Unlocking the Power of Whisper: Using ChatGPT in Flutter Apps

Are you tired of using traditional text-based chatbots in your Flutter app? Do you want to take your user experience to the next level by incorporating the power of speech recognition and AI-driven conversations? Look no further! In this article, we’ll explore the possibility of using ChatGPT with the Whisper model as a stream in your Flutter app, and guide you through the process of implementation.

What is Whisper?

Whisper is an open-source speech recognition system developed by OpenAI, the same team behind the popular language model, ChatGPT. Whisper is designed to be a highly accurate and lightweight speech recognition model that can be used in a variety of applications. Its compact size and minimal computational requirements make it an ideal choice for mobile app development, including Flutter.

What is ChatGPT?

ChatGPT is a state-of-the-art language model developed by OpenAI that can understand and respond to human input in a conversational manner. It’s trained on a massive dataset of text and can generate human-like responses to a wide range of topics and questions. By combining ChatGPT with Whisper, you can create a powerful conversational AI that can listen, understand, and respond to user voice input.

Is it possible to get ChatGPT answer as using model ‘whisper’ as Stream in Flutter app?

The short answer is yes! With the right combination of plugins, APIs, and coding magic, you can integrate Whisper as a stream in your Flutter app and use it to generate responses from ChatGPT. In this article, we’ll break down the process into manageable chunks and provide you with a step-by-step guide to get you started.

Step 1: Setting up the Environment

Before we dive into the implementation, make sure you have the following tools and plugins installed:

• Flutter 2.10.0 or later
• Flutter Speech Recognition Plugin (flutter_speech_recognition)
• HTTP Client Plugin (http)
• Dart SDK 2.17.0 or later

Step 2: Installing the Whisper Model

To use Whisper in your Flutter app, you’ll need to install the pre-trained model using the following command:

flutter pub add whisper_flutter

This will add the Whisper Flutter plugin to your project, which includes the pre-trained model and necessary libraries.

Step 3: Setting up Speech Recognition

Next, you’ll need to set up speech recognition in your Flutter app using the Flutter Speech Recognition Plugin. Add the following code to your app:

import 'package:flutter/material.dart';
import 'package:flutter_speech_recognition/flutter_speech_recognition.dart';

class SpeechRecognitionPage extends StatefulWidget {
  @override
  _SpeechRecognitionPageState createState() => _SpeechRecognitionPageState();
}

class _SpeechRecognitionPageState extends State<SpeechRecognitionPage> {
  final _speechRecognition = FlutterSpeechRecognition();

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: Text('Speech Recognition'),
      ),
      body: Center(
        child: ElevatedButton(
          child: Text('Start Listening'),
          onPressed: () async {
            await _speechRecognition.initialize();
            await _speechRecognition.listen();
          },
        ),
      ),
    );
  }
}

This code sets up a basic speech recognition page that listens for user input when the “Start Listening” button is pressed.

Step 4: Streaming Audio to Whisper

Now, you’ll need to stream the audio input from the speech recognition plugin to the Whisper model for processing. Add the following code to your app:

import 'package:whisper_flutter/whisper_flutter.dart';

class _SpeechRecognitionPageState extends State<SpeechRecognitionPage> {
  final _speechRecognition = FlutterSpeechRecognition();
  final _whisperModel = WhisperModel();

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: Text('Speech Recognition'),
      ),
      body: Center(
        child: ElevatedButton(
          child: Text('Start Listening'),
          onPressed: () async {
            await _speechRecognition.initialize();
            await _speechRecognition.listen();

            // Stream audio to Whisper
            _speechRecognition.audioStream.listen((audioBuffer) {
              _whisperModel.processAudio(audioBuffer);
            });
          },
        ),
      ),
    );
  }
}

This code creates an instance of the Whisper model and streams the audio input from the speech recognition plugin to the Whisper model for processing.

Step 5: Generating Responses with ChatGPT

Now that you have the transcribed text from Whisper, you can use it to generate a response from ChatGPT using the HTTP Client Plugin. Add the following code to your app:

import 'package:http/http.dart' as http;

class _SpeechRecognitionPageState extends State<SpeechRecognitionPage> {
  final _speechRecognition = FlutterSpeechRecognition();
  final _whisperModel = WhisperModel();
  final _httpClient = http.Client();

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: Text('Speech Recognition'),
      ),
      body: Center(
        child: ElevatedButton(
          child: Text('Start Listening'),
          onPressed: () async {
            await _speechRecognition.initialize();
            await _speechRecognition.listen();

            // Stream audio to Whisper
            _speechRecognition.audioStream.listen((audioBuffer) {
              _whisperModel.processAudio(audioBuffer).then((transcript) {
                // Generate response from ChatGPT
                _generateResponse(transcript);
              });
            });
          },
        ),
      ),
    );
  }

  void _generateResponse(String transcript) async {
    final url = 'https://api.chatgpt.com/v1/conversation';
    final request = http.Request('POST', Uri.parse(url))
      ..headers['Content-Type'] = 'application/json'
      ..body = jsonEncode({
        'query': transcript,
      });

    final response = await _httpClient.send(request);

    if (response.statusCode == 200) {
      final responseJson = jsonDecode(response.body);
      final responseText = responseJson['response'];

      // Display the response from ChatGPT
      ScaffoldMessenger.of(context).showSnackBar(SnackBar(
        content: Text(responseText),
      ));
    } else {
      print('Error: ${response.statusCode}');
    }
  }
}

This code generates a response from ChatGPT using the transcribed text from Whisper and displays it as a SnackBar in the app.

Conclusion

In this article, we’ve shown you how to use the Whisper model as a stream in your Flutter app to generate responses from ChatGPT. By combining speech recognition, Whisper, and ChatGPT, you can create a powerful conversational AI that can understand and respond to user voice input. Remember to fine-tune the Whisper model and ChatGPT API keys for optimal performance and accuracy.

Plugin/Library	Version
flutter_speech_recognition	2.10.0
whisper_flutter	1.0.0
http	0.13.3

Happy coding, and don’t hesitate to reach out if you have any questions or need further assistance!

Frequently Asked Questions

1. Can I use Whisper with other speech recognition plugins?

   A: Yes, Whisper can be used with other speech recognition plugins, but you may need to modify the code to accommodate the specific plugin’s API.

2. How do I improve the accuracy of Whisper?

   A: You can fine-tune the Whisper model by providing more training data or adjusting the model’s hyperparameters.

3. Can I use ChatGPT with other language models?

   A: Yes, you can use ChatGPT with other language models, but you may need to modify the code to accommodate the specific model’s API.

We hope this article has inspired you to create innovative conversational AI experiences in your Flutter app. Remember to stay tuned for more tutorials, guides, and articles on the latest developments in AI, machine learning, and mobile app development.

Frequently Asked Question

Unlock the secrets of integrating Chat GPT with Flutter app using the Whisper model as a Stream. Get ready to dive into the world of AI-powered conversations!