These include multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. The Whisper transformer sequence-to-sequence model was trained on various speech processing tasks. Be sure to try out both methodologies! Whisper review Source This code can be run in both a Gradient Notebook and a Deployment. In this tutorial, we will extend that work by integrating Whisper with Flask and MoviePy to automatically generate English subtitles for any video.įollow this tutorial to get insights into developing with Whisper, integrating ML/DL with the Flask application environment, and deploying ML/DL applications to the cloud on Gradient Deployment's powerful GPUs. In our last article, we showed how we can make use of the powerful tool in a Gradient Notebook or deploy it with Gradient Deployments in a simple Flask interface. Whisper represents an evidentiary step in that technological evolution, proving it is possible to quickly generate transcriptions at relatively low cost. The holy grail of speech integrated NLP is the ability to directly transcribe audio into words in real time with a high degree of precision. This extends across the 97 languages included in the training dataset, to varying degrees of success. This incredible model comes with a host of useful capabilities including multilingual speech recognition, transcription, and translation. We recently covered the release of Whisper, a brand new speech-to-text transcription model from Open AI.
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