How I Solved a Hidden Memory Bottleneck in Flask + StyleTTS2 TTS Pipeline

When building an AI-based backend with StyleTTS2 integrated into Flask, I encountered a puzzling issue: my server became sluggish and unresponsive after just a few TTS requests. Here's the full story of how I diagnosed and fixed it โ and how you can avoid the same trap.
๐ The Symptoms
The first TTS request worked great.
By the third request, the response time exploded.
The system would eventually crash or freeze.
Checking htop
, I noticed RAM usage was spiking over 90% even with small inputs. Clearly, something was being loaded repeatedly.
๐ Root Cause
After logging model loads and running a memory profiler, I confirmed:
Each request was reloading the entire StyleTTS2 model.
Thatโs hundreds of MB per call โ loaded into RAM every time.
Pythonโs default memory handling and the use of subprocesses (from my earlier naive setup) didnโt help. I was unintentionally forcing a fresh heap allocation per job.
โ The Fix: Persistent TTS Worker with Model Caching
I built an optimized worker architecture using Python's multiprocessing
:
Flask Main App โโถ Task Queue
โฌ๏ธ
๐ง Persistent Worker
โฌ๏ธ
StyleTTS2 Cached
โฌ๏ธ
.npy Output Queue
In styleTTS2_subprocess_optimized.py
:
The worker starts once.
Loads and caches the TTS model.
Processes queued requests and sends output back.
The Flask app stays responsive and lightweight.
๐ Results
๐ TTS generation time dropped by 40โ50%
๐พ RAM usage plateaued and stabilized
๐ Multiple requests now work smoothly without crashing
๐ก Takeaways
If you're integrating large ML models into web servers:
Avoid per-request model loads
Use a persistent subprocess with a task queue
Consider caching the model globally or in-memory
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