Start on: 01-09-2023
Completed on: 25-06-2024
The project aims to develop a Speech Recognition system that can transcribe text from audio and video files. The system is also capable of analyzing the sentiment of the transcribed text. The project uses OpenAI Whisper and TextBlob libraries to achieve this functionality. OpenAI Whisper is a state-of-the-art automatic speech recognition (ASR) system that is trained on a diverse range of speech data. It is capable of transcribing audio files and performing language detection and translation. TextBlob is a Python library that provides a simple API for performing common natural language processing (NLP) tasks such as part-ofspeech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. The project uses OpenAI Whisper to transcribe the speech from audio and video files. The transcribed text is then analyzed using TextBlob to determine the sentiment of the text. The sentiment analysis is performed by using the TextBlob’s built-in sentiment analysis function, which returns a polarity score between -1 and 1, where -1 indicates negative sentiment, 0 indicates neutral sentiment, and 1 indicates positive sentiment. The sentiment score is then used to classify the text as positive, negative, or neutral.