Audio to text converter: which ones actually work in 2026

Six months ago I tested six different audio to text converters on the same set of recordings: a clean podcast interview, a phone call, a meeting with four speakers, and a voice memo taken outside. The results were uneven. This guide covers what I found and how to pick the right tool for your situation.

What accuracy actually means

Most tools advertise accuracy numbers, but they measure on ideal conditions — studio audio, single speaker, minimal background noise. Real recordings are messier. The honest benchmark is word error rate (WER) on real-world audio.

On clean recordings, modern Whisper-based tools hit 3-5% WER (meaning 95-97% of words transcribed correctly). On noisy or multi-speaker audio, expect 10-20% WER, which means one in ten words is wrong or missing. That's still useful — you're correcting, not starting from scratch.

The tools I tested

TranscribTxt

Runs on Whisper. Upload a file, get a transcript in under a minute. The free plan gives you 5 files per month, each up to 30 minutes.

Accuracy on clean recordings: 96%. On the four-speaker meeting: 88%. It stumbled on names and industry-specific terminology, which is normal for general-purpose models.

Formats accepted: MP3, WAV, M4A, MP4, OGG, WEBM, FLAC. Max file size: 500MB.

Best for: People who want decent accuracy without any setup.

OpenAI Whisper (local)

The same model that powers most online tools, but running on your own hardware. Free, no file size limits, no monthly caps.

The tradeoff: you need Python installed, a command line you're comfortable with, and ideally a GPU. On a machine without a GPU, a 1-hour recording takes 20-30 minutes to process instead of 4-6 minutes. On a modern GPU it's faster than the online tools.

Best for: Developers, researchers, anyone processing large volumes with privacy requirements.

Google Docs voice typing

This only works with live microphone input — you speak, it transcribes in real time. You cannot upload a file and get a transcript.

If you have a recording and want to use this, you'd need to play the recording out loud into your microphone while the feature is active. That works in a quiet room, but you lose quality and it's slower than real-time.

Best for: Writing while talking, not transcribing recordings.

Otter.ai

Better than most for multi-speaker recordings because it actively separates speakers and labels them. It struggled more than Whisper on accented speech but performed well on clear interview audio.

Free plan: 300 minutes per month, 30 minutes per conversation. Paid plans start at $16.99/month.

Best for: Meeting transcription where speaker identification matters.

Descript

Combines transcription with audio editing. You edit the transcript and it edits the audio. Useful if you're producing a podcast and want to cut audio by editing text.

The transcription quality is similar to Whisper. The editing workflow is the main reason to use it — not accuracy.

Best for: Podcast editors who want to edit by transcript.

AssemblyAI

A developer API, not a consumer tool. You submit audio via HTTP request, get structured JSON back. It supports speaker diarization, sentiment analysis, and content moderation as paid add-ons.

Accurate and fast. Requires programming to use.

Best for: Developers building applications that need transcription.

What to look for before you pick

Speaker count. Single-speaker audio transcribes better than multi-speaker. If you have a multi-person recording, look for tools that offer speaker diarization (labeling who said what). Otter.ai and AssemblyAI handle this better than basic Whisper deployments.

Audio quality. Boost your results before uploading: reduce background noise with Audacity or Adobe Podcast's Enhance Speech (free). The 5 minutes you spend cleaning audio saves 20 minutes of transcript correction.

Language. Whisper supports 99 languages. If your audio is not in English, accuracy varies significantly by language. Spanish, French, German, and Portuguese are well-supported. Less common languages get lower accuracy.

Privacy. If your recording contains sensitive information, check where the file goes. Local tools like Whisper process on your machine. Online tools store files temporarily — TranscribTxt deletes files after transcription.

How to get better results

Three things that consistently improve transcription accuracy:

1. Normalize audio levels before uploading. Recordings where one speaker is much louder than another confuse transcription models. Aim for consistent volume.

2. Use the right format. WAV and FLAC give slightly better results than compressed MP3, especially on fast speech. If you have the choice, export uncompressed.

3. Correct as you go. Most tools produce transcripts fast enough that you can start correcting the beginning while the end is still processing. Don't wait for the full file.