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2025-08-24try finetuning encoder-decoder model like t5gemmaHEADmastervin1-1/+1
Unfortunately once it finishes the finetuning process, it doesn't actually output anything. With decoder-only models like small gemma3, it also doesn't output anything meaningful. I'm tempted to just classify each token as an ad or not, finetune an encoder like some long-context BERT, and then a post-processing script to convert back to tokens and their timestamps. May end up being more performant and efficient too...
2025-08-23add highlight/visualizer script and update system promptvin1-30/+37
2025-08-22update main scriptvin1-143/+127
2025-07-19provide better examples in system prompt and retry on errorvin1-69/+215
2025-07-19save output in data/, not where podcasts arevin1-4/+14
2025-07-19change chunking to overlapping time segmentsvin1-55/+52
2025-07-19output data in jsonlvin1-4/+6
2025-07-19respect ratelimit and change output extension to .gptvin1-40/+38
2025-07-19remove prototyping llm prints, use tqdm, use arguments as inputsvin1-140/+134
2025-07-18initial payloadvin1-0/+292