---
title: "Whisper speech recognition | Ethical Stack"
url: https://ethicalstack.ai/models/openai-whisper/
catalog_version: f6baff2119d0e9158520da7fbe4232011dac79f25bd08757436d7a1dbd593db2
---

# Whisper speech recognition

An open-source speech-recognition model that supports multilingual transcription, translation and language identification.

Canonical profile: https://ethicalstack.ai/models/openai-whisper/

Scope: OpenAI Whisper repository, model card and published downloadable weights.

Published overall: 4.8; 6/6 dimensions rated.

Round the equal-weight mean of available numerical ratings to one decimal; require at least four of six for an overall score. BDS status stays outside the mean; for eligible profiles, a boycott target caps the final score at 2/10 and a pressure target subtracts one point (minimum 1). Not listed and not checked do not adjust the score.

## Energy practices

Finding: 4/10

Assessment: https://ethicalstack.ai/models/openai-whisper/#energy

You can run this model on your own hardware; no comparative electricity figures are published.

Reason: 1 + 1 reporting + 0 electricity + 2 controls = 4/10. This grades documented practices and transparency.

Argument: This downloadable model profile scores disclosed deployment choices and operating controls, not measured energy savings. Local inference lets the operator select device, runtime and workload size, although the model card does not report task-level electricity.

Type: argument; confidence: medium

Scope (dimension): Whisper speech recognition

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Comparable per-minute or per-generation electricity, water use, lifecycle emissions and independently verified clean-power sourcing are not published in these sources.

```json
{
  "method": "practices-transparency-v1",
  "scope": "Whisper speech recognition",
  "components": {
    "disclosure": {
      "score": 1,
      "reason": "The model and local route are documented, but no environmental footprint is audited.",
      "sourceUrls": [
        "https://github.com/openai/whisper"
      ]
    },
    "electricity": {
      "score": 0,
      "reason": "No attributable clean-electricity procurement or hourly matching evidence was found in the cited primary material.",
      "sourceUrls": [
        "https://openai.com/about/"
      ]
    },
    "demand": {
      "score": 2,
      "reason": "Choosing a small model and local runtime allows workload and hardware controls; it is not measured energy reduction.",
      "sourceUrls": [
        "https://github.com/openai/whisper"
      ]
    }
  },
  "actualImpactMeasured": false
}
```

- [OpenAI Whisper official repository](https://github.com/openai/whisper) (primary; checked 2026-10-28)

- [OpenAI official company, product or policy source](https://openai.com/about/) (primary; checked 2026-10-28)

## Training data

Finding: 2/10

Assessment: https://ethicalstack.ai/models/openai-whisper/#training-rights

The model card discloses training source classes but does not publish a complete rights ledger.

Reason: 1 + 1 source transparency + 0 permission + 0 creator controls = 2/10, with the permission cap applied.

Argument: OpenAI’s Whisper model card says the release trained on 680,000 hours of audio and transcripts collected from the internet, with broad language proportions. It does not list source works, licenses or speaker consent. The MIT license applies to the code and weights, not to the original training recordings.

Type: argument; confidence: medium

Scope (profile): OpenAI Whisper repository, model card and published downloadable weights.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The full source inventory, speaker consent records, exact licenses for the remaining training data, rights-holder removal process and independent rights audit are not published in the reviewed model materials.

```json
{
  "method": "training-rights-evidence-v1",
  "scope": "The stated training release or selected model",
  "components": {
    "provenance": {
      "score": 1,
      "reason": "The card discloses the training data scale and broad web-derived source class, but no itemized source inventory.",
      "sourceUrls": [
        "https://github.com/openai/whisper/blob/main/model-card.md"
      ]
    },
    "permissions": {
      "score": 0,
      "reason": "Internet collection is described without affirmative source-level permission, license or consent evidence.",
      "sourceUrls": [
        "https://github.com/openai/whisper/blob/main/model-card.md"
      ]
    },
    "creatorControls": {
      "score": 0,
      "reason": "No rights-holder opt-out, removal or retraining remedy is documented in the model release sources.",
      "sourceUrls": [
        "https://github.com/openai/whisper/blob/main/model-card.md"
      ]
    }
  },
  "routeDependency": null,
  "modelReference": null
}
```

- [OpenAI Whisper model card](https://github.com/openai/whisper/blob/main/model-card.md) (primary; checked 2026-10-28)

## Your data & privacy

Finding: 8/10

Assessment: https://ethicalstack.ai/models/openai-whisper/#privacy

Local inference can keep the audio and transcript on your own device.

Reason: The published model can be downloaded and run locally. Once downloaded, inference need not send recordings or transcripts to the model publisher; software dependencies, telemetry, backups and any remote application remain separate data paths under the operator’s control.

Argument: The published model can be downloaded and run locally. Once downloaded, inference need not send recordings or transcripts to the model publisher; software dependencies, telemetry, backups and any remote application remain separate data paths under the operator’s control.

Type: argument; confidence: medium

Scope (profile): OpenAI Whisper repository, model card and published downloadable weights.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The model publisher does not audit the user’s runtime, network connections, logging, backups or downstream application. Verify those components for your installation.

- [OpenAI Whisper official repository](https://github.com/openai/whisper) (primary; checked 2026-10-28)

- [OpenAI official company, product or policy source](https://github.com/openai/whisper/blob/main/LICENSE) (primary; checked 2026-10-28)

## Openness

Finding: 8/10

Assessment: https://ethicalstack.ai/models/openai-whisper/#openness

The model weights and code are published under a permissive license; training remains difficult to reproduce.

Reason: OpenAI publishes Whisper code and model weights under MIT and provides inference instructions. This supports local deployment, while training data disclosure remains broad rather than a fully reproducible manifest.

Argument: OpenAI publishes Whisper code and model weights under MIT and provides inference instructions. This supports local deployment, while training data disclosure remains broad rather than a fully reproducible manifest.

Type: argument; confidence: medium

Scope (profile): OpenAI Whisper repository, model card and published downloadable weights.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: A complete data manifest, exact training recipe and independently reproduced end-to-end training are not supplied.

- [OpenAI Whisper official repository](https://github.com/openai/whisper) (primary; checked 2026-10-28)

## Worker protections

Finding: 4/10

Assessment: https://ethicalstack.ai/models/openai-whisper/#labour

OpenAI’s supplier code sets pay, safety and complaint rules for data workers. Public evidence of coverage and outcomes is limited.

Reason: 1 + 1 pay & conditions + 1 worker voice & remedy + 1 evidence & follow-through = 4/10. This grades documented worker safeguards, not a measured rate of mistreatment.

Argument: The October 2026 Supplier Code applies to suppliers, staffing agencies, subcontractors and sub-tier suppliers, and explicitly covers annotation, moderation, evaluation and RLHF workers: it requires task-risk notice, breaks, exposure limits/support, task opt-out or escalation without retaliation, reasonable paid performance expectations, and supplier records that OpenAI may validate. A 2023 TIME investigation into the earlier Sama contract documented workers’ accounts of very low take-home pay and distressing work; Sama disputed key characterizations and said it offered extensive counseling, while OpenAI said it had understood workers could opt out and access support, and said vendors set pay and care. These historical claims and counterclaims do not verify the current code’s implementation, worker voice or outcomes. This entry carries the same developer/operator finding to Whisper speech recognition; it does not establish that this product was deployed under every cited relationship.

Type: argument; confidence: medium

Scope (dimension): OpenAI: the documented developer/operator record, not proof of deployment of Whisper speech recognition.

Research reviewed: 2026-10-24; evidence date: not recorded

Unknowns: The current code does not publish supplier-level audit results, pay distributions, annotator coverage, worker survey results or independent remediation outcomes. The Sama project ended in 2022; no current vendor footprint is established.

```json
{
  "method": "worker-safeguards-evidence-v1",
  "entity": "openai",
  "company": "OpenAI",
  "scope": "OpenAI's workforce and disclosed contractors. Coverage of individual product teams may be incomplete.",
  "components": {
    "conditions": {
      "score": 1,
      "reason": "The October 2026 supplier code sets specific conditions for human-data workers, but it is a written requirement rather than evidence of current implementation. The only cited implementation account is the historical GPT-4-era work; TIME’s 2022 Sama reporting documented worker concerns and OpenAI/Sama disputed or qualified key claims.",
      "sourceUrls": [
        "https://openai.com/policies/supplier-code/",
        "https://cdn.openai.com/papers/gpt-4.pdf",
        "https://time.com/6247678/openai-chatgpt-kenya-workers/",
        "https://www.sama.com/blog/ethical-and-sustainable-sourcing"
      ]
    },
    "voice": {
      "score": 1,
      "reason": "The current code requires channels to pause, decline or escalate unsafe tasks without retaliation, but no public evidence verifies a current feedback channel’s use or outcomes. Historical reporting records workers raising concerns and the vendor/customer responses, not current implementation.",
      "sourceUrls": [
        "https://openai.com/policies/supplier-code/",
        "https://time.com/6247678/openai-chatgpt-kenya-workers/",
        "https://www.sama.com/blog/ethical-and-sustainable-sourcing"
      ]
    },
    "assurance": {
      "score": 1,
      "reason": "The code requires suppliers to retain worker and contract records and cooperate with OpenAI or authorized third-party validation on reasonable request, but OpenAI publishes no worker-specific audit results or independent outcomes.",
      "sourceUrls": [
        "https://openai.com/policies/supplier-code/"
      ]
    }
  },
  "disclosureLimited": false,
  "meaning": "This grades documented worker safeguards, not a measured rate of mistreatment."
}
```

- [OpenAI Supplier Code of Conduct](https://openai.com/policies/supplier-code/) (primary; checked 2026-10-23)

- [GPT-4 Technical Report](https://cdn.openai.com/papers/gpt-4.pdf) (primary; checked 2026-10-24)

- [OpenAI used Kenyan workers on less than $2 per hour: TIME investigation](https://time.com/6247678/openai-chatgpt-kenya-workers/) (reporting; checked 2026-10-23)

- [Sama and Content Moderation: What You Need to Know](https://www.sama.com/blog/ethical-and-sustainable-sourcing) (primary; checked 2026-10-24)

## Military ties

Finding: 3/10

Assessment: https://ethicalstack.ai/models/openai-whisper/#military-contracts

OpenAI describes a classified Department of War agreement with cloud-only deployment, named technical and personnel safeguards, and explicit restrictions, while lawful military use remains permitted.

Reason: 1 + 1 use restrictions + 2 enforcement & oversight + 2 relationship disclosure = 6, capped at 3/10. Known defense ties lower this rating; safeguards can improve it within the relationship’s cap.

Argument: The March 2026 agreement permits lawful purposes and classified deployment, so this is broad defense enablement; the public agreement does not identify actual targeting, weapons, or combat-intelligence deployments. It bars directing autonomous weapons and domestic surveillance of U.S. persons, including commercial personal data, and disallows high-stakes automated decisions where human approval is required. OpenAI says it retains the safety stack, deploys only through its cloud, uses classifiers and cleared personnel in the loop, and may terminate for breach. These are concrete company-described safeguards, but no independent enforcement outcomes are public, and the March amendment says NSA service would require a separate agreement. This entry carries the same developer/operator finding to Whisper speech recognition; it does not establish that this product was deployed under every cited relationship.

Type: argument; confidence: medium

Scope (dimension): OpenAI: the documented developer/operator record, not proof of deployment of Whisper speech recognition.

Research reviewed: 2026-10-24; evidence date: not recorded

Unknowns: The public agreement does not list task orders, deployed model versions, users, operational workflows, audit outcomes, or whether guardrails have been independently tested in use. Contract permission and deployment of a specific model version are not established here.

```json
{
  "method": "military-ties-priority-v2",
  "entity": "openai",
  "company": "OpenAI",
  "scope": "OpenAI's relationships and safeguards. A company relationship does not identify every product or release used.",
  "components": {
    "restrictions": {
      "score": 1,
      "reason": "The contract explicitly excludes autonomous weapon direction, domestic surveillance of U.S. persons, and specified unapproved high-stakes decisions, but lawful military activity remains authorized and not all harmful surveillance/weapon uses are categorically barred.",
      "sourceUrls": [
        "https://openai.com/index/our-agreement-with-the-department-of-war/"
      ]
    },
    "oversight": {
      "score": 2,
      "reason": "The agreement describes cloud-only deployment, OpenAI-controlled safety stack and classifiers, cleared staff in the loop, and termination rights; these are use-specific contractual and technical review mechanisms, with no independent outcome evidence.",
      "sourceUrls": [
        "https://openai.com/index/our-agreement-with-the-department-of-war/"
      ]
    },
    "transparency": {
      "score": 2,
      "reason": "OpenAI publishes the agreement’s scope, restrictions, architecture and NSA carve-out; it still does not disclose deployed mission/use-level details.",
      "sourceUrls": [
        "https://openai.com/index/our-agreement-with-the-department-of-war/"
      ]
    }
  },
  "relationship": "broad-defense",
  "relationshipReason": "A classified DoW contract authorizes broad lawful use, but the public evidence describes no actual targeting, strike, or combat-intelligence support; broad-defense cap applies.",
  "score": 3,
  "baseScore": 1,
  "safeguardPoints": 5,
  "safeguardAdjustment": 5,
  "rawScore": 6,
  "cap": 3,
  "disclosureLimited": false,
  "meaning": "Known military relationships take priority over missing safeguards. The rating does not establish the probability of an undisclosed contract."
}
```

- [Our agreement with the Department of War](https://openai.com/so-DJ/index/our-agreement-with-the-department-of-war/) (primary; checked 2026-10-24)

- [Our agreement with the Department of War](https://openai.com/index/our-agreement-with-the-department-of-war/) (primary; checked 2026-10-23)

## BDS targets

Finding: Not listed

Assessment: https://ethicalstack.ai/models/openai-whisper/#israel-palestine

We did not find OpenAI among the named targets in the two official BNC campaign lists checked on 2026-10-23. This is a limited list check, not BDS approval.

Reason: BDS campaign status is a factual category, not a 1–10 ethical rating.

Argument: OpenAI is not a named target in the checked lists, but BDS uses selective campaigns, so absence cannot establish a clean record. OpenAI’s April 2026 agreement names Microsoft as its primary cloud partner and a major shareholder, with revenue-share payments to Microsoft through 2030. That is a documented commercial link to a BDS target, not a separate official listing of OpenAI; local use of downloaded weights does not by itself establish a new payment to either company. This entry carries the same developer/operator finding to Whisper speech recognition; it does not establish that this product was deployed under every cited relationship.

Type: evidence; confidence: medium

Scope (dimension): OpenAI: the documented developer/operator record, not proof of deployment of Whisper speech recognition.

Research reviewed: 2026-10-23; evidence date: not recorded

Unknowns: This review covers the named targets in the BNC consumer/pressure guide and No Tech campaign, not every BDS campaign, local group, investment or supplier. Lists and ownership can change. A company’s absence from these lists is not an endorsement or proof that it has no relevant relationships.

- [BNC guide to boycott and pressure targets](https://bdsmovement.net/Guide-to-BDS-Boycott) (campaign; checked 2026-10-28)

- [BNC No Tech campaign](https://bdsmovement.net/no-tech-oppression-apartheid-or-genocide) (campaign; checked 2026-10-28)

- [OpenAI: amended Microsoft partnership, April 27 2026](https://openai.com/index/next-phase-of-microsoft-partnership/) (primary; checked 2026-10-23)

## Reading the evidence

- Assessments are provisional editorial judgments. Cite the profile and the underlying sources, and retain confidence, scope and unknowns.

- Unknown, not-applicable and model-dependent are different states. Missing evidence does not establish good or bad conduct.

- Company-level evidence does not establish use of a particular product or model. Account plan, model, host and route can change an assessment.

- Energy scores describe documented practices and transparency, not measured request emissions. Zero component points can mean an evidence gap.

- Published overall scores require at least four of six numerical dimensions. Preserve coverage and BDS adjustments; BDS itself is categorical.

- No named BDS target found is limited to the reviewed campaign sources and is not approval. Personalized priorities do not change the published overall score.

- Review and source-check dates describe the research, not this deployment. Fetch current primary sources when the decision requires current verification.

Methodology: https://ethicalstack.ai/methodology/