Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities

Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities

2024 | Zhifeng Kong, Arushi Goel, Rohan Badlani, Wei Ping, Rafael Valle, Bryan Catanzaro
Audio Flamingo is a novel audio language model that combines few-shot learning and dialogue abilities. It is designed to understand audio, including non-speech sounds and non-verbal speech, and can quickly adapt to new tasks through in-context learning and retrieval. The model is trained on a diverse set of audio-text pairs and demonstrates strong performance on various audio understanding tasks. It also supports multi-turn dialogues, allowing it to engage in extended conversations with users. Audio Flamingo achieves state-of-the-art results on multiple benchmarks, outperforming existing models in audio understanding, few-shot learning, and dialogue tasks. The model uses a novel architecture with a sliding window audio feature extractor and cross-attention mechanisms to enhance its ability to process and understand audio. It is trained using a combination of pre-training and supervised fine-tuning, with a focus on efficient use of in-context learning and retrieval. The model is open-sourced and available for research and development.Audio Flamingo is a novel audio language model that combines few-shot learning and dialogue abilities. It is designed to understand audio, including non-speech sounds and non-verbal speech, and can quickly adapt to new tasks through in-context learning and retrieval. The model is trained on a diverse set of audio-text pairs and demonstrates strong performance on various audio understanding tasks. It also supports multi-turn dialogues, allowing it to engage in extended conversations with users. Audio Flamingo achieves state-of-the-art results on multiple benchmarks, outperforming existing models in audio understanding, few-shot learning, and dialogue tasks. The model uses a novel architecture with a sliding window audio feature extractor and cross-attention mechanisms to enhance its ability to process and understand audio. It is trained using a combination of pre-training and supervised fine-tuning, with a focus on efficient use of in-context learning and retrieval. The model is open-sourced and available for research and development.
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