---
title: 'Robust Speech Recognition via Large-Scale Weak Supervision'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2212-04356-whisper.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: multimodal'
tags: arxiv, speech, whisper, multimodal
arxiv_id: 2212.04356
alias: 2212.04356-whisper
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Robust Speech Recognition via Large-Scale Weak Supervision'
    publisher: arXiv
    authors: 'Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever'
    url: https://arxiv.org/abs/2212.04356
    version_read: 'v1 (6 Dec 2022)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Robust Speech Recognition via Large-Scale Weak Supervision

## Summary
[Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) (2022) is the Whisper paper. It showed that training a speech model on a very large, weakly supervised multilingual audio-transcript collection yields a robust open recognition and transcription model, now used in a wide range of products and pipelines.

What it introduced: Whisper, a sequence-to-sequence speech model trained at scale on weakly labeled audio, covering recognition, translation, and related tasks, released as open weights.

What it led to: a default open speech frontend for assistants and for multimodal systems that need transcripts. Live notes: [ao-evals-multimodal-agents](https://iinventedlight.com/library/notes/ao-evals-multimodal-agents/).

If you need an open speech frontend for an assistant or a multimodal pipeline, this is the usual starting paper. Where to read it: https://arxiv.org/abs/2212.04356 (v1, 6 Dec 2022).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).