Whisper Transcription Online — Automatic Speech Recognition in 99 Languages

What Is Whisper?

Whisper is an open-source automatic speech recognition (ASR) model released by OpenAI in September 2022. It is a Transformer encoder-decoder trained on 680,000 hours of weakly supervised multilingual audio scraped from the public web, distributed under the MIT license. Roughly 117,000 hours of that training data are non-English, which is why a single set of weights can transcribe 99 languages and translate any of them into English.

Unlike commercial ASR systems that wrap proprietary acoustic and language models, Whisper is a single sequence-to-sequence Transformer. It performs language identification, voice activity detection, punctuation, and timestamp generation as part of the same forward pass. Because the model and weights are public, anyone can run Whisper locally on a GPU, fine-tune it for a specific domain, or — like VexaScribe — host it as a managed service.

The lineage matters when you read claims online. Whisper Large-v3 (November 2023) uses 128 mel-frequency bins and adds a Cantonese token. Whisper Turbo (officially large-v3-turbo, October 2024) has only 4 decoder layers — down from 32 — making it 4–8× faster at the cost of a small accuracy drop. As of 2026, OpenAI also offers two newer hosted-only models, gpt-4o-transcribe and gpt-4o-mini-transcribe, both released March 20, 2025; they are not the same as Whisper and have different licensing. See our deep-dive on Whisper accuracy.

Automatic Speech Recognition (ASR) — How Whisper Fits In

Automatic speech recognition (ASR) is the technology that converts spoken audio into written text. Every speech-to-text product — from a phone dictation app to a court transcription service — runs an ASR model at its core. What changed over the last decade is the dominant architecture.

Early ASR systems used GMM-HMM pipelines (Gaussian Mixture Model + Hidden Markov Model): a pronunciation dictionary mapped phonemes to words, and acoustic models were trained per language per domain. They needed clean audio, native speakers, and constant re-training for new vocabularies. Neural approaches (CTC, RNN-T) replaced the pronunciation dictionary with end-to-end gradient descent, but still required large labeled datasets per language. Whisper breaks this by using a sequence-to-sequence Transformer trained on 680,000 hours of weakly supervised multilingual audio — the model learns language identification, punctuation, and timestamps as part of the same forward pass, with no separate pronunciation model needed.

As of 2026, Whisper is the most-downloaded open-source ASR model on Hugging Face and the most-cited model on the Open ASR Leaderboard. When researchers or engineers say "Whisper speech to text," they mean running Whisper Large-v3 (or the Turbo distillation) as the ASR backbone — which is exactly what VexaScribe runs in the transcription tool above.

For a precise breakdown of where this ASR model gets numbers right and where it struggles — dialect accuracy, noisy environments, code-switching — see How Accurate Is Whisper? and AI transcription accuracy compared.

Whisper Model Family: Every Checkpoint Compared

Whisper ships in eight checkpoints from tiny (39M parameters, ~75 MB on disk) up to large-v3 (1,550M parameters, ~3.1 GB). The October 2024 large-v3-turbo is an 809M-parameter distillation that runs roughly 4–8× faster than Large-v3 with only a small accuracy penalty.

WER values on LibriSpeech test-clean. Large-v3 and large-v3-turbo numbers are from the official Hugging Face model cards; smaller-checkpoint WERs are from the OpenAI Whisper paper Table 9.

The English-only .en variants outperform their multilingual counterparts on English audio at the same parameter count, especially for tiny.en and base.en. Whisper Turbo is not trained for translation tasks — if you need to convert non-English speech into English subtitles, use multilingual large-v3.

Whisper Accuracy: WER by Language and Condition

Whisper Large-v3 averages 7.44% Word Error Rate across the eight-dataset Open ASR Leaderboard, but real-world WER ranges from ~2% on clean read English to over 20% on accented telephony or non-English low-resource speech.

The single biggest variable is not the model — it is the audio. Read speech in a quiet room is roughly 5× easier than the same speaker on a noisy Zoom call.

These ranges combine OpenAI's own FLEURS evaluation in the Whisper paper with reproductions on Common Voice and the Open ASR Leaderboard. For a deeper breakdown of WER methodology, see our Whisper accuracy guide.

How VexaScribe Uses Whisper Under the Hood

VexaScribe is a hosted Whisper service. Your file is resampled to 16 kHz mono, fed to Whisper Large-v3 on our GPUs, optionally diarized for speaker labels, then returned as SRT, VTT, plain text, or JSON.

1. 1

   Ingest

   Upload over TLS 1.2+ — files up to 5 GB. Audio extracted from video files automatically.

2. 2

   Decode and normalize

   FFmpeg decodes any of 17 supported formats and resamples to 16 kHz mono PCM — the input format Whisper expects.

3. 3

   Voice-activity detection

   Silence and non-speech regions are trimmed before the model sees them, reducing hallucination on quiet sections.

4. 4

   Whisper Large-v3 inference

   Default model. A 60-minute file completes in 5–10 minutes on our GPU pipeline.

5. 5

   Speaker diarization (optional)

   Speaker turns are identified and aligned with Whisper word timestamps to produce Speaker 1 / Speaker 2 output.

6. 6

   Export

   SRT, VTT, plain text, DOCX, or JSON with word-level timestamps and confidence scores.

Hosted Whisper vs OpenAI API vs Self-Hosted

If you transcribe under ~10 hours per month and you can write code, the OpenAI Whisper API is competitive at $0.006/minute. If you have an idle GPU, self-hosting is free per minute. VexaScribe is the right choice when you want speaker labels, files larger than 25 MB, no per-minute math, and a UI.

Read this table honestly: if your monthly volume is small and you are comfortable with code, the OpenAI API direct can be cheaper than VexaScribe per minute — that is just true. VexaScribe wins when (a) your files exceed 25 MB and you do not want to chunk them, (b) you need speaker labels without writing diarization code, (c) you want SRT/VTT formatted to broadcast standards, or (d) you want a flat monthly bill instead of per-minute math.

What Whisper Does Not Do Well

Whisper hallucinates entire sentences on silent or near-silent audio, performs poorly on song lyrics and overlapping speech, has no built-in speaker diarization, and cannot stream audio in real time without external chunking logic.

How to Use Whisper Transcription on VexaScribe

Drop in any audio or video file up to 5 GB, choose Whisper Large-v3 plus your language, and download the result as SRT, VTT, plain text, or JSON.

1. 1

   Upload your file

   Drag and drop any MP3, WAV, M4A, FLAC, OGG, MP4, MOV, MKV, or WebM file up to 5 GB. Files travel over TLS 1.2+ and are stored encrypted at rest.

2. 2

   Pick Large-v3 and language

   Whisper Large-v3 is the default — best accuracy. Choose one of 99 languages or leave on auto-detect. Toggle speaker diarization on if you need labels.

3. 3

   Export and share

   Download as SRT, VTT, plain text, DOCX, or JSON with word-level timestamps. Send the transcript straight to the AI summary tool.

Whisper-Supported Languages: All 99, with Quality Tiers

Whisper supports 99 languages out of a single Large-v3 checkpoint, with English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Mandarin, and Japanese all in the high-quality tier (sub-10% WER on clean read speech). Cantonese was added as a separate token in Large-v3 in November 2023.

For non-English audio, Whisper can also translate directly into English (the translate task) without a separate translation step. If you need to translate into a language other than English, see transcribe and translate audio.

Privacy and Data Handling

Your audio is encrypted in transit (TLS 1.2+) and at rest in AWS eu-west-2. We do not use your files to train Whisper or any other model, and you can delete any file with one click.

• Encryption: TLS 1.2+ in transit; encrypted at rest in AWS eu-west-2.
• No training on your data: Your audio and transcripts are never used to train or fine-tune any model. Whisper Large-v3 is a frozen open-source checkpoint we run as-is.
• Self-serve deletion: Delete any file at any time from your dashboard. Account deletion purges all transcripts and audio.
• Whisper inference on our infrastructure: The transcription step runs on our own GPU pipeline — your audio does not leave our environment for inference.

See our full privacy policy and editorial standards.

Pricing: VexaScribe vs OpenAI Whisper API

The OpenAI Whisper API is $0.006/minute ($0.36/hour) with no minimums or subscriptions, billed per second. VexaScribe paid plans bundle storage, diarization, summaries, and a UI into a flat monthly fee. The breakeven where VexaScribe becomes cheaper is around 50 hours of audio per month.

VexaScribe pricing reflects published Starter / Basic / Pro / Studio plans on /pricing. OpenAI rates from their official API pricing page (whisper-1, $0.006/minute, verified May 2026).