How to Benchmark Audio to Text Tools on Your Own Recordings

Almost every audio-to-text comparison online is a marketing list, and vendor accuracy figures are measured on the vendor's own audio. The only numbers that tell you which tool fits are the ones you get on your own recordings. This guide gives you a method: which files to pick, how to score each tool on word accuracy, speaker labels, output format and speed, what each type of tool does by construction, and the traps that make a quick comparison misleading. Disclosure: we build one of the tools discussed here, and we say plainly where it does not fit.

Pick test recordings that stress different things

Use real recordings from your own workload, not demo clips. If your work spans several situations, take one file per situation, each stressing a different dimension:

1. Podcast: two hosts, studio mics, no music bed. The easy baseline.
2. Meeting: a video call with several speakers and mixed mic quality (earbuds, webcam mics, an external USB mic).
3. Interview: two speakers in a busy cafe, a single phone on the table.
4. Lecture: one speaker at a lectern, recorder placed in the audience (back of room).
5. Field recording: one speaker outdoors, with traffic and wind.

Skip the situations you never record. If all your audio is clean podcast audio, a comparison decided by noisy field recordings will point you to the wrong tool.

Score each tool on four axes

• Word accuracy: measured against a human-checked reference transcript. Word Error Rate (WER) counts the substitutions, deletions and insertions against the number of words in the reference; lower is better. A few minutes from each file, typed and checked by hand, is enough reference to see differences.
• Speaker ID: on multi-speaker files, are turns labelled, and are the labels right? Count the swaps where words land on the wrong person.
• Output format: what you get back: plain text, structured Markdown, SRT, JSON, etc. Judge it against what you will do next with the file.
• Speed: wall-clock time per minute of audio, from upload to usable file (lower is better).

Weight the axes by your use. A journalist quoting a source cares most about accuracy on names and numbers; a sales team cares about speaker labels; someone feeding transcripts to an LLM cares about format as much as about a small difference in WER.

Tools worth including

• MDisBetter Audio to Markdown: web tool on a free account, returns a Markdown transcript with timestamps, split into speech segments, also downloadable as SRT subtitles (and VTT for a single file); accepts several files at once; no speaker identification
• TurboScribe: web app, paid plan offers unlimited transcription (~$10/mo), plain text + SRT output
• Otter.ai: meeting-bot product with file upload option, free tier 300 min/month
• Notta: claims 98.86% accuracy, 58 languages, real-time meeting bot
• HappyScribe: 150+ languages, optional human-transcription tier
• Descript: full audio/video editing suite with transcription as the foundation
• VOMO: also offers Markdown output, claims 99% accuracy
• ScreenApp: screen + audio recorder with built-in transcription
• Fireflies: meeting-bot with conversation intelligence, CRM sync
• Rev AI, pay-per-minute API (~$0.25/min for AI tier; human option separate)
• Whisper (OpenAI, run locally via openai-whisper Python package): open source, free if you self-host
• Sonix: pay-as-you-go web app, ~$10/hr

You don't need all twelve. Pick the three or four whose type matches your job (see the tradeoffs below) and run your files through those.

What each recording tells you

Podcast: clean studio audio

Clean two-speaker studio audio is the easy case for the top tier, so differences tend to show up in punctuation, capitalization and paragraph breaks rather than in the words. If your audio is always clean podcast quality, the decision points are cost, output format, and what you want to do next with the transcript.

Meeting: several speakers, mixed mic quality

This is where speaker diarization becomes the dominant factor. Count speaker swaps, not just word errors: a transcript with the right words under the wrong names is wrong exactly where it matters. Otter and Fireflies build diarization into their meeting bots; check whether it holds on your uploaded files too.

MDisBetter is out of the running on this axis: it does no speaker identification at all. It returns a Markdown transcript split into timestamped speech segments, but nothing in that file tells you which person was talking. Whisper's base model lacks diarization too (you need to bolt on WhisperX or pyannote, which is real engineering work).

Interview: cafe background noise

Background voices and clattering dishes are harder than steady noise, because the model has to decide which speech to transcribe. Listen for passages where a tool picks up the next table, or drops words under the noise.

Lecture: recorded from the back of the hall

Low signal-to-noise, single speaker, room reverb. Check the quiet stretches in particular: that is where a tool is most likely to drop words or produce garbled output.

Field recording: outdoor traffic

Wind and traffic test noise robustness. If you record outdoors, this is the file that should decide: a tool that does well on meetings tells you nothing about how it handles wind.

Speed

Time each tool from upload to usable file, per minute of audio. Cloud tools depend on their queue, so time more than one run. For Whisper running locally, speed depends on your hardware: a consumer GPU is much faster than CPU-only, and on CPU faster-whisper is the usual route. Local speed is "free" once you have the hardware; cloud speed is part of what you pay for.

Output format: the underrated axis

Most tools return either plain text or SRT. A few return JSON. Almost none return Markdown.

That matters more than people realize when the next step is feeding the transcript to an LLM. A 45-minute meeting as plain text is a wall of words; the same meeting as Markdown, with a timestamp on every segment and H2 headings at the topic shifts (added by you, or by a prompt that inserts them without touching the text), is something Claude or ChatGPT can actually navigate. Cite-by-section becomes possible. Semantic chunking becomes meaningful. We unpack the format-vs-AI quality argument in speech to text vs audio to Markdown.

Among the tools listed above, MDisBetter and VOMO offer Markdown output. Whisper plus a custom post-processing script can produce Markdown but you have to write the script.

Honest tradeoffs

Where MDisBetter fits

Markdown output by default: a timestamped transcript split into speech segments. Runs in the browser on a free account; each conversion uses credits. Works alongside our other Markdown converters, same UI, same downstream tooling. We do not ship speaker identification, a meeting bot, real-time captioning, CRM sync, or team workspace; for those, Otter or Fireflies is the right answer.

Where HappyScribe fits

150+ language support is the broadest in the market. Optional human transcription tier for when stakes are high (legal, journalism). Most expensive of the AI tier.

Where Whisper (local) fits

Free if you have the hardware. Total privacy: nothing leaves your machine. Ships in any language Whisper was trained on. Requires comfort with Python and (ideally) a GPU. No diarization out of the box.

Where Otter fits

Speaker labels built for meetings. Real-time meeting bot that joins your calls. Team workspace, shared notes, action item extraction. CRM sync. If your job is recurring multi-person meetings, Otter is purpose-built. See MDisBetter vs Otter for the head-to-head.

Where TurboScribe fits

Unlimited plan at around $10/month is the best raw-volume deal in the market. Polished UI for high-volume podcasters and journalists. See MDisBetter vs TurboScribe for the comparison.

Where Rev AI fits

Pay-per-minute API, no subscription overhang. Optional human transcription. The right pick for low-volume programmatic use without a monthly commitment.

Picking by use case

One-off file, want Markdown for AI: MDisBetter.
Recurring meetings with a team: Otter or Fireflies.
High-stakes legal/medical/journalism work: HappyScribe (consider human tier).
Privacy-critical or fully local: Whisper local.
Highest raw-volume ratio: TurboScribe unlimited.
Pay-per-minute, low frequency: Rev AI.
Editing a podcast end-to-end: Descript.

What about the PDF / URL side of the workflow?

Many AI workflows mix audio (interviews, podcast research) with documents (papers, web articles). For the document half see how to benchmark PDF to Markdown converters and how to benchmark URL to Markdown tools. The output Markdown composes cleanly across all three so you can feed a single combined corpus to the same chunker, embedder, and retrieval pipeline.

Traps that skew a quick comparison

• Vendor audio: headline accuracy figures are measured on the vendor's own test audio. They don't transfer to your files.
• A single file: audio quality varies wildly between recordings. A ranking built on one file tells you about that file.
• No reference: reading a transcript and checking that it "looks right" misses fluent errors. Compare against what was actually said.
• Ignoring the next step: a small WER difference matters less than whether the output fits what you do next (subtitles, a Word document, an LLM prompt).

The practical pattern: pick three representative recordings from your typical workload, run them through the three or four tools whose type fits your use case, and score them against your needs. Run it again when the kind of audio you record changes. See also our roundup of audio to text tools and how to measure transcription accuracy by audio quality.

Check Otter Basic limits: 300 monthly minutes, 30 minutes per transcript and three lifetime file imports.