Ask The Game, the Build Log

Speaker ID, Database Timeouts &  Content Hashing

Ever tried to build something that seemed straightforward, only to discover it's like peeling an onion? Each layer reveals another challenge you didn't see coming.

That's exactly what happened with my podcast processing pipeline for Alex Hormozi's "The Game" podcast. What started as a simple ETL project turned into an in-depth exploration of speaker identification algorithms, database optimisation, and content integrity systems.

Let me walk you through the journey.

The Vision (AKA "How Hard Could It Be?")

The goal was straightforward: build an AI-powered system that could process Alex Hormozi's podcast episodes and create a searchable knowledge base. You know, the kind of thing where you could ask "What did Alex say about sales funnels in episode 247?" and receive intelligent answers.

The pipeline needed to:

Sounds reasonable, right?

The Great Speaker Mystery

Here's where things became interesting. The pipeline was working... kind of. Episodes were getting processed, transcripts were being generated, and chunks were being stored in the database.

But there was one small issue: every speaker was labeled as "Unknown Guest."

Not ideal when you're trying to build an Alex Hormozi knowledge base and can't tell which wisdom actually came from Alex.

The Detective Work

After diving into the logs, the issue became clear. The speaker identification system had two parts:

  1. Pyannote.audio for diarization (figuring out when different people speak)
  2. SpeechBrain for speaker recognition (matching voices to known speakers)

The problem? A classic tensor dimension mismatch that was causing the similarity calculation to crash:

Error: XB must be a 2-dimensional array.

When the speaker identification failed, the system defaulted to generic labels. Hence: "Unknown Guest" everywhere.

The Fix That Changed Everything

The solution involved several key improvements:

Proper tensor reshaping

Ensure the reference voiceprint and current speaker embeddings were properly formatted as 2D arrays for the similarity calculation.

Stereo-to-mono conversion

The reference voiceprint was created from mono audio, but the pipeline was processing stereo. Consistency matters in audio processing.

Robust error handling

Instead of crashing when the speaker ID fails, the system now gracefully recovers and continues processing.

Enhanced debugging

Added comprehensive logging to see exactly what happened at each step.

The result?

Speaker identification went from 0% accuracy to correctly identifying Alex Hormozi with 85%+ confidence scores.

The Database Timeout Drama

Another challenge emerged when speaker identification was working beautifully: database timeouts.

The pipeline would successfully transcribe a whole episode (sometimes 19,000+ words), identify all the speakers correctly, generate embeddings... and then crash when trying to save to Supabase.

The culprit? I was storing the entire Deepgram response JSON in the database. For a full episode, that's a massive payload. Supabase wasn't happy about it.

The Elegant Solution

Instead of abandoning the raw transcription data (valuable for future reanalysis), I implemented a hybrid approach:

Local JSON backups: Save the full Deepgram response as local files, organized by episode ID.

Lean database records: Store only the essential metadata in Supabase (summary, topics, processing status).

Smart caching: Before making expensive Deepgram API calls, check if we already have the data locally.

This solved three problems at once:

The Content Hashing Cherry on Top

With the core pipeline working, there was one more enhancement that would make the system truly production-ready: content hashing.

Here's the thing about data integrity—you don't think about it until you need it. But when building a knowledge base that people will rely on, you want mathematical proof that your content hasn't been tampered with.

SHA-256 to the Rescue

I implemented SHA-256 content hashing for every text chunk:

def generate_content_hash(text: str) -> str:
    normalized_text = text.strip()
    text_bytes = normalized_text.encode('utf-8')
    hash_object = hashlib.sha256(text_bytes)
    return hash_object.hexdigest()

This simple addition provides:

The Technical Lessons

Building this pipeline taught me several valuable lessons:

Audio Processing is Tricky

Working with audio data requires attention to details like sample rates, channel counts, and tensor shapes. Small inconsistencies can cause big problems.

API Cost Management Matters

During development, you'll rerun your pipeline dozens of times. Smart caching can save you hundreds of dollars in API fees.

Error Handling is Everything

When you're chaining together multiple AI services (speech recognition, speaker identification, embeddings), any one component can fail. Graceful degradation keeps your pipeline running.

Data Integrity Pays Off

Content hashing might seem like over-engineering, but it's the difference between a hobby project and a production system.

The Architecture That Emerged

The final pipeline looks like this:

  1. RSS Processing: Fetch and parse podcast feeds
  2. Audio Download: Smart caching with local storage
  3. Speaker Diarization: Pyannote.audio identifies when people speak
  4. Speaker Recognition: SpeechBrain + custom voiceprints identify who speaks
  5. Transcription: Deepgram with smart local caching
  6. Temporal Alignment: Match Deepgram speakers with Pyannote timeline
  7. Chunk Processing: Create semantically meaningful text segments
  8. Embedding Generation: OpenAI embeddings for semantic search
  9. Content Hashing: SHA-256 fingerprints for data integrity
  10. Database Storage: Lean records in Supabase with full data backup

The Results

The transformation was dramatic:

Before: "Unknown Guest" speakers, database timeouts, expensive API re-runs After: 85%+ speaker identification accuracy, sub-second database inserts, intelligent API caching

More importantly, the system now correctly identifies Alex Hormozi's voice and properly labels all speakers. The knowledge base actually knows what Alex said versus what his guests contributed.

What's Next?

With the pipeline solid, the next phase focuses on the user experience:

The Takeaway

Building production AI systems is like solving a puzzle where the pieces keep changing shape.

You start with a simple goal—process some podcasts—and end up learning about tensor mathematics, database optimization, cryptographic hashing, and the subtle art of audio preprocessing.

But that's what makes it interesting. Every challenge teaches you something new. Every bug fixed makes the system more robust.

And when it finally works? When you can ask "What does Alex think about pricing strategies?" and get intelligent, accurate answers sourced from the right episodes with the right attribution?

That's when you remember why you started building in the first place.

– Benoit Meunier