This model is intended to be used for automatic podcast summarisation. As creator provided descriptions were used for training, the model also learned to generate promotional material (links, hashtags, etc) in its summaries, as such some post processing may be required on the model's outputs.

If using on Colab, the instance will crash if the number of tokens in the transcript exceeds 7000. I discovered that the model generated reasonable summaries even when the podcast transcript was truncated to reduce the number of tokens.

How to use

The model can be used with the summarisation as follows:

python

from transformers import pipeline

summarizer = pipeline("summarization", model="paulowoicho/t5-podcast-summarisation", tokenizer="paulowoicho/t5-podcast-summarisation")
summary = summarizer(podcast_transcript, min_length=5, max_length=20)

print(summary[0]['summary_text'])

Training data

This model is the result of fine-tuning t5-base on the Spotify Podcast Dataset. Pre-processing was done on the original data before fine-tuning.

Training procedure

Training was largely based on Fine-tune T5 for Summarization by Abhishek Kumar Mishra