Transcription from audio to text in Municipal Sessions in Planeta Rica
Keywords:
AI, artificial intelligence, audio-to-text, transcription, sessions, whisper, spleeterAbstract
The document addresses the issue of manually transcribing municipal sessions in Planeta Rica. It investigates the use of open-source tools to automate the transcription of audio to text in these sessions with the aim of improving efficiency and accuracy in this process. The importance of integrating models into the system to address different aspects and enhance transcription quality is emphasized. In this regard, two artificial intelligence models are mentioned: OpenAI's Whisper and Deezer's Spleeter. Whisper is a general-purpose speech recognition model. On the other hand, Spleeter is an audio track separation tool that utilizes pre-trained models to separate voices from any audio track. Furthermore, an architecture is developed to enable the automatic integration of these models. This architecture is based on the use of Python for managing the artificial intelligence models, while the application's backend is developed using Go and the frontend with Next.js/React. This allowed for the automation of transcriptions for Planeta Rica's municipal council sessions, improving both efficiency and precision in the process.
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References
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