Audio to text converter: which ones actually work in 2026

Audio to text converters look alike on their landing pages, but real recordings are uneven: a clean podcast interview, a phone call, a meeting with four speakers, a voice memo taken outside. This guide compares six common options and shows 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 make few errors. On noisy or multi-speaker audio, expect noticeably more words to be wrong or missing (what is word error rate explains how this is measured). That's still useful — you're correcting, not starting from scratch.

The tools compared

TranscribTxt

Runs on OpenAI's Whisper large-v3 turbo and detects 99 languages automatically. Upload a file and get the transcript back. In October 2026, a 145-minute recording took 2 min 45 s once uploaded. Your first file needs no account, and a free account gives you 3 files a day, each up to 30 minutes (a longer file gets its first 30 minutes free).

Accuracy follows the recording: clean audio gives a clean draft, and like other general-purpose models it stumbles on names and industry-specific terminology. It doesn't label speakers, so on a multi-speaker meeting you add names yourself.

Formats accepted: MP3, M4A, WAV, FLAC, OGG, OPUS, AAC, MP4, MOV, WebM, AVI, MKV. Max file size: 2 GB on every plan. Exports: TXT and SRT, plus JSON on paid plans.

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

Built for multi-speaker recordings: it separates speakers and labels them. How it compares with Whisper-based tools on accented speech depends on the recording, so try a clip of your own audio.

As of October 2026, Otter's pricing page lists a free plan with 300 minutes a month (30 minutes per conversation) and Pro at $8.33/month billed annually.

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 uploads after processing (a free-preview upload is kept up to 24 hours so you can finish the file) and doesn't keep transcripts on its servers.

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.