Cooking Issues transcripts

Transcripts of the Cooking Issues podcast, hosted by Dave Arnold. Transcribed locally using swappable backends, searchable via a static site.

How it works

A Rake pipeline pulls episodes from two RSS feeds (Heritage Radio Network and Acast), transcribes audio to JSON, renders readable text, and generates a searchable static site with Pagefind.

RSS feeds → download audio → transcribe (JSON) → render (text) → static site

Transcribers

Set the backend with TRANSCRIBER=name.

Name	Engine	Diarization	Notes
parakeet	Parakeet TDT via parakeet-mlx	No	Fast on Apple Silicon.
whisper-cpp-large	whisper.cpp (large-v3-turbo)	No	Fast on Apple Silicon via Metal.
whisper-cpp-tdrz	whisper.cpp (small.en-tdrz)	tinydiarize	Lightweight with basic speaker turns.
sous_chef	Apple SpeechAnalyzer	No	macOS 26+ only. Word-level segments.
mlx	mlx-whisper	No	Apple Silicon via MLX.
mlx-diarize	mlx-whisper + pyannote	Yes	Speaker labels. Requires HF token.
whisperx	WhisperX	Yes	CPU-based with pyannote diarization.

Why Parakeet

I run everything locally on a MacBook Pro, which is the primary constraint. That rules out most heavyweight transcription pipelines and anything that requires more GPU than Apple Silicon provides.

Apple's SpeechAnalyzer (sous_chef) is the fastest option, but Parakeet produces noticeably better transcripts — based on informal comparison of a handful of episodes, not a rigorous benchmark. Most of the other backends (WhisperX, mlx-whisper with diarization) were too resource-intensive to run comfortably on the same machine.

There are multiple transcriber backends wired up so it's easy to re-evaluate as models and hardware improve. The transcriber table above reflects what's implemented, not what's practical to run at scale on a single laptop.

Solutions don't need to stay local — cloud APIs, hosted inference, and CI-based pipelines are all welcome. I'm not looking to purchase or rent hardware for this, but I'm open to other approaches.

What's missing

Two features would meaningfully improve transcript quality but aren't available with the current setup.

Contextual hinting would let the transcriber bias toward domain-specific vocabulary — recurring guest names, cooking terminology (rotovap, Searzall, hydrocolloids), show-specific jargon.

Diarization so that transcripts could denote speakers. Pyannote-based diarization works (mlx-diarize, whisperx) but wasn't performant enough to run locally alongside transcription. The whisper-cpp-tdrz backend offers lightweight tinydiarize, but it uses a smaller model with lower transcription quality.

Usage

# Transcribe a single episode
rake transcribe[42]

# Transcribe all episodes
rake sync

# Re-transcribe from scratch
rake retranscribe[42]

# List episodes with transcription status
rake episodes

# Use a different transcriber
TRANSCRIBER=parakeet rake transcribe[1]

# Generate the searchable static site
rake pages

# Build and serve locally
rake serve

Transcribers that use pyannote (mlx-diarize, whisperx) need a Hugging Face token with access to the pyannote/speaker-diarization-3.1 and pyannote/segmentation-3.0 gated models:

export HUGGING_FACE_TOKEN=hf_...

Output

Only the rendered text files are committed to the repo, one per episode in transcripts/<transcriber>/:

transcripts/parakeet/001-episode-1-cooking-issues-debuts.txt

The intermediate JSON from each transcriber lives in cache/<transcriber>/ and is ignored. These files contain word- or sentence-level timestamps and are substantially larger than the rendered text. I have them locally for re-rendering or analysis but they're not worth carrying in the repo.

The text renderer groups segments into paragraphs. The heuristic varies by transcriber — silence gaps for most, sentence counts for Parakeet (whose chunking overlap produces overlapping timestamps).

Static site

rake pages generates a static site in pages/ with one HTML page per episode and a Pagefind search index. rake serve builds and serves it locally on port 8000. GitHub Actions deploys it to GitHub Pages on push to main.

Search

Beyond the Pagefind-powered static site, there are two CLI search tools for querying transcripts directly. Both index from the rendered text files and store their databases in cache/.

Full-text search uses SQLite FTS5 with BM25 ranking:

bin/fts-search index [transcripts/parakeet]
bin/fts-search search "agar clarification" -n 5

Semantic search uses sentence-transformers (all-MiniLM-L6-v2) with sqlite-vec for nearest-neighbor lookup:

bin/semantic-search index [transcripts/parakeet]
bin/semantic-search search "how to clarify juice" -n 5

Full-text search is fast and needs only SQLite. Semantic search is better for fuzzy or conceptual queries but requires downloading the embedding model on first run.

Requirements

• Ruby 3.x with Bundler
• Python 3.10+ with uv (for Parakeet, MLX, and Pagefind)
• At least one transcriber's dependencies:
  • parakeet — uv (dependencies managed automatically)
  • whisper-cpp — whisper-cli (Homebrew or build from source)
  • sous_chef — macOS 26+, Xcode 26+ (builds automatically)
  • mlx, mlx-diarize, whisperx — uv