WHY!!!! android run whisper tiny model need 10sec ! it very slow! How can i speed out!!!

[abcnm123456]

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I use Unity, and already set the multithread count = 4,
if the multithread count = 1, it spend 30 sec
WHY!!!! android run whisper tiny model need 10sec ! it very slow! How can i speed out!!!

[georgetoloraia]

Speed up a slow Android device

• Try the steps on this page if your phone:

Runs very slowly

Lags

• Takes a long time to respond
• After each step, restart your phone to see if it fixed the issue.

maybe it's help you https://support.google.com/android/answer/7667018?hl=en#zippy=

[abcnm123456]

my android device is very fast, only the whishper respond very slow

[Minecraft-111]

Same here!!!!!It takes 8000ms to recognize a sentence. Does anyone know how to optimize the model?

[georgetoloraia]

Multithreading

Multithreading ensures that your app's user interface remains responsive by offloading heavy computational tasks to a background thread. In Android, this can be achieved in several ways:

1. Using AsyncTask (Not Recommended for New Applications):

• AsyncTask was a common way to handle background operations and reflect the results on the UI thread without having to manage threads manually. However, it's been deprecated in API level 30.

2. Using Java Threads or Executors:

• You can manually create a new thread to run your model inference code. Java's ExecutorService can be used to effectively manage a thread pool for running tasks in the background.

ExecutorService executor = Executors.newSingleThreadExecutor();
executor.execute(() -> {
    // Your model inference code here
    runModel(input);
});

3. Using Kotlin Coroutines (For Kotlin Apps):

• If you're using Kotlin, coroutines provide a powerful and efficient way to handle background tasks.

GlobalScope.launch(Dispatchers.IO) {
    // Your model inference code here
    runModel(input)
}

Asynchronous Inference

Asynchronous inference allows your app to initiate a task and then continue with other work until the task completes. It's particularly useful when you're dealing with operations like model inference, which can take a significant amount of time.

1. Callback Mechanism:

• Implement a callback mechanism where the inference function accepts a callback that gets called when the inference is completed.

runModelAsync(input, result -> {
    // Update UI with the result on the main thread
    runOnUiThread(() -> updateUI(result));
});

2. Future and CompletableFuture (Java):

• Use Future or CompletableFuture to represent the result of an asynchronous computation. This allows you to write non-blocking code where your main thread can continue executing while the model runs in the background.

3. Kotlin Coroutines (For Kotlin Apps):

• Kotlin coroutines are well-suited for asynchronous work. They allow you to write code that's sequentially executed but non-blocking. You can use async and await to manage asynchronous tasks more intuitively.

Best Practices

• Error Handling: Ensure robust error handling in your background tasks to avoid crashes and unresponsive states.
• Resource Management: Manage your resources carefully. Background tasks, especially those involving model inference, can be resource-intensive.
• Testing: Thoroughly test the multithreaded aspects of your application. Concurrency can introduce complex bugs that are hard to reproduce and fix.
• UI Updates: Remember that any updates to the UI need to be done on the main thread. Android provides mechanisms like runOnUiThread or the ability to post tasks to a Handler associated with the main thread.

[abcnm123456]

i have already set the multithreaded to 2 or 4