In previous posts, I outlined the process of downloading and organizing thousands of podcast episodes for my AI-driven project. After addressing the chaos of managing and cleaning up nearly 7,000 files, the next hurdle became clear: transcription. Converting all of these audio files into readable, searchable text would unlock the real potential of my dataset, allowing me to analyze, tag, and connect ideas across episodes. Since then, I’ve expanded my collection to over 10,000 episodes, further increasing the importance of finding a scalable transcription solution.

Why is transcription so critical? Most AI tools available today aren’t optimized to handle audio data natively. They need input in a format they can process—typically text. Without transcription, it would be nearly impossible for my models to work with the podcast content, limiting their ability to understand the material, extract insights, or generate meaningful connections. Converting audio into text not only makes the data usable by AI models but also allows for deeper analysis, such as searching across episodes, generating summaries, and identifying recurring themes.

In this post, I’ll explore the various transcription methods I considered, from cloud services to local AI solutions, and how I ultimately arrived at the right balance of speed, accuracy, and cost.

What Makes a Good Transcription?

Before diving into the transcription options I explored, it’s important to outline what I consider to be the key elements of a good transcription. When working with large amounts of audio data—like podcasts—the quality of the transcription can make or break the usability of the resulting text. Here are the main criteria I looked for:

• Accuracy: The most obvious requirement is that the transcription needs to be accurate. It should capture what is said without altering the meaning. Misinterpretations, skipped words, or incorrect phrasing can lead to significant misunderstandings, especially when trying to analyze data from hours of dialogue.
• Speaker Diarization: Diarization is the process of distinguishing and labeling different speakers in an audio recording. Many of the podcasts in my dataset feature multiple speakers, and a good transcription should clearly indicate who is speaking at any given time. This makes the conversation easier to follow and is essential for both readability and for further processing, like analyzing individual speaker contributions or summarizing conversations.
• Punctuation and Formatting: Transcriptions need to be more than a raw dump of words. Proper punctuation and sentence structure make the resulting text more readable and usable for downstream tasks like summarization or natural language processing.
• Identifying Music and Sound Effects: Many podcasts feature music, sound effects, or background ambiance that are integral to the listening experience. A good transcription should be able to note when these elements occur, providing context about their role in the episode. This is especially important for audio that is heavily produced, as these non-verbal elements often contribute to the overall meaning or mood.
• Scalability: Finally, when dealing with tens of thousands of podcast episodes, scalability becomes critical. A transcription tool should not only work well for a single episode but also maintain performance when scaled to thousands of hours of audio. The ability to process large volumes of data efficiently without sacrificing quality is a key factor for a project of this scale.

These criteria shaped my approach to evaluating different transcription tools, helping me determine what worked—and what didn’t—for my specific needs.

Using Gemini for Transcription: A First Attempt

Since I work with Gemini and its APIs professionally (about me), I saw this transcription project as an opportunity to deepen my understanding of the system’s capabilities. My early experiments with Gemini were promising; the model produced highly accurate, diarized transcriptions for the first few podcast episodes I tested. I was excited by the results and the prospect of integrating Gemini into my workflow for this project. It seemed like a perfect fit—Gemini was delivering exactly what I needed in terms of transcription accuracy, making me optimistic about scaling this approach.

Early Success and Optimism

In those initial tests, Gemini excelled in several areas. The transcriptions were accurate, the diarization was clear, and the output was well-formatted. Given Gemini’s strength in understanding context and language, the transcripts felt polished, even in conversations with overlapping speech or complex dialogue. This early success gave me confidence that I had found a tool that could handle my vast dataset of podcasts while maintaining high quality.

The Challenges of Scaling

As I continued to test Gemini on a larger scale, I encountered two key issues that ultimately made the tool unsuitable for this project.

The biggest challenge was recitation errors. The Gemini API includes a mechanism that prevents it from returning text if it detects that it might be reciting copyrighted information. While this is an understandable safeguard, it became a major roadblock for my use case. Given that my project is dependent on converting copyrighted audio content into text, it wasn’t surprising that Gemini flagged some of this content during its recitation checks. However, when this error occurred, Gemini didn’t return any transcription, making the tool unreliable for my needs. I required a solution that could consistently transcribe all the audio I was working with, not just portions of it.

That said, when Gemini did return transcriptions, the quality was excellent. For instance, here’s a sample from one of the podcasts I processed using Gemini:

Where Does All The TSA Stuff Go?
0:00 - Intro music playing.
1:00 - [SOUND] Transition to podcast
1:01 - Kimberly: Hi, this is Kimberly, and we're at New York airport, and we just had our snow globe 
confiscated.
1:08 - Kimberly: Yeah, we're so pissed, and we want to know who gets all of the confiscated stuff, 
where does it go, and will we ever be able to even get our snow globe back?

In addition to the recitation issue, I didn’t want to rely on Gemini for some transcriptions and another tool for the rest. For this project, it was important to have a consistent output format across all my transcriptions. Switching between tools would introduce inconsistencies in the formatting and potentially complicate the next stages of analysis. I needed a single solution that could handle the entire podcast archive.

Using Whisper for High-Quality AI Transcription

After experiencing challenges with Gemini, I turned to OpenAI’s Whisper, a model specifically designed for speech recognition and transcription. Whisper is an open-source tool known for its accuracy in handling complex audio environments. Given that my podcast collection spans a variety of formats and sound qualities, Whisper quickly emerged as a viable solution.

Why Whisper?

• Accuracy: Whisper consistently delivered highly accurate transcriptions, even in cases with challenging audio quality, background noise, or overlapping speakers. It also performed well with speakers of different accents and speech patterns, which is critical for the diversity of content I’m working with.
• Diarization: While Whisper doesn’t have diarization built-in, its accuracy with speech segmentation allowed for easy integration with additional tools to identify and separate speakers. This flexibility allowed me to maintain clear, speaker-specific transcripts.
• Open Source Flexibility: Whisper’s open-source nature allowed me to deploy it locally on my Proxmox setup, leveraging the full power of my NVIDIA RTX 4090 GPU. This setup made it possible to transcribe podcasts in near real-time, which was crucial for processing a large dataset efficiently.

Performance on My Homelab Setup

By running Whisper locally with GPU acceleration, I saw significant improvements in processing time. For shorter podcasts, Whisper was able to transcribe episodes in a matter of minutes, while longer episodes could be transcribed in near real-time. This speed, combined with its accuracy, made Whisper a strong contender for handling my entire collection of over 10,000 episodes.

For instance, here’s the same podcast episode that was transcribed with Whisper:

Hi, this is Kimberly.
And we're at Newark Airport.
And we just had our snow globe confiscated.
Yeah, we're so pissed.
And we want to know who gets all of the confiscated stuff.
Where does it go?
And will we ever be able to even get our snow globe back?

Challenges and Considerations

While Whisper excelled in many areas, one consideration is its resource demand. Running Whisper locally with GPU acceleration requires substantial computational resources. For users without access to powerful hardware, this could be a limitation. Whisper also lacks built-in diarization, which means it cannot automatically differentiate between speakers. This requires additional post-processing or integration with other tools to achieve the same level of speaker clarity. However, for my setup, the performance trade-off was worth it, as it allowed me to maintain full control over the transcription process without relying on external services.

Comparing Transcription Methods and Moving Forward

After testing both Gemini and Whisper, it became clear that each tool has its strengths, but Whisper ultimately emerged as the best option for my project’s needs. While Gemini delivered higher-quality transcriptions overall, the recitation errors and lack of reliability when dealing with copyrighted material made it unsuitable for handling my entire dataset. Whisper, on the other hand, provided consistent, highly accurate transcriptions across the board and scaled well to the volume of audio I needed to process.

Gemini’s Strengths and Limitations

• Strengths: Gemini produced extremely polished and accurate transcriptions, outperforming Whisper in many cases. The diarization was clear, and the formatting made the transcripts easy to read and analyze.
• Limitations: Despite its transcription quality, Gemini’s API recitation checks became a major roadblock, which made it unreliable for my use case. Additionally, I needed a single solution that could provide consistent output across all episodes, which Gemini couldn’t guarantee due to these errors.

Whisper’s Strengths and Limitations

• Strengths: Whisper stood out for its high accuracy, scalability, and open-source flexibility. Running Whisper locally allowed me to transcribe thousands of episodes efficiently, while its robust handling of varied audio content—from background noise to multiple speakers—was a major advantage.
• Limitations: Whisper lacks built-in diarization, which means it cannot automatically differentiate between speakers. This requires additional post-processing or integration with other tools to achieve the same level of speaker clarity. Additionally, Whisper demands significant computational resources, which could be a barrier for users without access to powerful hardware.

Final Thoughts

As I move forward with this project, Whisper will be my go-to tool for transcribing the remaining episodes. Its ability to process large amounts of audio data reliably and consistently has made it the clear winner. While there may still be room for further exploration—particularly around post-processing clean-up or integrating diarization tools—Whisper has given me the foundation I need to turn my podcast archive into a fully searchable, AI-powered dataset.

In my next post, I’ll outline how I built my transcription system using Whisper to handle all of these episodes. It was a unique experience, as I used a model to write the entire application for this project. Stay tuned for a deep dive into the system’s architecture and the steps I took to automate the transcription process at scale.