I’m working with tflite on Android, and I have a model architecture that’s a MobilenetV2 backbone, and a GRU RNN, trained with CTC loss. I want to use ctc beam search decoding at inference time.
I’ve successfully made a .tflite model and tested that I can load it up and get the expected behaviour on my local machine.
BUT, it doesn’t work on my Android project as I get this error:
Could not build model from the provided pre-loaded flatbuffer: Unsupported custom op: FlexCTCBeamSearchDecoder, version: 1
The error is raised when I try to instantiate this class:
package com.stampfree.validation.tflite;
import android.app.Activity;
import java.io.IOException;
/** This TensorFlowLite classifier works with the float MobileNet model. */
public class DigicodeMobileNet extends Classifier {
    /**
     * Initializes a {@code ClassifierFloatMobileNet}.
     *
     * @param device a {@link Device} object to configure the hardware accelerator
     * @param numThreads the number of threads during the inference
     * @throws IOException if the model is not loaded correctly
     */
    public DigicodeMobileNet(Activity activity, Device device, int numThreads)
            throws IOException {
        super(activity, device, numThreads);
    }
    @Override
    protected String getModelPath() {
        return "tf_mobilenetv2_128x768_ctc_post_epoch08.tflite";
    }
}

I have tried this using the default dependencies:
dependencies {
    implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'
    implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly-SNAPSHOT'
}

and by building the AAR in a docker container.
Both approaches gave the same result.
Any tips? Happy to share more context on request.