Fix memory leak

Pure gpu setup
Upgrade onnxruntime
2026-06-03 07:37:02 +02:00 · 2025-07-23 10:11:59 +02:00 · 2025-02-24 10:27:42 +01:00 · 2024-11-03 03:55:22 +01:00 · 2024-08-12 13:43:53 +02:00 · 2024-07-18 16:27:38 +02:00
172 changed files with 15324 additions and 4802 deletions
@@ -1,19 +1,25 @@
-# Built application files
-*.apk
-*.ap_
+# Object files
+*.o

-# Java class files
-*.class
+# Temp files
+nohup.out

 # Gradle files
 .gradle/
-build/
 gradlew
 gradlew.bat
 gradle
-
-# Local configuration file (sdk path, etc)
 local.properties
+gradle.properties
+
+# Android
+android/build
+android/lib/build
+android/model-en/build
+android/model-en/src/main/assets/model-en-us
+android/repo
+*.apk
+*.ap_

 # Cmake
 .cxx
@@ -23,15 +29,43 @@ cmake_install.cmake
 wheelhouse
 __pycache__
 *.egg-info
-python/dist
+bindings/python/dist
+bindings/python/build
+bindings/python/vosk/*.so
+python/test/db
+python/test/hyp
+python/test/model
+python/test/ref
+python/test/result.txt
+python/test/wav.scp

 # Java
-*.so
-java/org
-java/model-en
-java/*.cc
+*.class
+bindings/java/lib/model
+bindings/java/demo/model
+bindings/java/lib/build
+bindings/java/demo/build

 # CSharp
-csharp/gen
-csharp/*.exe
-csharp/*.c
+*.dll
+*.so
+*.dylib
+*.nupkg
+csharp/demo/model
+csharp/demo/test.wav
+csharp/demo/bin
+csharp/demo/obj
+
+# Node
+nodejs/demo/model
+nodejs/demo/model-spk
+nodejs/demo/test.wav
+nodejs/node_modules
+nodejs/package-lock.json
+
+# C
+c/test_vosk
+c/test_vosk_speaker
+c/oprofile_data
+c/model
+c/test.wav
@@ -1,17 +0,0 @@
-notifications:
-  email: false
-
-matrix:
-  include:
-    - sudo: required
-      services:
-        - docker
-      env: DOCKER_IMAGE=alphacep/kaldi-manylinux:latest
-           PLAT=manylinux2010_x86_64
-
-install:
-  - docker pull $DOCKER_IMAGE
-
-script:
-  - docker run --rm -e PLAT=$PLAT -v `pwd`:/io $DOCKER_IMAGE $PRE_CMD /io/travis/build-wheels.sh
-  - ls wheelhouse/
@@ -1,142 +1,27 @@
-[![Build Status](https://travis-ci.com/alphacep/vosk-api.svg?branch=master)](https://travis-ci.com/alphacep/vosk-api)
+# Vosk Speech Recognition Toolkit

-[РУС](README.ru.md)
+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.

-[中文](README.zh.md)
+Vosk models are small (50 Mb) but provide continuous large vocabulary
+transcription, zero-latency response with streaming API, reconfigurable
+vocabulary and speaker identification.

-Vosk is a speech recognition toolkit. The best things in Vosk are:
+Speech recognition bindings implemented for various programming languages
+like Python, Java, Node.JS, C#, C++, Rust, Go and others.

-  1. Supports 9 languages - English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese. More to come.
-  1. Works offline, even on lightweight devices - Raspberry Pi, Android, iOS
-  1. Installs with simple `pip3 install vosk`
-  1. Portable per-language models are only 50Mb each, but there are much bigger server models available.
-  1. Provides streaming API for the best user experience (unlike popular speech-recognition python packages)
-  1. There are bindings for different programming languages, too - java/csharp/javascript etc.
-  1. Allows quick reconfiguration of vocabulary for best accuracy.
-  1. Supports speaker identification beside simple speech recognition.
+Vosk supplies speech recognition for chatbots, smart home appliances,
+virtual assistants. It can also create subtitles for movies,
+transcription for lectures and interviews.

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

-```
-cd android
-gradle build
-```
+# Documentation

-Please note that medium blog post about 64-bit is not relevant anymore, the script builds x86, arm64 and armv7 libraries automatically without any modifications.
-
-For example of Android application using Vosk-API check https://github.com/alphacep/kaldi-android-demo project
-
-## iOS build
-
-Available on request. Drop as a mail at [contact@alphacephei.com](mailto:contact@alphacephei.com).
-
-## Python installation from Pypi
-
-The easiest way to install vosk api is with pip. You do not have to compile anything. We currently support only Linux on x86_64 and Raspberry Pi. Other systems (windows, mac) will come soon.
-
-Make sure you have newer pip and python:
-
-  * Python version >= 3.4
-  * pip version >= 19.0
-
-Uprade python and pip if needed. Then install vosk on Linux with a simple command
-
-```
-pip3 install vosk
-```
-
-## Websocket Server and GRPC server
-
-We also provide a websocket server and grpc server which can be used in telephony and other applications. With bigger models adapted for 8khz audio it provides more accuracy.
-
-The server is installed with docker and can run with a single command:
-
-```
-docker run -d -p 2700:2700 alphacep/kaldi-en:latest
-```
-
-For details see https://github.com/alphacep/vosk-server
-
-
-## Compilation from source
-
-If you still want to build from scratch, you can compile Kaldi and Vosk yourself. The compilation is straightforward but might be a little confusing for newbie. In case you want to follow this, please watch the errors.
-
-#### Kaldi compilation for local python, node and java modules
-
-```
-git clone -b lookahead --single-branch https://github.com/alphacep/kaldi
-cd kaldi/tools
-make
-```
-
-install all dependencies and repeat `make` if needed
-
-```
-extras/install_openblas.sh
-cd ../src
-./configure --mathlib=OPENBLAS --shared --use-cuda=no
-make -j 10
-```
-
-#### Python module build
-
-Then build the python module
-
-```
-export KALDI_ROOT=<KALDI_ROOT>
-cd python
-python3 setup.py install
-```
-
-#### Running the example code with python
-
-Run like this:
-
-```
-cd vosk-api/python/example
-wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
-unzip alphacep-model-android-en-us-0.3.zip
-mv alphacep-model-android-en-us-0.3 model-en
-python3 ./test_simple.py test.wav
-```
-
-To run with your audio file make sure it has proper format - PCM 16khz 16bit mono, otherwise decoding will not work.
-
-You can find other examples of using a microphone, decoding with a fixed small vocabulary or speaker identification setup in  [python/example subfolder](https://github.com/alphacep/vosk-api/tree/master/python/example)
-
-#### Java example API build
-
-Or Java
-
-```
-cd java && KALDI_ROOT=<KALDI_ROOT> make
-wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
-unzip alphacep-model-android-en-us-0.3.zip
-mv alphacep-model-android-en-us-0.3 model
-make run
-```
-
-#### C# build
-
-Or C#
-
-```
-cd csharp && KALDI_ROOT=<KALDI_ROOT> make
-wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
-unzip alphacep-model-android-en-us-0.3.zip
-mv alphacep-model-android-en-us-0.3 model
-mono test.exe
-```
-
-## Models for different languages
-
-For information about models see [the documentation on available models](https://github.com/alphacep/vosk-api/blob/master/doc/models.md).
-
-## Contact Us
-
-If you have any questions, feel free to
-
-   * Post an issue here on github
-   * Send us an e-mail at [contact@alphacephei.com](mailto:contact@alphacephei.com)
-   * Join our group dedicated to speech recognition on Telegram [@speech_recognition](https://t.me/speech_recognition)
+For installation instructions, examples and documentation visit [Vosk
+Website](https://alphacephei.com/vosk).
@@ -1,149 +0,0 @@
-[![Build Status](https://travis-ci.com/alphacep/vosk-api.svg?branch=master)](https://travis-ci.com/alphacep/vosk-api)
-
-[EN](README.md)
-
-[中文](README.zh.md)
-
-Библитека для распознавания речи "Воск". Преимущества библиотеки:
-
-  1. Поддерживает 9 языков - русский, английский, немецкий, французский, португальский, испанский, китайский, турецкий, вьетнамский. В скором времени будут добавлены и другие
-  1. Работает без доступа к сети даже на мобильных устройствах - Raspberry Pi, Android, iOS
-  1. Устанавливается с помощью простой команды `pip3 install vosk` без дополнительных шагов
-  1. Модели для каждого языка занимают всего 50Мб, но есть и гораздо более точные большие модели для более точного распознавания
-  1. Сделана для потоковой обработки звука, что позволяет реализовать мгновенную реакцию на команды
-  1. Поддерживает несколько популярных языков программирования - Java, C#, Javascript
-  1. Позволяет быстро настраивать словарь распознавания для улучшения точности распознавания
-  1. Позволяет идентифицировать говорящего
-
-## Сборка для Android
-
-```
-cd android
-gradle build
-```
-
-Сборка включает платформы x86, armv7, arm64
-
-Для примера приложения, созданного с помощью библиотеки "Воск" смотрите [демо проект](https://github.com/alphacep/kaldi-android-demo).
-
-## Сборка для iOS
-
-Доступна позапросу. Напишите нам [contact@alphacephei.com](mailto:contact@alphacephei.com).
-
-## Установка для работы Python из Pypi
-
-Проще всего установить "Воск" с помощью pip. Собирать ничего не нужно. Мы поддерживаем платформы Linux, RPi и Windows. Сборка для OSX будет скоро доступна.
-
-Для начала убедитесь, что используются достаточно новые версии pip и Python:
-
-  * Python версия >= 3.5
-  * pip версия >= 19.0
-
-Обновите Python и Pip если нужно, а затем установите "Воск" такой командой:
-
-```
-pip3 install vosk
-```
-
-Для использования "Воск" смотрите примеры ниже.
-
-## Сервер для протоколов Websocket и GRPC
-
-We also provide a websocket server and grpc server which can be used in telephony and other applications. With bigger models adapted for 8khz audio it provides more accuracy.
-
-The server is installed with docker and can run with a single command:
-
-```
-docker run -d -p 2700:2700 alphacep/kaldi-en:latest
-```
-
-Смотрите проект https://github.com/alphacep/vosk-server
-
-
-## Сборка из исходников
-
-Если нужно собрать проект из исходного кода, необходимо будет собрать 
-Kaldi самостоятельно. Сборка досаточно простая и прямолинейная, но может
-быть непривычной для начинающих. Обращайте внимания на сообщения об ошибках
-в процессе сборки.
-
-#### Сборка Kaldi для модулей на Python, Java, C#
-
-```
-git clone https://github.com/kaldi-asr/kaldi
-cd kaldi/tools
-make
-```
-
-установите все рекомандуемые пакеты и повторите `make` если потребуется.
-
-```
-extras/install_openblas.sh
-cd ../src
-./configure --mathlib=OPENBLAS --shared --use-cuda=no
-make -j 10
-```
-
-#### Сборка модуля на Python
-
-После Kaldi можно собрать модуль Python
-
-```
-export KALDI_ROOT=<KALDI_ROOT>
-cd python
-python3 setup.py install
-```
-
-#### Запуск примера для Python
-
-Выполните следующие команды:
-
-```
-cd vosk-api/python/example
-wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
-unzip alphacep-model-android-en-us-0.3.zip
-mv alphacep-model-android-en-us-0.3 model-en
-python3 ./test_simple.py test.wav
-```
-
-Для того, чтобы распознавать другой файл, переведите его в нужный формат - PCM 16кГц 16бит 1канал. Это можно сделать с помощью ffmpeg.
-
-Другие примеры, в том числе использования микрофона, распознавание с небольшим словарём и распознавание говорящего можно найти в [каталоге python/example](https://github.com/alphacep/vosk-api/tree/master/python/example)
-
-#### Сборка для Java
-
-Перейдите в каталог Java и запустите сборку
-
-```
-cd java && KALDI_ROOT=<KALDI_ROOT> make
-wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
-unzip alphacep-model-android-en-us-0.3.zip
-mv alphacep-model-android-en-us-0.3 model
-make run
-```
-
-#### Сборка для C#
-
-Для сборки в среде Mono.
-
-```
-cd csharp && KALDI_ROOT=<KALDI_ROOT> make
-wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
-unzip alphacep-model-android-en-us-0.3.zip
-mv alphacep-model-android-en-us-0.3 model
-mono test.exe
-```
-
-.NET тоже должен работать, хотя мы не пробовали.
-
-## Модели для разных языков
-
-По информации о моделях смотрите соответствующую [страницу документации](https://github.com/alphacep/vosk-api/blob/master/doc/models.md).
-
-## Contact Us
-
-Если возникли вопросы, свяжитесь с нами:
-
-   * Создайте проблему тут на github
-   * Напишите нам по почте [contact@alphacephei.com](mailto:contact@alphacephei.com)
-   * Заходите в нашу группу в Телеграмме [@speech_recognition_ru](https://t.me/speech_recognition_ru)
@@ -1,12 +0,0 @@
-[![Build Status](https://travis-ci.com/alphacep/vosk-api.svg?branch=master)](https://travis-ci.com/alphacep/vosk-api)
-
-Vosk是言语识别工具包。Vosk最好的事情是：
-
-  1. 支持九种语言 - 中文, 英语，德语，法语，西班牙语，葡萄牙语，俄语，土耳其语，越南语
-  1. 移动设备上脱机工作-Raspberry Pi，Android，iOS
-  1. 使用简单的 pip3 install vosk 安装
-  1. 每种语言的手提式模型只有是50Mb, 但还有更大的服务器模型可用
-  1. 提供流媒体API，以提供最佳用户体验（与流行的语音识别python包不同）
-  1. 还有用于不同编程语言的包装器-java / csharp / javascript等
-  1. 可以快速重新配置词汇以实现最佳准确性
-  1. 支持说话人识别
@@ -1,64 +0,0 @@
-# Vosk CMake File
-
-cmake_minimum_required(VERSION 3.4.1)
-
-if ("x${ANDROID_ABI}" STREQUAL "xarmeabi-v7a")
-set(OPENBLAS_ARCH "armv7")
-set(KALDI_SUFFIX "arm_32")
-elseif ("x${ANDROID_ABI}" STREQUAL "xarm64-v8a")
-set(OPENBLAS_ARCH "armv8")
-set(KALDI_SUFFIX "arm_64")
-else ("x${ANDROID_ABI}" STREQUAL "xarmeabi-v7a")
-set(OPENBLAS_ARCH "atom")
-set(KALDI_SUFFIX "x86_64")
-endif ("x${ANDROID_ABI}" STREQUAL "xarmeabi-v7a")
-
-set(KALDI_ROOT "${PROJECT_SOURCE_DIR}/build/kaldi_${KALDI_SUFFIX}/kaldi")
-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"
-"${PROJECT_SOURCE_DIR}/../src/model.cc"
-"${PROJECT_SOURCE_DIR}/../src/model.h"
-"${PROJECT_SOURCE_DIR}/../src/spk_model.cc"
-"${PROJECT_SOURCE_DIR}/../src/spk_model.h"
-"${PROJECT_SOURCE_DIR}/../src/vosk_api.cc"
-"${PROJECT_SOURCE_DIR}/../src/vosk_api.h"
-)
-
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -DFST_NO_DYNAMIC_LINKING")
-
-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( kaldi_jni
-             ${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
-             ${LIB_ROOT}/lib/libfst.a
-             ${LIB_ROOT}/lib/libfstngram.a
-             ${LIB_ROOT}/lib/libopenblas_${OPENBLAS_ARCH}-r0.3.7.a
-             ${LIB_ROOT}/lib/libclapack.a
-             ${LIB_ROOT}/lib/liblapack.a
-             ${LIB_ROOT}/lib/libblas.a
-             ${LIB_ROOT}/lib/libf2c.a
-             log
-             )
@@ -1,22 +0,0 @@
-This is still work in progress, more to come
-
-## TODO
-
-   * Optimize graph construction, current one is below accuracy
-
-   * Load model from the AAR (mmap them in tflite style)
-
-   * Add decoding speed measurement
-
-   * Add wakeup word
-
-   * Add speakerid
-
-   * Integrate proper hardware optimized neural network library. Candidates are:
-
-      * https://github.com/XiaoMi/mace
-      * https://github.com/Tencent/ncnn
-      * https://developer.android.com/ndk/guides/neuralnetworks/ (since API level 27)
-      * https://github.com/google/XNNPACK
-
-   * Quantization for the models
@@ -1,130 +0,0 @@
-#!/bin/bash
-
-# Copyright 2019 Alpha Cephei Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#       http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-if [ "x$ANDROID_SDK_HOME" == "x" ]; then
-    echo "ANDROID_SDK_HOME environment variable is undefined, define it with local.properties or with export"
-    exit 1
-fi
-
-if [ ! -d "$ANDROID_SDK_HOME" ]; then
-    echo "ANDROID_SDK_HOME ($ANDROID_SDK_HOME) is missing. Make sure you have sdk installed"
-    exit 1
-fi
-
-if [ ! -d "$ANDROID_SDK_HOME/ndk-bundle" ]; then
-    echo "$ANDROID_SDK_HOME/ndk-bundle is missing. Make sure you have ndk installed within sdk"
-    exit 1
-fi
-
-set -x
-
-ANDROID_NDK_HOME=$ANDROID_SDK_HOME/ndk-bundle
-ANDROID_TOOLCHAIN_PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64
-WORKDIR_X86_64=`pwd`/build/kaldi_x86_64
-WORKDIR_ARM32=`pwd`/build/kaldi_arm_32
-WORKDIR_ARM64=`pwd`/build/kaldi_arm_64
-PATH=$PATH:$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64/bin
-OPENFST_VERSION=1.6.7
-
-mkdir -p $WORKDIR_ARM64/local/lib $WORKDIR_ARM32/local/lib $WORKDIR_X86_64/local/lib
-
-# Build standalone CLAPACK since gfortran is missing
-cd build
-git clone https://github.com/simonlynen/android_libs
-cd android_libs/lapack
-sed -i 's/APP_STL := gnustl_static/APP_STL := c++_static/g' jni/Application.mk && \
-sed -i 's/android-10/android-21/g' project.properties && \
-sed -i 's/APP_ABI := armeabi armeabi-v7a/APP_ABI := armeabi-v7a arm64-v8a x86_64/g' jni/Application.mk && \
-sed -i 's/LOCAL_MODULE:= testlapack/#LOCAL_MODULE:= testlapack/g' jni/Android.mk && \
-sed -i 's/LOCAL_SRC_FILES:= testclapack.cpp/#LOCAL_SRC_FILES:= testclapack.cpp/g' jni/Android.mk && \
-sed -i 's/LOCAL_STATIC_LIBRARIES := lapack/#LOCAL_STATIC_LIBRARIES := lapack/g' jni/Android.mk && \
-sed -i 's/include $(BUILD_SHARED_LIBRARY)/#include $(BUILD_SHARED_LIBRARY)/g' jni/Android.mk && \
-${ANDROID_NDK_HOME}/ndk-build && \
-cp obj/local/armeabi-v7a/*.a ${WORKDIR_ARM32}/local/lib && \
-cp obj/local/arm64-v8a/*.a ${WORKDIR_ARM64}/local/lib
-cp obj/local/x86_64/*.a ${WORKDIR_X86_64}/local/lib
-
-# Architecture-specific part
-
-
-for arch in arm32 arm64 x86_64; do
-#for arch in x86_64; do
-
-case $arch in
-    arm32)
-          BLAS_ARCH=ARMV7
-          WORKDIR=$WORKDIR_ARM32
-          HOST=arm-linux-androideabi
-          AR=arm-linux-androideabi-ar
-          CC=armv7a-linux-androideabi21-clang
-          CXX=armv7a-linux-androideabi21-clang++
-          ARCHFLAGS="-mfloat-abi=softfp -mfpu=neon"
-          ;;
-    arm64)
-          BLAS_ARCH=ARMV8
-          WORKDIR=$WORKDIR_ARM64
-          HOST=aarch64-linux-android
-          AR=aarch64-linux-android-ar
-          CC=aarch64-linux-android21-clang
-          CXX=aarch64-linux-android21-clang++
-          ARCHFLAGS=""
-          ;;
-    x86_64)
-          BLAS_ARCH=ATOM
-          WORKDIR=$WORKDIR_X86_64
-          HOST=x86_64-linux-android
-          AR=x86_64-linux-android-ar
-          CC=x86_64-linux-android21-clang
-          CXX=x86_64-linux-android21-clang++
-          ARCHFLAGS=""
-          ;;
-esac
-
-# openblas first
-cd $WORKDIR
-git clone -b v0.3.7 --single-branch https://github.com/xianyi/OpenBLAS
-make -C OpenBLAS TARGET=$BLAS_ARCH ONLY_CBLAS=1 AR=$AR CC=$CC HOSTCC=gcc ARM_SOFTFP_ABI=1 USE_THREAD=0 NUM_THREADS=1 -j4
-make -C OpenBLAS install PREFIX=$WORKDIR/local
-
-# tools directory --> we'll only compile OpenFST
-cd $WORKDIR
-wget -c -T 10 -t 1 http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-${OPENFST_VERSION}.tar.gz || \
-wget -c -T 10 -t 3 http://www.openslr.org/resources/2/openfst-${OPENFST_VERSION}.tar.gz
-
-tar -zxvf openfst-${OPENFST_VERSION}.tar.gz
-cd openfst-${OPENFST_VERSION}
-
-CXX=$CXX CXXFLAGS="$ARCHFLAGS -O3 -DFST_NO_DYNAMIC_LINKING" ./configure --prefix=${WORKDIR}/local \
-    --enable-shared --enable-static --with-pic --disable-bin \
-    --enable-lookahead-fsts --enable-ngram-fsts --host=$HOST --build=x86-linux-gnu
-make -j 8
-make install
-
-# Kaldi itself
-cd $WORKDIR
-git clone -b android-mix --single-branch https://github.com/alphacep/kaldi
-cd $WORKDIR/kaldi/src
-
-CXX=$CXX CXXFLAGS="$ARCHFLAGS -O3 -DFST_NO_DYNAMIC_LINKING" ./configure --use-cuda=no \
-    --mathlib=OPENBLAS --shared \
-    --android-incdir=${ANDROID_TOOLCHAIN_PATH}/sysroot/usr/include \
-    --host=$HOST --openblas-root=${WORKDIR}/local \
-    --fst-root=${WORKDIR}/local --fst-version=${OPENFST_VERSION}
-
-make -j 8 depend
-make -j 8 online2 lm
-
-done
@@ -1,68 +0,0 @@
-buildscript {
-    repositories {
-        google()
-        jcenter()
-    }
-    dependencies {
-        classpath 'com.android.tools.build:gradle:3.5.3'
-    }
-}
-
-allprojects {
-  repositories {
-      google()
-      jcenter()
-  }
-}
-
-apply plugin: 'com.android.library'
-
-android {
-    compileSdkVersion 29
-    defaultConfig {
-        minSdkVersion 21
-        targetSdkVersion 29
-        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"
-            }
-        }
-        ndk {
-            abiFilters 'armeabi-v7a', 'arm64-v8a', 'x86_64'
-        }
-    }
-    sourceSets {
-         main {
-              java.srcDirs = ['src/main/java', 'build/generated-src']
-         }
-    }
-    externalNativeBuild {
-       cmake {
-           path "CMakeLists.txt"
-       }
-    }
-}
-
-task swig {
-    doLast {
-        mkdir 'build/generated-src/java'
-        mkdir 'build/generated-src/cpp'
-        exec {
-            commandLine 'swig',
-            "-c++",
-            "-java", "-package", "org.kaldi",
-            "-outdir", "build/generated-src/java", "-o", "build/generated-src/cpp/vosk_wrap.cc",
-            "../src/vosk.i"
-        }
-    }
-}
-
-task kaldi(type: Exec) {
-    commandLine './build-kaldi.sh'
-    environment ANDROID_SDK_HOME: android.getSdkDirectory()
-}
-
-preBuild.dependsOn kaldi, swig
@@ -1,3 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<manifest xmlns:android="http://schemas.android.com/apk/res/android" package="org.kaldi">
-</manifest>
@@ -1,262 +0,0 @@
-// 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.
-
-package org.kaldi;
-
-import java.io.BufferedReader;
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-import java.io.OutputStream;
-import java.io.PrintWriter;
-import java.io.Reader;
-import java.util.ArrayDeque;
-import java.util.ArrayList;
-import java.util.Collection;
-import java.util.Collections;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Queue;
-
-import android.content.Context;
-import android.content.res.AssetManager;
-import android.os.Environment;
-import android.util.Log;
-
-/**
- * Provides utility methods to keep asset files to external storage to allow
- * further JNI code access assets from a filesystem.
- * 
- * There must be special file {@value #ASSET_LIST_NAME} among the application
- * assets containing relative paths of assets to synchronize. If the
- * corresponding path does not exist on the external storage it is copied. If
- * the path exists checksums are compared and the asset is copied only if there
- * is a mismatch. Checksum is stored in a separate asset with the name that
- * consists of the original name and a suffix that depends on the checksum
- * algorithm (e.g. MD5). Checksum files are copied along with the corresponding
- * asset files.
- * 
- * @author Alexander Solovets
- */
-public class Assets {
-
-    protected static final String TAG = Assets.class.getSimpleName();
-
-    public static final String ASSET_LIST_NAME = "assets.lst";
-    public static final String SYNC_DIR = "sync";
-    public static final String HASH_EXT = ".md5";
-
-    private final AssetManager assetManager;
-    private final File externalDir;
-
-    /**
-     * Creates new instance for asset synchronization
-     * 
-     * @param context
-     *            application context
-     * 
-     * @throws IOException
-     *             if the directory does not exist
-     * 
-     * @see android.content.Context#getExternalFilesDir
-     * @see android.os.Environment#getExternalStorageState
-     */
-    public Assets(Context context) throws IOException {
-        File appDir = context.getExternalFilesDir(null);
-        if (null == appDir)
-            throw new IOException("cannot get external files dir, "
-                    + "external storage state is " + Environment.getExternalStorageState());
-        externalDir = new File(appDir, SYNC_DIR);
-        assetManager = context.getAssets();
-    }
-
-    /**
-     * Creates new instance with specified destination for assets
-     * 
-     * @param context
-     *            application context to retrieve the assets
-     * @param path
-     *            path to sync the files
-     */ 
-    public Assets(Context context, String dest) {
-        externalDir = new File(dest);
-        assetManager = context.getAssets();
-    }
-
-    /**
-     * Returns destination path on external storage where assets are copied.
-     * 
-     * @return path to application directory or null if it does not exists
-     */
-    public File getExternalDir() {
-        return externalDir;
-    }
-
-    /**
-     * Returns the map of asset paths to the files checksums.
-     * 
-     * @return path to the root of resources directory on external storage
-     * @throws IOException
-     *             if an I/O error occurs or "assets.lst" is missing
-     */
-    public Map<String, String> getItems() throws IOException {
-        Map<String, String> items = new HashMap<String, String>();
-        for (String path : readLines(openAsset(ASSET_LIST_NAME))) {
-            Reader reader = new InputStreamReader(openAsset(path + HASH_EXT));
-            items.put(path, new BufferedReader(reader).readLine());
-        }
-        return items;
-    }
-
-    /**
-     * Returns path to hash mappings for the previously copied files. This
-     * method can be used to find out assets which must be updated.
-     */
-    public Map<String, String> getExternalItems() {
-        try {
-            Map<String, String> items = new HashMap<String, String>();
-            File assetFile = new File(externalDir, ASSET_LIST_NAME);
-            for (String line : readLines(new FileInputStream(assetFile))) {
-                String[] fields = line.split(" ");
-                items.put(fields[0], fields[1]);
-            }
-            return items;
-        } catch (IOException e) {
-            return Collections.emptyMap();
-        }
-    }
-
-    /**
-     * In case you want to create more smart sync implementation, this method
-     * returns the list of items which must be synchronized.
-     */
-    public Collection<String> getItemsToCopy(String path) throws IOException {
-        Collection<String> items = new ArrayList<String>();
-        Queue<String> queue = new ArrayDeque<String>();
-        queue.offer(path);
-
-        while (!queue.isEmpty()) {
-            path = queue.poll();
-            String[] list = assetManager.list(path);
-            for (String nested : list)
-                queue.offer(nested);
-
-            if (list.length == 0)
-                items.add(path);
-        }
-
-        return items;
-    }
-
-    private List<String> readLines(InputStream source) throws IOException {
-        List<String> lines = new ArrayList<String>();
-        BufferedReader br = new BufferedReader(new InputStreamReader(source));
-        String line;
-        while (null != (line = br.readLine()))
-            lines.add(line);
-        return lines;
-    }
-
-    private InputStream openAsset(String asset) throws IOException {
-        return assetManager.open(new File(SYNC_DIR, asset).getPath());
-    }
-
-    /**
-     * Saves the list of synchronized items. The list is stored as a two-column
-     * space-separated list of items in a text file. The file is located at the
-     * root of synchronization directory in the external storage.
-     * 
-     * @param items
-     *            the items
-     * @throws IOException
-     *             if an I/O error occurs
-     */
-    public void updateItemList(Map<String, String> items) throws IOException {
-        File assetListFile = new File(externalDir, ASSET_LIST_NAME);
-        PrintWriter pw = new PrintWriter(new FileOutputStream(assetListFile));
-        for (Map.Entry<String, String> entry : items.entrySet())
-            pw.format("%s %s\n", entry.getKey(), entry.getValue());
-        pw.close();
-    }
-
-    /**
-     * Copies raw asset resource to external storage of the device.
-     * 
-     * @param path
-     *            path of the asset to copy
-     * @throws IOException
-     *             if an I/O error occurs
-     */
-    public File copy(String asset) throws IOException {
-        InputStream source = openAsset(asset);
-        File destinationFile = new File(externalDir, asset);
-        destinationFile.getParentFile().mkdirs();
-        OutputStream destination = new FileOutputStream(destinationFile);
-        byte[] buffer = new byte[1024];
-        int nread;
-
-        while ((nread = source.read(buffer)) != -1) {
-            if (nread == 0) {
-                nread = source.read();
-                if (nread < 0)
-                    break;
-                destination.write(nread);
-                continue;
-            }
-            destination.write(buffer, 0, nread);
-        }
-        destination.close();
-        return destinationFile;
-    }
-
-    /**
-     * Performs the sync of assets in the application and on the external
-     * storage
-     * 
-     * @return The folder on external storage with data
-     * @throws IOException
-     */
-    public File syncAssets() throws IOException {
-        Collection<String> newItems = new ArrayList<String>();
-        Collection<String> unusedItems = new ArrayList<String>();
-        Map<String, String> items = getItems();
-        Map<String, String> externalItems = getExternalItems();
-
-        for (String path : items.keySet()) {
-            if (!items.get(path).equals(externalItems.get(path))
-                    || !(new File(externalDir, path).exists()))
-                newItems.add(path);
-        }
-
-        unusedItems.addAll(externalItems.keySet());
-        unusedItems.removeAll(items.keySet());
-
-        for (String path : newItems) {
-            File file = copy(path);
-        }
-
-        for (String path : unusedItems) {
-            File file = new File(externalDir, path);
-            file.delete();
-        }
-
-        updateItemList(items);
-        return externalDir;
-    }
-
-}
@@ -1,39 +0,0 @@
-// 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.
-
-package org.kaldi;
-
-/** Interface to receive recognition results */
-public interface RecognitionListener {
-
-    /**
-     * Called when partial recognition result is available.
-     */
-    public void onPartialResult(String hypothesis);
-
-    /**
-     * Called after the recognition is ended.
-     */
-    public void onResult(String hypothesis);
-
-    /**
-     * Called when an error occurs.
-     */
-    public void onError(Exception exception);
-
-    /**
-     * Called after timeout expired
-     */
-    public void onTimeout();
-}
@@ -1,286 +0,0 @@
-// 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.
-
-package org.kaldi;
-
-import static java.lang.String.format;
-
-import java.io.File;
-import java.io.IOException;
-import java.util.Collection;
-import java.util.HashSet;
-
-import android.media.AudioFormat;
-import android.media.AudioRecord;
-import android.media.MediaRecorder.AudioSource;
-import android.os.Handler;
-import android.os.Looper;
-import android.util.Log;
-
-/**
- * Main class to access recognizer functions. After configuration this class
- * starts a listener thread which records the data and recognizes it using
- * VOSK engine. Recognition events are passed to a client using
- * {@link RecognitionListener}
- * 
- */
-public class SpeechRecognizer {
-
-    protected static final String TAG = SpeechRecognizer.class.getSimpleName();
-
-    private final KaldiRecognizer recognizer;
-
-    private final int sampleRate;        
-    private final static float BUFFER_SIZE_SECONDS = 0.4f;
-    private int bufferSize;
-    private final AudioRecord recorder;
-    
-    private Thread recognizerThread;
-
-    private final Handler mainHandler = new Handler(Looper.getMainLooper());
-    
-    private final Collection<RecognitionListener> listeners = new HashSet<RecognitionListener>();
-    
-    /**
-     * 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 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, 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.");
-        }
-    }
-
-    /**
-     * Adds listener.
-     */
-    public void addListener(RecognitionListener listener) {
-        synchronized (listeners) {
-            listeners.add(listener);
-        }
-    }
-
-    /**
-     * Removes listener.
-     */
-    public void removeListener(RecognitionListener listener) {
-        synchronized (listeners) {
-            listeners.remove(listener);
-        }
-    }
-
-    /**
-     * Starts recognition. Does nothing if recognition is active.
-     * 
-     * @return true if recognition was actually started
-     */
-    public boolean startListening() {
-        if (null != recognizerThread)
-            return false;
-
-        recognizerThread = new RecognizerThread();
-        recognizerThread.start();
-        return true;
-    }
-
-    /**
-     * Starts recognition. After specified timeout listening stops and the
-     * endOfSpeech signals about that. Does nothing if recognition is active.
-     * 
-     * @timeout - timeout in milliseconds to listen.
-     * 
-     * @return true if recognition was actually started
-     */
-    public boolean startListening(int timeout) {
-        if (null != recognizerThread)
-            return false;
-
-        recognizerThread = new RecognizerThread(timeout);
-        recognizerThread.start();
-        return true;
-    }
-
-    private boolean stopRecognizerThread() {
-        if (null == recognizerThread)
-            return false;
-
-        try {
-            recognizerThread.interrupt();
-            recognizerThread.join();
-        } catch (InterruptedException e) {
-            // Restore the interrupted status.
-            Thread.currentThread().interrupt();
-        }
-
-        recognizerThread = null;
-        return true;
-    }
-
-    /**
-     * Stops recognition. All listeners should receive final result if there is
-     * any. Does nothing if recognition is not active.
-     * 
-     * @return true if recognition was actually stopped
-     */
-    public boolean stop() {
-        boolean result = stopRecognizerThread();
-        if (result) {
-            mainHandler.post(new ResultEvent(recognizer.Result(), true));
-        }
-        return result;
-    }
-
-    /**
-     * Cancels recognition. Listeners do not receive final result. Does nothing
-     * if recognition is not active.
-     * 
-     * @return true if recognition was actually canceled
-     */
-    public boolean cancel() {
-        boolean result = stopRecognizerThread();
-        recognizer.Result(); // Reset recognizer state
-        return result;
-    }
-    
-    /**
-     * Shutdown the recognizer and release the recorder
-     */
-    public void shutdown() {
-        recorder.release();
-    }
-    
-    private final class RecognizerThread extends Thread {
-        
-        private int remainingSamples;
-        private int timeoutSamples;
-        private final static int NO_TIMEOUT = -1;
-
-        public RecognizerThread(int timeout) {
-            if (timeout != NO_TIMEOUT)
-                this.timeoutSamples = timeout * sampleRate / 1000;
-            else
-                this.timeoutSamples = NO_TIMEOUT;
-            this.remainingSamples = this.timeoutSamples;
-        }
-
-        public RecognizerThread() {
-            this(NO_TIMEOUT);
-        }
-
-        @Override
-        public void run() {
-
-            recorder.startRecording();
-            if (recorder.getRecordingState() == AudioRecord.RECORDSTATE_STOPPED) {
-                recorder.stop();
-                IOException ioe = new IOException(
-                        "Failed to start recording. Microphone might be already in use.");
-                mainHandler.post(new OnErrorEvent(ioe));
-                return;
-            }
-
-            short[] buffer = new short[bufferSize];
-
-            while (!interrupted()
-                    && ((timeoutSamples == NO_TIMEOUT) || (remainingSamples > 0))) {
-                int nread = recorder.read(buffer, 0, buffer.length);
-
-                if (nread < 0) {
-                    throw new RuntimeException("error reading audio buffer");
-                } else {
-                    boolean isFinal = recognizer.AcceptWaveform(buffer, nread);
-                    if (isFinal) {
-                        mainHandler.post(new ResultEvent(recognizer.Result(), true));
-                    } else {
-                        mainHandler.post(new ResultEvent(recognizer.PartialResult(), false));
-                    }
-                }
-
-                if (timeoutSamples != NO_TIMEOUT) {
-                    remainingSamples = remainingSamples - nread;
-                }
-            }
-
-            recorder.stop();
-
-            // Remove all pending notifications.
-            mainHandler.removeCallbacksAndMessages(null);
-
-            // If we met timeout signal that speech ended
-            if (timeoutSamples != NO_TIMEOUT && remainingSamples <= 0) {
-                mainHandler.post(new TimeoutEvent());
-            }
-        }
-    }
-
-    private abstract class RecognitionEvent implements Runnable {
-        public void run() {
-            RecognitionListener[] emptyArray = new RecognitionListener[0];
-            for (RecognitionListener listener : listeners.toArray(emptyArray))
-                execute(listener);
-        }
-
-        protected abstract void execute(RecognitionListener listener);
-    }
-
-    private class ResultEvent extends RecognitionEvent {
-        protected final String hypothesis;
-        private final boolean finalResult;
-
-        ResultEvent(String hypothesis, boolean finalResult) {
-            this.hypothesis = hypothesis;
-            this.finalResult = finalResult;
-        }
-
-        @Override
-        protected void execute(RecognitionListener listener) {
-            if (finalResult)
-                listener.onResult(hypothesis);
-            else
-                listener.onPartialResult(hypothesis);
-        }
-    }
-
-    private class OnErrorEvent extends RecognitionEvent {
-        private final Exception exception;
-
-        OnErrorEvent(Exception exception) {
-            this.exception = exception;
-        }
-
-        @Override
-        protected void execute(RecognitionListener listener) {
-            listener.onError(exception);
-        }
-    }
-
-    private class TimeoutEvent extends RecognitionEvent {
-        @Override
-        protected void execute(RecognitionListener listener) {
-            listener.onTimeout();
-        }
-    }
-}
@@ -0,0 +1,16 @@
+CFLAGS=-I../../src
+LDFLAGS=-L../../src -lvosk -ldl -lpthread -Wl,-rpath,../../src
+
+all: test_vosk test_vosk_multi
+
+test_vosk: test_vosk.o
+	gcc $^ -o $@ $(LDFLAGS)
+
+test_vosk_multi: test_vosk_multi.o
+	gcc $^ -o $@ $(LDFLAGS) -lpthread
+
+%.o: %.c
+	gcc $(CFLAGS) -c -o $@ $<
+
+clean:
+	rm -f *.o *.a test_vosk test_vosk_multi
@@ -0,0 +1,37 @@
+#include <vosk_api.h>
+#include <stdio.h>
+#include <unistd.h>
+
+int main() {
+    FILE *wavin;
+    char buf[3200];
+    int nread, final;
+
+    VoskModel *model = vosk_model_new("vosk-model-small-ru");
+    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);
+         vosk_recognizer_accept_waveform(recognizer, buf, nread);
+         while (vosk_recognizer_get_num_pending_results(recognizer) > 0)
+             sleep(0.05);
+         while (!vosk_recognizer_results_empty(recognizer)) {
+             printf("%s\n", vosk_recognizer_result_front(recognizer));
+             vosk_recognizer_result_pop(recognizer);
+         }
+    }
+    vosk_recognizer_flush(recognizer);
+    while (vosk_recognizer_get_num_pending_results(recognizer) > 0)
+         sleep(0.05);
+    while (!vosk_recognizer_results_empty(recognizer)) {
+         printf("%s\n", vosk_recognizer_result_front(recognizer));
+         vosk_recognizer_result_pop(recognizer);
+    }
+
+    vosk_recognizer_free(recognizer);
+    vosk_model_free(model);
+    fclose(wavin);
+    return 0;
+}
@@ -0,0 +1,82 @@
+#include <vosk_api.h>
+#include <stdio.h>
+#include <unistd.h>
+#include <pthread.h>
+#include <time.h>
+#include <stdlib.h>
+#include <string.h>
+
+#define NUM_THREADS 10
+#define NUM_FILES_PER_THREAD 3
+#define NUM_FILES 400
+#define MAX_FILE 100
+
+VoskModel *model;
+char infiles[NUM_FILES][MAX_FILE];
+pthread_t thread_ids[NUM_THREADS];
+
+static void *worker(void *data) {
+    int j;
+
+    for (j = 0; j < NUM_FILES_PER_THREAD; j++) {
+        FILE *wavin;
+        char buf[4000];
+        int nread;
+        VoskRecognizer *recognizer = vosk_recognizer_new(model, 16000.0);
+        int foffset = rand() % NUM_FILES;
+
+        wavin = fopen(infiles[foffset], "rb");
+        fseek(wavin, 44, SEEK_SET);
+        while (!feof(wavin)) {
+             nread = fread(buf, 1, sizeof(buf), wavin);
+             vosk_recognizer_accept_waveform(recognizer, buf, nread);
+             while (vosk_recognizer_get_num_pending_results(recognizer) > 0)
+                 sleep(0.05);
+             while (!vosk_recognizer_results_empty(recognizer)) {
+                 printf("%s\n", vosk_recognizer_result_front(recognizer));
+                 vosk_recognizer_result_pop(recognizer);
+             }
+        }
+        vosk_recognizer_flush(recognizer);
+        while (vosk_recognizer_get_num_pending_results(recognizer) > 0)
+             sleep(0.05);
+        while (!vosk_recognizer_results_empty(recognizer)) {
+             printf("%s\n", vosk_recognizer_result_front(recognizer));
+             vosk_recognizer_result_pop(recognizer);
+        }
+
+        vosk_recognizer_free(recognizer);
+        fclose(wavin);
+    }
+}
+
+void read_file_list() {
+    FILE* infile;
+    infile = fopen("file.list", "r");
+    int i = 0;
+
+    while(i < NUM_FILES, fgets(infiles[i], MAX_FILE, infile)) {
+        infiles[i][strlen(infiles[i]) - 1] = 0;
+        printf("%s\n", infiles[i]);
+        i++;
+    }
+
+    fclose(infile);
+}
+
+int main() {
+    int i;
+    srand(0);
+
+    read_file_list();
+
+    model = vosk_model_new("vosk-model-small-ru");
+    for (i = 0; i < NUM_THREADS; i++) {
+        pthread_create(&thread_ids[i], NULL, worker, NULL);
+    }
+    for (i = 0; i < NUM_THREADS; i++) {
+        pthread_join(thread_ids[i], NULL);
+    }
+    vosk_model_free(model);
+    return 0;
+}
@@ -0,0 +1,7 @@
+Java bindings for Vosk API using jnr-ffi
+
+See demo project for details, build it with Gradle.
+
+Download model and unpack as "model" folder in the demo project.
+
+Make sure you are using recent JDK and Gradle.
@@ -0,0 +1,71 @@
+buildscript {
+  repositories {
+    mavenCentral()
+  }
+}
+
+plugins {
+    id 'java-library'
+    id 'maven-publish'
+    id 'com.vanniktech.maven.publish' version '0.18.0'
+}
+
+repositories {
+    mavenCentral()
+}
+
+archivesBaseName = 'vosk'
+group = 'com.alphacephei'
+version = '0.4.0'
+
+mavenPublish {
+    group = 'com.alphacephei'
+    version = version
+    sonatypeHost = 's01'
+}
+
+dependencies {
+    api group: 'net.java.dev.jna', name: 'jna', version: '5.13.0'
+    testImplementation 'junit:junit:4.13'
+}
+
+publishing {
+    publications {
+        mavenJava(MavenPublication) {
+            artifactId = 'vosk'
+            from components.java
+            pom {
+                name = 'Vosk'
+                description = 'Speech recognition library'
+                url = 'http://www.alphacephei.com.com/vosk/'
+                licenses {
+                    license {
+                        name = 'The Apache License, Version 2.0'
+                        url = 'http://www.apache.org/licenses/LICENSE-2.0.txt'
+                    }
+                }
+                developers {
+                    developer {
+                        id = 'alphacephei'
+                        name = 'Alpha Cephei Inc'
+                        email = 'contact@alphacephei.com'
+                    }
+                }
+                scm {
+                    connection = 'scm:git:git://github.com/alphacep/vosk-api.git'
+                    url = 'https://github.com/alphacep/vosk-api/'
+                }
+            }
+        }
+    }
+}
+
+test {
+    dependsOn cleanTest
+    testLogging.showStandardStreams = true
+}
+
+java {
+    withSourcesJar()
+    withJavadocJar()
+}
@@ -0,0 +1,86 @@
+package org.vosk;
+
+import com.sun.jna.Native;
+import com.sun.jna.Platform;
+import com.sun.jna.Pointer;
+import java.io.File;
+import java.io.InputStream;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.StandardCopyOption;
+
+public class LibVosk {
+
+    private static void unpackDll(File targetDir, String lib) throws IOException {
+        try (InputStream source = LibVosk.class.getResourceAsStream("/win32-x86-64/" + lib + ".dll")) {
+            Files.copy(source, new File(targetDir, lib + ".dll").toPath(), StandardCopyOption.REPLACE_EXISTING);
+        }
+    }
+
+    static {
+
+        if (Platform.isWindows()) {
+            // We have to unpack dependencies
+            try {
+                // To get a tmp folder we unpack small library and mark it for deletion
+                File tmpFile = Native.extractFromResourcePath("/win32-x86-64/empty", LibVosk.class.getClassLoader());
+                File tmpDir = tmpFile.getParentFile();
+                new File(tmpDir, tmpFile.getName() + ".x").createNewFile();
+
+                // Now unpack dependencies
+                unpackDll(tmpDir, "libwinpthread-1");
+                unpackDll(tmpDir, "libgcc_s_seh-1");
+                unpackDll(tmpDir, "libstdc++-6");
+
+            } catch (IOException e) {
+                // Nothing for now, it will fail on next step
+            } finally {
+                Native.register(LibVosk.class, "libvosk");
+            }
+        } else {
+            Native.register(LibVosk.class, "vosk");
+        }
+    }
+
+    public static native void vosk_set_log_level(int level);
+
+    public static native Pointer vosk_model_new(String path);
+
+    public static native void vosk_model_free(Pointer model);
+
+    public static native Pointer vosk_recognizer_new(Model model, float sample_rate);
+
+    public static native void vosk_recognizer_accept_waveform(Pointer recognizer, byte[] data, int len);
+
+    public static native void vosk_recognizer_accept_waveform_s(Pointer recognizer, short[] data, int len);
+
+    public static native void vosk_recognizer_accept_waveform_f(Pointer recognizer, float[] data, int len);
+
+    public static native void vosk_recognizer_flush(Pointer recognizer);
+
+    public static native String vosk_recognizer_result_front(Pointer recognizer);
+
+    public static native void vosk_recognizer_result_pop(Pointer recognizer);
+
+    public static native int vosk_recognizer_get_num_pending_results(Pointer recognizer);
+
+    public static native int vosk_recognizer_get_num_results(Pointer recognizer);
+
+    public static native int vosk_recognizer_results_empty(Pointer recognizer);
+
+    public static native void vosk_recognizer_reset(Pointer recognizer);
+
+    public static native void vosk_recognizer_free(Pointer recognizer);
+
+    /**
+     * Set log level for Kaldi messages.
+     *
+     *  @param loglevel the level
+     *     0 - default value to print info and error messages but no debug
+     *     less than 0 - don't print info messages
+     *     greater than 0 - more verbose mode
+     */
+    public static void setLogLevel(LogLevel loglevel) {
+        vosk_set_log_level(loglevel.getValue());
+    }
+}
@@ -0,0 +1,17 @@
+package org.vosk;
+
+public enum LogLevel {
+    WARNINGS(-1),  // Print warning and errors
+    INFO(0),       // Print info, along with warning and error messages, but no debug
+    DEBUG(1);      // Print debug info
+
+    private final int value;
+
+    LogLevel(int value) {
+        this.value = value;
+    }
+
+    public int getValue() {
+        return this.value;
+    }
+}
@@ -0,0 +1,22 @@
+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) throws IOException {
+        super(LibVosk.vosk_model_new(path));
+
+        if (getPointer() == null) {
+            throw new IOException("Failed to create a model");
+        }
+    }
+
+    @Override
+    public void close() {
+        LibVosk.vosk_model_free(this.getPointer());
+    }
+}
@@ -0,0 +1,113 @@
+package org.vosk;
+
+import com.sun.jna.PointerType;
+import java.io.IOException;
+
+public class Recognizer extends PointerType implements AutoCloseable {
+    /**
+     * Creates the 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 sampleRate The sample rate of the audio you are going to feed into the recognizer.
+     *                    Make sure this rate matches the audio content, it is a common
+     *                    issue causing accuracy problems.
+     * @throws IOException if the recognizer could not be created
+     */
+    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");
+        }
+    }
+
+    /**
+     * Accept and process new chunk of voice data.
+     *
+     *  @param data - audio data in PCM 16-bit mono format
+     *  @param len - length of the audio data
+     */
+    public void acceptWaveForm(byte[] data, int len) {
+        LibVosk.vosk_recognizer_accept_waveform(this.getPointer(), data, len);
+    }
+
+    public void acceptWaveForm(short[] data, int len) {
+        LibVosk.vosk_recognizer_accept_waveform_s(this.getPointer(), data, len);
+    }
+
+    public void acceptWaveForm(float[] data, int len) {
+        LibVosk.vosk_recognizer_accept_waveform_f(this.getPointer(), data, len);
+    }
+
+    public void flush() {
+        LibVosk.vosk_recognizer_flush(this.getPointer());
+    }
+
+    /**
+     * Returns speech recognition result
+     *
+     * @return 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 #setMaxAlternatives().
+     *
+     * If word times enabled returns word time, see also #setWordTimes().
+     */
+    public String getResult() {
+        return LibVosk.vosk_recognizer_result_front(this.getPointer());
+    }
+
+    /**
+     * Removes the latest result from the result queue
+     */
+    public void popResult() {
+        LibVosk.vosk_recognizer_result_pop(this.getPointer());
+    }
+
+    /**
+     * Counts pending results
+     */
+    public int getNumPendingResults() {
+        return LibVosk.vosk_recognizer_get_num_pending_results(this.getPointer());
+    }
+
+    /**
+     * Counts pending results
+     */
+    public int getNumResults() {
+        return LibVosk.vosk_recognizer_get_num_results(this.getPointer());
+    }
+
+    /**
+     * Check if output queue is empty
+     */
+    public boolean getResultsEmpty() {
+        return (LibVosk.vosk_recognizer_results_empty(this.getPointer()) == 1);
+    }
+
+    /**
+     * Resets the recognizer.
+     * Resets current results so the recognition can continue from scratch.
+     */
+    public void reset() {
+        LibVosk.vosk_recognizer_reset(this.getPointer());
+    }
+
+    /**
+     * Releases recognizer object.
+     * Underlying model is also unreferenced and if needed, released.
+     */
+    @Override
+    public void close() {
+        LibVosk.vosk_recognizer_free(this.getPointer());
+    }
+}
@@ -0,0 +1,86 @@
+package org.vosk.test;
+
+import java.io.FileInputStream;
+import java.io.BufferedInputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+
+import org.junit.Test;
+import org.junit.Assert;
+
+import javax.sound.sampled.AudioSystem;
+import javax.sound.sampled.UnsupportedAudioFileException;
+
+import org.vosk.LogLevel;
+import org.vosk.Recognizer;
+import org.vosk.LibVosk;
+import org.vosk.Model;
+
+public class DecoderTest {
+
+    @Test
+    public void decoderTest() throws IOException, UnsupportedAudioFileException, InterruptedException {
+        LibVosk.setLogLevel(LogLevel.DEBUG);
+
+        try (Model model = new Model("../../python/example/vosk-model-small-ru");
+            InputStream ais = AudioSystem.getAudioInputStream(new BufferedInputStream(new FileInputStream("../../python/example/test-ru.wav")));
+            Recognizer recognizer = new Recognizer(model, 16000)) {
+
+            int nbytes;
+            byte[] b = new byte[4000];
+            while ((nbytes = ais.read(b)) >= 0) {
+                recognizer.acceptWaveForm(b, nbytes);
+                while (recognizer.getNumPendingResults() > 0) {
+                    Thread.sleep(50);
+                }
+                while (!recognizer.getResultsEmpty()) {
+                    System.out.println(recognizer.getResult());
+                    recognizer.popResult();
+                }
+            }
+            recognizer.flush();
+            while (recognizer.getNumPendingResults() > 0) {
+                Thread.sleep(50);
+            }
+            while (!recognizer.getResultsEmpty()) {
+                System.out.println(recognizer.getResult());
+                recognizer.popResult();
+            }
+        }
+        Assert.assertTrue(true);
+    }
+
+    @Test
+    public void decoderTestBig() throws IOException, UnsupportedAudioFileException, InterruptedException {
+        LibVosk.setLogLevel(LogLevel.DEBUG);
+
+        try (Model model = new Model("../../python/example/vosk-model-small-ru");
+            InputStream ais = AudioSystem.getAudioInputStream(new BufferedInputStream(new FileInputStream("../../python/example/test-ru.wav")));
+            Recognizer recognizer = new Recognizer(model, 16000)) {
+
+            int nbytes;
+            byte[] b = new byte[400000];
+            while ((nbytes = ais.read(b)) >= 0) {
+                recognizer.acceptWaveForm(b, nbytes);
+                while (recognizer.getNumPendingResults() > 0) {
+                    Thread.sleep(50);
+                }
+                while (!recognizer.getResultsEmpty()) {
+                    System.out.println(recognizer.getResult());
+                    recognizer.popResult();
+                }
+            }
+            recognizer.flush();
+            while (recognizer.getNumPendingResults() > 0) {
+                Thread.sleep(50);
+            }
+            while (!recognizer.getResultsEmpty()) {
+                System.out.println(recognizer.getResult());
+                recognizer.popResult();
+            }
+        }
+        Assert.assertTrue(true);
+    }
+}
@@ -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
@@ -0,0 +1,25 @@
+This is a Python module for Vosk.
+
+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.
+
+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).
@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+
+import wave
+import sys
+import time
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
+# You can set log level to -1 to disable debug messages
+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("vosk-model-ru-0.53-private-0.1")
+model = Model("vosk-model-small-ru")
+
+rec = KaldiRecognizer(model, wf.getframerate())
+
+while True:
+    data = wf.readframes(4000)
+    if len(data) == 0:
+        break
+
+    # Feed into waveform
+    rec.AcceptWaveform(data)
+
+    # Wait for processing
+    while rec.GetNumPendingResults() > 0:
+        time.sleep(0.05)
+
+    # Retrieve the results
+    while not rec.ResultsEmpty():
+        print (rec.Result())
+        rec.Pop()
+
+rec.Flush()
+
+# Wait for processing
+while rec.GetNumPendingResults() > 0:
+    time.sleep(0.05)
+
+# Retrieve the results
+while not rec.ResultsEmpty():
+    print (rec.Result())
+    rec.Pop()
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+
+import wave
+import json
+import sys
+import os
+import time
+from timeit import default_timer as timer
+
+from multiprocessing.dummy import Pool
+from vosk import Model, KaldiRecognizer
+
+#model = Model("vosk-model-ru-0.54-private-0.1")
+model = Model("vosk-model-ru-0.62")
+#model = Model("vosk-model-small-ru")
+
+def recognize(line):
+    fn = line.strip()
+    wf = wave.open(fn, "rb")
+    rec = KaldiRecognizer(model, wf.getframerate())
+
+    results = []
+    while True:
+        data = wf.readframes(4000)
+        if len(data) == 0:
+            break
+
+        rec.AcceptWaveform(data)
+        while rec.GetNumPendingResults() > 0:
+            time.sleep(0.05)
+
+        while not rec.ResultsEmpty():
+            jres = json.loads(rec.Result())
+            if 'text' in jres:
+                print (jres)
+                results.append(jres['text'])
+            rec.Pop()
+
+    rec.Flush()
+
+    while rec.GetNumPendingResults() > 0:
+        time.sleep(0.05)
+
+    while not rec.ResultsEmpty():
+        jres = json.loads(rec.Result())
+        if 'text' in jres:
+            print (jres)
+            results.append(jres['text'])
+        rec.Pop()
+
+    owf = open(fn.replace(".wav", ".hyp"), "w")
+    owf.write("\n".join(results))
+
+def main():
+    start = timer()
+    p = Pool(20)
+    p.map(recognize, open(sys.argv[1]).readlines())
+    allsize = sum([os.path.getsize(f.strip()) for f in open(sys.argv[1]).readlines()]) / 8000.0 / 2
+    dur = timer() - start
+    print (f"Recognized {allsize:.2f} seconds in {dur} seconds speed {dur / allsize:.3f} x RT")
+
+main()
@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+
+import wave
+import sys
+import time
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
+# You can set log level to -1 to disable debug messages
+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("vosk-model-ru-0.53-private-0.1")
+model = Model("vosk-model-small-ru")
+
+rec = KaldiRecognizer(model, wf.getframerate())
+
+data = wf.readframes(min(wf.getnframes(), 320000))
+rec.AcceptWaveform(data)
+rec.Flush()
+
+while rec.GetNumPendingResults() > 0:
+    time.sleep(0.05)
+while not rec.ResultsEmpty():
+    print (rec.Result())
+    rec.Pop()
@@ -0,0 +1,75 @@
+import os
+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_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*.*")):
+    print ("Adding library", lib)
+    shutil.copy(lib, "vosk")
+
+# Create OS-dependent, but Python-independent wheels.
+try:
+    from wheel.bdist_wheel import bdist_wheel
+except ImportError:
+    cmdclass = {}
+else:
+    class bdist_wheel_tag_name(bdist_wheel):
+        def get_tag(self):
+            abi = 'none'
+            if system == 'Darwin':
+                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 machine == 'aarch64' and architecture == '64bit':
+                oses = 'manylinux2014_aarch64'
+            elif system == 'Linux':
+                oses = 'linux_' + machine
+            else:
+                raise TypeError("Unknown build environment")
+            return 'py3', abi, oses
+    cmdclass = {'bdist_wheel': bdist_wheel_tag_name}
+
+with open("README.md", "rb") as fh:
+    long_description = fh.read().decode("utf-8")
+
+setuptools.setup(
+    name="vosk",
+    version="0.4.0",
+    author="Alpha Cephei Inc",
+    author_email="contact@alphacephei.com",
+    description="Offline open source speech recognition API based on Kaldi and Vosk",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/alphacep/vosk-api",
+    packages=setuptools.find_packages(),
+    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',
+        'License :: OSI Approved :: Apache Software License',
+        'Operating System :: Microsoft :: Windows',
+        'Operating System :: POSIX :: Linux',
+        'Operating System :: MacOS :: MacOS X',
+        'Topic :: Software Development :: Libraries :: Python Modules'
+    ],
+    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', 'requests', 'tqdm', 'srt', 'websockets'],
+    install_requires=['cffi>=1.0', 'requests', 'tqdm', 'srt', 'websockets'],
+    cffi_modules=['vosk_builder.py:ffibuilder'],
+)
@@ -0,0 +1,247 @@
+import os
+import sys
+import srt
+import datetime
+import json
+import enum
+
+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":
+        # We want to load dependencies too
+        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":
+        return _ffi.dlopen(os.path.join(dlldir, "libvosk.so"))
+    elif sys.platform == "darwin":
+        return _ffi.dlopen(os.path.join(dlldir, "libvosk.dyld"))
+    else:
+        raise TypeError("Unsupported platform")
+
+_c = open_dll()
+
+def list_models():
+    response = requests.get(MODEL_LIST_URL, timeout=10)
+    for model in response.json():
+        print(model["name"])
+
+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):
+        if _c is not None:
+            _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"))
+
+    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"))
+
+        if self._handle == _ffi.NULL:
+            raise Exception("Failed to create a speaker model")
+
+    def __del__(self):
+        _c.vosk_spk_model_free(self._handle)
+
+class EndpointerMode(enum.Enum):
+    DEFAULT = 0
+    SHORT = 1
+    LONG = 2
+    VERY_LONG = 3
+
+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 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)
+
+    def SetMaxAlternatives(self, max_alternatives):
+        _c.vosk_recognizer_set_max_alternatives(self._handle, max_alternatives)
+
+    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 SetEndpointerMode(self, mode):
+        _c.vosk_recognizer_set_endpointer_mode(self._handle, mode.value)
+
+    def SetEndpointerDelays(self, t_start_max, t_end, t_max):
+        _c.vosk_recognizer_set_endpointer_delays(self._handle, t_start_max, t_end, t_max)
+
+    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):
+        _c.vosk_recognizer_accept_waveform(self._handle, data, len(data))
+
+    def Flush(self):
+        return _c.vosk_recognizer_flush(self._handle)
+
+    def Result(self):
+        return _ffi.string(_c.vosk_recognizer_result_front(self._handle)).decode("utf-8")
+
+    def Pop(self):
+        return _c.vosk_recognizer_result_pop(self._handle)
+
+    def GetNumPendingResults(self):
+        return _c.vosk_recognizer_get_num_pending_results(self._handle)
+
+    def GetNumResults(self):
+        return _c.vosk_recognizer_get_num_results(self._handle)
+
+    def ResultsEmpty(self):
+        return _c.vosk_recognizer_results_empty(self._handle)
+
+    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)
@@ -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", type=str,
+        help="use server for recognition. For example ws://localhost:2700")
+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()
@@ -0,0 +1,201 @@
+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"] != "":
+                    monologues["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)
+            await proc.wait()
+
+            # Bad input, continue
+            if tot_samples == 0:
+                 self.queue.task_done()
+                 continue
+
+            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)
+
+            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)
+        if tot_samples == 0:
+            return
+
+        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))
@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+
+import os
+from cffi import FFI
+
+vosk_root=os.environ.get("VOSK_SOURCE", "../..")
+cpp_command = "cpp " + vosk_root + "/src/vosk_api.h"
+
+ffibuilder = FFI()
+ffibuilder.set_source("vosk.vosk_cffi", None)
+ffibuilder.cdef(os.popen(cpp_command).read())
+
+if __name__ == '__main__':
+    ffibuilder.compile(verbose=True)
@@ -1,54 +0,0 @@
-KALDI_ROOT ?= $(HOME)/kaldi
-CFLAGS :=  -std=c++11 -g -O2 -DPIC -fPIC -Wno-unused-function
-CPPFLAGS := -I$(KALDI_ROOT)/src -I$(KALDI_ROOT)/tools/openfst/include -I../src -DFST_NO_DYNAMIC_LINKING
-
-KALDI_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/openfst/lib/libfst.a \
-             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a \
-             ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a \
-             -lgfortran -lstdc++
-
-all: test.exe
-
-test.exe: libkaldiwrap.so test.cs
-	mcs test.cs gen/*.cs
-
-VOSK_SOURCES = \
-	vosk_wrap.c \
-	../src/kaldi_recognizer.cc \
-	../src/kaldi_recognizer.h \
-	../src/model.cc \
-	../src/model.h \
-	../src/spk_model.cc \
-	../src/spk_model.h \
-	../src/vosk_api.cc \
-	../src/vosk_api.h
-
-libkaldiwrap.so: $(VOSK_SOURCES)
-	$(CXX) -fpermissive $(CFLAGS) $(CPPFLAGS) -shared -o $@ $(VOSK_SOURCES) $(KALDI_LIBS)
-
-vosk_wrap.c: ../src/vosk.i
-	swig -csharp -DSWIG_CSHARP_NO_EXCEPTION_HELPER -dllimport "libkaldiwrap" \
-	    -namespace "Kaldi" -outdir gen -o vosk_wrap.c ../src/vosk.i
-
-run: test.exe
-	mono test.exe
-
-clean:
-	$(RM) *.so vosk_wrap.c *.o gen/*.cs test.exe
@@ -1,47 +0,0 @@
-using System;
-using System.IO;
-using Kaldi;
-
-public class Test
-{
-   public static void Main()
-   {
-
-        Vosk.SetLogLevel(0);
-        Model model = new Model("model");
-        KaldiRecognizer rec = new KaldiRecognizer(model, 16000.0f);
-
-        using(Stream source = File.OpenRead("test.wav")) {
-            byte[] buffer = new byte[4096];
-            int bytesRead;
-            while((bytesRead = source.Read(buffer, 0, buffer.Length)) > 0) {
-                if (rec.AcceptWaveform(buffer, bytesRead)) {
-                    Console.WriteLine(rec.Result());
-                } else {
-                    Console.WriteLine(rec.PartialResult());
-                }
-            }
-        }
-        Console.WriteLine(rec.FinalResult());
-
-        rec = new KaldiRecognizer(model, 16000.0f);
-
-        using(Stream source = File.OpenRead("test.wav")) {
-            byte[] buffer = new byte[4096];
-            int bytesRead;
-            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);
-                }
-                if (rec.AcceptWaveform(fbuffer, fbuffer.Length)) {
-                    Console.WriteLine(rec.Result());
-                    GC.Collect();
-                } else {
-                    Console.WriteLine(rec.PartialResult());
-                }
-            }
-        }
-        Console.WriteLine(rec.FinalResult());
-   }
-}
@@ -1,21 +0,0 @@
-## Accuracy issues
-
-Accuracy of modern systems is still unstable, that means sometimes you can have a very good accuracy and sometimes it could be bad.
-It is hard to make a system that will work good. And there could be many reasons for that:
-
-  * Audio has very bad quality
-  * Vocabulary of the system doesn't match (yes, we still use fixed vocabulary)
-  * Audio conditions like accent were not really the ones that were used in training
-  * Some unpredictable audio issues like frame drop or frame coding bugs
-  * Software bugs
-
-It is hard to guess what is going on under the hood without getting your hands dirty. For that reason in case of any accuracy
-issues you must provide the following for analysis:
-
-  * Who are you, where are you from and why are you doing that. We don't like dealing with anonymous
-  * The complete and exact description of the system you want to build - what is it going to do, what do you want to build
-  * The precise description of hardware you are trying to run the system on
-  * The detailed list of software versions you are using
-  * Audio samples to demonstrate the problem together with the reference transcription for those samples
-
-Remember, the more information you provide the faster you get a solution.
@@ -1,42 +0,0 @@
-## Updating the language model
-
-The Kaldi model used in Vosk is compiled from 3 data sources:
-
-  * dictionary
-  * acoustic model
-  * language model
-
-You can rebuild all three with different level of effort, but sometimes you just
-need to adjust the probability of the words to improve the recognition. For
-that it is enough to recompile the language model from the text. To do that
-
-1) Take a text that reflects the speech you want to recognize
-2) Remove punctuation, convert everything to the lowercase, you can do it with a python script
-3) Build openfst and opengrm inside kaldi
-
-```
-export KALDI_ROOT=`pwd`/kaldi
-git clone https://github.com/kaldi-asr/kaldi
-cd kaldi/tools
-make
-# install all required dependencies and repeat `make` if needed
-extras/install_opengrm.sh
-```
-
-4) Now lets build a grammar
-
-```
-export PATH=$KALDI_ROOT/tools/openfst/bin:$PATH
-export LD_LIBRARY_PATH=$KALDI_ROOT/tools/openfst/lib/fst
-cd model
-fstsymbols --save_osymbols=words.txt Gr.fst > /dev/null
-farcompilestrings --fst_type=compact --symbols=words.txt --keep_symbols text.txt | \
-    ngramcount | ngrammake | \
-    fstconvert --fst_type=ngram > Gr.fst
-```
-
-Use created Gr.fst instead of standard one in your model.
-
-For more details see OpenGRM documentation http://www.opengrm.org/twiki/bin/view/GRM/NGramLibrary
-
-You can not introduce new words this way, that is something we will cover later.
@@ -1,81 +0,0 @@
-# Models
-
-This is the list of models compatible with Vosk-API.
-
-To add a new model here create an issue on Github.
-
-| Model                                                                                                     | Size  | Accuracy   | Notes                                                                                        |
-|-------------------------------------------------------------------------------------------------------------------------|-------|------------|----------------------------------------------------------------------------------------------|
-| **English**                                                                                               |       |            |  |
-| [vosk-model-en-us-aspire-0.2](http://alphacephei.com/kaldi/models/vosk-model-en-us-aspire-0.2.zip)        |  1.4G |   TBD      | Trained on Fisher + more or less recent LM. Should be pretty good for generic US English transcription |
-| [vosk-model-small-en-us-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip)          |  36M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-| **Chinese**                                                                                                   |       |            |  |
-| [vosk-model-cn-0.1.zip](http://alphacephei.com/kaldi/models/vosk-model-cn-0.1.zip)                        |  195M |   TBD      | Big narrowband Chinese model for server processing                                           |
-| [vosk-model-small-cn-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-cn-0.3.zip)                |  32M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-| **Russian**                                                                                                   |       |            |  |
-| [vosk-model-ru-0.10.zip](http://alphacephei.com/kaldi/models/vosk-model-ru-0.10.zip)                      |  2.5G |   TBD      | Big narrowband Russian model for server processing                                           |
-| [vosk-model-small-ru-0.4](http://alphacephei.com/kaldi/models/vosk-model-small-ru-0.4.zip)                |  39M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-| **French**                                                                                                |       |            |  |
-| [vosk-model-small-fr-pguyot-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-fr-pguyot-0.3.zip)                |  39M  |   TBD      | Lightweight wideband model for Android and RPi trained by [Paul Guyot](https://github.com/pguyot/zamia-speech/releases) |
-| **German**                                                                                                |       |            |  |
-| [tuda-de](http://ltdata1.informatik.uni-hamburg.de/kaldi_tuda_de/de_400k_nnet3chain_tdnn1f_2048_sp_bi.tar.bz2)    |  566M |   TBD      | Wideband server model from [tuda-de](https://github.com/uhh-lt/vosk-model-tuda-de)            |
-| [vosk-model-small-de-zamia-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-de-zamia-0.3.zip)            |  49M  |   TBD      | Lightweight wideband model for Android and RPi                                           |
-| **Spanish**                                                                                                |       |            |  |
-| [vosk-model-small-es-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-es-0.3.zip)                |  33M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-| **Portuguese**                                                                                                |       |            |  |
-| [vosk-model-small-pt-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-pt-0.3.zip)                |  31M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-| **Greek**                                                                                                |       |            |  |
-| [vosk-model-el-gr-0.6.zip](http://alphacephei.com/kaldi/models/vosk-model-el-gr-0.6.zip)                  |  1.1G |   TBD      | Big narrowband Greek model for server processing, not extremely accurate though        |
-| **Turkish**                                                                                                |       |            |  |
-| [vosk-model-small-tr-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-tr-0.3.zip)                |  35M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-| **Vietnamese**                                                                                                |       |            |  |
-| [vosk-model-small-vn-0.3](http://alphacephei.com/kaldi/models/vosk-model-small-vn-0.3.zip)                |  32M  |   TBD      | Lightweight wideband model for Android and RPi                                               |
-| **Speaker identification model**                                                                          |       |            |                                                                                              |
-| [vosk-model-spk-0.3](http://alphacephei.com/kaldi/models/vosk-model-spk-0.3.zip)                          |  13M  |   TBD      | Model for speaker identification, should work for all languages                              |
-
-
-## Other models
-
-Other places where you can check for models which might be compatible:
-
-  * http://kaldi-asr.org/models.html - variety of models from Kaldi - librispeech, aspire, chinese models
-  * https://github.com/daanzu/kaldi-active-grammar/blob/master/docs/models.md - Big dictation models
-  * http://zamia-speech.org/asr/ - German and English model from Zamia
-  * https://github.com/pguyot/zamia-speech/releases - French models for Zamia
-  * https://github.com/opensource-spraakherkenning-nl/Kaldi_NL - Dutch model
-  * https://montreal-forced-aligner.readthedocs.io/en/latest/pretrained_models.html (GMM models, not compatible but might be still useful)
-  * https://github.com/goodatlas/zeroth - Korean Kaldi (just a recipe and data to train)
-  * https://github.com/undertheseanlp/automatic_speech_recognition - Vietnamese Kaldi project
-
-## Training your own model
-
-You can train your model with Kaldi toolkit. The training is pretty standard - you need tdnn nnet3 model with ivectors. You can
-check mini_librispeech recipe for details. Some notes on training:
-
-  * For smaller mobile models watch number of parameters
-  * Train the model without pitch. It might be helpful for small amount of data, but for large database it doesn't give the advantage
-but complicates the processing and increases response time.
-  * Train ivector of dim 30 instead of standard 100 to save memory of mobile models.
-
-## Model structure
-
-Once you trained the model arrange the files according to the following layout (see en-us-aspire for details):
-
-  * `am/final.mdl` - acoustic model
-  * `conf/mfcc.conf` - mfcc config file. Make sure you take mfcc_hires.conf version if you are using hires model (most external ones)
-  * `conf/model.conf` - provide default decoding beams and silence phones. you have to create this file yourself, it is not present in kaldi model
-  * `ivector/final.dubm` - take ivector files from ivector extractor
-  * `ivector/final.ie`
-  * `ivector/final.mat`
-  * `ivector/splice.conf`
-  * `ivector/global_cmvn.stats`
-  * `ivector/online_cmvn.conf`
-  * `graph/phones/word_boundary.int` - from the graph
-  * `graph/HCLG.fst` - this is the decoding graph, if you are not using lookahead
-  * `graph/HCLr.fst` - use Gr.fst and HCLr.fst instead of one big HCLG.fst if you want to run rescoring
-  * `graph/Gr.fst`
-  * `graph/phones.txt` - from the graph
-  * `graph/words.txt` - from the graph
-  * `rescore/G.carpa` - carpa rescoring is optional but helpful in big models. Usually located inside data/lang_test_rescore
-  * `rescore/G.fst` - also optional if you want to use rescoring
-
@@ -1,3 +0,0 @@
-This is a baseline for the vosk-api iOS demo. It requires a build of a
-Vosk-API library, mail contact@alphacephei.com for the details.
-
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@@ -1,46 +0,0 @@
-//
-//  AppDelegate.swift
-//  VoskApiTest
-//
-//  Created by Niсkolay Shmyrev on 01.03.20.
-//  Copyright © 2020 Alpha Cephei. All rights reserved.
-//
-
-import UIKit
-
-@UIApplicationMain
-class AppDelegate: UIResponder, UIApplicationDelegate {
-
-    var window: UIWindow?
-
-
-    func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
-        // Override point for customization after application launch.
-        return true
-    }
-
-    func applicationWillResignActive(_ application: UIApplication) {
-        // Sent when the application is about to move from active to inactive state. This can occur for certain types of temporary interruptions (such as an incoming phone call or SMS message) or when the user quits the application and it begins the transition to the background state.
-        // Use this method to pause ongoing tasks, disable timers, and invalidate graphics rendering callbacks. Games should use this method to pause the game.
-    }
-
-    func applicationDidEnterBackground(_ application: UIApplication) {
-        // Use this method to release shared resources, save user data, invalidate timers, and store enough application state information to restore your application to its current state in case it is terminated later.
-        // If your application supports background execution, this method is called instead of applicationWillTerminate: when the user quits.
-    }
-
-    func applicationWillEnterForeground(_ application: UIApplication) {
-        // Called as part of the transition from the background to the active state; here you can undo many of the changes made on entering the background.
-    }
-
-    func applicationDidBecomeActive(_ application: UIApplication) {
-        // Restart any tasks that were paused (or not yet started) while the application was inactive. If the application was previously in the background, optionally refresh the user interface.
-    }
-
-    func applicationWillTerminate(_ application: UIApplication) {
-        // Called when the application is about to terminate. Save data if appropriate. See also applicationDidEnterBackground:.
-    }
-
-
-}
-
@@ -1,98 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "20x20",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "20x20",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "29x29",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "29x29",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "40x40",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "40x40",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "76x76",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "76x76",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "83.5x83.5",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ios-marketing",
-      "size" : "1024x1024",
-      "scale" : "1x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
@@ -1,27 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="11134" systemVersion="15F34" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" launchScreen="YES" useTraitCollections="YES" colorMatched="YES" initialViewController="01J-lp-oVM">
-    <dependencies>
-        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="11106"/>
-        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
-    </dependencies>
-    <scenes>
-        <!--View Controller-->
-        <scene sceneID="EHf-IW-A2E">
-            <objects>
-                <viewController id="01J-lp-oVM" sceneMemberID="viewController">
-                    <layoutGuides>
-                        <viewControllerLayoutGuide type="top" id="Llm-lL-Icb"/>
-                        <viewControllerLayoutGuide type="bottom" id="xb3-aO-Qok"/>
-                    </layoutGuides>
-                    <view key="view" contentMode="scaleToFill" id="Ze5-6b-2t3">
-                        <rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
-                        <autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
-                        <color key="backgroundColor" red="1" green="1" blue="1" alpha="1" colorSpace="custom" customColorSpace="sRGB"/>
-                    </view>
-                </viewController>
-                <placeholder placeholderIdentifier="IBFirstResponder" id="iYj-Kq-Ea1" userLabel="First Responder" sceneMemberID="firstResponder"/>
-            </objects>
-            <point key="canvasLocation" x="53" y="375"/>
-        </scene>
-    </scenes>
-</document>
@@ -1,32 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="13771" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" useTraitCollections="YES" colorMatched="YES" initialViewController="BYZ-38-t0r">
-    <device id="retina4_7" orientation="portrait">
-        <adaptation id="fullscreen"/>
-    </device>
-    <dependencies>
-        <deployment identifier="iOS"/>
-        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="13772"/>
-        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
-    </dependencies>
-    <scenes>
-        <!--View Controller-->
-        <scene sceneID="tne-QT-ifu">
-            <objects>
-                <viewController id="BYZ-38-t0r" customClass="ViewController" customModule="VoskApiTest" customModuleProvider="target" sceneMemberID="viewController">
-                    <textView key="view" clipsSubviews="YES" multipleTouchEnabled="YES" contentMode="scaleToFill" editable="NO" textAlignment="natural" id="CtX-mx-X98">
-                        <rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
-                        <autoresizingMask key="autoresizingMask" flexibleMaxX="YES" flexibleMaxY="YES"/>
-                        <color key="backgroundColor" white="1" alpha="1" colorSpace="calibratedWhite"/>
-                        <fontDescription key="fontDescription" type="system" pointSize="14"/>
-                        <textInputTraits key="textInputTraits" autocapitalizationType="sentences"/>
-                    </textView>
-                    <connections>
-                        <outlet property="mainText" destination="CtX-mx-X98" id="oJy-5J-NKp"/>
-                    </connections>
-                </viewController>
-                <placeholder placeholderIdentifier="IBFirstResponder" id="dkx-z0-nzr" sceneMemberID="firstResponder"/>
-            </objects>
-            <point key="canvasLocation" x="32.799999999999997" y="32.833583208395808"/>
-        </scene>
-    </scenes>
-</document>
@@ -1,45 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>CFBundleDevelopmentRegion</key>
-	<string>en</string>
-	<key>CFBundleExecutable</key>
-	<string>$(EXECUTABLE_NAME)</string>
-	<key>CFBundleIdentifier</key>
-	<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
-	<key>CFBundleInfoDictionaryVersion</key>
-	<string>6.0</string>
-	<key>CFBundleName</key>
-	<string>$(PRODUCT_NAME)</string>
-	<key>CFBundlePackageType</key>
-	<string>APPL</string>
-	<key>CFBundleShortVersionString</key>
-	<string>1.0</string>
-	<key>CFBundleVersion</key>
-	<string>1</string>
-	<key>LSRequiresIPhoneOS</key>
-	<true/>
-	<key>UILaunchStoryboardName</key>
-	<string>LaunchScreen</string>
-	<key>UIMainStoryboardFile</key>
-	<string>Main</string>
-	<key>UIRequiredDeviceCapabilities</key>
-	<array>
-		<string>armv7</string>
-	</array>
-	<key>UISupportedInterfaceOrientations</key>
-	<array>
-		<string>UIInterfaceOrientationPortrait</string>
-		<string>UIInterfaceOrientationLandscapeLeft</string>
-		<string>UIInterfaceOrientationLandscapeRight</string>
-	</array>
-	<key>UISupportedInterfaceOrientations~ipad</key>
-	<array>
-		<string>UIInterfaceOrientationPortrait</string>
-		<string>UIInterfaceOrientationPortraitUpsideDown</string>
-		<string>UIInterfaceOrientationLandscapeLeft</string>
-		<string>UIInterfaceOrientationLandscapeRight</string>
-	</array>
-</dict>
-</plist>
@@ -1,34 +0,0 @@
-//
-//  ViewController.swift
-//  VoskApiTest
-//
-//  Created by Niсkolay Shmyrev on 01.03.20.
-//  Copyright © 2020 Alpha Cephei. All rights reserved.
-//
-
-import UIKit
-
-class ViewController: UIViewController {
-
-    @IBOutlet var mainText: UITextView!
-
-    override func viewDidLoad() {
-        super.viewDidLoad()
-    
-        DispatchQueue.global(qos: .userInitiated).async {
-            DispatchQueue.main.async {
-                 self.mainText.text =  "Processing file..."
-            }
-            let vosk = Vosk()
-            let res = vosk.recognizeFile()
-            DispatchQueue.main.async {
-                self.mainText.text = res
-            }
-        }
-    }
-
-    override func didReceiveMemoryWarning() {
-        super.didReceiveMemoryWarning()
-    }
-}
-
@@ -1,37 +0,0 @@
-//
-//  Vosk.swift
-//  VoskApiTest
-//
-//  Created by Niсkolay Shmyrev on 01.03.20.
-//  Copyright © 2020 Alpha Cephei. All rights reserved.
-//
-
-import Foundation
-
-public final class Vosk {
-    
-    func recognizeFile() -> String {
-        var sres = ""
-        if let resourcePath = Bundle.main.resourcePath {
-
-            let modelPath = resourcePath + "/model-en"
-
-            let model = vosk_model_new(modelPath);
-            let recognizer = vosk_recognizer_new(model, 16000.0)
-
-            let audioFile = URL(fileURLWithPath: resourcePath + "/10001-90210-01803.wav")
-            if let data = try? Data(contentsOf: audioFile) {
-                let _ = data.withUnsafeBytes {
-                    vosk_recognizer_accept_waveform(recognizer, $0, Int32(data.count))
-                }
-                let res = vosk_recognizer_final_result(recognizer);
-                sres = String(validatingUTF8: res!)!;
-                print(sres);
-            }
-
-            vosk_recognizer_free(recognizer)
-            vosk_model_free(model)
-        }
-        return sres
-    }
-}
@@ -1,48 +0,0 @@
-// Copyright 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.
-
-
-#ifndef _VOSK_API_H_
-#define _VOSK_API_H_
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-typedef struct VoskModel VoskModel;
-typedef struct VoskSpkModel VoskSpkModel;
-typedef struct VoskRecognizer VoskRecognizer;
-
-VoskModel *vosk_model_new(const char *model_path);
-void vosk_model_free(VoskModel *model);
-
-VoskSpkModel *vosk_spk_model_new(const char *model_path);
-void vosk_spk_model_free(VoskSpkModel *model);
-
-VoskRecognizer *vosk_recognizer_new(VoskModel *model, float sample_rate);
-VoskRecognizer *vosk_recognizer_new_spk(VoskModel *model, VoskSpkModel *spk_model, float sample_rate);
-VoskRecognizer *vosk_recognizer_new_grm(VoskModel *model, float sample_rate, const char *grammar);
-int vosk_recognizer_accept_waveform(VoskRecognizer *recognizer, const char *data, int length);
-int vosk_recognizer_accept_waveform_s(VoskRecognizer *recognizer, const short *data, int length);
-int vosk_recognizer_accept_waveform_f(VoskRecognizer *recognizer, const float *data, int length);
-const char *vosk_recognizer_result(VoskRecognizer *recognizer);
-const char *vosk_recognizer_partial_result(VoskRecognizer *recognizer);
-const char *vosk_recognizer_final_result(VoskRecognizer *recognizer);
-void vosk_recognizer_free(VoskRecognizer *recognizer);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif /* _VOSK_API_H_ */
@@ -1 +0,0 @@
-#import "Vosk/vosk_api.h"
@@ -1,65 +0,0 @@
-KALDI_ROOT ?= $(HOME)/kaldi
-CFLAGS := -g -O2 -DPIC -fPIC -Wno-unused-function
-CPPFLAGS := -I$(JAVA_HOME)/include -I$(JAVA_HOME)/include/linux -I$(KALDI_ROOT)/src -I$(KALDI_ROOT)/tools/openfst/include -I../src
-
-KALDI_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/openfst/lib/libfst.a \
-             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a \
-             ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a \
-             -lgfortran
-
-all: libvosk_jni.so
-
-VOSK_SOURCES = \
-	vosk_wrap.cc \
-	../src/kaldi_recognizer.cc \
-	../src/kaldi_recognizer.h \
-	../src/model.cc \
-	../src/model.h \
-	../src/spk_model.cc \
-	../src/spk_model.h \
-	../src/vosk_api.cc \
-	../src/vosk_api.h
-
-libvosk_jni.so: $(VOSK_SOURCES)
-	$(CXX) -shared -o $@ $(CPPFLAGS) $(CFLAGS) $(VOSK_SOURCES) $(KALDI_LIBS)
-
-vosk_wrap.cc: ../src/vosk.i
-	mkdir -p org/kaldi
-	swig -c++ -I../src \
-		-java -package org.kaldi \
-		-outdir org/kaldi -o $@ $<
-
-clean:
-	$(RM) *.so *_wrap.cc *_wrap.o test/*.class
-	$(RM) -r org model-en
-
-model:
-	wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
-	unzip vosk-model-small-en-us-0.3.zip && rm vosk-model-small-en-us-0.3.zip
-	mv vosk-model-small-en-us-0.3 model
-
-model-spk:
-	wget https://alphacephei.com/kaldi/models/vosk-model-spk-0.3.zip
-	unzip vosk-model-spk-0.3.zip && rm vosk-model-spk-0.3.zip
-	mv vosk-model-spk-0.3 model-spk
-
-run: model model-spk
-	javac test/*.java org/kaldi/*.java
-	java -Djava.library.path=. -cp . test.DecoderTest
@@ -1,19 +0,0 @@
-Java API sample
-
-Doesn't work on Windows or Mac yet, help to prepare the packaged jars is welcome.
-
-For now to try it:
-
-On Linux you can do
-
-   1. Build recent kaldi
-   1. `git clone https://github.com/alphacep/vosk-api`
-   1. `cd vosk-api/java`
-   1. `export KALDI_ROOT=<KALDI_ROOT>`
-   1. `export JAVA_HOME=<JAVA_HOME>`
-   1. `make`
-   1. `make run`
-
-For details of the code you can check:
-
-https://github.com/alphacep/vosk-api/blob/master/java/test/DecoderTest.java
@@ -1,40 +0,0 @@
-package test;
-
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.DataOutputStream;
-import java.io.IOException;
-import java.net.URL;
-import java.nio.*;
-
-import org.kaldi.KaldiRecognizer;
-import org.kaldi.Model;
-import org.kaldi.SpkModel;
-import org.kaldi.Vosk;
-
-public class DecoderTest {
-    static {
-        System.loadLibrary("vosk_jni");
-    }
-
-    public static void main(String args[]) throws IOException {
-        Vosk.SetLogLevel(-10);
-
-        FileInputStream ais = new FileInputStream(new File("../python/example/test.wav"));
-        Model model = new Model("model");
-        SpkModel spkModel = new SpkModel("model-spk");
-        KaldiRecognizer rec = new KaldiRecognizer(model, spkModel, 16000.0f);
-
-        int nbytes;
-        byte[] b = new byte[4096];
-        while ((nbytes = ais.read(b)) >= 0) {
-            if (rec.AcceptWaveform(b)) {
-                System.out.println(rec.Result());
-            } else {
-                System.out.println(rec.PartialResult());
-            }
-        }
-        System.out.println(rec.FinalResult());
-    }
-}
@@ -1,3 +0,0 @@
-exports.printMsg = function() {
-  console.log("This is a message from the Vosk package");
-}
@@ -1,18 +0,0 @@
-{
-  "name": "vosk",
-  "version": "0.1.0",
-  "description": "Node binding for continuous voice recoginition through pocketsphinx.",
-  "repository": {
-    "type": "git",
-    "url": "git://github.com/alphacep/vosk-api.git"
-  },
-  "main": "index.js",
-  "keywords": [
-    "speech",
-    "speech recognition",
-    "voice"
-  ],
-  "author": "Alpha Cephei Inc.",
-  "license": "Apache 2.0",
-  "engines": { "node" : ">= 12.x.x" }
-}
@@ -1,51 +0,0 @@
-cmake_minimum_required(VERSION 3.12.0)
-project(vosk)
-
-set(TOP_SRCDIR "${CMAKE_SOURCE_DIR}/..")
-if("x$ENV{WHEEL_FLAGS}" STREQUAL "x")
-    find_package (Python3 COMPONENTS Interpreter Development)
-else()
-    # docker case
-    set(Python3_INCLUDE_DIRS "")
-    set(TOP_SRCDIR "/io")
-endif()
-
-set(KALDI_ROOT "$ENV{KALDI_ROOT}")
-set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DFST_NO_DYNAMIC_LINKING")
-set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} $ENV{WHEEL_FLAGS}")
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${CMAKE_C_FLAGS} -std=c++11")
-include_directories("${TOP_SRCDIR}/src" "${KALDI_ROOT}/src" "${KALDI_ROOT}/tools/openfst/include" ${Python3_INCLUDE_DIRS})
-
-find_package(SWIG REQUIRED)
-include(${SWIG_USE_FILE})
-
-swig_add_library(vosk TYPE SHARED LANGUAGE Python OUTPUT_DIR "${CMAKE_LIBRARY_OUTPUT_DIRECTORY}" OUTFILE_DIR "."
-    SOURCES "${TOP_SRCDIR}/src/kaldi_recognizer.cc"
-            "${TOP_SRCDIR}/src/spk_model.cc"
-            "${TOP_SRCDIR}/src/model.cc"
-            "${TOP_SRCDIR}/src/vosk_api.cc"
-            "${TOP_SRCDIR}/src/vosk.i")
-
-swig_link_libraries(vosk
-             ${KALDI_ROOT}/src/online2/kaldi-online2.a
-             ${KALDI_ROOT}/src/decoder/kaldi-decoder.a
-             ${KALDI_ROOT}/src/ivector/kaldi-ivector.a
-             ${KALDI_ROOT}/src/gmm/kaldi-gmm.a
-             ${KALDI_ROOT}/src/nnet3/kaldi-nnet3.a
-             ${KALDI_ROOT}/src/tree/kaldi-tree.a
-             ${KALDI_ROOT}/src/feat/kaldi-feat.a
-             ${KALDI_ROOT}/src/lat/kaldi-lat.a
-             ${KALDI_ROOT}/src/lm/kaldi-lm.a
-             ${KALDI_ROOT}/src/hmm/kaldi-hmm.a
-             ${KALDI_ROOT}/src/transform/kaldi-transform.a
-             ${KALDI_ROOT}/src/cudamatrix/kaldi-cudamatrix.a
-             ${KALDI_ROOT}/src/matrix/kaldi-matrix.a
-             ${KALDI_ROOT}/src/fstext/kaldi-fstext.a
-             ${KALDI_ROOT}/src/util/kaldi-util.a
-             ${KALDI_ROOT}/src/base/kaldi-base.a
-             ${KALDI_ROOT}/tools/openfst/lib/libfst.a
-             ${KALDI_ROOT}/tools/openfst/lib/libfstngram.a
-             ${KALDI_ROOT}/tools/OpenBLAS/libopenblas.a
-             -lgfortran -lstdc++)
-
-set_target_properties(_vosk PROPERTIES LINK_FLAGS_RELEASE -s)
@@ -1,3 +0,0 @@
-Python module for vosk-api
-
-See for details https://github.com/alphacep/vosk-api
@@ -1,76 +0,0 @@
-# From https://github.com/raydouglass/cmake_setuptools
-
-import os
-import subprocess
-import shutil
-import sys
-from setuptools import Extension
-from setuptools.command.build_ext import build_ext
-from setuptools.command.build_py import build_py
-
-CMAKE_EXE = os.environ.get('CMAKE_EXE', shutil.which('cmake'))
-
-
-def check_for_cmake():
-    if not CMAKE_EXE:
-        print('cmake executable not found. '
-              'Set CMAKE_EXE environment or update your path')
-        sys.exit(1)
-
-
-class CMakeExtension(Extension):
-    """
-    setuptools.Extension for cmake
-    """
-
-    def __init__(self, name, pkg_name, sourcedir=''):
-        check_for_cmake()
-        Extension.__init__(self, name, sources=[])
-        self.sourcedir = os.path.abspath(sourcedir)
-        self.pkg_name = pkg_name
-
-
-class CMakeBuildExt(build_ext):
-    """
-    setuptools build_exit which builds using cmake & make
-    You can add cmake args with the CMAKE_COMMON_VARIABLES environment variable
-    """
-
-    def build_extension(self, ext):
-        check_for_cmake()
-        if isinstance(ext, CMakeExtension):
-            output_dir = os.path.abspath(
-                os.path.dirname(self.get_ext_fullpath(ext.pkg_name + "/" + ext.name)))
-
-            build_type = 'Debug' if self.debug else 'Release'
-            cmake_args = [CMAKE_EXE,
-                          ext.sourcedir,
-                          '-Wno-dev',
-                          '-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + output_dir,
-                          '-DCMAKE_BUILD_TYPE=' + build_type]
-            cmake_args.extend(
-                [x for x in
-                 os.environ.get('CMAKE_COMMON_VARIABLES', '').split(' ')
-                 if x])
-
-            env = os.environ.copy()
-            if not os.path.exists(self.build_temp):
-                os.makedirs(self.build_temp)
-            subprocess.check_call(cmake_args,
-                                  cwd=self.build_temp,
-                                  env=env)
-            subprocess.check_call(['make', 'VERBOSE=1', ext.name],
-                                  cwd=self.build_temp,
-                                  env=env)
-            print()
-        else:
-            super().build_extension(ext)
-
-
-
-class CMakeBuildExtFirst(build_py):
-    def run(self):
-        self.run_command("build_ext")
-        return super().run()
-
-__all__ = ['CMakeBuildExt', 'CMakeExtension', 'CMakeBuildExtFirst']
@@ -1,11 +0,0 @@
-#!/usr/bin/python3
-
-from vosk import Model, KaldiRecognizer
-import sys
-import json
-
-model = Model("model")
-rec = KaldiRecognizer(model, 8000)
-
-res = json.loads(rec.FinalResult())
-print (res)
@@ -1,28 +0,0 @@
-#!/usr/bin/python3
-
-from vosk import Model, KaldiRecognizer
-import os
-
-if not os.path.exists("model"):
-    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
-
-p = pyaudio.PyAudio()
-stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=8000)
-stream.start_stream()
-
-model = Model("model")
-rec = KaldiRecognizer(model, 16000)
-
-while True:
-    data = stream.read(4000)
-    if len(data) == 0:
-        break
-    if rec.AcceptWaveform(data):
-        print(rec.Result())
-    else:
-        print(rec.PartialResult())
-
-print(rec.FinalResult())
@@ -1,31 +0,0 @@
-#!/usr/bin/python3
-
-from vosk import Model, KaldiRecognizer, SetLogLevel
-import sys
-import os
-import wave
-
-SetLogLevel(0)
-
-if not os.path.exists("model"):
-    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")
-if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
-
-model = Model("model")
-rec = KaldiRecognizer(model, wf.getframerate())
-
-while True:
-    data = wf.readframes(4000)
-    if len(data) == 0:
-        break
-    if rec.AcceptWaveform(data):
-        print(rec.Result())
-    else:
-        print(rec.PartialResult())
-
-print(rec.FinalResult())
@@ -1,52 +0,0 @@
-#!/usr/bin/python3
-
-from vosk import Model, KaldiRecognizer, SpkModel
-import sys
-import wave
-import json
-import os
-import numpy as np
-
-model_path = "model"
-spk_model_path = "model-spk"
-
-if not os.path.exists(model_path):
-    print ("Please download the model from https://github.com/alphacep/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://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")
-if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
-
-# Large vocabulary free form recognition
-model = Model(model_path)
-spk_model = SpkModel(spk_model_path)
-rec = KaldiRecognizer(model, spk_model, wf.getframerate())
-
-# We compare speakers with cosine distance. We can keep one or several fingerprints for the speaker in a database
-# to distingusih among users.
-spk_sig = [5.64308, 4.23898, 1.119433, -0.810904, 2.115443, 2.328436, 6.135152, 1.348195, 2.60771, 1.020717, 4.324225, -0.873012, 6.123375, 4.903791, 0.064803, 4.66212, 3.502724, 2.535861, 5.452417, 7.081769, -0.823969, -5.167974, 8.568919, 4.159035, 5.314441, 3.688272, 5.730379, 4.463213, 7.227232, 3.538961, 3.316218, 1.269628, -1.902378, 3.512679, -1.947611, -1.520158, 3.80928, -2.721601, 5.359588, 2.942463, -7.474174, 3.788054, 0.303426, 4.951366, 1.72281, -1.867125, -3.574615, 3.622509, 4.803109, 2.829714, 1.528521, 6.408293, 0.820131, 5.066522, 2.836125, 2.867029, 3.725267, 0.505927, 1.462984, 5.001863, -3.838309, -2.45902, 3.992581, 4.451616, 2.865211, -1.148313, 4.996399, -3.473454, 2.876967, 3.940124, 7.553079, 0.373356, 1.396561, 2.686691, 2.094895, 0.913796, -0.286909, 3.540179, 4.904687, 0.84554, 7.585956, 1.017081, 0.168355, 6.672327, 4.092033, -4.240158, -2.017081, -0.813043, 6.468298, 4.115041, 2.231936, 2.370055, 4.972295, 5.58382, 6.022872, 2.706988, 5.248096, -1.918003, 8.259204, -0.900911, 1.961962, 2.349709, 3.290093, 3.344172, 3.307027, 4.203372, -0.315103, 5.61919, -3.229496, 3.777309, 4.328595, 1.461014, 2.622894, 0.315525, 5.447259, 5.407609, 5.339016, 1.604555, 5.359932, 0.090242, 0.535306, 4.724705, 4.692502, 0.5783, -5.436688, -4.915511, 1.959807, 2.825248]
-
-def cosine_dist(x, y):
-    nx = np.array(x)
-    ny = np.array(y)
-    return 1 - np.dot(nx, ny) / np.linalg.norm(nx) / np.linalg.norm(ny)
-
-while True:
-    data = wf.readframes(4000)
-    if len(data) == 0:
-        break
-    if rec.AcceptWaveform(data):
-        res = json.loads(rec.Result())
-        print ("Text:", res['text'])
-        print ("Speaker distance:", cosine_dist(spk_sig, res['spk']))
-
-res = json.loads(rec.FinalResult())
-print ("Text:", res['text'])
-print ("Speaker distance:", cosine_dist(spk_sig, res['spk']))
-
@@ -1,33 +0,0 @@
-#!/usr/bin/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://github.com/alphacep/vosk-api/blob/master/doc/models.md and unpack as 'model' in the current folder.")
-    exit (1)
-
-
-model = Model("model")
-
-# Large vocabulary free form recognition
-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
-
-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'])
@@ -1,30 +0,0 @@
-#!/usr/bin/python3
-
-from vosk import Model, KaldiRecognizer
-import sys
-import os
-import wave
-
-if not os.path.exists("model"):
-    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")
-if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
-    print ("Audio file must be WAV format mono PCM.")
-    exit (1)
-
-model = Model("model")
-# 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)
-    if len(data) == 0:
-        break
-    if rec.AcceptWaveform(data):
-        print(rec.Result())
-    else:
-        print(rec.PartialResult())
-
-print(rec.FinalResult())
@@ -1,29 +0,0 @@
-import os
-import setuptools
-from cmake import *
-
-with open("README.md", "r") as fh:
-    long_description = fh.read()
-
-setuptools.setup(
-    name="vosk", # Replace with your own username
-    version="0.3.7",
-    author="Alpha Cephei Inc",
-    author_email="contact@alphacephei.com",
-    description="API for Kaldi and Vosk",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/alphacep/vosk-api",
-    packages=setuptools.find_packages(),
-    ext_modules=[CMakeExtension('_vosk', 'vosk')],
-    cmdclass={'build_ext': CMakeBuildExt, 'build_py' : CMakeBuildExtFirst},
-    classifiers=[
-        'Programming Language :: Python :: 3',
-        'License :: OSI Approved :: Apache Software License',
-        'Operating System :: Microsoft :: Windows',
-        'Operating System :: POSIX :: Linux',
-        'Operating System :: MacOS :: MacOS X',
-        'Topic :: Software Development :: Libraries :: Python Modules'
-    ],
-    python_requires='>=3.4',
-)
@@ -1,27 +0,0 @@
-#!/usr/bin/python3
-
-from vosk import Model, KaldiRecognizer
-import sys
-import os
-import wave
-import json
-
-model = Model("model")
-
-for line in open(sys.argv[1]):
-    uid, fn = line.split()
-    wf = wave.open(fn, "rb")
-    rec = KaldiRecognizer(model, wf.getframerate())
-
-    text = ""
-    while True:
-        data = wf.readframes(1000)
-        if len(data) == 0:
-            break
-        if rec.AcceptWaveform(data):
-            jres = json.loads(rec.Result())
-            text = text + " " + jres['text']
-    jres = json.loads(rec.FinalResult())
-    text = text + " " + jres['text']
-    print (uid + text)
-
@@ -1,2 +0,0 @@
-from .vosk import KaldiRecognizer, Model, SpkModel, SetLogLevel
-
@@ -0,0 +1,116 @@
+# ONNX
+ONNX_ROOT=$(abspath ../../onnxruntime-linux-x64-gpu-1.22.0)
+# Compiler
+CXX?=g++
+EXT?=so
+# Extra
+EXTRA_CFLAGS?=
+EXTRA_LDFLAGS?=
+OUTDIR?=.
+
+VOSK_SOURCES= \
+	circular-buffer.cc \
+	context-graph.cc \
+	feature-fbank.cc \
+	feature-functions.cc \
+	feature-extractor.cc \
+	feature-window.cc \
+	fftsg.cc \
+	file-utils.cc \
+	hypothesis.cc \
+	log.cc \
+	mel-computations.cc \
+	offline-lm.cc \
+	offline-lm-config.cc \
+	offline-model-config.cc \
+	offline-recognizer.cc \
+	offline-recognizer-impl.cc \
+	offline-rnn-lm.cc \
+	offline-stream.cc \
+	offline-transducer-greedy-search-decoder.cc \
+	offline-transducer-model.cc \
+	offline-transducer-model-config.cc \
+	offline-transducer-modified-beam-search-decoder.cc \
+	online-feature.cc \
+	onnx-utils.cc \
+	packed-sequence.cc \
+	pad-sequence.cc \
+	parse-options.cc \
+	provider.cc \
+	resample.cc \
+	rfft.cc \
+	session.cc \
+	silero-vad-model.cc \
+	silero-vad-model-config.cc \
+	slice.cc \
+	symbol-table.cc \
+	text-utils.cc \
+	transpose.cc \
+	utils.cc \
+	vad-model.cc \
+	vad-model-config.cc \
+	voice-activity-detector.cc \
+	vosk_api.cc
+
+VOSK_HEADERS= \
+	circular-buffer.h \
+	context-graph.h \
+	feature-fbank.h \
+	feature-functions.h \
+	feature-extractor.h \
+	feature-window.h \
+	file-utils.h \
+	hypothesis.h \
+	log.h \
+	macros.h \
+	math.h \
+	mel-computations.h \
+	offline-lm-config.h \
+	offline-lm.h \
+	offline-model-config.h \
+	offline-recognizer.h \
+	offline-recognizer-impl.h \
+	offline-recognizer-transducer-impl.h \
+	offline-rnn-lm.h \
+	offline-stream.h \
+	offline-transducer-decoder.h \
+	offline-transducer-greedy-search-decoder.h \
+	offline-transducer-model-config.h \
+	offline-transducer-model.h \
+	offline-transducer-modified-beam-search-decoder.h \
+	online-feature.h \
+	onnx-utils.h \
+	packed-sequence.h \
+	pad-sequence.h \
+	parse-options.h \
+	provider.h \
+	resample.h \
+	rfft.h \
+	session.h \
+	silero-vad-model-config.h \
+	silero-vad-model.h \
+	slice.h \
+	symbol-table.h \
+	text-utils.h \
+	transpose.h \
+	utils.h \
+	vad-model-config.h \
+	vad-model.h \
+	voice-activity-detector.h \
+	vosk_api.h
+
+CFLAGS=-g -O3 -mavx2 -fPIC -std=c++17 \
+       -I$(ONNX_ROOT)/include $(EXTRA_CFLAGS)
+
+LDFLAGS=-Wl,-rpath=$(ONNX_ROOT)/lib -L$(ONNX_ROOT)/lib -lonnxruntime 
+
+all: $(OUTDIR)/libvosk.$(EXT)
+
+$(OUTDIR)/libvosk.$(EXT): $(VOSK_SOURCES:%.cc=$(OUTDIR)/%.o)
+	$(CXX) --shared -o $@ $^ $(LDFLAGS) $(EXTRA_LDFLAGS)
+
+$(OUTDIR)/%.o: %.cc $(VOSK_HEADERS)
+	$(CXX) -c -o $@ $(CFLAGS) $<
+
+clean:
+	rm -f *.o *.so *.dll
@@ -0,0 +1,166 @@
+// circular-buffer.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "circular-buffer.h"
+
+#include <algorithm>
+
+#include "macros.h"
+
+namespace sherpa_onnx {
+
+CircularBuffer::CircularBuffer(int32_t capacity) {
+  if (capacity <= 0) {
+    SHERPA_ONNX_LOGE("Please specify a positive capacity. Given: %d\n",
+                     capacity);
+    exit(-1);
+  }
+  buffer_.resize(capacity);
+}
+
+void CircularBuffer::Resize(int32_t new_capacity) {
+  int32_t capacity = buffer_.size();
+  if (new_capacity <= capacity) {
+    SHERPA_ONNX_LOGE("new_capacity (%d) <= original capacity (%d). Skip it.",
+                     new_capacity, capacity);
+    return;
+  }
+
+  int32_t size = Size();
+  if (size == 0) {
+    buffer_.resize(new_capacity);
+    return;
+  }
+
+  std::vector<float> new_buffer(new_capacity);
+  int32_t start = head_ % capacity;
+  int32_t dest = head_ % new_capacity;
+
+  if (start + size <= capacity) {
+    if (dest + size <= new_capacity) {
+      std::copy(buffer_.begin() + start, buffer_.begin() + start + size,
+                new_buffer.begin() + dest);
+    } else {
+      int32_t part1_size = new_capacity - dest;
+
+      // copy [start, start+part1_size] to new_buffer
+      std::copy(buffer_.begin() + start, buffer_.begin() + start + part1_size,
+                new_buffer.begin() + dest);
+
+      // copy [start+part1_size, start+size] to new_buffer
+      std::copy(buffer_.begin() + start + part1_size,
+                buffer_.begin() + start + size, new_buffer.begin());
+    }
+  } else {
+    int32_t part1_size = capacity - start;
+    int32_t part2_size = size - part1_size;
+
+    // copy [start, start+part1_size] to new_buffer
+    if (dest + part1_size <= new_capacity) {
+      std::copy(buffer_.begin() + start, buffer_.begin() + start + part1_size,
+                new_buffer.begin() + dest);
+    } else {
+      int32_t first_part = new_capacity - dest;
+      int32_t second_part = part1_size - first_part;
+      std::copy(buffer_.begin() + start, buffer_.begin() + start + first_part,
+                new_buffer.begin() + dest);
+
+      std::copy(buffer_.begin() + start + first_part,
+                buffer_.begin() + start + part1_size, new_buffer.begin());
+    }
+
+    int32_t new_dest = (dest + part1_size) % new_capacity;
+
+    if (new_dest + part2_size <= new_capacity) {
+      std::copy(buffer_.begin(), buffer_.begin() + part2_size,
+                new_buffer.begin() + new_dest);
+    } else {
+      int32_t first_part = new_capacity - new_dest;
+      std::copy(buffer_.begin(), buffer_.begin() + first_part,
+                new_buffer.begin() + new_dest);
+      std::copy(buffer_.begin() + first_part, buffer_.begin() + part2_size,
+                new_buffer.begin());
+    }
+  }
+  buffer_.swap(new_buffer);
+}
+
+void CircularBuffer::Push(const float *p, int32_t n) {
+  int32_t capacity = buffer_.size();
+  int32_t size = Size();
+  if (n + size > capacity) {
+    int32_t new_capacity = std::max(capacity * 2, n + size);
+    SHERPA_ONNX_LOGE(
+        "Overflow! n: %d, size: %d, n+size: %d, capacity: %d. Increase "
+        "capacity to: %d",
+        n, size, n + size, capacity, new_capacity);
+    Resize(new_capacity);
+  }
+
+  int32_t start = tail_ % capacity;
+
+  tail_ += n;
+
+  if (start + n < capacity) {
+    std::copy(p, p + n, buffer_.begin() + start);
+    return;
+  }
+
+  int32_t part1_size = capacity - start;
+
+  std::copy(p, p + part1_size, buffer_.begin() + start);
+
+  std::copy(p + part1_size, p + n, buffer_.begin());
+}
+
+std::vector<float> CircularBuffer::Get(int32_t start_index, int32_t n) const {
+  if (start_index < head_ || start_index >= tail_) {
+    SHERPA_ONNX_LOGE("Invalid start_index: %d. head_: %d, tail_: %d",
+                     start_index, head_, tail_);
+    return {};
+  }
+
+  int32_t size = Size();
+  if (n < 0 || n > size) {
+    SHERPA_ONNX_LOGE("Invalid n: %d. size: %d", n, size);
+    return {};
+  }
+
+  int32_t capacity = buffer_.size();
+
+  if (start_index - head_ + n > size) {
+    SHERPA_ONNX_LOGE("Invalid start_index: %d and n: %d. head_: %d, size: %d",
+                     start_index, n, head_, size);
+    return {};
+  }
+
+  int32_t start = start_index % capacity;
+
+  if (start + n < capacity) {
+    return {buffer_.begin() + start, buffer_.begin() + start + n};
+  }
+
+  std::vector<float> ans(n);
+
+  std::copy(buffer_.begin() + start, buffer_.end(), ans.begin());
+
+  int32_t part1_size = capacity - start;
+  int32_t part2_size = n - part1_size;
+  std::copy(buffer_.begin(), buffer_.begin() + part2_size,
+            ans.begin() + part1_size);
+
+  return ans;
+}
+
+void CircularBuffer::Pop(int32_t n) {
+  int32_t size = Size();
+  if (n < 0 || n > size) {
+    SHERPA_ONNX_LOGE("Invalid n: %d. size: %d", n, size);
+    return;
+  }
+
+  head_ += n;
+}
+
+}  // namespace sherpa_onnx
@@ -0,0 +1,61 @@
+// circular-buffer.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_CIRCULAR_BUFFER_H_
+#define SHERPA_ONNX_CSRC_CIRCULAR_BUFFER_H_
+
+#include <cstdint>
+#include <vector>
+
+namespace sherpa_onnx {
+
+class CircularBuffer {
+ public:
+  // Capacity of this buffer. Should be large enough.
+  // If it is full, we just print a message and exit the program.
+  explicit CircularBuffer(int32_t capacity);
+
+  // Push an array
+  //
+  // @param p Pointer to the start address of the array
+  // @param n Number of elements in the array
+  //
+  // Note: If n + Size() > capacity, we print an error message and exit.
+  void Push(const float *p, int32_t n);
+
+  // @param start_index Should in the range [head_, tail_)
+  // @param n Number of elements to get
+  // @return Return a vector of size n containing the requested elements
+  std::vector<float> Get(int32_t start_index, int32_t n) const;
+
+  // Remove n elements from the buffer
+  //
+  // @param n Should be in the range [0, size_]
+  void Pop(int32_t n);
+
+  // Number of elements in the buffer.
+  int32_t Size() const { return tail_ - head_; }
+
+  // Current position of the head
+  int32_t Head() const { return head_; }
+
+  // Current position of the tail
+  int32_t Tail() const { return tail_; }
+
+  void Reset() {
+    head_ = 0;
+    tail_ = 0;
+  }
+
+  void Resize(int32_t new_capacity);
+
+ private:
+  std::vector<float> buffer_;
+
+  int32_t head_ = 0;  // linear index; always increasing; never wraps around
+  int32_t tail_ = 0;  // linear index, always increasing; never wraps around.
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_CIRCULAR_BUFFER_H_
@@ -0,0 +1,96 @@
+// sherpa-onnx/csrc/context-graph.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "context-graph.h"
+
+#include <cassert>
+#include <queue>
+#include <utility>
+
+namespace sherpa_onnx {
+void ContextGraph::Build(
+    const std::vector<std::vector<int32_t>> &token_ids) const {
+  for (int32_t i = 0; i < token_ids.size(); ++i) {
+    auto node = root_.get();
+    for (int32_t j = 0; j < token_ids[i].size(); ++j) {
+      int32_t token = token_ids[i][j];
+      if (0 == node->next.count(token)) {
+        bool is_end = j == token_ids[i].size() - 1;
+        node->next[token] = std::make_unique<ContextState>(
+            token, context_score_, node->node_score + context_score_,
+            is_end ? node->node_score + context_score_ : 0, is_end);
+      }
+      node = node->next[token].get();
+    }
+  }
+  FillFailOutput();
+}
+
+std::pair<float, const ContextState *> ContextGraph::ForwardOneStep(
+    const ContextState *state, int32_t token) const {
+  const ContextState *node;
+  float score;
+  if (1 == state->next.count(token)) {
+    node = state->next.at(token).get();
+    score = node->token_score;
+  } else {
+    node = state->fail;
+    while (0 == node->next.count(token)) {
+      node = node->fail;
+      if (-1 == node->token) break;  // root
+    }
+    if (1 == node->next.count(token)) {
+      node = node->next.at(token).get();
+    }
+    score = node->node_score - state->node_score;
+  }
+  SHERPA_ONNX_CHECK(nullptr != node);
+  return std::make_pair(score + node->output_score, node);
+}
+
+std::pair<float, const ContextState *> ContextGraph::Finalize(
+    const ContextState *state) const {
+  float score = -state->node_score;
+  return std::make_pair(score, root_.get());
+}
+
+void ContextGraph::FillFailOutput() const {
+  std::queue<const ContextState *> node_queue;
+  for (auto &kv : root_->next) {
+    kv.second->fail = root_.get();
+    node_queue.push(kv.second.get());
+  }
+  while (!node_queue.empty()) {
+    auto current_node = node_queue.front();
+    node_queue.pop();
+    for (auto &kv : current_node->next) {
+      auto fail = current_node->fail;
+      if (1 == fail->next.count(kv.first)) {
+        fail = fail->next.at(kv.first).get();
+      } else {
+        fail = fail->fail;
+        while (0 == fail->next.count(kv.first)) {
+          fail = fail->fail;
+          if (-1 == fail->token) break;
+        }
+        if (1 == fail->next.count(kv.first))
+          fail = fail->next.at(kv.first).get();
+      }
+      kv.second->fail = fail;
+      // fill the output arc
+      auto output = fail;
+      while (!output->is_end) {
+        output = output->fail;
+        if (-1 == output->token) {
+          output = nullptr;
+          break;
+        }
+      }
+      kv.second->output = output;
+      kv.second->output_score += output == nullptr ? 0 : output->output_score;
+      node_queue.push(kv.second.get());
+    }
+  }
+}
+}  // namespace sherpa_onnx
@@ -0,0 +1,66 @@
+// sherpa-onnx/csrc/context-graph.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_CONTEXT_GRAPH_H_
+#define SHERPA_ONNX_CSRC_CONTEXT_GRAPH_H_
+
+#include <memory>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "log.h"
+
+namespace sherpa_onnx {
+
+class ContextGraph;
+using ContextGraphPtr = std::shared_ptr<ContextGraph>;
+
+struct ContextState {
+  int32_t token;
+  float token_score;
+  float node_score;
+  float output_score;
+  bool is_end;
+  std::unordered_map<int32_t, std::unique_ptr<ContextState>> next;
+  const ContextState *fail = nullptr;
+  const ContextState *output = nullptr;
+
+  ContextState() = default;
+  ContextState(int32_t token, float token_score, float node_score,
+               float output_score, bool is_end)
+      : token(token),
+        token_score(token_score),
+        node_score(node_score),
+        output_score(output_score),
+        is_end(is_end) {}
+};
+
+class ContextGraph {
+ public:
+  ContextGraph() = default;
+  ContextGraph(const std::vector<std::vector<int32_t>> &token_ids,
+               float context_score)
+      : context_score_(context_score) {
+    root_ = std::make_unique<ContextState>(-1, 0, 0, 0, false);
+    root_->fail = root_.get();
+    Build(token_ids);
+  }
+
+  std::pair<float, const ContextState *> ForwardOneStep(
+      const ContextState *state, int32_t token_id) const;
+  std::pair<float, const ContextState *> Finalize(
+      const ContextState *state) const;
+
+  const ContextState *Root() const { return root_.get(); }
+
+ private:
+  float context_score_;
+  std::unique_ptr<ContextState> root_;
+  void Build(const std::vector<std::vector<int32_t>> &token_ids) const;
+  void FillFailOutput() const;
+};
+
+}  // namespace sherpa_onnx
+#endif  // SHERPA_ONNX_CSRC_CONTEXT_GRAPH_H_
@@ -0,0 +1,204 @@
+// features.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "feature-extractor.h"
+
+#include <algorithm>
+#include <memory>
+#include <mutex>  // NOLINT
+#include <sstream>
+#include <vector>
+#include <iostream>
+
+#include "online-feature.h"
+#include "macros.h"
+#include "resample.h"
+
+namespace sherpa_onnx {
+
+void FeatureExtractorConfig::Register(ParseOptions *po) {
+  po->Register("sample-rate", &sampling_rate,
+               "Sampling rate of the input waveform. "
+               "Note: You can have a different "
+               "sample rate for the input waveform. We will do resampling "
+               "inside the feature extractor");
+
+  po->Register("feat-dim", &feature_dim,
+               "Feature dimension. Must match the one expected by the model.");
+}
+
+std::string FeatureExtractorConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "FeatureExtractorConfig(";
+  os << "sampling_rate=" << sampling_rate << ", ";
+  os << "feature_dim=" << feature_dim << ")";
+
+  return os.str();
+}
+
+class FeatureExtractor::Impl {
+ public:
+  explicit Impl(const FeatureExtractorConfig &config) : config_(config) {
+    opts_.frame_opts.dither = 1;
+    opts_.frame_opts.snip_edges = false;
+    opts_.frame_opts.samp_freq = config.sampling_rate;
+
+    opts_.mel_opts.num_bins = config.feature_dim;
+    opts_.mel_opts.high_freq = -400;
+
+    fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
+  }
+
+  void AcceptWaveform(int32_t sampling_rate, const float *waveform, int32_t n) {
+    if (config_.normalize_samples) {
+      AcceptWaveformImpl(sampling_rate, waveform, n);
+    } else {
+      std::vector<float> buf(n);
+      for (int32_t i = 0; i != n; ++i) {
+        buf[i] = waveform[i] * 32768;
+      }
+      AcceptWaveformImpl(sampling_rate, buf.data(), n);
+    }
+  }
+
+  void AcceptWaveformImpl(int32_t sampling_rate, const float *waveform,
+                          int32_t n) {
+    std::lock_guard<std::mutex> lock(mutex_);
+
+    if (resampler_) {
+      if (sampling_rate != resampler_->GetInputSamplingRate()) {
+        SHERPA_ONNX_LOGE(
+            "You changed the input sampling rate!! Expected: %d, given: "
+            "%d",
+            resampler_->GetInputSamplingRate(), sampling_rate);
+        exit(-1);
+      }
+
+      std::vector<float> samples;
+      resampler_->Resample(waveform, n, false, &samples);
+      fbank_->AcceptWaveform(opts_.frame_opts.samp_freq, samples.data(),
+                             samples.size());
+      return;
+    }
+
+    if (sampling_rate != opts_.frame_opts.samp_freq) {
+      SHERPA_ONNX_LOGE(
+          "Creating a resampler:\n"
+          "   in_sample_rate: %d\n"
+          "   output_sample_rate: %d\n",
+          sampling_rate, static_cast<int32_t>(opts_.frame_opts.samp_freq));
+
+      float min_freq =
+          std::min<int32_t>(sampling_rate, opts_.frame_opts.samp_freq);
+      float lowpass_cutoff = 0.99 * 0.5 * min_freq;
+
+      int32_t lowpass_filter_width = 6;
+      resampler_ = std::make_unique<LinearResample>(
+          sampling_rate, opts_.frame_opts.samp_freq, lowpass_cutoff,
+          lowpass_filter_width);
+
+      std::vector<float> samples;
+      resampler_->Resample(waveform, n, false, &samples);
+      fbank_->AcceptWaveform(opts_.frame_opts.samp_freq, samples.data(),
+                             samples.size());
+      return;
+    }
+
+    fbank_->AcceptWaveform(sampling_rate, waveform, n);
+  }
+
+  void InputFinished() const {
+    std::lock_guard<std::mutex> lock(mutex_);
+    fbank_->InputFinished();
+  }
+
+  int32_t NumFramesReady() const {
+    std::lock_guard<std::mutex> lock(mutex_);
+    return fbank_->NumFramesReady();
+  }
+
+  bool IsLastFrame(int32_t frame) const {
+    std::lock_guard<std::mutex> lock(mutex_);
+    return fbank_->IsLastFrame(frame);
+  }
+
+  std::vector<float> GetFrames(int32_t frame_index, int32_t n) {
+    std::lock_guard<std::mutex> lock(mutex_);
+    if (frame_index + n > fbank_->NumFramesReady()) {
+      SHERPA_ONNX_LOGE("%d + %d > %d\n", frame_index, n,
+                       fbank_->NumFramesReady());
+      exit(-1);
+    }
+
+    int32_t discard_num = frame_index - last_frame_index_;
+    if (discard_num < 0) {
+      SHERPA_ONNX_LOGE("last_frame_index_: %d, frame_index_: %d",
+                       last_frame_index_, frame_index);
+      exit(-1);
+    }
+    fbank_->Pop(discard_num);
+
+    int32_t feature_dim = fbank_->Dim();
+    std::vector<float> features(feature_dim * n);
+
+    float *p = features.data();
+
+    for (int32_t i = 0; i != n; ++i) {
+      const float *f = fbank_->GetFrame(i + frame_index);
+
+//      std::cout << "Frame " << i;
+//      for (int j = 0; j < feature_dim; j++) {
+//          std::cout << " " << f[j];
+//      }
+//      std::cout << std::endl;
+
+      std::copy(f, f + feature_dim, p);
+      p += feature_dim;
+    }
+
+    last_frame_index_ = frame_index;
+
+    return features;
+  }
+
+  int32_t FeatureDim() const { return opts_.mel_opts.num_bins; }
+
+ private:
+  std::unique_ptr<knf::OnlineFbank> fbank_;
+  knf::FbankOptions opts_;
+  FeatureExtractorConfig config_;
+  mutable std::mutex mutex_;
+  std::unique_ptr<LinearResample> resampler_;
+  int32_t last_frame_index_ = 0;
+};
+
+FeatureExtractor::FeatureExtractor(const FeatureExtractorConfig &config /*={}*/)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+FeatureExtractor::~FeatureExtractor() = default;
+
+void FeatureExtractor::AcceptWaveform(int32_t sampling_rate,
+                                      const float *waveform, int32_t n) const {
+  impl_->AcceptWaveform(sampling_rate, waveform, n);
+}
+
+void FeatureExtractor::InputFinished() const { impl_->InputFinished(); }
+
+int32_t FeatureExtractor::NumFramesReady() const {
+  return impl_->NumFramesReady();
+}
+
+bool FeatureExtractor::IsLastFrame(int32_t frame) const {
+  return impl_->IsLastFrame(frame);
+}
+
+std::vector<float> FeatureExtractor::GetFrames(int32_t frame_index,
+                                               int32_t n) const {
+  return impl_->GetFrames(frame_index, n);
+}
+
+int32_t FeatureExtractor::FeatureDim() const { return impl_->FeatureDim(); }
+
+}  // namespace sherpa_onnx
@@ -0,0 +1,86 @@
+// features.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_FEATURES_H_
+#define SHERPA_ONNX_CSRC_FEATURES_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "parse-options.h"
+
+namespace sherpa_onnx {
+
+struct FeatureExtractorConfig {
+  // Sampling rate used by the feature extractor. If it is different from
+  // the sampling rate of the input waveform, we will do resampling inside.
+  int32_t sampling_rate = 16000;
+
+  // Feature dimension
+  int32_t feature_dim = 80;
+
+  // Set internally by some models, e.g., paraformer sets it to false.
+  // This parameter is not exposed to users from the commandline
+  // If true, the feature extractor expects inputs to be normalized to
+  // the range [-1, 1].
+  // If false, we will multiply the inputs by 32768
+  bool normalize_samples = true;
+
+  std::string ToString() const;
+
+  void Register(ParseOptions *po);
+};
+
+class FeatureExtractor {
+ public:
+  explicit FeatureExtractor(const FeatureExtractorConfig &config = {});
+  ~FeatureExtractor();
+
+  /**
+     @param sampling_rate The sampling_rate of the input waveform. If it does
+                          not equal to  config.sampling_rate, we will do
+                          resampling inside.
+     @param waveform Pointer to a 1-D array of size n. It must be normalized to
+                     the range [-1, 1].
+     @param n Number of entries in waveform
+   */
+  void AcceptWaveform(int32_t sampling_rate, const float *waveform,
+                      int32_t n) const;
+
+  /**
+   * InputFinished() tells the class you won't be providing any
+   * more waveform.  This will help flush out the last frame or two
+   * of features, in the case where snip-edges == false; it also
+   * affects the return value of IsLastFrame().
+   */
+  void InputFinished() const;
+
+  int32_t NumFramesReady() const;
+
+  /** Note: IsLastFrame() will only ever return true if you have called
+   * InputFinished() (and this frame is the last frame).
+   */
+  bool IsLastFrame(int32_t frame) const;
+
+  /** Get n frames starting from the given frame index.
+   *
+   * @param frame_index  The starting frame index
+   * @param n  Number of frames to get.
+   * @return Return a 2-D tensor of shape (n, feature_dim).
+   *         which is flattened into a 1-D vector (flattened in in row major)
+   */
+  std::vector<float> GetFrames(int32_t frame_index, int32_t n) const;
+
+  /// Return feature dim of this extractor
+  int32_t FeatureDim() const;
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_FEATURES_H_
@@ -0,0 +1,120 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE 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
+ *
+ * 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.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-fbank.cc
+//
+#include "feature-fbank.h"
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <vector>
+
+#include "feature-functions.h"
+
+namespace knf {
+
+static void Sqrt(float *in_out, int32_t n) {
+  for (int32_t i = 0; i != n; ++i) {
+    in_out[i] = std::sqrt(in_out[i]);
+  }
+}
+
+std::ostream &operator<<(std::ostream &os, const FbankOptions &opts) {
+  os << opts.ToString();
+  return os;
+}
+
+FbankComputer::FbankComputer(const FbankOptions &opts)
+    : opts_(opts), rfft_(opts.frame_opts.PaddedWindowSize()) {
+  if (opts.energy_floor > 0.0f) {
+    log_energy_floor_ = logf(opts.energy_floor);
+  }
+
+  // We'll definitely need the filterbanks info for VTLN warping factor 1.0.
+  // [note: this call caches it.]
+  GetMelBanks(1.0f);
+}
+
+FbankComputer::~FbankComputer() {
+  for (auto iter = mel_banks_.begin(); iter != mel_banks_.end(); ++iter)
+    delete iter->second;
+}
+
+const MelBanks *FbankComputer::GetMelBanks(float vtln_warp) {
+  MelBanks *this_mel_banks = nullptr;
+
+  // std::map<float, MelBanks *>::iterator iter = mel_banks_.find(vtln_warp);
+  auto iter = mel_banks_.find(vtln_warp);
+  if (iter == mel_banks_.end()) {
+    this_mel_banks = new MelBanks(opts_.mel_opts, opts_.frame_opts, vtln_warp);
+    mel_banks_[vtln_warp] = this_mel_banks;
+  } else {
+    this_mel_banks = iter->second;
+  }
+  return this_mel_banks;
+}
+
+void FbankComputer::Compute(float signal_raw_log_energy, float vtln_warp,
+                            std::vector<float> *signal_frame, float *feature) {
+  const MelBanks &mel_banks = *(GetMelBanks(vtln_warp));
+
+  SHERPA_ONNX_CHECK_EQ(signal_frame->size(), opts_.frame_opts.PaddedWindowSize());
+
+  // Compute energy after window function (not the raw one).
+  if (opts_.use_energy && !opts_.raw_energy) {
+    signal_raw_log_energy = std::log(
+        std::max<float>(InnerProduct(signal_frame->data(), signal_frame->data(),
+                                     signal_frame->size()),
+                        std::numeric_limits<float>::epsilon()));
+  }
+  rfft_.Compute(signal_frame->data());  // signal_frame is modified in-place
+  ComputePowerSpectrum(signal_frame);
+
+  // Use magnitude instead of power if requested.
+  if (!opts_.use_power) {
+    Sqrt(signal_frame->data(), signal_frame->size() / 2 + 1);
+  }
+
+  int32_t mel_offset = ((opts_.use_energy && !opts_.htk_compat) ? 1 : 0);
+
+  // Its length is opts_.mel_opts.num_bins
+  float *mel_energies = feature + mel_offset;
+
+  // Sum with mel filter banks over the power spectrum
+  mel_banks.Compute(signal_frame->data(), mel_energies);
+
+  if (opts_.use_log_fbank) {
+    // Avoid log of zero (which should be prevented anyway by dithering).
+    for (int32_t i = 0; i != opts_.mel_opts.num_bins; ++i) {
+      auto t = std::max(mel_energies[i], std::numeric_limits<float>::epsilon());
+      mel_energies[i] = std::log(t);
+    }
+  }
+
+  // Copy energy as first value (or the last, if htk_compat == true).
+  if (opts_.use_energy) {
+    if (opts_.energy_floor > 0.0 && signal_raw_log_energy < log_energy_floor_) {
+      signal_raw_log_energy = log_energy_floor_;
+    }
+    int32_t energy_index = opts_.htk_compat ? opts_.mel_opts.num_bins : 0;
+    feature[energy_index] = signal_raw_log_energy;
+  }
+}
+
+}  // namespace knf
@@ -0,0 +1,134 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE 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
+ *
+ * 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.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-fbank.h
+
+#ifndef KALDI_NATIVE_FBANK_CSRC_FEATURE_FBANK_H_
+#define KALDI_NATIVE_FBANK_CSRC_FEATURE_FBANK_H_
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "feature-window.h"
+#include "mel-computations.h"
+#include "rfft.h"
+
+namespace knf {
+
+struct FbankOptions {
+  FrameExtractionOptions frame_opts;
+  MelBanksOptions mel_opts;
+  // append an extra dimension with energy to the filter banks
+  bool use_energy = false;
+  float energy_floor = 0.0f;  // active iff use_energy==true
+
+  // If true, compute log_energy before preemphasis and windowing
+  // If false, compute log_energy after preemphasis ans windowing
+  bool raw_energy = true;  // active iff use_energy==true
+
+  // If true, put energy last (if using energy)
+  // If false, put energy first
+  bool htk_compat = false;  // active iff use_energy==true
+
+  // if true (default), produce log-filterbank, else linear
+  bool use_log_fbank = true;
+
+  // if true (default), use power in filterbank
+  // analysis, else magnitude.
+  bool use_power = true;
+
+  FbankOptions() { mel_opts.num_bins = 23; }
+
+  std::string ToString() const {
+    std::ostringstream os;
+    os << "frame_opts: \n";
+    os << frame_opts << "\n";
+    os << "\n";
+
+    os << "mel_opts: \n";
+    os << mel_opts << "\n";
+
+    os << "use_energy: " << use_energy << "\n";
+    os << "energy_floor: " << energy_floor << "\n";
+    os << "raw_energy: " << raw_energy << "\n";
+    os << "htk_compat: " << htk_compat << "\n";
+    os << "use_log_fbank: " << use_log_fbank << "\n";
+    os << "use_power: " << use_power << "\n";
+    return os.str();
+  }
+};
+
+std::ostream &operator<<(std::ostream &os, const FbankOptions &opts);
+
+class FbankComputer {
+ public:
+  using Options = FbankOptions;
+
+  explicit FbankComputer(const FbankOptions &opts);
+  ~FbankComputer();
+
+  int32_t Dim() const {
+    return opts_.mel_opts.num_bins + (opts_.use_energy ? 1 : 0);
+  }
+
+  // if true, compute log_energy_pre_window but after dithering and dc removal
+  bool NeedRawLogEnergy() const { return opts_.use_energy && opts_.raw_energy; }
+
+  const FrameExtractionOptions &GetFrameOptions() const {
+    return opts_.frame_opts;
+  }
+
+  const FbankOptions &GetOptions() const { return opts_; }
+
+  /**
+     Function that computes one frame of features from
+     one frame of signal.
+
+     @param [in] signal_raw_log_energy The log-energy of the frame of the signal
+         prior to windowing and pre-emphasis, or
+         log(numeric_limits<float>::min()), whichever is greater.  Must be
+         ignored by this function if this class returns false from
+         this->NeedsRawLogEnergy().
+     @param [in] vtln_warp  The VTLN warping factor that the user wants
+         to be applied when computing features for this utterance.  Will
+         normally be 1.0, meaning no warping is to be done.  The value will
+         be ignored for feature types that don't support VLTN, such as
+         spectrogram features.
+     @param [in] signal_frame  One frame of the signal,
+       as extracted using the function ExtractWindow() using the options
+       returned by this->GetFrameOptions().  The function will use the
+       vector as a workspace, which is why it's a non-const pointer.
+     @param [out] feature  Pointer to a vector of size this->Dim(), to which
+         the computed feature will be written. It should be pre-allocated.
+  */
+  void Compute(float signal_raw_log_energy, float vtln_warp,
+               std::vector<float> *signal_frame, float *feature);
+
+ private:
+  const MelBanks *GetMelBanks(float vtln_warp);
+
+  FbankOptions opts_;
+  float log_energy_floor_;
+  std::map<float, MelBanks *> mel_banks_;  // float is VTLN coefficient.
+  Rfft rfft_;
+};
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_FEATURE_FBANK_H_
@@ -0,0 +1,49 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE 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
+ *
+ * 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.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-functions.cc
+
+#include "feature-functions.h"
+
+#include <cstdint>
+#include <vector>
+
+namespace knf {
+
+void ComputePowerSpectrum(std::vector<float> *complex_fft) {
+  int32_t dim = complex_fft->size();
+
+  // now we have in complex_fft, first half of complex spectrum
+  // it's stored as [real0, realN/2, real1, im1, real2, im2, ...]
+
+  float *p = complex_fft->data();
+  int32_t half_dim = dim / 2;
+  float first_energy = p[0] * p[0];
+  float last_energy = p[1] * p[1];  // handle this special case
+
+  for (int32_t i = 1; i < half_dim; ++i) {
+    float real = p[i * 2];
+    float im = p[i * 2 + 1];
+    p[i] = real * real + im * im;
+  }
+  p[0] = first_energy;
+  p[half_dim] = last_energy;  // Will actually never be used, and anyway
+  // if the signal has been bandlimited sensibly this should be zero.
+}
+
+}  // namespace knf
@@ -0,0 +1,38 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE 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
+ *
+ * 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.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-functions.h
+#ifndef KALDI_NATIVE_FBANK_CSRC_FEATURE_FUNCTIONS_H_
+#define KALDI_NATIVE_FBANK_CSRC_FEATURE_FUNCTIONS_H_
+
+#include <vector>
+namespace knf {
+
+// ComputePowerSpectrum converts a complex FFT (as produced by the FFT
+// functions in csrc/rfft.h), and converts it into
+// a power spectrum.  If the complex FFT is a vector of size n (representing
+// half of the complex FFT of a real signal of size n, as described there),
+// this function computes in the first (n/2) + 1 elements of it, the
+// energies of the fft bins from zero to the Nyquist frequency.  Contents of the
+// remaining (n/2) - 1 elements are undefined at output.
+
+void ComputePowerSpectrum(std::vector<float> *complex_fft);
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_FEATURE_FUNCTIONS_H_
@@ -0,0 +1,258 @@
+// feature-window.cc
+//
+// Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-window.cc
+
+#include "feature-window.h"
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <vector>
+#include <random>
+
+#ifndef M_2PI
+#define M_2PI 6.283185307179586476925286766559005
+#endif
+
+namespace knf {
+
+std::ostream &operator<<(std::ostream &os, const FrameExtractionOptions &opts) {
+  os << opts.ToString();
+  return os;
+}
+
+FeatureWindowFunction::FeatureWindowFunction(const FrameExtractionOptions &opts)
+    : window_(opts.WindowSize()) {
+  int32_t frame_length = opts.WindowSize();
+  SHERPA_ONNX_CHECK_GT(frame_length, 0);
+
+  float *window_data = window_.data();
+
+  double a = M_2PI / (frame_length - 1);
+  if (opts.window_type == "hann") {
+    // see https://pytorch.org/docs/stable/generated/torch.hann_window.html
+    // We assume periodic is true
+    a = M_2PI / frame_length;
+  }
+
+  for (int32_t i = 0; i < frame_length; i++) {
+    double i_fl = static_cast<double>(i);
+    if (opts.window_type == "hanning") {
+      window_data[i] = 0.5 - 0.5 * cos(a * i_fl);
+    } else if (opts.window_type == "sine") {
+      // when you are checking ws wikipedia, please
+      // note that 0.5 * a = M_PI/(frame_length-1)
+      window_data[i] = sin(0.5 * a * i_fl);
+    } else if (opts.window_type == "hamming") {
+      window_data[i] = 0.54 - 0.46 * cos(a * i_fl);
+    } else if (opts.window_type == "hann") {
+      window_data[i] = 0.50 - 0.50 * cos(a * i_fl);
+    } else if (opts.window_type == "povey") {
+      // like hamming but goes to zero at edges.
+      window_data[i] = pow(0.5 - 0.5 * cos(a * i_fl), 0.85);
+    } else if (opts.window_type == "rectangular") {
+      window_data[i] = 1.0;
+    } else if (opts.window_type == "blackman") {
+      window_data[i] = opts.blackman_coeff - 0.5 * cos(a * i_fl) +
+                       (0.5 - opts.blackman_coeff) * cos(2 * a * i_fl);
+    } else {
+      SHERPA_ONNX_LOG(FATAL) << "Invalid window type " << opts.window_type;
+    }
+  }
+}
+
+void FeatureWindowFunction::Apply(float *wave) const {
+  int32_t window_size = window_.size();
+  const float *p = window_.data();
+  for (int32_t k = 0; k != window_size; ++k) {
+    wave[k] *= p[k];
+  }
+}
+
+int64_t FirstSampleOfFrame(int32_t frame, const FrameExtractionOptions &opts) {
+  int64_t frame_shift = opts.WindowShift();
+  if (opts.snip_edges) {
+    return frame * frame_shift;
+  } else {
+    int64_t midpoint_of_frame = frame_shift * frame + frame_shift / 2,
+            beginning_of_frame = midpoint_of_frame - opts.WindowSize() / 2;
+    return beginning_of_frame;
+  }
+}
+
+int32_t NumFrames(int64_t num_samples, const FrameExtractionOptions &opts,
+                  bool flush /*= true*/) {
+  int64_t frame_shift = opts.WindowShift();
+  int64_t frame_length = opts.WindowSize();
+  if (opts.snip_edges) {
+    // with --snip-edges=true (the default), we use a HTK-like approach to
+    // determining the number of frames-- all frames have to fit completely into
+    // the waveform, and the first frame begins at sample zero.
+    if (num_samples < frame_length)
+      return 0;
+    else
+      return (1 + ((num_samples - frame_length) / frame_shift));
+    // You can understand the expression above as follows: 'num_samples -
+    // frame_length' is how much room we have to shift the frame within the
+    // waveform; 'frame_shift' is how much we shift it each time; and the ratio
+    // is how many times we can shift it (integer arithmetic rounds down).
+  } else {
+    // if --snip-edges=false, the number of frames is determined by rounding the
+    // (file-length / frame-shift) to the nearest integer.  The point of this
+    // formula is to make the number of frames an obvious and predictable
+    // function of the frame shift and signal length, which makes many
+    // segmentation-related questions simpler.
+    //
+    // Because integer division in C++ rounds toward zero, we add (half the
+    // frame-shift minus epsilon) before dividing, to have the effect of
+    // rounding towards the closest integer.
+    int32_t num_frames = (num_samples + (frame_shift / 2)) / frame_shift;
+
+    if (flush) return num_frames;
+
+    // note: 'end' always means the last plus one, i.e. one past the last.
+    int64_t end_sample_of_last_frame =
+        FirstSampleOfFrame(num_frames - 1, opts) + frame_length;
+
+    // the following code is optimized more for clarity than efficiency.
+    // If flush == false, we can't output frames that extend past the end
+    // of the signal.
+    while (num_frames > 0 && end_sample_of_last_frame > num_samples) {
+      num_frames--;
+      end_sample_of_last_frame -= frame_shift;
+    }
+    return num_frames;
+  }
+}
+
+void ExtractWindow(int64_t sample_offset, const std::vector<float> &wave,
+                   int32_t f, const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function,
+                   std::vector<float> *window,
+                   float *log_energy_pre_window /*= nullptr*/) {
+  SHERPA_ONNX_CHECK(sample_offset >= 0 && wave.size() != 0);
+
+  int32_t frame_length = opts.WindowSize();
+  int32_t frame_length_padded = opts.PaddedWindowSize();
+
+  int64_t num_samples = sample_offset + wave.size();
+  int64_t start_sample = FirstSampleOfFrame(f, opts);
+  int64_t end_sample = start_sample + frame_length;
+
+  if (opts.snip_edges) {
+    SHERPA_ONNX_CHECK(start_sample >= sample_offset && end_sample <= num_samples);
+  } else {
+    SHERPA_ONNX_CHECK(sample_offset == 0 || start_sample >= sample_offset);
+  }
+
+  if (window->size() != frame_length_padded) {
+    window->resize(frame_length_padded);
+  }
+
+  // wave_start and wave_end are start and end indexes into 'wave', for the
+  // piece of wave that we're trying to extract.
+  int32_t wave_start = int32_t(start_sample - sample_offset);
+  int32_t wave_end = wave_start + frame_length;
+
+  if (wave_start >= 0 && wave_end <= wave.size()) {
+    // the normal case-- no edge effects to consider.
+    std::copy(wave.begin() + wave_start,
+              wave.begin() + wave_start + frame_length, window->data());
+  } else {
+    // Deal with any end effects by reflection, if needed.  This code will only
+    // be reached for about two frames per utterance, so we don't concern
+    // ourselves excessively with efficiency.
+    int32_t wave_dim = wave.size();
+    for (int32_t s = 0; s < frame_length; ++s) {
+      int32_t s_in_wave = s + wave_start;
+      while (s_in_wave < 0 || s_in_wave >= wave_dim) {
+        // reflect around the beginning or end of the wave.
+        // e.g. -1 -> 0, -2 -> 1.
+        // dim -> dim - 1, dim + 1 -> dim - 2.
+        // the code supports repeated reflections, although this
+        // would only be needed in pathological cases.
+        if (s_in_wave < 0)
+          s_in_wave = -s_in_wave - 1;
+        else
+          s_in_wave = 2 * wave_dim - 1 - s_in_wave;
+      }
+      (*window)[s] = wave[s_in_wave];
+    }
+  }
+
+  ProcessWindow(opts, window_function, window->data(), log_energy_pre_window);
+}
+
+static void RemoveDcOffset(float *d, int32_t n) {
+  float sum = 0;
+  for (int32_t i = 0; i != n; ++i) {
+    sum += d[i];
+  }
+
+  float mean = sum / n;
+
+  for (int32_t i = 0; i != n; ++i) {
+    d[i] -= mean;
+  }
+}
+
+float InnerProduct(const float *a, const float *b, int32_t n) {
+  float sum = 0;
+  for (int32_t i = 0; i != n; ++i) {
+    sum += a[i] * b[i];
+  }
+  return sum;
+}
+
+static std::default_random_engine de(1234); //seed
+
+void Dither(float *waveform, int32_t n, float dither_value) {
+  if (dither_value == 0.0)
+    return;
+  std::normal_distribution<float> nd(0, 1.0 / 32768.0); //mean followed by stdiv
+  for (int i = 0; i < n; i++)
+      waveform[i] += nd(de) * dither_value;
+}
+
+static void Preemphasize(float *d, int32_t n, float preemph_coeff) {
+  if (preemph_coeff == 0.0) {
+    return;
+  }
+
+  SHERPA_ONNX_CHECK(preemph_coeff >= 0.0 && preemph_coeff <= 1.0);
+
+  for (int32_t i = n - 1; i > 0; --i) {
+    d[i] -= preemph_coeff * d[i - 1];
+  }
+  d[0] -= preemph_coeff * d[0];
+}
+
+void ProcessWindow(const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function, float *window,
+                   float *log_energy_pre_window /*= nullptr*/) {
+  int32_t frame_length = opts.WindowSize();
+
+  if (opts.dither != 0.0) {
+    Dither(window, frame_length, opts.dither);
+  }
+
+  if (opts.remove_dc_offset) {
+    RemoveDcOffset(window, frame_length);
+  }
+
+  if (log_energy_pre_window != NULL) {
+    float energy = std::max<float>(InnerProduct(window, window, frame_length),
+                                   std::numeric_limits<float>::epsilon());
+    *log_energy_pre_window = std::log(energy);
+  }
+
+  if (opts.preemph_coeff != 0.0) {
+    Preemphasize(window, frame_length, opts.preemph_coeff);
+  }
+
+  window_function.Apply(window);
+}
+
+}  // namespace knf
@@ -0,0 +1,174 @@
+// feature-window.h
+//
+// Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-window.h
+
+#ifndef KALDI_NATIVE_FBANK_CSRC_FEATURE_WINDOW_H_
+#define KALDI_NATIVE_FBANK_CSRC_FEATURE_WINDOW_H_
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "log.h"
+
+namespace knf {
+
+inline int32_t RoundUpToNearestPowerOfTwo(int32_t n) {
+  // copied from kaldi/src/base/kaldi-math.cc
+  SHERPA_ONNX_CHECK_GT(n, 0);
+  n--;
+  n |= n >> 1;
+  n |= n >> 2;
+  n |= n >> 4;
+  n |= n >> 8;
+  n |= n >> 16;
+  return n + 1;
+}
+
+struct FrameExtractionOptions {
+  float samp_freq = 16000;
+  float frame_shift_ms = 10.0f;   // in milliseconds.
+  float frame_length_ms = 25.0f;  // in milliseconds.
+  float dither = 1.0f;            // Amount of dithering, 0.0 means no dither.
+  float preemph_coeff = 0.97f;    // Preemphasis coefficient.
+  bool remove_dc_offset = true;   // Subtract mean of wave before FFT.
+  std::string window_type = "povey";  // e.g. Hamming window
+  // May be "hamming", "rectangular", "povey", "hanning", "hann", "sine",
+  // "blackman".
+  // "povey" is a window I made to be similar to Hamming but to go to zero at
+  // the edges, it's pow((0.5 - 0.5*cos(n/N*2*pi)), 0.85) I just don't think the
+  // Hamming window makes sense as a windowing function.
+  bool round_to_power_of_two = true;
+  float blackman_coeff = 0.42f;
+  bool snip_edges = true;
+  // bool allow_downsample = false;
+  // bool allow_upsample = false;
+
+  int32_t WindowShift() const {
+    return static_cast<int32_t>(samp_freq * 0.001f * frame_shift_ms);
+  }
+  int32_t WindowSize() const {
+    return static_cast<int32_t>(samp_freq * 0.001f * frame_length_ms);
+  }
+  int32_t PaddedWindowSize() const {
+    return (round_to_power_of_two ? RoundUpToNearestPowerOfTwo(WindowSize())
+                                  : WindowSize());
+  }
+  std::string ToString() const {
+    std::ostringstream os;
+#define KNF_PRINT(x) os << #x << ": " << x << "\n"
+    KNF_PRINT(samp_freq);
+    KNF_PRINT(frame_shift_ms);
+    KNF_PRINT(frame_length_ms);
+    KNF_PRINT(dither);
+    KNF_PRINT(preemph_coeff);
+    KNF_PRINT(remove_dc_offset);
+    KNF_PRINT(window_type);
+    KNF_PRINT(round_to_power_of_two);
+    KNF_PRINT(blackman_coeff);
+    KNF_PRINT(snip_edges);
+    // KNF_PRINT(allow_downsample);
+    // KNF_PRINT(allow_upsample);
+#undef KNF_PRINT
+    return os.str();
+  }
+};
+
+std::ostream &operator<<(std::ostream &os, const FrameExtractionOptions &opts);
+
+class FeatureWindowFunction {
+ public:
+  FeatureWindowFunction() = default;
+  explicit FeatureWindowFunction(const FrameExtractionOptions &opts);
+  /**
+   * @param wave Pointer to a 1-D array of shape [window_size].
+   *             It is modified in-place: wave[i] = wave[i] * window_[i].
+   * @param
+   */
+  void Apply(float *wave) const;
+  const std::vector<float> &GetWindow() const { return window_; }
+
+ private:
+  std::vector<float> window_;  // of size opts.WindowSize()
+};
+
+int64_t FirstSampleOfFrame(int32_t frame, const FrameExtractionOptions &opts);
+
+/**
+   This function returns the number of frames that we can extract from a wave
+   file with the given number of samples in it (assumed to have the same
+   sampling rate as specified in 'opts').
+
+      @param [in] num_samples  The number of samples in the wave file.
+      @param [in] opts     The frame-extraction options class
+
+      @param [in] flush   True if we are asserting that this number of samples
+   is 'all there is', false if we expecting more data to possibly come in.  This
+   only makes a difference to the answer
+   if opts.snip_edges== false.  For offline feature extraction you always want
+   flush == true.  In an online-decoding context, once you know (or decide) that
+   no more data is coming in, you'd call it with flush == true at the end to
+   flush out any remaining data.
+*/
+int32_t NumFrames(int64_t num_samples, const FrameExtractionOptions &opts,
+                  bool flush = true);
+
+/*
+  ExtractWindow() extracts a windowed frame of waveform (possibly with a
+  power-of-two, padded size, depending on the config), including all the
+  processing done by ProcessWindow().
+
+  @param [in] sample_offset  If 'wave' is not the entire waveform, but
+                   part of it to the left has been discarded, then the
+                   number of samples prior to 'wave' that we have
+                   already discarded.  Set this to zero if you are
+                   processing the entire waveform in one piece, or
+                   if you get 'no matching function' compilation
+                   errors when updating the code.
+  @param [in] wave  The waveform
+  @param [in] f     The frame index to be extracted, with
+                    0 <= f < NumFrames(sample_offset + wave.Dim(), opts, true)
+  @param [in] opts  The options class to be used
+  @param [in] window_function  The windowing function, as derived from the
+                    options class.
+  @param [out] window  The windowed, possibly-padded waveform to be
+                     extracted.  Will be resized as needed.
+  @param [out] log_energy_pre_window  If non-NULL, the log-energy of
+                   the signal prior to pre-emphasis and multiplying by
+                   the windowing function will be written to here.
+*/
+void ExtractWindow(int64_t sample_offset, const std::vector<float> &wave,
+                   int32_t f, const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function,
+                   std::vector<float> *window,
+                   float *log_energy_pre_window = nullptr);
+
+/**
+  This function does all the windowing steps after actually
+  extracting the windowed signal: depending on the
+  configuration, it does dithering, dc offset removal,
+  preemphasis, and multiplication by the windowing function.
+   @param [in] opts  The options class to be used
+   @param [in] window_function  The windowing function-- should have
+                    been initialized using 'opts'.
+   @param [in,out] window  A vector of size opts.WindowSize().  Note:
+      it will typically be a sub-vector of a larger vector of size
+      opts.PaddedWindowSize(), with the remaining samples zero,
+      as the FFT code is more efficient if it operates on data with
+      power-of-two size.
+   @param [out]   log_energy_pre_window If non-NULL, then after dithering and
+      DC offset removal, this function will write to this pointer the log of
+      the total energy (i.e. sum-squared) of the frame.
+ */
+void ProcessWindow(const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function, float *window,
+                   float *log_energy_pre_window = nullptr);
+
+// Compute the inner product of two vectors
+float InnerProduct(const float *a, const float *b, int32_t n);
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_FEATURE_WINDOW_H_
@@ -0,0 +1,24 @@
+// file-utils.cc
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#include "file-utils.h"
+
+#include <fstream>
+#include <string>
+
+#include "log.h"
+
+namespace sherpa_onnx {
+
+bool FileExists(const std::string &filename) {
+  return std::ifstream(filename).good();
+}
+
+void AssertFileExists(const std::string &filename) {
+  if (!FileExists(filename)) {
+    SHERPA_ONNX_LOG(FATAL) << filename << " does not exist!";
+  }
+}
+
+}  // namespace sherpa_onnx
@@ -0,0 +1,28 @@
+// file-utils.h
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_FILE_UTILS_H_
+#define SHERPA_ONNX_CSRC_FILE_UTILS_H_
+
+#include <fstream>
+#include <string>
+
+namespace sherpa_onnx {
+
+/** Check whether a given path is a file or not
+ *
+ * @param filename Path to check.
+ * @return Return true if the given path is a file; return false otherwise.
+ */
+bool FileExists(const std::string &filename);
+
+/** Abort if the file does not exist.
+ *
+ * @param filename The file to check.
+ */
+void AssertFileExists(const std::string &filename);
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_FILE_UTILS_H_
@@ -0,0 +1,81 @@
+/**
+ * Copyright (c)  2023  Xiaomi Corporation
+ * Copyright (c)  2023  Pingfeng Luo
+ */
+
+#include "hypothesis.h"
+
+#include <algorithm>
+#include <utility>
+
+namespace sherpa_onnx {
+
+void Hypotheses::Add(Hypothesis hyp) {
+  auto key = hyp.Key();
+  auto it = hyps_dict_.find(key);
+  if (it == hyps_dict_.end()) {
+    hyps_dict_[key] = std::move(hyp);
+  } else {
+    it->second.log_prob = LogAdd<double>()(it->second.log_prob, hyp.log_prob);
+  }
+}
+
+Hypothesis Hypotheses::GetMostProbable(bool length_norm) const {
+  if (length_norm == false) {
+    return std::max_element(hyps_dict_.begin(), hyps_dict_.end(),
+                            [](const auto &left, auto &right) -> bool {
+                              return left.second.TotalLogProb() <
+                                     right.second.TotalLogProb();
+                            })
+        ->second;
+  } else {
+    // for length_norm is true
+    return std::max_element(
+               hyps_dict_.begin(), hyps_dict_.end(),
+               [](const auto &left, const auto &right) -> bool {
+                 return left.second.TotalLogProb() / left.second.ys.size() <
+                        right.second.TotalLogProb() / right.second.ys.size();
+               })
+        ->second;
+  }
+}
+
+std::vector<Hypothesis> Hypotheses::GetTopK(int32_t k, bool length_norm) const {
+  k = std::max(k, 1);
+  k = std::min(k, Size());
+
+  std::vector<Hypothesis> all_hyps = Vec();
+
+  if (length_norm == false) {
+    std::partial_sort(all_hyps.begin(), all_hyps.begin() + k, all_hyps.end(),
+                      [](const auto &a, const auto &b) {
+                        return a.TotalLogProb() > b.TotalLogProb();
+                      });
+  } else {
+    // for length_norm is true
+    std::partial_sort(all_hyps.begin(), all_hyps.begin() + k, all_hyps.end(),
+                      [](const auto &a, const auto &b) {
+                        return a.TotalLogProb() / a.ys.size() >
+                               b.TotalLogProb() / b.ys.size();
+                      });
+  }
+
+  return {all_hyps.begin(), all_hyps.begin() + k};
+}
+
+const std::vector<int32_t> GetHypsRowSplits(
+    const std::vector<Hypotheses> &hyps) {
+  std::vector<int32_t> row_splits;
+  row_splits.reserve(hyps.size() + 1);
+
+  row_splits.push_back(0);
+  int32_t s = 0;
+  for (const auto &h : hyps) {
+    s += h.Size();
+    row_splits.push_back(s);
+  }
+
+  return row_splits;
+}
+
+}  // namespace sherpa_onnx
@@ -0,0 +1,145 @@
+/**
+ * Copyright (c)  2023  Xiaomi Corporation
+ * Copyright (c)  2023  Pingfeng Luo
+ *
+ */
+
+#ifndef SHERPA_ONNX_CSRC_HYPOTHESIS_H_
+#define SHERPA_ONNX_CSRC_HYPOTHESIS_H_
+
+#include <sstream>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "context-graph.h"
+#include "math.h"
+#include "onnx-utils.h"
+
+namespace sherpa_onnx {
+
+struct Hypothesis {
+  // The predicted tokens so far. Newly predicated tokens are appended.
+  std::vector<int64_t> ys;
+
+  // timestamps[i] contains the frame number after subsampling
+  // on which ys[i] is decoded.
+  std::vector<int32_t> timestamps;
+
+  // The total score of ys in log space.
+  // It contains only acoustic scores
+  double log_prob = 0;
+
+  // LM log prob if any.
+  double lm_log_prob = 0;
+
+  // the nn lm score for next token given the current ys
+  CopyableOrtValue nn_lm_scores;
+  // the nn lm states
+  std::vector<CopyableOrtValue> nn_lm_states;
+
+  const ContextState *context_state;
+
+  // TODO(fangjun): Make it configurable
+  // the minimum of tokens in a chunk for streaming RNN LM
+  int32_t lm_rescore_min_chunk = 2;  // a const
+
+  int32_t num_trailing_blanks = 0;
+
+  Hypothesis() = default;
+  Hypothesis(const std::vector<int64_t> &ys, double log_prob,
+             const ContextState *context_state = nullptr)
+      : ys(ys), log_prob(log_prob), context_state(context_state) {}
+
+  double TotalLogProb() const { return log_prob + lm_log_prob; }
+
+  // If two Hypotheses have the same `Key`, then they contain
+  // the same token sequence.
+  std::string Key() const {
+    // TODO(fangjun): Use a hash function?
+    std::ostringstream os;
+    std::string sep;
+    for (auto i : ys) {
+      os << sep << i;
+      sep = "-";
+    }
+    return os.str();
+  }
+
+  // For debugging
+  std::string ToString() const {
+    std::ostringstream os;
+    os << "(" << Key() << ", " << log_prob << ")";
+    return os.str();
+  }
+};
+
+class Hypotheses {
+ public:
+  Hypotheses() = default;
+
+  explicit Hypotheses(std::vector<Hypothesis> hyps) {
+    for (auto &h : hyps) {
+      hyps_dict_[h.Key()] = std::move(h);
+    }
+  }
+
+  explicit Hypotheses(std::unordered_map<std::string, Hypothesis> hyps_dict)
+      : hyps_dict_(std::move(hyps_dict)) {}
+
+  // Add hyp to this object. If it already exists, its log_prob
+  // is updated with the given hyp using log-sum-exp.
+  void Add(Hypothesis hyp);
+
+  // Get the hyp that has the largest log_prob.
+  // If length_norm is true, hyp's log_prob is divided by
+  // len(hyp.ys) before comparison.
+  Hypothesis GetMostProbable(bool length_norm) const;
+
+  // Get the k hyps that have the largest log_prob.
+  // If length_norm is true, hyp's log_prob is divided by
+  // len(hyp.ys) before comparison.
+  std::vector<Hypothesis> GetTopK(int32_t k, bool length_norm) const;
+
+  int32_t Size() const { return hyps_dict_.size(); }
+
+  std::string ToString() const {
+    std::ostringstream os;
+    for (const auto &p : hyps_dict_) {
+      os << p.second.ToString() << "\n";
+    }
+    return os.str();
+  }
+
+  const auto begin() const { return hyps_dict_.begin(); }
+  const auto end() const { return hyps_dict_.end(); }
+
+  auto begin() { return hyps_dict_.begin(); }
+  auto end() { return hyps_dict_.end(); }
+
+  void Clear() { hyps_dict_.clear(); }
+
+ private:
+  // Return a list of hyps contained in this object.
+  std::vector<Hypothesis> Vec() const {
+    std::vector<Hypothesis> ans;
+    ans.reserve(hyps_dict_.size());
+    for (const auto &p : hyps_dict_) {
+      ans.push_back(p.second);
+    }
+    return ans;
+  }
+
+ private:
+  using Map = std ::unordered_map<std::string, Hypothesis>;
+  Map hyps_dict_;
+};
+
+const std::vector<int32_t> GetHypsRowSplits(
+    const std::vector<Hypotheses> &hyps);
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_HYPOTHESIS_H_
@@ -1,655 +0,0 @@
-// From https://github.com/nbsdx/SimpleJSON
-// SimpleJSON
-// Simple C++ JSON library
-//
-// License
-// Do what the fuck you want public license
-
-#pragma once
-
-#include <cstdint>
-#include <cmath>
-#include <cctype>
-#include <string>
-#include <deque>
-#include <map>
-#include <type_traits>
-#include <initializer_list>
-#include <ostream>
-#include <iostream>
-
-namespace json {
-
-using std::map;
-using std::deque;
-using std::string;
-using std::enable_if;
-using std::initializer_list;
-using std::is_same;
-using std::is_convertible;
-using std::is_integral;
-using std::is_floating_point;
-
-namespace {
-    string json_escape( const string &str ) {
-        string output;
-        for( unsigned i = 0; i < str.length(); ++i )
-            switch( str[i] ) {
-                case '\"': output += "\\\""; break;
-                case '\\': output += "\\\\"; break;
-                case '\b': output += "\\b";  break;
-                case '\f': output += "\\f";  break;
-                case '\n': output += "\\n";  break;
-                case '\r': output += "\\r";  break;
-                case '\t': output += "\\t";  break;
-                default  : output += str[i]; break;
-            }
-        return std::move( output );
-    }
-}
-
-class JSON
-{
-    union BackingData {
-        BackingData( double d ) : Float( d ){}
-        BackingData( long   l ) : Int( l ){}
-        BackingData( bool   b ) : Bool( b ){}
-        BackingData( string s ) : String( new string( s ) ){}
-        BackingData()           : Int( 0 ){}
-
-        deque<JSON>        *List;
-        map<string,JSON>   *Map;
-        string             *String;
-        double              Float;
-        long                Int;
-        bool                Bool;
-    } Internal;
-
-    public:
-        enum class Class {
-            Null,
-            Object,
-            Array,
-            String,
-            Floating,
-            Integral,
-            Boolean
-        };
-
-        template <typename Container>
-        class JSONWrapper {
-            Container *object;
-
-            public:
-                JSONWrapper( Container *val ) : object( val ) {}
-                JSONWrapper( std::nullptr_t )  : object( nullptr ) {}
-
-                typename Container::iterator begin() { return object ? object->begin() : typename Container::iterator(); }
-                typename Container::iterator end() { return object ? object->end() : typename Container::iterator(); }
-                typename Container::const_iterator begin() const { return object ? object->begin() : typename Container::iterator(); }
-                typename Container::const_iterator end() const { return object ? object->end() : typename Container::iterator(); }
-        };
-
-        template <typename Container>
-        class JSONConstWrapper {
-            const Container *object;
-
-            public:
-                JSONConstWrapper( const Container *val ) : object( val ) {}
-                JSONConstWrapper( std::nullptr_t )  : object( nullptr ) {}
-
-                typename Container::const_iterator begin() const { return object ? object->begin() : typename Container::const_iterator(); }
-                typename Container::const_iterator end() const { return object ? object->end() : typename Container::const_iterator(); }
-        };
-
-        JSON() : Internal(), Type( Class::Null ){}
-
-        JSON( initializer_list<JSON> list ) 
-            : JSON() 
-        {
-            SetType( Class::Object );
-            for( auto i = list.begin(), e = list.end(); i != e; ++i, ++i )
-                operator[]( i->ToString() ) = *std::next( i );
-        }
-
-        JSON( JSON&& other )
-            : Internal( other.Internal )
-            , Type( other.Type )
-        { other.Type = Class::Null; other.Internal.Map = nullptr; }
-
-        JSON& operator=( JSON&& other ) {
-            ClearInternal();
-            Internal = other.Internal;
-            Type = other.Type;
-            other.Internal.Map = nullptr;
-            other.Type = Class::Null;
-            return *this;
-        }
-
-        JSON( const JSON &other ) {
-            switch( other.Type ) {
-            case Class::Object:
-                Internal.Map = 
-                    new map<string,JSON>( other.Internal.Map->begin(),
-                                          other.Internal.Map->end() );
-                break;
-            case Class::Array:
-                Internal.List = 
-                    new deque<JSON>( other.Internal.List->begin(),
-                                      other.Internal.List->end() );
-                break;
-            case Class::String:
-                Internal.String = 
-                    new string( *other.Internal.String );
-                break;
-            default:
-                Internal = other.Internal;
-            }
-            Type = other.Type;
-        }
-
-        JSON& operator=( const JSON &other ) {
-            ClearInternal();
-            switch( other.Type ) {
-            case Class::Object:
-                Internal.Map = 
-                    new map<string,JSON>( other.Internal.Map->begin(),
-                                          other.Internal.Map->end() );
-                break;
-            case Class::Array:
-                Internal.List = 
-                    new deque<JSON>( other.Internal.List->begin(),
-                                      other.Internal.List->end() );
-                break;
-            case Class::String:
-                Internal.String = 
-                    new string( *other.Internal.String );
-                break;
-            default:
-                Internal = other.Internal;
-            }
-            Type = other.Type;
-            return *this;
-        }
-
-        ~JSON() {
-            switch( Type ) {
-            case Class::Array:
-                delete Internal.List;
-                break;
-            case Class::Object:
-                delete Internal.Map;
-                break;
-            case Class::String:
-                delete Internal.String;
-                break;
-            default:;
-            }
-        }
-
-        template <typename T>
-        JSON( T b, typename enable_if<is_same<T,bool>::value>::type* = 0 ) : Internal( b ), Type( Class::Boolean ){}
-
-        template <typename T>
-        JSON( T i, typename enable_if<is_integral<T>::value && !is_same<T,bool>::value>::type* = 0 ) : Internal( (long)i ), Type( Class::Integral ){}
-
-        template <typename T>
-        JSON( T f, typename enable_if<is_floating_point<T>::value>::type* = 0 ) : Internal( (double)f ), Type( Class::Floating ){}
-
-        template <typename T>
-        JSON( T s, typename enable_if<is_convertible<T,string>::value>::type* = 0 ) : Internal( string( s ) ), Type( Class::String ){}
-
-        JSON( std::nullptr_t ) : Internal(), Type( Class::Null ){}
-
-        static JSON Make( Class type ) {
-            JSON ret; ret.SetType( type );
-            return ret;
-        }
-
-        static JSON Load( const string & );
-
-        template <typename T>
-        void append( T arg ) {
-            SetType( Class::Array ); Internal.List->emplace_back( arg );
-        }
-
-        template <typename T, typename... U>
-        void append( T arg, U... args ) {
-            append( arg ); append( args... );
-        }
-
-        template <typename T>
-            typename enable_if<is_same<T,bool>::value, JSON&>::type operator=( T b ) {
-                SetType( Class::Boolean ); Internal.Bool = b; return *this;
-            }
-
-        template <typename T>
-            typename enable_if<is_integral<T>::value && !is_same<T,bool>::value, JSON&>::type operator=( T i ) {
-                SetType( Class::Integral ); Internal.Int = i; return *this;
-            }
-
-        template <typename T>
-            typename enable_if<is_floating_point<T>::value, JSON&>::type operator=( T f ) {
-                SetType( Class::Floating ); Internal.Float = f; return *this;
-            }
-
-        template <typename T>
-            typename enable_if<is_convertible<T,string>::value, JSON&>::type operator=( T s ) {
-                SetType( Class::String ); *Internal.String = string( s ); return *this;
-            }
-
-        JSON& operator[]( const string &key ) {
-            SetType( Class::Object ); return Internal.Map->operator[]( key );
-        }
-
-        JSON& operator[]( unsigned index ) {
-            SetType( Class::Array );
-            if( index >= Internal.List->size() ) Internal.List->resize( index + 1 );
-            return Internal.List->operator[]( index );
-        }
-
-        JSON &at( const string &key ) {
-            return operator[]( key );
-        }
-
-        const JSON &at( const string &key ) const {
-            return Internal.Map->at( key );
-        }
-
-        JSON &at( unsigned index ) {
-            return operator[]( index );
-        }
-
-        const JSON &at( unsigned index ) const {
-            return Internal.List->at( index );
-        }
-
-        int length() const {
-            if( Type == Class::Array )
-                return Internal.List->size();
-            else
-                return -1;
-        }
-
-        bool hasKey( const string &key ) const {
-            if( Type == Class::Object )
-                return Internal.Map->find( key ) != Internal.Map->end();
-            return false;
-        }
-
-        int size() const {
-            if( Type == Class::Object )
-                return Internal.Map->size();
-            else if( Type == Class::Array )
-                return Internal.List->size();
-            else
-                return -1;
-        }
-
-        Class JSONType() const { return Type; }
-
-        /// Functions for getting primitives from the JSON object.
-        bool IsNull() const { return Type == Class::Null; }
-
-        string ToString() const { bool b; return std::move( ToString( b ) ); }
-        string ToString( bool &ok ) const {
-            ok = (Type == Class::String);
-            return ok ? std::move( json_escape( *Internal.String ) ): string("");
-        }
-
-        double ToFloat() const { bool b; return ToFloat( b ); }
-        double ToFloat( bool &ok ) const {
-            ok = (Type == Class::Floating);
-            return ok ? Internal.Float : 0.0;
-        }
-
-        long ToInt() const { bool b; return ToInt( b ); }
-        long ToInt( bool &ok ) const {
-            ok = (Type == Class::Integral);
-            return ok ? Internal.Int : 0;
-        }
-
-        bool ToBool() const { bool b; return ToBool( b ); }
-        bool ToBool( bool &ok ) const {
-            ok = (Type == Class::Boolean);
-            return ok ? Internal.Bool : false;
-        }
-
-        JSONWrapper<map<string,JSON>> ObjectRange() {
-            if( Type == Class::Object )
-                return JSONWrapper<map<string,JSON>>( Internal.Map );
-            return JSONWrapper<map<string,JSON>>( nullptr );
-        }
-
-        JSONWrapper<deque<JSON>> ArrayRange() {
-            if( Type == Class::Array )
-                return JSONWrapper<deque<JSON>>( Internal.List );
-            return JSONWrapper<deque<JSON>>( nullptr );
-        }
-
-        JSONConstWrapper<map<string,JSON>> ObjectRange() const {
-            if( Type == Class::Object )
-                return JSONConstWrapper<map<string,JSON>>( Internal.Map );
-            return JSONConstWrapper<map<string,JSON>>( nullptr );
-        }
-
-
-        JSONConstWrapper<deque<JSON>> ArrayRange() const { 
-            if( Type == Class::Array )
-                return JSONConstWrapper<deque<JSON>>( Internal.List );
-            return JSONConstWrapper<deque<JSON>>( nullptr );
-        }
-
-        string dump( int depth = 1, string tab = "  ") const {
-            string pad = "";
-            for( int i = 0; i < depth; ++i, pad += tab );
-
-            switch( Type ) {
-                case Class::Null:
-                    return "null";
-                case Class::Object: {
-                    string s = "{\n";
-                    bool skip = true;
-                    for( auto &p : *Internal.Map ) {
-                        if( !skip ) s += ",\n";
-                        s += ( pad + "\"" + p.first + "\" : " + p.second.dump( depth + 1, tab ) );
-                        skip = false;
-                    }
-                    s += ( "\n" + pad.erase( 0, 2 ) + "}" ) ;
-                    return s;
-                }
-                case Class::Array: {
-                    string s = "[";
-                    bool skip = true;
-                    for( auto &p : *Internal.List ) {
-                        if( !skip ) s += ", ";
-                        s += p.dump( depth + 1, tab );
-                        skip = false;
-                    }
-                    s += "]";
-                    return s;
-                }
-                case Class::String:
-                    return "\"" + json_escape( *Internal.String ) + "\"";
-                case Class::Floating:
-                    return std::to_string( Internal.Float );
-                case Class::Integral:
-                    return std::to_string( Internal.Int );
-                case Class::Boolean:
-                    return Internal.Bool ? "true" : "false";
-                default:
-                    return "";
-            }
-            return "";
-        }
-
-        friend std::ostream& operator<<( std::ostream&, const JSON & );
-
-    private:
-        void SetType( Class type ) {
-            if( type == Type )
-                return;
-
-            ClearInternal();
-          
-            switch( type ) {
-            case Class::Null:      Internal.Map    = nullptr;                break;
-            case Class::Object:    Internal.Map    = new map<string,JSON>(); break;
-            case Class::Array:     Internal.List   = new deque<JSON>();     break;
-            case Class::String:    Internal.String = new string();           break;
-            case Class::Floating:  Internal.Float  = 0.0;                    break;
-            case Class::Integral:  Internal.Int    = 0;                      break;
-            case Class::Boolean:   Internal.Bool   = false;                  break;
-            }
-
-            Type = type;
-        }
-
-    private:
-      /* beware: only call if YOU know that Internal is allocated. No checks performed here. 
-         This function should be called in a constructed JSON just before you are going to 
-        overwrite Internal... 
-      */
-      void ClearInternal() {
-        switch( Type ) {
-          case Class::Object: delete Internal.Map;    break;
-          case Class::Array:  delete Internal.List;   break;
-          case Class::String: delete Internal.String; break;
-          default:;
-        }
-      }
-
-    private:
-
-        Class Type = Class::Null;
-};
-
-JSON Array() {
-    return std::move( JSON::Make( JSON::Class::Array ) );
-}
-
-template <typename... T>
-JSON Array( T... args ) {
-    JSON arr = JSON::Make( JSON::Class::Array );
-    arr.append( args... );
-    return std::move( arr );
-}
-
-JSON Object() {
-    return std::move( JSON::Make( JSON::Class::Object ) );
-}
-
-std::ostream& operator<<( std::ostream &os, const JSON &json ) {
-    os << json.dump();
-    return os;
-}
-
-namespace {
-    JSON parse_next( const string &, size_t & );
-
-    void consume_ws( const string &str, size_t &offset ) {
-        while( isspace( str[offset] ) ) ++offset;
-    }
-
-    JSON parse_object( const string &str, size_t &offset ) {
-        JSON Object = JSON::Make( JSON::Class::Object );
-
-        ++offset;
-        consume_ws( str, offset );
-        if( str[offset] == '}' ) {
-            ++offset; return std::move( Object );
-        }
-
-        while( true ) {
-            JSON Key = parse_next( str, offset );
-            consume_ws( str, offset );
-            if( str[offset] != ':' ) {
-                std::cerr << "Error: Object: Expected colon, found '" << str[offset] << "'\n";
-                break;
-            }
-            consume_ws( str, ++offset );
-            JSON Value = parse_next( str, offset );
-            Object[Key.ToString()] = Value;
-            
-            consume_ws( str, offset );
-            if( str[offset] == ',' ) {
-                ++offset; continue;
-            }
-            else if( str[offset] == '}' ) {
-                ++offset; break;
-            }
-            else {
-                std::cerr << "ERROR: Object: Expected comma, found '" << str[offset] << "'\n";
-                break;
-            }
-        }
-
-        return std::move( Object );
-    }
-
-    JSON parse_array( const string &str, size_t &offset ) {
-        JSON Array = JSON::Make( JSON::Class::Array );
-        unsigned index = 0;
-        
-        ++offset;
-        consume_ws( str, offset );
-        if( str[offset] == ']' ) {
-            ++offset; return std::move( Array );
-        }
-
-        while( true ) {
-            Array[index++] = parse_next( str, offset );
-            consume_ws( str, offset );
-
-            if( str[offset] == ',' ) {
-                ++offset; continue;
-            }
-            else if( str[offset] == ']' ) {
-                ++offset; break;
-            }
-            else {
-                std::cerr << "ERROR: Array: Expected ',' or ']', found '" << str[offset] << "'\n";
-                return std::move( JSON::Make( JSON::Class::Array ) );
-            }
-        }
-
-        return std::move( Array );
-    }
-
-    JSON parse_string( const string &str, size_t &offset ) {
-        JSON String;
-        string val;
-        for( char c = str[++offset]; c != '\"' ; c = str[++offset] ) {
-            if( c == '\\' ) {
-                switch( str[ ++offset ] ) {
-                case '\"': val += '\"'; break;
-                case '\\': val += '\\'; break;
-                case '/' : val += '/' ; break;
-                case 'b' : val += '\b'; break;
-                case 'f' : val += '\f'; break;
-                case 'n' : val += '\n'; break;
-                case 'r' : val += '\r'; break;
-                case 't' : val += '\t'; break;
-                case 'u' : {
-                    val += "\\u" ;
-                    for( unsigned i = 1; i <= 4; ++i ) {
-                        c = str[offset+i];
-                        if( (c >= '0' && c <= '9') || (c >= 'a' && c <= 'f') || (c >= 'A' && c <= 'F') )
-                            val += c;
-                        else {
-                            std::cerr << "ERROR: String: Expected hex character in unicode escape, found '" << c << "'\n";
-                            return std::move( JSON::Make( JSON::Class::String ) );
-                        }
-                    }
-                    offset += 4;
-                } break;
-                default  : val += '\\'; break;
-                }
-            }
-            else
-                val += c;
-        }
-        ++offset;
-        String = val;
-        return std::move( String );
-    }
-
-    JSON parse_number( const string &str, size_t &offset ) {
-        JSON Number;
-        string val, exp_str;
-        char c;
-        bool isDouble = false;
-        long exp = 0;
-        while( true ) {
-            c = str[offset++];
-            if( (c == '-') || (c >= '0' && c <= '9') )
-                val += c;
-            else if( c == '.' ) {
-                val += c; 
-                isDouble = true;
-            }
-            else
-                break;
-        }
-        if( c == 'E' || c == 'e' ) {
-            c = str[ offset++ ];
-            if( c == '-' ){ ++offset; exp_str += '-';}
-            while( true ) {
-                c = str[ offset++ ];
-                if( c >= '0' && c <= '9' )
-                    exp_str += c;
-                else if( !isspace( c ) && c != ',' && c != ']' && c != '}' ) {
-                    std::cerr << "ERROR: Number: Expected a number for exponent, found '" << c << "'\n";
-                    return std::move( JSON::Make( JSON::Class::Null ) );
-                }
-                else
-                    break;
-            }
-            exp = std::stol( exp_str );
-        }
-        else if( !isspace( c ) && c != ',' && c != ']' && c != '}' ) {
-            std::cerr << "ERROR: Number: unexpected character '" << c << "'\n";
-            return std::move( JSON::Make( JSON::Class::Null ) );
-        }
-        --offset;
-        
-        if( isDouble )
-            Number = std::stod( val ) * std::pow( 10, exp );
-        else {
-            if( !exp_str.empty() )
-                Number = std::stol( val ) * std::pow( 10, exp );
-            else
-                Number = std::stol( val );
-        }
-        return std::move( Number );
-    }
-
-    JSON parse_bool( const string &str, size_t &offset ) {
-        JSON Bool;
-        if( str.substr( offset, 4 ) == "true" )
-            Bool = true;
-        else if( str.substr( offset, 5 ) == "false" )
-            Bool = false;
-        else {
-            std::cerr << "ERROR: Bool: Expected 'true' or 'false', found '" << str.substr( offset, 5 ) << "'\n";
-            return std::move( JSON::Make( JSON::Class::Null ) );
-        }
-        offset += (Bool.ToBool() ? 4 : 5);
-        return std::move( Bool );
-    }
-
-    JSON parse_null( const string &str, size_t &offset ) {
-        JSON Null;
-        if( str.substr( offset, 4 ) != "null" ) {
-            std::cerr << "ERROR: Null: Expected 'null', found '" << str.substr( offset, 4 ) << "'\n";
-            return std::move( JSON::Make( JSON::Class::Null ) );
-        }
-        offset += 4;
-        return std::move( Null );
-    }
-
-    JSON parse_next( const string &str, size_t &offset ) {
-        char value;
-        consume_ws( str, offset );
-        value = str[offset];
-        switch( value ) {
-            case '[' : return std::move( parse_array( str, offset ) );
-            case '{' : return std::move( parse_object( str, offset ) );
-            case '\"': return std::move( parse_string( str, offset ) );
-            case 't' :
-            case 'f' : return std::move( parse_bool( str, offset ) );
-            case 'n' : return std::move( parse_null( str, offset ) );
-            default  : if( ( value <= '9' && value >= '0' ) || value == '-' )
-                           return std::move( parse_number( str, offset ) );
-        }
-        std::cerr << "ERROR: Parse: Unknown starting character '" << value << "'\n";
-        return JSON();
-    }
-}
-
-JSON JSON::Load( const string &str ) {
-    size_t offset = 0;
-    return std::move( parse_next( str, offset ) );
-}
-
-} // End Namespace json
@@ -1,404 +0,0 @@
-// 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.
-
-#include "kaldi_recognizer.h"
-#include "json.h"
-#include "fstext/fstext-utils.h"
-#include "lat/sausages.h"
-
-using namespace fst;
-using namespace kaldi::nnet3;
-
-KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency) : model_(model), spk_model_(0), sample_frequency_(sample_frequency) {
-
-    model_->Ref();
-
-    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
-
-    g_fst_ = NULL;
-    decode_fst_ = NULL;
-
-    if (!model_->hclg_fst_) {
-        if (model_->hcl_fst_ && model_->g_fst_) {
-            decode_fst_ = LookaheadComposeFst(*model_->hcl_fst_, *model_->g_fst_, model_->disambig_);
-        } else {
-            KALDI_ERR << "Can't create decoding graph";
-        }
-    }
-
-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
-            *model_->trans_model_,
-            *model_->decodable_info_,
-            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
-            feature_pipeline_);
-
-    frame_offset_ = 0;
-    input_finalized_ = false;
-    spk_feature_ = NULL;
-
-    InitRescoring();
-}
-
-KaldiRecognizer::KaldiRecognizer(Model *model, float sample_frequency, char const *grammar) : model_(model), spk_model_(0), sample_frequency_(sample_frequency)
-{
-    model_->Ref();
-
-    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
-
-    g_fst_ = new StdVectorFst();
-    if (model_->hcl_fst_) {
-        g_fst_->AddState();
-        g_fst_->SetStart(0);
-        g_fst_->AddState();
-        g_fst_->SetFinal(1, fst::TropicalWeight::One());
-        g_fst_->AddArc(1, StdArc(0, 0, fst::TropicalWeight::One(), 0));
-
-        // Create simple word loop FST
-        stringstream ss(grammar);
-        string token;
-
-        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_ERR << "Can't create decoding graph";
-    }
-
-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
-            *model_->trans_model_,
-            *model_->decodable_info_,
-            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
-            feature_pipeline_);
-
-    frame_offset_ = 0;
-    input_finalized_ = false;
-    spk_feature_ = NULL;
-
-    InitRescoring();
-}
-
-KaldiRecognizer::KaldiRecognizer(Model *model, SpkModel *spk_model, float sample_frequency) : model_(model), spk_model_(spk_model), sample_frequency_(sample_frequency) {
-
-    model_->Ref();
-    spk_model->Ref();
-
-    feature_pipeline_ = new kaldi::OnlineNnet2FeaturePipeline (model_->feature_info_);
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
-
-    decode_fst_ = NULL;
-    g_fst_ = NULL;
-
-    if (!model_->hclg_fst_) {
-        if (model_->hcl_fst_ && model_->g_fst_) {
-            decode_fst_ = LookaheadComposeFst(*model_->hcl_fst_, *model_->g_fst_, model_->disambig_);
-        } else {
-            KALDI_ERR << "Can't create decoding graph";
-        }
-    }
-
-    decoder_ = new kaldi::SingleUtteranceNnet3Decoder(model_->nnet3_decoding_config_,
-            *model_->trans_model_,
-            *model_->decodable_info_,
-            model_->hclg_fst_ ? *model_->hclg_fst_ : *decode_fst_,
-            feature_pipeline_);
-
-    frame_offset_ = 0;
-    input_finalized_ = false;
-
-    spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
-
-    InitRescoring();
-}
-
-KaldiRecognizer::~KaldiRecognizer() {
-    delete feature_pipeline_;
-    delete silence_weighting_;
-    delete decoder_;
-    delete g_fst_;
-    delete decode_fst_;
-    delete spk_feature_;
-    delete lm_fst_;
-
-    model_->Unref();
-    if (spk_model_)
-         spk_model_->Unref();
-}
-
-void KaldiRecognizer::InitRescoring()
-{
-    if (model_->std_lm_fst_) {
-        fst::CacheOptions cache_opts(true, 50000);
-        fst::MapFstOptions mapfst_opts(cache_opts);
-        fst::StdToLatticeMapper<kaldi::BaseFloat> mapper;
-        lm_fst_ = new fst::MapFst<fst::StdArc, kaldi::LatticeArc, fst::StdToLatticeMapper<kaldi::BaseFloat> >(*model_->std_lm_fst_, mapper, mapfst_opts);
-    } else {
-        lm_fst_ = NULL;
-    }
-}
-
-void KaldiRecognizer::CleanUp()
-{
-    delete silence_weighting_;
-    silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
-
-    frame_offset_ += decoder_->NumFramesDecoded();
-    decoder_->InitDecoding(frame_offset_);
-}
-
-void KaldiRecognizer::UpdateSilenceWeights()
-{
-    if (silence_weighting_->Active() && feature_pipeline_->NumFramesReady() > 0 &&
-        feature_pipeline_->IvectorFeature() != NULL) {
-        vector<pair<int32, BaseFloat> > delta_weights;
-        silence_weighting_->ComputeCurrentTraceback(decoder_->Decoder());
-        silence_weighting_->GetDeltaWeights(feature_pipeline_->NumFramesReady(),
-                                          frame_offset_ * 3,
-                                          &delta_weights);
-        feature_pipeline_->UpdateFrameWeights(delta_weights);
-    }
-}
-
-bool KaldiRecognizer::AcceptWaveform(const char *data, int len)
-{
-    Vector<BaseFloat> wave;
-    wave.Resize(len / 2, kUndefined);
-    for (int i = 0; i < len / 2; i++)
-        wave(i) = *(((short *)data) + i);
-    return AcceptWaveform(wave);
-}
-
-bool KaldiRecognizer::AcceptWaveform(const short *sdata, int len)
-{
-    Vector<BaseFloat> wave;
-    wave.Resize(len, kUndefined);
-    for (int i = 0; i < len; i++)
-        wave(i) = sdata[i];
-    return AcceptWaveform(wave);
-}
-
-bool KaldiRecognizer::AcceptWaveform(const float *fdata, int len)
-{
-    Vector<BaseFloat> wave;
-    wave.Resize(len, kUndefined);
-    for (int i = 0; i < len; i++)
-        wave(i) = fdata[i];
-    return AcceptWaveform(wave);
-}
-
-bool KaldiRecognizer::AcceptWaveform(Vector<BaseFloat> &wdata)
-{
-    if (input_finalized_) {
-        CleanUp();
-        input_finalized_ = false;
-    }
-
-    feature_pipeline_->AcceptWaveform(sample_frequency_, wdata);
-    UpdateSilenceWeights();
-    decoder_->AdvanceDecoding();
-
-    if (spk_feature_) {
-        spk_feature_->AcceptWaveform(sample_frequency_, wdata);
-    }
-
-    if (decoder_->EndpointDetected(model_->endpoint_config_)) {
-        return true;
-    }
-
-    return false;
-}
-
-// Computes an xvector from a chunk of speech features.
-static void RunNnetComputation(const MatrixBase<BaseFloat> &features,
-    const nnet3::Nnet &nnet, nnet3::CachingOptimizingCompiler *compiler,
-    Vector<BaseFloat> *xvector) 
-{
-    nnet3::ComputationRequest request;
-    request.need_model_derivative = false;
-    request.store_component_stats = false;
-    request.inputs.push_back(
-    nnet3::IoSpecification("input", 0, features.NumRows()));
-    nnet3::IoSpecification output_spec;
-    output_spec.name = "output";
-    output_spec.has_deriv = false;
-    output_spec.indexes.resize(1);
-    request.outputs.resize(1);
-    request.outputs[0].Swap(&output_spec);
-    shared_ptr<const nnet3::NnetComputation> computation = compiler->Compile(request);
-    nnet3::Nnet *nnet_to_update = NULL;  // we're not doing any update.
-    nnet3::NnetComputer computer(nnet3::NnetComputeOptions(), *computation,
-                    nnet, nnet_to_update);
-    CuMatrix<BaseFloat> input_feats_cu(features);
-    computer.AcceptInput("input", &input_feats_cu);
-    computer.Run();
-    CuMatrix<BaseFloat> cu_output;
-    computer.GetOutputDestructive("output", &cu_output);
-    xvector->Resize(cu_output.NumCols());
-    xvector->CopyFromVec(cu_output.Row(0));
-}
-
-void KaldiRecognizer::GetSpkVector(Vector<BaseFloat> &xvector)
-{
-    int num_frames = spk_feature_->NumFramesReady() - frame_offset_ * 3;
-    Matrix<BaseFloat> mfcc(num_frames, spk_feature_->Dim());
-    for (int i = 0; i < num_frames; ++i) {
-       Vector<BaseFloat> feat(spk_feature_->Dim());
-       spk_feature_->GetFrame(i + frame_offset_ * 3, &feat);
-       mfcc.CopyRowFromVec(feat, i);
-    }
-    SlidingWindowCmnOptions cmvn_opts;
-    Matrix<BaseFloat> features(mfcc.NumRows(), mfcc.NumCols(), kUndefined);
-    SlidingWindowCmn(cmvn_opts, mfcc, &features);
-
-    nnet3::NnetSimpleComputationOptions opts;
-    nnet3::CachingOptimizingCompilerOptions compiler_config;
-    nnet3::CachingOptimizingCompiler compiler(spk_model_->speaker_nnet, opts.optimize_config, compiler_config);
-
-    RunNnetComputation(features, spk_model_->speaker_nnet, &compiler, &xvector);
-}
-
-const char* KaldiRecognizer::Result()
-{
-
-    if (!input_finalized_) {
-        decoder_->FinalizeDecoding();
-        input_finalized_ = true;
-    }
-
-    if (decoder_->NumFramesDecoded() == 0) {
-        last_result_ = "{\"text\": \"\"}";
-        return last_result_.c_str();
-    }
-
-    kaldi::CompactLattice clat;
-    decoder_->GetLattice(true, &clat);
-
-    if (model_->std_lm_fst_) {
-        Lattice lat1;
-
-        ConvertLattice(clat, &lat1);
-        fst::ScaleLattice(fst::GraphLatticeScale(-1.0), &lat1);
-        fst::ArcSort(&lat1, fst::OLabelCompare<kaldi::LatticeArc>());
-        kaldi::Lattice composed_lat;
-        fst::Compose(lat1, *lm_fst_, &composed_lat);
-        fst::Invert(&composed_lat);
-        kaldi::CompactLattice determinized_lat;
-        DeterminizeLattice(composed_lat, &determinized_lat);
-        fst::ScaleLattice(fst::GraphLatticeScale(-1), &determinized_lat);
-        fst::ArcSort(&determinized_lat, fst::OLabelCompare<kaldi::CompactLatticeArc>());
-
-        kaldi::ConstArpaLmDeterministicFst const_arpa_fst(model_->const_arpa_);
-        kaldi::CompactLattice composed_clat;
-        kaldi::ComposeCompactLatticeDeterministic(determinized_lat, &const_arpa_fst, &composed_clat);
-        kaldi::Lattice composed_lat1;
-        ConvertLattice(composed_clat, &composed_lat1);
-        fst::Invert(&composed_lat1);
-        DeterminizeLattice(composed_lat1, &clat);
-    }
-
-    fst::ScaleLattice(fst::LatticeScale(9.0, 10.0), &clat);
-    CompactLattice aligned_lat;
-    if (model_->winfo_) {
-        WordAlignLattice(clat, *model_->trans_model_, *model_->winfo_, 0, &aligned_lat);
-    } else {
-        aligned_lat = clat;
-    }
-
-    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();
-
-    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"] = (frame_offset_ + times[i].first) * 0.03;
-        word["end"] = (frame_offset_ + times[i].second) * 0.03;
-        word["conf"] = conf[i];
-        obj["result"].append(word);
-
-        if (i) {
-            text << " ";
-        }
-        text << model_->word_syms_->Find(words[i]);
-    }
-    obj["text"] = text.str();
-
-    if (spk_model_) {
-        Vector<BaseFloat> xvector;
-        GetSpkVector(xvector);
-        for (int i = 0; i < xvector.Dim(); i++) {
-            obj["spk"].append(xvector(i));
-        }
-    }
-
-    last_result_ = obj.dump();
-    return last_result_.c_str();
-}
-
-const char* KaldiRecognizer::PartialResult()
-{
-    json::JSON res;
-    if (decoder_->NumFramesDecoded() == 0) {
-        res["partial"] = "";
-        last_result_ = res.dump();
-        return last_result_.c_str();
-    }
-
-    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 << " ";
-        }
-        text << model_->word_syms_->Find(words[i]);
-    }
-    res["partial"] = text.str();
-
-    last_result_ = res.dump();
-    return last_result_.c_str();
-}
-
-const char* KaldiRecognizer::FinalResult()
-{
-    if (!input_finalized_) {
-        feature_pipeline_->InputFinished();
-        UpdateSilenceWeights();
-        decoder_->AdvanceDecoding();
-        decoder_->FinalizeDecoding();
-        input_finalized_ = true;
-        return Result();
-    } else {
-        last_result_ = "{\"text\": \"\"}";
-        return last_result_.c_str();
-    }
-}
@@ -1,68 +0,0 @@
-// 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.
-
-#include "base/kaldi-common.h"
-#include "util/common-utils.h"
-#include "fstext/fstext-lib.h"
-#include "fstext/fstext-utils.h"
-#include "decoder/lattice-faster-decoder.h"
-#include "feat/feature-mfcc.h"
-#include "lat/kaldi-lattice.h"
-#include "lat/word-align-lattice.h"
-#include "nnet3/am-nnet-simple.h"
-#include "nnet3/nnet-am-decodable-simple.h"
-#include "nnet3/nnet-utils.h"
-
-#include "model.h"
-#include "spk_model.h"
-
-using namespace kaldi;
-
-class KaldiRecognizer {
-    public:
-        KaldiRecognizer(Model *model, float sample_frequency);
-        KaldiRecognizer(Model *model, SpkModel *spk_model, float sample_frequency);
-        KaldiRecognizer(Model *model, float sample_frequency, char const *grammar);
-        ~KaldiRecognizer();
-        bool AcceptWaveform(const char *data, int len);
-        bool AcceptWaveform(const short *sdata, int len);
-        bool AcceptWaveform(const float *fdata, int len);
-        const char* Result();
-        const char* FinalResult();
-        const char* PartialResult();
-
-    private:
-        void InitRescoring();
-        void CleanUp();
-        void UpdateSilenceWeights();
-        bool AcceptWaveform(Vector<BaseFloat> &wdata);
-        void GetSpkVector(Vector<BaseFloat> &xvector);
-
-        Model *model_;
-        SingleUtteranceNnet3Decoder *decoder_;
-        fst::LookaheadFst<fst::StdArc, int32> *decode_fst_;
-        fst::StdVectorFst *g_fst_; // dynamically constructed grammar
-        OnlineNnet2FeaturePipeline *feature_pipeline_;
-        OnlineSilenceWeighting *silence_weighting_;
-
-        SpkModel *spk_model_;
-        OnlineBaseFeature *spk_feature_;
-
-        fst::MapFst<fst::StdArc, kaldi::LatticeArc, fst::StdToLatticeMapper<kaldi::BaseFloat> > *lm_fst_;
-
-        float sample_frequency_;
-        int32 frame_offset_;
-        bool input_finalized_;
-        string last_result_;
-};
@@ -0,0 +1,122 @@
+// log.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "log.h"
+
+#ifdef SHERPA_ONNX_HAVE_EXECINFO_H
+#include <execinfo.h>  // To get stack trace in error messages.
+#ifdef SHERPA_ONNX_HAVE_CXXABI_H
+#include <cxxabi.h>  // For name demangling.
+// Useful to decode the stack trace, but only used if we have execinfo.h
+#endif  // SHERPA_ONNX_HAVE_CXXABI_H
+#endif  // SHERPA_ONNX_HAVE_EXECINFO_H
+
+#include <stdlib.h>
+
+#include <ctime>
+#include <iomanip>
+#include <string>
+
+namespace sherpa_onnx {
+
+std::string GetDateTimeStr() {
+  std::ostringstream os;
+  std::time_t t = std::time(nullptr);
+  std::tm tm = *std::localtime(&t);
+  os << std::put_time(&tm, "%F %T");  // yyyy-mm-dd hh:mm:ss
+  return os.str();
+}
+
+static bool LocateSymbolRange(const std::string &trace_name, std::size_t *begin,
+                              std::size_t *end) {
+  // Find the first '_' with leading ' ' or '('.
+  *begin = std::string::npos;
+  for (std::size_t i = 1; i < trace_name.size(); ++i) {
+    if (trace_name[i] != '_') {
+      continue;
+    }
+    if (trace_name[i - 1] == ' ' || trace_name[i - 1] == '(') {
+      *begin = i;
+      break;
+    }
+  }
+  if (*begin == std::string::npos) {
+    return false;
+  }
+  *end = trace_name.find_first_of(" +", *begin);
+  return *end != std::string::npos;
+}
+
+#ifdef SHERPA_ONNX_HAVE_EXECINFO_H
+static std::string Demangle(const std::string &trace_name) {
+#ifndef SHERPA_ONNX_HAVE_CXXABI_H
+  return trace_name;
+#else   // SHERPA_ONNX_HAVE_CXXABI_H
+  // Try demangle the symbol. We are trying to support the following formats
+  // produced by different platforms:
+  //
+  // Linux:
+  //   ./kaldi-error-test(_ZN5kaldi13UnitTestErrorEv+0xb) [0x804965d]
+  //
+  // Mac:
+  //   0 server 0x000000010f67614d _ZNK5kaldi13MessageLogger10LogMessageEv + 813
+  //
+  // We want to extract the name e.g., '_ZN5kaldi13UnitTestErrorEv' and
+  // demangle it info a readable name like kaldi::UnitTextError.
+  std::size_t begin, end;
+  if (!LocateSymbolRange(trace_name, &begin, &end)) {
+    return trace_name;
+  }
+  std::string symbol = trace_name.substr(begin, end - begin);
+  int status;
+  char *demangled_name = abi::__cxa_demangle(symbol.c_str(), 0, 0, &status);
+  if (status == 0 && demangled_name != nullptr) {
+    symbol = demangled_name;
+    free(demangled_name);
+  }
+  return trace_name.substr(0, begin) + symbol +
+         trace_name.substr(end, std::string::npos);
+#endif  // SHERPA_ONNX_HAVE_CXXABI_H
+}
+#endif  // SHERPA_ONNX_HAVE_EXECINFO_H
+
+std::string GetStackTrace() {
+  std::string ans;
+#ifdef SHERPA_ONNX_HAVE_EXECINFO_H
+  constexpr const std::size_t kMaxTraceSize = 50;
+  constexpr const std::size_t kMaxTracePrint = 50;  // Must be even.
+                                                    // Buffer for the trace.
+  void *trace[kMaxTraceSize];
+  // Get the trace.
+  std::size_t size = backtrace(trace, kMaxTraceSize);
+  // Get the trace symbols.
+  char **trace_symbol = backtrace_symbols(trace, size);
+  if (trace_symbol == nullptr) return ans;
+
+  // Compose a human-readable backtrace string.
+  ans += "[ Stack-Trace: ]\n";
+  if (size <= kMaxTracePrint) {
+    for (std::size_t i = 0; i < size; ++i) {
+      ans += Demangle(trace_symbol[i]) + "\n";
+    }
+  } else {  // Print out first+last (e.g.) 5.
+    for (std::size_t i = 0; i < kMaxTracePrint / 2; ++i) {
+      ans += Demangle(trace_symbol[i]) + "\n";
+    }
+    ans += ".\n.\n.\n";
+    for (std::size_t i = size - kMaxTracePrint / 2; i < size; ++i) {
+      ans += Demangle(trace_symbol[i]) + "\n";
+    }
+    if (size == kMaxTraceSize)
+      ans += ".\n.\n.\n";  // Stack was too long, probably a bug.
+  }
+
+  // We must free the array of pointers allocated by backtrace_symbols(),
+  // but not the strings themselves.
+  free(trace_symbol);
+#endif  // SHERPA_ONNX_HAVE_EXECINFO_H
+  return ans;
+}
+
+}  // namespace sherpa_onnx
@@ -0,0 +1,378 @@
+// log.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_LOG_H_
+#define SHERPA_ONNX_CSRC_LOG_H_
+
+#include <stdio.h>
+
+#include <mutex>  // NOLINT
+#include <sstream>
+#include <string>
+
+namespace sherpa_onnx {
+
+#if SHERPA_ONNX_ENABLE_CHECK
+
+#if defined(NDEBUG)
+constexpr bool kDisableDebug = true;
+#else
+constexpr bool kDisableDebug = false;
+#endif
+
+enum class LogLevel {
+  kTrace = 0,
+  kDebug = 1,
+  kInfo = 2,
+  kWarning = 3,
+  kError = 4,
+  kFatal = 5,  // print message and abort the program
+};
+
+// They are used in SHERPA_ONNX_LOG(xxx), so their names
+// do not follow the google c++ code style
+//
+// You can use them in the following way:
+//
+//  SHERPA_ONNX_LOG(TRACE) << "some message";
+//  SHERPA_ONNX_LOG(DEBUG) << "some message";
+#ifndef _MSC_VER
+constexpr LogLevel TRACE = LogLevel::kTrace;
+constexpr LogLevel DEBUG = LogLevel::kDebug;
+constexpr LogLevel INFO = LogLevel::kInfo;
+constexpr LogLevel WARNING = LogLevel::kWarning;
+constexpr LogLevel ERROR = LogLevel::kError;
+constexpr LogLevel FATAL = LogLevel::kFatal;
+#else
+#define TRACE LogLevel::kTrace
+#define DEBUG LogLevel::kDebug
+#define INFO LogLevel::kInfo
+#define WARNING LogLevel::kWarning
+#define ERROR LogLevel::kError
+#define FATAL LogLevel::kFatal
+#endif
+
+std::string GetStackTrace();
+
+/* Return the current log level.
+
+
+   If the current log level is TRACE, then all logged messages are printed out.
+
+   If the current log level is DEBUG, log messages with "TRACE" level are not
+   shown and all other levels are printed out.
+
+   Similarly, if the current log level is INFO, log message with "TRACE" and
+   "DEBUG" are not shown and all other levels are printed out.
+
+   If it is FATAL, then only FATAL messages are shown.
+ */
+inline LogLevel GetCurrentLogLevel() {
+  static LogLevel log_level = INFO;
+  static std::once_flag init_flag;
+  std::call_once(init_flag, []() {
+    const char *env_log_level = std::getenv("SHERPA_ONNX_LOG_LEVEL");
+    if (env_log_level == nullptr) return;
+
+    std::string s = env_log_level;
+    if (s == "TRACE")
+      log_level = TRACE;
+    else if (s == "DEBUG")
+      log_level = DEBUG;
+    else if (s == "INFO")
+      log_level = INFO;
+    else if (s == "WARNING")
+      log_level = WARNING;
+    else if (s == "ERROR")
+      log_level = ERROR;
+    else if (s == "FATAL")
+      log_level = FATAL;
+    else
+      fprintf(stderr,
+              "Unknown SHERPA_ONNX_LOG_LEVEL: %s"
+              "\nSupported values are: "
+              "TRACE, DEBUG, INFO, WARNING, ERROR, FATAL",
+              s.c_str());
+  });
+  return log_level;
+}
+
+inline bool EnableAbort() {
+  static std::once_flag init_flag;
+  static bool enable_abort = false;
+  std::call_once(init_flag, []() {
+    enable_abort = (std::getenv("SHERPA_ONNX_ABORT") != nullptr);
+  });
+  return enable_abort;
+}
+
+class Logger {
+ public:
+  Logger(const char *filename, const char *func_name, uint32_t line_num,
+         LogLevel level)
+      : filename_(filename),
+        func_name_(func_name),
+        line_num_(line_num),
+        level_(level) {
+    cur_level_ = GetCurrentLogLevel();
+    switch (level) {
+      case TRACE:
+        if (cur_level_ <= TRACE) fprintf(stderr, "[T] ");
+        break;
+      case DEBUG:
+        if (cur_level_ <= DEBUG) fprintf(stderr, "[D] ");
+        break;
+      case INFO:
+        if (cur_level_ <= INFO) fprintf(stderr, "[I] ");
+        break;
+      case WARNING:
+        if (cur_level_ <= WARNING) fprintf(stderr, "[W] ");
+        break;
+      case ERROR:
+        if (cur_level_ <= ERROR) fprintf(stderr, "[E] ");
+        break;
+      case FATAL:
+        if (cur_level_ <= FATAL) fprintf(stderr, "[F] ");
+        break;
+    }
+
+    if (cur_level_ <= level_) {
+      fprintf(stderr, "%s:%u:%s ", filename, line_num, func_name);
+    }
+  }
+
+  ~Logger() noexcept(false) {
+    static constexpr const char *kErrMsg = R"(
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of sherpa_onnx.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/csukuangfj/kaldi-native-fbank/issues/new
+    )";
+    if (level_ == FATAL) {
+      fprintf(stderr, "\n");
+      std::string stack_trace = GetStackTrace();
+      if (!stack_trace.empty()) {
+        fprintf(stderr, "\n\n%s\n", stack_trace.c_str());
+      }
+
+      fflush(nullptr);
+
+#ifndef __ANDROID_API__
+      if (EnableAbort()) {
+        // NOTE: abort() will terminate the program immediately without
+        // printing the Python stack backtrace.
+        abort();
+      }
+
+      throw std::runtime_error(kErrMsg);
+#else
+      abort();
+#endif
+    }
+  }
+
+  const Logger &operator<<(bool b) const {
+    if (cur_level_ <= level_) {
+      fprintf(stderr, b ? "true" : "false");
+    }
+    return *this;
+  }
+
+  const Logger &operator<<(int8_t i) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%d", i);
+    return *this;
+  }
+
+  const Logger &operator<<(const char *s) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%s", s);
+    return *this;
+  }
+
+  const Logger &operator<<(int32_t i) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%d", i);
+    return *this;
+  }
+
+  const Logger &operator<<(uint32_t i) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%u", i);
+    return *this;
+  }
+
+  const Logger &operator<<(uint64_t i) const {
+    if (cur_level_ <= level_)
+      fprintf(stderr, "%llu", (long long unsigned int)i);  // NOLINT
+    return *this;
+  }
+
+  const Logger &operator<<(int64_t i) const {
+    if (cur_level_ <= level_)
+      fprintf(stderr, "%lli", (long long int)i);  // NOLINT
+    return *this;
+  }
+
+  const Logger &operator<<(float f) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%f", f);
+    return *this;
+  }
+
+  const Logger &operator<<(double d) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%f", d);
+    return *this;
+  }
+
+  template <typename T>
+  const Logger &operator<<(const T &t) const {
+    // require T overloads operator<<
+    std::ostringstream os;
+    os << t;
+    return *this << os.str().c_str();
+  }
+
+  // specialization to fix compile error: `stringstream << nullptr` is ambiguous
+  const Logger &operator<<(const std::nullptr_t &null) const {
+    if (cur_level_ <= level_) *this << "(null)";
+    return *this;
+  }
+
+ private:
+  const char *filename_;
+  const char *func_name_;
+  uint32_t line_num_;
+  LogLevel level_;
+  LogLevel cur_level_;
+};
+#endif  // SHERPA_ONNX_ENABLE_CHECK
+
+class Voidifier {
+ public:
+#if SHERPA_ONNX_ENABLE_CHECK
+  void operator&(const Logger &) const {}
+#endif
+};
+#if !defined(SHERPA_ONNX_ENABLE_CHECK)
+template <typename T>
+const Voidifier &operator<<(const Voidifier &v, T &&) {
+  return v;
+}
+#endif
+
+}  // namespace sherpa_onnx
+
+#define SHERPA_ONNX_STATIC_ASSERT(x) static_assert(x, "")
+
+#ifdef SHERPA_ONNX_ENABLE_CHECK
+
+#if defined(__clang__) || defined(__GNUC__) || defined(__GNUG__) || \
+    defined(__PRETTY_FUNCTION__)
+// for clang and GCC
+#define SHERPA_ONNX_FUNC __PRETTY_FUNCTION__
+#else
+// for other compilers
+#define SHERPA_ONNX_FUNC __func__
+#endif
+
+#define SHERPA_ONNX_CHECK(x)                                            \
+  (x) ? (void)0                                                         \
+      : ::sherpa_onnx::Voidifier() &                                    \
+            ::sherpa_onnx::Logger(__FILE__, SHERPA_ONNX_FUNC, __LINE__, \
+                                  ::sherpa_onnx::FATAL)                 \
+                << "Check failed: " << #x << " "
+
+// WARNING: x and y may be evaluated multiple times, but this happens only
+// when the check fails. Since the program aborts if it fails, we don't think
+// the extra evaluation of x and y matters.
+//
+// CAUTION: we recommend the following use case:
+//
+//      auto x = Foo();
+//      auto y = Bar();
+//      SHERPA_ONNX_CHECK_EQ(x, y) << "Some message";
+//
+//  And please avoid
+//
+//      SHERPA_ONNX_CHECK_EQ(Foo(), Bar());
+//
+//  if `Foo()` or `Bar()` causes some side effects, e.g., changing some
+//  local static variables or global variables.
+#define _SHERPA_ONNX_CHECK_OP(x, y, op)                                        \
+  ((x)op(y)) ? (void)0                                                         \
+             : ::sherpa_onnx::Voidifier() &                                    \
+                   ::sherpa_onnx::Logger(__FILE__, SHERPA_ONNX_FUNC, __LINE__, \
+                                         ::sherpa_onnx::FATAL)                 \
+                       << "Check failed: " << #x << " " << #op << " " << #y    \
+                       << " (" << (x) << " vs. " << (y) << ") "
+
+#define SHERPA_ONNX_CHECK_EQ(x, y) _SHERPA_ONNX_CHECK_OP(x, y, ==)
+#define SHERPA_ONNX_CHECK_NE(x, y) _SHERPA_ONNX_CHECK_OP(x, y, !=)
+#define SHERPA_ONNX_CHECK_LT(x, y) _SHERPA_ONNX_CHECK_OP(x, y, <)
+#define SHERPA_ONNX_CHECK_LE(x, y) _SHERPA_ONNX_CHECK_OP(x, y, <=)
+#define SHERPA_ONNX_CHECK_GT(x, y) _SHERPA_ONNX_CHECK_OP(x, y, >)
+#define SHERPA_ONNX_CHECK_GE(x, y) _SHERPA_ONNX_CHECK_OP(x, y, >=)
+
+#define SHERPA_ONNX_LOG(x) \
+  ::sherpa_onnx::Logger(__FILE__, SHERPA_ONNX_FUNC, __LINE__, ::sherpa_onnx::x)
+
+// ------------------------------------------------------------
+//       For debug check
+// ------------------------------------------------------------
+// If you define the macro "-D NDEBUG" while compiling kaldi-native-fbank,
+// the following macros are in fact empty and does nothing.
+
+#define SHERPA_ONNX_DCHECK(x) \
+  ::sherpa_onnx::kDisableDebug ? (void)0 : SHERPA_ONNX_CHECK(x)
+
+#define SHERPA_ONNX_DCHECK_EQ(x, y) \
+  ::sherpa_onnx::kDisableDebug ? (void)0 : SHERPA_ONNX_CHECK_EQ(x, y)
+
+#define SHERPA_ONNX_DCHECK_NE(x, y) \
+  ::sherpa_onnx::kDisableDebug ? (void)0 : SHERPA_ONNX_CHECK_NE(x, y)
+
+#define SHERPA_ONNX_DCHECK_LT(x, y) \
+  ::sherpa_onnx::kDisableDebug ? (void)0 : SHERPA_ONNX_CHECK_LT(x, y)
+
+#define SHERPA_ONNX_DCHECK_LE(x, y) \
+  ::sherpa_onnx::kDisableDebug ? (void)0 : SHERPA_ONNX_CHECK_LE(x, y)
+
+#define SHERPA_ONNX_DCHECK_GT(x, y) \
+  ::sherpa_onnx::kDisableDebug ? (void)0 : SHERPA_ONNX_CHECK_GT(x, y)
+
+#define SHERPA_ONNX_DCHECK_GE(x, y) \
+  ::sherpa_onnx::kDisableDebug ? (void)0 : SHERPA_ONNX_CHECK_GE(x, y)
+
+#define SHERPA_ONNX_DLOG(x)    \
+  ::sherpa_onnx::kDisableDebug \
+      ? (void)0                \
+      : ::sherpa_onnx::Voidifier() & SHERPA_ONNX_LOG(x)
+
+#else
+
+#define SHERPA_ONNX_CHECK(x) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_LOG(x) ::sherpa_onnx::Voidifier()
+
+#define SHERPA_ONNX_CHECK_EQ(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_CHECK_NE(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_CHECK_LT(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_CHECK_LE(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_CHECK_GT(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_CHECK_GE(x, y) ::sherpa_onnx::Voidifier()
+
+#define SHERPA_ONNX_DCHECK(x) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_DLOG(x) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_DCHECK_EQ(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_DCHECK_NE(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_DCHECK_LT(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_DCHECK_LE(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_DCHECK_GT(x, y) ::sherpa_onnx::Voidifier()
+#define SHERPA_ONNX_DCHECK_GE(x, y) ::sherpa_onnx::Voidifier()
+
+#endif  // SHERPA_ONNX_CHECK_NE
+
+#endif  // SHERPA_ONNX_CSRC_LOG_H_
@@ -0,0 +1,115 @@
+// macros.h
+//
+// Copyright      2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_MACROS_H_
+#define SHERPA_ONNX_CSRC_MACROS_H_
+#include <stdio.h>
+
+#if __ANDROID_API__ >= 8
+#include "android/log.h"
+#define SHERPA_ONNX_LOGE(...)                                            \
+  do {                                                                   \
+    fprintf(stderr, "%s:%s:%d ", __FILE__, __func__,                     \
+            static_cast<int>(__LINE__));                                 \
+    fprintf(stderr, ##__VA_ARGS__);                                      \
+    fprintf(stderr, "\n");                                               \
+    __android_log_print(ANDROID_LOG_WARN, "sherpa-onnx", ##__VA_ARGS__); \
+  } while (0)
+#else
+#define SHERPA_ONNX_LOGE(...)                        \
+  do {                                               \
+    fprintf(stderr, "%s:%s:%d ", __FILE__, __func__, \
+            static_cast<int>(__LINE__));             \
+    fprintf(stderr, ##__VA_ARGS__);                  \
+    fprintf(stderr, "\n");                           \
+  } while (0)
+#endif
+
+// Read an integer
+#define SHERPA_ONNX_READ_META_DATA(dst, src_key)                        \
+  do {                                                                  \
+    auto value =                                                        \
+        meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
+    if (!value) {                                                       \
+      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);   \
+      exit(-1);                                                         \
+    }                                                                   \
+                                                                        \
+    dst = atoi(value.get());                                            \
+    if (dst < 0) {                                                      \
+      SHERPA_ONNX_LOGE("Invalid value %d for %s", dst, src_key);        \
+      exit(-1);                                                         \
+    }                                                                   \
+  } while (0)
+
+// read a vector of integers
+#define SHERPA_ONNX_READ_META_DATA_VEC(dst, src_key)                     \
+  do {                                                                   \
+    auto value =                                                         \
+        meta_data.LookupCustomMetadataMapAllocated(src_key, allocator);  \
+    if (!value) {                                                        \
+      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);    \
+      exit(-1);                                                          \
+    }                                                                    \
+                                                                         \
+    bool ret = SplitStringToIntegers(value.get(), ",", true, &dst);      \
+    if (!ret) {                                                          \
+      SHERPA_ONNX_LOGE("Invalid value %s for %s", value.get(), src_key); \
+      exit(-1);                                                          \
+    }                                                                    \
+  } while (0)
+
+// read a vector of floats
+#define SHERPA_ONNX_READ_META_DATA_VEC_FLOAT(dst, src_key)               \
+  do {                                                                   \
+    auto value =                                                         \
+        meta_data.LookupCustomMetadataMapAllocated(src_key, allocator);  \
+    if (!value) {                                                        \
+      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);    \
+      exit(-1);                                                          \
+    }                                                                    \
+                                                                         \
+    bool ret = SplitStringToFloats(value.get(), ",", true, &dst);        \
+    if (!ret) {                                                          \
+      SHERPA_ONNX_LOGE("Invalid value %s for %s", value.get(), src_key); \
+      exit(-1);                                                          \
+    }                                                                    \
+  } while (0)
+
+// read a vector of strings
+#define SHERPA_ONNX_READ_META_DATA_VEC_STRING(dst, src_key)                   \
+  do {                                                                        \
+    auto value =                                                              \
+        meta_data.LookupCustomMetadataMapAllocated(src_key, allocator);       \
+    if (!value) {                                                             \
+      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);         \
+      exit(-1);                                                               \
+    }                                                                         \
+    SplitStringToVector(value.get(), ",", false, &dst);                       \
+                                                                              \
+    if (dst.empty()) {                                                        \
+      SHERPA_ONNX_LOGE("Invalid value %s for %s. Empty vector!", value.get(), \
+                       src_key);                                              \
+      exit(-1);                                                               \
+    }                                                                         \
+  } while (0)
+
+// Read a string
+#define SHERPA_ONNX_READ_META_DATA_STR(dst, src_key)                    \
+  do {                                                                  \
+    auto value =                                                        \
+        meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
+    if (!value) {                                                       \
+      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);   \
+      exit(-1);                                                         \
+    }                                                                   \
+                                                                        \
+    dst = value.get();                                                  \
+    if (dst.empty()) {                                                  \
+      SHERPA_ONNX_LOGE("Invalid value for %s\n", src_key);              \
+      exit(-1);                                                         \
+    }                                                                   \
+  } while (0)
+
+#endif  // SHERPA_ONNX_CSRC_MACROS_H_
@@ -0,0 +1,115 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Daniel Povey)
+ * Copyright (c)  2023                     (Pingfeng Luo)
+ *
+ */
+// This file is copied from k2/csrc/utils.h
+#ifndef SHERPA_ONNX_CSRC_MATH_H_
+#define SHERPA_ONNX_CSRC_MATH_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <numeric>
+#include <vector>
+
+namespace sherpa_onnx {
+
+// logf(FLT_EPSILON)
+#define SHERPA_ONNX_MIN_LOG_DIFF_FLOAT -15.9423847198486328125f
+
+// log(DBL_EPSILON)
+#define SHERPA_ONNX_MIN_LOG_DIFF_DOUBLE \
+  -36.0436533891171535515240975655615329742431640625
+
+template <typename T>
+struct LogAdd;
+
+template <>
+struct LogAdd<double> {
+  double operator()(double x, double y) const {
+    double diff;
+
+    if (x < y) {
+      diff = x - y;
+      x = y;
+    } else {
+      diff = y - x;
+    }
+    // diff is negative.  x is now the larger one.
+
+    if (diff >= SHERPA_ONNX_MIN_LOG_DIFF_DOUBLE) {
+      double res;
+      res = x + log1p(exp(diff));
+      return res;
+    }
+
+    return x;  // return the larger one.
+  }
+};
+
+template <>
+struct LogAdd<float> {
+  float operator()(float x, float y) const {
+    float diff;
+
+    if (x < y) {
+      diff = x - y;
+      x = y;
+    } else {
+      diff = y - x;
+    }
+    // diff is negative.  x is now the larger one.
+
+    if (diff >= SHERPA_ONNX_MIN_LOG_DIFF_DOUBLE) {
+      float res;
+      res = x + log1pf(expf(diff));
+      return res;
+    }
+
+    return x;  // return the larger one.
+  }
+};
+
+template <class T>
+void LogSoftmax(T *input, int32_t input_len) {
+  assert(input);
+
+  T m = *std::max_element(input, input + input_len);
+
+  T sum = 0.0;
+  for (int32_t i = 0; i < input_len; i++) {
+    sum += exp(input[i] - m);
+  }
+
+  T offset = m + log(sum);
+  for (int32_t i = 0; i < input_len; i++) {
+    input[i] -= offset;
+  }
+}
+
+template <typename T>
+void LogSoftmax(T *in, int32_t w, int32_t h) {
+  for (int32_t i = 0; i != h; ++i) {
+    LogSoftmax(in, w);
+    in += w;
+  }
+}
+
+template <class T>
+std::vector<int32_t> TopkIndex(const T *vec, int32_t size, int32_t topk) {
+  std::vector<int32_t> vec_index(size);
+  std::iota(vec_index.begin(), vec_index.end(), 0);
+
+  std::partial_sort(vec_index.begin(), vec_index.begin() + topk,
+                    vec_index.end(), [vec](int32_t index_1, int32_t index_2) {
+                      return vec[index_1] > vec[index_2];
+                    });
+
+  int32_t k_num = std::min<int32_t>(size, topk);
+  std::vector<int32_t> index(vec_index.begin(), vec_index.begin() + k_num);
+  return index;
+}
+
+}  // namespace sherpa_onnx
+#endif  // SHERPA_ONNX_CSRC_MATH_H_
@@ -0,0 +1,282 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE 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
+ *
+ * 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.
+ */
+
+// This file is copied/modified from kaldi/src/feat/mel-computations.cc
+
+#include "mel-computations.h"
+
+#include <algorithm>
+#include <sstream>
+#include <vector>
+
+#include "feature-window.h"
+
+namespace knf {
+
+std::ostream &operator<<(std::ostream &os, const MelBanksOptions &opts) {
+  os << opts.ToString();
+  return os;
+}
+
+float MelBanks::VtlnWarpFreq(
+    float vtln_low_cutoff,  // upper+lower frequency cutoffs for VTLN.
+    float vtln_high_cutoff,
+    float low_freq,  // upper+lower frequency cutoffs in mel computation
+    float high_freq, float vtln_warp_factor, float freq) {
+  /// This computes a VTLN warping function that is not the same as HTK's one,
+  /// but has similar inputs (this function has the advantage of never producing
+  /// empty bins).
+
+  /// This function computes a warp function F(freq), defined between low_freq
+  /// and high_freq inclusive, with the following properties:
+  ///  F(low_freq) == low_freq
+  ///  F(high_freq) == high_freq
+  /// The function is continuous and piecewise linear with two inflection
+  ///   points.
+  /// The lower inflection point (measured in terms of the unwarped
+  ///  frequency) is at frequency l, determined as described below.
+  /// The higher inflection point is at a frequency h, determined as
+  ///   described below.
+  /// If l <= f <= h, then F(f) = f/vtln_warp_factor.
+  /// If the higher inflection point (measured in terms of the unwarped
+  ///   frequency) is at h, then max(h, F(h)) == vtln_high_cutoff.
+  ///   Since (by the last point) F(h) == h/vtln_warp_factor, then
+  ///   max(h, h/vtln_warp_factor) == vtln_high_cutoff, so
+  ///   h = vtln_high_cutoff / max(1, 1/vtln_warp_factor).
+  ///     = vtln_high_cutoff * min(1, vtln_warp_factor).
+  /// If the lower inflection point (measured in terms of the unwarped
+  ///   frequency) is at l, then min(l, F(l)) == vtln_low_cutoff
+  ///   This implies that l = vtln_low_cutoff / min(1, 1/vtln_warp_factor)
+  ///                       = vtln_low_cutoff * max(1, vtln_warp_factor)
+
+  if (freq < low_freq || freq > high_freq)
+    return freq;  // in case this gets called
+  // for out-of-range frequencies, just return the freq.
+
+  SHERPA_ONNX_CHECK_GT(vtln_low_cutoff, low_freq);
+  SHERPA_ONNX_CHECK_LT(vtln_high_cutoff, high_freq);
+
+  float one = 1.0f;
+  float l = vtln_low_cutoff * std::max(one, vtln_warp_factor);
+  float h = vtln_high_cutoff * std::min(one, vtln_warp_factor);
+  float scale = 1.0f / vtln_warp_factor;
+  float Fl = scale * l;  // F(l);
+  float Fh = scale * h;  // F(h);
+  SHERPA_ONNX_CHECK(l > low_freq && h < high_freq);
+  // slope of left part of the 3-piece linear function
+  float scale_left = (Fl - low_freq) / (l - low_freq);
+  // [slope of center part is just "scale"]
+
+  // slope of right part of the 3-piece linear function
+  float scale_right = (high_freq - Fh) / (high_freq - h);
+
+  if (freq < l) {
+    return low_freq + scale_left * (freq - low_freq);
+  } else if (freq < h) {
+    return scale * freq;
+  } else {  // freq >= h
+    return high_freq + scale_right * (freq - high_freq);
+  }
+}
+
+float MelBanks::VtlnWarpMelFreq(
+    float vtln_low_cutoff,  // upper+lower frequency cutoffs for VTLN.
+    float vtln_high_cutoff,
+    float low_freq,  // upper+lower frequency cutoffs in mel computation
+    float high_freq, float vtln_warp_factor, float mel_freq) {
+  return MelScale(VtlnWarpFreq(vtln_low_cutoff, vtln_high_cutoff, low_freq,
+                               high_freq, vtln_warp_factor,
+                               InverseMelScale(mel_freq)));
+}
+
+MelBanks::MelBanks(const MelBanksOptions &opts,
+                   const FrameExtractionOptions &frame_opts,
+                   float vtln_warp_factor)
+    : htk_mode_(opts.htk_mode) {
+  int32_t num_bins = opts.num_bins;
+  if (num_bins < 3) SHERPA_ONNX_LOG(FATAL) << "Must have at least 3 mel bins";
+
+  float sample_freq = frame_opts.samp_freq;
+  int32_t window_length_padded = frame_opts.PaddedWindowSize();
+  SHERPA_ONNX_CHECK_EQ(window_length_padded % 2, 0);
+
+  int32_t num_fft_bins = window_length_padded / 2;
+  float nyquist = 0.5f * sample_freq;
+
+  float low_freq = opts.low_freq, high_freq;
+  if (opts.high_freq > 0.0f)
+    high_freq = opts.high_freq;
+  else
+    high_freq = nyquist + opts.high_freq;
+
+  if (low_freq < 0.0f || low_freq >= nyquist || high_freq <= 0.0f ||
+      high_freq > nyquist || high_freq <= low_freq) {
+    SHERPA_ONNX_LOG(FATAL) << "Bad values in options: low-freq " << low_freq
+                   << " and high-freq " << high_freq << " vs. nyquist "
+                   << nyquist;
+  }
+
+  float fft_bin_width = sample_freq / window_length_padded;
+  // fft-bin width [think of it as Nyquist-freq / half-window-length]
+
+  float mel_low_freq = MelScale(low_freq);
+  float mel_high_freq = MelScale(high_freq);
+
+  debug_ = opts.debug_mel;
+
+  // divide by num_bins+1 in next line because of end-effects where the bins
+  // spread out to the sides.
+  float mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1);
+
+  float vtln_low = opts.vtln_low, vtln_high = opts.vtln_high;
+  if (vtln_high < 0.0f) {
+    vtln_high += nyquist;
+  }
+
+  if (vtln_warp_factor != 1.0f &&
+      (vtln_low < 0.0f || vtln_low <= low_freq || vtln_low >= high_freq ||
+       vtln_high <= 0.0f || vtln_high >= high_freq || vtln_high <= vtln_low)) {
+    SHERPA_ONNX_LOG(FATAL) << "Bad values in options: vtln-low " << vtln_low
+                   << " and vtln-high " << vtln_high << ", versus "
+                   << "low-freq " << low_freq << " and high-freq " << high_freq;
+  }
+
+  bins_.resize(num_bins);
+
+  for (int32_t bin = 0; bin < num_bins; ++bin) {
+    float left_mel = mel_low_freq + bin * mel_freq_delta,
+          center_mel = mel_low_freq + (bin + 1) * mel_freq_delta,
+          right_mel = mel_low_freq + (bin + 2) * mel_freq_delta;
+
+    if (vtln_warp_factor != 1.0f) {
+      left_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
+                                 vtln_warp_factor, left_mel);
+      center_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
+                                   vtln_warp_factor, center_mel);
+      right_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
+                                  vtln_warp_factor, right_mel);
+    }
+
+    // this_bin will be a vector of coefficients that is only
+    // nonzero where this mel bin is active.
+    std::vector<float> this_bin(num_fft_bins);
+
+    int32_t first_index = -1, last_index = -1;
+    for (int32_t i = 0; i < num_fft_bins; ++i) {
+      float freq = (fft_bin_width * i);  // Center frequency of this fft
+                                         // bin.
+      float mel = MelScale(freq);
+      if (mel > left_mel && mel < right_mel) {
+        float weight;
+        if (mel <= center_mel)
+          weight = (mel - left_mel) / (center_mel - left_mel);
+        else
+          weight = (right_mel - mel) / (right_mel - center_mel);
+        this_bin[i] = weight;
+        if (first_index == -1) first_index = i;
+        last_index = i;
+      }
+    }
+    SHERPA_ONNX_CHECK(first_index != -1 && last_index >= first_index &&
+              "You may have set num_mel_bins too large.");
+
+    bins_[bin].first = first_index;
+    int32_t size = last_index + 1 - first_index;
+    bins_[bin].second.insert(bins_[bin].second.end(),
+                             this_bin.begin() + first_index,
+                             this_bin.begin() + first_index + size);
+
+    // Replicate a bug in HTK, for testing purposes.
+    if (opts.htk_mode && bin == 0 && mel_low_freq != 0.0f) {
+      bins_[bin].second[0] = 0.0;
+    }
+  }  // for (int32_t bin = 0; bin < num_bins; ++bin) {
+
+  if (debug_) {
+    std::ostringstream os;
+    for (size_t i = 0; i < bins_.size(); i++) {
+      os << "bin " << i << ", offset = " << bins_[i].first << ", vec = ";
+      for (auto k : bins_[i].second) os << k << ", ";
+      os << "\n";
+    }
+    SHERPA_ONNX_LOG(INFO) << os.str();
+  }
+}
+
+MelBanks::MelBanks(const float *weights, int32_t num_rows, int32_t num_cols)
+    : debug_(false), htk_mode_(false) {
+  bins_.resize(num_rows);
+  for (int32_t bin = 0; bin < num_rows; ++bin) {
+    const float *this_bin = weights + bin * num_cols;
+
+    int32_t first_index = -1, last_index = -1;
+
+    for (int32_t i = 0; i < num_cols; ++i) {
+      if (this_bin[i] == 0) {
+        continue;
+      }
+      if (first_index == -1) first_index = i;
+      last_index = i;
+    }
+
+    SHERPA_ONNX_CHECK(first_index != -1 && last_index >= first_index &&
+              "You have an incorrect weight matrix.");
+
+    bins_[bin].first = first_index;
+    int32_t size = last_index + 1 - first_index;
+
+    bins_[bin].second.insert(bins_[bin].second.end(), this_bin + first_index,
+                             this_bin + first_index + size);
+  }
+}
+
+// "power_spectrum" contains fft energies.
+void MelBanks::Compute(const float *power_spectrum,
+                       float *mel_energies_out) const {
+  int32_t num_bins = bins_.size();
+
+  for (int32_t i = 0; i < num_bins; i++) {
+    int32_t offset = bins_[i].first;
+    const auto &v = bins_[i].second;
+    float energy = 0;
+    for (int32_t k = 0; k != v.size(); ++k) {
+      energy += v[k] * power_spectrum[k + offset];
+    }
+
+    // HTK-like flooring- for testing purposes (we prefer dither)
+    if (htk_mode_ && energy < 1.0) {
+      energy = 1.0;
+    }
+
+    mel_energies_out[i] = energy;
+
+    // The following assert was added due to a problem with OpenBlas that
+    // we had at one point (it was a bug in that library).  Just to detect
+    // it early.
+    SHERPA_ONNX_CHECK_EQ(energy, energy);  // check that energy is not nan
+  }
+
+  if (debug_) {
+    fprintf(stderr, "MEL BANKS:\n");
+    for (int32_t i = 0; i < num_bins; i++)
+      fprintf(stderr, " %f", mel_energies_out[i]);
+    fprintf(stderr, "\n");
+  }
+}
+
+}  // namespace knf
@@ -0,0 +1,119 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE 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
+ *
+ * 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.
+ */
+// This file is copied/modified from kaldi/src/feat/mel-computations.h
+#ifndef KALDI_NATIVE_FBANK_CSRC_MEL_COMPUTATIONS_H_
+#define KALDI_NATIVE_FBANK_CSRC_MEL_COMPUTATIONS_H_
+
+#include <cmath>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "feature-window.h"
+
+namespace knf {
+
+struct MelBanksOptions {
+  int32_t num_bins = 25;  // e.g. 25; number of triangular bins
+  float low_freq = 20;    // e.g. 20; lower frequency cutoff
+
+  // an upper frequency cutoff; 0 -> no cutoff, negative
+  // ->added to the Nyquist frequency to get the cutoff.
+  float high_freq = 0;
+
+  float vtln_low = 100;  // vtln lower cutoff of warping function.
+
+  // vtln upper cutoff of warping function: if negative, added
+  // to the Nyquist frequency to get the cutoff.
+  float vtln_high = -500;
+
+  bool debug_mel = false;
+  // htk_mode is a "hidden" config, it does not show up on command line.
+  // Enables more exact compatibility with HTK, for testing purposes.  Affects
+  // mel-energy flooring and reproduces a bug in HTK.
+  bool htk_mode = false;
+
+  std::string ToString() const {
+    std::ostringstream os;
+    os << "num_bins: " << num_bins << "\n";
+    os << "low_freq: " << low_freq << "\n";
+    os << "high_freq: " << high_freq << "\n";
+    os << "vtln_low: " << vtln_low << "\n";
+    os << "vtln_high: " << vtln_high << "\n";
+    os << "debug_mel: " << debug_mel << "\n";
+    os << "htk_mode: " << htk_mode << "\n";
+    return os.str();
+  }
+};
+
+std::ostream &operator<<(std::ostream &os, const MelBanksOptions &opts);
+
+class MelBanks {
+ public:
+  static inline float InverseMelScale(float mel_freq) {
+    return 700.0f * (expf(mel_freq / 1127.0f) - 1.0f);
+  }
+
+  static inline float MelScale(float freq) {
+    return 1127.0f * logf(1.0f + freq / 700.0f);
+  }
+
+  static float VtlnWarpFreq(
+      float vtln_low_cutoff,
+      float vtln_high_cutoff,  // discontinuities in warp func
+      float low_freq,
+      float high_freq,  // upper+lower frequency cutoffs in
+      // the mel computation
+      float vtln_warp_factor, float freq);
+
+  static float VtlnWarpMelFreq(float vtln_low_cutoff, float vtln_high_cutoff,
+                               float low_freq, float high_freq,
+                               float vtln_warp_factor, float mel_freq);
+
+  // TODO(fangjun): Remove vtln_warp_factor
+  MelBanks(const MelBanksOptions &opts,
+           const FrameExtractionOptions &frame_opts, float vtln_warp_factor);
+
+  // Initialize with a 2-d weights matrix
+  // @param weights Pointer to the start address of the matrix
+  // @param num_rows It equls to number of mel bins
+  // @param num_cols It equals to (number of fft bins)/2+1
+  MelBanks(const float *weights, int32_t num_rows, int32_t num_cols);
+
+  /// Compute Mel energies (note: not log energies).
+  /// At input, "fft_energies" contains the FFT energies (not log).
+  ///
+  /// @param fft_energies 1-D array of size num_fft_bins/2+1
+  /// @param mel_energies_out  1-D array of size num_mel_bins
+  void Compute(const float *fft_energies, float *mel_energies_out) const;
+
+  int32_t NumBins() const { return bins_.size(); }
+
+ private:
+  // the "bins_" vector is a vector, one for each bin, of a pair:
+  // (the first nonzero fft-bin), (the vector of weights).
+  std::vector<std::pair<int32_t, std::vector<float>>> bins_;
+
+  // TODO(fangjun): Remove debug_ and htk_mode_
+  bool debug_;
+  bool htk_mode_;
+};
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_MEL_COMPUTATIONS_H_
@@ -1,323 +0,0 @@
-// 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.
-
-
-//
-// Possible model layout:
-//
-// * Default kaldi model with HCLG.fst
-//
-// * Lookahead model with const G.fst
-//
-// * Lookahead model with ngram G.fst
-//
-// * File disambig_tid.int required only for lookadhead models
-//
-// * File word_boundary.int is required if we want to have precise word timing information
-//   otherwise we don't do any word alignment. Optionally lexicon alignment can be done
-//   with corresponding C++ code inside kaldi recognizer.
-
-#include "model.h"
-
-#include <sys/stat.h>
-#include <fst/fst.h>
-#include <fst/register.h>
-#include <fst/matcher-fst.h>
-#include <fst/extensions/ngram/ngram-fst.h>
-
-namespace fst {
-
-static FstRegisterer<StdOLabelLookAheadFst> OLabelLookAheadFst_StdArc_registerer;
-static FstRegisterer<NGramFst<StdArc>> NGramFst_StdArc_registerer;
-
-}  // namespace fst
-
-#ifdef __ANDROID__
-#include <android/log.h>
-static void KaldiLogHandler(const LogMessageEnvelope &env, const char *message)
-{
-  int priority;
-  if (env.severity > GetVerboseLevel())
-      return;
-
-  if (env.severity > LogMessageEnvelope::kInfo) {
-    priority = ANDROID_LOG_VERBOSE;
-  } else {
-    switch (env.severity) {
-    case LogMessageEnvelope::kInfo:
-      priority = ANDROID_LOG_INFO;
-      break;
-    case LogMessageEnvelope::kWarning:
-      priority = ANDROID_LOG_WARN;
-      break;
-    case LogMessageEnvelope::kAssertFailed:
-      priority = ANDROID_LOG_FATAL;
-      break;
-    case LogMessageEnvelope::kError:
-    default: // If not the ERROR, it still an error!
-      priority = ANDROID_LOG_ERROR;
-      break;
-    }
-  }
-
-  std::stringstream full_message;
-  full_message << env.func << "():" << env.file << ':'
-               << env.line << ") " << message;
-
-  __android_log_print(priority, "VoskAPI", "%s", full_message.str().c_str());
-}
-#else
-static void KaldiLogHandler(const LogMessageEnvelope &env, const char *message)
-{
-  if (env.severity > GetVerboseLevel())
-      return;
-
-  // Modified default Kaldi logging so we can disable LOG messages.
-  std::stringstream full_message;
-  if (env.severity > LogMessageEnvelope::kInfo) {
-    full_message << "VLOG[" << env.severity << "] (";
-  } else {
-    switch (env.severity) {
-    case LogMessageEnvelope::kInfo:
-      full_message << "LOG (";
-      break;
-    case LogMessageEnvelope::kWarning:
-      full_message << "WARNING (";
-      break;
-    case LogMessageEnvelope::kAssertFailed:
-      full_message << "ASSERTION_FAILED (";
-      break;
-    case LogMessageEnvelope::kError:
-    default: // If not the ERROR, it still an error!
-      full_message << "ERROR (";
-      break;
-    }
-  }
-  // Add other info from the envelope and the message text.
-  full_message << "VoskAPI" << ':'
-               << env.func << "():" << env.file << ':'
-               << env.line << ") " << message;
-
-  // Print the complete message to stderr.
-  full_message << "\n";
-  std::cerr << full_message.str();
-}
-#endif
-
-Model::Model(const char *model_path) : model_path_str_(model_path) {
-
-    SetLogHandler(KaldiLogHandler);
-
-    struct stat buffer;
-    string am_path = model_path_str_ + "/am/final.mdl";
-    if (stat(am_path.c_str(), &buffer) == 0) {
-         ConfigureV2();
-    } else {
-         ConfigureV1();
-    }
-
-    ReadDataFiles();
-
-    ref_cnt_ = 1;
-}
-
-// Old model layout without model configuration file
-
-void Model::ConfigureV1()
-{
-    const char *extra_args[] = {
-        "--min-active=200",
-        "--max-active=3000",
-        "--beam=10.0",
-        "--lattice-beam=2.0",
-        "--acoustic-scale=1.0",
-
-        "--frame-subsampling-factor=3",
-
-        "--endpoint.silence-phones=1:2:3:4:5:6:7:8:9:10",
-        "--endpoint.rule2.min-trailing-silence=0.5",
-        "--endpoint.rule3.min-trailing-silence=1.0",
-        "--endpoint.rule4.min-trailing-silence=2.0",
-
-        "--print-args=false",
-    };
-
-    kaldi::ParseOptions po("");
-    nnet3_decoding_config_.Register(&po);
-    endpoint_config_.Register(&po);
-    decodable_opts_.Register(&po);
-
-    vector<const char*> args;
-    args.push_back("vosk");
-    args.insert(args.end(), extra_args, extra_args + sizeof(extra_args) / sizeof(extra_args[0]));
-    po.Read(args.size(), args.data());
-
-    feature_info_.feature_type = "mfcc";
-    ReadConfigFromFile(model_path_str_ + "/mfcc.conf", &feature_info_.mfcc_opts);
-    feature_info_.mfcc_opts.frame_opts.allow_downsample = true; // It is safe to downsample
-
-    feature_info_.silence_weighting_config.silence_weight = 1e-3;
-    feature_info_.silence_weighting_config.silence_phones_str = endpoint_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";
-    feature_info_.use_ivectors = true;
-    feature_info_.ivector_extractor_info.Init(ivector_extraction_opts);
-
-    nnet3_rxfilename_ = model_path_str_ + "/final.mdl";
-    hclg_fst_rxfilename_ = model_path_str_ + "/HCLG.fst";
-    hcl_fst_rxfilename_ = model_path_str_ + "/HCLr.fst";
-    g_fst_rxfilename_ = model_path_str_ + "/Gr.fst";
-    disambig_rxfilename_ = model_path_str_ + "/disambig_tid.int";
-    word_syms_rxfilename_ = model_path_str_ + "/words.txt";
-    winfo_rxfilename_ = model_path_str_ + "/word_boundary.int";
-    carpa_rxfilename_ = model_path_str_ + "/rescore/G.carpa";
-    std_fst_rxfilename_ = model_path_str_ + "/rescore/G.fst";
-}
-
-void Model::ConfigureV2()
-{
-    kaldi::ParseOptions po("something");
-    nnet3_decoding_config_.Register(&po);
-    endpoint_config_.Register(&po);
-    decodable_opts_.Register(&po);
-    po.ReadConfigFile(model_path_str_ + "/conf/model.conf");
-
-    KALDI_LOG << "Decoding params beam=" << nnet3_decoding_config_.beam <<
-         " max-active=" << nnet3_decoding_config_.max_active <<
-         " lattice-beam=" << nnet3_decoding_config_.lattice_beam;
-    KALDI_LOG << "Silence phones " << endpoint_config_.silence_phones;
-
-    feature_info_.feature_type = "mfcc";
-    ReadConfigFromFile(model_path_str_ + "/conf/mfcc.conf", &feature_info_.mfcc_opts);
-    feature_info_.mfcc_opts.frame_opts.allow_downsample = true; // It is safe to downsample
-
-    feature_info_.silence_weighting_config.silence_weight = 1e-3;
-    feature_info_.silence_weighting_config.silence_phones_str = endpoint_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";
-    feature_info_.use_ivectors = true;
-    feature_info_.ivector_extractor_info.Init(ivector_extraction_opts);
-
-    nnet3_rxfilename_ = model_path_str_ + "/am/final.mdl";
-    hclg_fst_rxfilename_ = model_path_str_ + "/graph/HCLG.fst";
-    hcl_fst_rxfilename_ = model_path_str_ + "/graph/HCLr.fst";
-    g_fst_rxfilename_ = model_path_str_ + "/graph/Gr.fst";
-    disambig_rxfilename_ = model_path_str_ + "/graph/disambig_tid.int";
-    word_syms_rxfilename_ = model_path_str_ + "/graph/words.txt";
-    winfo_rxfilename_ = model_path_str_ + "/graph/phones/word_boundary.int";
-    carpa_rxfilename_ = model_path_str_ + "/rescore/G.carpa";
-    std_fst_rxfilename_ = model_path_str_ + "/rescore/G.fst";
-}
-
-void Model::ReadDataFiles()
-{
-    struct stat buffer;
-
-    trans_model_ = new kaldi::TransitionModel();
-    nnet_ = new kaldi::nnet3::AmNnetSimple();
-    {
-        bool binary;
-        kaldi::Input ki(nnet3_rxfilename_, &binary);
-        trans_model_->Read(ki.Stream(), binary);
-        nnet_->Read(ki.Stream(), binary);
-        SetBatchnormTestMode(true, &(nnet_->GetNnet()));
-        SetDropoutTestMode(true, &(nnet_->GetNnet()));
-        nnet3::CollapseModel(nnet3::CollapseModelConfig(), &(nnet_->GetNnet()));
-    }
-    decodable_info_ = new nnet3::DecodableNnetSimpleLoopedInfo(decodable_opts_,
-                                                               nnet_);
-
-    if (stat(hclg_fst_rxfilename_.c_str(), &buffer) == 0) {
-        KALDI_LOG << "Loading HCLG from " << hclg_fst_rxfilename_;
-        hclg_fst_ = fst::ReadFstKaldiGeneric(hclg_fst_rxfilename_);
-        hcl_fst_ = NULL;
-        g_fst_ = NULL;
-    } else {
-        KALDI_LOG << "Loading HCL and G from " << hcl_fst_rxfilename_ << " " << g_fst_rxfilename_;
-        hclg_fst_ = NULL;
-        hcl_fst_ = fst::StdFst::Read(hcl_fst_rxfilename_);
-        g_fst_ = fst::StdFst::Read(g_fst_rxfilename_);
-        ReadIntegerVectorSimple(disambig_rxfilename_, &disambig_);
-    }
-
-    word_syms_ = NULL;
-    if (hclg_fst_ && hclg_fst_->OutputSymbols()) {
-        word_syms_ = hclg_fst_->OutputSymbols();
-    } else if (g_fst_ && g_fst_->OutputSymbols()) {
-        word_syms_ = g_fst_->OutputSymbols();
-    }
-    if (!word_syms_) {
-        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_);
-    } else {
-        winfo_ = NULL;
-    }
-
-    if (stat(carpa_rxfilename_.c_str(), &buffer) == 0) {
-        KALDI_LOG << "Loading CARPA model from " << carpa_rxfilename_;
-        std_lm_fst_ = fst::ReadFstKaldi(std_fst_rxfilename_);
-        fst::Project(std_lm_fst_, fst::PROJECT_OUTPUT);
-        if (std_lm_fst_->Properties(fst::kILabelSorted, true) == 0) {
-            fst::ILabelCompare<fst::StdArc> ilabel_comp;
-            fst::ArcSort(std_lm_fst_, ilabel_comp);
-        }
-        ReadKaldiObject(carpa_rxfilename_, &const_arpa_);
-    } else {
-        std_lm_fst_ = NULL;
-    }
-}
-
-void Model::Ref() 
-{
-    ref_cnt_++;
-}
-
-void Model::Unref() 
-{
-    ref_cnt_--;
-    if (ref_cnt_ == 0) {
-        delete this;
-    }
-}
-
-Model::~Model() {
-    delete decodable_info_;
-    delete trans_model_;
-    delete nnet_;
-    delete winfo_;
-    delete hclg_fst_;
-    delete hcl_fst_;
-    delete g_fst_;
-}
@@ -1,87 +0,0 @@
-// 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 MODEL_H_
-#define MODEL_H_
-
-#include "base/kaldi-common.h"
-#include "fstext/fstext-lib.h"
-#include "fstext/fstext-utils.h"
-#include "online2/onlinebin-util.h"
-#include "online2/online-timing.h"
-#include "online2/online-endpoint.h"
-#include "online2/online-nnet3-decoding.h"
-#include "online2/online-feature-pipeline.h"
-#include "lat/lattice-functions.h"
-#include "lat/sausages.h"
-#include "lat/word-align-lattice.h"
-#include "lm/const-arpa-lm.h"
-#include "util/parse-options.h"
-#include "nnet3/nnet-utils.h"
-#include "rnnlm/rnnlm-utils.h"
-
-using namespace kaldi;
-using namespace std;
-
-class KaldiRecognizer;
-
-class Model {
-
-public:
-    Model(const char *model_path);
-    void Ref();
-    void Unref();
-
-protected:
-    ~Model();
-    void ConfigureV1();
-    void ConfigureV2();
-    void ReadDataFiles();
-
-    friend class KaldiRecognizer;
-
-    string model_path_str_;
-    string nnet3_rxfilename_;
-    string hclg_fst_rxfilename_;
-    string hcl_fst_rxfilename_;
-    string g_fst_rxfilename_;
-    string disambig_rxfilename_;
-    string word_syms_rxfilename_;
-    string winfo_rxfilename_;
-    string carpa_rxfilename_;
-    string std_fst_rxfilename_;
-
-    kaldi::OnlineEndpointConfig endpoint_config_;
-    kaldi::LatticeFasterDecoderConfig nnet3_decoding_config_;
-    kaldi::nnet3::NnetSimpleLoopedComputationOptions decodable_opts_;
-    kaldi::OnlineNnet2FeaturePipelineInfo feature_info_;
-
-    kaldi::nnet3::DecodableNnetSimpleLoopedInfo *decodable_info_;
-    kaldi::TransitionModel *trans_model_;
-    kaldi::nnet3::AmNnetSimple *nnet_;
-    const fst::SymbolTable *word_syms_;
-    kaldi::WordBoundaryInfo *winfo_;
-    vector<int32> disambig_;
-
-    fst::Fst<fst::StdArc> *hclg_fst_;
-    fst::Fst<fst::StdArc> *hcl_fst_;
-    fst::Fst<fst::StdArc> *g_fst_;
-
-    fst::VectorFst<fst::StdArc> *std_lm_fst_;
-    kaldi::ConstArpaLm const_arpa_;
-
-    int ref_cnt_;
-};
-
-#endif /* MODEL_H_ */
@@ -0,0 +1,42 @@
+// sherpa-onnx/csrc/offline-lm-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "offline-lm-config.h"
+
+#include <string>
+
+#include "file-utils.h"
+#include "macros.h"
+
+namespace sherpa_onnx {
+
+void OfflineLMConfig::Register(ParseOptions *po) {
+  po->Register("lm", &model, "Path to LM model.");
+  po->Register("lm-scale", &scale, "LM scale.");
+  po->Register("lm-num-threads", &lm_num_threads,
+              "Number of threads to run the neural network of LM model");
+  po->Register("lm-provider", &lm_provider,
+               "Specify a provider to LM model use: cpu, cuda, coreml");
+}
+
+bool OfflineLMConfig::Validate() const {
+  if (!FileExists(model)) {
+    SHERPA_ONNX_LOGE("%s does not exist", model.c_str());
+    return false;
+  }
+
+  return true;
+}
+
+std::string OfflineLMConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "OfflineLMConfig(";
+  os << "model=\"" << model << "\", ";
+  os << "scale=" << scale << ")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx
--- a/Show More
+++ b/Show More
				`@@ -1,2 +0,0 @@`
				`from .vosk import KaldiRecognizer, Model, SpkModel, SetLogLevel`