Compare Gglot vs. Whisper

Quickly convert audio to text online in various languages with Gglot's multilingual transcription service, which is ideal for interviews, content marketing, video production, and academic research. No matter the type of audio you have, our advanced AI transcription technology will seamlessly transform it into text. Gglot enables you to gather essential insights from both audio and video files without any hassle. Utilizing Artificial Intelligence, Gglot is an online platform that transcribes the audio and video files you upload with ease. It effectively recognizes human speech, overcoming challenges such as background noise, dialects, varying speeds, and different volumes. Enhance your audience's experience by incorporating English captions. Gglot not only adds captions to videos that reflect the dialogue but also highlights crucial non-verbal elements that enrich the context. Captions serve a greater purpose beyond mere transcription of audio into text; they enhance understanding and accessibility for all viewers. Ultimately, Gglot ensures that your content is both engaging and comprehensible for a diverse audience.

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.