Compare SpeechText.AI vs. Whisper

Convert audio and video files into written text effortlessly. Achieve high-quality transcriptions for podcasts utilizing specialized speech recognition tailored to specific industries. SpeechText.AI stands out as an advanced software solution designed for transforming spoken content into text format. Users can easily upload their audio or video files and benefit from AI transcription that accommodates various formats and languages. Choose your relevant domain and audio type from established categories to enhance the accuracy of transcribing industry-specific terminology. Upon selecting the appropriate settings, the sophisticated transcription engine employs cutting-edge deep neural network models to produce text that closely resembles human accuracy. Additionally, users can interactively edit, search, and validate their transcriptions using intuitive editing tools, with the flexibility to export the final content in multiple formats. The array of exceptional features within SpeechText.AI ensures that audio and video transcription is accomplished in mere seconds, thanks to its robust speech recognition capabilities. With its user-friendly interface and advanced technology, SpeechText.AI is poised to meet all your transcription 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.