Speech-to-Text Conversion and Text Summarization

The paper presents a new method based on Wav2Vec2 and Heckling Face Transformers (HFTs) speech-to-text conversion and text summarization in Natural Learning Processes for Chatbot systems. Wav2Vec2, being a stronger model in pattern extraction with the help of self-supervision learnings, performs this job well and not needing any manual annotation. In the parallel way, HFT which is an excellent performance when processing texts all around spectra, does very well in accomplishing text summarization. The model architecure merges both module for these tasks in parallel using Wav2Vec2 for speech recognition and fine-tuning for summaries using HFT. This consequently create unified pipeline which represent a fully fledged NLP model for multi-modal NLP tasks, resulting in the most error free of speech-to-text system and the most advanced text summarization model in the world.