Compare Speech to Note vs. Whisper

For those whose day is largely consumed by writing, Speech to Note is the perfect solution you've been seeking. With the power of GPT-4o, effortlessly convert your spoken words into quick summaries. A single click can turn your speech into an instant summary, capturing your message succinctly. Share your thoughts efficiently within a 15-minute timeframe, and receive a clear and precise summary tailored to your needs. You can select from various summary formats, including LinkedIn posts, formal emails, and minutes of meetings, ensuring your content meets your specific requirements. Customize your summaries to better fit your style and edit them to meet your preferences. Experience impeccable summaries provided in your preferred language, with support for multiple languages available seamlessly. Keep your content organized with personalized tags, making it simple to categorize and retrieve what you need effortlessly. You can easily incorporate additional ideas into your existing notes, ensuring that all your thoughts are effectively documented. Plus, enjoy access to your notes for up to 60 days, with only the audio files disappearing after that period while your summaries remain safe and sound. The tool not only enhances productivity but also keeps your creative process streamlined and efficient.

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.