That's a great question, as the transcription stack landscape is evolving quickly. As the founder of an AI collaboration platform (Markhub), we've tested severa

That's a great question, as the transcription stack landscape is evolving quickly. As the founder of an AI collaboration platform (Markhub), we've tested several options for our own internal "AI teammate," MAKi. Our stack is a hybrid approach, as we've found no single service is best for everything: For Real-Time & High-Volume Transcription: We use Google's Speech-to-Text API (via Vertex AI). We found its accuracy with various accents and noisy environments to be top-tier, which is critical for turning real-world meeting audio into usable data. It's not the cheapest, but the reliability is worth it for our core product. For Bundled Transcription + Summarization (What you're asking about): You're right to look at models that can handle this in one go. We use Gemini for this exact purpose. The ability to send an audio file and get back a structured summary, not just a raw transcript, is a huge workflow accelerator. We point our audio data to the model and ask it to not only transcribe but also extract action items, which is a core feature of our platform. For Local/CLI Batching (As you mentioned): We've had great success with OpenAI's Whisper running in a containerized environment for internal, less time-sensitive tasks. Using a fine-tuned version of Whisper on a dedicated machine for batch processing can be very cost-effective. Your M4 mini should be able to handle the base models of Whisper quite well, especially using a tool like whisper.cpp. My takeaway: For a production API, a robust cloud service like Google's or a powerful model like Gemini is more reliable. For local or batch processing, a self-hosted Whisper setup is a fantastic and powerful option. Good luck!