Handling audio data

A lot of work is happening in the audio processing space with the most significant advancements happening in automatic speech recognition (ASR) models. These models transform spoken language into written text, allowing the seamless integration of voice inputs into text-based workflows, thereby making it easier to analyze, search, and interact with. For instance, voice assistants, such as Siri and Google Assistant, rely on ASR to understand and respond to user commands, while transcription services convert meeting recordings into searchable text documents.

This conversion allows the passing of text input to LLMs to unlock powerful capabilities, such as sentiment analysis, topic modeling, automated summarization, and even supporting chat applications. For example, customer service call centers can use ASR to transcribe conversations, which can then be analyzed for customer sentiment or common issues, improving service quality and efficiency.

Handling audio data...