Whisper
Whisper is a speech recognition model from the company OpenAI that turns spoken language into written text, that is, does speech-to-text. It was trained on a very large amount of audio data and can recognise many languages and translate audio from one language directly into English. Whisper is available as open source, so it can be run locally and built into your own applications, and is additionally available through the OpenAI interface. The model is known for its robust recognition even with background noise, accents or poor recording quality. For companies, Whisper is therefore a versatile basis for making audio and video content usable automatically.
Also known as: OpenAI Whisper, speech-to-text, AI transcription
What is Whisper?
Whisper belongs to the category of automatic speech recognition, often abbreviated ASR. The model's task is to turn an audio recording into the corresponding text. Whisper recognises not just individual words but takes the context into account, which raises accuracy on ambiguous passages in particular.
One important feature is multilingualism. Whisper was trained to recognise numerous languages and can also translate audio from a foreign language directly into English. That makes it a flexible tool for internationally active companies with content in several languages.
Because Whisper is available as open source, it can be run locally without sending audio to an external service. That is valuable for data protection, for instance with confidential conversations or internal recordings. Alternatively, Whisper is available through the OpenAI–Interface available, which makes integration without your own hardware easier.
Where Whisper is used
Whisper can be used wherever spoken language is to be turned into text. The range runs from transcribing whole meetings through creating subtitles automatically to voice control in applications. Depending on the aim, different aspects come to the fore: accuracy, speed or data protection for instance.
The table below shows typical fields of use with a practical note for each. It makes clear that Whisper is not only a transcription tool but a foundation for many voice-based applications, from accessibility through to voice interfaces.
| Area of use | Benefit | Note |
|---|---|---|
| transcription | Meetings and interviews as text | Speaker attribution often needs extra tools |
| Subtitles | Accessible videos and greater reach | Plan for timestamps and corrections |
| translation | Foreign-language audio into English | Translation is into English only |
| voice interfaces | Voice control in applications | Test latency and recognition quality |
| content recycling | Reusing podcasts as blog text | Post-editing needed for clean text |
How do companies use Whisper?
For companies Whisper is a versatile building block for making audio and video content accessible automatically. Recorded meetings become searchable minutes, quotes can be drawn quickly from interviews, and podcasts can be reused as text. That saves considerable time compared with transcribing by hand.
In the Online marketing Whisper opens new possibilities for getting value from content. A single video can be transcribed automatically and turned into subtitles, a blog article or social snippets. Multiple value can thus be drawn from one piece of moving image, raising reach and improving accessibility at the same time.
In more complex AI solutions Whisper often forms the first stage. It turns speech into text, which is then processed by a Language model is processed further, for instance to summarise content, answer questions or trigger tasks. That creates voice-controlled applications and assistants in which Whisper reliably turns the spoken word into a machine-usable form.
- 1Provide an audio or video source, such as a meeting or podcast recording.
- 2Let Whisper turn the speech into text, locally or via the interface.
- 3Check and correct the transcript, especially for technical terms and names.
- 4Reuse the text as subtitles, minutes, a blog post or a search index.
- 5If needed, connect a language model to process the text further.
Data protection, limits and responsibility
In processing speech, data protection matters particularly, since recordings often contain personal or confidential content. Here Whisper plays to its advantage as an open-source model: run locally, the audio data does not leave your own system. That eases GDPR compliance, when sensitive conversations are transcribed for instance.
For all its strength, Whisper is not error-free. With poor recording quality, strong dialects, many people speaking at once or specialised terminology, errors can occur. Transcripts should therefore be checked and corrected before any binding use, particularly in legally or technically sensitive contexts.
Anyone recording and processing the speech of staff, customers or conversation partners bears responsibility for lawful use. That includes consent, transparency and careful handling of the texts produced. A clear internal policy helps use Whisper compliantly and responsibly.
Frequently asked questions
What is Whisper?
Whisper is a speech recognition model from OpenAI turning spoken language into written text, that is, doing speech-to-text. It was trained on a great deal of audio data, recognises numerous languages and can translate foreign-language audio into English. Whisper is available as open source and can additionally be used through the OpenAI interface.
What is Whisper used for?
Whisper is used wherever speech is to be turned into text. Typical applications are transcribing meetings and interviews, creating subtitles automatically, translating foreign-language audio and voice interfaces in applications. Reusing podcasts as blog text is a common use case too.
Is Whisper free?
Whisper is freely available as an open-source model and can be self-hosted, with the cost of your own hardware or cloud compute then applying. Whisper is also available through the OpenAI interface, billed by usage. Which route is cheaper depends on volume and requirements.
How accurate is Whisper?
Whisper counts as robust and often gives usable results even with background noise, accents or poor recording quality. It is not error-free, though: with bad sound quality, strong dialects, several people speaking at once or technical terms, errors can occur. Transcripts should therefore be checked and corrected before being used definitively.
Is Whisper privacy compliant?
Whisper can be used in a privacy-friendly way, because as an open-source model it can be run locally. The audio data then does not leave your own system, which eases GDPR compliance. Responsibility for lawful use stays with the operator, through consent, transparency and careful handling of the text produced for instance.
Can Whisper translate?
Yes, Whisper can not only transcribe foreign-language audio but translate it directly into English. It does not translate into arbitrary target languages, though, since the translation function is oriented on English. For other target languages the Whisper transcript is mostly combined with an additional translation step, through a separate translation tool or a language model downstream for instance.
Related terms
The AI research company behind GPT, ChatGPT and DALL·E, which makes its models available through an API.
AI models that understand and produce several kinds of data together — text, image, audio and video.
Generative AI independently creates new content — text, images, audio or code — based on patterns it has learned.
Content created with generative AI — text, images, video and audio — for marketing and the web.
The central open-source platform for AI models, datasets and demos, with its Model Hub, Transformers library and Spaces.
Deep learning uses deep neural networks to recognise complex patterns in large volumes of data automatically.
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