Whisper (OpenAI)
By OpenAISpeech-To-TextFree
Introducing Whisper: Advanced Multilingual ASR System
Last updated Apr 28, 2026
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What is Whisper (OpenAI)?
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.
Tags
Automatic Speech RecognitionASRSpeech RecognitionTranscriptionTranslationMultilingualOpenAITechnical LanguageTransformer ArchitectureLog-Mel SpectrogramsZero-Shot Performance
Whisper (OpenAI)'s Pricing
Free plan available
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OpenAIai lab
Creating safe AGI that benefits all of humanity
426 Tools6 ModelsFounded 2015San Francisco, CA
View full profileTools by OpenAI
Other AI tools from the same organization.
AI Models by OpenAI
Large language models from the same organization.
| Model | Context Window | Price (In / Out per M) |
|---|---|---|
| GPT-5.5Current | 1.1M | $5.00 / $30.00 |
| GPT-5.5 ProCurrent | 1.1M | $30.00 / $180.00 |
| GPT-5.4 MiniCurrent | 400K | $0.75 / $4.50 |
| GPT-5.4 NanoCurrent | 400K | $0.20 / $1.25 |
| GPT-5.4Current | 1.1M | $2.50 / $15.00 |
| GPT-5.4 ProCurrent | 1.1M | $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.