kwindla

import asyncio
import datetime
import os
import wave
from pathlib import Path

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams

kwindla

import asyncio
import os
from datetime import datetime

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner

kwindla

# export OPENAI_API_KEY=sk_proj-...
# uv run test-gpt5.py "Write a four-line poem about LLMs."

# For a complete, single-file conversational voice agent
# example using GPT-5, see this gist:
# https://gist.github.com/kwindla/678ea297d12e24b928b636db928226fb

#  /// script
# dependencies = ["openai"]
# ///

kwindla

# export OPENAI_API_KEY=sk_proj-...
# uv run gpt-5-voice-agent.py

# /// script
# dependencies = [
#   "numba==0.61.2",
#   "openai",
#   "python-dotenv",
#   "fastapi[all]",
#   "uvicorn",

kwindla

Advice on Voice AI, June 2025

My top three pieces of advice for people getting started with voice agents.

1. Spend time up front understanding why latency and instruction following accuracy drive voice AI tech choices.

2. You will need to add significant tooling complexity as you go from proof of concept to production. Prepare for that. Especially important: build lightweight evals as early as you can.

3. The right path is: start with a proven, "best practices" tech stack -> get everything working one piece at a time -> deploy to real-world users and collect data -> then think about optimizing cost/latency/etc.

kwindla

Task

I've been experimenting as much as I can with Gemini video understanding. Multiple videos, multi-step prompts, etc.

Prompt

Analyze this YouTube video.

https://www.youtube.com/watch?v=PgyJs0jfp_o

kwindla

Funny GPT-4o inference result.

Audible in this vide: https://youtu.be/PgyJs0jfp_o?si=43CJgmk954kulmgl&t=863

Output

It sounds like we're on an intriguing mission! I'm going to scan through the grand chandeliers and ornate carpets of the hotel for traces. Be right back with the results!

kwindla

from google import genai

import os

client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))

# filename_for_upload = "/Users/khkramer/Downloads/maven-lightning-trimmed.mp4"
# myfile = client.files.upload(file=filename_for_upload)
#
# print("My files:")

kwindla

# double transcription events
# pip install 'pipecat-ai[daily,silero,openai,cartesia]'==0.0.59 dotenv
#
# transcription events as expected
# pip install 'pipecat-ai[daily,silero,openai,cartesia]'==0.0.58 dotenv

import asyncio
import sys
import os

kwindla

#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import argparse
import asyncio
import os