Voice AI
Voice AI has moved from novelty to infrastructure. Transcription powers meeting summarization, accessibility tooling, and customer support automation. Text-to-speech drives voice assistants, audiobooks, and IVR systems. Both are production-critical, and both break in ways that traditional QA has no framework for handling. This guide covers how to
Whisper
OpenAI's Whisper and AssemblyAI are the two most common transcription APIs in production Python applications. They have different architectures, pricing models, and accuracy profiles — but they share the same testing challenges. This guide walks through a complete test suite for both, covering unit tests, integration tests, and edge
Speech Testing
Speech AI systems — transcription APIs, TTS engines, voice assistants — produce outputs that don't fit a binary pass/fail test. The question is never "did it produce output" but "is the output good enough." Answering that question consistently, automatically, and in CI requires evaluation frameworks