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flutter_tflite_audio

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Audio classification Tflite package for flutter (iOS & Android). Can support Google Teachable Machine models

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# TFlite Audio Plugin for Flutter

[![pub package](https://img.shields.io/pub/v/tflite_audio.svg?label=version&color=blue)](https://pub.dev/packages/tflite_audio)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![style: effective dart](https://img.shields.io/badge/style-effective_dart-40c4ff.svg)](https://pub.dev/packages/effective_dart)

Audio classification Tflite package for flutter (iOS & Android). Can also support Google Teachable Machine models.

If you are a complete newbie to audio classification, you can read the tutorial [here](https://carolinamalbuquerque.medium.com/audio-recognition-using-tensorflow-lite-in-flutter-application-8a4ad39964ae). Credit to [Carolina](https://github.com/cmalbuquerque) for writing a comprehensive article.

To keep this project alive, consider giving a star or a like. Pull requests or bug reports are also welcome.

Recording            |  Inference result
:-------------------------:|:-------------------------:
![](https://github.com/Caldarie/flutter_tflite_audio/blob/master/pictures/finish.png) | ![](https://github.com/Caldarie/flutter_tflite_audio/blob/master/pictures/start.png)

## Table of Contents

* [About this plugin](#about-this-plugin)
 * [Known Issues/Commonly asked questions](#known-issuescommonly-asked-questions)
 * [Please read if you are using Google's Teachable Machine. Otherwise skip.](#please-read-if-you-are-using-googles-teachable-machine-otherwise-skip)
 * [How to add tflite model and label to flutter](#how-to-add-tflite-model-and-label-to-flutter)
 * [How to use this plugin](#how-to-use-this-plugin)
 * [Rough guide on parameters](#rough-guide-on-the-parameters)
 * [Android Installation & Permissions](#android-installation--permissions)
 * [iOS Installation & Permissions](#ios-installation--permissions)
 * [References](#references)

## About This Plugin

### The plugin has several features:

1. Audio recognition for stored audio files. (Only mono wav files for now)

2. Audio recognition for recordings.

3. Tunable parameters for recording/inferences
   *  Please look a the [parameters](#rough-guide-on-the-parameters) below for more information.

4. Automatically reshape/transpose audio inputs.

### This plugin can support several model types:

1. Google Teachable Machine (Raw audio input)

* For beginners with little to no machine learning knowledge. You can read can read the tutorial [here](https://carolinamalbuquerque.medium.com/audio-recognition-using-tensorflow-lite-in-flutter-application-8a4ad39964ae) if you are a newbie.
   * Training can be done [here](https://teachablemachine.withgoogle.com/train/audio)

2. Raw audio input.

* Can recognize the following inputs: float32[audioLength, 1] or float32[1, audioLength]
   * For more information on how to train your own model, take a look [here](https://github.com/tensorflow/examples/tree/master/lite/examples/speech_commands/ml).

3. Decoded wav input.

* Supports two inputs: float32[audioLength, 1] and int32[1]
   * For more information on how to train your own model. Take a look [here](https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/audio_recognition.md)
   * To train a decoded wave with MFCC, take a look [here](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/examples/speech_commands)

4. **(Experimental feature)**  Spectogram, melspectrogram, and MFCC inputs.

* Please note that this feature is experimental, and results may not be accurate compared to raw audio / decoded wav.
   * Spectrogram model can be trained here [tutorial](https://www.tensorflow.org/tutorials/audio/simple_audio).

5. **(Currently worked on feature)** Multiple input and outputs.

## Known Issues/Commonly asked questions

1. **How to adjust the recording length/time**

There are two ways to reduce adjust recording length/time:

* You can increase the recording time by adjusting the bufferSize to a lower value. 
  
   * You can also increase recording time by lowering the sample rate.     
  
   **Note:** That stretching the value too low will cause problems with model accuracy. In that case, you may want to consider lowering your sample rate as well. Likewise, a very low sample rate can also cause problems with accuracy. It is your job to find the sweetspot for both values.

2. **How to reduce false positives in my model**

To reduce false positives, you may want to adjust the default values of `detectionThreshold=0.3` and `averageWindowDuration=1000` to a higher value. A good value for both respectively are `0.7`  and `1500`. For more details about these parameters, please visit this [section](#rough-guide-on-the-parameters).

3. **I am getting build errors on iOS**

There are several ways to fix this:

* Some have reported to fix this issue by replacing the following line:

```ruby
     target 'Runner' do
       use_frameworks! 
       use_modular_headers!
       #pod 'TensorFlowLiteSelectTfOps' #Old line
       pod'TensorFlowLiteSelectTfOps','~> 2.6.0' #New line

flutter_install_all_ios_pods File.dirname(File.realpath(__FILE__))
     end
     ```

* Others have fixed this issue building the app without the line: `pod 'TensorFlowLiteSelectTfOps`. Then rebuilding the app by re-adding the line again.

* Remember to run the following below:
   
   ```
     1. cd into iOS folder

2. Run `flutter pub get` on terminal

3. Run `pod install` on terminal

4. Run `flutter clean` on terminal

5. Run `flutter run` on terminal. All done!
   ```

4. **I am getting TensorFlow Lite Error on iOS. -  Regular TensorFlow ops are not supported by this interpreter. Make sure you apply/link the Flex delegate before inference**

*  Please make sure that you have enabled ops-select on your [podfile - step 4 & Xcode - step 5](#ios-if-you-are-using-googles-teachable-machine-model-otherwise-skip) and [build gradle - step 3](#android-if-you-are-using-googles-teachable-machine-otherwise-skip)

* If you tried above, please run the example on a device (not emulator). If you still recieved this error, its very likely that theres an issue with cocoapod or Xcode configuration. Please check the [issue #7](https://github.com/Caldarie/flutter_tflite_audio/issues/7)

* If you recieved this error from your custom model (not GTM), its likely that you're using unsupported tensorflow operators for tflite, as found in [issue #5](https://github.com/Caldarie/flutter_tflite_audio/issues/5#issuecomment-789260402). For more details on which operators are supported, look at the official documentation [here](https://www.tensorflow.org/lite/guide/ops_compatibility)

* Take a looking at issue number 3 if none of the above works.

5. **(iOS) App crashes when running Google's Teachable Machine model**

Please run your simulation on actual iOS device. Running your device on M1 macs should also be ok.
  
   As of this moment, there's [limited support](https://github.com/tensorflow/tensorflow/issues/44997#issuecomment-734001671) for x86_64 architectures from the Tensorflow Lite select-ops framework. If you absolutely need to run it on an emulator, you can consider building the select ops framework yourself. Instructions can be found [here](https://www.tensorflow.org/lite/guide/ops_select#ios)

6. **(Android) Fatal signal 11 (SIGSEGV), code 1 (SEGV_MAPERR), fault addr 0xfffffff4 in tid 5403**

It seems like the latest tflite package for android is causing this issue. Until this issue is fixed, please run this package on an actual Android Device.

## Please Read If You Are Using Google's Teachable Machine. (Otherwise Skip)

**BE AWARE:** Google's Teachable Machine requires [select tensorflow operators](https://www.tensorflow.org/lite/guide/ops_select#using_bazel_xcode) to work. This feature is experimental and will cause the following issues:

1. Increase the overall size of your app. If this is unnacceptable for you, it's recommended that you build your own custom model. Tutorials can be found in the [About this plugin section](#about-this-plugin)

2. Emulators for iOS do not work due to limited support for x86_64 architectures. You need to run your simulation on an actual device. Issue can be found [here](https://github.com/tensorflow/tensorflow/issues/44997)
  
3. You will need to manually implement ops-select on your [podfile - step 4 & Xcode - step 5](#note-skip-below-if-your-are-not-using-google-teachable-machine-ios) and [build gradle - step 3](#note-skip-below-if-your-are-not-using-google-teachable-machine-android)

## How to add tflite model and label to flutter:

1. Create an assets folder and then place your custom tflite model and labels inside.

![](https://github.com/Caldarie/flutter_tflite_audio/blob/master/pictures/model-label-asset.png)

2. In pubsec.yaml, link your tflite model and label under 'assets'. For example:

```
  assets:
    - assets/decoded_wav_model.tflite
    - assets/decoded_wav_label.txt

```

## How to use this plugin

Please look at the [example](https://github.com/Caldarie/flutter_tflite_audio/tree/master/example) on how to implement these futures.

1. To add the package in pubspec.yaml, open your terminal and run this line in your flutter project:

```
   flutter pub add tflite_audio
   ```

2. Import the plugin. For example:

```
   import 'package:tflite_audio/tflite_audio.dart';
   ```

3. To load your model:

```dart
   //Example for decodedWav models
    TfliteAudio.loadModel(
         model: 'assets/conv_actions_frozen.tflite',
         label: 'assets/conv_actions_label.txt',
         inputType: 'decodedWav');

//Example for Google's Teachable Machine models
     TfliteAudio.loadModel(
         model: 'assets/google_teach_machine_model.tflite',
         label: 'assets/google_teach_machine_label.txt',
         inputType: 'rawAudio');

//Example if you want to take advantage of all optional parameters from loadModel()
     TfliteAudio.loadModel(
       model: 'assets/conv_actions_frozen.tflite',
       label: 'assets/conv_actions_label.txt',
       inputType: 'decodedWav',
       outputRawScores: false, 
       numThreads: 1,
       isAsset: true,
     );
   ```

4. To start and listen to the stream for inference results:

* Declare stream value
 ```dart
 Stream<Map<dynamic, dynamic>> recognitionStream;
 ```

* If you want to use the recognition stream for recording:
      ```dart
      //Example values for Google's Teachable Machine models
      recognitionStream = TfliteAudio.startAudioRecognition(
        sampleRate: 44100,
        bufferSize: 22016,
        )

//Example values for decodedWav
      recognitionStream = TfliteAudio.startAudioRecognition(
        sampleRate: 16000,
        bufferSize: 2000,
        )
        
      //Example for advanced users who want to utilise all optional parameters from this package. 
      //Note the values are default.
      recognitionStream = TfliteAudio.startAudioRecognition(
        sampleRate: 44100,
        bufferSize: 22016,
        numOfInferences: 5,
        audioLength = 44032,
        detectionThreshold: 0.3, 
        averageWindowDuration = 1000,
        minimumTimeBetweenSamples = 30,
        suppressionTime = 1500,
        )

```

* If you want to use the recognition stream for stored audio files.

```dart
       //Example values for Google teachable models
       recognitionStream = TfliteAudio.startFileRecognition(
         sampleRate: 44100,
         audioDirectory: "assets/sampleAudio.wav",
         );

//Examples values for decodedWav
       recognitionStream = TfliteAudio.startFileRecognition(
         sampleRate: 16000,
         audioDirectory: "assets/sampleAudio.wav",
         );

//Example for advanced users who want to utilise all optional parameters from this package. 
       recognitionStream = TfliteAudio.startFileRecognition(
         sampleRate: 44100,
         audioDirectory: "assets/sampleAudio.wav",
         audioLength: 44032,
         detectionThreshold: 0.3,
         averageWindowDuration: 1000,
         minimumTimeBetweenSamples: 30,
         suppressionTime: 1500,
         );
       ```

* Listen for results 
      ```dart
      String result = '';
      int inferenceTime = 0;

recognitionStream.listen((event){
            result = event["inferenceTime"];
            inferenceTime = event["recognitionResult"];
            })
          .onDone(
             //Do something here when stream closes
           );
      ```

5. To forcibly cancel recognition stream
  
   ```dart
   TfliteAudio.stopAudioRecognition();
   ```

## Rough guide on the parameters
  
  * outputRawScores - Will output the result as an array in string format. For example `'[0.2, 0.6, 0.1, 0.1]'`

* numThreads -  Higher threads will reduce inferenceTime. However, will utilise the more cpu resource.

* isAsset - is your model, label or audio file in the asset file? If yes, set true. If the files are outside (such as external storage), set false.
  
  * numOfInferences - determines how many times you want to loop the recording and inference. For example:
`numOfInference = 3` will repeat the recording three times, so recording length will be (1 to 2 seconds) x 3 = (3 to 6 seconds). Also the model will output the scores three times.

* sampleRate - A higher sample rate may improve accuracy for recordings. Recommened values are 16000, 22050, 44100

* audioLength - Default is 0 as the plugin will determine the length for you. You can manually adjust this if you wish to shorten or extend the number of audio samples.

* bufferSize - A lower value will lengthen the recording. Likewise, a higehr value will shorten the recording. Make sure this value is equal or below your recording length. 
    
  * detectionThreshold - Will ignore any predictions where its probability does not exceed the detection threshold. Useful for situations where you pickup unwanted/unintentional sounds. Lower the value if your model's performance isn't doing too well.

* suppressionMs - If your detection triggers too early, the result may be poor or inaccurate. Adjust the values to avoid this situation.

* averageWindowDurationMs - Use to remove earlier results that are too old.

* minimumTimeBetweenSamples - Ignore any results that are coming in too frequently

## Android Installation & Permissions

1. Add the permissions below to your AndroidManifest. This could be found in `<YourApp>/android/app/src`. For example:

```
 <uses-permission android:name="android.permission.RECORD_AUDIO" />
 <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
 ```

2. Edit the following below to your build.gradle. This could be found in `<YourApp>/app/src/`. For example:

```Gradle
   aaptOptions {
           noCompress 'tflite'
   ```

#### **NOTE:** Skip below if your are not using Google Teachable Machine (Android)

1. Enable select-ops under dependencies in your build gradle.

```Gradle
   dependencies {
       compile 'org.tensorflow:tensorflow-lite-select-tf-ops:+'
   }
   ```

## iOS Installation & Permissions

1. Add the following key to Info.plist for iOS. This could be found in `<YourApp>/ios/Runner`
 ```
 <key>NSMicrophoneUsageDescription</key>
 <string>Record audio for playback</string>
 ```

2. Change the deployment target to a minumum of 12.0 or higher. This could be done by:

* Open your project workspace on xcode. Project workspace can be found here: `<YourApp>/ios/Runner.xcworkspace`
 
 * Select the top level Runner on the left panel
 
 * Select the Runner under Project.
 
 * Under the info tab, change the iOS deployment target to a minimum of 12.0 or higher

![](https://github.com/Caldarie/flutter_tflite_audio/blob/master/pictures/deployment-target.png)
 
3. Open your podfile (found here: `<YourApp>/ios/Podfile`) and change platform ios to a minimum 12 or higher.

```ruby
   platform :ios, '12.0'
   ```

#### **NOTE:** Skip below if your are not using Google Teachable Machine (iOS)

1. In the same podfile, add `pod 'TensorFlowLiteSelectTfOps' under target.

```ruby
   target 'Runner' do
     use_frameworks! 
     use_modular_headers!
     pod'TensorFlowLiteSelectTfOps','~> 2.6.0' #Add this line here

flutter_install_all_ios_pods File.dirname(File.realpath(__FILE__))
   end
   ```

2. Force load Select Ops for Tensorflow. To do that:

* Open your project on xcode 
    
   * click on runner under "Targets"
  
   * Click on "Build settings" tab

* Click on "All" tab

* Click on the empty space which is on the right side of "Other Links Flag"

* Add the following line: `-force_load $(SRCROOT)/Pods/TensorFlowLiteSelectTfOps/Frameworks/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps`

![](https://github.com/Caldarie/flutter_tflite_audio/blob/master/pictures/tflite-select-ops-installation.png)

3. Install the ops-select package to pod. To do this:

* cd into iOS folder

* Run `flutter pub get` on terminal

* Run `pod install` on terminal

* Run `flutter clean` on terminal

* Run `flutter run` on terminal. All done!

## References

This project wouldn't of been possible if it wasn't for the following:

1. Project is based on:
   * https://github.com/tensorflow/examples/tree/master/lite/examples/speech_commands
2. Tflite & select ops:
   * https://www.tensorflow.org/lite/guide/ops_select
   * https://libraries.io/cocoapods/TensorFlowLiteSelectTfOps
3. Spectogram libraries:
   * https://github.com/Subtitle-Synchronizer/jlibrosa
   * https://github.com/dhrebeniuk/RosaKit
4. RxJava and RxSwift
   * https://github.com/ReactiveX/RxAndroid
   * https://github.com/ReactiveX/RxJava
   * https://github.com/ReactiveX/RxSwift

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