tflite_audio 0.1.0

Tflite plugin for flutter. Can make audio classifications on both android and iOS with the user's specifications.

flutter_tflite_audio

This plugin allows you to use tflite to make audio/speech classifications. Can now support ios and android.

If you have any feature requests or would like to contribute to this plugin, please do not hesistate to contact me.

How to add tflite_audio as a dependency:

1. Add tflite_audio as a [dependency in your pubspec.yaml file]

How to add tflite model and label to flutter:

1. Place your custom tflite model and labels into the asset folder.
2. In pubsec.yaml, link your tflite model and label under 'assets'. For example:

  assets:
    - assets/conv_actions_frozen.tflite
    - assets/conv_actions_labels.txt

How to use this plugin

1. Import the plugin. For example:

import 'package:tflite_audio/tflite_audio.dart';

2. Use the following futures to make use of this plugin. Please look at the example on how to implement these futures.

//Loads your model
//Higher numThreads will be reduce inference times, but is more intensive on cpu
 Future loadModel({model, label, numThreads, isAsset}) async {
    return await TfliteAudio.loadModel(model, label, numThreads, isAsset);
  }

 Future<dynamic> startAudioRecognition(
      {int sampleRate, int recordingLength, int bufferSize}) async {
    return await TfliteAudio.startAudioRecognition(
        sampleRate, recordingLength, bufferSize);
  }

3. Call the future loadModel() and assign the appropriate arguments. The values for numThread and isAsset are on default as shown below:

loadModel(
        model: "assets/conv_actions_frozen.tflite",
        label: "assets/conv_actions_labels.txt",
        numThreads: 1,
        isAsset: true);

4. Call the future startAudioRecognition() and assign samplerate, recordinglength and buffersize. The values below are within the example model's input parameters.

//This future checks for permissions, records voice and starts audio recognition, then returns the result.
//Make sure the recordingLength fits your tensor input
//Higher buffer size = more latency, but less intensive on cpu. Also shorter recording time.
//Sample rate is the number of samples per second
  Future<String> startAudioRecognition() async {
     await startAudioRecognition(sampleRate: 16000, recordingLength: 16000, bufferSize: 1280)
  }

Android

Add the permissions below to your AndroidManifest. This could be found in

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

Edit the following below to your build.gradle. This could be found in

aaptOptions {
        noCompress 'tflite'

iOS

Also add the following key to Info.plist for iOS

<key>NSMicrophoneUsageDescription</key>
<string>Record audio for playback</string>

References

https://github.com/tensorflow/examples/tree/master/lite/examples/speech_commands