Whisper (OpenAI)

Speech-To-Text

Whisper (OpenAI)

Introducing Whisper: Advanced Multilingual ASR System

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About Whisper (OpenAI)

Introducing Whisper: OpenAI's Whisper is an advanced neural net aimed at achieving human-level robustness and accuracy in English speech recognition. Trained on a massive dataset of 680,000 hours of multilingual and multitask supervised data, Whisper excels in handling accents, background noise, and technical language. Its versatility includes transcribing multiple languages and translating them into English, leveraging an encoder-decoder Transformer architecture. Comparison to Existing Approaches: Unlike traditional models that rely on smaller, paired audio-text datasets, Whisper's training on a vast and diverse dataset provides unmatched robustness. Although it may not lead in specific benchmarks like LibriSpeech, Whisper demonstrates 50% fewer errors in zero-shot performance across varied datasets. Its capacity for speech-to-text translation, particularly outperforming the state-of-the-art in CoVoST2 to English translation, sets it apart. Impact and Availability: Whisper is poised to revolutionize application development by enabling integration of high-accuracy voice interfaces. OpenAI has made its paper, model card, and code publicly available, fostering further exploration and innovation in the field.

Key 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

Tags

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

FAQs

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.