Compare Beey vs. Whisper

Beey is a highly efficient application that transforms audio and video files into text within minutes, boasting remarkable accuracy. It supports speech recognition in 20 different languages, making it versatile for a global audience. Additionally, its intuitive editing tool allows users to refine the transcribed content, export it in multiple formats, and generate automatic subtitles or translations. The editing interface features a synchronized playback preview that aligns with the edited text, highlighted by a moving cursor, enabling seamless adjustments. Users can control the playback speed, slow it down, speed it up, or start from any chosen point in the transcription. Furthermore, Beey encompasses a range of supplementary tools: Link, Splitter, Stream, and Voice. The Link tool enables direct transcription of audio or video from major platforms like YouTube. The Splitter feature is particularly useful for lengthy recordings, breaking them into manageable segments for individual editing. Stream allows for real-time transcription and captioning of live broadcasts, while the Voice tool is designed for recording and transcribing live speech effortlessly. Overall, Beey provides a comprehensive suite of features that enhance the transcription experience, catering to various user needs.

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.