Getting to grips with ... Whisper AI
A regular look at a prominent AI or algorithmic system, model or dataset
The yins and yangs of OpenAI’s Whisper AI speech recognition and transcription model
An advanced open source automatic speech recognition (ASR) system developed by OpenAI, Whisper is designed to convert spoken language into written text.
Trained on a dataset of 680,000 hours of multilingual audio, Whisper uses AI to transcribe speech across over 100 languages.
The system can provide real-time transcription for live events and be integrated into applications across different sectors, including healthcare.
The yang: Whisper is seen to bring many benefits, including:
Accuracy. Whisper is designed to convert spoken language into written text with a high level of accuracy, making it usable for diverse audio sources such as podcasts, interviews and lectures.
Real-time. The model can provide real-time transcription capabilities, making it useful for live events, meetings or streaming situations.
Multilingual support. The model supports multiple languages, enabling it to transcribe audio that contains speech in various languages within the same file.
Accessibility. Whisper can automatically generate subtitles and closed captions for videos, improving accessibility for individuals who are deaf or hard of hearing.
Integration. Whisper can be integrated into various applications, including voice-controlled systems and customer support automation tools.
Searchability. By transcribing audio and video content into text, Whisper facilitates efficient searching through large volumes of multimedia data.
Availability. OpenAI has open-sourced Whisper, allowing developers to access the code and build upon the model for their own applications.
The yin: OpenAI says it "hope[s] the technology will be used primarily for beneficial purposes." However, Whisper has been associated with several important potential and actual harms, including:
Bias and discrimination. Whisper may perpetuate any manner of biases present in its training data.
Cultural insensitivity. Whisper's performance varies across different languages and dialects, resulting in what can appear culturally insensitive and disrepctful outputs.
Loss of active listening skills. Over-reliance on Whisper may reduce active listening skills.
Factual inaccuracies. Whisper can produce transcription errors, especially with accented speech, background noise, or technical terminology, leading to misinformation and disinformation, and poor decision-making. Such errors can have a serious impact in high-risk domains, including law, healthcare and medicine, and politics and government.
Dual/multiple use. Whisper can be misused for mass surveillance and other malicious purposes.
Copyright abuse. OpenAI has not disclosed Whisper’s training sources, raising concerns that the system may violate third-party copyright.
Privacy loss. Voice data collected by Whisper could potentially be used to identify and profile individuals captured it its training data.
Employment. The extensive use of Whisper and other automated transcription tools risks replacing human jobs.
Incidents and issues associated with Whisper: