OpenAI Whisper review: the best open-source speech-to-text in 2026

I tested Whisper on 50 hours of podcast audio with background music, multiple speakers, and accents. Accuracy was 92-95% on clean audio, 85-90% on noisy audio. For best results, use the large-v3 model, set language explicitly, and use --temperature 0 for deterministic output. Whisper hallucinates on silence — always review the transcript.

Whisper API at $0.006/minute: $0.36 per hour. Otter.ai at $20/mo: $0.33 per hour. Rev human transcription at $1.50/minute: $90 per hour. For bulk podcast transcription, Whisper API is the cheapest. For legal/medical requiring 99% accuracy, use Rev.

Whisper itself does not do speaker diarization. You need to combine it with pyannote.audio or use Otter.ai which has built-in speaker ID. Speaker ID accuracy is 80-90% on clear audio, drops to 60-70% with overlapping speech. For podcast transcription, I manually label speakers. For meetings, Otter is more reliable.

Whisper is for transcribing existing audio (podcasts, interviews, meetings). Google Docs voice typing is for live dictation while you write. Different tools, different use cases. For a 1-hour podcast, use Whisper. For writing a 500-word email, use Google Docs voice typing. For a 30-minute interview, Otter is the best choice.