Custom Summary allows you to distill a piece of audio into a few impactful sentences. You can give the model context to obtain more targeted results while outputting the results in a variety of formats described in human language.
How you want the summary to be returned. This can be any text. Examples: “TLDR”, “bullet points”
Context to provide the model. This can be a string or a free-form JSON value.
The model that is used for the final prompt after compression is performed.
Custom formatted transcript data. Maximum size is the context limit of the selected model, which defaults to 100000. Use either transcript_ids or input_text as input into LeMUR.
Max output size in tokens, up to 4000
The temperature to use for the model. Higher values result in answers that are more creative, lower values are more conservative. Can be any value between 0.0 and 1.0 inclusive.
A list of completed transcripts with text. Up to a maximum of 100 files or 100 hours, whichever is lower. Use either transcript_ids or input_text as input into LeMUR.
The ID of the LeMUR request
The response generated by LeMUR.
The usage numbers for the LeMUR request