farmaker47

if os.environ['COLAB_TPU_ADDR']:
  cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
  tf.config.experimental_connect_to_cluster(cluster_resolver)
  tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
  strategy = tf.distribute.TPUStrategy(cluster_resolver)
  print('Using TPU')
elif tf.test.is_gpu_available():
  strategy = tf.distribute.MirroredStrategy()
  print('Using GPU')
else:

farmaker47

fun convertArrayToBitmap(
            imageArray: Array<Array<Array<FloatArray>>>,
            imageWidth: Int,
            imageHeight: Int
        ): Pair<Bitmap, Bitmap> {

            // Convert multidimensional array to 1D
            val oneDFloatArray = ArrayList<Float>()

            for (m in imageArray[0].indices) {

farmaker47

fun bitmapToByteBuffer(
            bitmapIn: Bitmap,
            width: Int,
            height: Int,
            mean: Float = 0.0f,
            std: Float = 255.0f
        ): ByteBuffer {
            //var bitmap = cropBitmap(bitmapIn)
            //bitmap = scaleBitmapAndKeepRatio(bitmapIn, width, height)
            val bitmap = Bitmap.createScaledBitmap(bitmapIn, width, height, true)

farmaker47

fun bitmapToFloatArray(bitmap: Bitmap):
                Array<Array<Array<FloatArray>>> {
            val width: Int = bitmap.width
            val height: Int = bitmap.height
            val intValues = IntArray(width * height)
            bitmap.getPixels(intValues, 0, width, 0, 0, width, height)

            val floatArray = Array(1) {
                Array(3) {
                    Array(width) {

farmaker47

interpreter = tf.lite.Interpreter('/path/to/yamnet.tflite')

input_details = interpreter.get_input_details()
waveform_input_index = input_details[0]['index']
output_details = interpreter.get_output_details()
scores_output_index = output_details[0]['index']
embeddings_output_index = output_details[1]['index']
spectrogram_output_index = output_details[2]['index']

# Input: 3 seconds of silence as mono 16 kHz waveform samples.

farmaker47

private val readAudio = Runnable {
        var readBytes: Int
        buffer = ShortArray(BUFFER_SIZE)
        while (mRecording) {
            readBytes = mRecorder!!.read(buffer, 0, BUFFER_SIZE)

            //Higher volume of microphone
            //https://stackoverflow.com/questions/25441166/how-to-adjust-microphone-sensitivity-while-recording-audio-in-android
            if (readBytes > 0) {
                for (i in 0 until readBytes) {

farmaker47

fun startRecording() {
        mRecorder = AudioRecord.Builder().setAudioSource(AUDIO_SOURCE)
            .setAudioFormat(AUDIO_FORMAT)
            .setBufferSizeInBytes(BUFFER_SIZE)
            .build()

        done = false
        mRecording = true
        mRecorder?.startRecording()
        mThread = Thread(readAudio)

farmaker47

fun execute(floatsInput: FloatArray): Pair<ArrayList<String>, ArrayList<Float>> {

        .................................
        .................................
        
        val listOfArrayMeanScores = arrayMeanScores.toCollection(ArrayList())

        val listOfMaximumValues = arrayListOf<Float>()
        for (i in 0 until 10) {
            val number = listOfArrayMeanScores.max() ?: 0f

farmaker47

fun execute(floatsInput: FloatArray): Pair<ArrayList<String>, ArrayList<Float>> {

        predictTime = System.currentTimeMillis()
        val inputSize = floatsInput.size // ~2 seconds of sound
        //Log.i("YAMNET_INPUT_SIZE", inputSize.toString())

        val inputValues = floatsInput//FloatArray(inputSize)

        val inputs = arrayOf<Any>(inputValues)
        val outputs = HashMap<Int, Any>()

farmaker47

// Function for ML Binding
    fun executeWithMLBinding(
            contentImagePath: Bitmap,
            styleImageName: String,
            context: Context
    ): ModelExecutionResult {
        try {
            Log.i(TAG, "running models")

            fullExecutionTime = SystemClock.uptimeMillis()