cast2md 2026.1

Podcast transcription service - download episodes via RSS and transcribe with Whisper

cast2md

Podcast transcription service - download episodes via RSS and transcribe with Whisper. Automatically downloads publisher-provided transcripts when available (Podcasting 2.0) or fetches auto-generated transcripts from Pocket Casts.

Note: This is a personal project under active development. I'm sharing it in case others find it useful, but I'm not currently providing support or reviewing pull requests.

Features

• iTunes URL Support: Add podcasts via Apple Podcasts URLs (automatically resolves to RSS)
• RSS Feed Management: Add podcast feeds and automatically discover new episodes
• External Transcript Downloads: Fetches transcripts from Podcasting 2.0 tags and Pocket Casts before falling back to Whisper
• Whisper Transcription: Transcribe audio using faster-whisper or mlx-whisper
• Distributed Transcription: Use remote machines (M4 Macs, GPU PCs) to transcribe in parallel
• Full-Text Search: Search across episode metadata and transcripts
• Web Interface: Manage feeds, view episodes, and monitor progress
• REST API: Full API for integration with other tools
• MCP Server: Claude integration via Model Context Protocol

Installation

Docker

git clone https://github.com/meltforce/cast2md.git
cd cast2md
docker compose up -d

Manual Installation

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and install
git clone https://github.com/meltforce/cast2md.git
cd cast2md
uv sync --frozen

# Configure
cp .env.example .env
# Edit .env with your settings

# Initialize and run
uv run cast2md init-db
uv run cast2md serve

Configuration

Create a .env file:

DATABASE_PATH=./data/cast2md.db
STORAGE_PATH=./data/media
TEMP_DOWNLOAD_PATH=./data/temp

# Whisper settings
WHISPER_MODEL=medium          # tiny, base, small, medium, large-v3
WHISPER_DEVICE=cpu            # cpu or cuda
WHISPER_COMPUTE_TYPE=int8     # int8, float16, float32

Whisper Models

Model	Quality	Speed (CPU)	RAM
tiny	Basic	~10x realtime	1 GB
base	Good	~5x realtime	2 GB
small	Very good	~2x realtime	3 GB
medium	Excellent	~1x realtime	6 GB
large-v3	Best	~0.3x realtime	12 GB

Usage

Web Interface

Access at http://localhost:8000

• Feeds: Add and manage podcast RSS feeds
• Episodes: View episodes and transcription status
• Search: Search across titles, descriptions, and transcripts
• Admin: Monitor system health and processing queue

CLI

# Add a podcast (RSS or Apple Podcasts URL)
cast2md add-feed "https://example.com/feed.xml"
cast2md add-feed "https://podcasts.apple.com/us/podcast/example/id123456"

# List and manage
cast2md list-feeds
cast2md list-episodes <feed_id>

# Process episodes
cast2md download <episode_id>
cast2md transcribe <episode_id>
cast2md process <episode_id>  # download + transcribe

# Server
cast2md serve --host 0.0.0.0 --port 8000

# Backup/restore
cast2md backup -o backup.sql
cast2md restore backup.sql

MCP Server (Claude Integration)

{
  "mcpServers": {
    "podcasts": {
      "command": "/path/to/cast2md",
      "args": ["mcp"]
    }
  }
}

Available tools: search_transcripts, search_episodes, queue_episode, add_feed, refresh_feed

Distributed Transcription

Run transcription workers on remote machines:

# On remote machine (Mac with MLX, GPU PC)
cast2md node register --server http://server:8000 --name "Worker Name"
cast2md node start

See Distributed Transcription Setup for details.

API

Endpoint	Description
GET /api/feeds	List feeds
POST /api/feeds	Add feed
GET /api/episodes/{id}	Episode details
GET /api/episodes/{id}/transcript	Download transcript
POST /api/queue/episodes/{id}/process	Queue download
POST /api/queue/episodes/{id}/transcribe	Queue transcription
GET /api/queue/status	Queue status

Development

uv sync
uv run cast2md serve --reload
uv run pytest

License

MIT