Speech-to-Text, or Automatic Speech Recognition (ASR), is a technology that utilizes machine learning to convert spoken language into written text, and it has become increasingly prevalent in applications like TikTok, Instagram, Spotify, and Zoom. Traditional ASR systems rely on acoustic models, which involve a multi-step process for converting audio signals into linguistic units, while End-to-End ASR models use deep learning techniques to simplify this process, enhancing accuracy and accommodating diverse accents. Clarifai's AI platform offers advanced End-to-End ASR models that are easy to integrate, cost-effective, and emphasize data security, making them accessible for various uses. Notable models include Chirp, which excels in multilingual tasks, AssemblyAI's Conformer-2, which improves on its predecessor in handling noise and proper nouns, and Whisper, known for robust English transcription and zero-shot performance across languages. ASR models are evaluated using metrics like Word Error Rate (WER) and Character Error Rate (CER) to ensure high accuracy in applications such as closed captions, content creation, transcription services, and call centers.