Bump debian requirement

* added language model as parameter

* fix fore more help explanation

.gitignore

@@ -10,6 +10,7 @@ gradlew
 gradlew.bat
 gradle
 local.properties
+gradle.properties
 
 # Android
 android/build

CMakeLists.txt

@@ -0,0 +1,20 @@
+cmake_minimum_required(VERSION 3.13)
+project(vosk-api CXX)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_EXTENSIONS OFF)
+
+add_library(vosk
+  src/language_model.cc
+  src/model.cc
+  src/recognizer.cc
+  src/spk_model.cc
+  src/vosk_api.cc
+)
+
+find_package(kaldi REQUIRED)
+target_link_libraries(vosk PUBLIC kaldi-base kaldi-online2 kaldi-rnnlm fstngram)
+
+include(GNUInstallDirs)
+install(TARGETS vosk DESTINATION ${CMAKE_INSTALL_LIBDIR})
+install(FILES src/vosk_api.h DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})

README.md

@@ -1,17 +1,18 @@
-# About
+# Vosk Speech Recognition Toolkit
 
 Vosk is an offline open source speech recognition toolkit. It enables
-speech recognition models for 18 languages and dialects - English, Indian
+speech recognition for 20+ languages and dialects - English, Indian
 English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish,
 Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino,
-Ukrainian.
+Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish.
+More to come.
 
 Vosk models are small (50 Mb) but provide continuous large vocabulary
 transcription, zero-latency response with streaming API, reconfigurable
 vocabulary and speaker identification.
 
 Speech recognition bindings implemented for various programming languages
-like Python, Java, Node.JS, C#, C++ and others.
+like Python, Java, Node.JS, C#, C++, Rust, Go and others.
 
 Vosk supplies speech recognition for chatbots, smart home appliances,
 virtual assistants. It can also create subtitles for movies,

android/build.gradle

@@ -1,25 +1,36 @@
 buildscript {
     repositories {
         google()
-        jcenter()
+        mavenCentral()
     }
     dependencies {
-        classpath 'com.android.tools.build:gradle:4.1.3'
+        classpath 'com.android.tools.build:gradle:4.2.0'
+        classpath 'com.vanniktech:gradle-maven-publish-plugin:0.18.0'
     }
 }
 
 allprojects {
-    version = '0.3.30'
+    version = '0.3.45'
 }
 
 subprojects {
 
     apply plugin: 'com.android.library'
     apply plugin: 'maven-publish'
+    apply plugin: 'com.vanniktech.maven.publish'
+
+    plugins.withId('com.vanniktech.maven.publish') {
+        mavenPublish {
+            group = 'com.alphacephei'
+            version = version
+            sonatypeHost = 's01'
+            androidVariantToPublish = 'release'
+        }
+    }
 
     repositories {
         google()
-        jcenter()
+        mavenCentral()
     }
 
     publishing {
@@ -29,8 +40,8 @@ subprojects {
                 version version
                 pom {
                     url = 'http://www.alphacephei.com.com/vosk/'
-                        licenses {
-                            license {
+                    licenses {
+                        license {
                             name = 'The Apache License, Version 2.0'
                             url = 'http://www.apache.org/licenses/LICENSE-2.0.txt'
                         }
@@ -49,10 +60,6 @@ subprojects {
                 }
             }
         }
-        repositories {
-            maven {
-                url = "$rootDir/repo"
-            }
-        }
     }
+
 }

android/lib/build-vosk.sh

@@ -29,7 +29,7 @@ set -x
 OS_NAME=`echo $(uname -s) | tr '[:upper:]' '[:lower:]'`
 ANDROID_TOOLCHAIN_PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/${OS_NAME}-x86_64
 WORKDIR_BASE=`pwd`/build
-PATH=$PATH:$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/${OS_NAME}-x86_64/bin
+PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/${OS_NAME}-x86_64/bin:$PATH
 OPENFST_VERSION=1.8.0
 
 for arch in armeabi-v7a arm64-v8a x86_64 x86; do
@@ -40,7 +40,8 @@ case $arch in
     armeabi-v7a)
           BLAS_ARCH=ARMV7
           HOST=arm-linux-androideabi
-          AR=arm-linux-androideabi-ar
+          AR=llvm-ar
+          RANLIB=llvm-ranlib
           CC=armv7a-linux-androideabi21-clang
           CXX=armv7a-linux-androideabi21-clang++
           ARCHFLAGS="-mfloat-abi=softfp -mfpu=neon"
@@ -48,7 +49,8 @@ case $arch in
     arm64-v8a)
           BLAS_ARCH=ARMV8
           HOST=aarch64-linux-android
-          AR=aarch64-linux-android-ar
+          AR=llvm-ar
+          RANLIB=llvm-ranlib
           CC=aarch64-linux-android21-clang
           CXX=aarch64-linux-android21-clang++
           ARCHFLAGS=""
@@ -56,7 +58,8 @@ case $arch in
     x86_64)
           BLAS_ARCH=ATOM
           HOST=x86_64-linux-android
-          AR=x86_64-linux-android-ar
+          AR=llvm-ar
+          RANLIB=llvm-ranlib
           CC=x86_64-linux-android21-clang
           CXX=x86_64-linux-android21-clang++
           ARCHFLAGS=""
@@ -64,7 +67,8 @@ case $arch in
     x86)
           BLAS_ARCH=ATOM
           HOST=i686-linux-android
-          AR=i686-linux-android-ar
+          AR=llvm-ar
+          RANLIB=llvm-ranlib
           CC=i686-linux-android21-clang
           CXX=i686-linux-android21-clang++
           ARCHFLAGS=""
@@ -105,9 +109,9 @@ make install
 
 # Kaldi itself
 cd $WORKDIR
-git clone -b android-mix --single-branch https://github.com/alphacep/kaldi
+git clone -b vosk-android --single-branch https://github.com/alphacep/kaldi
 cd $WORKDIR/kaldi/src
-CXX=$CXX CXXFLAGS="$ARCHFLAGS -O3 -DFST_NO_DYNAMIC_LINKING" ./configure --use-cuda=no \
+CXX=$CXX AR=$AR RANLIB=$RANLIB CXXFLAGS="$ARCHFLAGS -O3 -DFST_NO_DYNAMIC_LINKING" ./configure --use-cuda=no \
     --mathlib=OPENBLAS_CLAPACK --shared \
     --android-incdir=${ANDROID_TOOLCHAIN_PATH}/sysroot/usr/include \
     --host=$HOST --openblas-root=${WORKDIR}/local \
@@ -118,12 +122,14 @@ make -j 8 online2 lm rnnlm
 
 # Vosk-api
 cd $WORKDIR
-#rm -rf vosk-api
-git clone -b master --single-branch https://github.com/alphacep/vosk-api
-cd vosk-api/src
-make -j 8 KALDI_ROOT=${WORKDIR}/kaldi OPENFST_ROOT=${WORKDIR}/local OPENBLAS_ROOT=${WORKDIR}/local CXX=$CXX EXTRA_LDFLAGS="-llog -static-libstdc++"
-
-# Copy JNI library to sources
-cp $WORKDIR/vosk-api/src/libvosk.so $WORKDIR/../../src/main/jniLibs/$arch/libvosk.so
+mkdir -p $WORKDIR/vosk
+make -j 8 -C ${WORKDIR_BASE}/../../../src \
+    OUTDIR=$WORKDIR/vosk \
+    KALDI_ROOT=${WORKDIR}/kaldi \
+    OPENFST_ROOT=${WORKDIR}/local \
+    OPENBLAS_ROOT=${WORKDIR}/local \
+    CXX=$CXX \
+    EXTRA_LDFLAGS="-llog -static-libstdc++ -Wl,-soname,libvosk.so"
+cp $WORKDIR/vosk/libvosk.so $WORKDIR/../../src/main/jniLibs/$arch/libvosk.so
 
 done

android/lib/build.gradle

@@ -10,6 +10,7 @@ android {
         versionCode 6
         versionName = version
         archivesBaseName = archiveName
+        ndkVersion = "22.1.7171670"
     }
     compileOptions {
         sourceCompatibility JavaVersion.VERSION_1_8
@@ -19,11 +20,11 @@ android {
 
 task buildVosk(type: Exec) {
     commandLine './build-vosk.sh'
-    environment ANDROID_NDK_HOME: android.getSdkDirectory()
+    environment ANDROID_NDK_HOME: android.getNdkDirectory()
 }
 
 dependencies {
-    implementation 'net.java.dev.jna:jna:4.4.0@aar'
+    api 'net.java.dev.jna:jna:4.4.0@aar'
 }
 
 //preBuild.dependsOn buildVosk
@@ -37,6 +38,18 @@ publishing {
                 name = pomName
                 description = pomDescription
             }
+            //generate pom nodes for dependencies
+            pom.withXml {
+                def dependenciesNode = asNode().appendNode('dependencies')
+                configurations.implementation.allDependencies.each { dependency ->
+                    if (dependency.name != 'unspecified') {
+                        def dependencyNode = dependenciesNode.appendNode('dependency')
+                        dependencyNode.appendNode('groupId', dependency.group)
+                        dependencyNode.appendNode('artifactId', dependency.name)
+                        dependencyNode.appendNode('version', dependency.version)
+                    }
+                }
+            }
         }
     }
 }

android/lib/settings.gradle

@@ -1 +0,0 @@
-include 'model-en'

android/lib/src/main/java/org/vosk/LibVosk.java

@@ -36,6 +36,8 @@ public class LibVosk {
 
     public static native void vosk_recognizer_set_words(Pointer recognizer, boolean words);
 
+    public static native void vosk_recognizer_set_partial_words(Pointer recognizer, boolean partial_words);
+
     public static native void vosk_recognizer_set_spk_model(Pointer recognizer, Pointer spk_model);
 
     public static native boolean vosk_recognizer_accept_waveform(Pointer recognizer, byte[] data, int len);

android/lib/src/main/java/org/vosk/Recognizer.java

@@ -23,6 +23,10 @@ public class Recognizer extends PointerType implements AutoCloseable {
         LibVosk.vosk_recognizer_set_words(this.getPointer(), words);
     }
 
+    public void setPartialWords(boolean partial_words) {
+        LibVosk.vosk_recognizer_set_partial_words(this.getPointer(), partial_words);
+    }
+
     public void setSpeakerModel(SpeakerModel spkModel) {
         LibVosk.vosk_recognizer_set_spk_model(this.getPointer(), spkModel.getPointer());
     }

c/Makefile

@@ -1,16 +1,16 @@
 CFLAGS=-I../src
-LDFLAGS=-L../src -lvosk -ldl -lpthread -Wl,-rpath=../src
+LDFLAGS=-L../src -lvosk -ldl -lpthread -Wl,-rpath,../src
 
 all: test_vosk test_vosk_speaker
 
 test_vosk: test_vosk.o
-	g++ $^ -o $@ $(LDFLAGS)
+	gcc $^ -o $@ $(LDFLAGS)
 
 test_vosk_speaker: test_vosk_speaker.o
-	g++ $^ -o $@ $(LDFLAGS)
+	gcc $^ -o $@ $(LDFLAGS)
 
 %.o: %.c
-	g++ $(CFLAGS) -c -o $@ $<
+	gcc $(CFLAGS) -c -o $@ $<
 
 clean:
 	rm -f *.o *.a test_vosk test_vosk_speaker

csharp/demo/VoskDemo.cs

@@ -34,7 +34,7 @@ public class VoskDemo
             while((bytesRead = source.Read(buffer, 0, buffer.Length)) > 0) {
                 float[] fbuffer = new float[bytesRead / 2];
                 for (int i = 0, n = 0; i < fbuffer.Length; i++, n+=2) {
-                    fbuffer[i] = (short)(buffer[n] | buffer[n+1] << 8);
+                    fbuffer[i] = BitConverter.ToInt16(buffer, n);
                 }
                 if (rec.AcceptWaveform(fbuffer, fbuffer.Length)) {
                     Console.WriteLine(rec.Result());

csharp/demo/VoskDemo.csproj

@@ -11,7 +11,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Vosk" Version="0.3.30" />
+    <PackageReference Include="Vosk" Version="0.3.45" />
   </ItemGroup>
 
 </Project>

csharp/nuget/Vosk.nuspec

@@ -2,13 +2,14 @@
 <package>
   <metadata>
     <id>Vosk</id>
-    <version>0.3.30</version>
+    <version>0.3.45</version>
     <authors>Alpha Cephei Inc</authors>
     <owners>Alpha Cephei Inc</owners>
     <license type="expression">Apache-2.0</license>
     <projectUrl>https://alphacephei.com/vosk/</projectUrl>
     <requireLicenseAcceptance>false</requireLicenseAcceptance>
-    <description>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.
+    <title>Vosk Speech Recognition Toolkit</title>
+    <description>Vosk is an offline open source speech recognition toolkit. It enables speech recognition models for 20+ languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech. More to come.
 
 Vosk models are small (50 Mb) but provide continuous large vocabulary transcription, zero-latency response with streaming API, reconfigurable vocabulary and speaker identification.
 
@@ -18,7 +19,8 @@ Vosk supplies speech recognition for chatbots, smart home appliances, virtual as
 
 Vosk scales from small devices like Raspberry Pi or Android smartphone to big clusters.</description>
     <releaseNotes>See for details https://github.com/alphacep/vosk-api/releases</releaseNotes>
-    <copyright>Copyright 2020 Alpha Cephei Inc</copyright>
+    <repository type="git" url="https://github.com/alphacep/vosk-api.git" branch="master"/>
+    <copyright>Copyright 2020-2050 Alpha Cephei Inc</copyright>
     <tags>speech recognition voice stt asr speech-to-text ai offline privacy</tags>
     <dependencies>
       <group targetFramework=".NETStandard2.0"/>

csharp/nuget/build/Vosk.targets

@@ -2,7 +2,7 @@
   <ItemGroup>
     <NativeLibs Include="$(MSBuildThisFileDirectory)\lib\linux-x64\*.so" Condition="'$([MSBuild]::IsOsPlatform(Linux))'" />
     <NativeLibs Include="$(MSBuildThisFileDirectory)\lib\win-x64\*.dll" Condition="'$([MSBuild]::IsOsPlatform(Windows))'" />
-    <NativeLibs Include="$(MSBuildThisFileDirectory)\lib\osx-x64\*.dylib" Condition="'$([MSBuild]::IsOsPlatform(OSX))'" />
+    <NativeLibs Include="$(MSBuildThisFileDirectory)\lib\osx-universal\*.dylib" Condition="'$([MSBuild]::IsOsPlatform(OSX))'" />
     <None Include="@(NativeLibs)">
       <Link>%(FileName)%(Extension)</Link>
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>

csharp/nuget/src/Model.cs

@@ -32,7 +32,7 @@ public class Model : global::System.IDisposable {
   public Model(string model_path) : this(VoskPINVOKE.new_Model(model_path)) {
   }
 
-  public int vosk_model_find_word(string word) {
+  public int FindWord(string word) {
     return VoskPINVOKE.Model_vosk_model_find_word(handle, word);
   }
 

csharp/nuget/src/VoskPINVOKE.cs

@@ -38,6 +38,9 @@ class VoskPINVOKE {
   [global::System.Runtime.InteropServices.DllImport("libvosk", EntryPoint="vosk_recognizer_set_words")]
   public static extern void VoskRecognizer_SetWords(global::System.Runtime.InteropServices.HandleRef jarg1, int jarg2);
 
+  [global::System.Runtime.InteropServices.DllImport("libvosk", EntryPoint="vosk_recognizer_set_partial_words")]
+  public static extern void VoskRecognizer_SetPartialWords(global::System.Runtime.InteropServices.HandleRef jarg1, int jarg2);
+
   [global::System.Runtime.InteropServices.DllImport("libvosk", EntryPoint="vosk_recognizer_set_spk_model")]
   public static extern void VoskRecognizer_SetSpkModel(global::System.Runtime.InteropServices.HandleRef jarg1, global::System.Runtime.InteropServices.HandleRef jarg2);
 

csharp/nuget/src/VoskRecognizer.cs

@@ -46,6 +46,10 @@ public class VoskRecognizer : System.IDisposable {
     VoskPINVOKE.VoskRecognizer_SetWords(handle, words ? 1 : 0);
   }
 
+  public void SetPartialWords(bool partial_words) {
+    VoskPINVOKE.VoskRecognizer_SetPartialWords(handle, partial_words ? 1 : 0);
+  }
+
   public void SetSpkModel(SpkModel spk_model) {
     VoskPINVOKE.VoskRecognizer_SetSpkModel(handle, SpkModel.getCPtr(spk_model));
   }

go/COPYING

@@ -0,0 +1,176 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
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+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
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+      designated in writing by the copyright owner as "Not a Contribution."
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+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
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+   4. Redistribution. You may reproduce and distribute copies of the
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+      (a) You must give any other recipients of the Work or
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+
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+
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. this License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
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+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
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+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
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+      on Your own behalf and on Your sole responsibility, not on behalf
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+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS

go/README.md

@@ -0,0 +1 @@
+See example subfolder for instructions how to use the module

go/doc.go

@@ -0,0 +1,7 @@
+// Go bindings for Vosk speech recognition toolkit. Vosk is an offline
+// open source speech to text API for Android, iOS, Raspberry Pi and
+// servers. It enables speech recognition models for 18 languages and
+// dialects - English, Indian English, German, French, Spanish, Portuguese,
+// Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic,
+// Greek, Farsi, Filipino, Ukrainian.
+package vosk

go/example/README.md

@@ -0,0 +1,31 @@
+To try this package do the following steps:
+
+On Linux (we download library and set LD_LIBRARY_PATH)
+
+```
+git clone https://github.com/alphacep/vosk-api
+cd vosk-api/go/example
+wget https://github.com/alphacep/vosk-api/releases/download/v0.3.45/vosk-linux-x86_64-0.3.45.zip
+unzip vosk-linux-x86_64-0.3.45.zip
+wget https://alphacephei.com/vosk/models/vosk-model-small-en-us-0.15.zip
+unzip vosk-model-small-en-us-0.15.zip
+mv vosk-model-small-en-us-0.15 model
+cp ../../python/example/test.wav .
+VOSK_PATH=`pwd`/vosk-linux-x86_64-0.3.45 LD_LIBRARY_PATH=$VOSK_PATH CGO_CPPFLAGS="-I $VOSK_PATH" CGO_LDFLAGS="-L $VOSK_PATH" go run . -f test.wav
+```
+
+for Windows (we place DLLs in current folder where linker finds them):
+
+```
+git clone https://github.com/alphacep/vosk-api
+cd vosk-api/go/example
+wget https://github.com/alphacep/vosk-api/releases/download/v0.3.45/vosk-linux-x86_64-0.3.45.zip
+unzip vosk-linux-x86_64-0.3.45.zip
+cp vosk-linux-x86_64-0.3.45/*.dll .
+cp vosk-linux-x86_64-0.3.45/*.h .
+wget https://alphacephei.com/vosk/models/vosk-model-small-en-us-0.15.zip
+unzip vosk-model-small-en-us-0.15.zip
+mv vosk-model-small-en-us-0.15 model
+cp ../../python/example/test.wav .
+VOSK_PATH=`pwd` LD_LIBRARY_PATH=$VOSK_PATH CGO_CPPFLAGS="-I $VOSK_PATH" CGO_LDFLAGS="-L $VOSK_PATH -lvosk -lpthread -dl" go run . -f test.wav
+```

go/example/doc.go

@@ -0,0 +1,2 @@
+// Example package for Vosk Go bindings.
+package main

go/example/main.go

@@ -1,36 +0,0 @@
-package main
-
-import (
-    "flag"
-    "os"
-    ".."
-)
-
-func main() {
-    var filename string
-    flag.StringVar(&filename, "f", "", "file to transcribe")
-    flag.Parse()
-    model, err := vosk.NewModel("model")
-    rec, err := vosk.NewRecognizer(model)
-
-    file, err := os.Open(filename)
-    if err != nil {
-        panic(err)
-    }
-    defer file.Close()
-
-    fileinfo, err := file.Stat()
-    if err != nil {
-        panic(err)
-    }
-
-    filesize := fileinfo.Size()
-    buffer := make([]byte, filesize)
-
-    _, err = file.Read(buffer)
-    if err != nil {
-        panic(err)
-    }
-
-    println(vosk.VoskFinalResult(rec, buffer))
-}

go/example/test_simple.go

@@ -0,0 +1,63 @@
+package main
+
+import (
+	"bufio"
+	"flag"
+	"fmt"
+	"io"
+	"log"
+	"os"
+	"encoding/json"
+
+	vosk "github.com/alphacep/vosk-api/go"
+)
+
+func main() {
+	var filename string
+	flag.StringVar(&filename, "f", "", "file to transcribe")
+	flag.Parse()
+
+	model, err := vosk.NewModel("model")
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// we can check if word is in the vocabulary
+	// fmt.Println(model.FindWord("air"))
+
+	sampleRate := 16000.0
+	rec, err := vosk.NewRecognizer(model, sampleRate)
+	if err != nil {
+		log.Fatal(err)
+	}
+	rec.SetWords(1)
+
+	file, err := os.Open(filename)
+	if err != nil {
+		panic(err)
+	}
+	defer file.Close()
+
+	reader := bufio.NewReader(file)
+	buf := make([]byte, 4096)
+
+	for {
+		_, err := reader.Read(buf)
+		if err != nil {
+			if err != io.EOF {
+				log.Fatal(err)
+			}
+
+			break
+		}
+
+		if rec.AcceptWaveform(buf) != 0 {
+			fmt.Println(rec.Result())
+		}
+	}
+
+	// Unmarshal example for final result
+	var jres map[string]interface{}
+	json.Unmarshal([]byte(rec.FinalResult()), &jres)
+	fmt.Println(jres["text"])
+}

go/go.mod

@@ -0,0 +1,7 @@
+module github.com/alphacep/vosk-api/go
+
+go 1.16
+
+replace (
+    github.com/alphacep/vosk-api/go => ./
+)

go/vosk.go

@@ -5,58 +5,158 @@ package vosk
 // #include <stdlib.h>
 // #include <vosk_api.h>
 import "C"
+import "unsafe"
 
 // VoskModel contains a reference to the C VoskModel
 type VoskModel struct {
-    model *C.struct_VoskModel
-}
-
-// VoskSpkModel contains a reference to the C VoskSpkModel
-type VoskSpkModel struct {
-    spkModel *C.struct_VoskSpkModel
-}
-
-// VoskRecognizer contains a reference to the C VoskRecognizer
-type VoskRecognizer struct {
-    rec *C.struct_VoskRecognizer
-}
-
-func VoskFinalResult(recognizer *VoskRecognizer, buffer []byte) string {
-    cbuf := C.CBytes(buffer)
-    defer C.free(cbuf)
-    _ = C.vosk_recognizer_accept_waveform(recognizer.rec, (*C.char)(cbuf), C.int(len(buffer)))
-    result := C.GoString(C.vosk_recognizer_final_result(recognizer.rec))
-    return result
+	model *C.struct_VoskModel
 }
 
 // NewModel creates a new VoskModel instance
 func NewModel(modelPath string) (*VoskModel, error) {
-    var internal *C.struct_VoskModel
-    internal = C.vosk_model_new(C.CString(modelPath))
-    model := &VoskModel{model: internal}
-    return model, nil
+	cmodelPath := C.CString(modelPath)
+	defer C.free(unsafe.Pointer(cmodelPath))
+	internal := C.vosk_model_new(cmodelPath)
+	model := &VoskModel{model: internal}
+	return model, nil
 }
 
-// NewRecognizer creates a new VoskRecognizer instance
-func NewRecognizer(model *VoskModel) (*VoskRecognizer, error) {
-    var internal *C.struct_VoskRecognizer
-    internal = C.vosk_recognizer_new(model.model, 16000.0)
-    rec := &VoskRecognizer{rec: internal}
-    return rec, nil
+func (m *VoskModel) Free() {
+	C.vosk_model_free(m.model)
 }
 
 func freeModel(model *VoskModel) {
-    C.vosk_model_free(model.model)
+	C.vosk_model_free(model.model)
 }
 
-func freeRecognizer(recognizer *VoskRecognizer) {
-    C.vosk_recognizer_free(recognizer.rec)
+// FindWord checks if a word can be recognized by the model.
+// Returns the word symbol if the word exists inside the model or
+// -1 otherwise.
+func (m *VoskModel) FindWord(word string) int {
+	cstr := C.CString(word)
+	defer C.free(unsafe.Pointer(cstr))
+	i := C.vosk_model_find_word(m.model, cstr)
+	return int(i)
+}
+
+// VoskSpkModel contains a reference to the C VoskSpkModel
+type VoskSpkModel struct {
+	spkModel *C.struct_VoskSpkModel
 }
 
 // NewSpkModel creates a new VoskSpkModel instance
 func NewSpkModel(spkModelPath string) (*VoskSpkModel, error) {
-    var internal *C.struct_VoskSpkModel
-    internal = C.vosk_spk_model_new(C.CString(spkModelPath))
-    spkModel := &VoskSpkModel{spkModel: internal}
-    return spkModel, nil
+	cspkModelPath := C.CString(spkModelPath)
+	defer C.free(unsafe.Pointer(cspkModelPath))
+	internal := C.vosk_spk_model_new(cspkModelPath)
+	spkModel := &VoskSpkModel{spkModel: internal}
+	return spkModel, nil
+}
+
+func freeSpkModel(model *VoskSpkModel) {
+	C.vosk_spk_model_free(model.spkModel)
+}
+
+func(s *VoskSpkModel) Free() {
+	C.vosk_spk_model_free(s.spkModel)
+}
+
+// VoskRecognizer contains a reference to the C VoskRecognizer
+type VoskRecognizer struct {
+	rec *C.struct_VoskRecognizer
+}
+
+func freeRecognizer(recognizer *VoskRecognizer) {
+	C.vosk_recognizer_free(recognizer.rec)
+}
+
+func (r *VoskRecognizer) Free() {
+	C.vosk_recognizer_free(r.rec)
+}
+
+// NewRecognizer creates a new VoskRecognizer instance
+func NewRecognizer(model *VoskModel, sampleRate float64) (*VoskRecognizer, error) {
+	internal := C.vosk_recognizer_new(model.model, C.float(sampleRate))
+	rec := &VoskRecognizer{rec: internal}
+	return rec, nil
+}
+
+// NewRecognizerSpk creates a new VoskRecognizer instance with a speaker model.
+func NewRecognizerSpk(model *VoskModel, sampleRate float64, spkModel *VoskSpkModel) (*VoskRecognizer, error) {
+	internal := C.vosk_recognizer_new_spk(model.model, C.float(sampleRate), spkModel.spkModel)
+	rec := &VoskRecognizer{rec: internal}
+	return rec, nil
+}
+
+// NewRecognizerGrm creates a new VoskRecognizer instance with the phrase list.
+func NewRecognizerGrm(model *VoskModel, sampleRate float64, grammar string) (*VoskRecognizer, error) {
+	cgrammar := C.CString(grammar)
+	defer C.free(unsafe.Pointer(cgrammar))
+	internal := C.vosk_recognizer_new_grm(model.model, C.float(sampleRate), cgrammar)
+	rec := &VoskRecognizer{rec: internal}
+	return rec, nil
+}
+
+// SetSpkModel adds a speaker model to an already initialized recognizer.
+func (r *VoskRecognizer) SetSpkModel(spkModel *VoskSpkModel) {
+	C.vosk_recognizer_set_spk_model(r.rec, spkModel.spkModel)
+}
+
+// SetMaxAlternatives configures the recognizer to output n-best results.
+func (r *VoskRecognizer) SetMaxAlternatives(maxAlternatives int) {
+	C.vosk_recognizer_set_max_alternatives(r.rec, C.int(maxAlternatives))
+}
+
+// SetWords enables words with times in the ouput.
+func (r *VoskRecognizer) SetWords(words int) {
+	C.vosk_recognizer_set_words(r.rec, C.int(words))
+}
+
+// SetPartialWords enables words with times in the partial ouput.
+func (r *VoskRecognizer) SetPartialWords(words int) {
+	C.vosk_recognizer_set_partial_words(r.rec, C.int(words))
+}
+
+// AcceptWaveform accepts and processes a new chunk of the voice data.
+func (r *VoskRecognizer) AcceptWaveform(buffer []byte) int {
+	cbuf := C.CBytes(buffer)
+	defer C.free(cbuf)
+	i := C.vosk_recognizer_accept_waveform(r.rec, (*C.char)(cbuf), C.int(len(buffer)))
+	return int(i)
+}
+
+// Result returns a speech recognition result.
+func (r *VoskRecognizer) Result() string {
+	return C.GoString(C.vosk_recognizer_result(r.rec))
+}
+
+// PartialResult returns a partial speech recognition result.
+func (r *VoskRecognizer) PartialResult() string {
+	return C.GoString(C.vosk_recognizer_partial_result(r.rec))
+}
+
+// FinalResult returns a speech recognition result. Same as result, but doesn't wait
+// for silence.
+func (r *VoskRecognizer) FinalResult() string {
+	return C.GoString(C.vosk_recognizer_final_result(r.rec))
+}
+
+// Reset resets the recognizer.
+func (r *VoskRecognizer) Reset() {
+	C.vosk_recognizer_reset(r.rec)
+}
+
+// SetLogLevel sets the log level for Kaldi messages.
+func SetLogLevel(logLevel int) {
+	C.vosk_set_log_level(C.int(logLevel))
+}
+
+// GPUInit automatically selects a CUDA device and allows multithreading.
+func GPUInit() {
+	C.vosk_gpu_init()
+}
+
+// GPUThreadInit inits CUDA device in a multi-threaded environment.
+func GPUThreadInit() {
+	C.vosk_gpu_thread_init()
 }

ios/VoskApiTest.xcodeproj/project.pbxproj

@@ -13,10 +13,10 @@
 		92375229240C550B00DD6076 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 92375228240C550B00DD6076 /* Assets.xcassets */; };
 		9237522C240C550B00DD6076 /* LaunchScreen.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = 9237522A240C550B00DD6076 /* LaunchScreen.storyboard */; };
 		92375234240C558900DD6076 /* Vosk.swift in Sources */ = {isa = PBXBuildFile; fileRef = 92375233240C558900DD6076 /* Vosk.swift */; };
-		9237523C240C642000DD6076 /* libkaldiwrap.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 9237523A240C642000DD6076 /* libkaldiwrap.a */; };
 		92375244240C6DAF00DD6076 /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 92375243240C6DAF00DD6076 /* Accelerate.framework */; };
-		92375246240C6DC900DD6076 /* libstdc++.tbd in Frameworks */ = {isa = PBXBuildFile; fileRef = 92375245240C6DC900DD6076 /* libstdc++.tbd */; };
 		92375274240C6F1E00DD6076 /* 10001-90210-01803.wav in Resources */ = {isa = PBXBuildFile; fileRef = 92375256240C6E3D00DD6076 /* 10001-90210-01803.wav */; };
+		925527A9273C492C00FFD9CC /* libvosk.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 925527A8273C492C00FFD9CC /* libvosk.a */; };
+		92833003273C466E00058B52 /* libc++.tbd in Frameworks */ = {isa = PBXBuildFile; fileRef = 92833002273C466E00058B52 /* libc++.tbd */; };
 		92BACED125BE125A00B5CC93 /* vosk-model-small-en-us-0.15 in Resources */ = {isa = PBXBuildFile; fileRef = 928CC50C25BE124400490481 /* vosk-model-small-en-us-0.15 */; };
 		92D6B8D325BDFEAC007FF08D /* VoskModel.swift in Sources */ = {isa = PBXBuildFile; fileRef = 92D6B8D225BDFEAC007FF08D /* VoskModel.swift */; };
 		92D86BD6253F823F0040D53F /* vosk-model-spk-0.4 in Resources */ = {isa = PBXBuildFile; fileRef = 92D86BD4253F823F0040D53F /* vosk-model-spk-0.4 */; };
@@ -31,10 +31,10 @@
 		9237522B240C550B00DD6076 /* Base */ = {isa = PBXFileReference; lastKnownFileType = file.storyboard; name = Base; path = Base.lproj/LaunchScreen.storyboard; sourceTree = "<group>"; };
 		9237522D240C550B00DD6076 /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist.xml; path = Info.plist; sourceTree = "<group>"; };
 		92375233240C558900DD6076 /* Vosk.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = Vosk.swift; sourceTree = "<group>"; };
-		9237523A240C642000DD6076 /* libkaldiwrap.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; path = libkaldiwrap.a; sourceTree = "<group>"; };
 		92375243240C6DAF00DD6076 /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
-		92375245240C6DC900DD6076 /* libstdc++.tbd */ = {isa = PBXFileReference; lastKnownFileType = "sourcecode.text-based-dylib-definition"; name = "libstdc++.tbd"; path = "usr/lib/libstdc++.tbd"; sourceTree = SDKROOT; };
 		92375256240C6E3D00DD6076 /* 10001-90210-01803.wav */ = {isa = PBXFileReference; lastKnownFileType = audio.wav; path = "10001-90210-01803.wav"; sourceTree = "<group>"; };
+		925527A8273C492C00FFD9CC /* libvosk.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; path = libvosk.a; sourceTree = "<group>"; };
+		92833002273C466E00058B52 /* libc++.tbd */ = {isa = PBXFileReference; lastKnownFileType = "sourcecode.text-based-dylib-definition"; name = "libc++.tbd"; path = "usr/lib/libc++.tbd"; sourceTree = SDKROOT; };
 		928CC50C25BE124400490481 /* vosk-model-small-en-us-0.15 */ = {isa = PBXFileReference; lastKnownFileType = folder; name = "vosk-model-small-en-us-0.15"; path = "/Users/shmyrev/Documents/IOS/VoskApiTest/VoskApiTest/Vosk/vosk-model-small-en-us-0.15"; sourceTree = "<absolute>"; };
 		92AA22AD244CDD1200DA464B /* vosk_api.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = vosk_api.h; sourceTree = "<group>"; };
 		92AA22AE244CDD5200DA464B /* bridging.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = bridging.h; sourceTree = "<group>"; };
@@ -47,9 +47,9 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
-				92375246240C6DC900DD6076 /* libstdc++.tbd in Frameworks */,
+				92833003273C466E00058B52 /* libc++.tbd in Frameworks */,
 				92375244240C6DAF00DD6076 /* Accelerate.framework in Frameworks */,
-				9237523C240C642000DD6076 /* libkaldiwrap.a in Frameworks */,
+				925527A9273C492C00FFD9CC /* libvosk.a in Frameworks */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 		};
@@ -93,11 +93,11 @@
 		92375239240C642000DD6076 /* Vosk */ = {
 			isa = PBXGroup;
 			children = (
-				928CC50C25BE124400490481 /* vosk-model-small-en-us-0.15 */,
 				92D86BD4253F823F0040D53F /* vosk-model-spk-0.4 */,
-				92375256240C6E3D00DD6076 /* 10001-90210-01803.wav */,
+				928CC50C25BE124400490481 /* vosk-model-small-en-us-0.15 */,
+				925527A8273C492C00FFD9CC /* libvosk.a */,
 				92AA22AD244CDD1200DA464B /* vosk_api.h */,
-				9237523A240C642000DD6076 /* libkaldiwrap.a */,
+				92375256240C6E3D00DD6076 /* 10001-90210-01803.wav */,
 			);
 			name = Vosk;
 			path = VoskApiTest/Vosk;
@@ -106,7 +106,7 @@
 		92375242240C6DAF00DD6076 /* Frameworks */ = {
 			isa = PBXGroup;
 			children = (
-				92375245240C6DC900DD6076 /* libstdc++.tbd */,
+				92833002273C466E00058B52 /* libc++.tbd */,
 				92375243240C6DAF00DD6076 /* Accelerate.framework */,
 			);
 			name = Frameworks;
@@ -145,7 +145,7 @@
 					9237521D240C550B00DD6076 = {
 						CreatedOnToolsVersion = 8.3.2;
 						LastSwiftMigration = 0920;
-						ProvisioningStyle = Automatic;
+						ProvisioningStyle = Manual;
 					};
 				};
 			};
@@ -223,7 +223,6 @@
 				ALWAYS_SEARCH_USER_PATHS = NO;
 				CLANG_ANALYZER_NONNULL = YES;
 				CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
-				CLANG_CXX_LANGUAGE_STANDARD = "gnu++0x";
 				CLANG_CXX_LIBRARY = "libc++";
 				CLANG_ENABLE_MODULES = YES;
 				CLANG_ENABLE_OBJC_ARC = YES;
@@ -245,7 +244,7 @@
 				CLANG_WARN_SUSPICIOUS_MOVE = YES;
 				CLANG_WARN_UNREACHABLE_CODE = YES;
 				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
-				"CODE_SIGN_IDENTITY[sdk=iphoneos*]" = "iPhone Developer";
+				"CODE_SIGN_IDENTITY[sdk=iphoneos*]" = "";
 				COPY_PHASE_STRIP = NO;
 				DEBUG_INFORMATION_FORMAT = dwarf;
 				ENABLE_STRICT_OBJC_MSGSEND = YES;
@@ -282,7 +281,6 @@
 				ALWAYS_SEARCH_USER_PATHS = NO;
 				CLANG_ANALYZER_NONNULL = YES;
 				CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
-				CLANG_CXX_LANGUAGE_STANDARD = "gnu++0x";
 				CLANG_CXX_LIBRARY = "libc++";
 				CLANG_ENABLE_MODULES = YES;
 				CLANG_ENABLE_OBJC_ARC = YES;
@@ -304,7 +302,7 @@
 				CLANG_WARN_SUSPICIOUS_MOVE = YES;
 				CLANG_WARN_UNREACHABLE_CODE = YES;
 				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
-				"CODE_SIGN_IDENTITY[sdk=iphoneos*]" = "iPhone Developer";
+				"CODE_SIGN_IDENTITY[sdk=iphoneos*]" = "";
 				COPY_PHASE_STRIP = NO;
 				DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
 				ENABLE_NS_ASSERTIONS = NO;
@@ -319,6 +317,7 @@
 				GCC_WARN_UNUSED_VARIABLE = YES;
 				IPHONEOS_DEPLOYMENT_TARGET = 10.3;
 				MTL_ENABLE_DEBUG_INFO = NO;
+				ONLY_ACTIVE_ARCH = YES;
 				SDKROOT = iphoneos;
 				SWIFT_OBJC_BRIDGING_HEADER = bridging.h;
 				SWIFT_OPTIMIZATION_LEVEL = "-Owholemodule";
@@ -333,7 +332,10 @@
 			buildSettings = {
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
 				CLANG_ENABLE_MODULES = YES;
-				ENABLE_BITCODE = YES;
+				"CODE_SIGN_IDENTITY[sdk=iphoneos*]" = "iPhone Developer";
+				CODE_SIGN_STYLE = Manual;
+				DEVELOPMENT_TEAM = "";
+				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = VoskApiTest/Info.plist;
 				LD_RUNPATH_SEARCH_PATHS = "$(inherited) @executable_path/Frameworks";
 				LIBRARY_SEARCH_PATHS = (
@@ -342,11 +344,13 @@
 				);
 				PRODUCT_BUNDLE_IDENTIFIER = com.alphacephei.VoskApiTest;
 				PRODUCT_NAME = "$(TARGET_NAME)";
+				PROVISIONING_PROFILE_SPECIFIER = "";
 				SWIFT_INSTALL_OBJC_HEADER = YES;
 				SWIFT_OBJC_BRIDGING_HEADER = VoskApiTest/bridging.h;
 				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
 				SWIFT_SWIFT3_OBJC_INFERENCE = Default;
 				SWIFT_VERSION = 4.0;
+				TARGETED_DEVICE_FAMILY = "1,2";
 			};
 			name = Debug;
 		};
@@ -355,7 +359,10 @@
 			buildSettings = {
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
 				CLANG_ENABLE_MODULES = YES;
-				ENABLE_BITCODE = YES;
+				"CODE_SIGN_IDENTITY[sdk=iphoneos*]" = "iPhone Developer";
+				CODE_SIGN_STYLE = Manual;
+				DEVELOPMENT_TEAM = "";
+				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = VoskApiTest/Info.plist;
 				LD_RUNPATH_SEARCH_PATHS = "$(inherited) @executable_path/Frameworks";
 				LIBRARY_SEARCH_PATHS = (
@@ -364,10 +371,12 @@
 				);
 				PRODUCT_BUNDLE_IDENTIFIER = com.alphacephei.VoskApiTest;
 				PRODUCT_NAME = "$(TARGET_NAME)";
+				PROVISIONING_PROFILE_SPECIFIER = "";
 				SWIFT_INSTALL_OBJC_HEADER = YES;
 				SWIFT_OBJC_BRIDGING_HEADER = VoskApiTest/bridging.h;
 				SWIFT_SWIFT3_OBJC_INFERENCE = Default;
 				SWIFT_VERSION = 4.0;
+				TARGETED_DEVICE_FAMILY = "1,2";
 			};
 			name = Release;
 		};

ios/VoskApiTest.xcodeproj/project.xcworkspace/contents.xcworkspacedata

@@ -1,9 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <Workspace
    version = "1.0">
-   <FileRef
-      location = "group:/Users/shmyrev/Documents/IOS/VoskApiTest/VoskApiTest/Vosk/vosk-model-small-en-us-0.15">
-   </FileRef>
    <FileRef
       location = "self:">
    </FileRef>

ios/VoskApiTest/Vosk.swift

@@ -14,7 +14,7 @@ public final class Vosk {
     var recognizer : OpaquePointer!
 
     init(model: VoskModel, sampleRate: Float) {
-        recognizer = vosk_recognizer_new_spk(model.model, model.spkModel, sampleRate)
+        recognizer = vosk_recognizer_new_spk(model.model, sampleRate, model.spkModel)
     }
     
     deinit {

ios/VoskApiTest/Vosk/vosk_api.h

@@ -1,4 +1,4 @@
-// Copyright 2020 Alpha Cephei Inc.
+// Copyright 2020-2021 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.
@@ -43,7 +43,7 @@ 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 */
+ * @returns model object or NULL if problem occured */
 VoskModel *vosk_model_new(const char *model_path);
 
 
@@ -55,10 +55,18 @@ VoskModel *vosk_model_new(const char *model_path);
 void vosk_model_free(VoskModel *model);
 
 
+/** Check if a word can be recognized by the model
+ * @param word: the word
+ * @returns the word symbol if @param word exists inside the model
+ * or -1 otherwise.
+ * Reminding that word symbol 0 is for <epsilon> */
+int vosk_model_find_word(VoskModel *model, const char *word);
+
+
 /** 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 */
+ * @returns model object or NULL if problem occured */
 VoskSpkModel *vosk_spk_model_new(const char *model_path);
 
 
@@ -71,9 +79,13 @@ 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 */
+ *  The recognizers process the speech and return text using shared model data
+ *  @param model       VoskModel containing static data for recognizer. Model can be
+ *                     shared across recognizers, even running in different threads.
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer.
+ *                     Make sure this rate matches the audio content, it is a common
+ *                     issue causing accuracy problems.
+ *  @returns recognizer object or NULL if problem occured */
 VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
 
 
@@ -82,10 +94,14 @@ VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
  *  With the speaker recognition mode the recognizer not just recognize
  *  text but also return speaker vectors one can use for speaker identification
  *
+ *  @param model       VoskModel containing static data for recognizer. Model can be
+ *                     shared across recognizers, even running in different threads.
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer.
+ *                     Make sure this rate matches the audio content, it is a common
+ *                     issue causing accuracy problems.
  *  @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);
+ *  @returns recognizer object or NULL if problem occured */
+VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, float sample_rate, VoskSpkModel *spk_model);
 
 
 /** Creates the recognizer object with the phrase list
@@ -98,42 +114,46 @@ VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, VoskSpkModel *spk_mode
  *  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 model       VoskModel containing static data for recognizer. Model can be
+ *                     shared across recognizers, even running in different threads.
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer.
+ *                     Make sure this rate matches the audio content, it is a common
+ *                     issue causing accuracy problems.
  *  @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 */
+ *  @returns recognizer object or NULL if problem occured */
 VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar);
 
 
-/** Accept voice data
+/** Adds speaker model to already initialized recognizer
  *
- *  accept and process new chunk of voice data
+ * Can add speaker recognition model to already created recognizer. Helps to initialize
+ * speaker recognition for grammar-based recognizer.
  *
- *  @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);
+ * @param spk_model Speaker recognition model */
+void vosk_recognizer_set_spk_model(VoskRecognizer *recognizer, VoskSpkModel *spk_model);
 
 
-/** 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
+/** Configures recognizer to output n-best results
+ *
+ * <pre>
+ *   {
+ *      "alternatives": [
+ *          { "text": "one two three four five", "confidence": 0.97 },
+ *          { "text": "one two three for five", "confidence": 0.03 },
+ *      ]
+ *   }
+ * </pre>
+ *
+ * @param max_alternatives - maximum alternatives to return from recognition results
+ */
+void vosk_recognizer_set_max_alternatives(VoskRecognizer *recognizer, int max_alternatives);
+
+
+/** Enables words with times in the output
  *
  * <pre>
- * {
  *   "result" : [{
  *       "conf" : 1.000000,
  *       "end" : 1.110000,
@@ -156,13 +176,54 @@ int vosk_recognizer_accept_waveform_f(VoskRecognizer *recognizer, const float *d
  *       "word" : "zero"
  *     }, {
  *       "conf" : 1.000000,
- *      "end" : 2.610000,
+ *       "end" : 2.610000,
  *       "start" : 2.340000,
  *       "word" : "one"
  *     }],
- *   "text" : "what zero zero zero one"
+ * </pre>
+ *
+ * @param words - boolean value
+ */
+void vosk_recognizer_set_words(VoskRecognizer *recognizer, int words);
+
+
+/** 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 1 if silence is occured and you can retrieve a new utterance with result method 
+ *           0 if decoding continues
+ *           -1 if exception occured */
+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>
+ *  {
+ *    "text" : "what zero zero zero one"
  *  }
  * </pre>
+ *
+ * If alternatives enabled it returns result with alternatives, see also vosk_recognizer_set_alternatives().
+ *
+ * If word times enabled returns word time, see also vosk_recognizer_set_word_times().
  */
 const char *vosk_recognizer_result(VoskRecognizer *recognizer);
 
@@ -174,7 +235,7 @@ const char *vosk_recognizer_result(VoskRecognizer *recognizer);
  *
  * <pre>
  * {
- *  "partial" : "cyril one eight zero"
+ *    "partial" : "cyril one eight zero"
  * }
  * </pre>
  */
@@ -190,6 +251,12 @@ const char *vosk_recognizer_partial_result(VoskRecognizer *recognizer);
 const char *vosk_recognizer_final_result(VoskRecognizer *recognizer);
 
 
+/** Resets the recognizer
+ *
+ *  Resets current results so the recognition can continue from scratch */
+void vosk_recognizer_reset(VoskRecognizer *recognizer);
+
+
 /** Releases recognizer object
  *
  *  Underlying model is also unreferenced and if needed released */
@@ -204,6 +271,20 @@ void vosk_recognizer_free(VoskRecognizer *recognizer);
  */
 void vosk_set_log_level(int log_level);
 
+/**
+ *  Init, automatically select a CUDA device and allow multithreading.
+ *  Must be called once from the main thread.
+ *  Has no effect if HAVE_CUDA flag is not set.
+ */
+void vosk_gpu_init();
+
+/**
+ *  Init CUDA device in a multi-threaded environment.
+ *  Must be called for each thread.
+ *  Has no effect if HAVE_CUDA flag is not set.
+ */
+void vosk_gpu_thread_init();
+
 #ifdef __cplusplus
 }
 #endif

java/demo/build.gradle

@@ -8,12 +8,9 @@ application {
 
 repositories {
     mavenCentral()
-    maven {
-        url 'https://alphacephei.com/maven/'
-    }
 }
 
 dependencies {
     implementation group: 'net.java.dev.jna', name: 'jna', version: '5.7.0'
-    implementation group: 'com.alphacephei', name: 'vosk', version: '0.3.30+'
+    implementation group: 'com.alphacephei', name: 'vosk', version: '0.3.40+'
 }

java/lib/build.gradle

@@ -1,18 +1,31 @@
+buildscript {
+  repositories {
+    mavenCentral()
+  }
+}
+
 plugins {
     id 'java-library'
     id 'maven-publish'
+    id 'com.vanniktech.maven.publish' version '0.18.0'
 }
 
-archivesBaseName = 'vosk'
-group = 'com.alphacephei'
-version = '0.3.30'
-
 repositories {
     mavenCentral()
 }
 
+archivesBaseName = 'vosk'
+group = 'com.alphacephei'
+version = '0.3.45'
+
+mavenPublish {
+    group = 'com.alphacephei'
+    version = version
+    sonatypeHost = 's01'
+}
+
 dependencies {
-    implementation group: 'net.java.dev.jna', name: 'jna', version: '5.7.0'
+    api group: 'net.java.dev.jna', name: 'jna', version: '5.7.0'
     testImplementation 'junit:junit:4.13'
 }
 
@@ -45,14 +58,14 @@ publishing {
             }
         }
     }
-    repositories {
-        maven {
-            url = "repo"
-        }
-    }
 }
 
 test {
     dependsOn cleanTest
     testLogging.showStandardStreams = true
 }
+
+java {
+    withSourcesJar()
+    withJavadocJar()
+}

java/lib/src/main/java/org/vosk/LibVosk.java

@@ -62,6 +62,8 @@ public class LibVosk {
 
     public static native void vosk_recognizer_set_words(Pointer recognizer, boolean words);
 
+    public static native void vosk_recognizer_set_partial_words(Pointer recognizer, boolean partial_words);
+
     public static native void vosk_recognizer_set_spk_model(Pointer recognizer, Pointer spk_model);
 
     public static native boolean vosk_recognizer_accept_waveform(Pointer recognizer, byte[] data, int len);

java/lib/src/main/java/org/vosk/Model.java

@@ -1,13 +1,18 @@
 package org.vosk;
 
+import java.io.IOException;
 import com.sun.jna.PointerType;
 
 public class Model extends PointerType implements AutoCloseable {
     public Model() {
     }
 
-    public Model(String path) {
+    public Model(String path) throws IOException {
         super(LibVosk.vosk_model_new(path));
+
+        if (getPointer() == null) {
+            throw new IOException("Failed to create a model");
+        }
     }
 
     @Override

java/lib/src/main/java/org/vosk/Recognizer.java

@@ -1,10 +1,15 @@
 package org.vosk;
 
 import com.sun.jna.PointerType;
+import java.io.IOException;
 
 public class Recognizer extends PointerType implements AutoCloseable {
-    public Recognizer(Model model, float sampleRate) {
+    public Recognizer(Model model, float sampleRate) throws IOException {
         super(LibVosk.vosk_recognizer_new(model, sampleRate));
+
+        if (getPointer() == null) {
+            throw new IOException("Failed to create a recognizer");
+        }
     }
 
     public Recognizer(Model model, float sampleRate, SpeakerModel spkModel) {
@@ -23,6 +28,10 @@ public class Recognizer extends PointerType implements AutoCloseable {
         LibVosk.vosk_recognizer_set_words(this.getPointer(), words);
     }
 
+    public void setPartialWords(boolean partial_words) {
+        LibVosk.vosk_recognizer_set_partial_words(this.getPointer(), partial_words);
+    }
+
     public void setSpeakerModel(SpeakerModel spkModel) {
         LibVosk.vosk_recognizer_set_spk_model(this.getPointer(), spkModel.getPointer());
     }

java/lib/src/main/java/org/vosk/SpeakerModel.java

@@ -1,13 +1,18 @@
 package org.vosk;
 
 import com.sun.jna.PointerType;
+import java.io.IOException;
 
 public class SpeakerModel extends PointerType implements AutoCloseable {
     public SpeakerModel() {
     }
 
-    public SpeakerModel(String path) {
+    public SpeakerModel(String path) throws IOException {
         super(LibVosk.vosk_spk_model_new(path));
+
+        if (getPointer() == null) {
+            throw new IOException("Failed to create a speaker model");
+        }
     }
 
     @Override

java/lib/src/test/java/org/vosk/test/DecoderTest.java

@@ -30,6 +30,7 @@ public class DecoderTest {
 
             recognizer.setMaxAlternatives(10);
             recognizer.setWords(true);
+            recognizer.setPartialWords(true);
 
             int nbytes;
             byte[] b = new byte[4096];
@@ -93,4 +94,10 @@ public class DecoderTest {
         }
         Assert.assertTrue(true);
     }
+
+
+    @Test(expected = IOException.class)
+    public void decoderTestException() throws IOException {
+        Model model = new Model("model_missing");
+    }
 }

nodejs/README.md

@@ -2,18 +2,19 @@ This is an FFI-NAPI wrapper for the Vosk library.
 
 ## Usage
 
-It mostly follows Vosk interface, some methods are not yet fully implemented.
+Bindings mostly follow Vosk interface, some methods are not yet fully implemented.
 
-To use it you need to compile libvosk library, see Python module build
-instructions for details. You can find prebuilt library inside python
-wheel.
+See [demo folder](https://github.com/alphacep/vosk-api/tree/master/nodejs/demo) for
+details.
 
 ## About
 
 Vosk is an offline open source speech recognition toolkit. It enables
-speech recognition models for 17 languages and dialects - English, Indian
+speech recognition for 20+ languages and dialects - English, Indian
 English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish,
-Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino.
+Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino,
+Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish.
+More to come.
 
 Vosk models are small (50 Mb) but provide continuous large vocabulary
 transcription, zero-latency response with streaming API, reconfigurable

nodejs/demo/test_microphone.js

@@ -18,11 +18,11 @@ const rec = new vosk.Recognizer({model: model, sampleRate: SAMPLE_RATE});
 var micInstance = mic({
     rate: String(SAMPLE_RATE),
     channels: '1',
-    debug: false
+    debug: false,
+    device: 'default',    
 });
 
 var micInputStream = micInstance.getAudioStream();
-micInstance.start();
 
 micInputStream.on('data', data => {
     if (rec.acceptWaveform(data))
@@ -31,9 +31,16 @@ micInputStream.on('data', data => {
         console.log(rec.partialResult());
 });
 
-process.on('SIGINT', function() {
+micInputStream.on('audioProcessExitComplete', function() {
+    console.log("Cleaning up");
     console.log(rec.finalResult());
-    console.log("\nDone");
     rec.free();
     model.free();
 });
+
+process.on('SIGINT', function() {
+    console.log("\nStopping");
+    micInstance.stop();
+});
+
+micInstance.start();

nodejs/demo/test_simple.js

@@ -29,10 +29,13 @@ wfReader.on('format', async ({ audioFormat, sampleRate, channels }) => {
     const rec = new vosk.Recognizer({model: model, sampleRate: sampleRate});
     rec.setMaxAlternatives(10);
     rec.setWords(true);
+    rec.setPartialWords(true);
     for await (const data of wfReadable) {
         const end_of_speech = rec.acceptWaveform(data);
         if (end_of_speech) {
               console.log(JSON.stringify(rec.result(), null, 4));
+        } else {
+              console.log(JSON.stringify(rec.partialResult(), null, 4));
         }
     }
     console.log(JSON.stringify(rec.finalResult(rec), null, 4));

nodejs/demo/test_srt.js

@@ -46,8 +46,8 @@ ffmpeg_run.on('exit', code => {
             subs.push({
                 type: 'cue',
                 data: {
-                start: words[0].start,
-                end: words[0].end,
+                start: words[0].start * 1000,
+                end: words[0].end * 1000,
                 text: words[0].word
                 }
             });
@@ -61,8 +61,8 @@ ffmpeg_run.on('exit', code => {
                 subs.push({
                     type: 'cue',
                     data: {
-                      start: words[start_index].start,
-                      end: words[i].end,
+                      start: words[start_index].start * 1000,
+                      end: words[i].end * 1000,
                       text: text.slice(0, text.length-1)
                     }
                 });
@@ -74,8 +74,8 @@ ffmpeg_run.on('exit', code => {
             subs.push({
                 type: 'cue',
                 data: {
-                  start: words[start_index].start,
-                  end: words[words.length-1].end,
+                  start: words[start_index].start * 1000,
+                  end: words[words.length-1].end * 1000,
                   text: text
                 }
             });

nodejs/index.js

@@ -72,11 +72,11 @@ if (os.platform() == 'win32') {
     // Update path to load dependent dlls
     let currentPath = process.env.Path;
     let dllDirectory = path.resolve(path.join(__dirname, "lib", "win-x86_64"));
-    process.env.Path = currentPath + path.delimiter + dllDirectory;
+    process.env.Path = dllDirectory + path.delimiter + currentPath;
 
     soname = path.join(__dirname, "lib", "win-x86_64", "libvosk.dll")
 } else if (os.platform() == 'darwin') {
-    soname = path.join(__dirname, "lib", "osx-x86_64", "libvosk.dylib")
+    soname = path.join(__dirname, "lib", "osx-universal", "libvosk.dylib")
 } else {
     soname = path.join(__dirname, "lib", "linux-x86_64", "libvosk.so")
 }
@@ -93,6 +93,7 @@ const libvosk = ffi.Library(soname, {
     'vosk_recognizer_free': ['void', [vosk_recognizer_ptr]],
     'vosk_recognizer_set_max_alternatives': ['void', [vosk_recognizer_ptr, 'int']],
     'vosk_recognizer_set_words': ['void', [vosk_recognizer_ptr, 'bool']],
+    'vosk_recognizer_set_partial_words': ['void', [vosk_recognizer_ptr, 'bool']],
     'vosk_recognizer_set_spk_model': ['void', [vosk_recognizer_ptr, vosk_spk_model_ptr]],
     'vosk_recognizer_accept_waveform': ['bool', [vosk_recognizer_ptr, 'pointer', 'int']],
     'vosk_recognizer_result': ['string', [vosk_recognizer_ptr]],
@@ -301,6 +302,11 @@ class Recognizer {
         libvosk.vosk_recognizer_set_words(this.handle, words);
     }
 
+    /** Same as above, but for partial results*/
+    setPartialWords(partial_words) {
+        libvosk.vosk_recognizer_set_partial_words(this.handle, partial_words);
+    }
+
     /** Adds speaker recognition model to already created recognizer. Helps to initialize
      * speaker recognition for grammar-based recognizer.
      *

nodejs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vosk",
-  "version": "0.3.30",
+  "version": "0.3.45",
   "description": "Node binding for continuous offline voice recoginition with Vosk library.",
   "repository": {
     "type": "git",

python/.pylintrc

@@ -0,0 +1,639 @@
+[MAIN]
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+# Load and enable all available extensions. Use --list-extensions to see a list
+# all available extensions.
+#enable-all-extensions=
+
+# In error mode, messages with a category besides ERROR or FATAL are
+# suppressed, and no reports are done by default. Error mode is compatible with
+# disabling specific errors.
+#errors-only=
+
+# Always return a 0 (non-error) status code, even if lint errors are found.
+# This is primarily useful in continuous integration scripts.
+#exit-zero=
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code.
+extension-pkg-allow-list=
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
+# for backward compatibility.)
+extension-pkg-whitelist=
+
+# Return non-zero exit code if any of these messages/categories are detected,
+# even if score is above --fail-under value. Syntax same as enable. Messages
+# specified are enabled, while categories only check already-enabled messages.
+fail-on=
+
+# Specify a score threshold under which the program will exit with error.
+fail-under=10
+
+# Interpret the stdin as a python script, whose filename needs to be passed as
+# the module_or_package argument.
+#from-stdin=
+
+# Files or directories to be skipped. They should be base names, not paths.
+ignore=CVS
+
+# Add files or directories matching the regular expressions patterns to the
+# ignore-list. The regex matches against paths and can be in Posix or Windows
+# format. Because '\' represents the directory delimiter on Windows systems, it
+# can't be used as an escape character.
+ignore-paths=
+
+# Files or directories matching the regular expression patterns are skipped.
+# The regex matches against base names, not paths. The default value ignores
+# Emacs file locks
+ignore-patterns=^\.#
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis). It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use, and will cap the count on Windows to
+# avoid hangs.
+jobs=1
+
+# Control the amount of potential inferred values when inferring a single
+# object. This can help the performance when dealing with large functions or
+# complex, nested conditions.
+limit-inference-results=100
+
+# List of plugins (as comma separated values of python module names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# Minimum Python version to use for version dependent checks. Will default to
+# the version used to run pylint.
+py-version=3.7
+
+# Discover python modules and packages in the file system subtree.
+recursive=no
+
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages.
+suggestion-mode=yes
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+# In verbose mode, extra non-checker-related info will be displayed.
+#verbose=
+
+
+[REPORTS]
+
+# Python expression which should return a score less than or equal to 10. You
+# have access to the variables 'fatal', 'error', 'warning', 'refactor',
+# 'convention', and 'info' which contain the number of messages in each
+# category, as well as 'statement' which is the total number of statements
+# analyzed. This score is used by the global evaluation report (RP0004).
+evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details.
+msg-template=
+
+# Set the output format. Available formats are text, parseable, colorized, json
+# and msvs (visual studio). You can also give a reporter class, e.g.
+# mypackage.mymodule.MyReporterClass.
+#output-format=
+
+# Tells whether to display a full report or only the messages.
+reports=no
+
+# Activate the evaluation score.
+score=yes
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE,
+# UNDEFINED.
+confidence=HIGH,
+           CONTROL_FLOW,
+           INFERENCE,
+           INFERENCE_FAILURE,
+           UNDEFINED
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once). You can also use "--disable=all" to
+# disable everything first and then re-enable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use "--disable=all --enable=classes
+# --disable=W".
+disable=raw-checker-failed,
+        bad-inline-option,
+        locally-disabled,
+        file-ignored,
+        suppressed-message,
+        useless-suppression,
+        deprecated-pragma,
+        use-symbolic-message-instead,
+	missing-module-docstring, # added
+	missing-class-docstring, # added
+	missing-function-docstring, # added
+	invalid-name, # added
+	broad-except, # added
+	wrong-import-order, # added
+	consider-using-f-string, # added
+	logging-format-interpolation, # added
+	no-member, # added
+	consider-using-with, # added
+	unused-argument,
+	no-else-return, # added
+	too-few-public-methods, # added
+	import-error, # added
+	protected-access, # added
+	unused-variable, # added
+	R0801 # added (similarity of two scripts)
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+enable=c-extension-no-member
+
+
+[BASIC]
+
+# Naming style matching correct argument names.
+argument-naming-style=snake_case
+
+# Regular expression matching correct argument names. Overrides argument-
+# naming-style. If left empty, argument names will be checked with the set
+# naming style.
+#argument-rgx=
+
+# Naming style matching correct attribute names.
+attr-naming-style=snake_case
+
+# Regular expression matching correct attribute names. Overrides attr-naming-
+# style. If left empty, attribute names will be checked with the set naming
+# style.
+#attr-rgx=
+
+# Bad variable names which should always be refused, separated by a comma.
+bad-names=foo,
+          bar,
+          baz,
+          toto,
+          tutu,
+          tata
+
+# Bad variable names regexes, separated by a comma. If names match any regex,
+# they will always be refused
+bad-names-rgxs=
+
+# Naming style matching correct class attribute names.
+class-attribute-naming-style=any
+
+# Regular expression matching correct class attribute names. Overrides class-
+# attribute-naming-style. If left empty, class attribute names will be checked
+# with the set naming style.
+#class-attribute-rgx=
+
+# Naming style matching correct class constant names.
+class-const-naming-style=UPPER_CASE
+
+# Regular expression matching correct class constant names. Overrides class-
+# const-naming-style. If left empty, class constant names will be checked with
+# the set naming style.
+#class-const-rgx=
+
+# Naming style matching correct class names.
+class-naming-style=PascalCase
+
+# Regular expression matching correct class names. Overrides class-naming-
+# style. If left empty, class names will be checked with the set naming style.
+#class-rgx=
+
+# Naming style matching correct constant names.
+const-naming-style=UPPER_CASE
+
+# Regular expression matching correct constant names. Overrides const-naming-
+# style. If left empty, constant names will be checked with the set naming
+# style.
+#const-rgx=
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+# Naming style matching correct function names.
+function-naming-style=snake_case
+
+# Regular expression matching correct function names. Overrides function-
+# naming-style. If left empty, function names will be checked with the set
+# naming style.
+#function-rgx=
+
+# Good variable names which should always be accepted, separated by a comma.
+good-names=i,
+           j,
+           k,
+           ex,
+           Run,
+           _,
+	   x,
+	   y,
+	   nx,
+	   ny
+
+# Good variable names regexes, separated by a comma. If names match any regex,
+# they will always be accepted
+good-names-rgxs=
+
+# Include a hint for the correct naming format with invalid-name.
+include-naming-hint=no
+
+# Naming style matching correct inline iteration names.
+inlinevar-naming-style=any
+
+# Regular expression matching correct inline iteration names. Overrides
+# inlinevar-naming-style. If left empty, inline iteration names will be checked
+# with the set naming style.
+#inlinevar-rgx=
+
+# Naming style matching correct method names.
+method-naming-style=snake_case
+
+# Regular expression matching correct method names. Overrides method-naming-
+# style. If left empty, method names will be checked with the set naming style.
+#method-rgx=
+
+# Naming style matching correct module names.
+module-naming-style=snake_case
+
+# Regular expression matching correct module names. Overrides module-naming-
+# style. If left empty, module names will be checked with the set naming style.
+#module-rgx=
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^_
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+# These decorators are taken in consideration only for invalid-name.
+property-classes=abc.abstractproperty
+
+# Regular expression matching correct type variable names. If left empty, type
+# variable names will be checked with the set naming style.
+#typevar-rgx=
+
+# Naming style matching correct variable names.
+variable-naming-style=snake_case
+
+# Regular expression matching correct variable names. Overrides variable-
+# naming-style. If left empty, variable names will be checked with the set
+# naming style.
+#variable-rgx=
+
+
+[VARIABLES]
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid defining new builtins when possible.
+additional-builtins=
+
+# Tells whether unused global variables should be treated as a violation.
+allow-global-unused-variables=yes
+
+# List of names allowed to shadow builtins
+allowed-redefined-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,
+          _cb
+
+# A regular expression matching the name of dummy variables (i.e. expected to
+# not be used).
+dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
+
+# Argument names that match this expression will be ignored.
+ignored-argument-names=_.*|^ignored_|^unused_
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
+
+
+[SIMILARITIES]
+
+# Comments are removed from the similarity computation
+ignore-comments=yes
+
+# Docstrings are removed from the similarity computation
+ignore-docstrings=yes
+
+# Imports are removed from the similarity computation
+ignore-imports=yes
+
+# Signatures are removed from the similarity computation
+ignore-signatures=yes
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,
+      XXX,
+      TODO
+
+# Regular expression of note tags to take in consideration.
+notes-rgx=
+
+
+[STRING]
+
+# This flag controls whether inconsistent-quotes generates a warning when the
+# character used as a quote delimiter is used inconsistently within a module.
+check-quote-consistency=no
+
+# This flag controls whether the implicit-str-concat should generate a warning
+# on implicit string concatenation in sequences defined over several lines.
+check-str-concat-over-line-jumps=no
+
+
+[IMPORTS]
+
+# List of modules that can be imported at any level, not just the top level
+# one.
+allow-any-import-level=
+
+# Allow wildcard imports from modules that define __all__.
+allow-wildcard-with-all=no
+
+# Deprecated modules which should not be used, separated by a comma.
+deprecated-modules=
+
+# Output a graph (.gv or any supported image format) of external dependencies
+# to the given file (report RP0402 must not be disabled).
+ext-import-graph=
+
+# Output a graph (.gv or any supported image format) of all (i.e. internal and
+# external) dependencies to the given file (report RP0402 must not be
+# disabled).
+import-graph=
+
+# Output a graph (.gv or any supported image format) of internal dependencies
+# to the given file (report RP0402 must not be disabled).
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=enchant
+
+# Couples of modules and preferred modules, separated by a comma.
+preferred-modules=
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when caught.
+overgeneral-exceptions=BaseException,
+                       Exception
+
+
+[DESIGN]
+
+# List of regular expressions of class ancestor names to ignore when counting
+# public methods (see R0903)
+exclude-too-few-public-methods=
+
+# List of qualified class names to ignore when counting class parents (see
+# R0901)
+ignored-parents=
+
+# Maximum number of arguments for function / method.
+max-args=5
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Maximum number of boolean expressions in an if statement (see R0916).
+max-bool-expr=5
+
+# Maximum number of branch for function / method body.
+max-branches=12
+
+# Maximum number of locals for function / method body.
+max-locals=15
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of return / yield for function / method body.
+max-returns=6
+
+# Maximum number of statements in function / method body.
+max-statements=50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+
+[METHOD_ARGS]
+
+# List of qualified names (i.e., library.method) which require a timeout
+# parameter e.g. 'requests.api.get,requests.api.post'
+timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request
+
+
+[LOGGING]
+
+# The type of string formatting that logging methods do. `old` means using %
+# formatting, `new` is for `{}` formatting.
+logging-format-style=old
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format.
+logging-modules=logging
+
+
+[FORMAT]
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren=4
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Maximum number of characters on a single line.
+max-line-length=100
+
+# Maximum number of lines in a module.
+max-module-lines=1000
+
+# Allow the body of a class to be on the same line as the declaration if body
+# contains single statement.
+single-line-class-stmt=no
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+
+[REFACTORING]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+# Complete name of functions that never returns. When checking for
+# inconsistent-return-statements if a never returning function is called then
+# it will be considered as an explicit return statement and no message will be
+# printed.
+never-returning-functions=sys.exit,argparse.parse_error
+
+
+[TYPECHECK]
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=
+
+# Tells whether to warn about missing members when the owner of the attribute
+# is inferred to be None.
+ignore-none=yes
+
+# This flag controls whether pylint should warn about no-member and similar
+# checks whenever an opaque object is returned when inferring. The inference
+# can return multiple potential results while evaluating a Python object, but
+# some branches might not be evaluated, which results in partial inference. In
+# that case, it might be useful to still emit no-member and other checks for
+# the rest of the inferred objects.
+ignore-on-opaque-inference=yes
+
+# List of symbolic message names to ignore for Mixin members.
+ignored-checks-for-mixins=no-member,
+                          not-async-context-manager,
+                          not-context-manager,
+                          attribute-defined-outside-init
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace
+
+# Show a hint with possible names when a member name was not found. The aspect
+# of finding the hint is based on edit distance.
+missing-member-hint=yes
+
+# The minimum edit distance a name should have in order to be considered a
+# similar match for a missing member name.
+missing-member-hint-distance=1
+
+# The total number of similar names that should be taken in consideration when
+# showing a hint for a missing member.
+missing-member-max-choices=1
+
+# Regex pattern to define which classes are considered mixins.
+mixin-class-rgx=.*[Mm]ixin
+
+# List of decorators that change the signature of a decorated function.
+signature-mutators=
+
+
+[CLASSES]
+
+# Warn about protected attribute access inside special methods
+check-protected-access-in-special-methods=no
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,
+                      __new__,
+                      setUp,
+                      __post_init__
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,
+                  _fields,
+                  _replace,
+                  _source,
+                  _make
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=cls
+
+
+[SPELLING]
+
+# Limits count of emitted suggestions for spelling mistakes.
+max-spelling-suggestions=4
+
+# Spelling dictionary name. Available dictionaries: none. To make it work,
+# install the 'python-enchant' package.
+spelling-dict=
+
+# List of comma separated words that should be considered directives if they
+# appear at the beginning of a comment and should not be checked.
+spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains the private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to the private dictionary (see the
+# --spelling-private-dict-file option) instead of raising a message.
+spelling-store-unknown-words=no

python/README.md

@@ -1,9 +1,11 @@
 This is a Python module for Vosk.
 
 Vosk is an offline open source speech recognition toolkit. It enables
-speech recognition models for 17 languages and dialects - English, Indian
+speech recognition for 20+ languages and dialects - English, Indian
 English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish,
-Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino.
+Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino,
+Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish.
+More to come.
 
 Vosk models are small (50 Mb) but provide continuous large vocabulary
 transcription, zero-latency response with streaming API, reconfigurable

python/example/colab/vosk-adaptation.ipynb

@@ -0,0 +1,558 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Vosk Adaptation",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "URzWMmv50-Ba",
+        "outputId": "0e096a99-74dd-42e2-efb1-9cba784c3664"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content\n",
+            "--2022-08-17 09:48:52--  https://alphacephei.com/vosk-colab/kaldi.tar.gz\n",
+            "Resolving alphacephei.com (alphacephei.com)... 188.40.21.16, 2a01:4f8:13a:279f::2\n",
+            "Connecting to alphacephei.com (alphacephei.com)|188.40.21.16|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 809174554 (772M) [application/octet-stream]\n",
+            "Saving to: ‘kaldi.tar.gz’\n",
+            "\n",
+            "kaldi.tar.gz        100%[===================>] 771.69M  20.3MB/s    in 40s     \n",
+            "\n",
+            "2022-08-17 09:49:33 (19.4 MB/s) - ‘kaldi.tar.gz’ saved [809174554/809174554]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "%cd /content\n",
+        "!wget -c https://alphacephei.com/vosk-colab/kaldi.tar.gz\n",
+        "!tar xzf kaldi.tar.gz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%cd /content/kaldi/egs/ac\n",
+        "!wget -c https://alphacephei.com/vosk-colab/vosk-model-small-en-us-0.15-compile-colab.tar.gz\n",
+        "!rm -rf vosk-model-small-en-us-0.15-compile-colab\n",
+        "!tar xf vosk-model-small-en-us-0.15-compile-colab.tar.gz"
+      ],
+      "metadata": {
+        "id": "-065p7WC2SHh",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "241c7473-7464-48d5-b48d-dc6e3bf4971d"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/kaldi/egs/ac\n",
+            "--2022-08-17 10:28:26--  https://alphacephei.com/vosk-colab/vosk-model-small-en-us-0.15-compile-colab.tar.gz\n",
+            "Resolving alphacephei.com (alphacephei.com)... 188.40.21.16, 2a01:4f8:13a:279f::2\n",
+            "Connecting to alphacephei.com (alphacephei.com)|188.40.21.16|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 59618100 (57M) [application/octet-stream]\n",
+            "Saving to: ‘vosk-model-small-en-us-0.15-compile-colab.tar.gz’\n",
+            "\n",
+            "vosk-model-small-en 100%[===================>]  56.86M  18.6MB/s    in 3.6s    \n",
+            "\n",
+            "2022-08-17 10:28:30 (15.7 MB/s) - ‘vosk-model-small-en-us-0.15-compile-colab.tar.gz’ saved [59618100/59618100]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%cd /content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab\n",
+        "!ls\n",
+        "!cat compile-graph.sh\n",
+        "!bash compile-graph.sh"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wuDjvNbd2sf9",
+        "outputId": "34a1d2fe-d443-4574-e25d-824e38eb3a78"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab\n",
+            "compile-graph.sh  data_test  decode.sh\texp\t      local  path.sh  steps\n",
+            "conf\t\t  db\t     dict.py\tget_vocab.py  mfcc   RESULTS  utils\n",
+            "#!/bin/bash\n",
+            "\n",
+            "set -x\n",
+            "\n",
+            ". path.sh\n",
+            "\n",
+            "pip3 install phonetisaurus\n",
+            "\n",
+            "rm -rf data\n",
+            "rm -rf exp/tdnn/lgraph\n",
+            "rm -rf exp/tdnn/lgraph_orig\n",
+            "\n",
+            "mkdir -p data/dict\n",
+            "cp db/phone/* data/dict\n",
+            "./dict.py > data/dict/lexicon.txt\n",
+            "\n",
+            "python3 ./get_vocab.py > data/mix.vocab\n",
+            "ngramsymbols data/mix.vocab data/mix.syms\n",
+            "farcompilestrings --fst_type=compact --symbols=data/mix.syms --keep_symbols --unknown_symbol=\"[unk]\" db/extra.txt data/extra.far\n",
+            "ngramcount --order=3 data/extra.far - |\n",
+            "    ngramprint --integers | grep -v \"<unk>\" | ngramread |\n",
+            "    ngramshrink --method=count_prune --count_pattern=\"3+:3\" |\n",
+            "    ngrammake --method=witten_bell - data/extra.mod\n",
+            "gunzip -c db/en-50k-0.4-android.lm.gz | ngramread --renormalize_arpa --ARPA --symbols=data/mix.syms - data/en-us.mod\n",
+            "ngrammerge --method=\"bayes_model_merge\" --normalize --alpha=0.95 --beta=0.05 data/en-us.mod data/extra.mod data/en-us-mix.mod\n",
+            "ngramprint --ARPA data/en-us-mix.mod | gzip -c > data/en-us-mix.lm.gz\n",
+            "\n",
+            "# Prune for the first stage if needed\n",
+            "# ngramshrink --method=relative_entropy --theta=2e-8 data/en-us-mix.mod data/en-us-mix-prune.mod\n",
+            "# ngramprint --ARPA data/en-us-mix-prune.mod | gzip -c > data/en-us-mix-small.lm.gz\n",
+            "\n",
+            "utils/prepare_lang.sh data/dict \"[unk]\" data/lang_local data/lang\n",
+            "utils/format_lm.sh data/lang db/en-50k-0.4-android.lm.gz data/dict/lexicon.txt data/lang_test\n",
+            "utils/format_lm.sh data/lang data/en-us-mix.lm.gz data/dict/lexicon.txt data/lang_test_adapt\n",
+            "\n",
+            "utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test exp/tdnn exp/tdnn/graph\n",
+            "utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test_adapt exp/tdnn exp/tdnn/graph_adapt\n",
+            "\n",
+            "# Lookahead part goes OOM\n",
+            "#utils/mkgraph_lookahead.sh \\\n",
+            "#          --self-loop-scale 1.0 data/lang \\\n",
+            "#          exp/tdnn data/en-us-mix.lm.gz exp/tdnn/lgraph\n",
+            "#utils/mkgraph_lookahead.sh \\\n",
+            "#          --self-loop-scale 1.0 data/lang \\\n",
+            "#          exp/tdnn db/en-50k-0.4-android.lm.gz exp/tdnn/lgraph_orig\n",
+            "+ . path.sh\n",
+            "+++ pwd\n",
+            "++ export KALDI_ROOT=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../..\n",
+            "++ KALDI_ROOT=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../..\n",
+            "++ export PATH=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/utils:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/openfst/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fstbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/gmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/featbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lm:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmm2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/latbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnetbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/online2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/ivectorbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/chainbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet3bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/sph2pipe_v2.5\n",
+            "++ PATH=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/utils:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/openfst/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fstbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/gmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/featbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lm:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmm2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/latbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnetbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/online2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/ivectorbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/chainbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet3bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/sph2pipe_v2.5\n",
+            "++ export PATH=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/ngram-1.3.7/src/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/utils:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/openfst/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fstbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/gmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/featbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lm:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmm2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/latbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnetbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/online2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/ivectorbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/chainbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet3bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/sph2pipe_v2.5\n",
+            "++ PATH=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/ngram-1.3.7/src/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/utils:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/openfst/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fstbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/gmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/featbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lm:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/sgmm2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/fgmmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/latbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnetbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/online2bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/ivectorbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/lmbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/chainbin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../src/nnet3bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/sph2pipe_v2.5\n",
+            "++ export LD_LIBRARY_PATH=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/openfst/lib/fst/\n",
+            "++ LD_LIBRARY_PATH=/content/kaldi/egs/ac/vosk-model-small-en-us-0.15-compile-colab/../../../tools/openfst/lib/fst/\n",
+            "++ export LC_ALL=C\n",
+            "++ LC_ALL=C\n",
+            "+ pip3 install phonetisaurus\n",
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: phonetisaurus in /usr/local/lib/python3.7/dist-packages (0.3.0)\n",
+            "+ rm -rf data\n",
+            "+ rm -rf exp/tdnn/lgraph\n",
+            "+ rm -rf exp/tdnn/lgraph_orig\n",
+            "+ mkdir -p data/dict\n",
+            "+ cp db/phone/extra_questions.txt db/phone/nonsilence_phones.txt db/phone/optional_silence.txt db/phone/silence_phones.txt data/dict\n",
+            "+ ./dict.py\n",
+            "+ python3 ./get_vocab.py\n",
+            "+ ngramsymbols data/mix.vocab data/mix.syms\n",
+            "+ farcompilestrings --fst_type=compact --symbols=data/mix.syms --keep_symbols '--unknown_symbol=[unk]' db/extra.txt data/extra.far\n",
+            "+ ngramcount --order=3 data/extra.far -\n",
+            "+ ngrammake --method=witten_bell - data/extra.mod\n",
+            "+ ngramshrink --method=count_prune --count_pattern=3+:3\n",
+            "+ ngramread\n",
+            "+ ngramprint --integers\n",
+            "+ grep -v '<unk>'\n",
+            "+ ngramread --renormalize_arpa --ARPA --symbols=data/mix.syms - data/en-us.mod\n",
+            "+ gunzip -c db/en-50k-0.4-android.lm.gz\n",
+            "+ ngrammerge --method=bayes_model_merge --normalize --alpha=0.95 --beta=0.05 data/en-us.mod data/extra.mod data/en-us-mix.mod\n",
+            "+ ngramprint --ARPA data/en-us-mix.mod\n",
+            "+ gzip -c\n",
+            "+ utils/prepare_lang.sh data/dict '[unk]' data/lang_local data/lang\n",
+            "utils/prepare_lang.sh data/dict [unk] data/lang_local data/lang\n",
+            "Checking data/dict/silence_phones.txt ...\n",
+            "--> reading data/dict/silence_phones.txt\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> data/dict/silence_phones.txt is OK\n",
+            "\n",
+            "Checking data/dict/optional_silence.txt ...\n",
+            "--> reading data/dict/optional_silence.txt\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> data/dict/optional_silence.txt is OK\n",
+            "\n",
+            "Checking data/dict/nonsilence_phones.txt ...\n",
+            "--> reading data/dict/nonsilence_phones.txt\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> data/dict/nonsilence_phones.txt is OK\n",
+            "\n",
+            "Checking disjoint: silence_phones.txt, nonsilence_phones.txt\n",
+            "--> disjoint property is OK.\n",
+            "\n",
+            "Checking data/dict/lexicon.txt\n",
+            "--> reading data/dict/lexicon.txt\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> data/dict/lexicon.txt is OK\n",
+            "\n",
+            "Checking data/dict/extra_questions.txt ...\n",
+            "--> data/dict/extra_questions.txt is empty (this is OK)\n",
+            "--> SUCCESS [validating dictionary directory data/dict]\n",
+            "\n",
+            "**Creating data/dict/lexiconp.txt from data/dict/lexicon.txt\n",
+            "fstaddselfloops data/lang/phones/wdisambig_phones.int data/lang/phones/wdisambig_words.int \n",
+            "prepare_lang.sh: validating output directory\n",
+            "utils/validate_lang.pl data/lang\n",
+            "Checking existence of separator file\n",
+            "separator file data/lang/subword_separator.txt is empty or does not exist, deal in word case.\n",
+            "Checking data/lang/phones.txt ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> data/lang/phones.txt is OK\n",
+            "\n",
+            "Checking words.txt: #0 ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> data/lang/words.txt is OK\n",
+            "\n",
+            "Checking disjoint: silence.txt, nonsilence.txt, disambig.txt ...\n",
+            "--> silence.txt and nonsilence.txt are disjoint\n",
+            "--> silence.txt and disambig.txt are disjoint\n",
+            "--> disambig.txt and nonsilence.txt are disjoint\n",
+            "--> disjoint property is OK\n",
+            "\n",
+            "Checking sumation: silence.txt, nonsilence.txt, disambig.txt ...\n",
+            "--> found no unexplainable phones in phones.txt\n",
+            "\n",
+            "Checking data/lang/phones/context_indep.{txt, int, csl} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 10 entry/entries in data/lang/phones/context_indep.txt\n",
+            "--> data/lang/phones/context_indep.int corresponds to data/lang/phones/context_indep.txt\n",
+            "--> data/lang/phones/context_indep.csl corresponds to data/lang/phones/context_indep.txt\n",
+            "--> data/lang/phones/context_indep.{txt, int, csl} are OK\n",
+            "\n",
+            "Checking data/lang/phones/nonsilence.{txt, int, csl} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 156 entry/entries in data/lang/phones/nonsilence.txt\n",
+            "--> data/lang/phones/nonsilence.int corresponds to data/lang/phones/nonsilence.txt\n",
+            "--> data/lang/phones/nonsilence.csl corresponds to data/lang/phones/nonsilence.txt\n",
+            "--> data/lang/phones/nonsilence.{txt, int, csl} are OK\n",
+            "\n",
+            "Checking data/lang/phones/silence.{txt, int, csl} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 10 entry/entries in data/lang/phones/silence.txt\n",
+            "--> data/lang/phones/silence.int corresponds to data/lang/phones/silence.txt\n",
+            "--> data/lang/phones/silence.csl corresponds to data/lang/phones/silence.txt\n",
+            "--> data/lang/phones/silence.{txt, int, csl} are OK\n",
+            "\n",
+            "Checking data/lang/phones/optional_silence.{txt, int, csl} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 1 entry/entries in data/lang/phones/optional_silence.txt\n",
+            "--> data/lang/phones/optional_silence.int corresponds to data/lang/phones/optional_silence.txt\n",
+            "--> data/lang/phones/optional_silence.csl corresponds to data/lang/phones/optional_silence.txt\n",
+            "--> data/lang/phones/optional_silence.{txt, int, csl} are OK\n",
+            "\n",
+            "Checking data/lang/phones/disambig.{txt, int, csl} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 14 entry/entries in data/lang/phones/disambig.txt\n",
+            "--> data/lang/phones/disambig.int corresponds to data/lang/phones/disambig.txt\n",
+            "--> data/lang/phones/disambig.csl corresponds to data/lang/phones/disambig.txt\n",
+            "--> data/lang/phones/disambig.{txt, int, csl} are OK\n",
+            "\n",
+            "Checking data/lang/phones/roots.{txt, int} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 41 entry/entries in data/lang/phones/roots.txt\n",
+            "--> data/lang/phones/roots.int corresponds to data/lang/phones/roots.txt\n",
+            "--> data/lang/phones/roots.{txt, int} are OK\n",
+            "\n",
+            "Checking data/lang/phones/sets.{txt, int} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 41 entry/entries in data/lang/phones/sets.txt\n",
+            "--> data/lang/phones/sets.int corresponds to data/lang/phones/sets.txt\n",
+            "--> data/lang/phones/sets.{txt, int} are OK\n",
+            "\n",
+            "Checking data/lang/phones/extra_questions.{txt, int} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 9 entry/entries in data/lang/phones/extra_questions.txt\n",
+            "--> data/lang/phones/extra_questions.int corresponds to data/lang/phones/extra_questions.txt\n",
+            "--> data/lang/phones/extra_questions.{txt, int} are OK\n",
+            "\n",
+            "Checking data/lang/phones/word_boundary.{txt, int} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 166 entry/entries in data/lang/phones/word_boundary.txt\n",
+            "--> data/lang/phones/word_boundary.int corresponds to data/lang/phones/word_boundary.txt\n",
+            "--> data/lang/phones/word_boundary.{txt, int} are OK\n",
+            "\n",
+            "Checking optional_silence.txt ...\n",
+            "--> reading data/lang/phones/optional_silence.txt\n",
+            "--> data/lang/phones/optional_silence.txt is OK\n",
+            "\n",
+            "Checking disambiguation symbols: #0 and #1\n",
+            "--> data/lang/phones/disambig.txt has \"#0\" and \"#1\"\n",
+            "--> data/lang/phones/disambig.txt is OK\n",
+            "\n",
+            "Checking topo ...\n",
+            "\n",
+            "Checking word_boundary.txt: silence.txt, nonsilence.txt, disambig.txt ...\n",
+            "--> data/lang/phones/word_boundary.txt doesn't include disambiguation symbols\n",
+            "--> data/lang/phones/word_boundary.txt is the union of nonsilence.txt and silence.txt\n",
+            "--> data/lang/phones/word_boundary.txt is OK\n",
+            "\n",
+            "Checking word-level disambiguation symbols...\n",
+            "--> data/lang/phones/wdisambig.txt exists (newer prepare_lang.sh)\n",
+            "Checking word_boundary.int and disambig.int\n",
+            "--> generating a 98 word/subword sequence\n",
+            "--> resulting phone sequence from L.fst corresponds to the word sequence\n",
+            "--> L.fst is OK\n",
+            "--> generating a 49 word/subword sequence\n",
+            "--> resulting phone sequence from L_disambig.fst corresponds to the word sequence\n",
+            "--> L_disambig.fst is OK\n",
+            "\n",
+            "Checking data/lang/oov.{txt, int} ...\n",
+            "--> text seems to be UTF-8 or ASCII, checking whitespaces\n",
+            "--> text contains only allowed whitespaces\n",
+            "--> 1 entry/entries in data/lang/oov.txt\n",
+            "--> data/lang/oov.int corresponds to data/lang/oov.txt\n",
+            "--> data/lang/oov.{txt, int} are OK\n",
+            "\n",
+            "--> data/lang/L.fst is olabel sorted\n",
+            "--> data/lang/L_disambig.fst is olabel sorted\n",
+            "--> SUCCESS [validating lang directory data/lang]\n",
+            "+ utils/format_lm.sh data/lang db/en-50k-0.4-android.lm.gz data/dict/lexicon.txt data/lang_test\n",
+            "Converting 'db/en-50k-0.4-android.lm.gz' to FST\n",
+            "arpa2fst --disambig-symbol=#0 --read-symbol-table=data/lang_test/words.txt - data/lang_test/G.fst \n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:94) Reading \\data\\ section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:149) Reading \\1-grams: section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:149) Reading \\2-grams: section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:149) Reading \\3-grams: section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:RemoveRedundantStates():arpa-lm-compiler.cc:359) Reduced num-states from 1217362 to 185036\n",
+            "fstisstochastic data/lang_test/G.fst \n",
+            "0.476411 -3.03779\n",
+            "Succeeded in formatting LM: 'db/en-50k-0.4-android.lm.gz'\n",
+            "+ utils/format_lm.sh data/lang data/en-us-mix.lm.gz data/dict/lexicon.txt data/lang_test_adapt\n",
+            "Converting 'data/en-us-mix.lm.gz' to FST\n",
+            "arpa2fst --disambig-symbol=#0 --read-symbol-table=data/lang_test_adapt/words.txt - data/lang_test_adapt/G.fst \n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:94) Reading \\data\\ section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:149) Reading \\1-grams: section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:149) Reading \\2-grams: section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:Read():arpa-file-parser.cc:149) Reading \\3-grams: section.\n",
+            "LOG (arpa2fst[5.5.1046~1-76cd5]:RemoveRedundantStates():arpa-lm-compiler.cc:359) Reduced num-states from 1217646 to 185095\n",
+            "fstisstochastic data/lang_test_adapt/G.fst \n",
+            "6.81902e-07 -3.03779\n",
+            "Succeeded in formatting LM: 'data/en-us-mix.lm.gz'\n",
+            "+ utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test exp/tdnn exp/tdnn/graph\n",
+            "tree-info exp/tdnn/tree \n",
+            "tree-info exp/tdnn/tree \n",
+            "fstdeterminizestar --use-log=true \n",
+            "fsttablecompose data/lang_test/L_disambig.fst data/lang_test/G.fst \n",
+            "fstminimizeencoded \n",
+            "fstpushspecial \n",
+            "fstisstochastic data/lang_test/tmp/LG.fst \n",
+            "-0.145498 -0.146281\n",
+            "[info]: LG not stochastic.\n",
+            "fstcomposecontext --context-size=2 --central-position=1 --read-disambig-syms=data/lang_test/phones/disambig.int --write-disambig-syms=data/lang_test/tmp/disambig_ilabels_2_1.int data/lang_test/tmp/ilabels_2_1.905 data/lang_test/tmp/LG.fst \n",
+            "fstisstochastic data/lang_test/tmp/CLG_2_1.fst \n",
+            "-0.145498 -0.146281\n",
+            "[info]: CLG not stochastic.\n",
+            "make-h-transducer --disambig-syms-out=exp/tdnn/graph/disambig_tid.int --transition-scale=1.0 data/lang_test/tmp/ilabels_2_1 exp/tdnn/tree exp/tdnn/final.mdl \n",
+            "fstrmepslocal \n",
+            "fsttablecompose exp/tdnn/graph/Ha.fst data/lang_test/tmp/CLG_2_1.fst \n",
+            "fstdeterminizestar --use-log=true \n",
+            "fstminimizeencoded \n",
+            "fstrmsymbols exp/tdnn/graph/disambig_tid.int \n",
+            "fstisstochastic exp/tdnn/graph/HCLGa.fst \n",
+            "-0.109817 -0.571742\n",
+            "HCLGa is not stochastic\n",
+            "add-self-loops --self-loop-scale=1.0 --reorder=true exp/tdnn/final.mdl exp/tdnn/graph/HCLGa.fst \n",
+            "fstisstochastic exp/tdnn/graph/HCLG.fst \n",
+            "1.90465e-09 -0.415046\n",
+            "[info]: final HCLG is not stochastic.\n",
+            "+ utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test_adapt exp/tdnn exp/tdnn/graph_adapt\n",
+            "tree-info exp/tdnn/tree \n",
+            "tree-info exp/tdnn/tree \n",
+            "fstdeterminizestar --use-log=true \n",
+            "fsttablecompose data/lang_test_adapt/L_disambig.fst data/lang_test_adapt/G.fst \n",
+            "fstminimizeencoded \n",
+            "fstpushspecial \n",
+            "fstisstochastic data/lang_test_adapt/tmp/LG.fst \n",
+            "-0.148474 -0.149181\n",
+            "[info]: LG not stochastic.\n",
+            "fstcomposecontext --context-size=2 --central-position=1 --read-disambig-syms=data/lang_test_adapt/phones/disambig.int --write-disambig-syms=data/lang_test_adapt/tmp/disambig_ilabels_2_1.int data/lang_test_adapt/tmp/ilabels_2_1.979 data/lang_test_adapt/tmp/LG.fst \n",
+            "fstisstochastic data/lang_test_adapt/tmp/CLG_2_1.fst \n",
+            "-0.148474 -0.149181\n",
+            "[info]: CLG not stochastic.\n",
+            "make-h-transducer --disambig-syms-out=exp/tdnn/graph_adapt/disambig_tid.int --transition-scale=1.0 data/lang_test_adapt/tmp/ilabels_2_1 exp/tdnn/tree exp/tdnn/final.mdl \n",
+            "fstrmepslocal \n",
+            "fsttablecompose exp/tdnn/graph_adapt/Ha.fst data/lang_test_adapt/tmp/CLG_2_1.fst \n",
+            "fstdeterminizestar --use-log=true \n",
+            "fstminimizeencoded \n",
+            "fstrmsymbols exp/tdnn/graph_adapt/disambig_tid.int \n",
+            "fstisstochastic exp/tdnn/graph_adapt/HCLGa.fst \n",
+            "-0.113907 -0.5857\n",
+            "HCLGa is not stochastic\n",
+            "add-self-loops --self-loop-scale=1.0 --reorder=true exp/tdnn/final.mdl exp/tdnn/graph_adapt/HCLGa.fst \n",
+            "fstisstochastic exp/tdnn/graph_adapt/HCLG.fst \n",
+            "1.90465e-09 -0.423618\n",
+            "[info]: final HCLG is not stochastic.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!cat decode.sh\n",
+        "!bash decode.sh"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Sl3QBI1MXpc-",
+        "outputId": "affac8a3-782f-4000-e31f-81bfed47a37a"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "#!/bin/bash\n",
+            "\n",
+            ". path.sh\n",
+            "\n",
+            "steps/make_mfcc.sh --nj 10 data_test/test_small exp/make_mfcc/test mfcc\n",
+            "steps/compute_cmvn_stats.sh data_test/test_small exp/make_mfcc/test mfcc\n",
+            "utils/fix_data_dir.sh data_test/test_small\n",
+            "\n",
+            "steps/online/nnet2/extract_ivectors_online.sh --nj 4 \\\n",
+            "          data_test/test_small exp/extractor \\\n",
+            "          exp/ivectors_test\n",
+            "\n",
+            "steps/nnet3/decode.sh --nj 4 \\\n",
+            "          --acwt 1.0 --post-decode-acwt 10.0 \\\n",
+            "          --online-ivector-dir exp/ivectors_test \\\n",
+            "          exp/tdnn/graph_adapt data_test/test_small exp/tdnn/decode_test_adapt\n",
+            "\n",
+            "steps/nnet3/decode.sh --nj 4 \\\n",
+            "          --acwt 1.0 --post-decode-acwt 10.0 \\\n",
+            "          --online-ivector-dir exp/ivectors_test \\\n",
+            "          exp/tdnn/graph data_test/test_small exp/tdnn/decode_test\n",
+            "\n",
+            "#steps/nnet3/decode_lookahead.sh --nj 4 \\\n",
+            "#          --acwt 1.0 --post-decode-acwt 10.0 \\\n",
+            "#          --online-ivector-dir exp/ivectors_test \\\n",
+            "#          exp/tdnn/lgraph data_test/test_small exp/tdnn/decode_test_adapt\n",
+            "#steps/nnet3/decode_lookahead.sh --nj 4 \\\n",
+            "#          --acwt 1.0 --post-decode-acwt 10.0 \\\n",
+            "#          --online-ivector-dir exp/ivectors_test \\\n",
+            "#          exp/tdnn/lgraph_orig data_test/test_small exp/tdnn/decode_test\n",
+            "steps/make_mfcc.sh --nj 10 data_test/test_small exp/make_mfcc/test mfcc\n",
+            "steps/make_mfcc.sh: moving data_test/test_small/feats.scp to data_test/test_small/.backup\n",
+            "utils/validate_data_dir.sh: Successfully validated data-directory data_test/test_small\n",
+            "steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.\n",
+            "steps/make_mfcc.sh: Succeeded creating MFCC features for test_small\n",
+            "steps/compute_cmvn_stats.sh data_test/test_small exp/make_mfcc/test mfcc\n",
+            "Succeeded creating CMVN stats for test_small\n",
+            "fix_data_dir.sh: kept all 50 utterances.\n",
+            "fix_data_dir.sh: old files are kept in data_test/test_small/.backup\n",
+            "steps/online/nnet2/extract_ivectors_online.sh --nj 4 data_test/test_small exp/extractor exp/ivectors_test\n",
+            "steps/online/nnet2/extract_ivectors_online.sh: extracting iVectors\n",
+            "steps/online/nnet2/extract_ivectors_online.sh: combining iVectors across jobs\n",
+            "steps/online/nnet2/extract_ivectors_online.sh: done extracting (online) iVectors to exp/ivectors_test using the extractor in exp/extractor.\n",
+            "steps/nnet3/decode.sh --nj 4 --acwt 1.0 --post-decode-acwt 10.0 --online-ivector-dir exp/ivectors_test exp/tdnn/graph_adapt data_test/test_small exp/tdnn/decode_test_adapt\n",
+            "steps/nnet2/check_ivectors_compatible.sh: WARNING: One of the directories do not contain iVector ID.\n",
+            "steps/nnet2/check_ivectors_compatible.sh: WARNING: That means it's you who's reponsible for keeping \n",
+            "steps/nnet2/check_ivectors_compatible.sh: WARNING: the directories compatible\n",
+            "steps/nnet3/decode.sh: feature type is raw\n",
+            "steps/diagnostic/analyze_lats.sh --cmd run.pl --iter final exp/tdnn/graph_adapt exp/tdnn/decode_test_adapt\n",
+            "steps/diagnostic/analyze_lats.sh: see stats in exp/tdnn/decode_test_adapt/log/analyze_alignments.log\n",
+            "Overall, lattice depth (10,50,90-percentile)=(1,1,4) and mean=2.4\n",
+            "steps/diagnostic/analyze_lats.sh: see stats in exp/tdnn/decode_test_adapt/log/analyze_lattice_depth_stats.log\n",
+            "score best paths\n",
+            "local/score.sh --cmd run.pl data_test/test_small exp/tdnn/graph_adapt exp/tdnn/decode_test_adapt\n",
+            "local/score.sh: scoring with word insertion penalty=0.0,0.5,1.0\n",
+            "score confidence and timing with sclite\n",
+            "Decoding done.\n",
+            "steps/nnet3/decode.sh --nj 4 --acwt 1.0 --post-decode-acwt 10.0 --online-ivector-dir exp/ivectors_test exp/tdnn/graph data_test/test_small exp/tdnn/decode_test\n",
+            "steps/nnet2/check_ivectors_compatible.sh: WARNING: One of the directories do not contain iVector ID.\n",
+            "steps/nnet2/check_ivectors_compatible.sh: WARNING: That means it's you who's reponsible for keeping \n",
+            "steps/nnet2/check_ivectors_compatible.sh: WARNING: the directories compatible\n",
+            "steps/nnet3/decode.sh: feature type is raw\n",
+            "steps/diagnostic/analyze_lats.sh --cmd run.pl --iter final exp/tdnn/graph exp/tdnn/decode_test\n",
+            "steps/diagnostic/analyze_lats.sh: see stats in exp/tdnn/decode_test/log/analyze_alignments.log\n",
+            "Overall, lattice depth (10,50,90-percentile)=(1,5,23) and mean=10.4\n",
+            "steps/diagnostic/analyze_lats.sh: see stats in exp/tdnn/decode_test/log/analyze_lattice_depth_stats.log\n",
+            "score best paths\n",
+            "local/score.sh --cmd run.pl data_test/test_small exp/tdnn/graph exp/tdnn/decode_test\n",
+            "local/score.sh: scoring with word insertion penalty=0.0,0.5,1.0\n",
+            "score confidence and timing with sclite\n",
+            "Decoding done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!bash RESULTS"
+      ],
+      "metadata": {
+        "id": "ABtcNyUDX4S8",
+        "outputId": "d5e50be7-3293-4a59-94b8-9bfa46736481",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "%WER 11.77 [ 107 / 909, 13 ins, 7 del, 87 sub ] exp/tdnn/decode_test/wer_7_1.0\n",
+            "%WER 0.22 [ 2 / 909, 0 ins, 1 del, 1 sub ] exp/tdnn/decode_test_adapt/wer_10_1.0\n"
+          ]
+        }
+      ]
+    }
+  ]
+}

python/example/test_alternatives.py

@@ -1,23 +1,19 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer, SetLogLevel
-import sys
-import os
 import wave
+import sys
 import json
 
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
 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")
 if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
+    print("Audio file must be WAV format mono PCM.")
+    sys.exit(1)
 
-model = Model("model")
+model = Model(lang="en-us")
 rec = KaldiRecognizer(model, wf.getframerate())
 rec.SetMaxAlternatives(10)
 rec.SetWords(True)

python/example/test_empty.py

@@ -1,11 +1,11 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer
-import sys
 import json
 
-model = Model("model")
+from vosk import Model, KaldiRecognizer
+
+model = Model(lang="en-us")
 rec = KaldiRecognizer(model, 8000)
 
 res = json.loads(rec.FinalResult())
-print (res)
+print(res)

python/example/test_ffmpeg.py

@@ -1,33 +1,29 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer, SetLogLevel
-import sys
-import os
-import wave
 import subprocess
+import sys
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
+SAMPLE_RATE = 16000
 
 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)
+model = Model(lang="en-us")
+rec = KaldiRecognizer(model, SAMPLE_RATE)
 
-sample_rate=16000
-model = Model("model")
-rec = KaldiRecognizer(model, sample_rate)
-
-process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
+with subprocess.Popen(["ffmpeg", "-loglevel", "quiet", "-i",
                             sys.argv[1],
-                            '-ar', str(sample_rate) , '-ac', '1', '-f', 's16le', '-'],
-                            stdout=subprocess.PIPE)
+                            "-ar", str(SAMPLE_RATE) , "-ac", "1", "-f", "s16le", "-"],
+                            stdout=subprocess.PIPE) as process:
 
-while True:
-    data = process.stdout.read(4000)
-    if len(data) == 0:
-        break
-    if rec.AcceptWaveform(data):
-        print(rec.Result())
-    else:
-        print(rec.PartialResult())
+    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())
+    print(rec.FinalResult())

python/example/test_gpu_batch.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+
+import sys
+import json
+
+from vosk import BatchModel, BatchRecognizer, GpuInit
+from timeit import default_timer as timer
+
+TOT_SAMPLES = 0
+
+GpuInit()
+
+model = BatchModel("model")
+
+with open(sys.argv[1]) as fn:
+    fnames = fn.readlines()
+    fds = [open(x.strip(), "rb") for x in fnames]
+    uids = [fname.strip().split("/")[-1][:-4] for fname in fnames]
+    recs = [BatchRecognizer(model, 16000) for x in fnames]
+    results = [""] * len(fnames)
+
+ended = set()
+
+start_time = timer()
+
+while True:
+
+    # Feed in the data
+    for i, fd in enumerate(fds):
+        if i in ended:
+            continue
+        data = fd.read(8000)
+        if len(data) == 0:
+            recs[i].FinishStream()
+            ended.add(i)
+            continue
+        recs[i].AcceptWaveform(data)
+        TOT_SAMPLES += len(data)
+
+    # Wait for results from CUDA
+    model.Wait()
+
+    # Retrieve and add results
+    for i, fd in enumerate(fds):
+        res = recs[i].Result()
+        if len(res) != 0:
+            results[i] = results[i] + " " + json.loads(res)["text"]
+
+    if len(ended) == len(fds):
+        break
+
+end_time = timer()
+
+for i, res in enumerate(results):
+    print(uids[i], res.strip())
+
+print("Processed %.3f seconds of audio in %.3f seconds (%.3f xRT)"
+    % (TOT_SAMPLES / 16000.0 / 2,
+    end_time - start_time,
+    (TOT_SAMPLES / 16000.0 / 2 / (end_time - start_time))),
+    file=sys.stderr)

python/example/test_gradio.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+
+import json
+import gradio as gr
+
+from vosk import KaldiRecognizer, Model
+
+model = Model(lang="en-us")
+
+def transcribe(data, state):
+    sample_rate, audio_data = data
+    audio_data = (audio_data >> 16).astype("int16").tobytes()
+
+    if state is None:
+        rec = KaldiRecognizer(model, sample_rate)
+        result = []
+    else:
+        rec, result = state
+
+    if rec.AcceptWaveform(audio_data):
+        text_result = json.loads(rec.Result())["text"]
+        if text_result != "":
+            result.append(text_result)
+        partial_result = ""
+    else:
+        partial_result = json.loads(rec.PartialResult())["partial"] + " "
+
+    return "\n".join(result) + "\n" + partial_result, (rec, result)
+
+gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type="numpy", streaming=True),
+        "state"
+    ],
+    outputs=[
+        "textbox",
+        "state"
+    ],
+    live=True).launch(share=True)

python/example/test_microphone.py

@@ -1,11 +1,15 @@
 #!/usr/bin/env python3
 
+# prerequisites: as described in https://alphacephei.com/vosk/install and also python module `sounddevice` (simply run command `pip install sounddevice`)
+# Example usage using Dutch (nl) recognition model: `python test_microphone.py -m nl`
+# For more help run: `python test_microphone.py -h`
+
 import argparse
-import os
 import queue
-import sounddevice as sd
-import vosk
 import sys
+import sounddevice as sd
+
+from vosk import Model, KaldiRecognizer
 
 q = queue.Queue()
 
@@ -24,8 +28,8 @@ def callback(indata, frames, time, status):
 
 parser = argparse.ArgumentParser(add_help=False)
 parser.add_argument(
-    '-l', '--list-devices', action='store_true',
-    help='show list of audio devices and exit')
+    "-l", "--list-devices", action="store_true",
+    help="show list of audio devices and exit")
 args, remaining = parser.parse_known_args()
 if args.list_devices:
     print(sd.query_devices())
@@ -35,55 +39,51 @@ parser = argparse.ArgumentParser(
     formatter_class=argparse.RawDescriptionHelpFormatter,
     parents=[parser])
 parser.add_argument(
-    '-f', '--filename', type=str, metavar='FILENAME',
-    help='audio file to store recording to')
+    "-f", "--filename", type=str, metavar="FILENAME",
+    help="audio file to store recording to")
 parser.add_argument(
-    '-m', '--model', type=str, metavar='MODEL_PATH',
-    help='Path to the model')
+    "-d", "--device", type=int_or_str,
+    help="input device (numeric ID or substring)")
 parser.add_argument(
-    '-d', '--device', type=int_or_str,
-    help='input device (numeric ID or substring)')
+    "-r", "--samplerate", type=int, help="sampling rate")
 parser.add_argument(
-    '-r', '--samplerate', type=int, help='sampling rate')
+    "-m", "--model", type=str, help="language model; e.g. en-us, fr, nl; default is en-us")
 args = parser.parse_args(remaining)
 
 try:
-    if args.model is None:
-        args.model = "model"
-    if not os.path.exists(args.model):
-        print ("Please download a model for your language from https://alphacephei.com/vosk/models")
-        print ("and unpack as 'model' in the current folder.")
-        parser.exit(0)
     if args.samplerate is None:
-        device_info = sd.query_devices(args.device, 'input')
+        device_info = sd.query_devices(args.device, "input")
         # soundfile expects an int, sounddevice provides a float:
-        args.samplerate = int(device_info['default_samplerate'])
-
-    model = vosk.Model(args.model)
+        args.samplerate = int(device_info["default_samplerate"])
+        
+    if args.model is None:
+        model = Model(lang="en-us")
+    else:
+        model = Model(lang=args.model)
 
     if args.filename:
         dump_fn = open(args.filename, "wb")
     else:
         dump_fn = None
 
-    with sd.RawInputStream(samplerate=args.samplerate, blocksize = 8000, device=args.device, dtype='int16',
-                            channels=1, callback=callback):
-            print('#' * 80)
-            print('Press Ctrl+C to stop the recording')
-            print('#' * 80)
+    with sd.RawInputStream(samplerate=args.samplerate, blocksize = 8000, device=args.device,
+            dtype="int16", channels=1, callback=callback):
+        print("#" * 80)
+        print("Press Ctrl+C to stop the recording")
+        print("#" * 80)
 
-            rec = vosk.KaldiRecognizer(model, args.samplerate)
-            while True:
-                data = q.get()
-                if rec.AcceptWaveform(data):
-                    print(rec.Result())
-                else:
-                    print(rec.PartialResult())
-                if dump_fn is not None:
-                    dump_fn.write(data)
+        rec = KaldiRecognizer(model, args.samplerate)
+        while True:
+            data = q.get()
+            if rec.AcceptWaveform(data):
+                print(rec.Result())
+            else:
+                print(rec.PartialResult())
+            if dump_fn is not None:
+                dump_fn.write(data)
 
 except KeyboardInterrupt:
-    print('\nDone')
+    print("\nDone")
     parser.exit(0)
 except Exception as e:
-    parser.exit(type(e).__name__ + ': ' + str(e))
+    parser.exit(type(e).__name__ + ": " + str(e))

python/example/test_nlsml.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+
+import wave
+import sys
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
+SetLogLevel(0)
+
+wf = wave.open(sys.argv[1], "rb")
+if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
+    print("Audio file must be WAV format mono PCM.")
+    sys.exit(1)
+
+model = Model(lang="en-us")
+rec = KaldiRecognizer(model, wf.getframerate())
+rec.SetMaxAlternatives(10)
+rec.SetNLSML(True)
+
+while True:
+    data = wf.readframes(4000)
+    if len(data) == 0:
+        break
+    if rec.AcceptWaveform(data):
+        print(rec.Result())
+
+print(rec.FinalResult())

python/example/test_reset.py

@@ -1,23 +1,19 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer, SetLogLevel
-import sys
-import os
 import wave
+import sys
 import json
 
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
 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")
 if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
+    print("Audio file must be WAV format mono PCM.")
+    sys.exit(1)
 
-model = Model("model")
+model = Model(lang="en-us")
 rec = KaldiRecognizer(model, wf.getframerate())
 
 while True:
@@ -26,12 +22,12 @@ while True:
         break
     if rec.AcceptWaveform(data):
         print(rec.Result())
-        break
+        sys.exit(1)
 
     else:
         jres = json.loads(rec.PartialResult())
         print(jres)
 
-        if jres['partial'] == "one zero zero zero":
+        if jres["partial"] == "one zero zero zero":
             print("We can reset recognizer here and start over")
-            rec.Reset();
+            rec.Reset()

python/example/test_simple.py

@@ -1,24 +1,27 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer, SetLogLevel
-import sys
-import os
 import wave
+import sys
 
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
+# You can set log level to -1 to disable debug messages
 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")
 if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
+    print("Audio file must be WAV format mono PCM.")
+    sys.exit(1)
+
+model = Model(lang="en-us")
+
+# You can also init model by name or with a folder path
+# model = Model(model_name="vosk-model-en-us-0.21")
+# model = Model("models/en")
 
-model = Model("model")
 rec = KaldiRecognizer(model, wf.getframerate())
 rec.SetWords(True)
+rec.SetPartialWords(True)
 
 while True:
     data = wf.readframes(4000)

python/example/test_speaker.py

@@ -1,38 +1,54 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer, SpkModel
+import os
 import sys
 import wave
 import json
-import os
 import numpy as np
 
-model_path = "model"
-spk_model_path = "model-spk"
+from vosk import Model, KaldiRecognizer, SpkModel
 
-if not os.path.exists(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)
+SPK_MODEL_PATH = "model-spk"
 
-if not os.path.exists(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)
+if not os.path.exists(SPK_MODEL_PATH):
+    print("Please download the speaker model from "
+        "https://alphacephei.com/vosk/models and unpack as {SPK_MODEL_PATH} "
+        "in the current folder.")
+    sys.exit(1)
 
 wf = wave.open(sys.argv[1], "rb")
 if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
+    print("Audio file must be WAV format mono PCM.")
+    sys.exit(1)
 
 # Large vocabulary free form recognition
-model = Model(model_path)
-spk_model = SpkModel(spk_model_path)
+model = Model(lang="en-us")
+spk_model = SpkModel(SPK_MODEL_PATH)
 #rec = KaldiRecognizer(model, wf.getframerate(), spk_model)
 rec = KaldiRecognizer(model, wf.getframerate())
 rec.SetSpkModel(spk_model)
 
-# We compare speakers with cosine distance. We can keep one or several fingerprints for the speaker in a database
+# 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 = [-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]
+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)
@@ -45,16 +61,18 @@ while True:
         break
     if rec.AcceptWaveform(data):
         res = json.loads(rec.Result())
-        print ("Text:", res['text'])
-        if 'spk' in res:
-            print ("X-vector:", res['spk'])
-            print ("Speaker distance:", cosine_dist(spk_sig, res['spk']), "based on", res['spk_frames'], "frames")
+        print("Text:", res["text"])
+        if "spk" in res:
+            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")
+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'])
-if 'spk' in res:
-   print ("X-vector:", res['spk'])
-   print ("Speaker distance:", cosine_dist(spk_sig, res['spk']), "based on", res['spk_frames'], "frames")
-
+print("Text:", res["text"])
+if "spk" in res:
+    print("X-vector:", res["spk"])
+    print("Speaker distance:", cosine_dist(spk_sig, res["spk"]),
+        "based on", res["spk_frames"], "frames")

python/example/test_srt.py

@@ -1,56 +1,21 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer, SetLogLevel
-import sys
-import os
-import wave
 import subprocess
-import srt
-import json
-import datetime
+import sys
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
+SAMPLE_RATE = 16000
 
 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)
+model = Model(lang="en-us")
+rec = KaldiRecognizer(model, SAMPLE_RATE)
 rec.SetWords(True)
 
-process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
+with subprocess.Popen(["ffmpeg", "-loglevel", "quiet", "-i",
                             sys.argv[1],
-                            '-ar', str(sample_rate) , '-ac', '1', '-f', 's16le', '-'],
-                            stdout=subprocess.PIPE)
+                            "-ar", str(SAMPLE_RATE) , "-ac", "1", "-f", "s16le", "-"],
+                            stdout=subprocess.PIPE).stdout as stream:
 
-
-WORDS_PER_LINE = 7
-
-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)
-       if not 'result' in jres:
-           continue
-       words = jres['result']
-       for j in range(0, len(words), WORDS_PER_LINE):
-           line = words[j : j + WORDS_PER_LINE] 
-           s = srt.Subtitle(index=len(subs), 
-                   content=" ".join([l['word'] for l in line]),
-                   start=datetime.timedelta(seconds=line[0]['start']), 
-                   end=datetime.timedelta(seconds=line[-1]['end']))
-           subs.append(s)
-    return subs
-
-print (srt.compose(transcribe()))
+    print(rec.SrtResult(stream))

python/example/test_text.py

@@ -1,16 +1,11 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer
 import sys
 import json
-import os
 
-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)
+from vosk import Model, KaldiRecognizer
 
-
-model = Model("model")
+model = Model(lang="en-us")
 
 # Large vocabulary free form recognition
 rec = KaldiRecognizer(model, 16000)
@@ -18,16 +13,16 @@ rec = KaldiRecognizer(model, 16000)
 # You can also specify the possible word list
 #rec = KaldiRecognizer(model, 16000, "zero oh one two three four five six seven eight nine")
 
-wf = open(sys.argv[1], "rb")
-wf.read(44) # skip header
+with open(sys.argv[1], "rb") as wf:
+    wf.read(44) # skip header
 
-while True:
-    data = wf.read(4000)
-    if len(data) == 0:
-        break
-    if rec.AcceptWaveform(data):
-        res = json.loads(rec.Result())
-        print (res['text'])
+    while True:
+        data = wf.read(4000)
+        if len(data) == 0:
+            break
+        if rec.AcceptWaveform(data):
+            res = json.loads(rec.Result())
+            print(res["text"])
 
-res = json.loads(rec.FinalResult())
-print (res['text'])
+    res = json.loads(rec.FinalResult())
+    print(res["text"])

python/example/test_webvtt.py

@@ -1,72 +1,67 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer, SetLogLevel
-from webvtt import WebVTT, Caption
 import sys
-import os
 import subprocess
 import json
 import textwrap
 
-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)
-rec.SetWords(True)
+from webvtt import WebVTT, Caption
+from vosk import Model, KaldiRecognizer, SetLogLevel
 
+SAMPLE_RATE = 16000
 WORDS_PER_LINE = 7
 
+SetLogLevel(-1)
 
-def timeString(seconds):
+model = Model(lang="en-us")
+rec = KaldiRecognizer(model, SAMPLE_RATE)
+rec.SetWords(True)
+
+
+def timestring(seconds):
     minutes = seconds / 60
     seconds = seconds % 60
     hours = int(minutes / 60)
     minutes = int(minutes % 60)
-    return '%i:%02i:%06.3f' % (hours, minutes, seconds)
+    return "%i:%02i:%06.3f" % (hours, minutes, seconds)
 
 
 def transcribe():
-    command = ['ffmpeg', '-nostdin', '-loglevel', 'quiet', '-i', sys.argv[1],
-               '-ar', str(sample_rate), '-ac', '1', '-f', 's16le', '-']
-    process = subprocess.Popen(command, stdout=subprocess.PIPE)
+    command = ["ffmpeg", "-nostdin", "-loglevel", "quiet", "-i", sys.argv[1],
+               "-ar", str(SAMPLE_RATE), "-ac", "1", "-f", "s16le", "-"]
+    with subprocess.Popen(command, stdout=subprocess.PIPE) as process:
 
-    results = []
-    while True:
-        data = process.stdout.read(4000)
-        if len(data) == 0:
-            break
-        if rec.AcceptWaveform(data):
-            results.append(rec.Result())
-    results.append(rec.FinalResult())
+        results = []
+        while True:
+            data = process.stdout.read(4000)
+            if len(data) == 0:
+                break
+            if rec.AcceptWaveform(data):
+                results.append(rec.Result())
+        results.append(rec.FinalResult())
 
-    vtt = WebVTT()
-    for i, res in enumerate(results):
-        words = json.loads(res).get('result')
-        if not words:
-            continue
+        vtt = WebVTT()
+        for _, res in enumerate(results):
+            words = json.loads(res).get("result")
+            if not words:
+                continue
 
-        start = timeString(words[0]['start'])
-        end = timeString(words[-1]['end'])
-        content = ' '.join([w['word'] for w in words])
+            start = timestring(words[0]["start"])
+            end = timestring(words[-1]["end"])
+            content = " ".join([w["word"] for w in words])
 
-        caption = Caption(start, end, textwrap.fill(content))
-        vtt.captions.append(caption)
+            caption = Caption(start, end, textwrap.fill(content))
+            vtt.captions.append(caption)
 
-    # save or return webvtt
-    if len(sys.argv) > 2:
-        vtt.save(sys.argv[2])
-    else:
-        print(vtt.content)
+        # save or return webvtt
+        if len(sys.argv) > 2:
+            vtt.save(sys.argv[2])
+        else:
+            print(vtt.content)
 
 
-if __name__ == '__main__':
-    if not (1 < len(sys.argv) < 4):
-        print(f'Usage: {sys.argv[0]} audiofile [output file]')
-        exit(1)
+if __name__ == "__main__":
+    if not 1 < len(sys.argv) < 4:
+        print("Usage: {} audiofile [output file]".format(sys.argv[0]))
+        sys.exit(1)
     transcribe()

python/example/test_words.py

@@ -1,23 +1,22 @@
 #!/usr/bin/env python3
 
-from vosk import Model, KaldiRecognizer
-import sys
-import os
 import wave
+import sys
 
-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)
+from vosk import Model, KaldiRecognizer
 
 wf = wave.open(sys.argv[1], "rb")
 if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
+    print("Audio file must be WAV format mono PCM.")
+    sys.exit(1)
 
-model = Model("model")
+model = Model(lang="en-us")
 
-# 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]"]')
+# 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(4000)
@@ -25,6 +24,7 @@ while True:
         break
     if rec.AcceptWaveform(data):
         print(rec.Result())
+        rec.SetGrammar('["one zero one two three oh", "four five six", "seven eight nine zero", "[unk]"]')
     else:
         print(rec.PartialResult())
 

python/setup.py

@@ -1,14 +1,15 @@
 import os
-import sys
 import setuptools
 import shutil
 import glob
 import platform
 
 # Figure out environment for cross-compile
-vosk_source = os.getenv("VOSK_SOURCE", os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-system = os.environ.get('VOSK_PLATFORM', platform.system())
+vosk_source = os.getenv("VOSK_SOURCE", os.path.abspath(os.path.join(os.path.dirname(__file__),
+    "..")))
+system = os.environ.get('VOSK_SYSTEM', platform.system())
 architecture = os.environ.get('VOSK_ARCHITECTURE', platform.architecture()[0])
+machine = os.environ.get('VOSK_MACHINE', platform.machine())
 
 # Copy precompmilled libraries
 for lib in glob.glob(os.path.join(vosk_source, "src/lib*.*")):
@@ -25,26 +26,26 @@ else:
         def get_tag(self):
             abi = 'none'
             if system == 'Darwin':
-                oses = 'macosx_10_6_x86_64'
+                oses = 'macosx_10_6_universal2'
             elif system == 'Windows' and architecture == '32bit':
                 oses = 'win32'
             elif system == 'Windows' and architecture == '64bit':
                 oses = 'win_amd64'
-            elif system == 'Linux' and architecture == '64bit':
-                oses = 'linux_x86_64'
+            elif system == 'Linux' and machine == 'aarch64' and architecture == '64bit':
+                oses = 'manylinux2014_aarch64'
             elif system == 'Linux':
-                oses = 'linux_' + architecture
+                oses = 'linux_' + machine
             else:
                 raise TypeError("Unknown build environment")
             return 'py3', abi, oses
     cmdclass = {'bdist_wheel': bdist_wheel_tag_name}
 
-with open("README.md", "r") as fh:
-    long_description = fh.read()
+with open("README.md", "rb") as fh:
+    long_description = fh.read().decode("utf-8")
 
 setuptools.setup(
     name="vosk",
-    version="0.3.30",
+    version="0.3.45",
     author="Alpha Cephei Inc",
     author_email="contact@alphacephei.com",
     description="Offline open source speech recognition API based on Kaldi and Vosk",
@@ -53,6 +54,9 @@ setuptools.setup(
     url="https://github.com/alphacep/vosk-api",
     packages=setuptools.find_packages(),
     package_data = {'vosk': ['*.so', '*.dll', '*.dyld']},
+    entry_points = {
+        'console_scripts': ['vosk-transcriber=vosk.transcriber.cli:main'],
+    },
     include_package_data=True,
     classifiers=[
         'Programming Language :: Python :: 3',
@@ -65,7 +69,7 @@ setuptools.setup(
     cmdclass=cmdclass,
     python_requires='>=3',
     zip_safe=False, # Since we load so file from the filesystem, we can not run from zip file
-    setup_requires=['cffi>=1.0'],
-    install_requires=['cffi>=1.0'],
+    setup_requires=['cffi>=1.0', 'requests', 'tqdm', 'srt', 'websockets'],
+    install_requires=['cffi>=1.0', 'requests', 'tqdm', 'srt', 'websockets'],
     cffi_modules=['vosk_builder.py:ffibuilder'],
 )

python/test/transcribe_scp.py

@@ -1,14 +1,13 @@
 #!/usr/bin/env python3
 
+import wave
+import json
+import sys
+
 from multiprocessing.dummy import Pool
 from vosk import Model, KaldiRecognizer
 
-import sys
-import os
-import wave
-import json
-
-model = Model("model")
+model = Model("en-us")
 
 def recognize(line):
     uid, fn = line.split()
@@ -22,14 +21,14 @@ def recognize(line):
             break
         if rec.AcceptWaveform(data):
             jres = json.loads(rec.Result())
-            text = text + " " + jres['text']
+            text = text + " " + jres["text"]
     jres = json.loads(rec.FinalResult())
-    text = text + " " + jres['text']
-    return (uid + text)
+    text = text + " " + jres["text"]
+    return uid + text
 
 def main():
     p = Pool(8)
-    texts = p.map(recognize, open(sys.argv[1]).readlines())
+    texts = p.map(recognize, open(sys.argv[1], encoding="uft-8").readlines())
     print ("\n".join(texts))
 
 main()

python/vosk/__init__.py

@@ -1,56 +1,162 @@
 import os
 import sys
+import srt
+import datetime
+import json
 
+import requests
+from urllib.request import urlretrieve
+from zipfile import ZipFile
+from re import match
+from pathlib import Path
 from .vosk_cffi import ffi as _ffi
+from tqdm import tqdm
+
+# Remote location of the models and local folders
+MODEL_PRE_URL = "https://alphacephei.com/vosk/models/"
+MODEL_LIST_URL = MODEL_PRE_URL + "model-list.json"
+MODEL_DIRS = [os.getenv("VOSK_MODEL_PATH"), Path("/usr/share/vosk"),
+        Path.home() / "AppData/Local/vosk", Path.home() / ".cache/vosk"]
 
 def open_dll():
     dlldir = os.path.abspath(os.path.dirname(__file__))
-    if sys.platform == 'win32':
+    if sys.platform == "win32":
         # We want to load dependencies too
-        os.environ["PATH"] = dlldir + os.pathsep + os.environ['PATH']
-        if hasattr(os, 'add_dll_directory'):
+        os.environ["PATH"] = dlldir + os.pathsep + os.environ["PATH"]
+        if hasattr(os, "add_dll_directory"):
             os.add_dll_directory(dlldir)
         return _ffi.dlopen(os.path.join(dlldir, "libvosk.dll"))
-    elif sys.platform == 'linux':
+    elif sys.platform == "linux":
         return _ffi.dlopen(os.path.join(dlldir, "libvosk.so"))
-    elif sys.platform == 'darwin':
+    elif sys.platform == "darwin":
         return _ffi.dlopen(os.path.join(dlldir, "libvosk.dyld"))
     else:
         raise TypeError("Unsupported platform")
 
 _c = open_dll()
 
-class Model(object):
+def list_models():
+    response = requests.get(MODEL_LIST_URL, timeout=10)
+    for model in response.json():
+        print(model["name"])
 
-    def __init__(self, model_path):
-        self._handle = _c.vosk_model_new(model_path.encode('utf-8'))
+def list_languages():
+    response = requests.get(MODEL_LIST_URL, timeout=10)
+    languages = {m["lang"] for m in response.json()}
+    for lang in languages:
+        print (lang)
+
+class Model:
+    def __init__(self, model_path=None, model_name=None, lang=None):
+        if model_path is not None:
+            self._handle = _c.vosk_model_new(model_path.encode("utf-8"))
+        else:
+            model_path = self.get_model_path(model_name, lang)
+            self._handle = _c.vosk_model_new(model_path.encode("utf-8"))
+        if self._handle == _ffi.NULL:
+            raise Exception("Failed to create a model")
 
     def __del__(self):
         _c.vosk_model_free(self._handle)
 
     def vosk_model_find_word(self, word):
-        return _c.vosk_model_find_word(self._handle, word.encode('utf-8'))
+        return _c.vosk_model_find_word(self._handle, word.encode("utf-8"))
 
-class SpkModel(object):
+    def get_model_path(self, model_name, lang):
+        if model_name is None:
+            model_path = self.get_model_by_lang(lang)
+        else:
+            model_path = self.get_model_by_name(model_name)
+        return str(model_path)
+
+    def get_model_by_name(self, model_name):
+        for directory in MODEL_DIRS:
+            if directory is None or not Path(directory).exists():
+                continue
+            model_file_list = os.listdir(directory)
+            model_file = [model for model in model_file_list if model == model_name]
+            if model_file != []:
+                return Path(directory, model_file[0])
+        response = requests.get(MODEL_LIST_URL, timeout=10)
+        result_model = [model["name"] for model in response.json() if model["name"] == model_name]
+        if result_model == []:
+            print("model name %s does not exist" % (model_name))
+            sys.exit(1)
+        else:
+            self.download_model(Path(directory, result_model[0]))
+            return Path(directory, result_model[0])
+
+    def get_model_by_lang(self, lang):
+        for directory in MODEL_DIRS:
+            if directory is None or not Path(directory).exists():
+                continue
+            model_file_list = os.listdir(directory)
+            model_file = [model for model in model_file_list if
+                    match(r"vosk-model(-small)?-{}".format(lang), model)]
+            if model_file != []:
+                return Path(directory, model_file[0])
+        response = requests.get(MODEL_LIST_URL, timeout=10)
+        result_model = [model["name"] for model in response.json() if
+                model["lang"] == lang and model["type"] == "small" and model["obsolete"] == "false"]
+        if result_model == []:
+            print("lang %s does not exist" % (lang))
+            sys.exit(1)
+        else:
+            self.download_model(Path(directory, result_model[0]))
+            return Path(directory, result_model[0])
+
+    def download_model(self, model_name):
+        if not (model_name.parent).exists():
+            (model_name.parent).mkdir(parents=True)
+        with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1,
+                desc=(MODEL_PRE_URL + str(model_name.name) + ".zip").rsplit("/",
+                    maxsplit=1)[-1]) as t:
+            reporthook = self.download_progress_hook(t)
+            urlretrieve(MODEL_PRE_URL + str(model_name.name) + ".zip",
+                    str(model_name) + ".zip", reporthook=reporthook, data=None)
+            t.total = t.n
+            with ZipFile(str(model_name) + ".zip", "r") as model_ref:
+                model_ref.extractall(model_name.parent)
+            Path(str(model_name) + ".zip").unlink()
+
+    def download_progress_hook(self, t):
+        last_b = [0]
+        def update_to(b=1, bsize=1, tsize=None):
+            if tsize not in (None, -1):
+                t.total = tsize
+            displayed = t.update((b - last_b[0]) * bsize)
+            last_b[0] = b
+            return displayed
+        return update_to
+
+class SpkModel:
 
     def __init__(self, model_path):
-        self._handle = _c.vosk_spk_model_new(model_path.encode('utf-8'))
+        self._handle = _c.vosk_spk_model_new(model_path.encode("utf-8"))
+
+        if self._handle == _ffi.NULL:
+            raise Exception("Failed to create a speaker model")
 
     def __del__(self):
         _c.vosk_spk_model_free(self._handle)
 
-class KaldiRecognizer(object):
+class KaldiRecognizer:
 
     def __init__(self, *args):
         if len(args) == 2:
             self._handle = _c.vosk_recognizer_new(args[0]._handle, args[1])
-        elif len(args) == 3 and type(args[2]) is SpkModel:
-            self._handle = _c.vosk_recognizer_new_spk(args[0]._handle, args[1], args[2]._handle)
-        elif len(args) == 3 and type(args[2]) is str:
-            self._handle = _c.vosk_recognizer_new_grm(args[0]._handle, args[1], args[2].encode('utf-8'))
+        elif len(args) == 3 and isinstance(args[2], SpkModel):
+            self._handle = _c.vosk_recognizer_new_spk(args[0]._handle,
+                    args[1], args[2]._handle)
+        elif len(args) == 3 and isinstance(args[2], str):
+            self._handle = _c.vosk_recognizer_new_grm(args[0]._handle,
+                    args[1], args[2].encode("utf-8"))
         else:
             raise TypeError("Unknown arguments")
 
+        if self._handle == _ffi.NULL:
+            raise Exception("Failed to create a recognizer")
+
     def __del__(self):
         _c.vosk_recognizer_free(self._handle)
 
@@ -60,24 +166,62 @@ class KaldiRecognizer(object):
     def SetWords(self, enable_words):
         _c.vosk_recognizer_set_words(self._handle, 1 if enable_words else 0)
 
+    def SetPartialWords(self, enable_partial_words):
+        _c.vosk_recognizer_set_partial_words(self._handle, 1 if enable_partial_words else 0)
+
+    def SetNLSML(self, enable_nlsml):
+        _c.vosk_recognizer_set_nlsml(self._handle, 1 if enable_nlsml else 0)
+
     def SetSpkModel(self, spk_model):
         _c.vosk_recognizer_set_spk_model(self._handle, spk_model._handle)
 
+    def SetGrammar(self, grammar):
+        _c.vosk_recognizer_set_grm(self._handle, grammar.encode("utf-8"))
+
     def AcceptWaveform(self, data):
-        return _c.vosk_recognizer_accept_waveform(self._handle, data, len(data))
+        res = _c.vosk_recognizer_accept_waveform(self._handle, data, len(data))
+        if res < 0:
+            raise Exception("Failed to process waveform")
+        return res
 
     def Result(self):
-        return _ffi.string(_c.vosk_recognizer_result(self._handle)).decode('utf-8')
+        return _ffi.string(_c.vosk_recognizer_result(self._handle)).decode("utf-8")
 
     def PartialResult(self):
-        return _ffi.string(_c.vosk_recognizer_partial_result(self._handle)).decode('utf-8')
+        return _ffi.string(_c.vosk_recognizer_partial_result(self._handle)).decode("utf-8")
 
     def FinalResult(self):
-        return _ffi.string(_c.vosk_recognizer_final_result(self._handle)).decode('utf-8')
+        return _ffi.string(_c.vosk_recognizer_final_result(self._handle)).decode("utf-8")
 
     def Reset(self):
         return _c.vosk_recognizer_reset(self._handle)
 
+    def SrtResult(self, stream, words_per_line = 7):
+        results = []
+
+        while True:
+            data = stream.read(4000)
+            if len(data) == 0:
+                break
+            if self.AcceptWaveform(data):
+                results.append(self.Result())
+        results.append(self.FinalResult())
+
+        subs = []
+        for res in results:
+            jres = json.loads(res)
+            if not "result" in jres:
+                continue
+            words = jres["result"]
+            for j in range(0, len(words), words_per_line):
+                line = words[j : j + words_per_line]
+                s = srt.Subtitle(index=len(subs),
+                        content=" ".join([l["word"] for l in line]),
+                        start=datetime.timedelta(seconds=line[0]["start"]),
+                        end=datetime.timedelta(seconds=line[-1]["end"]))
+                subs.append(s)
+
+        return srt.compose(subs)
 
 def SetLogLevel(level):
     return _c.vosk_set_log_level(level)
@@ -89,3 +233,43 @@ def GpuInit():
 
 def GpuThreadInit():
     _c.vosk_gpu_thread_init()
+
+class BatchModel:
+
+    def __init__(self, model_path, *args):
+        self._handle = _c.vosk_batch_model_new(model_path.encode('utf-8'))
+
+        if self._handle == _ffi.NULL:
+            raise Exception("Failed to create a model")
+
+    def __del__(self):
+        _c.vosk_batch_model_free(self._handle)
+
+    def Wait(self):
+        _c.vosk_batch_model_wait(self._handle)
+
+class BatchRecognizer:
+
+    def __init__(self, *args):
+        self._handle = _c.vosk_batch_recognizer_new(args[0]._handle, args[1])
+
+        if self._handle == _ffi.NULL:
+            raise Exception("Failed to create a recognizer")
+
+    def __del__(self):
+        _c.vosk_batch_recognizer_free(self._handle)
+
+    def AcceptWaveform(self, data):
+        res = _c.vosk_batch_recognizer_accept_waveform(self._handle, data, len(data))
+
+    def Result(self):
+        ptr = _c.vosk_batch_recognizer_front_result(self._handle)
+        res = _ffi.string(ptr).decode("utf-8")
+        _c.vosk_batch_recognizer_pop(self._handle)
+        return res
+
+    def FinishStream(self):
+        _c.vosk_batch_recognizer_finish_stream(self._handle)
+
+    def GetPendingChunks(self):
+        return _c.vosk_batch_recognizer_get_pending_chunks(self._handle)

python/vosk/transcriber/cli.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+
+import argparse
+import logging
+import sys
+import os
+
+from pathlib import Path
+from vosk import list_models, list_languages
+from vosk.transcriber.transcriber import Transcriber
+
+parser = argparse.ArgumentParser(
+        description = "Transcribe audio file and save result in selected format")
+parser.add_argument(
+        "--model", "-m", type=str,
+        help="model path")
+parser.add_argument(
+        "--server", "-s", const="ws://localhost:2700", action="store_const",
+        help="use server for recognition")
+parser.add_argument(
+        "--list-models", default=False, action="store_true",
+        help="list available models")
+parser.add_argument(
+        "--list-languages", default=False, action="store_true",
+        help="list available languages")
+parser.add_argument(
+        "--model-name", "-n", type=str,
+        help="select model by name")
+parser.add_argument(
+        "--lang", "-l", default="en-us", type=str,
+        help="select model by language")
+parser.add_argument(
+        "--input", "-i", type=str,
+        help="audiofile")
+parser.add_argument(
+        "--output", "-o", default="", type=str,
+        help="optional output filename path")
+parser.add_argument(
+        "--output-type", "-t", default="txt", type=str,
+        help="optional arg output data type")
+parser.add_argument(
+        "--tasks", "-ts", default=10, type=int,
+        help="number of parallel recognition tasks")
+parser.add_argument(
+        "--log-level", default="INFO",
+        help="logging level")
+
+def main():
+
+    args = parser.parse_args()
+    log_level = args.log_level.upper()
+    logging.getLogger().setLevel(log_level)
+
+    if args.list_models is True:
+        list_models()
+        return
+
+    if args.list_languages is True:
+        list_languages()
+        return
+
+    if not args.input:
+        logging.info("Please specify input file or directory")
+        sys.exit(1)
+
+    if not Path(args.input).exists():
+        logging.info("File/folder {args.input} does not exist, "\
+            "please specify an existing file/directory")
+        sys.exit(1)
+
+    transcriber = Transcriber(args)
+
+    if Path(args.input).is_dir():
+        task_list = [(Path(args.input, fn),
+            Path(args.output,
+            Path(fn).stem).with_suffix("." + args.output_type)) for fn in os.listdir(args.input)]
+    elif Path(args.input).is_file():
+        if args.output == "":
+            task_list = [(Path(args.input), args.output)]
+        else:
+            task_list = [(Path(args.input), Path(args.output))]
+    else:
+        logging.info("Wrong arguments")
+        sys.exit(1)
+
+    transcriber.process_task_list(task_list)
+
+if __name__ == "__main__":
+    main()

python/vosk/transcriber/transcriber.py

@@ -0,0 +1,195 @@
+import json
+import logging
+import asyncio
+import websockets
+import srt
+import datetime
+import shlex
+import subprocess
+
+from vosk import KaldiRecognizer, Model
+from queue import Queue
+from timeit import default_timer as timer
+from multiprocessing.dummy import Pool
+
+CHUNK_SIZE = 4000
+SAMPLE_RATE = 16000.0
+
+class Transcriber:
+
+    def __init__(self, args):
+        self.model = Model(model_path=args.model, model_name=args.model_name, lang=args.lang)
+        self.args = args
+        self.queue = Queue()
+
+    def recognize_stream(self, rec, stream):
+        tot_samples = 0
+        result = []
+
+        while True:
+            data = stream.stdout.read(CHUNK_SIZE)
+
+            if len(data) == 0:
+                break
+
+            tot_samples += len(data)
+            if rec.AcceptWaveform(data):
+                jres = json.loads(rec.Result())
+                logging.info(jres)
+                result.append(jres)
+            else:
+                jres = json.loads(rec.PartialResult())
+                if jres["partial"] != "":
+                    logging.info(jres)
+
+        jres = json.loads(rec.FinalResult())
+        result.append(jres)
+
+        return result, tot_samples
+
+    async def recognize_stream_server(self, proc):
+        async with websockets.connect(self.args.server) as websocket:
+            tot_samples = 0
+            result = []
+
+            await websocket.send('{ "config" : { "sample_rate" : %f } }' % (SAMPLE_RATE))
+            while True:
+                data = await proc.stdout.read(CHUNK_SIZE)
+                tot_samples += len(data)
+                if len(data) == 0:
+                    break
+                await websocket.send(data)
+                jres = json.loads(await websocket.recv())
+                logging.info(jres)
+                if not "partial" in jres:
+                    result.append(jres)
+            await websocket.send('{"eof" : 1}')
+            jres = json.loads(await websocket.recv())
+            logging.info(jres)
+            result.append(jres)
+
+            return result, tot_samples
+
+
+    def format_result(self, result, words_per_line=7):
+        processed_result = ""
+        if self.args.output_type == "srt":
+            subs = []
+
+            for _, res in enumerate(result):
+                if not "result" in res:
+                    continue
+                words = res["result"]
+
+                for j in range(0, len(words), words_per_line):
+                    line = words[j : j + words_per_line]
+                    s = srt.Subtitle(index=len(subs),
+                            content = " ".join([l["word"] for l in line]),
+                            start=datetime.timedelta(seconds=line[0]["start"]),
+                            end=datetime.timedelta(seconds=line[-1]["end"]))
+                    subs.append(s)
+            processed_result = srt.compose(subs)
+
+        elif self.args.output_type == "txt":
+            for part in result:
+                if part["text"] != "":
+                    processed_result += part["text"] + "\n"
+
+        elif self.args.output_type == "json":
+            monologues = {"schemaVersion":"2.0", "monologues":[], "text":[]}
+            for part in result:
+                if part["text"] != "":
+                    monologue["text"] += part["text"]
+            for _, res in enumerate(result):
+                if not "result" in res:
+                    continue
+                monologue = { "speaker": {"id": "unknown", "name": None}, "start": 0, "end": 0, "terms": []}
+                monologue["start"] = res["result"][0]["start"]
+                monologue["end"] = res["result"][-1]["end"]
+                monologue["terms"] = [{"confidence": t["conf"], "start": t["start"], "end": t["end"], "text": t["word"], "type": "WORD" } for t in res["result"]]
+                monologues["monologues"].append(monologue)
+            processed_result = json.dumps(monologues)
+        return processed_result
+
+    def resample_ffmpeg(self, infile):
+        cmd = shlex.split("ffmpeg -nostdin -loglevel quiet "
+                "-i \'{}\' -ar {} -ac 1 -f s16le -".format(str(infile), SAMPLE_RATE))
+        stream = subprocess.Popen(cmd, stdout=subprocess.PIPE)
+        return stream
+
+    async def resample_ffmpeg_async(self, infile):
+        cmd = "ffmpeg -nostdin -loglevel quiet "\
+        "-i \'{}\' -ar {} -ac 1 -f s16le -".format(str(infile), SAMPLE_RATE)
+        return await asyncio.create_subprocess_shell(cmd, stdout=subprocess.PIPE)
+
+    async def server_worker(self):
+        while True:
+            try:
+                input_file, output_file = self.queue.get_nowait()
+            except Exception:
+                break
+
+            logging.info("Recognizing {}".format(input_file))
+            start_time = timer()
+            proc = await self.resample_ffmpeg_async(input_file)
+            result, tot_samples = await self.recognize_stream_server(proc)
+
+            processed_result = self.format_result(result)
+            if output_file != "":
+                logging.info("File {} processing complete".format(output_file))
+                with open(output_file, "w", encoding="utf-8") as fh:
+                    fh.write(processed_result)
+            else:
+                print(processed_result)
+
+            await proc.wait()
+
+            elapsed = timer() - start_time
+            logging.info("Execution time: {:.3f} sec; "\
+                    "xRT {:.3f}".format(elapsed, float(elapsed) * (2 * SAMPLE_RATE) / tot_samples))
+            self.queue.task_done()
+
+    def pool_worker(self, inputdata):
+        logging.info("Recognizing {}".format(inputdata[0]))
+        start_time = timer()
+
+        try:
+            stream = self.resample_ffmpeg(inputdata[0])
+        except FileNotFoundError as e:
+            print(e, "Missing FFMPEG, please install and try again")
+            return
+        except Exception as e:
+            logging.info(e)
+            return
+
+        rec = KaldiRecognizer(self.model, SAMPLE_RATE)
+        rec.SetWords(True)
+        result, tot_samples = self.recognize_stream(rec, stream)
+        processed_result = self.format_result(result)
+
+        if inputdata[1] != "":
+            logging.info("File {} processing complete".format(inputdata[1]))
+            with open(inputdata[1], "w", encoding="utf-8") as fh:
+                fh.write(processed_result)
+        else:
+            print(processed_result)
+
+        elapsed = timer() - start_time
+        logging.info("Execution time: {:.3f} sec; "\
+                "xRT {:.3f}".format(elapsed, float(elapsed) * (2 * SAMPLE_RATE) / tot_samples))
+
+    async def process_task_list_server(self, task_list):
+        for x in task_list:
+            self.queue.put(x)
+        workers = [asyncio.create_task(self.server_worker()) for i in range(self.args.tasks)]
+        await asyncio.gather(*workers)
+
+    def process_task_list_pool(self, task_list):
+        with Pool() as pool:
+            pool.map(self.pool_worker, task_list)
+
+    def process_task_list(self, task_list):
+        if self.args.server is None:
+            self.process_task_list_pool(task_list)
+        else:
+            asyncio.run(self.process_task_list_server(task_list))

ruby/lib/vosk.rb

@@ -0,0 +1,5 @@
+class Vosk
+  def self.hi
+    puts "Hello world!"
+  end
+end

ruby/vosk.gemspec

@@ -0,0 +1,12 @@
+Gem::Specification.new do |s|
+  s.name        = "vosk"
+  s.version     = "0.3.45"
+  s.summary     = "Offline speech recognition API"
+  s.description = "Vosk is an offline open source speech recognition toolkit. It enables speech recognition for 20+ languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish. More to come."
+  s.authors     = ["Alpha Cephei Inc"]
+  s.email       = "contact@alphacphei.com"
+  s.files       = ["lib/vosk.rb"]
+  s.homepage    =
+    "https://rubygems.org/gems/vosk"
+  s.license       = "Apache 2.0"
+end

rust/README.md

@@ -0,0 +1,5 @@
+See
+
+https://github.com/Bear-03/vosk-rs
+
+https://crates.io/crates/vosk

src/Makefile

@@ -1,71 +1,114 @@
+# Locations of the dependencies
 KALDI_ROOT?=$(HOME)/travis/kaldi
 OPENFST_ROOT?=$(KALDI_ROOT)/tools/openfst
 OPENBLAS_ROOT?=$(KALDI_ROOT)/tools/OpenBLAS/install
-HAVE_CUDA?=0
+MKL_ROOT?=/opt/intel/mkl
 CUDA_ROOT?=/usr/local/cuda
-EXT?=so
-CXX?=g++
-HAVE_OPENBLAS_CLAPACK=1
+USE_SHARED?=0
+# Math libraries
+HAVE_OPENBLAS_CLAPACK?=1
+HAVE_MKL?=0
 HAVE_ACCELERATE=0
-EXTRA_CFLGAS?=
+HAVE_CUDA?=0
+# Compiler
+CXX?=g++
+EXT?=so
+# Extra
+EXTRA_CFLAGS?=
 EXTRA_LDFLAGS?=
+OUTDIR?=.
 
 VOSK_SOURCES= \
-	kaldi_recognizer.cc \
+	recognizer.cc \
 	language_model.cc \
 	model.cc \
 	spk_model.cc \
 	vosk_api.cc
 
-CFLAGS=-g -O2 -std=c++17 -fPIC -DFST_NO_DYNAMIC_LINKING $(EXTRA_CFLAGS) \
-	-I. -I$(KALDI_ROOT)/src -I$(OPENFST_ROOT)/include -I$(OPENBLAS_ROOT)/include
+VOSK_HEADERS= \
+	recognizer.h \
+	language_model.h \
+	model.h \
+	spk_model.h \
+	vosk_api.h
 
-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/rnnlm/kaldi-rnnlm.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 \
-	$(OPENFST_ROOT)/lib/libfst.a \
-	$(OPENFST_ROOT)/lib/libfstngram.a
+CFLAGS=-g -O3 -std=c++17 -Wno-deprecated-declarations -fPIC -DFST_NO_DYNAMIC_LINKING \
+       -I. -I$(KALDI_ROOT)/src -I$(OPENFST_ROOT)/include $(EXTRA_CFLAGS)
 
+LDFLAGS=
+
+ifeq ($(USE_SHARED), 0)
+    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/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/rnnlm/kaldi-rnnlm.a \
+        $(KALDI_ROOT)/src/hmm/kaldi-hmm.a \
+        $(KALDI_ROOT)/src/nnet3/kaldi-nnet3.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 \
+        $(OPENFST_ROOT)/lib/libfst.a \
+        $(OPENFST_ROOT)/lib/libfstngram.a
+else
+    LDFLAGS += \
+        -L$(KALDI_ROOT)/libs \
+        -lkaldi-online2 -lkaldi-decoder -lkaldi-ivector -lkaldi-gmm -lkaldi-tree \
+        -lkaldi-feat -lkaldi-lat -lkaldi-lm -lkaldi-rnnlm -lkaldi-hmm -lkaldi-nnet3 \
+        -lkaldi-transform -lkaldi-cudamatrix -lkaldi-matrix -lkaldi-fstext \
+        -lkaldi-util -lkaldi-base -lfst -lfstngram
+endif
 
 ifeq ($(HAVE_OPENBLAS_CLAPACK), 1)
-LIBS += \
-	$(OPENBLAS_ROOT)/lib/libopenblas.a \
-	$(OPENBLAS_ROOT)/lib/liblapack.a \
-	$(OPENBLAS_ROOT)/lib/libblas.a \
-	$(OPENBLAS_ROOT)/lib/libf2c.a
+    CFLAGS += -I$(OPENBLAS_ROOT)/include
+    ifeq ($(USE_SHARED), 0)
+        LIBS += \
+            $(OPENBLAS_ROOT)/lib/libopenblas.a \
+            $(OPENBLAS_ROOT)/lib/liblapack.a \
+            $(OPENBLAS_ROOT)/lib/libblas.a \
+            $(OPENBLAS_ROOT)/lib/libf2c.a
+    else
+        LDFLAGS += -lopenblas -llapack -lblas -lf2c
+    endif
+endif
+
+ifeq ($(HAVE_MKL), 1)
+    CFLAGS += -DHAVE_MKL=1 -I$(MKL_ROOT)/include
+    LDFLAGS += -L$(MKL_ROOT)/lib/intel64 -Wl,-rpath=$(MKL_ROOT)/lib/intel64 -lmkl_rt -lmkl_intel_lp64 -lmkl_core -lmkl_sequential
 endif
 
 ifeq ($(HAVE_ACCELERATE), 1)
-LIBS += \
-	-framework Accelerate
+    LDFLAGS += -framework Accelerate
 endif
 
 ifeq ($(HAVE_CUDA), 1)
-CFLAGS+=-DHAVE_CUDA=1 -I$(CUDA_ROOT)/include
-LIBS+=-L$(CUDA_ROOT)/lib64 -lcublas -lcusparse -lcudart -lcurand -lcufft -lcusolver -lnvToolsExt
+    VOSK_SOURCES += batch_recognizer.cc batch_model.cc
+    VOSK_HEADERS += batch_recognizer.h batch_model.h
+
+    CFLAGS+=-DHAVE_CUDA=1 -I$(CUDA_ROOT)/include
+
+    LIBS := \
+        $(KALDI_ROOT)/src/cudadecoder/kaldi-cudadecoder.a \
+        $(KALDI_ROOT)/src/cudafeat/kaldi-cudafeat.a \
+        $(LIBS)
+
+    LDFLAGS += -L$(CUDA_ROOT)/lib64 -lcuda -lcublas -lcusparse -lcudart -lcurand -lcufft -lcusolver -lnvToolsExt
 endif
 
-all: libvosk.$(EXT)
+all: $(OUTDIR)/libvosk.$(EXT)
 
-libvosk.$(EXT): $(VOSK_SOURCES:.cc=.o)
-	$(CXX) --shared -s -o $@ $^ $(LIBS) -lm -latomic $(EXTRA_LDFLAGS)
+$(OUTDIR)/libvosk.$(EXT): $(VOSK_SOURCES:%.cc=$(OUTDIR)/%.o) $(LIBS)
+	$(CXX) --shared -s -o $@ $^ $(LDFLAGS) $(EXTRA_LDFLAGS)
 
-%.o: %.cc
+$(OUTDIR)/%.o: %.cc $(VOSK_HEADERS)
 	$(CXX) $(CFLAGS) -c -o $@ $<
 
 clean:

src/batch_model.cc

@@ -0,0 +1,135 @@
+// 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.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "batch_model.h"
+
+#include <sys/stat.h>
+
+using namespace fst;
+using namespace kaldi::nnet3;
+using CorrelationID = CudaOnlinePipelineDynamicBatcher::CorrelationID;
+
+BatchModel::BatchModel(const char *model_path) : model_path_str_(model_path) {
+    BatchedThreadedNnet3CudaOnlinePipelineConfig batched_decoder_config;
+
+    kaldi::ParseOptions po("something");
+    batched_decoder_config.Register(&po);
+    po.ReadConfigFile(model_path_str_ + "/conf/model.conf");
+
+    struct stat buffer;
+
+    string nnet3_rxfilename_ = model_path_str_ + "/am/final.mdl";
+    string hclg_fst_rxfilename_ = model_path_str_ + "/graph/HCLG.fst";
+    string word_syms_rxfilename_ = model_path_str_ + "/graph/words.txt";
+    string winfo_rxfilename_ = model_path_str_ + "/graph/phones/word_boundary.int";
+    string std_fst_rxfilename_ = model_path_str_ + "/rescore/G.fst";
+    string carpa_rxfilename_ = model_path_str_ + "/rescore/G.carpa";
+
+    trans_model_ = new kaldi::TransitionModel();
+    nnet_ = new kaldi::nnet3::AmNnetSimple();
+    {
+        bool binary;
+        kaldi::Input ki(nnet3_rxfilename_, &binary);
+        trans_model_->Read(ki.Stream(), binary);
+        nnet_->Read(ki.Stream(), binary);
+        SetBatchnormTestMode(true, &(nnet_->GetNnet()));
+        SetDropoutTestMode(true, &(nnet_->GetNnet()));
+        nnet3::CollapseModel(nnet3::CollapseModelConfig(), &(nnet_->GetNnet()));
+    }
+
+    if (stat(hclg_fst_rxfilename_.c_str(), &buffer) == 0) {
+        KALDI_LOG << "Loading HCLG from " << hclg_fst_rxfilename_;
+        hclg_fst_ = fst::ReadFstKaldiGeneric(hclg_fst_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_;
+    }
+    KALDI_ASSERT(word_syms_);
+
+    if (stat(winfo_rxfilename_.c_str(), &buffer) == 0) {
+        KALDI_LOG << "Loading winfo " << winfo_rxfilename_;
+        kaldi::WordBoundaryInfoNewOpts opts;
+        winfo_ = new kaldi::WordBoundaryInfo(opts, winfo_rxfilename_);
+    }
+
+    batched_decoder_config.num_worker_threads = -1;
+    batched_decoder_config.max_batch_size = 32;
+    batched_decoder_config.num_channels = 600;
+    batched_decoder_config.reset_on_endpoint = true;
+    batched_decoder_config.use_gpu_feature_extraction = true;
+
+    feature_info_.feature_type = "mfcc";
+    ReadConfigFromFile(model_path_str_ + "/conf/mfcc.conf", &feature_info_.mfcc_opts);
+    feature_info_.silence_weighting_config.silence_weight = 1e-3;
+    feature_info_.silence_weighting_config.silence_phones_str = batched_decoder_config.decoder_opts.endpointing_config.silence_phones;
+
+    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);
+
+
+    batched_decoder_config.decoder_opts.max_active = 7000;
+    batched_decoder_config.decoder_opts.default_beam = 13.0;
+    batched_decoder_config.decoder_opts.lattice_beam = 6.0;
+    batched_decoder_config.compute_opts.acoustic_scale = 1.0;
+    batched_decoder_config.compute_opts.frame_subsampling_factor = 3;
+
+    int32 nnet_left_context, nnet_right_context;
+    nnet3::ComputeSimpleNnetContext(nnet_->GetNnet(), &nnet_left_context,
+                                    &nnet_right_context);
+
+    batched_decoder_config.compute_opts.frames_per_chunk = std::max(51, (nnet_right_context + 3 - nnet_right_context % 3));
+
+    cuda_pipeline_ = new BatchedThreadedNnet3CudaOnlinePipeline 
+         (batched_decoder_config, feature_info_, *hclg_fst_, *nnet_, *trans_model_);
+    cuda_pipeline_->SetSymbolTable(*word_syms_);
+
+    CudaOnlinePipelineDynamicBatcherConfig dynamic_batcher_config;
+    dynamic_batcher_ = new CudaOnlinePipelineDynamicBatcher(dynamic_batcher_config,
+                                                            *cuda_pipeline_);
+
+    samples_per_chunk_ = cuda_pipeline_->GetNSampsPerChunk();
+    last_id_ = 0;
+}
+
+uint64_t BatchModel::GetID(BatchRecognizer *recognizer) {
+    return last_id_++;
+}
+
+BatchModel::~BatchModel() {
+
+    delete trans_model_;
+    delete nnet_;
+    delete word_syms_;
+    delete winfo_;
+    delete hclg_fst_;
+
+    delete cuda_pipeline_;
+    delete dynamic_batcher_;
+}
+
+void BatchModel::WaitForCompletion()
+{
+    dynamic_batcher_->WaitForCompletion();
+}

src/batch_model.h

@@ -0,0 +1,71 @@
+// Copyright 2019 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.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef VOSK_BATCH_MODEL_H
+#define VOSK_BATCH_MODEL_H
+
+#include "base/kaldi-common.h"
+#include "util/common-utils.h"
+#include "fstext/fstext-lib.h"
+#include "fstext/fstext-utils.h"
+#include "decoder/lattice-faster-decoder.h"
+#include "feat/feature-mfcc.h"
+#include "lat/kaldi-lattice.h"
+#include "lat/word-align-lattice.h"
+#include "lat/compose-lattice-pruned.h"
+#include "nnet3/am-nnet-simple.h"
+#include "nnet3/nnet-am-decodable-simple.h"
+#include "nnet3/nnet-utils.h"
+
+#include "cudadecoder/cuda-online-pipeline-dynamic-batcher.h"
+#include "cudadecoder/batched-threaded-nnet3-cuda-online-pipeline.h"
+#include "cudadecoder/batched-threaded-nnet3-cuda-pipeline2.h"
+#include "cudadecoder/cuda-pipeline-common.h"
+
+#include "model.h"
+
+using namespace kaldi;
+using namespace kaldi::cuda_decoder;
+
+class BatchRecognizer;
+
+class BatchModel {
+    public:
+        BatchModel(const char *model_path);
+        ~BatchModel();
+
+        uint64_t GetID(BatchRecognizer *recognizer);
+        void WaitForCompletion();
+
+    private:
+        friend class BatchRecognizer;
+
+        std::string model_path_str_;
+
+        OnlineNnet2FeaturePipelineInfo feature_info_;
+        kaldi::TransitionModel *trans_model_ = nullptr;
+        kaldi::nnet3::AmNnetSimple *nnet_ = nullptr;
+        const fst::SymbolTable *word_syms_ = nullptr;
+
+        fst::Fst<fst::StdArc> *hclg_fst_ = nullptr;
+        kaldi::WordBoundaryInfo *winfo_ = nullptr;
+
+        BatchedThreadedNnet3CudaOnlinePipeline *cuda_pipeline_ = nullptr;
+        CudaOnlinePipelineDynamicBatcher *dynamic_batcher_ = nullptr;
+
+        int32 samples_per_chunk_;
+        uint64_t last_id_;
+};
+
+#endif /* VOSK_BATCH_MODEL_H */

src/batch_recognizer.cc

@@ -0,0 +1,202 @@
+// 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.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "batch_recognizer.h"
+
+#include "fstext/fstext-utils.h"
+#include "lat/sausages.h"
+#include "json.h"
+
+BatchRecognizer::BatchRecognizer(BatchModel *model, float
+                                 sample_frequency) : model_(model), sample_frequency_(sample_frequency),
+                                 initialized_(false), callbacks_set_(false), nlsml_(false) {
+    id_ = model->GetID(this);
+
+
+    resampler_ = new LinearResample(
+        sample_frequency, 16000.0f,
+        std::min(sample_frequency / 2, 16000.0f / 2), 6);
+}
+
+BatchRecognizer::~BatchRecognizer() {
+    delete resampler_;
+    // Drop the ID
+}
+
+void BatchRecognizer::FinishStream()
+{
+    SubVector<BaseFloat> chunk = buffer_.Range(0, buffer_.Dim());
+    model_->dynamic_batcher_->Push(id_, !initialized_, true, chunk);
+}
+
+void BatchRecognizer::PushLattice(CompactLattice &clat, BaseFloat offset)
+{
+    fst::ScaleLattice(fst::GraphLatticeScale(0.9), &clat);
+
+    CompactLattice aligned_lat;
+    WordAlignLattice(clat, *model_->trans_model_, *model_->winfo_, 0, &aligned_lat);
+
+    MinimumBayesRisk mbr(aligned_lat);
+    const vector<BaseFloat> &conf = mbr.GetOneBestConfidences();
+    const vector<int32> &words = mbr.GetOneBest();
+    const vector<pair<BaseFloat, BaseFloat> > &times =
+          mbr.GetOneBestTimes();
+
+    int size = words.size();
+
+    if (nlsml_) {
+
+        std::stringstream ss;
+        std::stringstream text;
+        ss << "<?xml version=\"1.0\"?>\n";
+        ss << "<result grammar=\"default\">\n";
+        BaseFloat confidence = 0.0;
+        for (int i = 0; i < size; i++) {
+            if (i) {
+                text << " ";
+            }
+            confidence += conf[i];
+            text << model_->word_syms_->Find(words[i]);
+        }
+        confidence /= size;
+
+        ss << "<interpretation grammar=\"default\" confidence=\"" << confidence << "\">\n";
+        ss << "<input mode=\"speech\">" << text.str() << "</input>\n";
+        ss << "<instance>" << text.str() << "</instance>\n";
+        ss << "</interpretation>\n";
+        ss << "</result>\n";
+
+        results_.push(ss.str());
+
+    } else {
+        json::JSON obj;
+        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"] = round(times[i].first) * 0.03 + offset;
+            word["end"] = round(times[i].second) * 0.03 + offset;
+            word["conf"] = conf[i];
+            obj["result"].append(word);
+
+            if (i) {
+                text << " ";
+            }
+            text << model_->word_syms_->Find(words[i]);
+        }
+        obj["text"] = text.str();
+
+//      KALDI_LOG << "Result " << id << " " << obj.dump();
+
+        results_.push(obj.dump());
+    }
+}
+
+void BatchRecognizer::SetNLSML(bool nlsml)
+{
+    nlsml_ = nlsml;
+}
+
+
+void BatchRecognizer::AcceptWaveform(const char *data, int len)
+{
+    uint64_t id = id_;
+    if (!callbacks_set_) {
+        // Define the callback for results.
+#if 0
+         model_->cuda_pipeline_->SetBestPathCallback(
+          id,
+          [&, id](const std::string &str, bool partial,
+                       bool endpoint_detected) {
+              if (partial) {
+                  KALDI_LOG << "id #" << id << " [partial] : " << str << ":";
+              }
+
+              if (endpoint_detected) {
+                  KALDI_LOG << "id #" << id << " [endpoint detected]";
+              }
+
+              if (!partial) {
+                  KALDI_LOG << "id #" << id << " : " << str;
+              }
+            });
+#endif
+        model_->cuda_pipeline_->SetLatticeCallback(
+          id,
+          [&, id](SegmentedLatticeCallbackParams& params) {
+              if (params.results.empty()) {
+                  KALDI_WARN << "Empty result for callback";
+                  return;
+              }
+              CompactLattice *clat = params.results[0].GetLatticeResult();
+              BaseFloat offset = params.results[0].GetTimeOffsetSeconds();
+              PushLattice(*clat, offset);
+          },
+          CudaPipelineResult::RESULT_TYPE_LATTICE);
+        callbacks_set_ = true;
+    }
+
+    Vector<BaseFloat> input_wave(len / 2);
+    for (int i = 0; i < len / 2; i++)
+        input_wave(i) = *(((short *)data) + i);
+
+    Vector<BaseFloat> resampled_wave;
+    resampler_->Resample(input_wave, true, &resampled_wave);
+
+    int32 end = buffer_.Dim();
+    buffer_.Resize(end + resampled_wave.Dim(), kCopyData);
+    buffer_.Range(end, resampled_wave.Dim()).CopyFromVec(resampled_wave);
+
+    // Pick chunks and submit them to the batcher
+    int32 i = 0;
+    while (i + model_->samples_per_chunk_ <= buffer_.Dim()) {
+        model_->dynamic_batcher_->Push(id_, !initialized_, false,
+                                       buffer_.Range(i, model_->samples_per_chunk_));
+        initialized_ = true;
+        i += model_->samples_per_chunk_;
+    }
+
+    // Keep remaining data
+    if (i > 0) {
+        int32 tail = buffer_.Dim() - i;
+        for (int j = 0; j < tail; j++) {
+            buffer_(j) = buffer_(i + j);
+        }
+        buffer_.Resize(tail, kCopyData);
+    }
+}
+
+const char* BatchRecognizer::FrontResult()
+{
+    if (results_.empty()) {
+        return "";
+    }
+    return results_.front().c_str();
+}
+
+void BatchRecognizer::Pop()
+{
+    if (results_.empty()) {
+        return;
+    }
+    results_.pop();
+}
+
+int BatchRecognizer::GetNumPendingChunks()
+{
+    return model_->dynamic_batcher_->GetNumPendingChunks(id_);
+}

src/batch_recognizer.h

@@ -0,0 +1,55 @@
+// Copyright 2019 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.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef VOSK_BATCH_RECOGNIZER_H
+#define VOSK_BATCH_RECOGNIZER_H
+
+#include "base/kaldi-common.h"
+#include "util/common-utils.h"
+#include "feat/resample.h"
+
+#include <queue>
+
+#include "batch_model.h"
+
+using namespace kaldi;
+
+class BatchRecognizer {
+    public:
+        BatchRecognizer(BatchModel *model, float sample_frequency);
+        ~BatchRecognizer();
+
+        void AcceptWaveform(const char *data, int len);
+        int GetNumPendingChunks();
+        const char *FrontResult();
+        void Pop();
+        void FinishStream();
+        void SetNLSML(bool nlsml);
+
+    private:
+
+        void PushLattice(CompactLattice &clat, BaseFloat offset);
+
+        BatchModel *model_;
+        uint64_t id_;
+        bool initialized_;
+        bool callbacks_set_;
+        bool nlsml_;
+        float sample_frequency_;
+        std::queue<std::string> results_;
+        LinearResample *resampler_;
+        kaldi::Vector<BaseFloat> buffer_;
+};
+
+#endif /* VOSK_BATCH_RECOGNIZER_H */

src/json.h

@@ -424,7 +424,7 @@ class JSON
         Class Type = Class::Null;
 };
 
-JSON Array() {
+inline JSON Array() {
     return JSON::Make( JSON::Class::Array );
 }
 
@@ -435,11 +435,11 @@ JSON Array( T... args ) {
     return arr;
 }
 
-JSON Object() {
+inline JSON Object() {
     return JSON::Make( JSON::Class::Object );
 }
 
-std::ostream& operator<<( std::ostream &os, const JSON &json ) {
+inline std::ostream& operator<<( std::ostream &os, const JSON &json ) {
     os << json.dump();
     return os;
 }
@@ -647,7 +647,7 @@ namespace {
     }
 }
 
-JSON JSON::Load( const string &str ) {
+inline JSON JSON::Load( const string &str ) {
     size_t offset = 0;
     return parse_next( str, offset );
 }

src/model.cc

@@ -109,10 +109,10 @@ Model::Model(const char *model_path) : model_path_str_(model_path) {
 
     struct stat buffer;
     string am_v2_path = model_path_str_ + "/am/final.mdl";
-    string mfcc_v2_path = model_path_str_ + "/conf/mfcc.conf";
+    string model_conf_v2_path = model_path_str_ + "/conf/model.conf";
     string am_v1_path = model_path_str_ + "/final.mdl";
     string mfcc_v1_path = model_path_str_ + "/mfcc.conf";
-    if (stat(am_v2_path.c_str(), &buffer) == 0 && stat(mfcc_v2_path.c_str(), &buffer) == 0) {
+    if (stat(am_v2_path.c_str(), &buffer) == 0 && stat(model_conf_v2_path.c_str(), &buffer) == 0) {
         ConfigureV2();
         ReadDataFiles();
     } else if (stat(am_v1_path.c_str(), &buffer) == 0 && stat(mfcc_v1_path.c_str(), &buffer) == 0) {
@@ -168,13 +168,13 @@ void Model::ConfigureV1()
     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";
+    fbank_conf_rxfilename_ = model_path_str_ + "/fbank.conf";
     global_cmvn_stats_rxfilename_ = model_path_str_ + "/global_cmvn.stats";
     pitch_conf_rxfilename_ = model_path_str_ + "/pitch.conf";
     rnnlm_word_feats_rxfilename_ = model_path_str_ + "/rnnlm/word_feats.txt";
     rnnlm_feat_embedding_rxfilename_ = model_path_str_ + "/rnnlm/feat_embedding.final.mat";
     rnnlm_config_rxfilename_ = model_path_str_ + "/rnnlm/special_symbol_opts.conf";
     rnnlm_lm_rxfilename_ = model_path_str_ + "/rnnlm/final.raw";
-    rnnlm_lm_fst_rxfilename_ = model_path_str_ + "/rescore/G.fst";
 }
 
 void Model::ConfigureV2()
@@ -197,13 +197,13 @@ void Model::ConfigureV2()
     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";
+    fbank_conf_rxfilename_ = model_path_str_ + "/conf/fbank.conf";
     global_cmvn_stats_rxfilename_ = model_path_str_ + "/am/global_cmvn.stats";
     pitch_conf_rxfilename_ = model_path_str_ + "/conf/pitch.conf";
     rnnlm_word_feats_rxfilename_ = model_path_str_ + "/rnnlm/word_feats.txt";
     rnnlm_feat_embedding_rxfilename_ = model_path_str_ + "/rnnlm/feat_embedding.final.mat";
     rnnlm_config_rxfilename_ = model_path_str_ + "/rnnlm/special_symbol_opts.conf";
     rnnlm_lm_rxfilename_ = model_path_str_ + "/rnnlm/final.raw";
-    rnnlm_lm_fst_rxfilename_ = model_path_str_ + "/rescore/G.fst";
 }
 
 void Model::ReadDataFiles()
@@ -215,9 +215,17 @@ void Model::ReadDataFiles()
          " lattice-beam=" << nnet3_decoding_config_.lattice_beam;
     KALDI_LOG << "Silence phones " << endpoint_config_.silence_phones;
 
-    feature_info_.feature_type = "mfcc";
-    ReadConfigFromFile(mfcc_conf_rxfilename_, &feature_info_.mfcc_opts);
-    feature_info_.mfcc_opts.frame_opts.allow_downsample = true; // It is safe to downsample
+    if (stat(mfcc_conf_rxfilename_.c_str(), &buffer) == 0) {
+        feature_info_.feature_type = "mfcc";
+        ReadConfigFromFile(mfcc_conf_rxfilename_, &feature_info_.mfcc_opts);
+        feature_info_.mfcc_opts.frame_opts.allow_downsample = true; // It is safe to downsample
+    } else if (stat(fbank_conf_rxfilename_.c_str(), &buffer) == 0) {
+        feature_info_.feature_type = "fbank";
+        ReadConfigFromFile(fbank_conf_rxfilename_, &feature_info_.fbank_opts);
+        feature_info_.fbank_opts.frame_opts.allow_downsample = true; // It is safe to downsample
+    } else {
+        KALDI_ERR << "Failed to find feature config file";
+    }
 
     feature_info_.silence_weighting_config.silence_weight = 1e-3;
     feature_info_.silence_weighting_config.silence_phones_str = endpoint_config_.silence_phones;
@@ -233,9 +241,9 @@ void Model::ReadDataFiles()
         SetDropoutTestMode(true, &(nnet_->GetNnet()));
         nnet3::CollapseModel(nnet3::CollapseModelConfig(), &(nnet_->GetNnet()));
     }
+
     decodable_info_ = new nnet3::DecodableNnetSimpleLoopedInfo(decodable_opts_,
                                                                nnet_);
-
     if (stat(final_ie_rxfilename_.c_str(), &buffer) == 0) {
         KALDI_LOG << "Loading i-vector extractor from " << final_ie_rxfilename_;
 
@@ -263,7 +271,8 @@ void Model::ReadDataFiles()
     if (stat(pitch_conf_rxfilename_.c_str(), &buffer) == 0) {
         KALDI_LOG << "Using pitch in feature pipeline";
         feature_info_.add_pitch = true;
-        ReadConfigFromFile(pitch_conf_rxfilename_, &feature_info_.pitch_opts);
+        ReadConfigsFromFile(pitch_conf_rxfilename_,
+                            &feature_info_.pitch_opts, &feature_info_.pitch_process_opts);
     }
 
     if (stat(hclg_fst_rxfilename_.c_str(), &buffer) == 0) {
@@ -273,7 +282,10 @@ void Model::ReadDataFiles()
         KALDI_LOG << "Loading HCL and G from " << hcl_fst_rxfilename_ << " " << g_fst_rxfilename_;
         hcl_fst_ = fst::StdFst::Read(hcl_fst_rxfilename_);
         g_fst_ = fst::StdFst::Read(g_fst_rxfilename_);
-        ReadIntegerVectorSimple(disambig_rxfilename_, &disambig_);
+        if (!ReadIntegerVectorSimple(disambig_rxfilename_, &disambig_)) {
+            KALDI_ERR << "Could not read disambig symbol table from file "
+                      << disambig_rxfilename_;
+        }
     }
 
     if (hclg_fst_ && hclg_fst_->OutputSymbols()) {
@@ -288,7 +300,9 @@ void Model::ReadDataFiles()
                       << word_syms_rxfilename_;
         word_syms_loaded_ = word_syms_;
     }
-    KALDI_ASSERT(word_syms_);
+    if (!word_syms_) {
+        KALDI_ERR << "Word symbol table empty";
+    }
 
     if (stat(winfo_rxfilename_.c_str(), &buffer) == 0) {
         KALDI_LOG << "Loading winfo " << winfo_rxfilename_;
@@ -296,12 +310,19 @@ void Model::ReadDataFiles()
         winfo_ = new kaldi::WordBoundaryInfo(opts, winfo_rxfilename_);
     }
 
+    if (stat(carpa_rxfilename_.c_str(), &buffer) == 0) {
+
+        KALDI_LOG << "Loading subtract G.fst model from " << std_fst_rxfilename_;
+        graph_lm_fst_ = fst::ReadAndPrepareLmFst(std_fst_rxfilename_);
+        KALDI_LOG << "Loading CARPA model from " << carpa_rxfilename_;
+        ReadKaldiObject(carpa_rxfilename_, &const_arpa_);
+    }
+
     // RNNLM Rescoring
     if (stat(rnnlm_lm_rxfilename_.c_str(), &buffer) == 0) {
         KALDI_LOG << "Loading RNNLM model from " << rnnlm_lm_rxfilename_;
 
         ReadKaldiObject(rnnlm_lm_rxfilename_, &rnnlm);
-        rnnlm_lm_fst_ = fst::ReadAndPrepareLmFst(rnnlm_lm_fst_rxfilename_);
         Matrix<BaseFloat> feature_embedding_mat;
         ReadKaldiObject(rnnlm_feat_embedding_rxfilename_, &feature_embedding_mat);
         SparseMatrix<BaseFloat> word_feature_mat;
@@ -319,17 +340,9 @@ void Model::ReadDataFiles()
 
         ReadConfigFromFile(rnnlm_config_rxfilename_, &rnnlm_compute_opts);
 
-    } else 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::ProjectType::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_);
+        rnnlm_enabled_ = true;
     }
+
 }
 
 void Model::Ref() 
@@ -363,5 +376,5 @@ Model::~Model() {
     delete hclg_fst_;
     delete hcl_fst_;
     delete g_fst_;
-    delete std_lm_fst_;
+    delete graph_lm_fst_;
 }

src/model.h

@@ -21,7 +21,7 @@
 #include "online2/onlinebin-util.h"
 #include "online2/online-timing.h"
 #include "online2/online-endpoint.h"
-#include "online2/online-nnet3-decoding.h"
+#include "online2/online-nnet3-incremental-decoding.h"
 #include "online2/online-feature-pipeline.h"
 #include "lat/lattice-functions.h"
 #include "lat/sausages.h"
@@ -36,7 +36,7 @@
 using namespace kaldi;
 using namespace std;
 
-class KaldiRecognizer;
+class Recognizer;
 
 class Model {
 
@@ -52,7 +52,7 @@ protected:
     void ConfigureV2();
     void ReadDataFiles();
 
-    friend class KaldiRecognizer;
+    friend class Recognizer;
 
     string model_path_str_;
     string nnet3_rxfilename_;
@@ -66,17 +66,17 @@ protected:
     string std_fst_rxfilename_;
     string final_ie_rxfilename_;
     string mfcc_conf_rxfilename_;
+    string fbank_conf_rxfilename_;
     string global_cmvn_stats_rxfilename_;
     string pitch_conf_rxfilename_;
 
     string rnnlm_word_feats_rxfilename_;
     string rnnlm_feat_embedding_rxfilename_;
     string rnnlm_config_rxfilename_;
-    string rnnlm_lm_fst_rxfilename_;
     string rnnlm_lm_rxfilename_;
 
     kaldi::OnlineEndpointConfig endpoint_config_;
-    kaldi::LatticeFasterDecoderConfig nnet3_decoding_config_;
+    kaldi::LatticeIncrementalDecoderConfig nnet3_decoding_config_;
     kaldi::nnet3::NnetSimpleLoopedComputationOptions decodable_opts_;
     kaldi::OnlineNnet2FeaturePipelineInfo feature_info_;
 
@@ -92,13 +92,13 @@ protected:
     fst::Fst<fst::StdArc> *hcl_fst_ = nullptr;
     fst::Fst<fst::StdArc> *g_fst_ = nullptr;
 
-    fst::VectorFst<fst::StdArc> *std_lm_fst_ = nullptr;
+    fst::VectorFst<fst::StdArc> *graph_lm_fst_ = nullptr;
     kaldi::ConstArpaLm const_arpa_;
 
     kaldi::rnnlm::RnnlmComputeStateComputationOptions rnnlm_compute_opts;
     CuMatrix<BaseFloat> word_embedding_mat;
-    fst::VectorFst<fst::StdArc> *rnnlm_lm_fst_ = NULL;
     kaldi::nnet3::Nnet rnnlm;
+    bool rnnlm_enabled_ = false;
 
     std::atomic<int> ref_cnt_;
 };

src/recognizer.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "kaldi_recognizer.h"
+#include "recognizer.h"
 #include "json.h"
 #include "fstext/fstext-utils.h"
 #include "lat/sausages.h"
@@ -21,7 +21,7 @@
 using namespace fst;
 using namespace kaldi::nnet3;
 
-KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency) : model_(model), spk_model_(0), sample_frequency_(sample_frequency) {
+Recognizer::Recognizer(Model *model, float sample_frequency) : model_(model), spk_model_(0), sample_frequency_(sample_frequency) {
 
     model_->Ref();
 
@@ -36,7 +36,7 @@ KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency) : model_(
         }
     }
 
-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+    decoder_ = new kaldi::SingleUtteranceNnet3IncrementalDecoder(model_->nnet3_decoding_config_,
             *model_->trans_model_,
             *model_->decodable_info_,
             model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
@@ -46,7 +46,7 @@ KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency) : model_(
     InitRescoring();
 }
 
-KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, char const *grammar) : model_(model), spk_model_(0), sample_frequency_(sample_frequency)
+Recognizer::Recognizer(Model *model, float sample_frequency, char const *grammar) : model_(model), spk_model_(0), sample_frequency_(sample_frequency)
 {
     model_->Ref();
 
@@ -54,50 +54,12 @@ KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, char cons
     silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
 
     if (model_->hcl_fst_) {
-        json::JSON obj;
-        obj = json::JSON::Load(grammar);
-
-        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_);
-        }
+        UpdateGrammarFst(grammar);
     } else {
         KALDI_WARN << "Runtime graphs are not supported by this model";
     }
 
-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+    decoder_ = new kaldi::SingleUtteranceNnet3IncrementalDecoder(model_->nnet3_decoding_config_,
             *model_->trans_model_,
             *model_->decodable_info_,
             model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
@@ -107,7 +69,7 @@ KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, char cons
     InitRescoring();
 }
 
-KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, SpkModel *spk_model) : model_(model), spk_model_(spk_model), sample_frequency_(sample_frequency) {
+Recognizer::Recognizer(Model *model, float sample_frequency, SpkModel *spk_model) : model_(model), spk_model_(spk_model), sample_frequency_(sample_frequency) {
 
     model_->Ref();
     spk_model->Ref();
@@ -123,7 +85,7 @@ KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, SpkModel
         }
     }
 
-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+    decoder_ = new kaldi::SingleUtteranceNnet3IncrementalDecoder(model_->nnet3_decoding_config_,
             *model_->trans_model_,
             *model_->decodable_info_,
             model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
@@ -135,27 +97,27 @@ KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, SpkModel
     InitRescoring();
 }
 
-KaldiRecognizer::~KaldiRecognizer() {
+Recognizer::~Recognizer() {
     delete decoder_;
     delete feature_pipeline_;
     delete silence_weighting_;
     delete g_fst_;
     delete decode_fst_;
     delete spk_feature_;
-    delete lm_fst_;
 
-    delete info;
-    delete lm_to_subtract_det_backoff;
-    delete lm_to_subtract_det_scale;
-    delete lm_to_add_orig;
-    delete lm_to_add;
+    delete lm_to_subtract_;
+    delete carpa_to_add_;
+    delete carpa_to_add_scale_;
+    delete rnnlm_info_;
+    delete rnnlm_to_add_;
+    delete rnnlm_to_add_scale_;
 
     model_->Unref();
     if (spk_model_)
          spk_model_->Unref();
 }
 
-void KaldiRecognizer::InitState()
+void Recognizer::InitState()
 {
     frame_offset_ = 0;
     samples_processed_ = 0;
@@ -164,27 +126,28 @@ void KaldiRecognizer::InitState()
     state_ = RECOGNIZER_INITIALIZED;
 }
 
-void KaldiRecognizer::InitRescoring()
+void Recognizer::InitRescoring()
 {
-    if (model_->rnnlm_lm_fst_) {
-        float lm_scale = 0.5;
-        int lm_order = 4;
+    if (model_->graph_lm_fst_) {
 
-        info = new kaldi::rnnlm::RnnlmComputeStateInfo(model_->rnnlm_compute_opts, model_->rnnlm, model_->word_embedding_mat);
-        lm_to_subtract_det_backoff = new fst::BackoffDeterministicOnDemandFst<fst::StdArc>(*model_->rnnlm_lm_fst_);
-        lm_to_subtract_det_scale = new fst::ScaleDeterministicOnDemandFst(-lm_scale, lm_to_subtract_det_backoff);
-        lm_to_add_orig = new kaldi::rnnlm::KaldiRnnlmDeterministicFst(lm_order, *info);
-        lm_to_add = new fst::ScaleDeterministicOnDemandFst(lm_scale, lm_to_add_orig);
-
-    } else if (model_->std_lm_fst_) {
-        fst::CacheOptions cache_opts(true, 50000);
+        fst::CacheOptions cache_opts(true, -1);
         fst::ArcMapFstOptions mapfst_opts(cache_opts);
-        fst::StdToLatticeMapper<kaldi::BaseFloat> mapper;
-        lm_fst_ = new fst::ArcMapFst<fst::StdArc, kaldi::LatticeArc, fst::StdToLatticeMapper<kaldi::BaseFloat> >(*model_->std_lm_fst_, mapper, mapfst_opts);
+        fst::StdToLatticeMapper<BaseFloat> mapper;
+
+        lm_to_subtract_ = new fst::ArcMapFst<fst::StdArc, LatticeArc, fst::StdToLatticeMapper<BaseFloat> >(*model_->graph_lm_fst_, mapper, mapfst_opts);
+        carpa_to_add_ = new ConstArpaLmDeterministicFst(model_->const_arpa_);
+
+        if (model_->rnnlm_enabled_) {
+           int lm_order = 4;
+           rnnlm_info_ = new kaldi::rnnlm::RnnlmComputeStateInfo(model_->rnnlm_compute_opts, model_->rnnlm, model_->word_embedding_mat);
+           rnnlm_to_add_ = new kaldi::rnnlm::KaldiRnnlmDeterministicFst(lm_order, *rnnlm_info_);
+           rnnlm_to_add_scale_ = new fst::ScaleDeterministicOnDemandFst(0.5, rnnlm_to_add_);
+           carpa_to_add_scale_ = new fst::ScaleDeterministicOnDemandFst(-0.5, carpa_to_add_);
+        }
     }
 }
 
-void KaldiRecognizer::CleanUp()
+void Recognizer::CleanUp()
 {
     delete silence_weighting_;
     silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
@@ -207,7 +170,7 @@ void KaldiRecognizer::CleanUp()
         delete feature_pipeline_;
 
         feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
-        decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
+        decoder_ = new kaldi::SingleUtteranceNnet3IncrementalDecoder(model_->nnet3_decoding_config_,
             *model_->trans_model_,
             *model_->decodable_info_,
             model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
@@ -222,7 +185,7 @@ void KaldiRecognizer::CleanUp()
     }
 }
 
-void KaldiRecognizer::UpdateSilenceWeights()
+void Recognizer::UpdateSilenceWeights()
 {
     if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0 &&
         feature_pipeline_->IvectorFeature() != nullptr) {
@@ -235,17 +198,27 @@ void KaldiRecognizer::UpdateSilenceWeights()
     }
 }
 
-void KaldiRecognizer::SetMaxAlternatives(int max_alternatives)
+void Recognizer::SetMaxAlternatives(int max_alternatives)
 {
     max_alternatives_ = max_alternatives;
 }
 
-void KaldiRecognizer::SetWords(bool words)
+void Recognizer::SetWords(bool words)
 {
     words_ = words;
 }
 
-void KaldiRecognizer::SetSpkModel(SpkModel *spk_model)
+void Recognizer::SetPartialWords(bool partial_words)
+{
+    partial_words_ = partial_words;
+}
+
+void Recognizer::SetNLSML(bool nlsml)
+{
+    nlsml_ = nlsml;
+}
+
+void Recognizer::SetSpkModel(SpkModel *spk_model)
 {
     if (state_ == RECOGNIZER_RUNNING) {
         KALDI_ERR << "Can't add speaker model to already running recognizer";
@@ -256,7 +229,97 @@ void KaldiRecognizer::SetSpkModel(SpkModel *spk_model)
     spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
 }
 
-bool KaldiRecognizer::AcceptWaveform(const char *data, int len)
+void Recognizer::SetGrm(char const *grammar)
+{
+    if (state_ == RECOGNIZER_RUNNING) {
+        KALDI_ERR << "Can't add speaker model to already running recognizer";
+        return;
+    }
+
+    if (!model_->hcl_fst_) {
+        KALDI_WARN << "Runtime graphs are not supported by this model";
+        return;
+    }
+
+    delete decode_fst_;
+
+    if (!strcmp(grammar, "[]")) {
+        decode_fst_ = LookaheadComposeFst(*model_->hcl_fst_, *model_->g_fst_, model_->disambig_);
+    } else {
+        UpdateGrammarFst(grammar);
+    }
+
+    samples_round_start_ += samples_processed_;
+    samples_processed_ = 0;
+    frame_offset_ = 0;
+
+    delete decoder_;
+    delete feature_pipeline_;
+    delete silence_weighting_;
+
+    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
+    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
+    decoder_ = new kaldi::SingleUtteranceNnet3IncrementalDecoder(model_->nnet3_decoding_config_,
+            *model_->trans_model_,
+            *model_->decodable_info_,
+            *decode_fst_,
+            feature_pipeline_);
+
+    if (spk_model_) {
+        delete spk_feature_;
+        spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
+    }
+
+    state_ = RECOGNIZER_INITIALIZED;
+}
+
+
+void Recognizer::UpdateGrammarFst(char const *grammar)
+{
+    json::JSON obj;
+    obj = json::JSON::Load(grammar);
+
+    if (obj.length() <= 0) {
+        KALDI_WARN << "Expecting array of strings, got: '" << grammar << "'";
+        return;
+    }
+
+    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_);
+}
+
+
+bool Recognizer::AcceptWaveform(const char *data, int len)
 {
     Vector<BaseFloat> wave;
     wave.Resize(len / 2, kUndefined);
@@ -265,7 +328,7 @@ bool KaldiRecognizer::AcceptWaveform(const char *data, int len)
     return AcceptWaveform(wave);
 }
 
-bool KaldiRecognizer::AcceptWaveform(const short *sdata, int len)
+bool Recognizer::AcceptWaveform(const short *sdata, int len)
 {
     Vector<BaseFloat> wave;
     wave.Resize(len, kUndefined);
@@ -274,7 +337,7 @@ bool KaldiRecognizer::AcceptWaveform(const short *sdata, int len)
     return AcceptWaveform(wave);
 }
 
-bool KaldiRecognizer::AcceptWaveform(const float *fdata, int len)
+bool Recognizer::AcceptWaveform(const float *fdata, int len)
 {
     Vector<BaseFloat> wave;
     wave.Resize(len, kUndefined);
@@ -283,7 +346,7 @@ bool KaldiRecognizer::AcceptWaveform(const float *fdata, int len)
     return AcceptWaveform(wave);
 }
 
-bool KaldiRecognizer::AcceptWaveform(Vector<BaseFloat> &wdata)
+bool Recognizer::AcceptWaveform(Vector<BaseFloat> &wdata)
 {
     // Cleanup if we finalized previous utterance or the whole feature pipeline
     if (!(state_ == RECOGNIZER_RUNNING || state_ == RECOGNIZER_INITIALIZED)) {
@@ -342,7 +405,7 @@ static void RunNnetComputation(const MatrixBase<BaseFloat> &features,
 
 #define MIN_SPK_FEATS 50
 
-bool KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &out_xvector, int *num_spk_frames)
+bool Recognizer::GetSpkVector(Vector<BaseFloat> &out_xvector, int *num_spk_frames)
 {
     vector<int32> nonsilence_frames;
     if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0) {
@@ -407,14 +470,23 @@ bool KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &out_xvector, int *num_spk_
     return true;
 }
 
-
-const char *KaldiRecognizer::MbrResult(CompactLattice &rlat)
+// If we can't align, we still need to prepare for MBR
+static void CopyLatticeForMbr(CompactLattice &lat, CompactLattice *lat_out)
 {
+    *lat_out = lat;
+    RmEpsilon(lat_out, true);
+    fst::CreateSuperFinal(lat_out);
+    TopSortCompactLatticeIfNeeded(lat_out);
+}
+
+const char *Recognizer::MbrResult(CompactLattice &rlat)
+{
+
     CompactLattice aligned_lat;
     if (model_->winfo_) {
         WordAlignLattice(rlat, *model_->trans_model_, *model_->winfo_, 0, &aligned_lat);
     } else {
-        aligned_lat = rlat;
+        CopyLatticeForMbr(rlat, &aligned_lat);
     }
 
     MinimumBayesRisk mbr(aligned_lat);
@@ -522,7 +594,7 @@ static bool CompactLatticeToWordAlignmentWeight(const CompactLattice &clat,
 }
 
 
-const char *KaldiRecognizer::NbestResult(CompactLattice &clat)
+const char *Recognizer::NbestResult(CompactLattice &clat)
 {
     Lattice lat;
     Lattice nbest_lat;
@@ -533,15 +605,15 @@ const char *KaldiRecognizer::NbestResult(CompactLattice &clat)
     fst::ConvertNbestToVector(nbest_lat, &nbest_lats);
 
     json::JSON obj;
-    std::stringstream ss;
     for (int k = 0; k < nbest_lats.size(); k++) {
 
       Lattice nlat = nbest_lats[k];
-      RmEpsilon(&nlat);
-      CompactLattice nclat;
-      CompactLattice aligned_nclat;
-      ConvertLattice(nlat, &nclat);
 
+      CompactLattice nclat;
+      fst::Invert(&nlat);
+      DeterminizeLattice(nlat, &nclat);
+
+      CompactLattice aligned_nclat;
       if (model_->winfo_) {
           WordAlignLattice(nclat, *model_->trans_model_, *model_->winfo_, 0, &aligned_nclat);
       } else {
@@ -559,7 +631,7 @@ const char *KaldiRecognizer::NbestResult(CompactLattice &clat)
       stringstream text;
       json::JSON entry;
 
-      for (int i = 0; i < words.size(); i++) {
+      for (int i = 0, first = 1; i < words.size(); i++) {
         json::JSON word;
         if (words[i] == 0)
             continue;
@@ -569,8 +641,12 @@ const char *KaldiRecognizer::NbestResult(CompactLattice &clat)
             word["end"] = samples_round_start_ / sample_frequency_ + (frame_offset_ + begin_times[i] + lengths[i]) * 0.03;
             entry["result"].append(word);
         }
-        if (i)
+
+        if (first)
+          first = 0;
+        else
           text << " ";
+
         text << model_->word_syms_->Find(words[i]);
       }
 
@@ -582,56 +658,122 @@ const char *KaldiRecognizer::NbestResult(CompactLattice &clat)
     return StoreReturn(obj.dump());
 }
 
-const char* KaldiRecognizer::GetResult()
+const char *Recognizer::NlsmlResult(CompactLattice &clat)
+{
+    Lattice lat;
+    Lattice nbest_lat;
+    std::vector<Lattice> nbest_lats;
+
+    ConvertLattice (clat, &lat);
+    fst::ShortestPath(lat, &nbest_lat, max_alternatives_);
+    fst::ConvertNbestToVector(nbest_lat, &nbest_lats);
+
+    std::stringstream ss;
+    ss << "<?xml version=\"1.0\"?>\n";
+    ss << "<result grammar=\"default\">\n";
+
+    for (int k = 0; k < nbest_lats.size(); k++) {
+
+      Lattice nlat = nbest_lats[k];
+
+      CompactLattice nclat;
+      fst::Invert(&nlat);
+      DeterminizeLattice(nlat, &nclat);
+
+      CompactLattice aligned_nclat;
+      if (model_->winfo_) {
+          WordAlignLattice(nclat, *model_->trans_model_, *model_->winfo_, 0, &aligned_nclat);
+      } else {
+          aligned_nclat = nclat;
+      }
+
+      std::vector<int32> words;
+      std::vector<int32> begin_times;
+      std::vector<int32> lengths;
+      CompactLattice::Weight weight;
+
+      CompactLatticeToWordAlignmentWeight(aligned_nclat, &words, &begin_times, &lengths, &weight);
+      float likelihood = -(weight.Weight().Value1() + weight.Weight().Value2());
+
+      stringstream text;
+      for (int i = 0, first = 1; i < words.size(); i++) {
+        if (words[i] == 0)
+            continue;
+
+        if (first)
+          first = 0;
+        else
+          text << " ";
+
+        text << model_->word_syms_->Find(words[i]);
+      }
+
+      ss << "<interpretation grammar=\"default\" confidence=\"" << likelihood << "\">\n";
+      ss << "<input mode=\"speech\">" << text.str() << "</input>\n";
+      ss << "<instance>" << text.str() << "</instance>\n";
+      ss << "</interpretation>\n";
+    }
+    ss << "</result>\n";
+
+    return StoreReturn(ss.str());
+}
+
+const char* Recognizer::GetResult()
 {
     if (decoder_->NumFramesDecoded() == 0) {
         return StoreEmptyReturn();
     }
 
-    kaldi::CompactLattice clat;
-    kaldi::CompactLattice rlat;
-    decoder_->GetLattice(true, &clat);
+    // Original from decoder, subtracted graph weight, rescored with carpa, rescored with rnnlm
+    CompactLattice clat, slat, tlat, rlat;
 
-    if (model_->rnnlm_lm_fst_) {
-        kaldi::ComposeLatticePrunedOptions compose_opts;
-        compose_opts.lattice_compose_beam = 3.0;
-        compose_opts.max_arcs = 3000;
+    clat = decoder_->GetLattice(decoder_->NumFramesDecoded(), true);
 
-        TopSortCompactLatticeIfNeeded(&clat);
-        fst::ComposeDeterministicOnDemandFst<fst::StdArc> combined_lms(lm_to_subtract_det_scale, lm_to_add);
-        CompactLattice composed_clat;
-        ComposeCompactLatticePruned(compose_opts, clat,
-                                    &combined_lms, &rlat);
-        lm_to_add_orig->Clear();
-    } else if (model_->std_lm_fst_) {
-        Lattice lat1;
+    if (lm_to_subtract_ && carpa_to_add_) {
+        Lattice lat, composed_lat;
 
-        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);
+        // Delete old score
+        ConvertLattice(clat, &lat);
+        fst::ScaleLattice(fst::GraphLatticeScale(-1.0), &lat);
+        fst::Compose(lat, *lm_to_subtract_, &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>());
+        DeterminizeLattice(composed_lat, &slat);
+        fst::ScaleLattice(fst::GraphLatticeScale(-1.0), &slat);
 
-        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, &rlat);
+        // Add CARPA score
+        TopSortCompactLatticeIfNeeded(&slat);
+        ComposeCompactLatticeDeterministic(slat, carpa_to_add_, &tlat);
+
+        // Rescore with RNNLM score on top if needed
+        if (rnnlm_to_add_scale_) {
+             ComposeLatticePrunedOptions compose_opts;
+             compose_opts.lattice_compose_beam = 3.0;
+             compose_opts.max_arcs = 3000;
+             fst::ComposeDeterministicOnDemandFst<StdArc> combined_rnnlm(carpa_to_add_scale_, rnnlm_to_add_scale_);
+
+             TopSortCompactLatticeIfNeeded(&tlat);
+             ComposeCompactLatticePruned(compose_opts, tlat,
+                                         &combined_rnnlm, &rlat);
+             rnnlm_to_add_->Clear();
+        } else {
+             rlat = tlat;
+        }
     } else {
         rlat = clat;
     }
 
-    fst::ScaleLattice(fst::GraphLatticeScale(0.9), &rlat); // Apply rescoring weight
+    // Pruned composition can return empty lattice. It should be rare
+    if (rlat.Start() != 0) {
+       return StoreEmptyReturn();
+    }
+
+    // Apply rescoring weight
+    fst::ScaleLattice(fst::GraphLatticeScale(0.9), &rlat);
 
     if (max_alternatives_ == 0) {
         return MbrResult(rlat);
+    } else if (nlsml_) {
+        return NlsmlResult(rlat);
     } else {
         return NbestResult(rlat);
     }
@@ -639,7 +781,7 @@ const char* KaldiRecognizer::GetResult()
 }
 
 
-const char* KaldiRecognizer::PartialResult()
+const char* Recognizer::PartialResult()
 {
     if (state_ != RECOGNIZER_RUNNING) {
         return StoreEmptyReturn();
@@ -647,30 +789,75 @@ const char* KaldiRecognizer::PartialResult()
 
     json::JSON res;
 
-    if (decoder_->NumFramesDecoded() == 0) {
-        res["partial"] = "";
-        return StoreReturn(res.dump());
-    }
+    if (partial_words_) {
 
-    kaldi::Lattice lat;
-    decoder_->GetBestPath(false, &lat);
-    vector<kaldi::int32> alignment, words;
-    LatticeWeight weight;
-    GetLinearSymbolSequence(lat, &alignment, &words, &weight);
-
-    ostringstream text;
-    for (size_t i = 0; i < words.size(); i++) {
-        if (i) {
-            text << " ";
+        if (decoder_->NumFramesInLattice() == 0) {
+            res["partial"] = "";
+            return StoreReturn(res.dump());
         }
-        text << model_->word_syms_->Find(words[i]);
+
+        CompactLattice clat;
+        CompactLattice aligned_lat;
+
+        clat = decoder_->GetLattice(decoder_->NumFramesInLattice(), false);
+        if (model_->winfo_) {
+            WordAlignLatticePartial(clat, *model_->trans_model_, *model_->winfo_, 0, &aligned_lat);
+        } else {
+            CopyLatticeForMbr(clat, &aligned_lat);
+        }
+
+        MinimumBayesRisk mbr(aligned_lat);
+        const vector<BaseFloat> &conf = mbr.GetOneBestConfidences();
+        const vector<int32> &words = mbr.GetOneBest();
+        const vector<pair<BaseFloat, BaseFloat> > &times = mbr.GetOneBestTimes();
+
+        int size = words.size();
+
+        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"] = 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];
+            res["partial_result"].append(word);
+
+            if (i) {
+                text << " ";
+            }
+            text << model_->word_syms_->Find(words[i]);
+        }
+        res["partial"] = text.str();
+
+    } else {
+
+        if (decoder_->NumFramesDecoded() == 0) {
+            res["partial"] = "";
+            return StoreReturn(res.dump());
+        }
+        Lattice lat;
+        decoder_->GetBestPath(false, &lat);
+        vector<kaldi::int32> alignment, words;
+        LatticeWeight weight;
+        GetLinearSymbolSequence(lat, &alignment, &words, &weight);
+
+        ostringstream text;
+        for (size_t i = 0; i < words.size(); i++) {
+            if (i) {
+                text << " ";
+            }
+            text << model_->word_syms_->Find(words[i]);
+        }
+        res["partial"] = text.str();
     }
-    res["partial"] = text.str();
 
     return StoreReturn(res.dump());
 }
 
-const char* KaldiRecognizer::Result()
+const char* Recognizer::Result()
 {
     if (state_ != RECOGNIZER_RUNNING) {
         return StoreEmptyReturn();
@@ -680,7 +867,7 @@ const char* KaldiRecognizer::Result()
     return GetResult();
 }
 
-const char* KaldiRecognizer::FinalResult()
+const char* Recognizer::FinalResult()
 {
     if (state_ != RECOGNIZER_RUNNING) {
         return StoreEmptyReturn();
@@ -708,7 +895,7 @@ const char* KaldiRecognizer::FinalResult()
     return last_result_.c_str();
 }
 
-void KaldiRecognizer::Reset()
+void Recognizer::Reset()
 {
     if (state_ == RECOGNIZER_RUNNING) {
         decoder_->FinalizeDecoding();
@@ -717,17 +904,25 @@ void KaldiRecognizer::Reset()
     state_ = RECOGNIZER_ENDPOINT;
 }
 
-const char *KaldiRecognizer::StoreEmptyReturn()
+const char *Recognizer::StoreEmptyReturn()
 {
     if (!max_alternatives_) {
         return StoreReturn("{\"text\": \"\"}");
+    } else if (nlsml_) {
+        return StoreReturn("<?xml version=\"1.0\"?>\n"
+                           "<result grammar=\"default\">\n"
+                           "<interpretation confidence=\"1.0\">\n"
+                           "<instance/>\n"
+                           "<input><noinput/></input>\n"
+                           "</interpretation>\n"
+                           "</result>\n");
     } else {
         return StoreReturn("{\"alternatives\" : [{\"text\": \"\", \"confidence\" : 1.0}] }");
     }
 }
 
 // Store result in recognizer and return as const string
-const char *KaldiRecognizer::StoreReturn(const string &res)
+const char *Recognizer::StoreReturn(const string &res)
 {
     last_result_ = res;
     return last_result_.c_str();

src/recognizer.h

@@ -33,22 +33,25 @@
 
 using namespace kaldi;
 
-enum KaldiRecognizerState {
+enum RecognizerState {
     RECOGNIZER_INITIALIZED,
     RECOGNIZER_RUNNING,
     RECOGNIZER_ENDPOINT,
     RECOGNIZER_FINALIZED
 };
 
-class KaldiRecognizer {
+class Recognizer {
     public:
-        KaldiRecognizer(Model *model, float sample_frequency);
-        KaldiRecognizer(Model *model, float sample_frequency, SpkModel *spk_model);
-        KaldiRecognizer(Model *model, float sample_frequency, char const *grammar);
-        ~KaldiRecognizer();
+        Recognizer(Model *model, float sample_frequency);
+        Recognizer(Model *model, float sample_frequency, SpkModel *spk_model);
+        Recognizer(Model *model, float sample_frequency, char const *grammar);
+        ~Recognizer();
         void SetMaxAlternatives(int max_alternatives);
         void SetSpkModel(SpkModel *spk_model);
+        void SetGrm(char const *grammar);
         void SetWords(bool words);
+        void SetPartialWords(bool partial_words);
+        void SetNLSML(bool nlsml);
         bool AcceptWaveform(const char *data, int len);
         bool AcceptWaveform(const short *sdata, int len);
         bool AcceptWaveform(const float *fdata, int len);
@@ -62,6 +65,7 @@ class KaldiRecognizer {
         void InitRescoring();
         void CleanUp();
         void UpdateSilenceWeights();
+        void UpdateGrammarFst(char const *grammar);
         bool AcceptWaveform(Vector<BaseFloat> &wdata);
         bool GetSpkVector(Vector<BaseFloat> &out_xvector, int *frames);
         const char *GetResult();
@@ -69,9 +73,10 @@ class KaldiRecognizer {
         const char *StoreReturn(const string &res);
         const char *MbrResult(CompactLattice &clat);
         const char *NbestResult(CompactLattice &clat);
+        const char *NlsmlResult(CompactLattice &clat);
 
         Model *model_ = nullptr;
-        SingleUtteranceNnet3Decoder *decoder_ = nullptr;
+        SingleUtteranceNnet3IncrementalDecoder *decoder_ = nullptr;
         fst::LookaheadFst<fst::StdArc, int32> *decode_fst_ = nullptr;
         fst::StdVectorFst *g_fst_ = nullptr; // dynamically constructed grammar
         OnlineNnet2FeaturePipeline *feature_pipeline_ = nullptr;
@@ -82,17 +87,20 @@ class KaldiRecognizer {
         OnlineBaseFeature *spk_feature_ = nullptr;
 
         // Rescoring
-        fst::ArcMapFst<fst::StdArc, kaldi::LatticeArc, fst::StdToLatticeMapper<kaldi::BaseFloat> > *lm_fst_ = nullptr;
-
+        fst::ArcMapFst<fst::StdArc, LatticeArc, fst::StdToLatticeMapper<BaseFloat> > *lm_to_subtract_ = nullptr;
+        kaldi::ConstArpaLmDeterministicFst *carpa_to_add_ = nullptr;
+        fst::ScaleDeterministicOnDemandFst *carpa_to_add_scale_ = nullptr;
         // RNNLM rescoring
-        kaldi::rnnlm::RnnlmComputeStateInfo *info = nullptr;
-        fst::ScaleDeterministicOnDemandFst *lm_to_subtract_det_scale = nullptr;
-        fst::BackoffDeterministicOnDemandFst<fst::StdArc> *lm_to_subtract_det_backoff = nullptr;
-        kaldi::rnnlm::KaldiRnnlmDeterministicFst* lm_to_add_orig = nullptr;
-        fst::DeterministicOnDemandFst<fst::StdArc> *lm_to_add = nullptr;
+        kaldi::rnnlm::KaldiRnnlmDeterministicFst* rnnlm_to_add_ = nullptr;
+        fst::DeterministicOnDemandFst<fst::StdArc> *rnnlm_to_add_scale_ = nullptr;
+        kaldi::rnnlm::RnnlmComputeStateInfo *rnnlm_info_ = nullptr;
 
+
+        // Other
         int max_alternatives_ = 0; // Disable alternatives by default
         bool words_ = false;
+        bool partial_words_ = false;
+        bool nlsml_ = false;
 
         float sample_frequency_;
         int32 frame_offset_;
@@ -100,7 +108,7 @@ class KaldiRecognizer {
         int64 samples_processed_;
         int64 samples_round_start_;
 
-        KaldiRecognizerState state_;
+        RecognizerState state_;
         string last_result_;
 };
 

src/spk_model.h

@@ -22,7 +22,7 @@
 
 using namespace kaldi;
 
-class KaldiRecognizer;
+class Recognizer;
 
 class SpkModel {
 
@@ -32,7 +32,7 @@ public:
     void Unref();
 
 protected:
-    friend class KaldiRecognizer;
+    friend class Recognizer;
     ~SpkModel() {};
 
     kaldi::nnet3::Nnet speaker_nnet;

src/vosk_api.cc

@@ -13,12 +13,14 @@
 // limitations under the License.
 
 #include "vosk_api.h"
-#include "kaldi_recognizer.h"
+
+#include "recognizer.h"
 #include "model.h"
 #include "spk_model.h"
 
 #if HAVE_CUDA
 #include "cudamatrix/cu-device.h"
+#include "batch_recognizer.h"
 #endif
 
 #include <string.h>
@@ -27,11 +29,18 @@ using namespace kaldi;
 
 VoskModel *vosk_model_new(const char *model_path)
 {
-    return (VoskModel *)new Model(model_path);
+    try {
+        return (VoskModel *)new Model(model_path);
+    } catch (...) {
+        return nullptr;
+    }
 }
 
 void vosk_model_free(VoskModel *model)
 {
+    if (model == nullptr) {
+       return;
+    }
     ((Model *)model)->Unref();
 }
 
@@ -42,82 +51,134 @@ int vosk_model_find_word(VoskModel *model, const char *word)
 
 VoskSpkModel *vosk_spk_model_new(const char *model_path)
 {
-    return (VoskSpkModel *)new SpkModel(model_path);
+    try {
+        return (VoskSpkModel *)new SpkModel(model_path);
+    } catch (...) {
+        return nullptr;
+    }
 }
 
 void vosk_spk_model_free(VoskSpkModel *model)
 {
+    if (model == nullptr) {
+       return;
+    }
     ((SpkModel *)model)->Unref();
 }
 
 VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate)
 {
-    return (VoskRecognizer *)new KaldiRecognizer((Model *)model, sample_rate);
+    try {
+        return (VoskRecognizer *)new Recognizer((Model *)model, sample_rate);
+    } catch (...) {
+        return nullptr;
+    }
 }
 
 VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, float sample_rate, VoskSpkModel *spk_model)
 {
-    return (VoskRecognizer *)new KaldiRecognizer((Model *)model, sample_rate, (SpkModel *)spk_model);
+    try {
+        return (VoskRecognizer *)new Recognizer((Model *)model, sample_rate, (SpkModel *)spk_model);
+    } catch (...) {
+        return nullptr;
+    }
 }
 
 VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar)
 {
-    return (VoskRecognizer *)new KaldiRecognizer((Model *)model, sample_rate, grammar);
+    try {
+        return (VoskRecognizer *)new Recognizer((Model *)model, sample_rate, grammar);
+    } catch (...) {
+        return nullptr;
+    }
 }
 
 void vosk_recognizer_set_max_alternatives(VoskRecognizer *recognizer, int max_alternatives)
 {
-    ((KaldiRecognizer *)recognizer)->SetMaxAlternatives(max_alternatives);
+    ((Recognizer *)recognizer)->SetMaxAlternatives(max_alternatives);
 }
 
 void vosk_recognizer_set_words(VoskRecognizer *recognizer, int words)
 {
-    ((KaldiRecognizer *)recognizer)->SetWords((bool)words);
+    ((Recognizer *)recognizer)->SetWords((bool)words);
+}
+
+void vosk_recognizer_set_partial_words(VoskRecognizer *recognizer, int partial_words)
+{
+    ((Recognizer *)recognizer)->SetPartialWords((bool)partial_words);
+}
+
+void vosk_recognizer_set_nlsml(VoskRecognizer *recognizer, int nlsml)
+{
+    ((Recognizer *)recognizer)->SetNLSML((bool)nlsml);
 }
 
 void vosk_recognizer_set_spk_model(VoskRecognizer *recognizer, VoskSpkModel *spk_model)
 {
-    ((KaldiRecognizer *)recognizer)->SetSpkModel((SpkModel *)spk_model);
+    if (recognizer == nullptr || spk_model == nullptr) {
+       return;
+    }
+    ((Recognizer *)recognizer)->SetSpkModel((SpkModel *)spk_model);
+}
+
+void vosk_recognizer_set_grm(VoskRecognizer *recognizer, char const *grammar)
+{
+    if (recognizer == nullptr) {
+       return;
+    }
+    ((Recognizer *)recognizer)->SetGrm(grammar);
 }
 
 int vosk_recognizer_accept_waveform(VoskRecognizer *recognizer, const char *data, int length)
 {
-    return ((KaldiRecognizer *)(recognizer))->AcceptWaveform(data, length);
+    try {
+        return ((Recognizer *)(recognizer))->AcceptWaveform(data, length);
+    } catch (...) {
+        return -1;
+    }
 }
 
 int vosk_recognizer_accept_waveform_s(VoskRecognizer *recognizer, const short *data, int length)
 {
-    return ((KaldiRecognizer *)(recognizer))->AcceptWaveform(data, length);
+    try {
+        return ((Recognizer *)(recognizer))->AcceptWaveform(data, length);
+    } catch (...) {
+        return -1;
+    }
 }
 
 int vosk_recognizer_accept_waveform_f(VoskRecognizer *recognizer, const float *data, int length)
 {
-    return ((KaldiRecognizer *)(recognizer))->AcceptWaveform(data, length);
+    try {
+        return ((Recognizer *)(recognizer))->AcceptWaveform(data, length);
+    } catch (...) {
+        return -1;
+    }
 }
 
 const char *vosk_recognizer_result(VoskRecognizer *recognizer)
 {
-    return ((KaldiRecognizer *)recognizer)->Result();
+    return ((Recognizer *)recognizer)->Result();
 }
 
 const char *vosk_recognizer_partial_result(VoskRecognizer *recognizer)
 {
-    return ((KaldiRecognizer *)recognizer)->PartialResult();
+    return ((Recognizer *)recognizer)->PartialResult();
 }
 
 const char *vosk_recognizer_final_result(VoskRecognizer *recognizer)
 {
-    return ((KaldiRecognizer *)recognizer)->FinalResult();
+    return ((Recognizer *)recognizer)->FinalResult();
 }
 
 void vosk_recognizer_reset(VoskRecognizer *recognizer)
 {
-    ((KaldiRecognizer *)recognizer)->Reset();
+    ((Recognizer *)recognizer)->Reset();
 }
 
 void vosk_recognizer_free(VoskRecognizer *recognizer)
 {
-    delete (KaldiRecognizer *)(recognizer);
+    delete (Recognizer *)(recognizer);
 }
 
 void vosk_set_log_level(int log_level)
@@ -128,6 +189,8 @@ void vosk_set_log_level(int log_level)
 void vosk_gpu_init()
 {
 #if HAVE_CUDA
+//    kaldi::CuDevice::EnableTensorCores(true);
+//    kaldi::CuDevice::EnableTf32Compute(true);
     kaldi::CuDevice::Instantiate().SelectGpuId("yes");
     kaldi::CuDevice::Instantiate().AllowMultithreading();
 #endif
@@ -139,3 +202,89 @@ void vosk_gpu_thread_init()
     kaldi::CuDevice::Instantiate();
 #endif
 }
+
+VoskBatchModel *vosk_batch_model_new(const char *model_path)
+{
+#if HAVE_CUDA
+    return (VoskBatchModel *)(new BatchModel(model_path));
+#else
+    return NULL;
+#endif
+}
+
+void vosk_batch_model_free(VoskBatchModel *model)
+{
+#if HAVE_CUDA
+    delete ((BatchModel *)model);
+#endif
+}
+
+void vosk_batch_model_wait(VoskBatchModel *model)
+{
+#if HAVE_CUDA
+    ((BatchModel *)model)->WaitForCompletion();
+#endif
+}
+
+VoskBatchRecognizer *vosk_batch_recognizer_new(VoskBatchModel *model, float sample_rate)
+{
+#if HAVE_CUDA
+    return (VoskBatchRecognizer *)(new BatchRecognizer((BatchModel *)model, sample_rate));
+#else
+    return NULL;
+#endif
+}
+
+void vosk_batch_recognizer_free(VoskBatchRecognizer *recognizer)
+{
+#if HAVE_CUDA
+    delete ((BatchRecognizer *)recognizer);
+#endif
+}
+
+void vosk_batch_recognizer_accept_waveform(VoskBatchRecognizer *recognizer, const char *data, int length)
+{
+#if HAVE_CUDA
+    ((BatchRecognizer *)recognizer)->AcceptWaveform(data, length);
+#endif
+}
+
+void vosk_batch_recognizer_set_nlsml(VoskBatchRecognizer *recognizer, int nlsml)
+{
+#if HAVE_CUDA
+    ((BatchRecognizer *)recognizer)->SetNLSML((bool)nlsml);
+#endif
+}
+
+void vosk_batch_recognizer_finish_stream(VoskBatchRecognizer *recognizer)
+{
+#if HAVE_CUDA
+    ((BatchRecognizer *)recognizer)->FinishStream();
+#endif
+}
+
+const char *vosk_batch_recognizer_front_result(VoskBatchRecognizer *recognizer)
+{
+#if HAVE_CUDA
+    return ((BatchRecognizer *)recognizer)->FrontResult();
+#else
+    return NULL;
+#endif
+}
+
+void vosk_batch_recognizer_pop(VoskBatchRecognizer *recognizer)
+{
+#if HAVE_CUDA
+    ((BatchRecognizer *)recognizer)->Pop();
+#endif
+}
+
+
+int vosk_batch_recognizer_get_pending_chunks(VoskBatchRecognizer *recognizer)
+{
+#if HAVE_CUDA
+    return ((BatchRecognizer *)recognizer)->GetNumPendingChunks();
+#else
+    return 0;
+#endif
+}

src/vosk_api.h

@@ -40,10 +40,21 @@ typedef struct VoskSpkModel VoskSpkModel;
 typedef struct VoskRecognizer VoskRecognizer;
 
 
+/**
+ * Batch model object
+ */
+typedef struct VoskBatchModel VoskBatchModel;
+
+/**
+ * Batch recognizer object
+ */
+typedef struct VoskBatchRecognizer VoskBatchRecognizer;
+
+
 /** 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 */
+ * @returns model object or NULL if problem occured */
 VoskModel *vosk_model_new(const char *model_path);
 
 
@@ -66,7 +77,7 @@ int vosk_model_find_word(VoskModel *model, const char *word);
 /** 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 */
+ * @returns model object or NULL if problem occured */
 VoskSpkModel *vosk_spk_model_new(const char *model_path);
 
 
@@ -79,9 +90,13 @@ 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 */
+ *  The recognizers process the speech and return text using shared model data
+ *  @param model       VoskModel containing static data for recognizer. Model can be
+ *                     shared across recognizers, even running in different threads.
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer.
+ *                     Make sure this rate matches the audio content, it is a common
+ *                     issue causing accuracy problems.
+ *  @returns recognizer object or NULL if problem occured */
 VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
 
 
@@ -90,9 +105,13 @@ VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
  *  With the speaker recognition mode the recognizer not just recognize
  *  text but also return speaker vectors one can use for speaker identification
  *
- *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
+ *  @param model       VoskModel containing static data for recognizer. Model can be
+ *                     shared across recognizers, even running in different threads.
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer.
+ *                     Make sure this rate matches the audio content, it is a common
+ *                     issue causing accuracy problems.
  *  @param spk_model speaker model for speaker identification
- *  @returns recognizer object */
+ *  @returns recognizer object or NULL if problem occured */
 VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, float sample_rate, VoskSpkModel *spk_model);
 
 
@@ -106,11 +125,15 @@ VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, float sample_rate, Vos
  *  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 model       VoskModel containing static data for recognizer. Model can be
+ *                     shared across recognizers, even running in different threads.
+ *  @param sample_rate The sample rate of the audio you going to feed into the recognizer.
+ *                     Make sure this rate matches the audio content, it is a common
+ *                     issue causing accuracy problems.
  *  @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 */
+ *  @returns recognizer object or NULL if problem occured */
 VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar);
 
 
@@ -123,6 +146,15 @@ VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, con
 void vosk_recognizer_set_spk_model(VoskRecognizer *recognizer, VoskSpkModel *spk_model);
 
 
+/** Reconfigures recognizer to use grammar
+ *
+ * @param recognizer   Already running VoskRecognizer
+ * @param grammar      Set of phrases in JSON array of strings or "[]" to use default model graph.
+ *                     See also vosk_recognizer_new_grm
+ */
+void vosk_recognizer_set_grm(VoskRecognizer *recognizer, char const *grammar);
+
+
 /** Configures recognizer to output n-best results
  *
  * <pre>
@@ -174,6 +206,17 @@ void vosk_recognizer_set_max_alternatives(VoskRecognizer *recognizer, int max_al
  */
 void vosk_recognizer_set_words(VoskRecognizer *recognizer, int words);
 
+/** Like above return words and confidences in partial results
+ *
+ * @param partial_words - boolean value
+ */
+void vosk_recognizer_set_partial_words(VoskRecognizer *recognizer, int partial_words);
+
+/** Set NLSML output
+ * @param nlsml - boolean value
+ */
+void vosk_recognizer_set_nlsml(VoskRecognizer *recognizer, int nlsml);
+
 
 /** Accept voice data
  *
@@ -181,7 +224,9 @@ void vosk_recognizer_set_words(VoskRecognizer *recognizer, int words);
  *
  *  @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 */
+ *  @returns 1 if silence is occured and you can retrieve a new utterance with result method 
+ *           0 if decoding continues
+ *           -1 if exception occured */
 int vosk_recognizer_accept_waveform(VoskRecognizer *recognizer, const char *data, int length);
 
 
@@ -207,7 +252,7 @@ int vosk_recognizer_accept_waveform_f(VoskRecognizer *recognizer, const float *d
  *  }
  * </pre>
  *
- * If alternatives enabled it returns result with alternatives, see also vosk_recognizer_set_alternatives().
+ * If alternatives enabled it returns result with alternatives, see also vosk_recognizer_set_max_alternatives().
  *
  * If word times enabled returns word time, see also vosk_recognizer_set_word_times().
  */
@@ -271,6 +316,44 @@ void vosk_gpu_init();
  */
 void vosk_gpu_thread_init();
 
+/** Creates the batch recognizer object
+ *
+ *  @returns model object or NULL if problem occured */
+VoskBatchModel *vosk_batch_model_new(const char *model_path);
+
+/** Releases batch model object */
+void vosk_batch_model_free(VoskBatchModel *model);
+
+/** Wait for the processing */
+void vosk_batch_model_wait(VoskBatchModel *model);
+
+/** Creates batch recognizer object
+ *  @returns recognizer object or NULL if problem occured */
+VoskBatchRecognizer *vosk_batch_recognizer_new(VoskBatchModel *model, float sample_rate);
+ 
+/** Releases batch recognizer object */
+void vosk_batch_recognizer_free(VoskBatchRecognizer *recognizer);
+
+/** Accept batch voice data */
+void vosk_batch_recognizer_accept_waveform(VoskBatchRecognizer *recognizer, const char *data, int length);
+
+/** Set NLSML output
+ * @param nlsml - boolean value
+ */
+void vosk_batch_recognizer_set_nlsml(VoskBatchRecognizer *recognizer, int nlsml);
+
+/** Closes the stream */
+void vosk_batch_recognizer_finish_stream(VoskBatchRecognizer *recognizer);
+
+/** Return results */
+const char *vosk_batch_recognizer_front_result(VoskBatchRecognizer *recognizer);
+
+/** Release and free first retrieved result */
+void vosk_batch_recognizer_pop(VoskBatchRecognizer *recognizer);
+
+/** Get amount of pending chunks for more intelligent waiting */
+int vosk_batch_recognizer_get_pending_chunks(VoskBatchRecognizer *recognizer);
+
 #ifdef __cplusplus
 }
 #endif

training/README.md

@@ -0,0 +1,3 @@
+A proper simple setup to train a Vosk model
+
+More documentation later

training/RESULTS

@@ -0,0 +1,2 @@
+#!/bin/bash
+for x in exp/*/decode* exp/chain*/*/decode*; do [ -d $x ] && [[ $x =~ "$1" ]] && grep WER $x/wer_* | utils/best_wer.sh; done

training/RESULTS.txt

@@ -0,0 +1,2 @@
+%WER 14.10 [ 2839 / 20138, 214 ins, 487 del, 2138 sub ] exp/chain/tdnn/decode_test/wer_11_0.0
+%WER 12.67 [ 2552 / 20138, 215 ins, 406 del, 1931 sub ] exp/chain/tdnn/decode_test_rescore/wer_11_0.0

training/cmd.sh

@@ -0,0 +1,5 @@
+export train_cmd="run.pl"
+export decode_cmd="run.pl"
+export mkgraph_cmd="run.pl"
+export cuda_cmd="run.pl"
+export get_egs_cmd="run.pl"

training/conf/mfcc.conf

@@ -0,0 +1,7 @@
+--use-energy=false
+--num-mel-bins=40
+--num-ceps=40
+--low-freq=20
+--high-freq=-400
+--allow-upsample=true
+--allow-downsample=true

training/local/chain/run_ivector_common.sh

@@ -0,0 +1,86 @@
+#!/bin/bash
+
+set -euo pipefail
+
+# This script is called from local/chain_${suffix}/run_tdnn.sh and
+# local/chain_${suffix}/run_tdnn.sh (and may eventually be called by more
+# scripts).  It contains the common feature preparation and
+# iVector-related parts of the script.  See those scripts for examples
+# of usage.
+
+stage=0
+train_set=train
+gmm=tri3
+suffix=""
+
+. ./cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+gmm_dir=exp/${gmm}
+ali_dir=exp/${gmm}_ali
+
+if [ $stage -le 4 ]; then
+  echo "$0: computing a subset of data to train the diagonal UBM."
+  # We'll use about a quarter of the data.
+  mkdir -p exp/chain${suffix}/diag_ubm
+  temp_data_root=exp/chain${suffix}/diag_ubm
+
+  num_utts_total=$(wc -l <data/${train_set}/utt2spk)
+  num_utts=$[$num_utts_total/4]
+  utils/data/subset_data_dir.sh data/${train_set} \
+     $num_utts ${temp_data_root}/${train_set}_subset
+
+  echo "$0: computing a PCA transform from the data."
+  steps/online/nnet2/get_pca_transform.sh --cmd "$train_cmd" \
+      --splice-opts "--left-context=3 --right-context=3" \
+      --max-utts 10000 --subsample 2 \
+       ${temp_data_root}/${train_set}_subset \
+       exp/chain${suffix}/pca_transform
+
+  echo "$0: training the diagonal UBM."
+  # Use 512 Gaussians in the UBM.
+  steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 10 \
+    --num-frames 700000 \
+    --num-threads 8 \
+    ${temp_data_root}/${train_set}_subset 512 \
+    exp/chain${suffix}/pca_transform exp/chain${suffix}/diag_ubm
+fi
+
+if [ $stage -le 5 ]; then
+  # Train the iVector extractor.  Use all of the speed-perturbed data since iVector extractors
+  # can be sensitive to the amount of data.  The script defaults to an iVector dimension of
+  # 100.
+  echo "$0: training the iVector extractor"
+  steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 2 \
+     --ivector-dim 40 \
+     data/${train_set} exp/chain${suffix}/diag_ubm \
+     exp/chain${suffix}/extractor || exit 1;
+fi
+
+
+if [ $stage -le 6 ]; then
+  # We extract iVectors on the speed-perturbed training data after combining
+  # short segments, which will be what we train the system on.  With
+  # --utts-per-spk-max 2, the script pairs the utterances into twos, and treats
+  # each of these pairs as one speaker; this gives more diversity in iVectors..
+  # Note that these are extracted 'online'.
+
+  # note, we don't encode the 'max2' in the name of the ivectordir even though
+  # that's the data we extract the ivectors from, as it's still going to be
+  # valid for the non-'max2' data, the utterance list is the same.
+
+  ivectordir=exp/chain${suffix}/ivectors_${train_set}
+
+  # having a larger number of speakers is helpful for generalization, and to
+  # handle per-utterance decoding well (iVector starts at zero).
+  temp_data_root=${ivectordir}
+  utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
+    data/${train_set} ${temp_data_root}/${train_set}_max2
+
+  steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 10 \
+    ${temp_data_root}/${train_set}_max2 \
+    exp/chain${suffix}/extractor $ivectordir
+fi
+
+exit 0

training/local/chain/run_tdnn.sh

@@ -0,0 +1,165 @@
+#!/bin/bash
+
+# Set -e here so that we catch if any executable fails immediately
+set -euo pipefail
+
+# (some of which are also used in this script directly).
+stage=-1
+decode_nj=10
+train_set=train
+gmm=tri3
+nnet3_affix=
+suffix=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+# training options
+# training chunk-options
+chunk_width=140,100,160
+common_egs_dir=
+xent_regularize=0.1
+dropout_schedule='0,0@0.20,0.5@0.50,0'
+
+# training options
+srand=0
+remove_egs=true
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+ali_dir=exp/${gmm}_ali
+tree_dir=exp/chain${suffix}/tree${tree_affix:+_$tree_affix}
+lang=data/lang_chain${suffix}
+lat_dir=exp/chain${suffix}/${gmm}_${train_set}_lats
+dir=exp/chain${suffix}/tdnn${affix}
+train_data_dir=data/${train_set}
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $ali_dir/ali.1.gz; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 9 ]; then
+    local/chain/run_ivector_common.sh \
+                          --train-set ${train_set} \
+                          --gmm ${gmm} \
+                          --suffix "${suffix}"
+fi
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  rm -rf $lang
+  cp -r data/lang $lang
+  silphonelist=$(cat $lang/phones/silence.csl) 
+  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) 
+  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+fi
+
+if [ $stage -le 11 ]; then
+    steps/align_fmllr_lats.sh --nj 20 --cmd "$train_cmd" ${train_data_dir} \
+         data/lang $gmm_dir $lat_dir
+fi
+
+if [ $stage -le 12 ]; then
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --context-opts "--context-width=2 --central-position=1" \
+    --cmd "$train_cmd" 2500 ${train_data_dir} \
+    $lang $ali_dir $tree_dir
+fi
+
+if [ $stage -le 13 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
+
+  affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
+  tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.75"
+  linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
+  prefinal_opts="l2-regularize=0.008"
+  output_opts="l2-regularize=0.002"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+
+  input dim=40 name=ivector
+  input dim=40 name=input
+
+  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
+  batchnorm-component name=batchnorm0 input=idct
+  spec-augment-layer name=spec-augment freq-max-proportion=0.5 time-zeroed-proportion=0.2 time-mask-max-frames=20
+  delta-layer name=delta input=spec-augment
+
+  no-op-component name=input2 input=Append(delta, ReplaceIndex(ivector, t, 0))
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-dropout-layer name=tdnn1 $affine_opts dim=512 input=input2
+  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=1
+  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=1
+  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=1
+  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=0
+  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
+  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
+  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
+  tdnnf-layer name=tdnnf9 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
+  tdnnf-layer name=tdnnf10 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
+  tdnnf-layer name=tdnnf11 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
+  tdnnf-layer name=tdnnf12 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
+  linear-component name=prefinal-l dim=192 $linear_opts
+
+  ## adding the layers for chain branch
+  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts small-dim=192 big-dim=512
+  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
+
+  # adding the layers for xent branch
+  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts small-dim=192 big-dim=512
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
+
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 14 ]; then
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$cuda_cmd" \
+    --feat.online-ivector-dir exp/chain${suffix}/ivectors_${train_set} \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.0 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --egs.cmd "$get_egs_cmd" \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0 --constrained false" \
+    --egs.chunk-width $chunk_width \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.add-option="--optimization.memory-compression-level=2" \
+    --trainer.num-chunk-per-minibatch 64 \
+    --trainer.frames-per-iter 2500000 \
+    --trainer.num-epochs 20 \
+    --trainer.optimization.num-jobs-initial 1 \
+    --trainer.optimization.num-jobs-final 1 \
+    --trainer.optimization.initial-effective-lrate 0.001 \
+    --trainer.optimization.final-effective-lrate 0.0001 \
+    --trainer.max-param-change 2.0 \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir $train_data_dir \
+    --tree-dir $tree_dir \
+    --lat-dir $lat_dir \
+    --dir $dir  || exit 1;
+fi

training/local/data_prep.sh

@@ -0,0 +1,85 @@
+#!/usr/bin/env bash
+
+# Copyright 2014  Vassil Panayotov
+#           2014  Johns Hopkins University (author: Daniel Povey)
+# Apache 2.0
+
+if [ "$#" -ne 2 ]; then
+  echo "Usage: $0 <src-dir> <dst-dir>"
+  echo "e.g.: $0 /export/a15/vpanayotov/data/LibriSpeech/dev-clean data/dev-clean"
+  exit 1
+fi
+
+src=$1
+dst=$2
+
+# all utterances are FLAC compressed
+if ! which flac >&/dev/null; then
+   echo "Please install 'flac' on ALL worker nodes!"
+   exit 1
+fi
+
+spk_file=$src/../SPEAKERS.TXT
+
+mkdir -p $dst || exit 1;
+
+[ ! -d $src ] && echo "$0: no such directory $src" && exit 1;
+[ ! -f $spk_file ] && echo "$0: expected file $spk_file to exist" && exit 1;
+
+
+wav_scp=$dst/wav.scp; [[ -f "$wav_scp" ]] && rm $wav_scp
+trans=$dst/text; [[ -f "$trans" ]] && rm $trans
+utt2spk=$dst/utt2spk; [[ -f "$utt2spk" ]] && rm $utt2spk
+spk2gender=$dst/spk2gender; [[ -f $spk2gender ]] && rm $spk2gender
+
+for reader_dir in $(find -L $src -mindepth 1 -maxdepth 1 -type d | sort); do
+  reader=$(basename $reader_dir)
+  if ! [ $reader -eq $reader ]; then  # not integer.
+    echo "$0: unexpected subdirectory name $reader"
+    exit 1;
+  fi
+
+  reader_gender=$(egrep "^$reader[ ]+\|" $spk_file | awk -F'|' '{gsub(/[ ]+/, ""); print tolower($2)}')
+  if [ "$reader_gender" != 'm' ] && [ "$reader_gender" != 'f' ]; then
+    echo "Unexpected gender: '$reader_gender'"
+    exit 1;
+  fi
+
+  for chapter_dir in $(find -L $reader_dir/ -mindepth 1 -maxdepth 1 -type d | sort); do
+    chapter=$(basename $chapter_dir)
+    if ! [ "$chapter" -eq "$chapter" ]; then
+      echo "$0: unexpected chapter-subdirectory name $chapter"
+      exit 1;
+    fi
+
+    find -L $chapter_dir/ -iname "*.flac" | sort | xargs -I% basename % .flac | \
+      awk -v "dir=$chapter_dir" '{printf "%s flac -c -d -s %s/%s.flac |\n", $0, dir, $0}' >>$wav_scp|| exit 1
+
+    chapter_trans=$chapter_dir/${reader}-${chapter}.trans.txt
+    [ ! -f  $chapter_trans ] && echo "$0: expected file $chapter_trans to exist" && exit 1
+    cat $chapter_trans >>$trans
+
+    # NOTE: For now we are using per-chapter utt2spk. That is each chapter is considered
+    #       to be a different speaker. This is done for simplicity and because we want
+    #       e.g. the CMVN to be calculated per-chapter
+    awk -v "reader=$reader" -v "chapter=$chapter" '{printf "%s %s-%s\n", $1, reader, chapter}' \
+      <$chapter_trans >>$utt2spk || exit 1
+
+    # reader -> gender map (again using per-chapter granularity)
+    echo "${reader}-${chapter} $reader_gender" >>$spk2gender
+  done
+done
+
+spk2utt=$dst/spk2utt
+utils/utt2spk_to_spk2utt.pl <$utt2spk >$spk2utt || exit 1
+
+ntrans=$(wc -l <$trans)
+nutt2spk=$(wc -l <$utt2spk)
+! [ "$ntrans" -eq "$nutt2spk" ] && \
+  echo "Inconsistent #transcripts($ntrans) and #utt2spk($nutt2spk)" && exit 1;
+
+utils/validate_data_dir.sh --no-feats $dst || exit 1;
+
+echo "$0: successfully prepared data in $dst"
+
+exit 0

training/local/download_and_untar.sh

@@ -0,0 +1,100 @@
+#!/usr/bin/env bash
+
+# Copyright   2014  Johns Hopkins University (author: Daniel Povey)
+#             2017  Luminar Technologies, Inc. (author: Daniel Galvez)
+# Apache 2.0
+
+remove_archive=false
+
+if [ "$1" == --remove-archive ]; then
+  remove_archive=true
+  shift
+fi
+
+if [ $# -ne 3 ]; then
+  echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
+  echo "e.g.: $0 /export/a05/dgalvez/ www.openslr.org/resources/31 dev-clean-2"
+  echo "With --remove-archive it will remove the archive after successfully un-tarring it."
+  echo "<corpus-part> can be one of: dev-clean-2, test-clean-5, dev-other, test-other,"
+  echo "          train-clean-100, train-clean-360, train-other-500."
+fi
+
+data=$1
+url=$2
+part=$3
+
+if [ ! -d "$data" ]; then
+  echo "$0: no such directory $data"
+  exit 1;
+fi
+
+data=$(readlink -f $data)
+
+part_ok=false
+list="dev-clean-2 train-clean-5"
+for x in $list; do
+  if [ "$part" == $x ]; then part_ok=true; fi
+done
+if ! $part_ok; then
+  echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
+  exit 1;
+fi
+
+if [ -z "$url" ]; then
+  echo "$0: empty URL base."
+  exit 1;
+fi
+
+if [ -f $data/LibriSpeech/$part/.complete ]; then
+  echo "$0: data part $part was already successfully extracted, nothing to do."
+  exit 0;
+fi
+
+
+#sizes="126046265 332747356"
+sizes="126046265 332954390"
+
+if [ -f $data/$part.tar.gz ]; then
+  size=$(/bin/ls -l $data/$part.tar.gz | awk '{print $5}')
+  size_ok=false
+  for s in $sizes; do if [ $s == $size ]; then size_ok=true; fi; done
+  if ! $size_ok; then
+    echo "$0: removing existing file $data/$part.tar.gz because its size in bytes $size"
+    echo "does not equal the size of one of the archives."
+    rm $data/$part.tar.gz
+  else
+    echo "$data/$part.tar.gz exists and appears to be complete."
+  fi
+fi
+
+if [ ! -f $data/$part.tar.gz ]; then
+  if ! which wget >/dev/null; then
+    echo "$0: wget is not installed."
+    exit 1;
+  fi
+  full_url=$url/$part.tar.gz
+  echo "$0: downloading data from $full_url.  This may take some time, please be patient."
+
+  cd $data
+  if ! wget --no-check-certificate $full_url; then
+    echo "$0: error executing wget $full_url"
+    exit 1;
+  fi
+  cd -
+fi
+
+cd $data
+
+if ! tar -xvzf $part.tar.gz; then
+  echo "$0: error un-tarring archive $data/$part.tar.gz"
+  exit 1;
+fi
+
+touch $data/LibriSpeech/$part/.complete
+
+echo "$0: Successfully downloaded and un-tarred $data/$part.tar.gz"
+
+if $remove_archive; then
+  echo "$0: removing $data/$part.tar.gz file since --remove-archive option was supplied."
+  rm $data/$part.tar.gz
+fi

training/local/download_lm.sh

@@ -0,0 +1,73 @@
+#!/usr/bin/env bash
+
+# Copyright 2014  Vassil Panayotov
+#           2017  Daniel Povey
+# Apache 2.0
+
+if [ $# -ne "3" ]; then
+  echo "Usage: $0 <base-url> <download_dir> <local?"
+  echo "e.g.: $0 http://www.openslr.org/resources/11 ./corpus/ data/local/lm"
+  exit 1
+fi
+
+base_url=$1
+dst_dir=$2
+local_dir=$3
+
+# given a filename returns the corresponding file size in bytes
+# The switch cases below can be autogenerated by entering the data directory and running:
+# for f in *; do echo "\"$f\") echo \"$(du -b $f | awk '{print $1}')\";;"; done
+function filesize() {
+  case $1 in
+    "3-gram.arpa.gz") echo "759636181";;
+    "3-gram.pruned.1e-7.arpa.gz") echo "34094057";;
+    "3-gram.pruned.3e-7.arpa.gz") echo "13654242";;
+    "librispeech-lexicon.txt") echo "5627653";;
+    "librispeech-vocab.txt") echo "1737588";;
+    *) echo "";;
+  esac
+}
+
+function check_and_download () {
+  [[ $# -eq 1 ]] || { echo "check_and_download() expects exactly one argument!"; return 1; }
+  fname=$1
+  echo "Downloading file '$fname' into '$dst_dir'..."
+  expect_size="$(filesize $fname)"
+  [[ ! -z "$expect_size" ]] || { echo "Unknown file size for '$fname'"; return 1; }
+  if [[ -s $dst_dir/$fname ]]; then
+    # In the following statement, the first version works on linux, and the part
+    # after '||' works on Linux.
+    f=$dst_dir/$fname
+    fsize=$(set -o pipefail; du -b $f 2>/dev/null | awk '{print $1}' || stat '-f %z' $f)
+    if [[ "$fsize" -eq "$expect_size" ]]; then
+      echo "'$fname' already exists and appears to be complete"
+      return 0
+    else
+      echo "WARNING: '$fname' exists, but the size is wrong - re-downloading ..."
+    fi
+  fi
+  wget --no-check-certificate -O $dst_dir/$fname $base_url/$fname || {
+    echo "Error while trying to download $fname!"
+    return 1
+  }
+  f=$dst_dir/$fname
+  # In the following statement, the first version works on linux, and the part after '||'
+  # works on Linux.
+  fsize=$(set -o pipefail; du -b $f 2>/dev/null | awk '{print $1}' || stat '-f %z' $f)
+  [[ "$fsize" -eq "$expect_size" ]] || { echo "$fname: file size mismatch!"; return 1; }
+  return 0
+}
+
+mkdir -p $dst_dir $local_dir
+
+for f in 3-gram.pruned.1e-7.arpa.gz 3-gram.pruned.3e-7.arpa.gz  \
+         librispeech-vocab.txt librispeech-lexicon.txt; do
+  check_and_download $f || exit 1
+done
+
+dst_dir=$(readlink -f $dst_dir)
+ln -sf $dst_dir/3-gram.pruned.1e-7.arpa.gz $local_dir/lm_tgmed.arpa.gz
+ln -sf $dst_dir/3-gram.pruned.3e-7.arpa.gz $local_dir/lm_tgsmall.arpa.gz
+ln -sf $dst_dir/librispeech-lexicon.txt $local_dir/librispeech-lexicon.txt
+ln -sf $dst_dir/librispeech-vocab.txt $local_dir/librispeech-vocab.txt
+exit 0

training/local/prepare_dict.sh

@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+echo "Preparing phone lists and lexicon"
+
+src_dir=$1
+dst_dir=$2
+
+mkdir -p $dst_dir
+cat $src_dir/librispeech-lexicon.txt | sed 's:[012]::g' > $dst_dir/lexicon_raw_nosil.txt
+
+(echo SIL; echo SPN;) > $dst_dir/silence_phones.txt
+echo SIL > $dst_dir/optional_silence.txt
+echo "" > $dst_dir/extra_questions
+
+cat $dst_dir/lexicon_raw_nosil.txt |  awk '{ for(n=2;n<=NF;n++){ phones[$n] = 1; }} END{for (p in phones) print p;}' | \
+  grep -v SIL | sort > $dst_dir/nonsilence_phones.txt
+
+(echo '!SIL SIL'; echo '<UNK> SPN'; ) | cat - $dst_dir/lexicon_raw_nosil.txt | sort | uniq >$dst_dir/lexicon.txt

training/local/score.sh

@@ -0,0 +1,155 @@
+#!/bin/bash
+# Copyright 2012-2014  Johns Hopkins University (Author: Daniel Povey, Yenda Trmal)
+# Apache 2.0
+
+[ -f ./path.sh ] && . ./path.sh
+
+# begin configuration section.
+cmd=run.pl
+stage=0
+decode_mbr=false
+reverse=false
+stats=true
+beam=6
+word_ins_penalty=0.0
+min_lmwt=7
+max_lmwt=13
+iter=final
+#end configuration section.
+
+echo "$0 $@"  # Print the command line for logging
+[ -f ./path.sh ] && . ./path.sh
+. parse_options.sh || exit 1;
+
+if [ $# -ne 3 ]; then
+  echo "Usage: local/score.sh [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>"
+  echo " Options:"
+  echo "    --cmd (run.pl|queue.pl...)      # specify how to run the sub-processes."
+  echo "    --stage (0|1|2)                 # start scoring script from part-way through."
+  echo "    --decode_mbr (true/false)       # maximum bayes risk decoding (confusion network)."
+  echo "    --min_lmwt <int>                # minumum LM-weight for lattice rescoring "
+  echo "    --max_lmwt <int>                # maximum LM-weight for lattice rescoring "
+  echo "    --reverse (true/false)          # score with time reversed features "
+  exit 1;
+fi
+
+data=$1
+lang_or_graph=$2
+dir=$3
+
+symtab=$lang_or_graph/words.txt
+
+for f in $symtab $dir/lat.1.gz $data/text; do
+  [ ! -f $f ] && echo "score.sh: no such file $f" && exit 1;
+done
+
+
+ref_filtering_cmd="cat"
+[ -x local/wer_output_filter ] && ref_filtering_cmd="local/wer_output_filter"
+[ -x local/wer_ref_filter ] && ref_filtering_cmd="local/wer_ref_filter"
+hyp_filtering_cmd="cat"
+[ -x local/wer_output_filter ] && hyp_filtering_cmd="local/wer_output_filter"
+[ -x local/wer_hyp_filter ] && hyp_filtering_cmd="local/wer_hyp_filter"
+
+
+if $decode_mbr ; then
+  echo "$0: scoring with MBR, word insertion penalty=$word_ins_penalty"
+else
+  echo "$0: scoring with word insertion penalty=$word_ins_penalty"
+fi
+
+
+mkdir -p $dir/scoring_kaldi
+cat $data/text | $ref_filtering_cmd > $dir/scoring_kaldi/test_filt.txt || exit 1;
+
+if [ $stage -le 0 ]; then
+
+  for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do
+    mkdir -p $dir/scoring_kaldi/penalty_$wip/log
+
+    if $decode_mbr ; then
+      $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring_kaldi/penalty_$wip/log/best_path.LMWT.log \
+        acwt=\`perl -e \"print 1.0/LMWT\"\`\; \
+        lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
+        lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \
+        lattice-prune --beam=$beam ark:- ark:- \| \
+        lattice-mbr-decode  --word-symbol-table=$symtab \
+        ark:- ark,t:- \| \
+        utils/int2sym.pl -f 2- $symtab \| \
+        $hyp_filtering_cmd '>' $dir/scoring_kaldi/penalty_$wip/LMWT.txt || exit 1;
+
+    else
+      $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring_kaldi/penalty_$wip/log/best_path.LMWT.log \
+        lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
+        lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \
+        lattice-best-path --word-symbol-table=$symtab ark:- ark,t:- \| \
+        utils/int2sym.pl -f 2- $symtab \| \
+        $hyp_filtering_cmd '>' $dir/scoring_kaldi/penalty_$wip/LMWT.txt || exit 1;
+    fi
+
+    if $reverse; then # rarely-used option, ignore this.
+      for lmwt in `seq $min_lmwt $max_lmwt`; do
+        mv $dir/scoring_kaldi/penalty_$wip/$lmwt.txt $dir/scoring_kaldi/penalty_$wip/$lmwt.txt.orig
+        awk '{ printf("%s ",$1); for(i=NF; i>1; i--){ printf("%s ",$i); } printf("\n"); }' \
+          <$dir/scoring_kaldi/penalty_$wip/$lmwt.txt.orig >$dir/scoring_kaldi/penalty_$wip/$lmwt.txt
+      done
+    fi
+
+    $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring_kaldi/penalty_$wip/log/score.LMWT.log \
+      cat $dir/scoring_kaldi/penalty_$wip/LMWT.txt \| \
+      compute-wer --text --mode=present \
+      ark:$dir/scoring_kaldi/test_filt.txt  ark,p:- ">&" $dir/wer_LMWT_$wip || exit 1;
+
+  done
+fi
+
+
+
+if [ $stage -le 1 ]; then
+
+  for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do
+    for lmwt in $(seq $min_lmwt $max_lmwt); do
+      # adding /dev/null to the command list below forces grep to output the filename
+      grep WER $dir/wer_${lmwt}_${wip} /dev/null
+    done
+  done | utils/best_wer.sh  >& $dir/scoring_kaldi/best_wer || exit 1
+
+  best_wer_file=$(awk '{print $NF}' $dir/scoring_kaldi/best_wer)
+  best_wip=$(echo $best_wer_file | awk -F_ '{print $NF}')
+  best_lmwt=$(echo $best_wer_file | awk -F_ '{N=NF-1; print $N}')
+
+  if [ -z "$best_lmwt" ]; then
+    echo "$0: we could not get the details of the best WER from the file $dir/wer_*.  Probably something went wrong."
+    exit 1;
+  fi
+
+  if $stats; then
+    mkdir -p $dir/scoring_kaldi/wer_details
+    echo $best_lmwt > $dir/scoring_kaldi/wer_details/lmwt # record best language model weight
+    echo $best_wip > $dir/scoring_kaldi/wer_details/wip # record best word insertion penalty
+
+    $cmd $dir/scoring_kaldi/log/stats1.log \
+      cat $dir/scoring_kaldi/penalty_$best_wip/$best_lmwt.txt \| \
+      align-text --special-symbol="'***'" ark:$dir/scoring_kaldi/test_filt.txt ark:- ark,t:- \|  \
+      utils/scoring/wer_per_utt_details.pl --special-symbol "'***'" \| tee $dir/scoring_kaldi/wer_details/per_utt \|\
+       utils/scoring/wer_per_spk_details.pl $data/utt2spk \> $dir/scoring_kaldi/wer_details/per_spk || exit 1;
+
+    $cmd $dir/scoring_kaldi/log/stats2.log \
+      cat $dir/scoring_kaldi/wer_details/per_utt \| \
+      utils/scoring/wer_ops_details.pl --special-symbol "'***'" \| \
+      sort -b -i -k 1,1 -k 4,4rn -k 2,2 -k 3,3 \> $dir/scoring_kaldi/wer_details/ops || exit 1;
+
+    $cmd $dir/scoring_kaldi/log/wer_bootci.log \
+      compute-wer-bootci --mode=present \
+        ark:$dir/scoring_kaldi/test_filt.txt ark:$dir/scoring_kaldi/penalty_$best_wip/$best_lmwt.txt \
+        '>' $dir/scoring_kaldi/wer_details/wer_bootci || exit 1;
+
+  fi
+fi
+
+# If we got here, the scoring was successful.
+# As a  small aid to prevent confusion, we remove all wer_{?,??} files;
+# these originate from the previous version of the scoring files
+rm $dir/wer_{?,??} 2>/dev/null
+
+exit 0;

training/path.sh

@@ -0,0 +1,6 @@
+export KALDI_ROOT=`pwd`/../../..
+[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
+export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
+[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
+. $KALDI_ROOT/tools/config/common_path.sh
+export LC_ALL=C