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Build A Transcription App with Strapi, ChatGPT, & Whisper: Part 3

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Build A Transcription App with Strapi, ChatGPT, & Whisper: Part 3

Introduction

In this third and final part of our series, we'll delve into integrating the powerful capabilities of ChatGPT and Whisper into our transcription application built using Strapi. We'll leverage these tools to enhance the accuracy and user experience of our transcription service. We'll explore how to connect these services, process transcription outputs, and refine them using ChatGPT's advanced language understanding and generation abilities.

Key Concepts and Techniques

This section will cover the main concepts and techniques we'll be using in this tutorial:

Strapi API Integration

We will use Strapi's built-in RESTful API to communicate with our front-end application. This API allows us to create, read, update, and delete transcriptions and related data.

Whisper API Integration

We will use the Whisper API, available through the OpenAI API, to perform automatic speech recognition (ASR) on audio files uploaded by users. Whisper is known for its high accuracy and multilingual capabilities.

ChatGPT API Integration

We'll connect to the ChatGPT API to enhance transcription outputs. This API enables us to leverage ChatGPT's language understanding and generation abilities to refine the transcribed text, improve context, and address any potential errors.

Error Handling and Validation

We'll implement robust error handling mechanisms to gracefully handle errors that may arise during the transcription process, such as network issues, API errors, and invalid audio files. We'll also include input validation to ensure data integrity.

Step-by-Step Guide

This section will guide you through the implementation process, starting with setting up the backend and then moving to the front-end integration.

Setting up the Backend

1.1. Install Necessary Dependencies

npm install openai axios

1.2. Create Strapi Routes

We'll create two routes in Strapi: one for handling audio uploads and another for processing transcriptions.

audioUpload.js:

'use strict';

const { createCoreController } = require('@strapi/strapi').factories;

module.exports = createCoreController('api::audio.audio', ({ strapi }) =&gt; ({
  async create(ctx) {
    const { data: { audioFile } } = ctx.request.files;

    // Validate audio file type and size

    const fileData = await strapi.plugins['upload'].services.upload.upload({
      data: audioFile,
      files: ctx.request.files,
      model: 'api::audio.audio',
    });

    // Create a new audio entry in Strapi

    const audioEntry = await strapi.entityService.create('api::audio.audio', {
      data: {
        name: audioFile.name,
        file: fileData,
      },
    });

    // Trigger transcription process (to be implemented in the next step)

    return audioEntry;
  },
}));

processTranscription.js:

'use strict';

const { createCoreController } = require('@strapi/strapi').factories;
const openai = require('openai');

module.exports = createCoreController('api::audio.audio', ({ strapi }) =&gt; ({
  async update(ctx) {
    const { id } = ctx.params;
    const { data: { transcription } } = ctx.request.body;

    // Retrieve the audio entry from Strapi

    const audioEntry = await strapi.entityService.findOne('api::audio.audio', id);

    // Update the transcription in Strapi

    await strapi.entityService.update('api::audio.audio', id, {
      data: { transcription },
    });

    return audioEntry;
  },
  async transcribe(ctx) {
    const { id } = ctx.params;

    // Retrieve the audio entry from Strapi

    const audioEntry = await strapi.entityService.findOne('api::audio.audio', id);

    // Call Whisper API for transcription

    const transcription = await transcribeAudio(audioEntry.file.url);

    // Refine transcription using ChatGPT

    const refinedTranscription = await refineTranscription(transcription);

    // Update the transcription in Strapi

    await strapi.entityService.update('api::audio.audio', id, {
      data: { transcription: refinedTranscription },
    });

    return audioEntry;
  },
}));

// Helper functions for Whisper and ChatGPT interaction

async function transcribeAudio(audioUrl) {
  // Implement Whisper API integration here
}

async function refineTranscription(transcription) {
  // Implement ChatGPT API integration here
}

1.3. Implement Whisper API Integration

const openai = require('openai');
openai.apiKey = process.env.OPENAI_API_KEY;

async function transcribeAudio(audioUrl) {
  try {
    const response = await openai.audio.transcriptions.create({
      file: audioUrl,
      model: 'whisper-1', // Adjust model based on your needs
      language: 'en', // Adjust language based on audio
    });

    return response.text;
  } catch (error) {
    console.error('Whisper transcription error:', error);
    throw error;
  }
}

1.4. Implement ChatGPT API Integration

async function refineTranscription(transcription) {
  try {
    const response = await openai.chat.completions.create({
      model: 'gpt-3.5-turbo', // Adjust model based on your needs
      messages: [
        { role: 'user', content: `Please refine the following transcription:\n\n${transcription}` },
      ],
    });

    return response.choices[0].message.content;
  } catch (error) {
    console.error('ChatGPT refinement error:', error);
    throw error;
  }
}

Setting up the Frontend

2.1. Create React App

npx create-react-app transcription-app

2.2. Install Dependencies

npm install axios

2.3. Build UI Components

Create components for file upload, transcription display, and progress indicators.

FileUpload.jsx:

import React, { useState } from 'react';
import axios from 'axios';

const FileUpload = () =&gt; {
  const [selectedFile, setSelectedFile] = useState(null);
  const [isUploading, setIsUploading] = useState(false);
  const [uploadProgress, setUploadProgress] = useState(0);

  const handleFileChange = (event) =&gt; {
    setSelectedFile(event.target.files[0]);
  };

  const handleFileUpload = async () =&gt; {
    if (!selectedFile) return;

    setIsUploading(true);

    try {
      const formData = new FormData();
      formData.append('audioFile', selectedFile);

      const response = await axios.post('/api/audio', formData, {
        onUploadProgress: (progressEvent) =&gt; {
          setUploadProgress(Math.round((progressEvent.loaded / progressEvent.total) * 100));
        },
      });

      // After upload, trigger transcription
      const transcriptionResponse = await axios.put(`/api/audio/${response.data.id}/transcribe`);

      // Display transcribed text
    } catch (error) {
      console.error('Upload or transcription error:', error);
    } finally {
      setIsUploading(false);
      setUploadProgress(0);
    }
  };

  return (
  <div>
   <input accept="audio/*" onchange="{handleFileChange}" type="file"/>
   <button disabled="{isUploading}" onclick="{handleFileUpload}">
    {isUploading ? 'Uploading...' : 'Upload'}
   </button>
   {isUploading &amp;&amp; (
   <div>
    Upload Progress: {uploadProgress}%
   </div>
   )}
  </div>
  );
};

export default FileUpload;

TranscriptionDisplay.jsx:

import React from 'react';

const TranscriptionDisplay = ({ transcription }) =&gt; {
  return (
  <div>
   <h2>
    Transcription:
   </h2>
   <p>
    {transcription}
   </p>
  </div>
  );
};

export default TranscriptionDisplay;

2.4. Integrate with Strapi API

Fetch transcription data from Strapi and update the UI.

App.jsx:

import React, { useState, useEffect } from 'react';
import FileUpload from './FileUpload';
import TranscriptionDisplay from './TranscriptionDisplay';

const App = () =&gt; {
  const [transcription, setTranscription] = useState('');

  useEffect(() =&gt; {
    // Fetch transcription data from Strapi
    const fetchTranscription = async () =&gt; {
      // ... (Implement API call to Strapi)
      // ... (Update 'transcription' state)
    };

    fetchTranscription();
  }, []);

  return (
  <div>
   <h1>
    Transcription App
   </h1>
   <fileupload>
   </fileupload>
   <transcriptiondisplay transcription="{transcription}">
   </transcriptiondisplay>
  </div>
  );
};

export default App;

Conclusion

In this tutorial, we have successfully integrated Strapi, Whisper, and ChatGPT to build a robust transcription application. We've covered the essential steps, from setting up the backend with API routes and integration to building a user-friendly front-end with file upload and transcription display functionality. This application leverages the power of open-source tools and cloud-based services to provide a seamless and accurate transcription experience.

Here are some key takeaways and best practices:

Choose the right Whisper model and language for your specific audio data.
Implement error handling and validation to ensure a reliable and robust system.
Consider using a task queue or background processing to handle transcriptions asynchronously.
Customize the ChatGPT prompts to refine the transcription according to your specific requirements.
Monitor the performance and accuracy of your system and make necessary adjustments.

This project can be further extended by incorporating features like user authentication, audio editing capabilities, and integration with other services like cloud storage. By building upon this foundation, you can create a powerful and customizable transcription application tailored to your specific needs.