Welcome back! You have already learned how to set up your C# environment, connect to the OpenAI API, and make your first audio transcription request. In this lesson, you will take your transcription skills to the next level by exploring advanced options that can make your transcriptions more accurate, detailed, and useful for real-world applications.

By the end of this lesson, you will know how to customize your transcription requests using additional parameters such as language selection, prompts, temperature, and timestamp granularity. This will help you get the most out of OpenAI’s transcription models and prepare you for more complex audio-to-text tasks.

OpenAI’s transcription API allows you to fine-tune your requests using the AudioTranscriptionOptions class. As of now, only the Whisper models (such as whisper-1) support advanced metadata features like word and segment timestamps, detailed response formats, and more. These options can significantly enhance your transcriptions, especially for specialized applications.

Customizing these parameters lets you adapt the transcription process for a variety of real-world needs. Whether you're building tools for education, accessibility, research, or content analysis, fine-tuning transcription requests helps you get exactly the information you need. Understanding these options is essential for leveraging the full potential of OpenAI's transcription technology.

Below, we’ll explore the most useful parameters you can set.

Providing a well-crafted prompt is one of the most effective ways to improve transcription quality in specialized or ambiguous scenarios. The Prompt parameter allows you to give the model extra context about the audio. For example, if your audio is from a language proficiency test, you can tell the model about the test or the expected topics. This is particularly helpful when the audio content is technical, contains names or jargon, or follows a certain format.

A clear, detailed prompt helps the model understand context, recognize speaker intentions, and reduce misinterpretations. Use this especially when your audio includes uncommon words, non-standard speech, or specialized subject matter.

The Language parameter allows you to specify the language of the audio using a standard language code (such as "en" for English, "fr" for French, etc). This helps the model transcribe more accurately, especially with non-English audio. In multilingual scenarios, specifying the language can prevent the model from making incorrect assumptions and ensures the output matches your requirements.

Explicitly setting the language is recommended whenever you know it, as this removes ambiguity and can improve both accuracy and speed. If the language code is omitted, the model will try to detect the language automatically, but specifying it directly is more reliable.

Transcription usually means converting spoken words into written text as accurately as possible. However, models like Whisper can sometimes interpret unclear audio, heavy accents, or ambiguous phrases in different ways. This is where the Temperature parameter comes into play—it controls how "creative" or flexible the model is when making those choices.

Lower values like 0.2 produce more predictable, stable text. Higher values like 0.7 allow for more varied interpretations, which may be useful for informal, creative, or less-structured audio content. For clear, factual recordings, a low temperature is usually best. For conversational or expressive audio, increasing the temperature can yield more natural or flexible outputs.

In most transcription scenarios, a moderate or low temperature is best to ensure reliable and consistent output. Consider raising the temperature if you notice the transcriptions are too rigid or lack nuance.

The TimestampGranularities parameter lets you specify how detailed you want the timestamps to be. You can choose to receive timestamps for each word, each segment, or both. This feature is especially useful for applications that need precise alignment between audio and text, such as language learning tools, detailed analytics, or accessibility solutions.

To access the timestamps returned in the transcription result, you can use the Words or Segments properties of the AudioTranscription object (depending on the granularity you requested). Here’s how you might print out word-level timestamps:

For segment-level timestamps, you can do:

This allows you to see exactly when each word or segment occurs in the audio, which is useful for syncing text with playback or highlighting spoken words in real time.

Enabling word-level timestamps will slightly increase the response size but gives you fine-grained control for highlighting or syncing text to audio playback.

With the ResponseFormat parameter, you can control how much detail is included in the response and how the output is structured. Several formats are available:

• Text: Only the transcribed text, no extra metadata.
• Simple: Transcribed text in a simple JSON structure.
• Verbose: Json with additional metadata, such as duration, detected language, and timestamps.
• Srt: SubRip subtitle format, useful for generating subtitles for video players.
• Vtt: WebVTT subtitle format, commonly used for web-based video players.

Choose the response format that fits your needs—Verbose for detailed metadata, or Text, Simple, Srt, or Vtt for simpler output or subtitle generation.

Once you’ve set your desired parameters, you simply pass your AudioTranscriptionOptions object to the TranscribeAudio method. Here is how you bring it all together:

The result will include the transcribed text, and—if you used the advanced options—additional metadata such as audio duration and detected language. You can print these out as needed:

A sample output might look like this:

This output shows the transcription, the duration of the audio in seconds, and the language detected by the model.

Advanced transcription options are especially valuable in real-world scenarios where you need more than just plain text. If you are building a language learning app, word-level timestamps let you highlight each word as it is spoken. In legal or medical settings, segment timestamps make it easier to review and reference specific parts of a conversation. Providing a context-specific prompt can help the model produce accurate and relevant results, particularly when working with jargon or specialized topics.

Always consider your application’s requirements. Use only the options you need: enable timestamps if you need alignment, specify language for non-English audio, and provide a prompt for better context. Remember, more detailed responses may take longer to process and can increase output size, so balance your needs with performance.

In this lesson, you learned how to enhance your audio transcription requests in C# by using advanced options like prompts, language selection, temperature, timestamp granularity, and response format. These features allow you to obtain more accurate and useful transcriptions tailored to your needs. You also saw how to set these parameters and use the enhanced metadata in your application.

Now you are ready to practice using these advanced options in your own code. In the next set of exercises, you will apply what you have learned to real-world scenarios, deepening your understanding and building confidence. Keep experimenting with different parameters to see how they affect your results—this is the best way to master advanced transcription with OpenAI’s API. Great work so far!