Compare Otter.ai vs. Whisper

Otter is where conversations are. With Otter, your AI-powered assistant, you can create rich notes for interviews, meetings, lectures, and other important voice conversation. The Otter advantage is a benefit for organizations. Otter is trusted by all sizes of teams to transcribe important conversations. Otter 2.0, our shiny new release, offers more functionality to enhance collaboration and productivity. The Teams plan is designed for small and medium-sized businesses as well as teams in larger companies. You can record and review your conversations in real-time. You can search, play, edit, organize and share your conversations on any device. Otter allows you to record conversations on your smartphone or web browser. You can import or sync recordings from other services. Zoom can be integrated. Real-time streaming transcripts are available. Within minutes, rich, searchable notes can be created with text, audio, images and speaker ID. To inform others and stay on the same page, you can share or export voice notes.

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.