Compare Speak vs. Whisper

Transform your language data into valuable insights quickly and effortlessly, without any coding required. Join a community of over 10,000 companies, researchers, and marketers leveraging Speak to minimize manual tasks, gain a competitive edge, foster deeper customer connections, and enhance decision-making processes. Speak is equipped to support various essential organizational functions, including qualitative research, academic studies, marketing analysis, and competitive intelligence. With features that allow for seamless individual and bulk uploads of audio, video, and text data, users can easily convert audio and video files into text through automated transcription, import CSVs for comprehensive analysis, and utilize an embeddable recorder for capturing recordings. Additionally, you can create content directly within Speak or integrate with popular tools to streamline data capture. Whether dealing with customer interviews, Zoom sessions, YouTube content, podcasts, focus group discussions, Amazon reviews, tweets, or other significant qualitative feedback sources, Speak empowers users to uncover actionable insights that drive competitive advantages and inform strategic decisions. Ultimately, by harnessing the capabilities of Speak, organizations can not only improve efficiency but also enhance their understanding of customer needs and market trends.

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.