LLark: A Multimodal Instruction-Following Language Model for Music (2310.07160v3)

Abstract: Music has a unique and complex structure which is challenging for both expert humans and existing AI systems to understand, and presents unique challenges relative to other forms of audio. We present LLark, an instruction-tuned multimodal model for \emph{music} understanding. We detail our process for dataset creation, which involves augmenting the annotations of diverse open-source music datasets and converting them to a unified instruction-tuning format. We propose a multimodal architecture for LLark, integrating a pretrained generative model for music with a pretrained LLM. In evaluations on three types of tasks (music understanding, captioning, reasoning), we show that LLark matches or outperforms existing baselines in music understanding, and that humans show a high degree of agreement with its responses in captioning and reasoning tasks. LLark is trained entirely from open-source music data and models, and we make our training code available along with the release of this paper. Additional results and audio examples are at https://bit.ly/llark, and our source code is available at https://github.com/spotify-research/llark .

• The paper introduces LLark, a multimodal instruction-tuned model that fuses a generative music encoder with a Llama 2 language model to advance music understanding.
• It details an innovative data pipeline that augments open-source music datasets with annotations like tempo, key, and chords for instruction tuning.
• Empirical results show LLark outperforming models on music tasks, matching state-of-the-art performance in classification, captioning, and reasoning.

LLark: A Multimodal Instruction-Following LLM for Music

The paper introduces LLark, a multimodal LLM specifically designed to improve understanding and processing of musical content through instruction-tuning methodologies. LLark leverages a combination of a pretrained generative music model and a LLM, aiming to address existing gaps in music understanding when compared to advancements in other modalities such as vision and speech.

Dataset Development and Architecture

To address the specific challenges of music understanding, LLark incorporates an innovative data creation process. The authors develop a comprehensive instruction-tuning pipeline, which begins with the augmentation of several open-source music datasets. These datasets are annotated to include critical musical features such as tempo, key, beat grids, and chords. The resulting data are then transformed into a unified instruction-tuning format through the use of LLMs to generate diverse question-answer pairs based on the music's annotated features.

LLark's architecture integrates three main components:

1. Generative Audio Encoder: Utilizes Jukebox's pretrained audio encoders to extract substantial musical embeddings.
2. LLM: Employs a Llama 2-based LLM, designed to generate text responses from embedded audio features.
3. Multimodal Projection Module: This simple linear layer harmonizes the audio and textual feature spaces for further processing by the LLM.

Empirical Evaluation and Results

LLark's performance was benchmarked across a variety of musical tasks: music understanding, captioning, and reasoning. For music understanding tasks involving classification and regression (such as key, tempo, genre, and instrument identification), LLark outperformed comparable models, including those not initially designed with a strong focus on music. Notably, LLark matched or closely approached the performance of task-specific state-of-the-art models trained on the same datasets.

In human evaluations of music captioning tasks, LLark's captions were consistently preferred over other models, especially on datasets with richly musical contexts. The model demonstrated enhanced musical detail in its generated outputs, verified using assessments by a LLM (GPT-4), highlighting the effectiveness of the metadata augmentation strategy.

Implications and Future Work

The contributions of LLark extend beyond music information retrieval to general AI, presenting a compelling case for the utility of instruction-tuning in specialized domains. The paper suggests future opportunities to refine such models through improved dataset diversity and suggests enhancements to audio encoders and LLMs to further expand multimodal capabilities.

Potential future directions include extending LLark to handle longer audio sequences, integrating more diverse music datasets, and developing specialized benchmarks for music understanding and reasoning tasks. Such advancements promise to bolster not only the model's versatility and performance but also its fidelity in reflecting the complexities inherent in musical compositions.

In summation, LLark marks a significant step forward in the field of music AI, offering a template for future explorations in leveraging multimodal datasets and advanced modeling techniques to bridge existing gaps in AI's musical comprehension.