gencast 1.0.0

Generate conversational podcasts from documents using AI

gencast

Generate conversational podcasts from documents using AI. A cost-effective, customisable, local-first alternative to NotebookLM.

gencast notebook.yaml  ->  podcast.m4a (with embedded subtitles)

Install

pip install gencast

System dependency: ffmpeg (for audio combining and M4A muxing).

API keys (export or use gencast init to be prompted):

export OPENAI_API_KEY="sk-..."          # required (TTS + Whisper)
export ANTHROPIC_API_KEY="sk-ant-..."   # required (default outline + transcript)
export MISTRAL_API_KEY="..."            # optional (better PDF extraction)

Quickstart

gencast init                        # interactive notebook wizard
gencast preview notebook.yaml       # outline-only dry run (free)
gencast generate notebook.yaml      # full pipeline -> out/<basename>.m4a

Or one-shot from a markdown file (uses default profiles):

gencast generate path/to/lecture.md

Three-axis profile system

Each notebook composes three orthogonal profiles:

speaker_profile: revision-duo       # WHO speaks (1-4 voices, personas)
episode_profile: exam-revision      # WHAT kind of podcast (briefing, segments, models)
room_profile:    small-room         # HOW it sounds (spatial pipeline)

List bundled profiles:

gencast list-profiles --type speakers
gencast list-profiles --type episodes
gencast list-profiles --type rooms

Profiles cascade: ./gencast/profiles/<kind>/<name>.yaml (project)

~/.config/gencast/profiles/<kind>/<name>.yaml (XDG) bundled defaults. Override per-notebook via overrides: block in the notebook YAML.

Worked example

./photosynthesis/notebook.yaml:

title: Photosynthesis revision
sources:
  - lectures/photosynthesis.md
  - lectures/calvin-cycle.md
speaker_profile: revision-duo
episode_profile: exam-revision
room_profile: small-room
output:
  basename: photosynthesis-revision
  formats: [m4a]
overrides:
  briefing_suffix: |
    Pay specific attention to the distinction between the light-dependent
    reactions and the Calvin cycle. Include one worked Q&A on this distinction.

gencast generate photosynthesis/notebook.yaml
# -> photosynthesis/out/photosynthesis-revision.m4a

Cost

Typical 10-min podcast (~5K-token source, 6 segments, 2 speakers):

Component	Default model	Cost
Outline	claude-haiku-4-5	~$0.005
Transcript (with prompt cache)	claude-sonnet-4-5	~$0.10
TTS	openai/tts-1-hd	~$0.06
Subtitles	native (no Whisper)	$0.00
Total		~$0.17

Use --model overrides or different episode profiles to trade quality for cost.

Caches

• TTS cache -- ~/.cache/gencast/tts/ -- always on. Re-runs cost only changed sentences.
• LLM cache -- ~/.cache/gencast/llm/ -- opt-in via --cache-llm. Off by default since dialogue is non-deterministic.
• PDF extract cache -- ~/.cache/gencast/extract/ -- always on for Mistral PDF extraction.

Manage:

gencast cache status
gencast cache clear --type tts --yes

CLI reference

gencast NB.yaml                       generate (alias for `gencast generate NB.yaml`)
gencast init [--copy NB] [--minimal]  interactive notebook wizard
gencast preview NB.yaml               outline-only dry run
gencast generate NB.yaml              full pipeline -> m4a + sidecars
gencast list-profiles [--type X]      enumerate profiles in cascade
gencast subtitle audio.mp3            re-subtitle external audio (Whisper)
gencast cache status [--type X]       inspect cache sizes
gencast cache clear [--type X] [--yes]

Verbosity: -v, -vv, -q, --silent, --log-file PATH.

Tests

pytest tests/unit                          # fast, no API calls
pytest tests/component                     # vcrpy cassettes, no keys needed once recorded
GENCAST_TEST_E2E=1 pytest tests/e2e        # real API calls, costs a few cents
GENCAST_TEST_AUDIO=1 pytest tests/audio    # TTS + spatial audio (requires OPENAI_API_KEY)

Specs and design

• v1.0 design
• Plan A -- foundation
• Plan B -- pipeline
• Plan C -- finishing
• Future work

License

MIT.