Convert Speech to Text: Flutter App

Table of Contents

1. Introduction
2. Understanding OpenAI's Whisper API
3. Setting up the Flutter Application
4. Picking a File from the Device System Files
5. Calling OpenAI's Whisper API for Transcription
6. Getting the Secret Key for API Access
7. Creating the URL for the HTTP Request
8. Adding the Header for Authorization
9. Creating the Request Body
10. Executing the HTTP Request
11. Decoding and Displaying the Response Data
12. Conclusion

Introduction

Welcome back to my Channel! In this tutorial, I will be showing You how to Create a speech-to-text application in Flutter using OpenAI's Whisper API. Whisper API is a hosted version of the open-source Whisper speech-to-text model that OpenAI released in September. This automatic speech recognition system claims to be able to translate multiple languages into English. It can also handle files in various formats, such as m4a, mp3, and mp4.

Understanding OpenAI's Whisper API

OpenAI's Whisper API is a powerful tool that allows developers to convert speech into text using the Whisper speech-to-text model. With the help of this API, you can effortlessly transcribe audio files in different languages into English. It provides accurate and reliable speech-to-text conversion, making it a valuable tool for various applications.

Setting up the Flutter Application

Before we dive into the code, we need to set up our Flutter application. In this tutorial, we will be using two main packages: HTTP and file picker. To avoid any version errors, make sure you are using the same environment SDK version as mine.

First, we need to create a simple Flutter application that consists of an elevated button and a text widget. The purpose of this application is to open the system file when the button is clicked and allow the user to pick an audio file for transcription.

Picking a File from the Device System Files

To pick a file from the device system files, we will use the file picker Package. This package provides a function that allows us to select a file from the device. It returns a file picker result, which we can use to retrieve the selected file.

Let's create a function called pickFileFromDevice that invokes the file picker's pickFiles function. This function will be triggered when the elevated button is pressed.

Future<void> pickFileFromDevice() async {
  FilePickerResult? result = await FilePicker.platform.pickFiles();

  // Check if a file was selected
  if (result != null) {
    // Proceed with file processing
  } else {
    // Handle the case when no file was selected
  }
}

Calling OpenAI's Whisper API for Transcription

After picking a file from the device system files, we need to call OpenAI's Whisper API for transcription. In this step, we will create a function called convertSpeechToText that will handle the API call. This function takes a STRING parameter which represents the path to the audio file.

To call OpenAI's API, we need to obtain a secret key. I will demonstrate how to get the secret key in the following section. Once we have the secret key, we can create a constant file to store it securely.

Here's an example of how to call the API using the HTTP package:

Future<String> convertSpeechToText(String filePath) async {
  final apiSecretKey = Constants.apiSecretKey; // Replace with your API secret key

  final url = "https://api.openai.com/v1/anything"; // Replace with the appropriate API endpoint

  // Perform the API request and handle the response
  // ...

  return "Transcription text"; // Replace with the actual transcription text
}

Getting the Secret Key for API Access

To gain access to OpenAI's API, you need to create an account on their Website and obtain a secret key. Follow these steps to retrieve your secret key:

1. Log in to the OpenAI website using your account credentials.
2. Click on your profile in the top right corner.
3. Select "View API Keys."
4. Create a new secret key. Be extremely cautious and avoid sharing your API secret key with anyone.

It is important to store the secret key securely. To prevent accidental release of the key, save it in a separate file that can be ignored when pushing the code to a public repository, such as GitHub.

const apiSecretKey = "YOUR_API_SECRET_KEY";

Now, import the API secret key variable into the main file, ensuring that there are no naming conflicts. If necessary, adjust the variable name to avoid conflicts.

import 'constants.Dart' as Constants;

Creating the URL for the HTTP Request

In this step, we will create the URL for the HTTP request. The URL represents the path to the API endpoint. Refer to OpenAI's API reference to find the appropriate path for the specific API you are using.

Here's an example of how to add the URL path to the code:

final url = "https://api.openai.com/v1/your-api-endpoint";

Adding the Header for Authorization

To authenticate our API calls, we need to add the header for authorization. The header should include the API secret key. This ensures that each API request is properly validated by OpenAI's server.

Here's an example of how to add the authorization header:

final headers = {
  'Authorization': 'Bearer $apiSecretKey',
};

Creating the Request Body

Next, we need to create the request body for the API call. The request body includes parameters required by the API, such as the file and the model to be used. Ensure that the field names match those specified in the API documentation to avoid any errors.

Here's an example of how to add the request body parameters for the Whisper API:

final body = {
  'file': 'path/to/audio/file',
  'model': 'whisper-1',
  // Additional optional parameters can be added here
};

Executing the HTTP Request

Once we have set up the URL, headers, and request body, we can proceed to execute the HTTP request. To do this, we use the http package to send the request and receive the response.

Here's an example of how to execute the HTTP request:

final response = await http.post(
  Uri.parse(url),
  headers: headers,
  body: body,
);

// Check the status code and handle the response
if (response.statusCode == 200) {
  // Handle a successful response
} else {
  // Handle an error response
}

Decoding and Displaying the Response Data

After receiving the response from the API call, we need to decode the response data and display the transcribed text in our Flutter application. The structure of the response data is typically in JSON format, containing the transcription text.

Here's an example of how to decode and display the response data:

final responseData = json.decode(response.body);
final transcriptionText = responseData['transcription'];

setState(() {
  textWidgetText = transcriptionText;
});

Conclusion

Congratulations! You have successfully learned how to create a speech-to-text application in Flutter using OpenAI's Whisper API. This powerful API enables accurate and efficient speech recognition, opening up possibilities for various applications. Remember to keep your API secret key secure and avoid sharing it publicly. Feel free to explore advanced features and customize the application to suit your needs.

Thank you for watching this tutorial. Don't forget to like, share, and subscribe to my channel for more exciting tutorials. See you in the next video!

Highlights

• Create a speech-to-text application in Flutter using OpenAI's Whisper API
• Transcribe audio files from different languages into English
• Use the HTTP and file picker packages for file handling
• Obtain the API secret key from OpenAI's website
• Build the URL and headers for the API request
• Decode and display the response data in your application
• Securely store the API secret key to prevent unauthorized access

FAQ

Q: Can I transcribe audio files in languages other than English? A: Yes, OpenAI's Whisper API supports transcribing multiple languages into English. You can easily convert speech from different languages into text using this API.

Q: How can I ensure the security of my API secret key? A: It is crucial to keep your API secret key secure and avoid sharing it with anyone. Store the key in a separate file that can be ignored when pushing the code to public repositories. This way, the key won't be leaked to the public.

Q: Are there any additional parameters I can use when calling the Whisper API? A: Yes, besides the mandatory parameters like file and model, there are optional parameters you can use, such as language and format. Refer to the API documentation for more information on these optional parameters.

Q: Can I use the Whisper API for real-time speech recognition? A: The Whisper API is primarily designed for processing audio files. While real-time speech recognition is possible, it requires additional implementation to handle audio streaming and continuous transcription.

Q: What formats of audio files does the Whisper API support? A: The Whisper API supports various formats, including m4a, mp3, and mp4. Make sure the audio file you provide is in one of these supported formats for successful transcription.