Resolved

TTS system fails to handle multilingual content with proper pronunciation

TTS
Python
AI/ML
Localization
Audio Processing
Text-to-Speech
Multilingual
Pipeline Architecture

Single-language TTS pipeline mispronounces code segments when generating videos in non-English languages | Implemented dual-pipeline architecture to maintain English pronunciation for code while using native pronunciation for text content

Issue

The text-to-speech (TTS) system only supported English pronunciation, which led to mispronunciation of technical terms (e.g., Python/JavaScript code) when generating multilingual videos. For example, non-English pipelines would incorrectly pronounce code keywords using native language phonetics, reducing intelligibility.

Culprit

The system used a single pipeline for all content types, forcing either English-only output or native language conversion without distinguishing between code and text segments. This caused:

  • Code segments being pronounced with native phonetics (e.g., ‘def’ pronounced in Japanese).
  • No separation between phonetic processing (code required English) and voice selection (text could use native language).

Solution

A dual pipeline architecture was implemented to handle content-type routing:

  1. Code segments were processed through an English pipeline to ensure correct pronunciation of technical terms.
  2. Text segments used native language pipelines with language-specific voices.
  3. Configuration was simplified via environment variables to support multiple languages.
  4. Fallback behavior ensured backward compatibility with existing English-only workflows.

This approach resolved mispronunciation issues while enabling multilingual support.