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Whisper (OpenAI)

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Last updated: August 8, 2024

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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.

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Whisper (OpenAI)'s Top Features

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

Frequently asked questions about Whisper (OpenAI)

Whisper (OpenAI)'s pricing

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    Use Cases

    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.