Incorporating audio comprehension into language models represents a significant leap forward in the pursuit of more dynamic and contextually aware artificial intelligence systems. The novel integration of audio knowledge into the Mistral LLM offers an innovative approach to enhancing its interpretative capabilities, enabling it to process and understand auditory information alongside textual data. Through architectural modifications such as the introduction of an audio encoder and attention-based fusion mechanism, the model demonstrated marked improvements in tasks ranging from transcription accuracy to sentiment analysis and contextual summarization. The empirical evaluation revealed that the audio-augmented model achieved lower Word Error Rates across multiple datasets and higher BLEU scores for audio-textual comprehension, showcasing its ability to handle complex and diverse linguistic environments. Moreover, the model exhibited robustness to background noise and proficiency in cross-language transcription, further highlighting its versatility and potential applications. Although the integration of audio knowledge increased the computational demands of the model, the resultant enhancement in audio comprehension justifies this trade-off, presenting a valuable tool for applications requiring sophisticated spoken language understanding.