This crashes after using too much ram, so I'm thinking it's not the way to go. Silly to make something that relies on paying money to run it. Finetuning the model around new data would require learning the new dataformat but have reusable work around backpropagating information to the model weights, which I'm planning to do anyway. But maybe it would make sense to just finetune the model around something small, such as the heuristic idea. I'm thinking of spending some time just looking into things. Unsure. What seems exciting is maybe just finetuning the model around human data :/ . There's a lot of manual transcription available for this recording already, and it is easy to generate by hand. I'm thinking this approach could be generalised so as to require only a little example data, and learn the rest. The pattern space is small compared to the capacity of the model, since the output is limited and the speech is repetitive. ("pattern space" is a phrase i just made up for the space of relations between the data and its intended meaning) This is hard for me. It's hard to think about nonnormative transfer learning. It's hard! But I just posted about it to the list. What did I post?