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

K Chiranjiv RaoK Chiranjiv Rao
2 min read

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.


๐Ÿ“‰ 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.


๐Ÿ” Root Cause

After logging model loads and running a memory profiler, I confirmed:

Each request was reloading the entire StyleTTS2 model.

Thatโ€™s hundreds of MB per call โ€” loaded into RAM every time.

Pythonโ€™s default memory handling and the use of subprocesses (from my earlier naive setup) didnโ€™t help. I was unintentionally forcing a fresh heap allocation per job.


โœ… The Fix: Persistent TTS Worker with Model Caching

I built an optimized worker architecture using Python's multiprocessing:

Flask Main App  โ”€โ–ถ  Task Queue
                โฌ‡๏ธ
        ๐Ÿง  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.


๐Ÿ“ˆ Results

  • ๐Ÿ•’ TTS generation time dropped by 40โ€“50%

  • ๐Ÿ’พ RAM usage plateaued and stabilized

  • ๐Ÿ”„ Multiple requests now work smoothly without crashing


๐Ÿ’ก 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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K Chiranjiv Rao
K Chiranjiv Rao