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Cohere Analyze
We all understand what Cohere Generate and Classify do. But embed ...?! Introducing ... Cohere Analyze. You can upload any csv file. You then have 3 options. EDA gives you an overview of the data and you can get some general exploratory data analysis. Cluster, allows you to do some group analysis, with keywords generated from the body of the text and using the titles. And finally, Search, allows you to query the data and retrieve the closest match. This has significant business value because now you can gain business insights using cluster that are non-trivial. You can search a knowledge-base or other internal data sources for information. Cohere Analyze is what we have all been waiting for, to better utilize Embed.
Phoenix Whisper
According to research made by J. Birulés-Muntané1 and S. Soto-Faraco (10.1371/journal.pone.0158409), watching movies with subtitles can help us learn a new language more effectively. However, the traditional way of showing subtitles in YouTube or Netflix does not provide us the best way to check the meaning of new vocabulary nor understand complex slang and abbreviation. Therefore, we found out that if we display dual subtitles (the original subtitle of the video and the translated one), the learning curve immediately improves. In research conducted in Japan, the authors concluded that the participants who viewed the episode with dual subtitles did significantly better (http://callej.org/journal/22-3/Dizon-Thanyawatpokin2021.pdf). After understanding both the problem and the solution, we decided to create a platform for learning new languages with dual active transcripts. When you enter a YouTube URL or upload an MP4 file in our web application, the app will produce a web page where you can view the video and have a transcript running next to it in two different languages. We have accomplished this goal and successfully integrated OpenAI Whisper, GPT and Facebook's language model for the backend of the app. At first, we use Streamlit for the app, but it does not provide a transcript that automatically move with the audio timeline, also Streamlit does not give us the ability to design the user interface, so we create our own full stack application using Bootstrap, Flask, HTML, CSS and Javascript. Our business model is subscription-based and/or one-time purchase based on the usage. Our app isn’t just for language learners. It can also be used for writers, singers, YouTubers, or anyone who would like to make their content reach out to more people by adding different languages to their videos/audios. Due to the limitation of free hosting plan, we could not deploy the app on cloud for now but we have a simple website that you can have a quick look at what we are creating (https://phoenixwhisper.onrender.com/success/BzKtI9OfEpk/en).
