diff --git a/StreamingASR/CMakeLists.txt b/StreamingASR/CMakeLists.txt
deleted file mode 100644
index bfc11b57..00000000
--- a/StreamingASR/CMakeLists.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-cmake_minimum_required(VERSION 3.4.1)
-
-project(StreamingASR)
-
-add_subdirectory(external/eigen)
-add_subdirectory(StreamingASR/app/src/main/cpp)
diff --git a/StreamingASR/README.md b/StreamingASR/README.md
index 33ac5d39..4958338c 100644
--- a/StreamingASR/README.md
+++ b/StreamingASR/README.md
@@ -6,9 +6,9 @@ In the Speech Recognition Android [demo app](https://github.com/pytorch/android-
 
 ## Prerequisites
 
-* PyTorch 1.11 and torchaudio 0.11 or above (Optional)
+* PyTorch 1.12 and torchaudio 0.12 or above (Optional)
 * Python 3.8 (Optional)
-* Android Pytorch library org.pytorch:pytorch_android_lite:1.11.0
+* Android Pytorch library org.pytorch:pytorch_android_lite:1.12.2
 * Android Studio 4.0.1 or later
 
 ## Quick Start
@@ -22,39 +22,32 @@ git clone https://github.com/pytorch/android-demo-app
 cd android-demo-app/StreamingASR
 ```
 
-If you don't have PyTorch 1.11 and torchaudio 0.11 installed or want to have a quick try of the demo app, you can download the optimized scripted model file [streaming_asr.ptl](https://drive.google.com/file/d/1awT_1S6H5IXSOOqpFLmpeg0B-kQVWG2y/view?usp=sharing), then drag and drop it to the `StreamingASR/app/src/main/assets` folder inside `android-demo-app/StreamingASR`, and continue to Step 3.
-
-Also you need to download [Eigen](https://eigen.tuxfamily.org/), a C++ template library for linear algebra, for Android NDK build required to run the app (see last section of this README for more info):
-```
-mkdir external; cd external
-git clone https://github.com/jeffxtang/eigen
-```
+If you don't have PyTorch 1.12 and torchaudio 0.12 installed or want to have a quick try of the demo app, you can download the optimized scripted model file [streaming_asrv2.ptl](https://drive.google.com/file/d/1XRCAFpMqOSz5e7VP0mhiACMGCCcYfpk-/view?usp=sharing), then drag and drop it to the `StreamingASR/app/src/main/assets` folder inside `android-demo-app/StreamingASR`, and continue to Step 3.
 
 ### 2. Test and Prepare the Model
 
-To install PyTorch 1.11, torchaudio 0.11, and other required Python packages (numpy and pyaudio), do something like this:
+To install PyTorch 1.12, torchaudio 0.12, and other required packages (numpy, pyaudio, and fairseq), do something like this:
 
 ```
-conda create -n pt1.11 python=3.8.5
-conda activate pt1.11
-pip install torch torchaudio numpy pyaudio
+conda create -n pt1.12 python=3.8.5
+conda activate pt1.12
+pip install torch torchaudio numpy pyaudio fairseq
 ```
 
-Now download the streaming ASR model file
-[scripted_wrapper_tuple_no_transform.pt](https://drive.google.com/file/d/1_49DwHS_a3p3THGdHZj3TXmjNJj60AhP/view?usp=sharing) to the `android-demo-app/StreamingASR` directory.
+First, create the model file `scripted_wrapper_tuple.pt` by running `python generate_ts.py`.
 
-To test the model, run `python run_sasr.py`. After you see:
+Then, to test the model, run `python run_sasr.py`. After you see:
 ```
 Initializing model...
 Initialization complete.
 ```
-say something like "good afternoon happy new year", and you'll likely see the streaming recognition results `▁good ▁afternoon ▁happy ▁new ▁year` while you speak. Hit Ctrl-C to end.
+say something like "good afternoon happy new year", and you'll likely see the streaming recognition results `good afternoon happy new year` while you speak. Hit Ctrl-C to end.
 
-To optimize and convert the model to the format that can run on Android, run the following commands:
+Finally, to optimize and convert the model to the format that can run on Android, run the following commands:
 ```
 mkdir -p StreamingASR/app/src/main/assets
 python save_model_for_mobile.py
-mv streaming_asr.ptl StreamingASR/app/src/main/assets
+mv streaming_asrv2.ptl StreamingASR/app/src/main/assets
 ```
 
 ### 3. Build and run with Android Studio
@@ -67,10 +60,6 @@ Start Android Studio, open the project located in `android-demo-app/StreamingASR
 
 ## Librosa C++, Eigen, and JNI
 
-Note that this demo uses a [C++ port](https://github.com/ewan-xu/LibrosaCpp/) of [Librosa](https://librosa.org), a popular audio processing library in Python, to perform the MelSpectrogram transform. In the Python script `run_sasr.py` above, the torchaudio's [MelSpectrogram](https://pytorch.org/audio/stable/transforms.html#melspectrogram) is used, but you can achieve the same transform result by replacing `spectrogram = transform(tensor).transpose(1, 0)`, line 46 of run_sasr.py with:
-```
-mel = librosa.feature.melspectrogram(np_array, sr=16000, n_fft=400, n_mels=80, hop_length=160)
-spectrogram = torch.tensor(mel).transpose(1, 0)
-```
+The first version of this demo uses a [C++ port](https://github.com/ewan-xu/LibrosaCpp/) of [Librosa](https://librosa.org), a popular audio processing library in Python, to perform the MelSpectrogram transform, because torchaudio before version 0.11 doesn't support fft on Android (see [here](https://github.com/pytorch/audio/issues/408)). Using the Librosa C++ port and [JNI](https://developer.android.com/training/articles/perf-jni) (Java Native Interface) on Android makes the MelSpectrogram possible on Android. Furthermore, the Librosa C++ port requires [Eigen](https://eigen.tuxfamily.org/), a C++ template library for linear algebra, so both the port and the Eigen library are included in the first version of the demo app and built as JNI.
 
-Because torchaudio currently doesn't support fft on Android (see [here](https://github.com/pytorch/audio/issues/408)), using the Librosa C++ port and [JNI](https://developer.android.com/training/articles/perf-jni) (Java Native Interface) on Android makes the MelSpectrogram possible on Android. Furthermore, the Librosa C++ port requires [Eigen](https://eigen.tuxfamily.org/), a C++ template library for linear algebra, so both the port and the Eigen library are included in the demo app and built as JNI, using the `CMakeLists.txt` and `MainActivityJNI.cpp` in `StreamingASR/app/src/main/cpp`.
+See [here](https://github.com/jeffxtang/android-demo-app/tree/librosa_jni/StreamingASR) for the first version of the demo if interested in an example of using native C++ to expand operations not yet supported in PyTorch or one of its domain libraries.
diff --git a/StreamingASR/StreamingASR/app/build.gradle b/StreamingASR/StreamingASR/app/build.gradle
index 288702a7..1bda3c67 100644
--- a/StreamingASR/StreamingASR/app/build.gradle
+++ b/StreamingASR/StreamingASR/app/build.gradle
@@ -13,13 +13,6 @@ android {
         versionName "1.0"
 
         testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
-
-        externalNativeBuild {
-            cmake {
-                cppFlags ""
-                arguments "-DLOGGER_BUILD_HEADER_LIB=ON", "-DBUILD_TESTING=OFF"
-            }
-        }
     }
 
     buildTypes {
@@ -32,13 +25,6 @@ android {
         sourceCompatibility JavaVersion.VERSION_1_8
         targetCompatibility JavaVersion.VERSION_1_8
     }
-
-    externalNativeBuild {
-        cmake {
-            path "../../CMakeLists.txt"
-            version "3.10.2"
-        }
-    }
 }
 
 dependencies {
@@ -50,5 +36,5 @@ dependencies {
     androidTestImplementation 'androidx.test.ext:junit:1.1.3'
     androidTestImplementation 'androidx.test.espresso:espresso-core:3.4.0'
 
-    implementation 'org.pytorch:pytorch_android_lite:1.11'
+    implementation 'org.pytorch:pytorch_android_lite:1.12.2'
 }
\ No newline at end of file
diff --git a/StreamingASR/StreamingASR/app/src/main/cpp/CMakeLists.txt b/StreamingASR/StreamingASR/app/src/main/cpp/CMakeLists.txt
deleted file mode 100644
index d52e785d..00000000
--- a/StreamingASR/StreamingASR/app/src/main/cpp/CMakeLists.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-cmake_minimum_required(VERSION 3.4.1)
-project(CTest LANGUAGES C CXX)
-
-add_library( MainActivityJNI SHARED MainActivityJNI.cpp )
-target_link_libraries( MainActivityJNI Eigen3::Eigen)
diff --git a/StreamingASR/StreamingASR/app/src/main/cpp/MainActivityJNI.cpp b/StreamingASR/StreamingASR/app/src/main/cpp/MainActivityJNI.cpp
deleted file mode 100644
index ff5086d4..00000000
--- a/StreamingASR/StreamingASR/app/src/main/cpp/MainActivityJNI.cpp
+++ /dev/null
@@ -1,92 +0,0 @@
-#include <jni.h>
-#include <string>
-#include <Eigen/Dense>
-#include "librosa/librosa.h"
-#include <iostream>
-#include <vector>
-#include <chrono>
-#include <numeric>
-#include <algorithm>
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <stdint.h>
-
-using namespace std;
-
-using Eigen::MatrixXd;
-
-extern "C" JNIEXPORT jobject JNICALL
-Java_org_pytorch_demo_streamingasr_MainActivity_melSpectrogram(JNIEnv* env, jobject obj,
-                                                                    jdoubleArray data) {
-    int len = env -> GetArrayLength(data);
-    std::vector<float> x;
-    jdouble *element = env->GetDoubleArrayElements(data, 0);
-    for(int i=0; i<len; ++i) {
-        x.push_back((float)element[i]);
-    }
-
-    int n_fft = 400;
-    int n_hop = 160;
-    int n_mel = 80;
-    int fmin = 0;
-    int fmax = 8000;
-    int sr = 16000;
-
-    std::vector<std::vector<std::complex<float>>> X = librosa::Feature::stft(x, n_fft, n_hop, "hann", true, "reflect");
-
-    std::vector<std::vector<float>> mels = librosa::Feature::melspectrogram(x, sr, n_fft, n_hop, "hann", true, "reflect", 2.f, n_mel, fmin, fmax);
-
-    jclass vectorClass = env->FindClass("java/util/Vector");
-    if(vectorClass == NULL) {
-        return NULL;
-    }
-
-    jclass floatClass = env->FindClass("java/lang/Float");
-    if(floatClass == NULL) {
-        return NULL;
-    }
-
-    jmethodID vectorConstructorID = env->GetMethodID(
-            vectorClass, "<init>", "()V");
-    if(vectorConstructorID == NULL) {
-        return NULL;
-    }
-
-    jmethodID addMethodID = env->GetMethodID(
-            vectorClass, "add", "(Ljava/lang/Object;)Z" );
-    if(addMethodID == NULL) {
-        return NULL;
-    }
-
-    jmethodID floatConstructorID = env->GetMethodID(floatClass, "<init>", "(F)V");
-    if(floatConstructorID == NULL) {
-        return NULL;
-    }
-
-    jobject outerVector = env->NewObject(vectorClass, vectorConstructorID);
-    if(outerVector == NULL) {
-        return NULL;
-    }
-
-    for(vector<float> i : mels) {
-        jobject innerVector = env->NewObject(vectorClass, vectorConstructorID);
-
-        for(float f : i) {
-            jobject floatValue = env->NewObject(floatClass, floatConstructorID, f);
-            if(floatValue == NULL) {
-                return NULL;
-            }
-
-            env->CallBooleanMethod(innerVector, addMethodID, floatValue);
-        }
-
-        env->CallBooleanMethod(outerVector, addMethodID, innerVector);
-    }
-
-    env->DeleteLocalRef(vectorClass);
-    env->DeleteLocalRef(floatClass);
-
-    return outerVector;
-}
diff --git a/StreamingASR/StreamingASR/app/src/main/cpp/librosa/librosa.h b/StreamingASR/StreamingASR/app/src/main/cpp/librosa/librosa.h
deleted file mode 100644
index 30a2f9e7..00000000
--- a/StreamingASR/StreamingASR/app/src/main/cpp/librosa/librosa.h
+++ /dev/null
@@ -1,265 +0,0 @@
-/* ------------------------------------------------------------------
-* Copyright (C) 2020 ewan xu<ewan_xu@outlook.com>
-*
-* 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 LIBROSA_H_
-#define LIBROSA_H_
-
-#include "Eigen/Core"
-#include "unsupported/Eigen/FFT"
-
-#include <vector>
-#include <complex>
-#include <iostream>
-
-///
-/// \brief c++ implemention of librosa
-///
-namespace librosa{
-
-#ifndef M_PI
-#define M_PI 3.14159265358979323846
-#endif // !M_PI
-
-typedef Eigen::Matrix<float, 1, Eigen::Dynamic, Eigen::RowMajor> Vectorf;
-typedef Eigen::Matrix<std::complex<float>, 1, Eigen::Dynamic, Eigen::RowMajor> Vectorcf;
-typedef Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> Matrixf;
-typedef Eigen::Matrix<std::complex<float>, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> Matrixcf;
-
-namespace internal{
-
-static Vectorf pad(Vectorf &x, int left, int right, const std::string &mode, float value){
-  Vectorf x_paded = Vectorf::Constant(left+x.size()+right, value);
-  x_paded.segment(left, x.size()) = x;
-
-  if (mode.compare("reflect") == 0){
-    for (int i = 0; i < left; ++i){
-      x_paded[i] = x[left-i];
-    }
-    for (int i = left; i < left+right; ++i){
-      x_paded[i+x.size()] = x[x.size()-2-i+left];
-    }
-  }
-
-  if (mode.compare("symmetric") == 0){
-    for (int i = 0; i < left; ++i){
-      x_paded[i] = x[left-i-1];
-    }
-    for (int i = left; i < left+right; ++i){
-      x_paded[i+x.size()] = x[x.size()-1-i+left];
-    }
-  }
-
-  if (mode.compare("edge") == 0){
-    for (int i = 0; i < left; ++i){
-      x_paded[i] = x[0];
-    }
-    for (int i = left; i < left+right; ++i){
-      x_paded[i+x.size()] = x[x.size()-1];
-    }
-  }
-  return x_paded;
-}
-
-static Matrixcf stft(Vectorf &x, int n_fft, int n_hop, const std::string &win, bool center, const std::string &mode){
-  // hanning
-  Vectorf window = 0.5*(1.f-(Vectorf::LinSpaced(n_fft, 0.f, static_cast<float>(n_fft-1))*2.f*M_PI/n_fft).array().cos());
-
-  int pad_len = center ? n_fft / 2 : 0;
-  Vectorf x_paded = pad(x, pad_len, pad_len, mode, 0.f);
-
-  int n_f = n_fft/2+1;
-  int n_frames = 1+(x_paded.size()-n_fft) / n_hop;
-  Matrixcf X(n_frames, n_fft);
-  Eigen::FFT<float> fft;
-
-  for (int i = 0; i < n_frames; ++i){
-    Vectorf x_frame = window.array()*x_paded.segment(i*n_hop, n_fft).array();
-    X.row(i) = fft.fwd(x_frame);
-  }
-  return X.leftCols(n_f);
-}
-
-static Matrixf spectrogram(Matrixcf &X, float power = 1.f){
-  return X.cwiseAbs().array().pow(power);
-}
-
-static Matrixf melfilter(int sr, int n_fft, int n_mels, int fmin, int fmax){
-  int n_f = n_fft/2+1;
-  Vectorf fft_freqs = (Vectorf::LinSpaced(n_f, 0.f, static_cast<float>(n_f-1))*sr)/n_fft;
-
-  float f_min = 0.f;
-  float f_sp = 200.f/3.f;
-  float min_log_hz = 1000.f;
-  float min_log_mel = (min_log_hz-f_min)/f_sp;
-  float logstep = logf(6.4f)/27.f;
-
-  auto hz_to_mel = [=](int hz, bool htk = false) -> float {
-    if (htk){
-      return 2595.0f*log10f(1.0f+hz/700.0f);
-    }
-    float mel = (hz-f_min)/f_sp;
-    if (hz >= min_log_hz){
-      mel = min_log_mel+logf(hz/min_log_hz)/logstep;
-    }
-    return mel;
-  };
-  auto mel_to_hz = [=](Vectorf &mels, bool htk = false) -> Vectorf {
-    if (htk){
-      return 700.0f*(Vectorf::Constant(n_mels+2, 10.f).array().pow(mels.array()/2595.0f)-1.0f);
-    }
-    return (mels.array()>min_log_mel).select(((mels.array()-min_log_mel)*logstep).exp()*min_log_hz, (mels*f_sp).array()+f_min);
-  };
-
-  float min_mel = hz_to_mel(fmin);
-  float max_mel = hz_to_mel(fmax);
-  Vectorf mels = Vectorf::LinSpaced(n_mels+2, min_mel, max_mel);
-  Vectorf mel_f = mel_to_hz(mels);
-  Vectorf fdiff = mel_f.segment(1, mel_f.size() - 1) - mel_f.segment(0, mel_f.size() - 1);
-  Matrixf ramps = mel_f.replicate(n_f, 1).transpose().array() - fft_freqs.replicate(n_mels + 2, 1).array();
-
-  Matrixf lower = -ramps.topRows(n_mels).array()/fdiff.segment(0, n_mels).transpose().replicate(1, n_f).array();
-  Matrixf upper = ramps.bottomRows(n_mels).array()/fdiff.segment(1, n_mels).transpose().replicate(1, n_f).array();
-  Matrixf weights = (lower.array()<upper.array()).select(lower, upper).cwiseMax(0);
-
-  auto enorm = (2.0/(mel_f.segment(2, n_mels)-mel_f.segment(0, n_mels)).array()).transpose().replicate(1, n_f);
-  weights = weights.array()*enorm;
-
-  return weights;
-}
-
-static Matrixf melspectrogram(Vectorf &x, int sr, int n_fft, int n_hop,
-                        const std::string &win, bool center,
-                        const std::string &mode, float power,
-                        int n_mels, int fmin, int fmax){
-  Matrixcf X = stft(x, n_fft, n_hop, win, center, mode);
-  Matrixf mel_basis = melfilter(sr, n_fft, n_mels, fmin, fmax);
-  Matrixf sp = spectrogram(X, power);
-  Matrixf mel = mel_basis*sp.transpose();
-  return mel;
-}
-
-static Matrixf power2db(Matrixf& x) {
-  auto log_sp = 10.0f*x.array().max(1e-10).log10();
-  return log_sp.cwiseMax(log_sp.maxCoeff() - 80.0f);
-}
-
-static Matrixf dct(Matrixf& x, bool norm, int type) {
-  int N = x.cols();
-  Matrixf xi = Matrixf::Zero(N, N);
-  xi.rowwise() += Vectorf::LinSpaced(N, 0.f, static_cast<float>(N-1));
-  // type 2
-  Matrixf coeff = 2*(M_PI*xi.transpose().array()/N*(xi.array()+0.5)).cos();
-  Matrixf dct = x*coeff.transpose();
-  // ortho
-  if (norm) {
-    Vectorf ortho = Vectorf::Constant(N, std::sqrtf(0.5f/N));
-    ortho[0] = std::sqrtf(0.25f/N);
-    dct = dct*ortho.asDiagonal();
-  }
-  return dct;
-}
-
-} // namespace internal
-
-class Feature
-{
-public:
-  /// \brief      short-time fourier transform similar with librosa.feature.stft
-  /// \param      x             input audio signal
-  /// \param      n_fft         length of the FFT size
-  /// \param      n_hop         number of samples between successive frames
-  /// \param      win           window function. currently only supports 'hann'
-  /// \param      center        same as librosa
-  /// \param      mode          pad mode. support "reflect","symmetric","edge"
-  /// \return     complex-valued matrix of short-time fourier transform coefficients.
-  static std::vector<std::vector<std::complex<float>>> stft(std::vector<float> &x,
-                                                            int n_fft, int n_hop,
-                                                            const std::string &win, bool center,
-                                                            const std::string &mode){
-    Vectorf map_x = Eigen::Map<Vectorf>(x.data(), x.size());
-    Matrixcf X = internal::stft(map_x, n_fft, n_hop, win, center, mode);
-    std::vector<std::vector<std::complex<float>>> X_vector(X.rows(), std::vector<std::complex<float>>(X.cols(), 0));
-    for (int i = 0; i < X.rows(); ++i){
-      auto &row = X_vector[i];
-      Eigen::Map<Vectorcf>(row.data(), row.size()) = X.row(i);
-    }
-    return X_vector;
-  }
-
-  /// \brief      compute mel spectrogram similar with librosa.feature.melspectrogram
-  /// \param      x             input audio signal
-  /// \param      sr            sample rate of 'x'
-  /// \param      n_fft         length of the FFT size
-  /// \param      n_hop         number of samples between successive frames
-  /// \param      win           window function. currently only supports 'hann'
-  /// \param      center        same as librosa
-  /// \param      mode          pad mode. support "reflect","symmetric","edge"
-  /// \param      power         exponent for the magnitude melspectrogram
-  /// \param      n_mels        number of mel bands
-  /// \param      f_min         lowest frequency (in Hz)
-  /// \param      f_max         highest frequency (in Hz)
-  /// \return     mel spectrogram matrix
-  static std::vector<std::vector<float>> melspectrogram(std::vector<float> &x, int sr, 
-                                                        int n_fft, int n_hop, const std::string &win, bool center, const std::string &mode,
-                                                        float power, int n_mels, int fmin, int fmax){
-    Vectorf map_x = Eigen::Map<Vectorf>(x.data(), x.size());
-    Matrixf mel = internal::melspectrogram(map_x, sr, n_fft, n_hop, win, center, mode, power, n_mels, fmin, fmax).transpose();
-    std::vector<std::vector<float>> mel_vector(mel.rows(), std::vector<float>(mel.cols(), 0.f));
-    for (int i = 0; i < mel.rows(); ++i){
-      auto &row = mel_vector[i];
-      Eigen::Map<Vectorf>(row.data(), row.size()) = mel.row(i);
-    }
-    return mel_vector;
-  }
-
-  /// \brief      compute mfcc similar with librosa.feature.mfcc
-  /// \param      x             input audio signal
-  /// \param      sr            sample rate of 'x'
-  /// \param      n_fft         length of the FFT size
-  /// \param      n_hop         number of samples between successive frames
-  /// \param      win           window function. currently only supports 'hann'
-  /// \param      center        same as librosa
-  /// \param      mode          pad mode. support "reflect","symmetric","edge"
-  /// \param      power         exponent for the magnitude melspectrogram
-  /// \param      n_mels        number of mel bands
-  /// \param      f_min         lowest frequency (in Hz)
-  /// \param      f_max         highest frequency (in Hz)
-  /// \param      n_mfcc        number of mfccs
-  /// \param      norm          ortho-normal dct basis
-  /// \param      type          dct type. currently only supports 'type-II'
-  /// \return     mfcc matrix
-  static std::vector<std::vector<float>> mfcc(std::vector<float> &x, int sr,
-                                              int n_fft, int n_hop, const std::string &win, bool center, const std::string &mode,
-                                              float power, int n_mels, int fmin, int fmax,
-                                              int n_mfcc, bool norm, int type) {
-    Vectorf map_x = Eigen::Map<Vectorf>(x.data(), x.size());
-    Matrixf mel = internal::melspectrogram(map_x, sr, n_fft, n_hop, win, center, mode, power, n_mels, fmin, fmax).transpose();
-    Matrixf mel_db = internal::power2db(mel);
-    Matrixf dct = internal::dct(mel_db, norm, type).leftCols(n_mfcc);
-    std::vector<std::vector<float>> mfcc_vector(dct.rows(), std::vector<float>(dct.cols(), 0.f));
-    for (int i = 0; i < dct.rows(); ++i) {
-      auto &row = mfcc_vector[i];
-      Eigen::Map<Vectorf>(row.data(), row.size()) = dct.row(i);
-    }
-    return mfcc_vector;
-  }
-};
-
-} // namespace librosa
-
-#endif
diff --git a/StreamingASR/StreamingASR/app/src/main/java/org/pytorch/demo/streamingasr/MainActivity.java b/StreamingASR/StreamingASR/app/src/main/java/org/pytorch/demo/streamingasr/MainActivity.java
index 12abe2ec..59999930 100644
--- a/StreamingASR/StreamingASR/app/src/main/java/org/pytorch/demo/streamingasr/MainActivity.java
+++ b/StreamingASR/StreamingASR/app/src/main/java/org/pytorch/demo/streamingasr/MainActivity.java
@@ -24,7 +24,6 @@
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.nio.FloatBuffer;
-import java.util.Vector;
 
 import org.pytorch.LiteModuleLoader;
 
@@ -41,187 +40,11 @@ public class MainActivity extends AppCompatActivity implements Runnable {
     private final static int SAMPLE_RATE = 16000;
     private final static int CHUNK_TO_READ = 5;
     private final static int CHUNK_SIZE = 640;
-    private final static int SPECTROGRAM_X = 21;
-    private final static int SPECTROGRAM_Y = 80;
+    private final static int INPUT_SIZE = 3200;
 
     private IValue hypo = null;
     private IValue state = null;
 
-    private static final double[] MEAN =  {
-            16.462461471557617,
-            17.020158767700195,
-            17.27733039855957,
-            17.273637771606445,
-            17.78028678894043,
-            18.112783432006836,
-            18.322141647338867,
-            18.3536319732666,
-            18.220436096191406,
-            17.93610191345215,
-            17.650646209716797,
-            17.505868911743164,
-            17.450956344604492,
-            17.420780181884766,
-            17.36254119873047,
-            17.24843978881836,
-            17.073762893676758,
-            16.893953323364258,
-            16.62371826171875,
-            16.279895782470703,
-            16.046218872070312,
-            15.789617538452148,
-            15.458984375,
-            15.335075378417969,
-            15.103074073791504,
-            14.993032455444336,
-            14.818647384643555,
-            14.713132858276367,
-            14.576343536376953,
-            14.482580184936523,
-            14.431093215942383,
-            14.392385482788086,
-            14.357626914978027,
-            14.335031509399414,
-            14.344644546508789,
-            14.341029167175293,
-            14.338135719299316,
-            14.311485290527344,
-            14.266831398010254,
-            14.205205917358398,
-            14.159194946289062,
-            14.07589054107666,
-            14.02244758605957,
-            13.954248428344727,
-            13.897454261779785,
-            13.856722831726074,
-            13.80321216583252,
-            13.75955867767334,
-            13.718783378601074,
-            13.67695426940918,
-            13.626880645751953,
-            13.554975509643555,
-            13.465453147888184,
-            13.372663497924805,
-            13.269320487976074,
-            13.184920310974121,
-            13.094778060913086,
-            12.998514175415039,
-            12.891039848327637,
-            12.765382766723633,
-            12.638651847839355,
-            12.50733470916748,
-            12.345802307128906,
-            12.195826530456543,
-            12.019110679626465,
-            11.842704772949219,
-            11.680868148803711,
-            11.518675804138184,
-            11.37252426147461,
-            11.252099990844727,
-            11.12936019897461,
-            11.029287338256836,
-            10.927411079406738,
-            10.825841903686523,
-            10.717211723327637,
-            10.499553680419922,
-            9.722028732299805,
-            8.256664276123047,
-            7.897761344909668,
-            7.252806663513184
-    };
-
-    private static final double[] INVSTDDEV = {
-            0.2532021571066031,
-            0.2597563367511928,
-            0.2579079373215276,
-            0.2416085222005694,
-            0.23003407153886749,
-            0.21714598348479108,
-            0.20868966256973892,
-            0.20397882792073063,
-            0.20346486748979434,
-            0.20568288111895272,
-            0.20795624145573485,
-            0.20848980415063503,
-            0.20735096423640872,
-            0.2060772210458722,
-            0.20577174595523076,
-            0.20655349986725383,
-            0.2080547906859301,
-            0.21015748217276387,
-            0.2127639989370032,
-            0.2156462785763535,
-            0.21848300746868443,
-            0.22174608140608748,
-            0.22541974458780933,
-            0.22897465119671973,
-            0.23207484606149037,
-            0.2353556049061462,
-            0.23820711835547867,
-            0.24016651485087528,
-            0.24200318561465783,
-            0.2435905301766702,
-            0.24527147180928432,
-            0.2493368450351618,
-            0.25120444993308483,
-            0.2521961451825939,
-            0.25358032484699955,
-            0.25349767201088286,
-            0.2534676894845623,
-            0.25149125467665234,
-            0.25001929593946776,
-            0.25064096375066197,
-            0.25194505955280033,
-            0.25270402089338095,
-            0.2535205901701615,
-            0.25363568106276674,
-            0.2535307075541985,
-            0.25315144026701186,
-            0.2523683857532224,
-            0.25200854739575596,
-            0.2516561583169735,
-            0.25147053419035553,
-            0.25187638352086095,
-            0.25176343344798546,
-            0.25256615785525305,
-            0.25310796555079107,
-            0.2535568871416053,
-            0.2542411936874833,
-            0.2544978632482573,
-            0.2553210332506536,
-            0.2567248511819892,
-            0.2559665595456875,
-            0.2564729970835735,
-            0.2585267417223537,
-            0.2573770145474615,
-            0.2585495460828127,
-            0.2593605768768532,
-            0.25906572100606984,
-            0.26026752519153573,
-            0.2609952847918467,
-            0.26222905157170767,
-            0.26395874733435604,
-            0.26404203898769246,
-            0.26501581381370537,
-            0.2666259054856709,
-            0.2676190865432322,
-            0.26813030555166134,
-            0.26873271506658997,
-            0.2624062353014993,
-            0.2289515918968408,
-            0.22755587298227964,
-            0.24719513536827162
-    };
-
-    private static final double DECIBEL = 2 * 20 * Math.log10(32767);
-    private static final double GAIN = Math.pow(10, 0.05 * DECIBEL);
-
-    public native Vector<Vector<Float>> melSpectrogram(double[] data);
-
-    static {
-        System.loadLibrary("MainActivityJNI");
-    }
-
     @Override
     protected void onCreate(Bundle savedInstanceState) {
         super.onCreate(savedInstanceState);
@@ -231,7 +54,7 @@ protected void onCreate(Bundle savedInstanceState) {
         mTextView = findViewById(R.id.tvResult);
 
         if (mModuleEncoder == null) {
-            mModuleEncoder = LiteModuleLoader.load(assetFilePath(getApplicationContext(), "streaming_asr.ptl"));
+            mModuleEncoder = LiteModuleLoader.load(assetFilePath(getApplicationContext(), "streaming_asrv2.ptl"));
         }
 
         mButton.setOnClickListener(new View.OnClickListener() {
@@ -341,36 +164,12 @@ public void run() {
     }
 
     private String recognize(double[] inputBuffer) {
-
-        double[][] spectrogram = new double[SPECTROGRAM_X][SPECTROGRAM_Y];
-        Vector<Vector< Float >> result = melSpectrogram(inputBuffer);
-        for (int i = 0; i < result.size(); i++) {
-            for (int j = 0; j < result.get(i).size(); j++) {
-                spectrogram[i][j] = result.get(i).get(j);
-            }
-        }
-
-        for (int i = 0; i < spectrogram.length; i++) {
-            for (int j = 0; j < spectrogram[i].length; j++) {
-                spectrogram[i][j] *= GAIN;
-                if (spectrogram[i][j] > Math.E)
-                    spectrogram[i][j] = Math.log(spectrogram[i][j]);
-                else
-                    spectrogram[i][j] /= Math.E;
-            }
-        }
-
-        FloatBuffer inTensorBuffer = Tensor.allocateFloatBuffer((SPECTROGRAM_X-1) * SPECTROGRAM_Y);
-        // get rid of the last row and transform the others
-        for (int i = 0; i < spectrogram.length - 1; i++) {
-            for (int j = 0; j < spectrogram[i].length; j++) {
-                spectrogram[i][j] -= MEAN[j];
-                spectrogram[i][j] *= INVSTDDEV[j];
-                inTensorBuffer.put((float) spectrogram[i][j]);
-            }
+        FloatBuffer inTensorBuffer = Tensor.allocateFloatBuffer(INPUT_SIZE);
+        for (int i = 0; i < inputBuffer.length - 1; i++) {
+            inTensorBuffer.put((float) inputBuffer[i]);
         }
 
-        final Tensor inTensor = Tensor.fromBlob(inTensorBuffer, new long[]{1, SPECTROGRAM_X - 1, SPECTROGRAM_Y});
+        final Tensor inTensor = Tensor.fromBlob(inTensorBuffer, new long[]{INPUT_SIZE});
         final long startTime = SystemClock.elapsedRealtime();
         IValue[] outputTuple;
         if (hypo == null && state == null)
diff --git a/StreamingASR/generate_ts.py b/StreamingASR/generate_ts.py
new file mode 100644
index 00000000..e6c19eae
--- /dev/null
+++ b/StreamingASR/generate_ts.py
@@ -0,0 +1,100 @@
+from typing import Dict, List, Optional, Tuple
+import json
+import math
+
+from fairseq.data import Dictionary
+import torch
+import torchaudio
+from torchaudio.pipelines import EMFORMER_RNNT_BASE_LIBRISPEECH
+from torchaudio.models import Hypothesis
+
+
+def get_hypo_tokens(hypo: Hypothesis) -> List[int]:
+    return hypo[0]
+
+
+def get_hypo_score(hypo: Hypothesis) -> float:
+    return hypo[3]
+
+
+def to_string(input: List[int], tgt_dict: List[str], bos_idx: int = 0, eos_idx: int = 2, separator: str = "",) -> str:
+    # torchscript dislikes sets
+    extra_symbols_to_ignore: Dict[int, int] = {}
+    extra_symbols_to_ignore[eos_idx] = 1
+    extra_symbols_to_ignore[bos_idx] = 1
+
+    # it also dislikes comprehensions with conditionals
+    filtered_idx: List[int] = []
+    for idx in input:
+        if idx not in extra_symbols_to_ignore:
+            filtered_idx.append(idx)
+
+    return separator.join([tgt_dict[idx] for idx in filtered_idx]).replace("\u2581", " ")
+
+
+def post_process_hypos(
+    hypos: List[Hypothesis], tgt_dict: List[str],
+) -> List[Tuple[str, List[float], List[int]]]:
+    post_process_remove_list = [
+        3,  # unk
+        2,  # eos
+        1,  # pad
+    ]
+    hypos_str: List[str] = []
+    for h in hypos:
+        filtered_tokens: List[int] = []
+        for token_index in get_hypo_tokens(h)[1:]:
+            if token_index not in post_process_remove_list:
+                filtered_tokens.append(token_index)
+        string = to_string(filtered_tokens, tgt_dict)
+        hypos_str.append(string)
+
+    hypos_ids = [get_hypo_tokens(h)[1:] for h in hypos]
+    hypos_score = [[math.exp(get_hypo_score(h))] for h in hypos]
+
+    nbest_batch = list(zip(hypos_str, hypos_score, hypos_ids))
+
+    return nbest_batch
+
+
+def _piecewise_linear_log(x):
+    x[x > math.e] = torch.log(x[x > math.e])
+    x[x <= math.e] = x[x <= math.e] / math.e
+    return x
+
+
+class ModelWrapper(torch.nn.Module):
+    def __init__(self, tgt_dict: List[str]):
+        super().__init__()
+        self.transform = torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=400, n_mels=80, hop_length=160)
+
+        self.decoder = EMFORMER_RNNT_BASE_LIBRISPEECH.get_decoder()
+
+        self.tgt_dict = tgt_dict
+
+        with open("global_stats.json") as f:
+            blob = json.loads(f.read())
+
+        self.mean = torch.tensor(blob["mean"])
+        self.invstddev = torch.tensor(blob["invstddev"])
+
+        self.decibel = 2 * 20 * math.log10(32767)
+        self.gain = pow(10, 0.05 * self.decibel)
+
+    def forward(
+        self, input: torch.Tensor, prev_hypo: Optional[Hypothesis], prev_state: Optional[List[List[torch.Tensor]]]
+    ) -> Tuple[str, Hypothesis, Optional[List[List[torch.Tensor]]]]:
+        spectrogram = self.transform(input).transpose(1, 0)
+        features = _piecewise_linear_log(spectrogram * self.gain).unsqueeze(0)[:, :-1]
+        features = (features - self.mean) * self.invstddev
+        length = torch.tensor([features.shape[1]])
+
+        hypotheses, state = self.decoder.infer(features, length, 10, state=prev_state, hypothesis=prev_hypo)
+        transcript = post_process_hypos(hypotheses[:1], self.tgt_dict)[0][0]
+        return transcript, hypotheses[0], state
+
+
+tgt_dict = Dictionary.load("spm_bpe_4096_fairseq.dict")
+wrapper = ModelWrapper(tgt_dict.symbols)
+wrapper = torch.jit.script(wrapper)
+wrapper.save("scripted_wrapper_tuple.pt")
diff --git a/StreamingASR/run_sasr.py b/StreamingASR/run_sasr.py
index 68276a7e..06042049 100644
--- a/StreamingASR/run_sasr.py
+++ b/StreamingASR/run_sasr.py
@@ -1,5 +1,3 @@
-import math
-import json
 import pyaudio
 import queue
 import numpy as np
@@ -8,53 +6,40 @@
 
 
 def get_demo_wrapper():
-    wrapper = torch.jit.load("scripted_wrapper_tuple_no_transform.pt")
+    wrapper = torch.jit.load("scripted_wrapper_tuple.pt")
     return wrapper
 
+
 wrapper = get_demo_wrapper()
+
+
+################################################################
+
+
 data_queue = queue.Queue()
 
+
 def callback(in_data, frame_count, time_info, status):
     global data_queue
     data_queue.put(in_data)
     return in_data, pyaudio.paContinue
 
-def _piecewise_linear_log(x):
-    x[x > math.e] = torch.log(x[x > math.e])
-    x[x <= math.e] = x[x <= math.e] / math.e
-    return x
-
-transform = torchaudio.transforms.MelSpectrogram(
-    sample_rate=16000, n_fft=400, n_mels=80, hop_length=160,
-)
-
-with open("global_stats.json") as f:
-    blob = json.loads(f.read())
-
-_mean = torch.tensor(blob["mean"])
-_invstddev = torch.tensor(blob["invstddev"])
-
-_decibel = 2 * 20 * math.log10(32767)
-_gain = pow(10, 0.05 * _decibel)
 
 state = None
 hypo = None
 
+
 def transcribe(np_array, should_print=True):
     global state, hypo
     tensor = torch.tensor(np_array)
-    spectrogram = transform(tensor).transpose(1, 0)
-
-    features = _piecewise_linear_log(spectrogram * _gain)
-    features = features.unsqueeze(0)[:, :-1]
-    features = (features - _mean) * _invstddev
-
-    transcript, hypo, state = wrapper(features, hypo, state)
+    transcript, hypo, state = wrapper(tensor, hypo, state)
     if should_print and transcript:
-        print(transcript, end=" ", flush=True)
+        print(transcript, end="", flush=True)
+
 
 previous_right_context = None
 
+
 def process(should_print=True):
     global previous_right_context
     if previous_right_context is None:
diff --git a/StreamingASR/save_model_for_mobile.py b/StreamingASR/save_model_for_mobile.py
index f6412417..9bac252a 100644
--- a/StreamingASR/save_model_for_mobile.py
+++ b/StreamingASR/save_model_for_mobile.py
@@ -3,11 +3,11 @@
 from torch.utils.mobile_optimizer import optimize_for_mobile
 
 def get_demo_wrapper():
-    wrapper = torch.jit.load("scripted_wrapper_tuple_no_transform.pt")
+    wrapper = torch.jit.load("scripted_wrapper_tuple.pt")
     return wrapper
 
 wrapper = get_demo_wrapper()
 scripted_model = torch.jit.script(wrapper)
 optimized_model = optimize_for_mobile(scripted_model)
-optimized_model._save_for_lite_interpreter("streaming_asr.ptl")
+optimized_model._save_for_lite_interpreter("streaming_asrv2.ptl")
 print("Done _save_for_lite_interpreter")
diff --git a/StreamingASR/spm_bpe_4096_fairseq.dict b/StreamingASR/spm_bpe_4096_fairseq.dict
new file mode 100644
index 00000000..2480c719
--- /dev/null
+++ b/StreamingASR/spm_bpe_4096_fairseq.dict
@@ -0,0 +1,4092 @@
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