# OpenWhispr vs Whisper (OpenAI)

OpenWhispr records 291,756 monthly visits with -13.3% month-over-month growth and Free pricing. Whisper (OpenAI) records 0 monthly visits with — growth and Free pricing.

| Metric | [OpenWhispr](https://openaitools.app/tool/openwhispr) | [Whisper (OpenAI)](https://openaitools.app/tool/whisper-github) |
| :--- | :--- | :--- |
| Website URL | https://openwhispr.com | https://github.com/openai/whisper |
| Categories | AI Speech-to-Text, AI Transcription, Audio To Text AI, AI Note Taker | AI Speech Recognition, AI Speech-to-Text, AI Transcriber, AI Translate |
| Pricing Model | Free | Free |
| Monthly Visits | 291,756 | 0 |
| MoM Growth Rate | -13.3% | — |
| Primary Market | United States | Global |
| Direct Visits | 72,621 | 0 |
| Review Score | 4.5 | 4.8 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Open Source |

## OpenWhispr

OpenWhispr tackles a simple arithmetic problem: most people type around 40 words per minute but speak closer to 150. The app listens through a configurable hotkey, streams your speech through a speech recognition model, and pastes the result into whatever text field is focused — ChatGPT, Claude, Cursor, Slack, Docs, Gmail, or an IDE. Because it operates at the operating-system level rather than inside one app, it becomes a universal input method rather than a single-purpose tool.

The distinguishing choice is where the audio goes. Local mode runs on-device models such as Whisper or NVIDIA Parakeet entirely on your hardware, so nothing leaves the machine. Cloud mode is optional and lets you plug in your own provider keys, with recordings deleted once transcription finishes. The project is published under the MIT license with a public GitHub repository, and it publishes compliance credentials including SOC 2 Type II, ISO 27001, HIPAA and GDPR alignment — unusual for a free utility, and aimed squarely at teams whose security reviewers would otherwise block a dictation tool.

Beyond raw transcription, OpenWhispr layers on voice commands and AI cleanup. You can say things like "clean this up" or "draft an email to Mike" and the model restructures the rough transcript accordingly. A custom vocabulary system learns names and domain jargon such as product names, internal services or database identifiers directly from corrections you make, which is where generic dictation tools usually fall apart. Language support spans 100+ languages with automatic detection, so switching languages mid-conversation keeps working.

**Pricing & Plans:** Free to download and use, and the code is open source under the MIT license. Local on-device transcription and bring-your-own-API-key cloud transcription cost nothing extra in software fees; if you choose a paid cloud provider, you pay that provider directly. A separate pricing page and enterprise contact option exist for organisational deployments.

- **System-wide dictation:** Press a configurable hotkey and your words are typed into whichever app has focus — chat clients, editors, browsers and terminals alike.
- **Local or cloud processing:** Run on-device models such as Whisper or NVIDIA Parakeet for full privacy, or switch to cloud transcription with your own provider API keys.
- **AI cleanup and voice commands:** Spoken instructions like "clean this up" or "draft an email" let the model restructure and polish the raw transcript before it lands.
- **Custom vocabulary learning:** Names, jargon and technical terms are auto-learned from your corrections so recurring vocabulary stops being misheard.
- **100+ languages and offline mode:** Automatic language detection across more than a hundred languages, with everything still working without an internet connection.

**Strengths:** Genuinely free and open source under the MIT license, with the code available for inspection.; On-device models mean dictation can work fully offline with no audio leaving your computer.; Works across all apps rather than being locked inside one editor or chat window.; Publishes SOC 2 Type II, ISO 27001, HIPAA and GDPR-oriented credentials, which is rare for a free tool.

**Limitations:** Local models require a reasonable amount of disk space and benefit from decent hardware.; You must supply and manage your own API keys if you want cloud processing.; Cloud features depend on third-party providers, so quality and cost vary by provider.

[Profile (Markdown)](https://openaitools.app/tool/openwhispr.md)

## Whisper (OpenAI)

Whisper was introduced by OpenAI as a general-purpose speech recognition model trained on a very large, diverse dataset of web audio. Instead of stacking separate acoustic, language and punctuation modules, it treats every speech task as a token prediction problem: transcription, translation, language identification and voice activity detection all share one Transformer encoder-decoder and are distinguished by special task tokens. That single-model design is why a straightforward `pip install` is enough to get multilingual transcription running on a laptop, and why Whisper has become a de facto baseline for open speech-to-text work.

The repository ships six model sizes — tiny, base, small, medium, large and a faster turbo variant — trading accuracy for memory and speed. Each size also has an English-only `.en` counterpart that tends to score better on English audio, while the multilingual checkpoints are the ones to use when translating non-English speech into English. Practical VRAM needs range from roughly 1 GB for tiny up to about 10 GB for large, so the smaller checkpoints run comfortably on consumer hardware or even CPU, while large models are usually paired with a GPU.

Because Whisper is a model and a Python package rather than a hosted product, there is no dashboard, account or quota. You install it, point the command line at audio files, and write your own integration on top. It supports many audio formats through ffmpeg, and its per-language accuracy varies widely — strong for high-resource languages, weaker for low-resource ones — which is worth knowing before adopting it for production transcription.

**Pricing & Plans:** Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

- **Multitask single model:** One Transformer handles transcription, speech translation into English, language identification and voice activity detection, selected through special task tokens.
- **Multiple model sizes:** Choose from tiny, base, small, medium, large and turbo checkpoints, with English-only .en variants, to balance accuracy against VRAM and speed.
- **Broad language coverage:** Multilingual checkpoints transcribe and translate dozens of languages, with automatic language detection on mixed-language audio.
- **Command-line and Python API:** Install via pip with openai-whisper and transcribe files from the terminal or from your own Python code, with ffmpeg handling audio decoding.
- **Permissive MIT license:** Freely embed, modify and redistribute the code and weights in commercial and personal projects without licensing fees.

**Strengths:** Completely free and open source under a permissive MIT license; Strong multilingual accuracy across many languages with automatic detection; Runs locally, so audio never has to leave your hardware; Several model sizes let you match accuracy to available compute

**Limitations:** No hosted UI, account or support — you must install and operate it yourself; Large models need substantial VRAM and a GPU for reasonable speed; Accuracy drops noticeably on low-resource languages and noisy audio; Setup requires Python, PyTorch and ffmpeg, which can be fiddly for non-developers

[Profile (Markdown)](https://openaitools.app/tool/whisper-github.md)

## Frequently Asked Questions

### How do OpenWhispr and Whisper (OpenAI) compare in monthly traffic?

OpenWhispr has 291,756 monthly visits, while Whisper (OpenAI) has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

OpenWhispr has a month-over-month growth rate of -13.3%; Whisper (OpenAI) has —.

### How does pricing compare?

OpenWhispr: Free. Free to download and use, and the code is open source under the MIT license. Local on-device transcription and bring-your-own-API-key cloud transcription cost nothing extra in software fees; if you choose a paid cloud provider, you pay that provider directly. A separate pricing page and enterprise contact option exist for organisational deployments. Whisper (OpenAI): Free. Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [TurboScribe](https://openaitools.app/tool/turboscribe-ai) | 27,661,771 | +2.1% | Freemium | Unlimited AI transcription of audio and video in 98+ languages | [.md](https://openaitools.app/tool/turboscribe-ai.md) |
| 2 | [Otter AI](https://openaitools.app/tool/otter-ai) | 7,360,981 | +9.6% | Freemium | AI meeting agent that transcribes, summarises, and turns every conversation into searchable knowledge | [.md](https://openaitools.app/tool/otter-ai.md) |
| 3 | [Happy Scribe](https://openaitools.app/tool/happyscribe-com) | 4,088,330 | +8% | Freemium, Free Trial, Paid | AI transcription and meeting notes in 150+ languages | [.md](https://openaitools.app/tool/happyscribe-com.md) |
| 4 | [Video Transcriber AI](https://openaitools.app/tool/video-transcriber-ai) | 1,320,064 | +11% | Freemium | Turn any video or audio into text in seconds | [.md](https://openaitools.app/tool/video-transcriber-ai.md) |
| 5 | [Deepgram Voice AI](https://openaitools.app/tool/deepgram-voice-ai) | 863,852 | +31.8% | Free Trial | Real-time speech-to-text, text-to-speech and voice agents in one API platform | [.md](https://openaitools.app/tool/deepgram-voice-ai.md) |
| 6 | [Transkriptor](https://openaitools.app/tool/transkriptor-com) | 628,671 | -3.6% | Free Trial, Paid | AI transcription that turns audio and video into searchable text | [.md](https://openaitools.app/tool/transkriptor-com.md) |
| 7 | [AssemblyAI](https://openaitools.app/tool/assemblyai) | 453,591 | -6.9% | Freemium, Contact for Pricing | Speech-to-text and voice intelligence APIs for developers | [.md](https://openaitools.app/tool/assemblyai.md) |
| 8 | [Tongyi Tingwu](https://openaitools.app/tool/tongyi-tingwu) | 425,484 | -10% | Free | Your AI assistant for work and study. | [.md](https://openaitools.app/tool/tongyi-tingwu.md) |
| 9 | [Superwhisper](https://openaitools.app/tool/superwhisper) | 238,286 | -13.9% | Freemium | Offers a more polished cross-platform dictation experience, but lacks OpenWhispr's native open-source flexibility and self-hosting options. | [.md](https://openaitools.app/tool/superwhisper.md) |
| 10 | [Gladia](https://openaitools.app/tool/gladia) | 232,451 | +6.4% | Freemium, Contact for Pricing | Speech-to-text and audio intelligence API for production voice products | [.md](https://openaitools.app/tool/gladia.md) |

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Source: https://openaitools.app/compare/openwhispr-vs-whisper-github

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