Another timestamp bugfix

Release memory when final result is received to reduce memory pressure.
Actually show the distance
2020-06-29 14:11:05 +02:00 · 2020-06-24 18:37:53 +02:00 · 2020-06-23 22:14:39 +02:00 · 2020-06-23 21:54:47 +03:00 · 2020-06-23 20:11:22 +02:00
40 changed files with 172 additions and 1002 deletions
@@ -5,9 +5,6 @@
 # Java class files
 *.class

-# Object files
-*.o
-
 # Gradle files
 .gradle/
 build/
@@ -1,25 +1,26 @@
-# About
-
-Vosk is an offline open source speech recognition toolkit. It enables
-speech recognition models for 16 languages and dialects - English, Indian
-English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish,
-Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi.
-
-Vosk models are small (50 Mb) but provide continuous large vocabulary
-transcription, zero-latency response with streaming API, reconfigurable
+Vosk is an open source speech recognition toolkit which supports 9
+languages - English, German, French, Spanish, Portuguese, Chinese,
+Russian, Turkish, Vietnamese. Vosk works offline with small (50 Mb), but
+accurate model, 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.
+### Installation and usage

-Vosk supplies speech recognition for chatbots, smart home appliances,
-virtual assistants. It can also create subtitles for movies,
-transcription for lectures and interviews.
+For Vosk installation instructions, examples and turorial and documentation visit https://alphacephei.com/vosk

-Vosk scales from small devices like Raspberry Pi or Android smartphone to
-big clusters.
+### Build

-# Documentation
+[![Build Status](https://travis-ci.com/alphacep/vosk-api.svg?branch=master)](https://travis-ci.com/alphacep/vosk-api)

-For installation instructions, examples and documentation visit [Vosk
-Website](https://alphacephei.com/vosk).
+### Models for different languages
+
+For information about models see [the documentation on available models](https://alphacephei.com/vosk/models.html).
+
+### Contact Us
+
+If you have any questions, feel free to:
+
+   * Post an issue here on github
+   * Send us an e-mail at [contact@alphacephei.com](mailto:contact@alphacephei.com)
+   * Join our group dedicated to speech recognition on Telegram [@speech_recognition](https://t.me/speech_recognition)
+   * We have a Wechat group which is pretty big, so it is invitation-only. Mail us to join the group and provide some information about yourself.
@@ -22,8 +22,6 @@ set(LIB_ROOT "${PROJECT_SOURCE_DIR}/build/kaldi_${KALDI_SUFFIX}/local")
 set(API_SOURCES
 "${PROJECT_SOURCE_DIR}/../src/kaldi_recognizer.cc"
 "${PROJECT_SOURCE_DIR}/../src/kaldi_recognizer.h"
-"${PROJECT_SOURCE_DIR}/../src/language_model.cc"
-"${PROJECT_SOURCE_DIR}/../src/language_model.h"
 "${PROJECT_SOURCE_DIR}/../src/model.cc"
 "${PROJECT_SOURCE_DIR}/../src/model.h"
 "${PROJECT_SOURCE_DIR}/../src/spk_model.cc"
@@ -34,14 +32,14 @@ set(API_SOURCES

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

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

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

-target_link_libraries( vosk_jni
+target_link_libraries( kaldi_jni
             ${KALDI_ROOT}/src/online2/kaldi-online2.a
             ${KALDI_ROOT}/src/decoder/kaldi-decoder.a
             ${KALDI_ROOT}/src/ivector/kaldi-ivector.a
@@ -5,17 +5,9 @@ buildscript {
    }
    dependencies {
        classpath 'com.android.tools.build:gradle:3.5.3'
-        classpath 'com.jfrog.bintray.gradle:gradle-bintray-plugin:1.8.5'
    }
 }

-plugins {
-    id "com.jfrog.bintray" version "1.8.5"
-}
-
-def archiveName = "vosk-android"
-def libVersion = "0.3.15"
-
 allprojects {
  repositories {
      google()
@@ -24,16 +16,15 @@ allprojects {
 }

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

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

-Properties properties = new Properties()
-properties.load(project.rootProject.file('local.properties').newDataInputStream())
-
-bintray {
-    user = properties.getProperty("bintray.user")
-    key = properties.getProperty("bintray.apikey")
-    pkg {
-        repo = 'vosk'
-        name = 'vosk-android'
-        userOrg = "alphacep"
-        licenses = ['Apache2.0']
-        websiteUrl = 'https://github.com/alphacep/vosk-api'
-        issueTrackerUrl = 'https://github.com/alphacep/vosk-api/issues'
-        vcsUrl = 'https://github.com/alphacep/vosk-api'
-        version {
-            name = libVersion
-            vcsTag = libVersion
-        }
-    }
-    publications = ['aar']
-}
-
-publishing {
-    publications {
-        aar(MavenPublication) {
-            groupId 'com.alphacep'
-            artifactId archiveName
-            version libVersion
-            artifact("$buildDir/outputs/aar/$archiveName-release.aar")
-        }
-    }
-}
-
 task swig {
    doLast {
        mkdir 'build/generated-src/java'
@@ -29,18 +29,19 @@ import android.os.Looper;
 import android.util.Log;

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

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

    private final KaldiRecognizer recognizer;

-    private final int sampleRate;
+    private final int sampleRate;        
    private final static float BUFFER_SIZE_SECONDS = 0.4f;
    private int bufferSize;
    private final AudioRecord recorder;
@@ -52,18 +53,33 @@ public class SpeechService {
    private final Collection<RecognitionListener> listeners = new HashSet<RecognitionListener>();
    
    /**
-     * Creates speech service. Service holds the AudioRecord object, so you 
+     * Creates speech recognizer. Recognizer holds the AudioRecord object, so you 
     * need to call {@link release} in order to properly finalize it.
     * 
     * @throws IOException thrown if audio recorder can not be created for some reason.
     */
-    public SpeechService(KaldiRecognizer recognizer, float sampleRate) throws IOException {
-        this.recognizer = recognizer;
-        this.sampleRate = (int)sampleRate;
-
-        bufferSize = Math.round(this.sampleRate * BUFFER_SIZE_SECONDS);
+    public SpeechRecognizer(Model model) throws IOException {
+        recognizer = new KaldiRecognizer(model, 16000.0f);
+        sampleRate = 16000;
+        bufferSize = Math.round(sampleRate * BUFFER_SIZE_SECONDS);
        recorder = new AudioRecord(
-                AudioSource.VOICE_RECOGNITION, this.sampleRate,
+                AudioSource.VOICE_RECOGNITION, sampleRate,
+                AudioFormat.CHANNEL_IN_MONO,
+                AudioFormat.ENCODING_PCM_16BIT, bufferSize * 2);
+
+        if (recorder.getState() == AudioRecord.STATE_UNINITIALIZED) {
+            recorder.release();
+            throw new IOException(
+                    "Failed to initialize recorder. Microphone might be already in use.");
+        }
+    }
+
+    public SpeechRecognizer(Model model, SpkModel spkModel) throws IOException {
+        recognizer = new KaldiRecognizer(model, spkModel, 16000.0f);
+        sampleRate = 16000;
+        bufferSize = Math.round(sampleRate * BUFFER_SIZE_SECONDS);
+        recorder = new AudioRecord(
+                AudioSource.VOICE_RECOGNITION, sampleRate,
                AudioFormat.CHANNEL_IN_MONO,
                AudioFormat.ENCODING_PCM_16BIT, bufferSize * 2);

@@ -171,9 +187,9 @@ public class SpeechService {
    public void shutdown() {
        recorder.release();
    }
-
+    
    private final class RecognizerThread extends Thread {
-
+        
        private int remainingSamples;
        private int timeoutSamples;
        private final static int NO_TIMEOUT = -1;
@@ -1,50 +0,0 @@
-KALDI_ROOT=$(HOME)/travis/kaldi
-
-VOSK_SOURCES= \
-    ../src/kaldi_recognizer.cc \
-    ../src/language_model.cc \
-    ../src/model.cc \
-    ../src/spk_model.cc \
-    ../src/vosk_api.cc
-
-CFLAGS=-g -O2 -DFST_NO_DYNAMIC_LINKING -I../src -I$(KALDI_ROOT)/src -I$(KALDI_ROOT)/tools/openfst/include
-LIBS= \
-	$(KALDI_ROOT)/src/online2/kaldi-online2.a \
-	$(KALDI_ROOT)/src/decoder/kaldi-decoder.a \
-	$(KALDI_ROOT)/src/ivector/kaldi-ivector.a \
-	$(KALDI_ROOT)/src/gmm/kaldi-gmm.a \
-	$(KALDI_ROOT)/src/nnet3/kaldi-nnet3.a \
-	$(KALDI_ROOT)/src/tree/kaldi-tree.a \
-	$(KALDI_ROOT)/src/feat/kaldi-feat.a \
-	$(KALDI_ROOT)/src/lat/kaldi-lat.a \
-	$(KALDI_ROOT)/src/lm/kaldi-lm.a \
-	$(KALDI_ROOT)/src/hmm/kaldi-hmm.a \
-	$(KALDI_ROOT)/src/transform/kaldi-transform.a \
-	$(KALDI_ROOT)/src/cudamatrix/kaldi-cudamatrix.a \
-	$(KALDI_ROOT)/src/matrix/kaldi-matrix.a \
-	$(KALDI_ROOT)/src/fstext/kaldi-fstext.a \
-	$(KALDI_ROOT)/src/util/kaldi-util.a \
-	$(KALDI_ROOT)/src/base/kaldi-base.a \
-	$(KALDI_ROOT)/tools/OpenBLAS/libopenblas.a \
-	$(KALDI_ROOT)/tools/openfst/lib/libfst.a \
-	$(KALDI_ROOT)/tools/openfst/lib/libfstngram.a
-
-all: test_vosk test_vosk_speaker
-
-test_vosk: test_vosk.o libvosk.a
-	g++ $^ -o $@ $(LIBS) -lgfortran -lpthread
-
-test_vosk_speaker: test_vosk_speaker.o libvosk.a
-	g++ $^ -o $@ $(LIBS) -lgfortran -lpthread
-
-libvosk.a: $(VOSK_SOURCES:.cc=.o)
-	ar rcs $@ $^
-
-%.o: %.c
-	g++ $(CFLAGS) -c -o $@ $<
-
-%.o: %.cc
-	g++ -std=c++11 $(CFLAGS) -c -o $@ $<
-
-clean:
-	rm -f *.o *.a test_vosk
@@ -1,28 +0,0 @@
-#include <vosk_api.h>
-#include <stdio.h>
-
-int main() {
-    FILE *wavin;
-    char buf[3200];
-    int nread, final;
-
-    VoskModel *model = vosk_model_new("model");
-    VoskRecognizer *recognizer = vosk_recognizer_new(model, 16000.0);
-
-    wavin = fopen("test.wav", "rb");
-    fseek(wavin, 44, SEEK_SET);
-    while (!feof(wavin)) {
-         nread = fread(buf, 1, sizeof(buf), wavin);
-         final = vosk_recognizer_accept_waveform(recognizer, buf, nread);
-         if (final) {
-             printf("%s\n", vosk_recognizer_result(recognizer));
-         } else {
-             printf("%s\n", vosk_recognizer_partial_result(recognizer));
-         }
-    }
-    printf("%s\n", vosk_recognizer_final_result(recognizer));
-
-    vosk_recognizer_free(recognizer);
-    vosk_model_free(model);
-    return 0;
-}
@@ -1,30 +0,0 @@
-#include <vosk_api.h>
-#include <stdio.h>
-
-int main() {
-    FILE *wavin;
-    char buf[3200];
-    int nread, final;
-
-    VoskModel *model = vosk_model_new("model");
-    VoskSpkModel *spk_model = vosk_spk_model_new("spk-model");
-    VoskRecognizer *recognizer = vosk_recognizer_new_spk(model, spk_model, 16000.0);
-
-    wavin = fopen("test.wav", "rb");
-    fseek(wavin, 44, SEEK_SET);
-    while (!feof(wavin)) {
-         nread = fread(buf, 1, sizeof(buf), wavin);
-         final = vosk_recognizer_accept_waveform(recognizer, buf, nread);
-         if (final) {
-             printf("%s\n", vosk_recognizer_result(recognizer));
-         } else {
-             printf("%s\n", vosk_recognizer_partial_result(recognizer));
-         }
-    }
-    printf("%s\n", vosk_recognizer_final_result(recognizer));
-
-    vosk_recognizer_free(recognizer);
-    vosk_spk_model_free(spk_model);
-    vosk_model_free(model);
-    return 0;
-}
@@ -20,12 +20,9 @@ KALDI_LIBS = \
             ${KALDI_ROOT}/src/util/kaldi-util.a \
             ${KALDI_ROOT}/src/base/kaldi-base.a \
             ${KALDI_ROOT}/tools/openfst/lib/libfst.a \
-             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a
-
-# On Linux
-MATH_LIBS = ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a -lgfortran
-# On OSX
-# MATH_LIBS = -framework Accelerate
+             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a \
+             ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a \
+             -lgfortran -lstdc++

 all: test.exe

@@ -35,20 +32,16 @@ test.exe: libkaldiwrap.so test.cs
 VOSK_SOURCES = \
 	vosk_wrap.c \
 	../src/kaldi_recognizer.cc \
-	../src/language_model.cc \
-	../src/model.cc \
-	../src/spk_model.cc \
-	../src/vosk_api.cc
-
-VOSK_HEADERS = \
 	../src/kaldi_recognizer.h \
-	../src/language_model.h \
+	../src/model.cc \
 	../src/model.h \
-	../src/vosk_api.h \
-	../src/spk_model.h
+	../src/spk_model.cc \
+	../src/spk_model.h \
+	../src/vosk_api.cc \
+	../src/vosk_api.h

-libkaldiwrap.so: $(VOSK_SOURCES) $(VOSK_HEADERS)
-	$(CXX) -fpermissive $(CFLAGS) $(CPPFLAGS) -shared -o $@ $(VOSK_SOURCES) $(KALDI_LIBS) $(MATH_LIBS)
+libkaldiwrap.so: $(VOSK_SOURCES)
+	$(CXX) -fpermissive $(CFLAGS) $(CPPFLAGS) -shared -o $@ $(VOSK_SOURCES) $(KALDI_LIBS)

 vosk_wrap.c: ../src/vosk.i
 	mkdir -p gen
@@ -1 +1 @@
-See https://alphacephei.com/vosk/accuracy
+See https://alphacephei.com/vosk/accuracy.html
@@ -1 +1 @@
-See https://alphacephei.com/vosk/adaptation
+See https://alphacephei.com/vosk/adaptation.html
@@ -1 +1 @@
-See https://alphacephei.com/vosk/models
+See https://alphacephei.com/vosk/models.html
@@ -12,199 +12,37 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

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

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

 #ifdef __cplusplus
 extern "C" {
 #endif

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

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

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

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

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

-#endif /* VOSK_API_H */
+#endif /* _VOSK_API_H_ */
@@ -30,8 +30,6 @@ VOSK_SOURCES = \
 	vosk_wrap.cc \
 	../src/kaldi_recognizer.cc \
 	../src/kaldi_recognizer.h \
-	../src/language_model.cc \
-	../src/language_model.h \
 	../src/model.cc \
 	../src/model.h \
 	../src/spk_model.cc \
@@ -14,6 +14,9 @@ import org.kaldi.SpkModel;
 import org.kaldi.Vosk;

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

    public static void main(String args[]) throws IOException {
        Vosk.SetLogLevel(-10);
@@ -9,7 +9,7 @@ Still, you need swig of newest version 4.0.1
 Build like this

 ```
-npm install --kaldi_root=/home/user/kaldi
+npm install --kaldi_root=/home/suser/kaldi
 ```

 Then test with
@@ -5,32 +5,22 @@
      'sources': [
         '../src/kaldi_recognizer.cc',
         '../src/model.cc',
-         '../src/language_model.cc',
         '../src/spk_model.cc',
         '../src/vosk_api.cc',
         'vosk_wrap.cc',
      ],
      'cflags': [
-          '-std=c++11',
-          '-DFST_NO_DYNAMIC_LINKING',
-          '-Wno-deprecated-declarations',
-          '-Wno-sign-compare',
-          '-Wno-unused-local-typedefs',
-          '-Wno-ignored-quaifiers',
-          '-Wno-extra',
+           '-std=c++11',
+           '-DFST_NO_DYNAMIC_LINKING',
+           '-Wno-deprecated-declarations',
+           '-Wno-sign-compare',
+           '-Wno-unused-local-typedefs',
+           '-Wno-ignored-quaifiers',
+           '-Wno-extra',
      ],
      'cflags_cc!' : [
-          '-fno-rtti',
-          '-fno-exceptions',
-      ],
-      'conditions': [
-          ['OS == "mac"', {
-              'xcode_settings': {
-                  'GCC_ENABLE_CPP_EXCEPTIONS': 'YES',
-                  'GCC_ENABLE_CPP_RTTI': 'YES',
-                  'CLANG_CXX_LANGUAGE_STANDARD': 'c++11'
-              }
-          }]
+           '-fno-rtti',
+           '-fno-exceptions',
      ],
      'actions': [
        {
@@ -24,25 +24,25 @@ const wfReader = new wav.Reader()
 const model = new Model('model')
 const spkModel = new SpkModel('model-spk')

-const spk_sig = [-0.56648, 0.030579, 1.730239, 0.239899, -1.194183,
-0.251954, 0.540388, -0.971872, -0.020963, 0.085036, 0.563973, -0.019682,
-0.597381, 1.094719, -0.566738, 0.29819, 0.171165, 0.370341, -0.033539,
-0.09757, 1.228286, 0.485949, 0.427826, 0.147762, -0.015112, 0.599513,
-2.040655, -0.490882, 0.440161, -0.072991, 0.835955, -0.496124, 0.952978,
-0.85356, -1.096116, 0.107764, -0.385486, 1.410305, 0.609147, -0.457014,
-1.542864, 0.343669, 0.171913, -0.627281, -1.281781, -1.134276,
-0.639895, 1.190183, -0.700537, 1.063457, 0.206946, 0.342198, -1.165625,
-1.475955, -0.089007, -2.555155, 0.551438, -0.212736, 1.025625, -1.631965,
-0.716256, -1.295995, 1.554956, -1.866009, -1.010782, -1.43231, 0.109027,
-2.123925, 1.703283, -0.784997, 2.730568, 0.755113, 0.0617, 0.128955,
-0.054047, 1.359119, -0.611666, -1.105754, -0.631353, 0.052109, 0.729386,
-0.769876, 1.250235, -1.463298, 0.648176, -0.73239, -0.385239,
-1.661856, 0.602106, -0.45567, -1.438431, -0.836673, 0.033557, 0.373597,
-1.343341, -0.181095, 0.237287, -0.522005, -1.722836, 0.932333,
-0.092861, -0.219254, 0.476182, 1.033803, -1.633563, -0.874341, 1.039064,
-1.758573, -0.838422, -0.324336, -0.924634, 1.962594, 2.152814, 1.2521,
-0.46172, -1.50271, 1.685691, 0.403097, -0.819042, 0.866403, -0.591716,
-0.578645, -0.553839, 0.381861, -1.051647, -1.477578, 0.524005, 0.925245]
+const spk_sig = [4.658117, 1.277387, 3.346158, -1.473036, -2.15727,
+2.461757, 3.76756, -1.241252, 2.333765, 0.642588, -2.848165, 1.229534,
+3.907015, 1.726496, -1.188692, 1.16322, -0.668811, -0.623309, 4.628018,
+0.407197, 0.089955, 0.920438, 1.47237, -0.311365, -0.437051, -0.531738,
+-1.591781, 3.095415, 0.439524, -0.274787, 4.03165, 2.665864, 4.815553,
+1.581063, 1.078242, 5.017717, -0.089395, -3.123428, 5.34038, 0.456982,
+2.465727, 2.131833, 4.056272, 1.178392, -2.075712, -1.568503, 0.847139,
+0.409214, 1.84727, 0.986758, 4.222116, 2.235512, 1.369377, 4.283126,
+2.278125, -1.467577, -0.999971, 3.070041, 1.462214, 0.423204, 2.143578,
+0.567174, -2.294655, 1.864723, 4.307356, 2.610872, -1.238721, 0.551861,
+2.861954, 0.59613, -0.715396, -1.395357, 2.706177, -2.004444, 2.055255,
+0.458283, 1.231968, 3.48234, 2.993858, 0.402819, 0.940885, 0.360162,
+-2.173674, -2.504609, 0.329541, 3.653913, 3.638025, -1.406409, 2.14059,
+1.662765, -0.991323, 0.770921, 0.010094, 3.775469, 1.847511, 2.074432,
+-1.928593, 0.807414, 2.964505, 0.128597, 1.297962, 2.645227, 0.136405,
+-2.543087, 0.932246, 2.405783, -2.122267, 3.044013, 0.486728, 4.395338,
+0.474267, 0.781297, 1.694144, -0.831078, -0.462362, -0.964715, 3.187863,
+6.008708, 1.725954, 3.667886, -1.467623, 3.370667, 2.72555, -0.796541,
+2.416543, 0.675401, -0.737634, -1.709676]

 function dotp(x, y) {
    function dotp_sum(a, b) {
@@ -1,7 +1,7 @@
 {
  "name": "vosk",
-  "version": "0.3.15",
-  "description": "Node binding for continuous offline voice recoginition with Vosk library.",
+  "version": "0.3.8",
+  "description": "Node binding for continuous voice recoginition through pocketsphinx.",
  "repository": {
    "type": "git",
    "url": "git://github.com/alphacep/vosk-api.git"
@@ -1,23 +1,3 @@
-This is a Python module for Vosk.
+Python module for vosk-api

-Vosk is an offline open source speech recognition toolkit. It enables
-speech recognition models for 16 languages and dialects - English, Indian
-English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish,
-Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi.
-
-Vosk models are small (50 Mb) but provide continuous large vocabulary
-transcription, zero-latency response with streaming API, reconfigurable
-vocabulary and speaker identification.
-
-Vosk supplies speech recognition for chatbots, smart home appliances,
-virtual assistants. It can also create subtitles for movies,
-transcription for lectures and interviews.
-
-Vosk scales from small devices like Raspberry Pi or Android smartphone to
-big clusters.
-
-# Documentation
-
-For installation instructions, examples and documentation visit [Vosk
-Website](https://alphacephei.com/vosk). See also our project on
-[Github](https://github.com/alphacep/vosk-api).
+See for details https://github.com/alphacep/vosk-api
@@ -9,7 +9,7 @@ import subprocess
 SetLogLevel(0)

 if not os.path.exists("model"):
-    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
+    print ("Please download the model from https://github.com/alphacep/vosk-api/blob/master/doc/models.md and unpack as 'model' in the current folder.")
    exit (1)

 sample_rate=16000
@@ -4,7 +4,7 @@ from vosk import Model, KaldiRecognizer
 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.")
+    print ("Please download the model from https://github.com/alphacep/vosk-api/blob/master/doc/models.md and unpack as 'model' in the current folder.")
    exit (1)

 import pyaudio
@@ -8,7 +8,7 @@ import wave
 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.")
+    print ("Please download the model from https://github.com/alphacep/vosk-api/blob/master/doc/models.md and unpack as 'model' in the current folder.")
    exit (1)

 wf = wave.open(sys.argv[1], "rb")
@@ -11,11 +11,11 @@ model_path = "model"
 spk_model_path = "model-spk"

 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))
+    print ("Please download the model from https://github.com/alphacep/vosk-api/blob/master/doc/models.md and unpack as {} in the current folder.".format(model_path))
    exit (1)

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

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

 # We compare speakers with cosine distance. We can keep one or several fingerprints for the speaker in a database
 # to distingusih among users.
-spk_sig = [-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 = [4.658117, 1.277387, 3.346158, -1.473036, -2.15727, 2.461757, 3.76756, -1.241252, 2.333765, 0.642588, -2.848165, 1.229534, 3.907015, 1.726496, -1.188692, 1.16322, -0.668811, -0.623309, 4.628018, 0.407197, 0.089955, 0.920438, 1.47237, -0.311365, -0.437051, -0.531738, -1.591781, 3.095415, 0.439524, -0.274787, 4.03165, 2.665864, 4.815553, 1.581063, 1.078242, 5.017717, -0.089395, -3.123428, 5.34038, 0.456982, 2.465727, 2.131833, 4.056272, 1.178392, -2.075712, -1.568503, 0.847139, 0.409214, 1.84727, 0.986758, 4.222116, 2.235512, 1.369377, 4.283126, 2.278125, -1.467577, -0.999971, 3.070041, 1.462214, 0.423204, 2.143578, 0.567174, -2.294655, 1.864723, 4.307356, 2.610872, -1.238721, 0.551861, 2.861954, 0.59613, -0.715396, -1.395357, 2.706177, -2.004444, 2.055255, 0.458283, 1.231968, 3.48234, 2.993858, 0.402819, 0.940885, 0.360162, -2.173674, -2.504609, 0.329541, 3.653913, 3.638025, -1.406409, 2.14059, 1.662765, -0.991323, 0.770921, 0.010094, 3.775469, 1.847511, 2.074432, -1.928593, 0.807414, 2.964505, 0.128597, 1.297962, 2.645227, 0.136405, -2.543087, 0.932246, 2.405783, -2.122267, 3.044013, 0.486728, 4.395338, 0.474267, 0.781297, 1.694144, -0.831078, -0.462362, -0.964715, 3.187863, 6.008708, 1.725954, 3.667886, -1.467623, 3.370667, 2.72555, -0.796541, 2.416543, 0.675401, -0.737634, -1.709676]

 def cosine_dist(x, y):
    nx = np.array(x)
@@ -45,12 +45,9 @@ while True:
        res = json.loads(rec.Result())
        print ("Text:", res['text'])
        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 ("Speaker distance:", cosine_dist(spk_sig, res['spk']))

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

@@ -1,49 +0,0 @@
-#!/usr/bin/env python3
-
-from vosk import Model, KaldiRecognizer, SetLogLevel
-import sys
-import os
-import wave
-import subprocess
-import srt
-import json
-import datetime
-
-SetLogLevel(-1)
-
-if not os.path.exists("model"):
-    print ("Please download the model from https://alphacephei.com/vosk/models and unpack as 'model' in the current folder.")
-    exit (1)
-
-sample_rate=16000
-model = Model("model")
-rec = KaldiRecognizer(model, sample_rate)
-
-process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
-                            sys.argv[1],
-                            '-ar', str(sample_rate) , '-ac', '1', '-f', 's16le', '-'],
-                            stdout=subprocess.PIPE)
-
-
-def transcribe():
-    results = []
-    subs = []
-    while True:
-       data = process.stdout.read(4000)
-       if len(data) == 0:
-           break
-       if rec.AcceptWaveform(data):
-           results.append(rec.Result())
-    results.append(rec.FinalResult())
-
-    for i, res in enumerate(results):
-       jres = json.loads(res)
-       s = srt.Subtitle(index=i, 
-                   content=jres['text'], 
-                   start=datetime.timedelta(seconds=jres['result'][0]['start']), 
-                   end=datetime.timedelta(seconds=jres['result'][-1]['end']))
-       subs.append(s)
-    return subs
-
-print (srt.compose(transcribe()))
-
@@ -6,7 +6,7 @@ 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.")
+    print ("Please download the model from https://github.com/alphacep/vosk-api/blob/master/doc/models.md and unpack as 'model' in the current folder.")
    exit (1)


@@ -6,7 +6,7 @@ import os
 import wave

 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.")
+    print ("Please download the model from https://github.com/alphacep/vosk-api/blob/master/doc/models.md and unpack as 'model' in the current folder.")
    exit (1)

 wf = wave.open(sys.argv[1], "rb")
@@ -15,9 +15,8 @@ if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE
    exit (1)

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

 while True:
    data = wf.readframes(4000)
@@ -52,14 +52,14 @@ kaldi_libraries = []

 if sys.platform.startswith('darwin'):
    kaldi_link_args.extend(['-Wl,-undefined,dynamic_lookup', '-framework', 'Accelerate'])
-elif kaldi_mkl == "1":
+elif kaldi_mkl != None:
    kaldi_link_args.extend(['-L/opt/intel/mkl/lib/intel64', '-Wl,-rpath=/opt/intel/mkl/lib/intel64'])
    kaldi_libraries.extend(['mkl_rt', 'mkl_intel_lp64', 'mkl_core', 'mkl_sequential'])
 else:
    kaldi_static_libs.append('tools/OpenBLAS/libopenblas.a')
    kaldi_libraries.append('gfortran')

-sources = ['kaldi_recognizer.cc', 'model.cc', 'spk_model.cc', 'vosk_api.cc', 'language_model.cc', 'vosk.i']
+sources = ['kaldi_recognizer.cc', 'model.cc', 'spk_model.cc', 'vosk_api.cc', 'vosk.i']

 vosk_ext = Extension('vosk._vosk',
                    define_macros = [('FST_NO_DYNAMIC_LINKING', '1')],
@@ -72,11 +72,11 @@ vosk_ext = Extension('vosk._vosk',
                    extra_compile_args = ['-std=c++11', '-Wno-sign-compare', '-Wno-unused-variable', '-Wno-unused-local-typedefs'])

 setuptools.setup(
-    name="vosk",
-    version="0.3.15",
+    name="vosk", # Replace with your own username
+    version="0.3.10",
    author="Alpha Cephei Inc",
    author_email="contact@alphacephei.com",
-    description="Offline open source speech recognition API based on Kaldi and Vosk",
+    description="API for Kaldi and Vosk",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/alphacep/vosk-api",
@@ -16,7 +16,6 @@
 #include "json.h"
 #include "fstext/fstext-utils.h"
 #include "lat/sausages.h"
-#include "language_model.h"

 using namespace fst;
 using namespace kaldi::nnet3;
@@ -58,49 +57,28 @@ KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, char cons
    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);

+    g_fst_ = new StdVectorFst();
    if (model_->hcl_fst_) {
-        json::JSON obj;
-        obj = json::JSON::Load(grammar);
+        g_fst_->AddState();
+        g_fst_->SetStart(0);
+        g_fst_->AddState();
+        g_fst_->SetFinal(1, fst::TropicalWeight::One());
+        g_fst_->AddArc(1, StdArc(0, 0, fst::TropicalWeight::One(), 0));

-        if (obj.length() <= 0) {
-            KALDI_WARN << "Expecting array of strings, got: '" << grammar << "'";
-        } else {
-            KALDI_LOG << obj;
+        // Create simple word loop FST
+        stringstream ss(grammar);
+        string token;

-            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_);
+        while (getline(ss, token, ' ')) {
+            int32 id = model_->word_syms_->Find(token);
+            g_fst_->AddArc(0, StdArc(id, id, fst::TropicalWeight::One(), 1));
        }
+        ArcSort(g_fst_, ILabelCompare<StdArc>());
+
+        decode_fst_ = LookaheadComposeFst(*model_->hcl_fst_, *g_fst_, model_->disambig_);
    } else {
        decode_fst_ = NULL;
-        KALDI_WARN << "Runtime graphs are not supported by this model";
+        KALDI_ERR << "Can't create decoding graph";
    }

    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
@@ -186,6 +164,11 @@ void KaldiRecognizer::CleanUp()
    delete silence_weighting_;
    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);

+    if (spk_model_) {
+        delete spk_feature_;
+        spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
+    }
+
    if (decoder_)
       frame_offset_ += decoder_->NumFramesDecoded();

@@ -209,11 +192,6 @@ void KaldiRecognizer::CleanUp()
            *model_->decodable_info_,
            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
            feature_pipeline_);
-
-        if (spk_model_) {
-            delete spk_feature_;
-            spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
-        }
    } else {
        decoder_->InitDecoding(frame_offset_);
    }
@@ -316,9 +294,7 @@ static void RunNnetComputation(const MatrixBase<BaseFloat> &features,
    xvector->CopyFromVec(cu_output.Row(0));
 }

-#define MIN_SPK_FEATS 50
-
-bool KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &out_xvector, int *num_spk_frames)
+void KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &xvector)
 {
    vector<int32> nonsilence_frames;
    if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0) {
@@ -328,36 +304,17 @@ bool KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &out_xvector, int *num_spk_
                                          &nonsilence_frames);
    }

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

@@ -365,22 +322,7 @@ bool KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &out_xvector, int *num_spk_
    nnet3::CachingOptimizingCompilerOptions compiler_config;
    nnet3::CachingOptimizingCompiler compiler(spk_model_->speaker_nnet, opts.optimize_config, compiler_config);

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

 const char* KaldiRecognizer::GetResult()
@@ -415,7 +357,7 @@ const char* KaldiRecognizer::GetResult()
        DeterminizeLattice(composed_lat1, &clat);
    }

-    fst::ScaleLattice(fst::GraphLatticeScale(0.9), &clat); // Apply rescoring weight
+    fst::ScaleLattice(fst::LatticeScale(9.0, 10.0), &clat);
    CompactLattice aligned_lat;
    if (model_->winfo_) {
        WordAlignLattice(clat, *model_->trans_model_, *model_->winfo_, 0, &aligned_lat);
@@ -452,12 +394,9 @@ const char* KaldiRecognizer::GetResult()

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

@@ -12,9 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#ifndef VOSK_KALDI_RECOGNIZER_H
-#define VOSK_KALDI_RECOGNIZER_H
-
 #include "base/kaldi-common.h"
 #include "util/common-utils.h"
 #include "fstext/fstext-lib.h"
@@ -58,7 +55,7 @@ class KaldiRecognizer {
        void CleanUp();
        void UpdateSilenceWeights();
        bool AcceptWaveform(Vector<BaseFloat> &wdata);
-        bool GetSpkVector(Vector<BaseFloat> &out_xvector, int *frames);
+        void GetSpkVector(Vector<BaseFloat> &xvector);
        const char *GetResult();
        const char *StoreReturn(const string &res);

@@ -83,5 +80,3 @@ class KaldiRecognizer {
        KaldiRecognizerState state_;
        string last_result_;
 };
-
-#endif /* VOSK_KALDI_RECOGNIZER_H */
@@ -1,211 +0,0 @@
-// Copyright      2015   Johns Hopkins University (author: Daniel Povey)
-
-// See ../../COPYING for clarification regarding multiple authors
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//  http://www.apache.org/licenses/LICENSE-2.0
-//
-// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
-// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
-// MERCHANTABLITY OR NON-INFRINGEMENT.
-// See the Apache 2 License for the specific language governing permissions and
-// limitations under the License.
-
-// A modified version from chain/language-model.cc for static backoff
-
-#include <algorithm>
-#include <numeric>
-
-#include "language_model.h"
-
-using namespace kaldi;
-
-void LanguageModelEstimator::AddCounts(const std::vector<int32> &sentence) {
-  KALDI_ASSERT(opts_.ngram_order >= 2 && "--ngram-order must be >= 2");
-  int32 order = opts_.ngram_order;
-  // 0 is used for left-context at the beginning of the file.. treat it as BOS.
-  std::vector<int32> history(0);
-  std::vector<int32>::const_iterator iter = sentence.begin(),
-      end = sentence.end();
-  for (; iter != end; ++iter) {
-    KALDI_ASSERT(*iter != 0);
-    IncrementCount(history, *iter);
-    history.push_back(*iter);
-    if (history.size() >= order)
-      history.erase(history.begin());
-  }
-  // Probability of end of sentence.  This will end up getting ignored later, but
-  // it still makes a difference for probability-normalization reasons.
-  IncrementCount(history, 0);
-}
-
-void LanguageModelEstimator::IncrementCount(const std::vector<int32> &history,
-                                            int32 next_phone) {
-  int32 lm_state_index = FindOrCreateLmStateIndexForHistory(history);
-  if (lm_states_[lm_state_index].tot_count == 0) {
-    num_active_lm_states_++;
-  }
-  lm_states_[lm_state_index].AddCount(next_phone, 1);
-}
-
-void LanguageModelEstimator::SetParentCounts() {
-  int32 num_lm_states = lm_states_.size();
-  for (int32 l = 0; l < num_lm_states; l++) {
-    int32 l_iter = lm_states_[l].backoff_lmstate_index;
-    while (l_iter != -1) {
-      lm_states_[l_iter].Add(lm_states_[l]);
-      l_iter = lm_states_[l_iter].backoff_lmstate_index;
-    }
-  }
-}
-
-int32 LanguageModelEstimator::FindLmStateIndexForHistory(
-    const std::vector<int32> &hist) const {
-  MapType::const_iterator iter = hist_to_lmstate_index_.find(hist);
-  if (iter == hist_to_lmstate_index_.end())
-    return -1;
-  else
-    return iter->second;
-}
-
-int32 LanguageModelEstimator::FindNonzeroLmStateIndexForHistory(
-    std::vector<int32> hist) const {
-  while (1) {
-    int32 l = FindLmStateIndexForHistory(hist);
-    if (l == -1 || lm_states_[l].tot_count == 0) {
-      // no such state or state has zero count.
-      if (hist.empty())
-        KALDI_ERR << "Error looking up LM state index for history "
-                  << "(likely code bug)";
-      hist.erase(hist.begin());  // back off.
-    } else {
-      return l;
-    }
-  }
-}
-
-int32 LanguageModelEstimator::FindOrCreateLmStateIndexForHistory(
-    const std::vector<int32> &hist) {
-  MapType::const_iterator iter = hist_to_lmstate_index_.find(hist);
-  if (iter != hist_to_lmstate_index_.end())
-    return iter->second;
-  int32 ans = lm_states_.size();  // index of next element
-  // next statement relies on default construct of LmState.
-  lm_states_.resize(lm_states_.size() + 1);
-  lm_states_.back().history = hist;
-  hist_to_lmstate_index_[hist] = ans;
-
-  // make sure backoff_lmstate_index is set
-  if (hist.size() > 0) {
-    std::vector<int32> backoff_hist(hist.begin() + 1,
-                                    hist.end());
-    int32 backoff_lm_state = FindOrCreateLmStateIndexForHistory(backoff_hist);
-    lm_states_[ans].backoff_lmstate_index = backoff_lm_state;
-  }
-  return ans;
-}
-
-void LanguageModelEstimator::LmState::AddCount(int32 phone, int32 count) {
-  std::map<int32, int32>::iterator iter = phone_to_count.find(phone);
-  if (iter == phone_to_count.end())
-    phone_to_count[phone] = count;
-  else
-    iter->second += count;
-  tot_count += count;
-}
-
-void LanguageModelEstimator::LmState::Add(const LmState &other) {
-  KALDI_ASSERT(&other != this);
-  std::map<int32, int32>::const_iterator iter = other.phone_to_count.begin(),
-      end = other.phone_to_count.end();
-  for (; iter != end; ++iter)
-    AddCount(iter->first, iter->second);
-}
-
-int32 LanguageModelEstimator::AssignFstStates() {
-  int32 num_lm_states = lm_states_.size();
-  int32 current_fst_state = 0;
-  for (int32 l = 0; l < num_lm_states; l++) {
-    if (lm_states_[l].tot_count != 0) {
-      lm_states_[l].fst_state = current_fst_state++;
-    }
-  }
-  KALDI_ASSERT(current_fst_state == num_active_lm_states_);
-  return current_fst_state;
-}
-
-void LanguageModelEstimator::Estimate(fst::StdVectorFst *fst) {
-  KALDI_LOG << "Estimating language model with ngram-order="
-            << opts_.ngram_order << ", discount="
-            << opts_.discount;
-  SetParentCounts();
-  int32 num_fst_states = AssignFstStates();
-  OutputToFst(num_fst_states, fst);
-}
-
-int32 LanguageModelEstimator::FindInitialFstState() const {
-  std::vector<int32> history(0);
-  int32 l = FindNonzeroLmStateIndexForHistory(history);
-  KALDI_ASSERT(l != -1 && lm_states_[l].fst_state != -1);
-  return lm_states_[l].fst_state;
-}
-
-void LanguageModelEstimator::OutputToFst(
-    int32 num_states,
-    fst::StdVectorFst *fst) const {
-  KALDI_ASSERT(num_states == num_active_lm_states_);
-  fst->DeleteStates();
-  for (int32 i = 0; i < num_states; i++)
-    fst->AddState();
-  fst->SetStart(FindInitialFstState());
-
-  int64 tot_count = 0;
-  double tot_logprob = 0.0;
-
-  int32 num_lm_states = lm_states_.size();
-  // note: not all lm-states end up being 'active'.
-  for (int32 l = 0; l < num_lm_states; l++) {
-    const LmState &lm_state = lm_states_[l];
-    if (lm_state.fst_state == -1) {
-      continue;
-    }
-    int32 state_count = lm_state.tot_count;
-    KALDI_ASSERT(state_count != 0);
-    std::map<int32, int32>::const_iterator
-        iter = lm_state.phone_to_count.begin(),
-        end = lm_state.phone_to_count.end();
-    for (; iter != end; ++iter) {
-      int32 phone = iter->first, count = iter->second;
-      BaseFloat logprob = log(count * opts_.discount / state_count);
-      tot_count += count;
-      tot_logprob += logprob * count;
-      if (phone == 0) {  // Go to final state
-        fst->SetFinal(lm_state.fst_state, fst::TropicalWeight(-logprob));
-      } else {  // It becomes a transition.
-        std::vector<int32> next_history(lm_state.history);
-        next_history.push_back(phone);
-        int32 dest_lm_state = FindNonzeroLmStateIndexForHistory(next_history),
-            dest_fst_state = lm_states_[dest_lm_state].fst_state;
-        KALDI_ASSERT(dest_fst_state != -1);
-        fst->AddArc(lm_state.fst_state,
-                    fst::StdArc(phone, phone, fst::TropicalWeight(-logprob),
-                                dest_fst_state));
-      }
-    }
-    if (lm_state.backoff_lmstate_index >= 0) {
-      fst->AddArc(lm_state.fst_state, fst::StdArc(0, 0, fst::TropicalWeight(-log(1 - opts_.discount)), lm_states_[lm_state.backoff_lmstate_index].fst_state));
-    }
-  }
-  fst::Connect(fst);
-  // Make sure that Connect does not delete any states.
-  int32 num_states_connected = fst->NumStates();
-  KALDI_ASSERT(num_states_connected == num_states);
-  // arc-sort.  ilabel or olabel doesn't matter, it's an acceptor.
-  fst::ArcSort(fst, fst::ILabelCompare<fst::StdArc>());
-  KALDI_LOG << "Created language model with " << num_states
-            << " states and " << fst::NumArcs(*fst) << " arcs.";
-}
@@ -1,150 +0,0 @@
-// Copyright      2015  Johns Hopkins University (Author: Daniel Povey)
-
-// See ../../COPYING for clarification regarding multiple authors
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//  http://www.apache.org/licenses/LICENSE-2.0
-//
-// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
-// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
-// MERCHANTABLITY OR NON-INFRINGEMENT.
-// See the Apache 2 License for the specific language governing permissions and
-// limitations under the License.
-
-
-#ifndef VOSK_LANGUAGE_MODEL_H
-#define VOSK_LANGUAGE_MODEL_H
-
-#include <vector>
-#include <map>
-
-#include "base/kaldi-common.h"
-#include "util/common-utils.h"
-#include "fstext/fstext-lib.h"
-#include "lat/kaldi-lattice.h"
-
-using namespace kaldi;
-
-// Very simply lm construction with absolute discounting
-
-struct LanguageModelOptions {
-  int32 ngram_order;  // you might want to tune this
-  BaseFloat discount; // discount for backoff
-
-  LanguageModelOptions():
-      ngram_order(3),
-      discount(0.5)
-      { }
-
-  void Register(OptionsItf *opts) {
-    opts->Register("ngram-order", &ngram_order, "n-gram order for the phone "
-                   "language model used for the 'denominator model'");
-    opts->Register("discount", &discount, "Discount for backoff");
-  }
-};
-
-class LanguageModelEstimator {
- public:
-  LanguageModelEstimator(LanguageModelOptions &opts): opts_(opts),
-                                                      num_active_lm_states_(0) {
-    KALDI_ASSERT(opts.ngram_order >= 1);
-  }
-
-  // Adds counts for this sentence.  Basically does: for each n-gram in the
-  // sentence, count[n-gram] += 1.  The only constraint on 'sentence' is that it
-  // should contain no zeros.
-  void AddCounts(const std::vector<int32> &sentence);
-
-  // Estimates the LM and outputs it as an FST.  Note: there is
-  // no concept here of backoff arcs.
-  void Estimate(fst::StdVectorFst *fst);
-
- protected:
-  struct LmState {
-    // the phone history associated with this state (length can vary).
-    std::vector<int32> history;
-
-    // maps from
-    std::map<int32, int32> phone_to_count;
-
-    // total count of this state.  As we back off states to lower-order states
-    // (and note that this is a hard backoff where we completely remove un-needed
-    // states) this tot_count may become zero.
-    int32 tot_count;
-
-    // LM-state index of the backoff LM state (if it exists, else -1)...
-    // provided for convenience.
-    int32 backoff_lmstate_index;
-
-    // this is only set after we decide on the FST state numbering (at the end).
-    // If not set, it's -1.
-    int32 fst_state;
-
-    void AddCount(int32 phone, int32 count);
-
-    // Add the contents of another LmState.
-    void Add(const LmState &other);
-
-    LmState(): tot_count(0), backoff_lmstate_index(-1),
-               fst_state(-1) { }
-    LmState(const LmState &other):
-        history(other.history), phone_to_count(other.phone_to_count),
-        tot_count(other.tot_count),
-        backoff_lmstate_index(other.backoff_lmstate_index),
-        fst_state(other.fst_state) { }
-  };
-
-  // maps from history to int32
-  typedef unordered_map<std::vector<int32>, int32, VectorHasher<int32> > MapType;
-
-  LanguageModelOptions opts_;
-
-  MapType hist_to_lmstate_index_;
-  std::vector<LmState> lm_states_;  // indexed by lmstate_index, the LmStates.
-
-  // Keeps track of the number of lm states that have nonzero counts.
-  int32 num_active_lm_states_;
-
-
-  // adds the counts for this ngram (called from AddCounts()).
-  inline void IncrementCount(const std::vector<int32> &history,
-                             int32 next_phone);
-
-  // sets up tot_count_with_parents in all the lm-states
-  void SetParentCounts();
-
-  // Finds and returns an LM-state index for a history -- or -1 if it doesn't
-  // exist.  No backoff is done.
-  int32 FindLmStateIndexForHistory(const std::vector<int32> &hist) const;
-
-  // Finds and returns an LM-state index for a history -- and creates one if
-  // it doesn't exist -- and also creates any backoff states needed, down
-  // to history-length no_prune_ngram_order - 1.
-  int32 FindOrCreateLmStateIndexForHistory(const std::vector<int32> &hist);
-
-  // Finds and returns the most specific LM-state index for a history or
-  // backed-off versions of it, that exists and has nonzero count.  Will die if
-  // there is no such history.  [e.g. if there is no unigram backoff state,
-  // which generally speaking there won't be.]
-  int32 FindNonzeroLmStateIndexForHistory(std::vector<int32> hist) const;
-
-  // after all backoff has been done, assigns FST state indexes to all states
-  // that exist and have nonzero count.  Returns the number of states.
-  int32 AssignFstStates();
-
-  // find the FST index of the initial-state, and returns it.
-  int32 FindInitialFstState() const;
-
-  // Write to an FST
-  void OutputToFst(
-      int32 num_fst_states,
-      fst::StdVectorFst *fst) const;
-
-};
-
-#endif
-
@@ -225,8 +225,6 @@ void Model::ReadDataFiles()
        ivector_extraction_opts.global_cmvn_stats_rxfilename = model_path_str_ + "/ivector/global_cmvn.stats";
        ivector_extraction_opts.diag_ubm_rxfilename = model_path_str_ + "/ivector/final.dubm";
        ivector_extraction_opts.ivector_extractor_rxfilename = model_path_str_ + "/ivector/final.ie";
-        ivector_extraction_opts.max_count = 100;
-
        feature_info_.use_ivectors = true;
        feature_info_.ivector_extractor_info.Init(ivector_extraction_opts);
    } else {
@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

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

 #include "base/kaldi-common.h"
 #include "fstext/fstext-lib.h"
@@ -86,4 +86,4 @@ protected:
    int ref_cnt_;
 };

-#endif /* VOSK_MODEL_H */
+#endif /* MODEL_H_ */
@@ -25,9 +25,6 @@ SpkModel::SpkModel(const char *speaker_path) {
    SetDropoutTestMode(true, &speaker_nnet);
    CollapseModel(nnet3::CollapseModelConfig(), &speaker_nnet);

-    ReadKaldiObject(speaker_path_str + "/mean.vec", &mean);
-    ReadKaldiObject(speaker_path_str + "/transform.mat", &transform);
-
    ref_cnt_ = 1;
 }

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

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

 #include "base/kaldi-common.h"
 #include "online2/online-feature-pipeline.h"
@@ -35,12 +35,9 @@ protected:
    ~SpkModel() {};

    kaldi::nnet3::Nnet speaker_nnet;
-    kaldi::Vector<BaseFloat> mean;
-    kaldi::Matrix<BaseFloat> transform;
-
    MfccOptions spkvector_mfcc_opts;

    int ref_cnt_;
 };

-#endif /* VOSK_SPK_MODEL_H */
+#endif /* SPK_MODEL_H_ */
@@ -34,11 +34,6 @@ import java.nio.ByteOrder;
    return AcceptWaveform(bdata, bdata.length);
  }
 %}
-%pragma(java) jniclasscode=%{
-    static {
-        System.loadLibrary("vosk_jni");
-    }
-%}
 #endif

 #if SWIGCSHARP
@@ -14,8 +14,8 @@

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

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

 #ifdef __cplusplus
 extern "C" {
@@ -88,10 +88,10 @@ VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
 VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, VoskSpkModel *spk_model, float sample_rate);


-/** Creates the recognizer object with the phrase list
+/** Creates the recognizer object with the grammar
 *
 *  Sometimes when you want to improve recognition accuracy and when you don't need
- *  to recognize large vocabulary you can specify a list of phrases to recognize. This
+ *  to recognize large vocabulary you can specify a list of words to recognize. This
 *  will improve recognizer speed and accuracy but might return [unk] if user said
 *  something different.
 *
@@ -99,8 +99,7 @@ VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, VoskSpkModel *spk_mode
 *  Precompiled HCLG graph models are not supported.
 *
 *  @param sample_rate The sample rate of the audio you going to feed into the recognizer
- *  @param grammar The string with the list of phrases to recognize as JSON array of strings,
- *                 for example "["one two three four five", "[unk]"]".
+ *  @param grammar The string with the list of words to recognize, for example "one two three four five [unk]"
 *
 *  @returns recognizer object */
 VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar);
@@ -208,4 +207,4 @@ void vosk_set_log_level(int log_level);
 }
 #endif

-#endif /* VOSK_API_H */
+#endif /* _VOSK_API_H_ */
@@ -90,7 +90,7 @@ RUN cd /opt \
    && cd kaldi/tools \
    && git clone -b v0.3.7 --single-branch https://github.com/xianyi/OpenBLAS \
    && make PREFIX=$(pwd)/OpenBLAS/install TARGET="${OPENBLAS_ARCH}" HOSTCC=gcc USE_LOCKING=1 USE_THREAD=0 -C OpenBLAS all install \
-    && git clone -b old-gcc --single-branch https://github.com/alphacep/openfst openfst \
+    && git clone https://github.com/alphacep/openfst openfst \
    && cd openfst \
    && autoreconf -i \
    && ./configure --prefix=/opt/kaldi/tools/openfst --enable-static --enable-shared --enable-far --enable-ngram-fsts --enable-lookahead-fsts --with-pic --disable-bin --host=${CROSS_TRIPLE} --build=x86-linux-gnu \
@@ -24,7 +24,7 @@ RUN cd /opt \
    && git clone -b lookahead --single-branch https://github.com/alphacep/kaldi \
    && cd kaldi/tools \
    && git clone -b v0.3.7 --single-branch https://github.com/xianyi/OpenBLAS \
-    && make PREFIX=$(pwd)/OpenBLAS/install DYNAMIC_ARCH=1 USE_LOCKING=1 USE_THREAD=0 -C OpenBLAS all install \
+    && make PREFIX=$(pwd)/OpenBLAS/install TARGET=NEHALEM USE_LOCKING=1 USE_THREAD=0 -C OpenBLAS all install \
    && git clone https://github.com/alphacep/openfst openfst \
    && cd openfst \
    && autoreconf -i \
@@ -32,5 +32,5 @@ RUN cd /opt \
    && make -j 10 && make install \
    && cd /opt/kaldi/src \
    && ./configure --mathlib=OPENBLAS --shared --use-cuda=no \
-    && make -j 10 online2 lm rnnlm \
+    && make -j 10 online2 lm \
    && find /opt/kaldi -name "*.o" -exec rm {} \;
Author	SHA1	Message	Date
Nickolay Shmyrev	8dd2166443	Another timestamp bugfix	2020-06-29 14:11:05 +02:00
Nickolay Shmyrev	7f3f25e170	Release memory when final result is received to reduce memory pressure.	2020-06-24 18:37:53 +02:00
Nickolay Shmyrev	f48e4624bf	Actually show the distance	2020-06-23 22:14:39 +02:00
Nickolay V. Shmyrev	856c935e92	Merge pull request #153 from He1nr1chK/master Added cosine distance function	2020-06-23 21:54:47 +03:00
He1nr1chK	444123b37e	Merge pull request #1 from He1nr1chK/He1nr1chK-Nodejs Added cosine distance function	2020-06-23 20:11:22 +02:00