Maybe he could transfer the cadence over, since that’s what I have to filter out as “not information” as I listen to the podcast. I’m not aware of any reliable way to do that however, since the highest quality synthesis and voice recognition methods rely on pretrained neural networks. If you could get reliable millisecond-level timestamps for when each word starts and stops, both in the original and in the initial generated speech, you could use the Rubberband library to build the final speech as a piecewise assembly of scaled segments such that the word boundary timings exactly match in the real voice input and the synthesized output. I don’t particularly think that would sound great without substantial tuning, partially because the process of determining the exact time a word starts and ends is a matter of taste, so it may not be worth it under current conditions.
Maybe he could transfer the cadence over, since that’s what I have to filter out as “not information” as I listen to the podcast. I’m not aware of any reliable way to do that however, since the highest quality synthesis and voice recognition methods rely on pretrained neural networks. If you could get reliable millisecond-level timestamps for when each word starts and stops, both in the original and in the initial generated speech, you could use the Rubberband library to build the final speech as a piecewise assembly of scaled segments such that the word boundary timings exactly match in the real voice input and the synthesized output. I don’t particularly think that would sound great without substantial tuning, partially because the process of determining the exact time a word starts and ends is a matter of taste, so it may not be worth it under current conditions.