Artificial intelligence (AI) powered automatic audio transcription technology uses machine learning algorithms to convert spoken words into written text. The process involves several steps, including audio input, preprocessing, feature extraction, decoding, and post-processing.
The first step is to receive audio input, which can be a live recording or a prerecorded file. The audio is then preprocessed to remove noise and enhance the quality. This is followed by feature extraction, where the audio is converted into a spectrogram, a visual representation of the audio's frequency and time characteristics.
The spectrogram is then fed into a decoder, which is trained to predict the corresponding text caption, along with special tokens that direct the model to generate the correct text. The decoder uses a combination of natural language processing and machine learning algorithms to generate the text transcript.
Finally, the transcript is post-processed to correct any errors and improve readability. The resulting text can be used for a variety of applications, such as transcribing podcasts, videos, and meetings, or generating subtitles for videos.
Overall, AI-powered automatic audio transcription technology has revolutionized the way we process and consume audio content, making it more accessible and inclusive for people around the world.