I recently encountered a frustrating issue with my 432Hz Frequency Checker application. Users were reporting crashes when uploading larger audio files (around 20MB+), and my hosting provider (Render) kept sending me alerts about memory limits being exceeded.
Here is the story of how I investigated the leak, identified the culprit, and optimized the backend to handle large files with minimal memory footprint.
The Problem: "Out of Memory" on Large Files
My FastAPI backend was designed to accept an audio file, analyze its dominant frequency, and determine if it was tuned to 432Hz.
The original implementation looked something like this:
# 🚫 The Problematic Code
@app.post("/upload")
async def analyze_audio(file: UploadFile = File(...)):
# 1. Read the ENTIRE file into memory
audio_data = io.BytesIO(await file.read())
# 2. Decode the ENTIRE audio to PCM data
audio = AudioSegment.from_file(audio_data).set_channels(1)
samples = np.array(audio.get_array_of_samples(), dtype=np.float32)
# 3. Perform FFT...
Why this failed
When you upload a 20MB MP3, it's compressed. When pydub (backed by ffmpeg) decodes it into raw PCM data (uncompressed audio samples) for analysis, that 20MB file can easily explode into hundreds of megabytes of data in RAM.
For a free-tier server with 512MB or 1GB of RAM, processing just one or two of these requests concurrently causes the system to kill the process (OOM Kill).
The Solution: Smart Sampling with Librosa
I realized that to find the "tuning" of a song, I don't need to analyze every millisecond of a 5-minute track. The tuning is constant. Analyzing a 60-second sample is statistically just as accurate as analyzing the whole hour.
I switched from pydub to librosa, a powerful library for audio analysis that supports streaming and partial loading.
The Fix
Here is the optimized code:
# ✅ The Optimized Code
import librosa
@app.post("/upload")
async def analyze_audio(file: UploadFile = File(...)):
# ... validation ...
# Load audio using librosa
# duration=60: Only load the first 60 seconds!
# sr=None: Keep original sampling rate
# mono=True: Downmix to mono immediately
y, sr = librosa.load(file.file, sr=None, mono=True, duration=60)
# Perform FFT on this smaller chunk
fft_vals = rfft(y)
# ...
Key Improvements
- No
await file.read(): We pass the file-like object directly tolibrosa, avoiding a duplicate copy in Python memory. -
duration=60: We strictly limit the decoded audio to 60 seconds. This caps the memory usage to fixed size (approx 10-20MB of RAM for the array) regardless of whether the uploaded file is 20MB or 2GB. - Faster Processing: FFT on 60 seconds of data is significantly faster than on 5 minutes.
Verification
I verified the fix by generating a synthetic 120-second wav file (approx 20MB) and sending it to the new endpoint.
Before:
- RAM usage spiked to >500MB.
- Processing time: ~5-10 seconds.
After:
- RAM usage remained flat/negligible.
- Processing time: < 1 second.
- Result: STILL ACCURATE (Detected 432Hz correctly).
Conclusion
If you are doing data analysis on user uploads, always ask: "Do I need all the data?"
By sampling just what we needed, we turned a crash-prone application into a stable, high-performance service.
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