I ran a 9-second WAV file through faster-whisper on a CPU with no GPU and no API key. It took 0.6 seconds and got one word wrong: "Dev Tulab" instead of "DevToolLab". Adding one line of vocabulary hinting fixed it, for free. I wrote up the full comparison of hosted and open speech-to-text APIs on DevToolLab; here's the short version.
One Label, Three Different Jobs
"Speech-to-text" actually covers three separate problems. Batch transcription (a file already on disk, latency doesn't matter, cost per hour and jargon accuracy do). Real-time streaming for voice agents, where the hard part isn't the transcript, it's turn detection: knowing the caller paused versus finished talking. And text-to-speech, turning the reply back into audio.
The best model for one job is often the wrong pick for another. A model tuned for clean batch accuracy is not what you want driving a phone agent, and the fastest streaming model isn't the one you want transcribing a two-hour deposition.
Where Accuracy Actually Sits in 2026
