi/tools/openfst --enable-static --enable-shared --enable-far --enable-ngram-fsts --enable-lookahead-fsts --with-pic --disable-bin --host=${CROSS_TRIPLE} --build=x86-linux-gnu \
+    && make -j 10 && make install \
+    && cd /opt/kaldi/src \
+    && sed -i "s:TARGET_ARCH=\"\`uname -m\`\":TARGET_ARCH=$(echo $CROSS_TRIPLE|cut -d - -f 1):g" configure \
+    && sed -i "s:-mfloat-abi=hard -mfpu=neon:${ARM_HARDWARE_OPTS}:g" makefiles/linux_openblas_arm.mk \
+    && sed -i "s: -O1 : -O3 :g" makefiles/linux_openblas_arm.mk \
+    && ./configure --mathlib=OPENBLAS --shared --use-cuda=no \
+    && make -j 10 online2 lm \
+    && find /opt/kaldi -name "*.o" -exec rm {} \;
@@ -7,29 +7,11 @@ RUN yum -y update && yum -y install \
    wget \
    openssl-devel \
    pcre-devel \
+    automake \
+    autoconf \
+    libtool \
    && yum clean all

-RUN cd /opt \
-    && wget https://github.com/Kitware/CMake/releases/download/v3.16.2/cmake-3.16.2.tar.gz \
-    && tar xf cmake-3.16.2.tar.gz \
-    && cd cmake-3.16.2 \
-    && ./configure --prefix=/usr && make -j 10 && make install \
-    && cd .. \
-    && rm -rf cmake-3.16.2 cmake-3.16.2.tar.gz
-
-RUN cd /opt \
-    && export OPENFST_CONFIGURE="--enable-static --enable-shared --enable-far --enable-ngram-fsts --enable-lookahead-fsts --with-pic --disable-bin" \
-    && git clone -b lookahead --single-branch https://github.com/alphacep/kaldi \
-    && cd kaldi/tools \
-    && git clone -b v0.3.7 --single-branch https://github.com/xianyi/OpenBLAS \
-    && make PREFIX=$(pwd)/OpenBLAS/install TARGET=NEHALEM USE_LOCKING=1 USE_THREAD=0 -C OpenBLAS all install \
-    && sed -i 's:status=0:exit 0:g' extras/check_dependencies.sh \
-    && make -j 10 openfst \
-    && cd ../src \
-    && ./configure --mathlib=OPENBLAS --shared --use-cuda=no \
-    && make -j 10 online2 \
-    && find /opt/kaldi -name "*.o" -exec rm {} \;
-
 RUN cd /opt \
    && wget -O swig-4.0.1.tar.gz https://sourceforge.net/projects/swig/files/swig/swig-4.0.1/swig-4.0.1.tar.gz/download \
    && tar xf swig-4.0.1.tar.gz \
@@ -37,3 +19,18 @@ RUN cd /opt \
    && ./configure --prefix=/usr && make -j 10 && make install \
    && cd .. \
    && rm -rf swig-4.0.1.tar.gz swig-4.0.1
+
+RUN cd /opt \
+    && git clone -b lookahead --single-branch https://github.com/alphacep/kaldi \
+    && cd kaldi/tools \
+    && git clone -b v0.3.7 --single-branch https://github.com/xianyi/OpenBLAS \
+    && make PREFIX=$(pwd)/OpenBLAS/install DYNAMIC_ARCH=1 USE_LOCKING=1 USE_THREAD=0 -C OpenBLAS all install \
+    && git clone https://github.com/alphacep/openfst openfst \
+    && cd openfst \
+    && autoreconf -i \
+    && ./configure --prefix=/opt/kaldi/tools/openfst --enable-static --enable-shared --enable-far --enable-ngram-fsts --enable-lookahead-fsts --with-pic --disable-bin \
+    && make -j 10 && make install \
+    && cd /opt/kaldi/src \
+    && ./configure --mathlib=OPENBLAS --shared --use-cuda=no \
+    && make -j 10 online2 lm rnnlm \
+    && find /opt/kaldi -name "*.o" -exec rm {} \;
@@ -3,11 +3,9 @@
 set -e
 set -x

-skip() {
 docker build --build-arg="DOCKCROSS_IMAGE=linux-armv7" --build-arg="OPENBLAS_ARCH=ARMV7" --file Dockerfile.dockcross --tag alphacep/kaldi-dockcross-armv7:latest .
 docker build --build-arg="DOCKCROSS_IMAGE=linux-armv6" --build-arg="OPENBLAS_ARCH=ARMV6" --build-arg="ARM_HARDWARE_OPTS=" --file Dockerfile.dockcross --tag alphacep/kaldi-dockcross-armv6:latest .
 docker build --build-arg="DOCKCROSS_IMAGE=linux-arm64" --build-arg="OPENBLAS_ARCH=ARMV8" --file Dockerfile.dockcross --tag alphacep/kaldi-dockcross-arm64:latest .
-}

 docker run --rm -v /home/shmyrev/travis/vosk-api/:/io alphacep/kaldi-dockcross-armv6 /io/travis/build-wheels-dockcross.sh
 docker run --rm -v /home/shmyrev/travis/vosk-api/:/io alphacep/kaldi-dockcross-armv7 /io/travis/build-wheels-dockcross.sh
@@ -2,24 +2,36 @@
 set -e -x

 ORIG_PATH=$PATH
-for pyver in 3.6 3.7; do
+for pyver in 3.6 3.7 3.8; do

    export KALDI_ROOT=/opt/kaldi
-    export WHEEL_FLAGS=`$CROSS_ROOT/bin/python${pyver}-config --cflags`
    export PATH=/opt/python/cp${pyver}-cp${pyver}m/bin:$ORIG_PATH
-    echo $CROSS_TRIPLE
+    export VOSK_SOURCE=/io/src
+
+    # Python 3.8 somehow changed syconfig file name
+    sysconfig_bit="m"
+    if [ $pyver == "3.8" ]; then
+         sysconfig_bit=""
+    fi
+
    case $CROSS_TRIPLE in
        *arm-*)
            export _PYTHON_HOST_PLATFORM=linux-armv6l
+            export _PYTHON_SYSCONFIGDATA_NAME=_sysconfigdata_${sysconfig_bit}_linux_arm-linux-gnueabihf
            ;;
        *armv7-*)
            export _PYTHON_HOST_PLATFORM=linux-armv7l
+            export _PYTHON_SYSCONFIGDATA_NAME=_sysconfigdata_${sysconfig_bit}_linux_arm-linux-gnueabihf
            ;;
        *aarch64-*)
            export _PYTHON_HOST_PLATFORM=linux-aarch64
+            export _PYTHON_SYSCONFIGDATA_NAME=_sysconfigdata_${sysconfig_bit}_linux_aarch64-linux-gnu
            ;;
    esac
+    export PYTHONHOME=$CROSS_ROOT
+    export PYTHONPATH=/opt/python/cp${pyver}-cp${pyver}m/lib/python${pyver}/site-packages:/opt/python/cp${pyver}-cp${pyver}m/lib/python${pyver}/lib-dynload

-    pip3 wheel /io/python -w /io/wheelhouse
+    rm -rf /io/python/build
+    pip${pyver} -v wheel /io/python -w /io/wheelhouse

 done
@@ -5,9 +5,10 @@ export KALDI_ROOT=/opt/kaldi

 # Compile wheels
 for pypath in /opt/python/cp3[56789]*; do
-    export WHEEL_FLAGS=`${pypath}/bin/python3-config --cflags`
+    export VOSK_SOURCE=/io/src
    mkdir -p /opt/wheelhouse
-    "${pypath}/bin/pip" wheel /io/python -w /opt/wheelhouse
+    rm -rf /io/python/build
+    "${pypath}/bin/pip" -v wheel /io/python -w /opt/wheelhouse
 done

 # Bundle external shared libraries into the wheels
@@ -0,0 +1,3 @@
+exports.printMsg = function() {
+  console.log("This is a message from the Vosk package");
+}
@@ -0,0 +1,20 @@
+{
+  "name": "vosk-js",
+  "version": "0.3.0",
+  "description": "Node binding for continuous voice recoginition through vosk-api.",
+  "repository": {
+    "type": "git",
+    "url": "git://github.com/alphacep/vosk-api.git"
+  },
+  "main": "index.js",
+  "keywords": [
+    "speech",
+    "speech recognition",
+    "voice"
+  ],
+  "author": "Alpha Cephei Inc.",
+  "license": "Apache 2.0",
+  "engines": {
+    "node": ">= 12.x.x"
+  }
+}
Author	SHA1	Message	Date
Nickolay Shmyrev	0269a10833	Critical ivector bugfix and version 0.3.15	2020-10-13 01:28:46 +02:00
Nickolay Shmyrev	7af3e9a334	Add srt example	2020-10-07 13:43:09 +02:00
Nickolay Shmyrev	d666876be1	Revert compile flags	2020-10-05 01:24:32 +02:00
Nickolay Shmyrev	564fab7ec1	Update documentation	2020-10-05 00:16:15 +02:00
Nickolay Shmyrev	155c6c2a2a	Don't crash with grammar and static graphs	2020-10-05 00:13:25 +02:00
Nickolay Shmyrev	d43cbe9344	Add 0.3.14	2020-10-04 23:45:34 +02:00
Nickolay Shmyrev	57cc474c9f	Build bigram language model from grammars	2020-10-04 23:42:50 +02:00
Nickolay Shmyrev	586603f8e1	Add Farsi	2020-10-03 12:42:54 +02:00
Nickolay Shmyrev	d57887d22a	Add Greek	2020-09-30 13:42:20 +02:00
Nickolay Shmyrev	6183bcfc5f	Add Arabic model	2020-09-30 13:29:43 +02:00
Nickolay Shmyrev	65f6113b4d	Add Catalan	2020-09-27 11:57:20 +02:00
Nickolay Shmyrev	8d88b89db1	Fix reserved identifier violation. Closes issue #226 Thanks to Markus Elfring	2020-09-23 21:16:34 +02:00
Nickolay Shmyrev	62885e8963	Update descriptions	2020-09-22 11:30:27 +02:00
qo6xup6	9fc094a5da	Update kaldi_recognizer.h	2020-09-22 10:11:29 +08:00
Nickolay Shmyrev	f97383c17f	Update models location	2020-09-22 00:00:24 +02:00
Nickolay Shmyrev	4b892ec5e7	Bump version	2020-09-21 23:49:55 +02:00
Nickolay Shmyrev	41035485db	Fix x-vectors, now they actually work. Requires new version spk-model-0.4 with whitening transform matrix	2020-09-21 23:31:30 +02:00
Nickolay Shmyrev	9696f4c917	Mention Dutch	2020-09-18 17:31:17 +02:00
Nickolay Shmyrev	55abf5f5ac	Dynamic arch in pip module	2020-09-13 22:08:36 +02:00
Nickolay Shmyrev	a1b2e41710	Mention we support Indian English	2020-09-09 23:28:55 +02:00
Nickolay Shmyrev	dff4ab26e4	Library is now vosk_jni	2020-09-01 18:27:54 +02:00
Nickolay Shmyrev	83b6e1cdf7	Speech service for more flexible recognizer initialization Publish repo on jcenter	2020-09-01 16:32:15 +02:00
Nickolay Shmyrev	38dbaa15ea	Load JNI inside library itself	2020-09-01 12:44:44 +02:00
Nickolay Shmyrev	0e531b6061	Organize Makefile	2020-08-23 20:10:48 +02:00
Nickolay Shmyrev	de94ef5537	Add speaker C demo	2020-08-14 11:33:41 +02:00
Nickolay Shmyrev	6ef9d13877	Add Italian	2020-08-03 01:31:30 +02:00
Nickolay Shmyrev	1c7b94757d	Don't attempt to resize to empty matrix	2020-07-31 16:45:05 +02:00
Nickolay Shmyrev	83486e0bef	Spk vector fix	2020-07-31 12:58:23 +02:00
Nickolay Shmyrev	9787e8a53f	No .html in links	2020-07-21 10:15:27 +02:00
Nickolay Shmyrev	f59d6685ad	Fix confidences	2020-07-15 10:24:08 +02:00
Nickolay Shmyrev	8a986ef384	Require KALDI_MKL to be 1	2020-07-12 11:03:22 +02:00
Nickolay Shmyrev	78f9f55e14	Fixes description	2020-07-12 10:37:56 +02:00
Nickolay Shmyrev	c2e006f664	Some probably helpful flags for npm on osx	2020-07-11 20:54:13 +02:00
Nickolay V. Shmyrev	4b8e9737a5	Update README.md	2020-07-08 01:38:03 +02:00
Nickolay Shmyrev	722b09eaa4	Ignore words missing in the vocabulary	2020-07-08 01:35:50 +02:00
Nickolay Shmyrev	f5b4f5a1f2	Added C test	2020-07-08 01:35:50 +02:00
Nickolay Shmyrev	4407d8da55	Another timestamp bugfix	2020-07-08 01:35:50 +02:00
Nickolay Shmyrev	73b73527cd	Release memory when final result is received to reduce memory pressure.	2020-07-08 01:35:50 +02:00
Nickolay Shmyrev	4df0e3a741	Actually show the distance	2020-07-08 01:35:49 +02:00
He1nr1chK	1a771e0172	Added cosine distance function	2020-06-23 20:10:14 +02:00
Nickolay Shmyrev	4333c3c242	Build dockcross with python 3.8	2020-06-23 01:11:07 +02:00
Nickolay V. Shmyrev	b831c9ad57	Merge pull request #151 from hviana/patch-1 Add support for speaker model in Android	2020-06-23 00:07:26 +03:00
Nickolay Shmyrev	e5c08f7710	Allow to continue after final result. See for discussion https://github.com/alphacep/vosk-api/issues/146	2020-06-22 23:04:09 +02:00
Henrique Emanoel Viana	51c2968595	fix	2020-06-22 18:01:42 -03:00
Henrique Emanoel Viana	5993376322	Add suport to SpkModel	2020-06-22 17:51:55 -03:00
Nickolay Shmyrev	c4281622b9	Added node speaker demo	2020-06-22 21:45:50 +02:00
Nickolay Shmyrev	9d26014de2	Use wider beams by default to avoid accuracy confusion	2020-06-19 16:10:30 +02:00
Nickolay Shmyrev	f6c115d215	Update to 0.3.9 and use our fork of openfst based on openfst 1.7.7	2020-06-16 19:13:57 +02:00
Nickolay Shmyrev	6bd102d778	Fixes timing issue #125	2020-06-15 02:04:02 +02:00
Nickolay Shmyrev	d3d6af5712	Define os in a single place	2020-06-12 10:35:58 +02:00
Nickolay V. Shmyrev	876093446f	Merge pull request #127 from nnkalita/master added android build support on macOS	2020-06-12 11:32:27 +03:00
Nickolay Shmyrev	336f219f09	No Qr code for wechat anymore	2020-06-11 12:57:32 +02:00
Nickolay Shmyrev	584251cbdc	Remove chinese-only	2020-06-11 09:18:34 +02:00
Nickolay Shmyrev	a34995a788	Process audio in chunks for imporved accuracy	2020-06-11 01:06:11 +02:00
Nickolay Shmyrev	998e5da227	Update rules for joining wechat	2020-06-10 15:07:10 +02:00
Nickolay Shmyrev	e04c15e367	Fix	2020-06-06 11:15:23 +02:00
Nickolay Shmyrev	b81f69d407	Avoid overflow. See issue #128	2020-06-06 10:43:44 +02:00
Nickolay Shmyrev	0ac2064281	Few more words	2020-06-06 10:07:12 +02:00
Nickolay Shmyrev	1d00bd244e	Multithreaded testing	2020-06-06 00:40:15 +02:00
Nickolay Shmyrev	8f5efc58c9	Disable ivector pipeline without ivectors	2020-06-05 19:34:04 +02:00
Nayan Kalita	a0c5ae1b5e	Update build-kaldi.sh added android build from mac.	2020-06-05 20:10:28 +05:30
Nayan Kalita	99f48f9de1	Merge remote-tracking branch 'upstream/master'	2020-06-05 19:15:42 +05:30
Nayan Kalita	1948b23f32	Update build-kaldi.sh Updated based feedback.	2020-06-05 18:03:01 +05:30
Nickolay Shmyrev	c9eb572fc5	Move documentation to our website	2020-06-05 01:31:13 +02:00
Nickolay Shmyrev	7d9895ff81	Add API documentation in the header	2020-06-04 22:47:54 +02:00
Nickolay Shmyrev	d507210ef8	Add npmignore and pure javascript package	2020-06-03 17:47:15 +02:00
Nickolay Shmyrev	db0a3d23d5	Add repackages librispeech model	2020-06-03 17:24:28 +02:00
Nickolay Shmyrev	f4f920f3cd	Proper cross-compilation with setuptools	2020-06-02 00:36:16 +02:00
Nickolay Shmyrev	75993ea276	Update to 0.3.8	2020-06-01 00:55:28 +02:00
Nickolay Shmyrev	ee9bacb092	Reset pipeline to save memory	2020-06-01 00:54:47 +02:00
Nickolay Shmyrev	b1e775c67b	Better speaker identification without silence frames	2020-05-31 23:46:42 +02:00
Nickolay Shmyrev	d631e567aa	Add README	2020-05-31 02:21:07 +02:00
Nickolay Shmyrev	9edf45be42	Sync version	2020-05-31 02:08:35 +02:00
Nickolay Shmyrev	8b4b3c646a	Nodejs support	2020-05-31 02:07:01 +02:00
Nickolay Shmyrev	31bb0557d9	Add mkl support	2020-05-30 23:22:14 +02:00
Nickolay Shmyrev	4593183cf9	Create gen if doens't exist. Thanks to BMI24.	2020-05-25 00:32:42 +02:00
Nickolay Shmyrev	fdc45f1187	Better link on wechat	2020-05-23 21:13:23 +02:00
Nickolay Shmyrev	b3d3c6d12c	Fix mistake, thanks to Shaheen	2020-05-23 01:12:31 +02:00
Nickolay Shmyrev	f5f0794def	Add logo	2020-05-22 15:24:57 +02:00
Nickolay Shmyrev	55664fcca0	Add wechat group	2020-05-22 09:56:54 +02:00
Nickolay Shmyrev	afbf330f16	Make ivector extractor optional	2020-05-18 23:56:42 +02:00
Nickolay Shmyrev	25aadf61bc	Fix travis	2020-05-09 17:36:55 +02:00
Nickolay Shmyrev	be47056467	Updates for osx	2020-05-09 15:49:24 +03:00
Nickolay Shmyrev	8f623e0aea	Get rid of broken cmake	2020-05-09 14:32:46 +02:00
Nickolay Shmyrev	78025435e4	Windows is fully supported now	2020-05-09 09:18:10 +02:00
Nickolay Shmyrev	910455802e	Don't link to the library explicitely to allow relocations	2020-05-08 15:16:50 +03:00
Nickolay Shmyrev	b12914955c	Add x86 build	2020-05-08 11:33:25 +02:00
Nickolay Shmyrev	dfe11eaf83	Linux build fix	2020-05-08 09:04:14 +02:00
Nickolay Shmyrev	37fbe1a52b	OSX build	2020-05-08 09:55:15 +03:00
Nickolay Shmyrev	af11bb2361	No need for external dependencies	2020-05-07 21:48:03 +02:00
Nickolay Shmyrev	8da8697c1e	Add ffmpeg test	2020-05-07 21:07:17 +02:00
Nickolay V. Shmyrev	944dc87531	Update readme to mention aarch64	2020-05-05 00:55:29 +02:00
Nickolay Shmyrev	5c4dd4644e	Update version	2020-05-03 20:52:43 +02:00
Nickolay Shmyrev	9bbd172cfd	Some more links	2020-05-02 01:37:10 +02:00
Nickolay Shmyrev	dbf9de77c3	Document model training and model structure	2020-05-02 01:11:25 +02:00
Nickolay Shmyrev	8b790cd162	Better android logging	2020-05-01 20:02:46 +02:00
Nickolay Shmyrev	80219066e9	Expose verbose level in the API	2020-05-01 19:02:57 +02:00
Nickolay Shmyrev	26fa5f098f	Updated link to English model	2020-04-30 15:19:13 +02:00
Nickolay Shmyrev	d75bb36131	Fix model download path	2020-04-30 11:04:35 +02:00
Nickolay Shmyrev	30c5e8ca79	Link to lm library for rescoring	2020-04-30 10:52:15 +02:00
Nickolay Shmyrev	c00e36fab6	Rearrange models	2020-04-30 10:47:10 +02:00
Nickolay Shmyrev	80e60b9118	LM Rescoring	2020-04-28 22:17:34 +02:00
Nickolay Shmyrev	e3a95a44bc	Added Turkish model	2020-04-28 10:10:35 +02:00
Nickolay Shmyrev	86caf526f5	New model layout and model config file with beams for decoding	2020-04-28 01:06:41 +02:00
Nickolay Shmyrev	e09f32b4b4	Properly stop listening so it can continue later	2020-04-27 09:00:32 +02:00
Nickolay Shmyrev	3803ab345d	Proper reference couting of the models to avoid memory issus	2020-04-23 23:37:52 +02:00
Nickolay V. Shmyrev	889b43136f	Update README.zh.md	2020-04-23 12:43:00 +02:00
John Baber-Lucero	b517cf46af	Fix some grammar/punctuation	2020-04-23 00:28:34 -04:00
Nickolay Shmyrev	ffd810fe00	Fix threading bug	2020-04-22 22:15:45 +02:00
Nickolay V. Shmyrev	a1a0ed70a1	Update README.zh.md	2020-04-22 18:29:01 +03:00
Nickolay Shmyrev	1554d9ede7	Added per-language readme	2020-04-22 13:21:36 +02:00
Nayan Kalita	bca0b86e37	android build support on macOS	2020-03-06 20:57:56 +05:30