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How Accurate Is Whisper in 2026?
OpenAI Whisper Large-v3 achieves ~2.7% Word Error Rate (WER) on the LibriSpeech benchmark and 8–12% on real-world English audio. Accuracy varies dramatically by language, audio quality, and speaker conditions — here's what the numbers actually mean.
By VexaScribe Editorial · Published May 3, 2026 · Updated August 25, 2026
OpenAI Whisper is the open-source speech recognition model behind many AI transcription products, including VexaScribe (formerly NovaScribe). The current best version, Whisper Large-v3, achieves around 2.7% Word Error Rate (WER) on the LibriSpeech test-clean benchmark — clean audiobook audio with one speaker. On real-world English audio (meetings, podcasts, phone calls), WER rises to roughly 8–12%. Whisper supports 99 languages, but accuracy varies sharply: major Western European languages perform near English-level, while low-resource languages can have 25%+ WER. Whisper does not perform speaker diarization natively — that requires a separate model (WhisperX + pyannote is the standard stack). OpenAI also released GPT-4o-transcribe and GPT-4o-mini-transcribe in March 2025 as API-only successors with reportedly lower WER, though they're not open-source like Whisper — see can ChatGPT transcribe audio and video for a breakdown of Record mode, GPT-4o file upload, and the API tiers side-by-side. The model is MIT-licensed and free to self-host, with the largest version requiring about 10 GB of GPU memory. The OpenAI Whisper API costs $0.006 per minute. This page breaks down what those numbers actually mean for transcription accuracy across audio types, languages, and conditions. For a broader cross-engine benchmark including AssemblyAI, Deepgram Nova-3, and GPT-4o alongside Whisper, see our 2026 transcription accuracy comparison.
Whisper Accuracy at a Glance
Whisper WER by Language (Answer-First)
This is the answer most searchers want up top. Whisper Large-v3 WER, grouped from best (Tier 1 languages near English performance) to worst (low-resource languages, effectively unusable for production). Numbers are typical real-world ranges observed across published benchmarks; your specific audio may fall outside these ranges.
| Language / Condition | Typical WER | Tier |
|---|---|---|
| English (LibriSpeech clean) | ~2.7% | Best-case benchmark |
| English (real-world meetings) | 8–12% | Typical business audio |
| English (phone call, noisy) | 15–25% | Bandwidth + noise |
| Spanish | 4–6% | Near English-level |
| French | 4–6% | Near English-level |
| German | 5–7% | Strong Tier 1 |
| Portuguese | 5–7% | Strong Tier 1 |
| Italian | 5–7% | Strong Tier 1 |
| Japanese | 8–12% | Strong Tier 2 |
| Korean | 8–13% | Strong Tier 2 |
| Mandarin Chinese | 9–14% | Strong Tier 2 |
| Russian | 8–14% | Variable Tier 2 |
| Turkish | 12–18% | Variable Tier 2 |
| Arabic | 15–22% | Variable Tier 2 |
| Hindi | 15–25% | Below par Tier 3 |
| Low-resource (Amharic, Yoruba, Welsh) | 25–50%+ | Poor / experimental |
Real-world WER ranges combine the OpenAI Whisper paper (Appendix D, FLEURS benchmark) with published third-party evaluations. Individual audio quality (mic, noise, accent, domain vocabulary) can shift these numbers by ±5 points. For a full tier breakdown across all 99 supported languages, see the language section below.
Whisper Is a Family of Models, Not One Model
Whisper releases come in 7 sizes from Tiny (39M params) to Large-v3 (1.55B params). Speed and accuracy trade off significantly. Most commercial products use Large-v3 or Large-v3 Turbo (a distilled version released September 2024) for production. If you're specifically deciding between Large-v3 and Turbo, our dedicated Whisper Large-v3 vs Turbo comparison has per-dataset WER, speed benchmarks by hardware, cost math, and a decision flowchart.
| Model | Parameters | VRAM | Relative speed | Use case |
|---|---|---|---|---|
| Tiny | 39M | ~1 GB | ~10× faster than Large | Mobile, edge devices |
| Base | 74M | ~1 GB | ~7× faster | Constrained environments |
| Small | 244M | ~2 GB | ~4× faster | Balanced quality/speed |
| Medium | 769M | ~5 GB | ~2× faster | Quality-focused |
| Large-v2 | 1.55B | ~10 GB | 1× (baseline) | Production (older) |
| Large-v3 ★ | 1.55B | ~10 GB | 1× | Production (current best) |
| Large-v3 Turbo | 809M | ~6 GB | ~8× faster | Fast production (Sept 2024) |
Speed multipliers are relative to Large-v3, not real-time. Specific WER per model size requires reading the OpenAI Whisper paper appendix; we don't reproduce unverified per-size numbers here. Source: openai/whisper README.
Why Real-World WER Differs From Benchmark WER
LibriSpeech audiobook benchmarks measure ideal conditions — single speaker, studio recording, scripted speech. Real audio is messy. The same Whisper Large-v3 model produces dramatically different results across audio types.
| Audio condition | Approximate WER | Notes |
|---|---|---|
| LibriSpeech test-clean (audiobook benchmark) | ~2.7% | Industry baseline, best case |
| Clean studio podcast (one speaker) | ~3–6% | Real-world but ideal conditions |
| Conference call, 2 speakers | ~7–12% | Business meeting baseline |
| Zoom/Teams call (3+ speakers) | ~10–15% | Common business reality |
| Phone audio (8 kHz bandwidth) | Higher than studio | Bandwidth-limited; specific delta unverified |
| Strong accents | +5–10% over baseline | Documented disparity (JASA Express Letters 2024) |
| Heavy background noise | +5–15% over baseline | Cafés, traffic, music |
| Multiple overlapping speakers | Significant degradation | Whisper doesn't separate speakers |
WER ranges represent typical observations across published benchmarks, not a single controlled study. Use them as directional guidance.
Why Whisper Sometimes Makes Things Up
A peer-reviewed study presented at ACM FAccT 2024 documented that Whisper occasionally fabricates content during silences and audio with frequent pauses. Researchers have reported hallucination rates from 1% to 80% of segments depending on conditions. The problem is most pronounced with:
- ●Long silences at segment boundaries (greater than 30 seconds)
- ●Audio with frequent pauses or speech disfluencies
- ●Recordings starting or ending with silence
- ●Background noise that resembles speech
This is not unique to Whisper — most automatic speech recognition models have this issue — but Whisper's tendency was specifically flagged in healthcare contexts (Healthcare Brew, November 2024) where transcript fabrication has serious consequences. For production use, always treat AI transcripts as drafts, not records of truth.
Sources: ACM FAccT 2024; Cornell coverage (June 2024); Calm-Whisper paper.
Correction: our July 2026 benchmark has been withdrawn
Updated August 25, 2026. This page previously included results from our own July 2026 benchmark. An internal review identified methodology problems, so we withdrew those results rather than restate them. Third-party and vendor-published figures on this page are cited to their sources and are unaffected. If you believe you have found an error in anything still published here, please tell us — see our editorial standards for how we handle corrections.
Open ASR Leaderboard: 8-Benchmark Composite
The Hugging Face Open ASR Leaderboard publishes standardized WER for open and commercial models across a fixed set of English benchmarks (LibriSpeech clean/other, TED-LIUM 3, AMI, GigaSpeech, Earnings22, CallHome, CommonVoice). The composite average is the aggregate WER across all 8. Whisper Large-v3 currently sits at ~7.44% composite; NVIDIA Parakeet-TDT 0.6B v2 leads the English-only leaderboard at ~6.05%.
| Model | LS clean | LS other | TED-LIUM 3 | AMI | GigaSpeech | Earnings22 | Composite |
|---|---|---|---|---|---|---|---|
| NVIDIA Parakeet-TDT 0.6B v2 | 1.6% | 3.1% | 3.4% | 15.5% | 9.8% | 10.7% | 6.05% |
| Speechmatics Melia-1 | 3.3% | 5.6% | 4.1% | 17.2% | 14.6% | 13.6% | 6.4% |
| Speechmatics Enhanced | 4% | 6.9% | 3.5% | 17.3% | 14.2% | 11% | 6.9% |
| OpenAI Whisper Large-v3 | 2.7% | 5.2% | 3.9% | 15.9% | 10.1% | 11.3% | 7.44% |
| OpenAI Whisper Large-v3 Turbo | 2.7% | 5.4% | 4% | 16.4% | 10.4% | 11.9% | 7.7% |
| AssemblyAI Universal-3.5 Pro | 3.9% | 8.1% | 4.8% | 22.6% | 14.1% | 12.4% | 7% |
Composite averages are aggregate WER across the 8 benchmark datasets on the Hugging Face Open ASR Leaderboard. Verified 2026-08-19. Numbers drift as vendors submit updated models. CallHome is included in the leaderboard composite but not shown per-column here — see the leaderboard for that dataset.
Whisper Accuracy Across 99 Languages
Whisper's training data is heavily English-weighted; performance scales roughly with how much training data exists per language. The table below reports FLEURS benchmark WER for Whisper Large-v2, extracted directly from Table 13, Appendix D of the OpenAI Whisper paper (arXiv:2212.04356). Whisper Large-v3 (released November 2023) reduces errors by 10–20% across most of these languages per OpenAI's release note.
| Language | Whisper Large-v2 error rate | Metric | Tier |
|---|---|---|---|
| Spanish | 3.0% | WER | Tier 1 |
| Italian | 4.0% | WER | Tier 1 |
| English | 4.2% | WER | Tier 1 |
| Portuguese | 4.3% | WER | Tier 1 |
| German | 4.5% | WER | Tier 1 |
| Japanese | 5.3% | CER | Tier 1 |
| Polish | 5.4% | WER | Tier 1 |
| Russian | 5.6% | WER | Tier 1 |
| Dutch | 6.7% | WER | Tier 1 |
| Indonesian | 7.1% | WER | Tier 1 |
| French | 8.3% | WER | Tier 1 |
| Turkish | 8.4% | WER | Tier 2 |
| Ukrainian | 8.6% | WER | Tier 2 |
| Vietnamese | 10.3% | WER | Tier 2 |
| Korean | 14.3% | CER | Tier 2 |
| Chinese | 14.7% | CER | Tier 2 |
| Arabic | 16.0% | WER | Tier 2 |
| Hindi | 21.5% | WER | Tier 3 |
Notes. Whisper Large-v2 FLEURS numbers from Table 13, Appendix D of the OpenAI paper. Character Error Rate (CER) is used for Chinese, Japanese, and Korean per the paper's convention (word segmentation is not well-defined in these scripts). Tiers reflect production-suitability: Tier 1 (<8%) is production-ready, Tier 2 (8–20%) needs a human review pass for professional output, Tier 3 (>20%) needs domain fine-tuning to be usable.
Language coverage is narrower than the token count suggests
Whisper can emit 99 language tokens (100 since Cantonese was added for Large-v3), but the Whisper paper reports speech-recognition training data for 75 languages (Section 3.3). The paper also finds a 0.83 squared correlation between the log of per-language training data and the log of word error rate, and that WER halves for every 16× increase in training data. Accuracy on the lowest-resource languages should be validated per language against the FLEURS results in Appendix D rather than assumed from the supported-language count.
Correction, August 25, 2026. An earlier version of this section stated that “OpenAI flags 20 of the 99 supported languages as having no training data.” That was a misreading of the paper. The figure appears in Section 3.6 and Table 5, where it refers to 20 of the 102 languages in the FLEURS benchmark for which Whisper has no training data — languages largely outside Whisper's supported set — cited there to explain a depressed language-identification score. OpenAI does not designate any supported language as untrained.
How Whisper Compares to Commercial APIs
Vendor-published and independent WER figures are typically produced on different datasets, with different text normalization, model settings, and aggregation methods. Compare absolute WER values only when the methodologies are sufficiently similar; across published studies, absolute numbers for the same engine commonly vary by 2–3 percentage points.
| Engine | Real-world English WER | Pricing | Source |
|---|---|---|---|
| Whisper Large-v3 (open source) | ~10.6% (independent) | Free self-host / $0.006/min API | Modal, ionio.ai 2025 |
| Mistral Voxtral Mini Transcribe 2 | 4.0% FLEURS (vendor, top-10 avg) | $0.003/min batch | Mistral AI Feb 2026 |
| Deepgram Nova-3 | ~5.26% batch (vendor) | $0.0043/min | Deepgram 2026 |
| AssemblyAI Universal-2 | ~8.4% (vendor) | $0.00025/sec | AssemblyAI |
| OpenAI GPT-4o-transcribe | Lower than Whisper-v3 (vendor) | $0.006/min | OpenAI March 2025 |
| OpenAI GPT-4o-mini-transcribe | Higher than 4o full (vendor) | $0.003/min | OpenAI March 2025 |
| Google Cloud Speech / Chirp | Specific WER unverified | $0.016/min | Vendor docs |
| AWS Transcribe | Specific WER unverified | $0.024/min | Vendor docs |
The takeaway: Whisper, Deepgram, AssemblyAI, and OpenAI's GPT-4o-transcribe are all in the same general accuracy class for English. Mistral's Voxtral (released July 2025, Transcribe 2 in February 2026) is the new price-performance leader on FLEURS at $0.003/min. Differences become more pronounced for non-English languages, custom vocabulary, and specific audio conditions. For a vendor-by-vendor comparison with verified pricing, see our developer transcription API comparison (12 APIs) and the podcast transcription tools comparison (10 podcaster-focused tools).
Whisper vs human transcription
Professional human transcribers hit 5.1–5.9% WER on the NIST Switchboard benchmark and 6.8–11.3% on CallHome (Xu et al., arXiv:1708.08615). Whisper Large-v3's real-world English WER of 8–12% puts it roughly at the top edge of human performance on clean audio and 1.5–2× the human error rate on noisy multi-speaker audio.
Whisper vs NVIDIA Parakeet
NVIDIA Parakeet-TDT 0.6B v2 currently leads the Hugging Face Open ASR Leaderboard on English aggregate WER at ~6.05% — better than Whisper Large-v3's ~7.44% composite. Parakeet is also significantly faster in real-time factor. The trade-offs are ecosystem and scope.
| Dimension | Whisper Large-v3 | Parakeet-TDT 0.6B v2 |
|---|---|---|
| Parameters | 1.55B | 0.6B |
| Open ASR Leaderboard composite (English) | ~7.44% | ~6.05% |
| Languages supported | 99 | English only |
| License | MIT | CC-BY-4.0 |
| Real-time factor (relative) | 1× baseline | 3–5× faster |
| Ecosystem maturity | Very large (faster-whisper, WhisperX, distil-whisper, whisper.cpp) | Growing (NeMo, Riva) |
| Diarization pairing | Mature (pyannote, WhisperX) | Via NeMo pipeline |
Verdict. For English-only production ASR where throughput matters, evaluate Parakeet. For multilingual workflows, mature diarization pairing, or ecosystem-dependent tooling (faster-whisper, WhisperX), Whisper remains the safer default.
What Whisper Doesn't Do
Whisper is a transcription model. Real production transcription tools need several things Whisper doesn't natively provide:
- Speaker diarizationWhisper transcribes all speech but doesn't identify who said what. Pair with pyannote-audio (open source) or WhisperX (combines both) for speaker labels. Commercial tools built on Whisper — including VexaScribe — bundle diarization.
- Live streamingWhisper is batch-oriented. For real-time transcription, look at faster-whisper (CTranslate2 reimplementation) or commercial streaming APIs (Deepgram, Speechmatics, AssemblyAI Universal-Streaming).
- Custom vocabulary / domain termsWhisper has limited support for biasing toward specific terms (brand names, technical jargon). Commercial APIs like Deepgram (keyterm prompting, up to 100 terms), AssemblyAI (promptable models that accept domain context alongside the audio), and Google offer better custom vocabulary handling.
- Voice activity detection (VAD)Whisper struggles with empty audio (contributes to the hallucination problem). Most production setups pre-process audio with a VAD model to skip silence.
- Q&A interface over the transcriptWhisper produces text — it doesn't let you query that text. Commercial tools layer this on top: VexaScribe's AI Chat lets users ask natural-language questions about a transcript and get answers with timestamps validated against the source text. Useful when an accurate transcript is the foundation but not the end goal.
Tools and Frameworks Built on Whisper
Whisper underpins many products and open-source projects. If you're evaluating which Whisper-based tool to use:
Open-source frameworks
- •
faster-whisper— CTranslate2 reimplementation, ~4× faster - •
WhisperX— adds diarization + word-level timestamps - •
distil-whisper— HuggingFace, 6× faster, within 1% WER - •
Calm-Whisper— silence-handling improvements
Commercial products
VexaScribe (formerly NovaScribe), TurboScribe, Descript, and many others run Whisper Large-v3 in production with their own UI, editing, exports, and diarization layers.
Two workflow-specific packagings of Whisper worth knowing: AI note taker for live meetings (bot joins Zoom/Teams/Meet and layers diarization + action-item extraction on top of Whisper) and video-to-transcript for MP4/MOV/MKV files up to 5 GB (video-specific workflow with audio auto-extraction — same Whisper model under the hood).
OpenAI's own offerings
- • Whisper API (whisper-1) — $0.006/min
- • GPT-4o-transcribe — $0.006/min (Mar 2025)
- • GPT-4o-mini-transcribe — $0.003/min (Mar 2025)
Should You Run Whisper Yourself?
| Self-host Whisper | Use a service like VexaScribe |
|---|---|
| Free model, but you pay for GPU/cloud (~$0.50–$2/hr GPU time) | $0.006–$0.05/min (typically cheaper at moderate volume) |
| You handle VAD, diarization, exports, retries | All bundled — diarization, exports, summaries |
| Full data privacy (data never leaves your infrastructure) | Cloud-based; check provider's privacy policy |
| Need GPU + ML expertise | Browser upload, no setup |
| Best for: high-volume, privacy-sensitive, custom pipelines | Best for: occasional/regular use, no infra |
If you're transcribing more than ~50 hours/month and you're comfortable with ML infra, self-hosting can be cheaper. For everyone else, a managed service is simpler.
Middle-ground option. If you don't want GPU infra but per-minute API pricing is unpredictable at your volume, predictable-cost bulk transcription delivers Whisper Large-v3 output at a flat monthly rate for up to 6,000 minutes — cheaper than self-hosting at that scale, more predictable than per-minute APIs.
Methodology & Sources
How we define Word Error Rate (WER)
WER is the sum of word substitutions, insertions, and deletions in a hypothesis transcript divided by the total number of words in the reference. Lower is better. Reported as a percentage. Where WER figures are cited on this page, they come from published benchmarks and vendor documentation, each noted with its source.
Our July 2026 benchmark — withdrawn
This section previously described the methodology for a first-party benchmark of 14 speech-to-text models. Those results were withdrawn on August 25, 2026 following an internal review — see the correction above for what was wrong and why. The methodology description has been removed along with the results rather than left standing behind data we no longer publish. Third-party figures elsewhere on this page are cited to their original sources and are unaffected.
Primary sources cited
- •Robust Speech Recognition via Large-Scale Weak Supervision (Radford et al., OpenAI Whisper paper, arXiv:2212.04356) — Table 13, Appendix D for FLEURS Large-v2 numbers.
- •Hugging Face Open ASR Leaderboard — 8-benchmark composite English WER, verified 2026-08-19.
- •openai/whisper README — model sizes, parameter counts, and VRAM requirements.
- •OpenAI Whisper announcement and release notes for Large-v3 (November 2023) and Large-v3 Turbo (September 2024).
- •ACM FAccT 2024 hallucination study; Cornell coverage (June 2024); Calm-Whisper silence-handling improvements paper.
- •Xu et al., arXiv:1708.08615 — professional human transcription WER on Switchboard and CallHome benchmarks.
- •LibriSpeech, AMI Meeting Corpus, TED-LIUM 3, GigaSpeech, Earnings21, VoxConverse, FLEURS (Google), CommonVoice (Mozilla), Multilingual LibriSpeech — dataset licenses vary; check per-dataset documentation before commercial republication.
Verification & update window
First-party WER, latency, and DER figures were withdrawn on 2026-08-25 (see the correction above). Open ASR Leaderboard composite figures are pulled 2026-08-19 and drift as vendors submit updated models. FLEURS per-language figures are from Table 13, Appendix D of the OpenAI Whisper paper and describe Large-v2. Vendor pricing referenced on this page was checked 2026-08-19 against vendor pricing pages. Corrections are shipped in-line and dated as we find them.
Independence and third-party names
VexaScribe (formerly NovaScribe) is an independent commercial transcription product and is not affiliated with, sponsored by, or endorsed by any of the vendors discussed here. Company, product, and model names are used solely to identify the services described and remain the property of their respective owners. Some vendors discussed compete with VexaScribe.
We use Speechmatics Melia-1 as our primary engine with Whisper Large-v3 fallback for languages Melia-1 doesn't cover. We do not sell a transcription API and do not resell any vendor discussed in this document.
Corrections. If you identify a reproducible error in anything published here, contact us with the specific claim and supporting evidence and we will investigate and publish a dated correction where warranted. Editorial standards at /about/editorial-standards.
Frequently Asked Questions
What is Whisper's word error rate?
Whisper Large-v3 achieves approximately 2.7% Word Error Rate (WER) on the LibriSpeech test-clean benchmark — clean audiobook audio with one speaker. On real-world English audio (meetings, podcasts, phone calls), WER rises to roughly 8–12% based on independent benchmarks. Accuracy drops further on noisy audio, strong accents, or languages with limited training data.
Is Whisper better than Deepgram, AssemblyAI, or Google?
It depends on the dataset, the text normalization, and how results are aggregated — the same engines can change places when any of those change. On English audio, the leading engines tend to cluster within a few points of one another, so the ordering is rarely stable across evaluations. For a standardized public comparison run under a common protocol, see the Hugging Face Open ASR Leaderboard. The most reliable answer for your project is to test the candidates on your own audio, in your own conditions.
Which Whisper model is most accurate?
Whisper Large-v3 (1.55B parameters) is the most accurate Whisper model. Large-v3 Turbo (released September 2024) is a distilled version with about 8× the speed and most of the accuracy. Smaller models — Tiny, Base, Small, Medium — trade accuracy for speed. For production use cases, most commercial transcription products run Large-v3 or Large-v3 Turbo.
Is Whisper accurate for languages other than English?
Whisper supports 99 languages, but accuracy varies significantly. Major Western European languages (Spanish, French, German, Italian, Portuguese, Dutch, Polish) perform near English-level. Japanese, Korean, Russian, Arabic, Hindi, Turkish, and Vietnamese typically have higher WER than English. The OpenAI Whisper paper (arXiv:2212.04356, Section 3.3) reports speech-recognition training data for 75 languages against 99 language tokens (100 since Cantonese was added for Large-v3), and finds a 0.83 squared correlation between log per-language training data and log WER, with WER halving for every 16× increase in training data. Languages with little training data should be treated as experimental regardless of what the language ID system reports.
What is GPT-4o-transcribe and how does it differ from Whisper?
OpenAI released GPT-4o-transcribe and GPT-4o-mini-transcribe in March 2025 as API-only successors to Whisper. Unlike Whisper, GPT-4o models are not open-source — you cannot self-host them. Whisper remains MIT-licensed. The OpenAI Whisper API costs $0.006/min; GPT-4o-mini-transcribe is $0.003/min.
Is GPT-4o Transcribe more accurate than Whisper?
We previously published a comparison here based on our own July 2026 benchmark. Those results have been withdrawn — our long-form measurements captured a limit on how much text the API returns for a single long file rather than its transcription accuracy, so the conclusions we drew were not supported. For a standardized public comparison, see the Hugging Face Open ASR Leaderboard. If long-form audio matters for your use case, test both on your own files at the durations you actually work with.
Whisper Large-v3 vs Large-v3 Turbo — which should I use?
Large-v3 (1.55B parameters, ~10 GB VRAM) is the most accurate. Large-v3 Turbo (809M parameters, ~6 GB VRAM, released September 2024) runs about 8× faster with most of the accuracy retained through knowledge distillation. Rule of thumb: use Turbo when latency or GPU cost matters (real-time UIs, high-volume batch), use Large-v3 when accuracy is critical (research, publishing, legal). On low-resource languages and heavily accented audio, Large-v3 pulls further ahead of Turbo.
Which OpenAI transcription model should I use for long recordings?
We previously answered this here based on our own July 2026 benchmark. Those results have been withdrawn — our long-form measurements captured a limit on how much text the API returns for a single long file rather than its transcription accuracy, so the conclusions we drew were not supported. For a standardized public comparison, see the Hugging Face Open ASR Leaderboard. Because long recordings are exactly where behavior differs most, test the candidate models on your own files at the durations you actually work with before committing.
Which speech-to-text API is best in 2026?
There is no single winner — "best" depends on what you are optimizing for. Batch accuracy, streaming latency, language coverage (some providers support 125+ languages versus Whisper's 99), price per minute, and compliance requirements like data residency all point to different providers. On English accuracy the leading engines sit close together, so the deciding factor is usually one of the other constraints. For a standardized public accuracy comparison, see the Hugging Face Open ASR Leaderboard, and see our full API comparison for the trade-offs across pricing, features, and deployment.
What is the most accurate transcription app?
Consumer transcription apps typically wrap Speechmatics, AssemblyAI, OpenAI Whisper, or Deepgram under the hood. VexaScribe runs Speechmatics Melia-1 with Whisper Large-v3 fallback for languages Melia-1 doesn't cover. Otter, Descript, Rev, and TurboScribe use various combinations of the same underlying engines. Accuracy differences between consumer apps come more from how they handle audio preprocessing, diarization, and editing than from the raw ASR engine.
Which transcription models are better than Whisper on English?
The standardized public source for this question is the Hugging Face Open ASR Leaderboard, which evaluates models on a common English suite under a shared protocol. Several commercial engines score close to or better than Whisper Large-v3 there, and NVIDIA Parakeet-TDT 0.6B v2 currently leads the English aggregate at around 6.05% versus Whisper Large-v3's ~7.44%. Rankings shift as models are updated, and the gaps at the top are small enough that results on your own audio may order them differently — check the leaderboard directly and test on representative files.
Why does Whisper sometimes fabricate text (hallucinations)?
A peer-reviewed study presented at ACM FAccT 2024 documented that Whisper occasionally fabricates content during silences and audio with frequent pauses. The hallucination rate varies dramatically by study and conditions (researchers have reported rates from 1% to 80% of segments). The problem is most pronounced with long silences, audio starting or ending in silence, and background noise that resembles speech. For production use, always treat AI transcripts as drafts, not records of truth.
Does Whisper hallucinate?
Yes, sometimes. A peer-reviewed ACM FAccT 2024 study documented that Whisper fabricates content during silences and audio with frequent pauses. Hallucination rates vary from ~1% to 80% of segments depending on audio conditions. Most affected: long silences at segment boundaries (over 30 seconds), audio starting or ending in silence, background noise that resembles speech. Mitigations include running voice activity detection (VAD) preprocessing before Whisper and using Calm-Whisper variants for silence handling. Always treat AI transcripts as drafts.
Can Whisper handle multiple speakers?
Whisper transcribes all speech but does not natively identify speakers (no diarization). For speaker labels, you need to combine Whisper with tools like pyannote-audio or use WhisperX, which adds forced alignment and diarization to Whisper output. Commercial tools built on Whisper — including VexaScribe (formerly NovaScribe) — bundle diarization automatically.
Is Whisper free to use commercially?
Yes. Whisper is released under the MIT license, which permits unrestricted commercial use. You can self-host, modify, and include it in products you sell. OpenAI also offers a paid Whisper API ($0.006/min) for those who don't want to self-host.
Is Whisper AI safe to use?
Whisper is safe in the operational sense — it runs locally, is MIT-licensed, does not phone home, and does not transmit audio anywhere if self-hosted. The safety concern is transcript quality: the ACM FAccT 2024 hallucination study showed Whisper occasionally fabricates content, which becomes a safety issue in medical, legal, or evidentiary contexts where inserted-but-plausible text could mislead. Rule of thumb: safe as a productivity tool for review-first workflows; not safe as an unattended source of record.
Is Parakeet better than Whisper?
On some English benchmarks yes, and it's faster. NVIDIA Parakeet-TDT 0.6B v2 currently leads the Hugging Face Open ASR Leaderboard on English-only aggregate WER — around 6.05% vs Whisper Large-v3's 7.44%. Parakeet is also significantly faster (higher real-time factor). Trade-offs: Parakeet is English-only, Whisper covers 99 languages; Parakeet has a smaller ecosystem (fewer wrappers, fewer language variants, less mature diarization pairing); Parakeet is CC-BY-4.0 licensed while Whisper is MIT. For English production ASR where speed matters, evaluate Parakeet. For multilingual or ecosystem-dependent workflows, Whisper is still the safer pick.
Does Whisper work offline?
Yes. Once the model is downloaded, Whisper runs entirely locally with no internet connection required. This makes it suitable for privacy-sensitive applications, offline environments, and air-gapped systems. Model sizes range from about 75 MB (Tiny) to about 3 GB on disk (Large-v3).
What hardware do I need to run Whisper Large-v3?
Whisper Large-v3 requires approximately 10 GB of GPU VRAM to run efficiently. Large-v3 Turbo needs about 6 GB. Smaller models (Tiny, Base, Small) can run on CPU but with significantly slower throughput. For self-hosting at scale, a modern NVIDIA GPU (RTX 3090, 4090, A6000, A100) gives best results. CPU-only inference is possible but typically 5–20× slower.
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