We use smol-podcaster to take care of most of Latent Space transcription work. What it will do for you:
- Generate a clean, diarized transcript of the podcast with speaker labels and timestamps
- Generate a list of chapters with timestamps for the episode
- Give you title ideas based on previous ones (modify the prompt to give examples of your own, it comes with Latent Space ones)
- Give you ideas for tweets to announce the podcast
To run:
python smol_podcaster.py AUDIO_FILE_URL GUEST_NAME NUMBER_OF_SPEAKERS
The URL needs to be a direct download link, it can't be a GDrive. For files <100MB you can use tmpfiles.org, otherwise Dropbox. For example:
python smol_podcaster.py "https://dl.dropboxusercontent.com/XXXX" "Tianqi" 3
The script will automatically switch https://www.dropbox.com to https://dl.dropboxusercontent.com in the link.
If you want to run a bunch in parallel (or remotely) you can use the web UI + celery. Before running, you'll need a broker for celery (I use RabbitMQ).
celery -A tasks worker --loglevel=INFO
flask --app web.py --debug run
Then simply go to localhost:5000 and fill out the form. The files will be saved locally as /podcast-results just like the cli version.
Activate virtualenv with
source venv/bin/activate
Install dependencies with
pip install -r requirements.txt
Make a copy of the .env.sample and replace it with your keys:
mv .env.sample .env
Run with the command above
MIT License