Podcast Reader

Transcribe podcast audio files or YouTube videos into readable, styled HTML transcripts with timestamps.

Uses whisper-ctranslate2 for audio files (GPU-accelerated, 4x faster than OpenAI's Whisper) and youtube-transcript-api for YouTube videos (fetches existing captions — no audio download needed).

Usage

./transcribe.sh <url-or-file> [title]

Examples

# From a YouTube video (uses existing captions, no download)
./transcribe.sh https://www.youtube.com/watch?v=VIDEO_ID "Episode Title"

# From a URL (downloads automatically)
./transcribe.sh https://media.rss.com/show/episode.mp3 "Episode 42: The Answer"

# From a local file
./transcribe.sh ~/Downloads/interview.mp3 "Interview with Dr. Smith"

# With speaker diarization
HF_TOKEN=hf_xxx ./transcribe.sh episode.mp3 "Panel Discussion"

# With chapter generation (requires Anthropic API key)
ANTHROPIC_API_KEY=sk-ant-xxx ./transcribe.sh episode.mp3 "Episode 42"

# Customize model and paragraph size
WHISPER_MODEL=medium SENTENCES=3 ./transcribe.sh episode.mp3

Output

The script produces:

• <name>.json — Transcript segments with timestamps (from Whisper or YouTube captions)
• <name>_chapters.json — Chapter markers with titles, abstracts, key points, pull quotes, and type tags (if ANTHROPIC_API_KEY is set)
• <name>.html — Styled, readable transcript with timestamp badges

For YouTube videos, <name> is the video ID (e.g., fkKh_WBT5BM.json). The title is auto-extracted from YouTube if not provided.

When chapters are generated, the HTML includes:

• Table of contents with chapter titles, timestamps, and abstracts
• Chapter sections with headings and summaries
• Key points — bullet-point summaries in a sticky right gutter (hidden on narrow screens)
• Pull quotes — standout phrases rendered as bold inline text after each chapter abstract
• Sponsor dimming — sponsor/ad segments are visually muted (hover to reveal)
• Anchor navigation — click any TOC entry to jump to that section

The HTML supports both dark and light themes automatically via prefers-color-scheme.

Requirements

• Python 3.10+
• uv for Python package management
• For audio transcription: NVIDIA GPU recommended (falls back to CPU), ffmpeg
• For YouTube: no additional requirements (captions are fetched directly)

Setup

uv venv .venv
source .venv/bin/activate
uv pip install -r requirements.txt

Or just run ./transcribe.sh — it bootstraps the venv automatically on first run.