Python on

Pro Tools Markers from Resolve CSV Markers

Thu, 06 Nov 2025 19:49:39 -0800

Just a little sketch of this; it’s very easy to whip something like this up in a few minutes with py-ptsl.

import csv
import sys, re

import ptsl
from ptsl import PTSL_pb2 as pt


def create_marker(client: ptsl.Client, time: str, notes: str):
 print("Time: {}\n\t{}".format(time, notes))

 result = client.run_command(pt.CId_CreateMemoryLocation, {
 'name': notes[0:32],
 'start_time': time,
 'end_time': time,
 'time_properties': 'TProperties_Marker',
 'reference': 'MLReference_Absolute',
 'comments': notes,
 'color_index': 7,
 'location': 'MarkerLocation_NamedRuler',
 'track_name': 'Robert Notes'
 })

 print(result)

def main():
 client = ptsl.Client(company_name="Squad 51",
 application_name='csv_import')
 for p in sys.argv[1:]:
 with open(p, "r") as f:
 table = csv.DictReader(f)
 for row in table:
 time = row['Record In']
 notes = row['Notes'].strip()
 if md := re.match(r'^Marker \d+ - (.*)$', notes):
 notes = md.group(1)

 create_marker(client, time, notes)

 client.close()


if __name__ == "__main__":
 main()

UCSINFER for Renaming Sounds

Sat, 30 Aug 2025 21:23:54 -0700

After goofing around a bit with the ucsinfer gather and evaluate I’ve built out the recommend feature a bit.

Note 9/6/2025: This article was written around the time ucsinfer was at commit 1a42d7d9. There have been signififcant changes to the codebase since that time, including enhancements to the recommend feature, but it should still mostly work the way depicted here. Check the online documentation for all the latest updates.

Category inference experiments with UCSINFER

Mon, 25 Aug 2025 14:24:06 -0700

I’ve had some success auto-categorizing sounds into their corresponding UCS categories by using an LLM and the technique of sentence embedding. I’m writing up my process here and hope people find it useful and can maybe take this forward to do other things.

This is also my first post to the “Notebook” and am testing the posting system.

How I Learned What ChatGPT Can’t Do

One of my original experiments with ChatGPT was to try to use it to automatically select a UCS category for a given text description. This seemed like exactly the kind of problem ChatGPT should be able to solve well: it knows that a Ford is a car, that explosions go bang and may invovlve gasoline or black powder, inferring a category given the text description should be easy.