Bringing It All Together: Chunked Transcription and Automated Summarization

Welcome back! This is the last lesson in this section, where you’ll bring together everything you’ve learned to build a complete, end-to-end transcription and summarization pipeline in C#. In this lesson, you’ll split a long audio file into manageable chunks, transcribe each chunk, combine the results, use GPT to generate an automated summary, and learn how to save the full transcript and summary to files for future reference.

When working with long audio files, it’s not practical to transcribe the entire file in one go. Instead, as before, you will split the audio into smaller, 30-second chunks and transcribe each chunk individually. After that, you’ll combine the transcriptions into a single full transcript, summarize it using an AI-powered function, and save both the transcript and summary to files. This approach is efficient, scalable, and prepares you for real-world scenarios where audio files can be long and complex.

Here’s a quick overview of the end-to-end process you’ll implement:

• Audio Chunking: Split long audio files into manageable 30-second segments.
• Chunk Transcription: Transcribe each audio chunk separately to avoid memory and processing issues.
• Transcript Combination: Merge all chunk transcriptions into a single, complete transcript.
• Automated Summarization: Use an AI model to generate a concise summary of the full transcript.
• Saving Results: Write both the full transcript and the summary to files for future reference.
• Cleanup: Remove any temporary files created during the chunking and transcription process.

By following these steps, you’ll be able to handle audio files of any length and automate the process of transcription and summarization from start to finish.

As you’ve done in previous lessons, you’ll use the AudioProcessor to split the audio into 30-second chunks and then transcribe each chunk using the TranscriptionService. The process is the same as before: split, transcribe, and collect the results for each chunk.

Once you have the transcriptions for each chunk, you need to combine them into a single, complete transcript. This is necessary so you can work with the entire content of the audio file as one block of text, which is required for summarization and saving the results.

string fullTranscript = string.Join(" ", transcriptions);

Now, fullTranscript contains the entire transcription of your audio file, ready for further processing.

With the full transcript ready, you can generate a summary using the SummarizeTranscript method from the TranscriptionService class. This method uses a chat model (such as GPT-4o) to read the transcript and produce a concise summary that highlights the main points, decisions, and outcomes.

Here’s how the SummarizeTranscript method works in detail. It first checks if the transcript is empty, and if so, returns a message indicating that no summary can be generated. If the transcript contains text, it prepares a list of chat messages: a system message that instructs the model to act as a skilled summarizer, and a user message containing the transcript itself. These messages are sent to the OpenAI API using the chat client, which processes the input and generates a summary. The method then extracts and returns the summary text from the model’s response.

public string SummarizeTranscript(string transcript)
{
    if (string.IsNullOrWhiteSpace(transcript))
    {
        return "The transcript was empty; no summary could be generated.";
    }

    // Prepare the list of messages to send to the chat model.
    var chatMessages = new List<ChatMessage>
    {
        new SystemChatMessage("You are a highly skilled AI assistant trained to create concise and accurate summaries of long transcripts. Your goal is to provide a summary that captures the key points, decisions, and outcomes from the text."),
        new UserChatMessage(transcript)
    };
    
    // Send the chat messages to the OpenAI API and get the summary.
    ChatCompletion completion = _chatClient.CompleteChat(chatMessages);

    // Return the summary text from the model’s response.
    return completion.Content[0].Text;
}

This approach allows you to automatically generate a summary for any transcript, making it much easier to quickly understand the main content of the audio without reading the entire transcript.

Console.WriteLine("\nSummarizing the transcript...");
string summary = transcriptionService.SummarizeTranscript(fullTranscript);

Console.WriteLine("\n--- Transcript Summary ---");
Console.WriteLine(summary);

The summary is generated automatically and provides a quick way to understand the main content of the audio without reading the entire transcript.

After generating the transcript and summary, it’s important to save them for future use. The TranscriptionService provides a convenient method for this. The SaveTextToFile method ensures that the directory for the file exists before writing the text to the specified file path. If the directory does not exist, it is created automatically, and then the text is written to the file.

Here’s how the SaveTextToFile method works:

public void SaveTextToFile(string text, string filePath)
{
    // Ensure the directory exists before writing the file.
    string? directory = Path.GetDirectoryName(filePath);
    if (directory != null)
    {
        Directory.CreateDirectory(directory);
    }

    File.WriteAllText(filePath, text);
}

This method makes it easy to reliably save both the full transcript and the summary, regardless of whether the target directory already exists.

string transcriptionPath = Path.Combine("Assets", "transcription.txt");
string summaryPath = Path.Combine("Assets", "summary.txt");

transcriptionService.SaveTextToFile(fullTranscript, transcriptionPath);
transcriptionService.SaveTextToFile(summary, summaryPath);

Console.WriteLine("\nTranscription and summary saved to the Assets directory.");

The SaveTextToFile method ensures the directory exists and writes the provided text to the specified file path.

As before, you should clean up any temporary chunk files created during the process to keep your workspace tidy.

In this final lesson, you learned how to bring together all the steps needed to process long audio files: splitting them into manageable chunks, transcribing each chunk, combining the results, generating an automated summary, and saving the outputs. This end-to-end workflow allows you to handle real-world audio files of any length, automate the transcription and summarization process, and store the results for later use. You are now ready to practice these skills in the upcoming exercises!

Well done and good luck in your future studies!