# whisper_android

**Repository Path**: github_syn/whisper_android

## Basic Information

- **Project Name**: whisper_android
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-13
- **Last Updated**: 2025-10-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Offline Speech Recognition with Whisper & TFLite

This repository offers two Android apps leveraging the OpenAI Whisper speech-to-text model. One app uses the TensorFlow Lite Java API for easy Java integration, while the other employs the TensorFlow Lite Native API for enhanced performance. It also includes a Python script for model generation and pre-built APKs for straightforward deployment.


## 📂 Folder Structure

- **whisper_java**:  
  An Android app using the TensorFlow Lite Java API for model inference with Whisper, ideal for Java developers integrating TensorFlow Lite.

- **whisper_native**:  
  An Android app utilizing the TensorFlow Lite Native API for model inference, offering optimized performance for developers preferring native code.

- **models_and_scripts**:  
  Contains a Python script to convert Whisper models into TensorFlow Lite format and includes pre-generated TFLite models.
  - `generate_model.py`: Script for generating TFLite models.
  - `generated_model`: Directory with optimized TFLite models.

- **demo_and_apk**:  
  Contains pre-built APKs for direct Android installation.

## 🚀 How to Use

- **Running the Whisper Java App**
  1. Navigate to the `whisper_java` folder.
  2. Open the project in Android Studio.
  3. Build and run on an Android device or emulator.

- **Running the Whisper Native App**
  - Follow similar steps as above for the `whisper_native` app.

## Whisper ASR Integration Guide
This guide explains how to integrate Whisper and Recorder class in Android apps for audio recording and speech recognition.

Here are separate code snippets for using `Whisper` and `Recorder`:

### Whisper (Speech Recognition)

**Initialization and Configuration:**
```java
// Initialize Whisper
Whisper mWhisper = new Whisper(this); // Create Whisper instance

// Load model and vocabulary for Whisper
String modelPath = "path/to/whisper-tiny.tflite"; // Provide model file path
String vocabPath = "path/to/filters_vocab_multilingual.bin"; // Provide vocabulary file path
mWhisper.loadModel(modelPath, vocabPath, true); // Load model and set multilingual mode

// Set a listener for Whisper to handle updates and results
mWhisper.setListener(new IWhisperListener() {
    @Override
    public void onUpdateReceived(String message) {
        // Handle Whisper status updates
    }

    @Override
    public void onResultReceived(String result) {
        // Handle transcribed results
    }
});
```

**Transcription:**
```java
// Set the audio file path for transcription. Audio format should be in 16K, mono, 16bits
String waveFilePath = "path/to/your_audio_file.wav"; // Provide audio file path
mWhisper.setFilePath(waveFilePath); // Set audio file path

// Start transcription
mWhisper.setAction(Whisper.ACTION_TRANSCRIBE); // Set action to transcription
mWhisper.start(); // Start transcription

// Perform other operations
// Add your additional code here

// Stop transcription
mWhisper.stop(); // Stop transcription
```

### Recorder (Audio Recording)

**Initialization and Configuration:**
```java
// Initialize Recorder
Recorder mRecorder = new Recorder(this); // Create Recorder instance

// Set a listener for Recorder to handle updates and audio data
mRecorder.setListener(new IRecorderListener() {
    @Override
    public void onUpdateReceived(String message) {
        // Handle Recorder status updates
    }

    @Override
    public void onDataReceived(float[] samples) {
        // Handle audio data received during recording
        // You can forward this data to Whisper for live recognition using writeBuffer()
        // mWhisper.writeBuffer(samples);
    }
});
```

**Recording:**
```java
// Check and request recording permissions
checkRecordPermission(); // Check and request recording permissions

// Set the audio file path for recording. It record audio in 16K, mono, 16bits format
String waveFilePath = "path/to/your_audio_file.wav"; // Provide audio file path
mRecorder.setFilePath(waveFilePath); // Set audio file path

// Start recording
mRecorder.start(); // Start recording

// Perform other operations
// Add your additional code here

// Stop recording
mRecorder.stop(); // Stop recording
```

Please adapt these code snippets to your specific use case, provide the correct file paths, and handle exceptions appropriately in your application.

**Note**: Ensure that you have the necessary permissions, error handling, and file path management in your application when using the `Recorder` class.


## Demo Video
[![Video](https://img.youtube.com/vi/w9pohi9NQrg/0.jpg)](https://www.youtube.com/watch?v=w9pohi9NQrg)

## Live Transcription
[![Video](https://img.youtube.com/vi/qUg6_v7b9Io/0.jpg)](https://www.youtube.com/watch?v=qUg6_v7b9Io)

## Important Note

Whisper ASR is a powerful tool for transcribing speech into text. However, keep in mind that handling audio data and transcriptions may require careful synchronization and error handling in your Android application to ensure a smooth user experience.


Enjoy using the Whisper ASR Android app to enhance your speech recognition capabilities!

## 💖 Support This Project
Maintaining this project requires time and effort. If you find it useful and would like to support its development, you can contribute via PayPal:

🔹 PayPal Direct Link: [https://www.paypal.com/vilassn](https://www.paypal.me/vilassn)

For any inquiries or business-related discussions, feel free to reach out:
📧 Email: vilassninawe@gmail.com

Thank you for your support! 🚀