Compare Transcribe vs. Whisper

Transcribe significantly reduces the time spent on transcription each month for journalists, lawyers, podcasters, students, and professional transcriptionists globally, potentially saving thousands of hours. Boost your efficiency and reclaim valuable time by transforming a wide variety of audio content, including interviews, lectures, speeches, and podcasts, into written text. Simply put on your headphones, play your audio at a slower pace, and articulate what you hear—it's really that straightforward. Our dictation technology allows for real-time speech-to-text conversion, offering a speedier alternative to traditional typing methods. We cater to a diverse range of languages, including English, Spanish, French, Hindi, and nearly all other languages from Europe and Asia, making transcription accessible for a global audience. This versatility ensures that users from different linguistic backgrounds can benefit from our service seamlessly.

We have developed and are releasing an open-source neural network named Whisper, which achieves levels of accuracy and resilience in English speech recognition that are comparable to human performance. This automatic speech recognition (ASR) system is trained on an extensive dataset comprising 680,000 hours of multilingual and multitask supervised information gathered from online sources. Our research demonstrates that leveraging such a comprehensive and varied dataset significantly enhances the system's capability to handle different accents, ambient noise, and specialized terminology. Additionally, Whisper facilitates transcription across various languages and provides translation into English from those languages. We are making available both the models and the inference code to support the development of practical applications and to encourage further exploration in the field of robust speech processing. The architecture of Whisper follows a straightforward end-to-end design, utilizing an encoder-decoder Transformer framework. The process begins with dividing the input audio into 30-second segments, which are then transformed into log-Mel spectrograms before being input into the encoder. By making this technology accessible, we aim to foster innovation in speech recognition technologies.