Whisper (OpenAI) screenshot

Whisper (OpenAI)

By OpenAI
Speech-To-TextFree

Introducing Whisper: Advanced Multilingual ASR System

Last updated Apr 28, 2026

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What is Whisper (OpenAI)?

Whisper is a cutting-edge automatic speech recognition (ASR) system created by OpenAI. Trained on 680,000 hours of multilingual and multitask supervised data from the web, Whisper boasts improved robustness to accents, background noise, and technical language. It provides transcription services in multiple languages and translates those languages into English. Whisper uses an encoder-decoder Transformer architecture that captures 30-second audio chunks, converts them to log-Mel spectrograms, and predicts corresponding text captions. Its large and diverse dataset helps Whisper outperform existing systems in zero-shot performance across diverse scenarios.

Whisper (OpenAI)'s Top Features

Key capabilities that make Whisper (OpenAI) stand out.

High robustness to accents and background noise

Supports multiple languages

Translates languages into English

Encoder-decoder Transformer architecture

Processes 30-second audio chunks

Predicts text captions with special tokens integration

Improved zero-shot performance

Open-source with detailed resources

Enables voice interfaces for applications

Outperforms on CoVoST2 for English translation

Use Cases

Who benefits most from this tool.

Developers

Adding voice interfaces to applications.

Global businesses

Transcribing and translating multilingual communication.

Content creators

Accurate transcription and translation of audio content for diverse audiences.

Researchers

Studying performance across diverse audio data without fine-tuning.

Language learners

Translating non-English audio to English for learning purposes.

Accessibility advocates

Creating accessible content for people with hearing impairments.

Customer service teams

Transcribing customer interactions for better service and analysis.

Educators

Transcribing lectures and translating educational content.

Media professionals

Automating subtitles and translations for multimedia content.

Tech enthusiasts

Experimenting with and contributing to the open-source ASR model.

Explore Top AI Use Cases

Tags

Automatic Speech RecognitionASRSpeech RecognitionTranscriptionTranslationMultilingualOpenAITechnical LanguageTransformer ArchitectureLog-Mel SpectrogramsZero-Shot Performance

Whisper (OpenAI)'s Pricing

Free plan available

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GPT-5.4Current1.1M$2.50 / $15.00
GPT-5.4 ProCurrent1.1M$30.00 / $180.00

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Frequently Asked Questions

What is Whisper?
Whisper is an automatic speech recognition (ASR) system developed by OpenAI, designed for high robustness and accuracy using a vast, diverse dataset.
How was Whisper trained?
Whisper was trained on 680,000 hours of multilingual and multitask supervised data collected from the web.
What languages can Whisper transcribe?
Whisper can transcribe speech in multiple languages and translate these languages into English.
What architecture does Whisper use?
Whisper uses an encoder-decoder Transformer architecture.
How does Whisper handle input audio?
Input audio is split into 30-second chunks, converted into log-Mel spectrograms, and then passed into an encoder to predict text captions.
What are the special tokens used for in Whisper?
Special tokens in Whisper are used for language identification, phrase-level timestamps, multilingual speech transcription, and translation into English.
How does Whisper compare to existing ASR systems?
Whisper is more robust, with 50% fewer errors in zero-shot performance across diverse datasets due to its large and diverse training dataset.
Is Whisper specialized for certain benchmarks?
While Whisper doesn't excel in specialized benchmarks like LibriSpeech, it outperforms state-of-the-art models in zero-shot translations on datasets like CoVoST2.
What resources are available for Whisper?
OpenAI has made the paper, model card, and code for Whisper available for more detailed understanding and experimentation.
What impact does Whisper aim to have?
Whisper aims to enable developers to add voice interfaces easily to various applications due to its high accuracy and ease of use.