Audio to Text Benchmark: 12 Tools Tested for Accuracy & Speed

Almost every audio-to-text comparison online is a marketing list. We wanted real numbers. So we picked five recordings that span the actual use cases users care about — a clean podcast, a five-person Zoom meeting, a one-on-one interview in a coffee shop, a 45-minute lecture from the back of a hall, and a field recording with traffic noise — and ran them through 12 tools. Some of those tools are ours. Some are not. The ranking below is what the data showed, not what we hoped it would show.

Test methodology

Five audio files, each chosen to stress a different dimension:

1. Podcast — 24-minute two-host conversation, studio mics, no music bed. The easy one.
2. Meeting — 38-minute Zoom call, 5 speakers, mixed mic quality (some AirPods, some webcam mics, one external USB).
3. Interview — 47 minutes, two speakers, recorded in a busy cafe with a single iPhone on the table.
4. Lecture — 51 minutes, single speaker at a lectern, recorder placed in the audience (back of room).
5. Field recording — 12 minutes, single speaker outdoors, traffic + wind + occasional dog.

Each tool scored on four axes:

• Word accuracy — measured against a human transcript. Word Error Rate (WER) inverted to a 0-100 score.
• Speaker ID — correct speaker labeling on multi-speaker files (0-10).
• Output format — what you get back: plain text, structured Markdown, SRT, JSON, etc. (0-5).
• Speed — wall-clock time per minute of audio (lower is better).

Disclosure: we built one of the 12 tools. Where competitors win, we say so plainly. The five audio files are not specially curated — they were the next five real recordings we got our hands on at the time of testing.

Tools tested

• MDisBetter Audio to Markdown — web tool, free tier, structured Markdown output (speakers + H2 sections + timestamps)
• 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 600 min/month
• Notta — claims 98.86% accuracy, 58 languages, real-time meeting bot
• HappyScribe — 150+ languages, optional human-transcription tier (highest accuracy on the market)
• 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

Aggregate results

The top of the table is tightly packed — HappyScribe, Whisper, MDisBetter, Notta, and Rev AI are within four points of each other. Differences only become decisive when you slice by audio type.

Per-recording winners

Podcast — clean studio audio

This is where everyone scores well. WER on clean two-speaker studio audio is essentially solved for the top tier. HappyScribe came out marginally ahead on punctuation and capitalization quality. Whisper large-v3 (run locally) and MDisBetter tied for second. TurboScribe was clean but used fewer paragraph breaks.

If your audio is always clean podcast quality, the accuracy difference between any of the top eight tools won't matter. The decision points are: cost, output format, and what you want to do next with the transcript.

Meeting — 5 speakers, mixed mic quality

This is where speaker diarization becomes the dominant factor. Otter is the king here — they have invested heavily in meeting-specific diarization for their bot product, and that quality shows up on uploaded files too. They correctly identified all five speakers across the 38 minutes with only two swap errors.

MDisBetter was a close second on diarization (three swap errors) and produced cleaner Markdown output with H2 section breaks at topic shifts. Fireflies tied with Otter on diarization but gave less-clean output formatting.

TurboScribe and Whisper local both struggled here — Whisper's base model lacks diarization (you need to bolt on WhisperX or pyannote, which is real engineering work). TurboScribe's diarization is functional but tends to merge similar voices.

Interview — cafe background noise

Background noise destroys the bottom of the table. ScreenApp dropped to 84% accuracy. Descript dropped to 87%. The top performers held up.

HappyScribe's noise-robust model edged ahead. Notta close behind. Whisper large-v3 was solid but slightly worse than its lecture/podcast scores — Whisper handles steady-state noise (traffic) better than non-stationary noise (other voices, dishes clattering).

Lecture — recorded from the back of the hall

Low signal-to-noise, single speaker, room reverb. Whisper large-v3 won this one, narrowly. The model has seen enough varied training data to handle distance-mic recordings. HappyScribe was second, MDisBetter third.

The bottom of the table really suffered here — ScreenApp lost the speaker entirely during a quiet stretch and inserted three minutes of garbled output.

Field recording — outdoor traffic

Whisper large-v3 wins again. The model is genuinely impressive on noisy outdoor audio. Rev AI was second. The big-platform tools (Otter, Fireflies) lagged here because their models are tuned for meeting/conference acoustic conditions, not outdoor recordings.

Speed

Speed varies wildly. TurboScribe was the fastest of the cloud tools (about 12 seconds per minute of audio). Notta close behind. The open-source Whisper running locally on a consumer GPU (RTX 4070) was about 36 seconds per minute on large-v3 — slower than the cloud, but "free" once you have the hardware. CPU-only Whisper is much slower (3-5x real time depending on the chip); use faster-whisper if you go that route.

Output format — the underrated axis

Most tools return either plain text or SRT. A few return JSON. Almost none return structured 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 speaker labels, H2 section headers at topic shifts, and timestamp anchors 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.

Of the 12 tools tested, only two ship structured-Markdown output by default: MDisBetter and VOMO. Whisper plus a custom post-processing script can produce Markdown but you have to write the script.

Honest tradeoffs

Where MDisBetter wins

Structured Markdown output by default (speakers + H2 sections + timestamps). Free tier with no signup. Works alongside our 20+ other Markdown converters — same UI, same downstream tooling. We do not ship a meeting bot, real-time captioning, CRM sync, or team workspace; for those, Otter or Fireflies is the right answer.

Where HappyScribe wins

Highest AI accuracy in our tests, by a small margin. 150+ language support is the broadest in the market. Optional human transcription tier produces near-100% accuracy when stakes are high (legal, journalism). Most expensive of the AI tier.

Where Whisper (local) wins

Free if you have the hardware. Best handling of noisy/distant audio. 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 wins

Best meeting diarization. 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 wins

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

Where Rev AI wins

Pay-per-minute API — no subscription overhang. Optional human transcription. Strong accuracy. 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 the parallel PDF benchmark and the URL benchmark. The output Markdown composes cleanly across all three so you can feed a single combined corpus to the same chunker, embedder, and retrieval pipeline.

Reproducibility notes

Audio quality varies wildly between recordings. The exact WER numbers above are specific to the five files we tested. The broad ranking (top tier within 4-6 points of each other; ScreenApp at the bottom; Whisper-large-v3 winning the noisy categories) has been stable across multiple test rounds and is unlikely to change.

If you want to verify on your own corpus, the right pattern is: pick three representative recordings from your typical workload, run them through the top-three tools for your use case, and score against your needs. We re-run this benchmark quarterly. See also our 2026 ranked review and the accuracy-by-audio-quality deep dive.