Debugging Audio Buffer Overruns: When Python Asyncio Drops the Ball

We are so used to building asynchronous web applications governed by REST APIs and GraphQL mutations that we have collectively forgotten what “real-time” actually means.

If a database query takes 500 milliseconds instead of 50 milliseconds, the user’s progress spinner rotates a fraction of a second longer. It is annoying, but it is not a catastrophic failure. The application state remains coherent. The UI eventually updates. We log the slow query to Sentry, add an index to the PostgreSQL table during the next sprint, and move on.

Real-time audio streaming does not afford you this luxury.

When you stream raw PCM audio down a WebRTC connection or a WebSocket to a user’s browser, you are feeding a hungry machine. The user’s audio device expects a continuous, unbroken supply of audio samples.

If the device plays through its currently buffered audio and reaches into your application’s buffer to find it completely empty, it does not politely show a spinning wheel.

This is an Audio Buffer Underrun. On the flip side, if your application receives audio packets from the user faster than it can process them, your input buffer overflows and drops critical packets of data. This is a Buffer Overrun.

To the user, both failures sound identical: the audio glitches. The seamless conversational illusion of your Voice Agent is instantly destroyed.

And if you are orchestrating this sophisticated stream using Python’s asyncio loop running on a standard Kubernetes pod, you are walking through a minefield.

The Illusion of Asynchronous Concurrency

Python’s asyncio is elegant, lightweight, and incredibly effective at managing highly I/O-bound web servers. But it is vital to remember the underlying architectural truth: an asyncio event loop executes on a single, solitary operating system thread.

It provides concurrency, not parallelism.

When you write await db.fetch(), you are instructing the Python interpreter to suspend the current coroutine, place it on a shelf, and go execute a different coroutine until the database network packet arrives.

This is brilliant for stateless HTTP endpoints. But its not practical for real-time audio loop management if you introduce blocking code.

Imagine you have a WebSocket handler continuously receiving 20ms audio chunks from the user. You need to pull those chunks off the network socket, append them to a bytearray, and run a fast Voice Activity Detection (VAD) algorithm over them to check for silence.

async def audio_receiver(websocket):
    while True:
        try:
            # We await the next 20ms chunk of audio
            chunk = await websocket.receive_bytes()

            # We append it to our ongoing buffer
            session_buffer.append(chunk)

            # We check if the user is still speaking
            is_speaking = fast_vad_check(chunk)

            if not is_speaking:
                await trigger_llm_reasoning()

        except WebSocketDisconnect:
            break

This code looks perfectly standard. It will compile, it will run, and it will fail catastrophically in production under load.

The CPU Bound Trap

The vulnerability lies in the fast_vad_check(chunk) function.

VAD algorithms, even ultra-fast, highly optimized ones like Silero, require mathematical execution. They compute Mel-frequency cepstral coefficients (MFCCs), run a fast Fourier transform, or evaluate a small ONNX neural network model. This is CPU-bound work. It is not waiting on a network packet; it is actively crunching numbers on the processor.

Because fast_vad_check is entirely synchronous CPU work, it physically hijacks the single asyncio thread.

If the ONNX model takes 35 milliseconds to evaluate that 20ms chunk of audio, the asyncio event loop is frozen for 35 milliseconds. It cannot process incoming HTTP requests. It cannot flush the outbound audio queue to the speaker. Every other concurrent task is starved of execution time.

By the time the single thread finishes the VAD check and loops back up to await websocket.receive_bytes(), the client has already sent two more 20ms audio chunks. The TCP buffer begins to fill. The network starts dropping packets. You just hit a Buffer Overrun.

The Diagnostic Tooling

When the audio starts glitching in production, you cannot just look at a standard CPU metrics dashboard. Your GKE Pod might show 15% overall CPU utilization while your audio streams are tearing themselves apart.

You do not have a resource problem; you have an execution starvation problem. You need to profile the asyncio event loop latency.

You must instrument your application to aggressively monitor how long it takes for a dormant task to wake up.

import asyncio
import time
import logging

log = logging.getLogger("event_loop_monitor")

async def monitor_loop_lag(threshold_ms: int = 10):
    """
    A canary coroutine that constantly sleeps and measures
    the delay between when it expected to wake up and when
    it actually got scheduled by the event loop.
    """
    while True:
        start = time.perf_counter()
        # Ask to be woken up in roughly 0 milliseconds (yield control)
        await asyncio.sleep(0)
        end = time.perf_counter()

        lag_ms = (end - start) * 1000

        if lag_ms > threshold_ms:
            log.warning(f"CRITICAL: Event loop lag spiked to {lag_ms:.2f}ms!")

        await asyncio.sleep(0.1) # Check 10 times a second

Running this specific canary task alongside your audio handlers provides immediate visibility. When your logs scream CRITICAL: Event loop lag spiked to 85.3ms!, you know precisely why your outbound audio buffer emptied out and the speaker clicked. A synchronous function hijacked the thread.

The Escape Hatch: Executors and Multiprocessing

Once you identify the synchronous villain—whether it’s the VAD model, an intensive JSON parsing step, or a deeply nested dictionary manipulation—you must forcefully eject it from the main asyncio thread.

You have two primary options in Python:

1. run_in_executor (Thread Pool): For tasks involving blocking I/O (like reading a huge file from disk that doesn’t have an async interface) or calling C-extensions that release the Global Interpreter Lock (GIL), you can push the work to a separate background thread. The asyncio thread continues marching forward.

import asyncio
import concurrent.futures

executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)

async def audio_receiver(websocket):
    loop = asyncio.get_running_loop()
    while True:
        chunk = await websocket.receive_bytes()

        # Offload the VAD to a separate background thread
        # The main loop yields control while waiting for the result
        is_speaking = await loop.run_in_executor(
            executor,
            fast_vad_check,
            chunk
        )

2. ProcessPoolExecutor (Multiprocessing): If the function is heavily CPU-bound in pure Python (meaning it holds the GIL tightly), pushing it to a thread solves nothing; the GIL will still lock up alternating threads. You must create an entirely new, independent Python process.

Heavy matrix math for feature extraction must run in a background process, communicating with the main async loop via inter-process communication (IPC) queues.

The Unpredictability of the Garbage Collector

Even if you flawlessy manage your event loop, decouple all your CPU-bound logic, and pre-allocate your numpy arrays, you still face one final, unpredictable challenge: the Python Garbage Collector.

Python uses reference counting backed by a generational garbage collector to identify circular references and free memory. When the GC triggered, it historically invoked a “stop-the-world” pause.

In a standard web API, a 40ms GC pause is invisible. In a streaming audio engine where you must push a new frame every 20ms, a 40ms GC pause is a guaranteed audio glitch.

If you profile your application and discover random, inexplicable spikes in your monitor_loop_lag logs correlating exactly with memory usage drops, it is the GC.

In extreme low-latency environments like pushing PCM audio from Gemini 2.5 Pro directly to a WebRTC peer, you might need to take drastic manual control.

import gc

def disable_gc_during_stream():
    """
    When the user starts a lively, fast-paced conversation,
    we disable automatic garbage collection to prevent unpredictable
    latency spikes. We accept slightly higher memory utilization.
    """
    gc.disable()

def flush_gc_during_silence():
    """
    When the VAD detects the user has stopped speaking and the
    LLM is merely 'listening' or idle, we manually force a collection.
    """
    if not gc.isenabled():
        gc.collect()

Moving Beyond Scripts

Many developers arrive at AI through building straightforward Jupyter notebooks or simple web dashboards. They learn that chaining APIs together is easy. They assume building a Voice Agent is just chaining a Speech-to-Text API, an LLM API, and a Text-to-Speech API.

The architecture required to maintain a seamless, glitch-free, bi-directional audio stream across a noisy internet connection demands a totally different discipline. It demands defensive programming against network jitter, aggressive management of thread starvation, and an intimate understanding of event loop mechanics.

Audio buffer overruns are not a minor bug. They are the physical manifestation of architectural failure. If you want your agent to speak intelligently, you must first build the plumbing capable of carrying the signal.