Fine-Tuning a Language Model for Spatial Scenes

Module B: Training your own audio AI. Lesson 4 of 20, 30 min.

Abstract

Learn how to adapt an open language model to turn stems into structured spatial scene descriptions. The lesson covers dataset design and consent, parameter-efficient fine-tuning, preference optimization and constrained decoding, and how to evaluate placements with schema checks, angular error and listening tests.

By the end you can

  • Design a licensed, documented stems-to-scene dataset with a frozen schema and leak-free splits
  • Fine-tune a language model with LoRA or QLoRA using loss-masked supervised examples
  • Apply preference optimization and constrained JSON decoding to improve and guarantee scene outputs
  • Evaluate spatial predictions using schema validity, great-circle positional error and blind listening tests

A spatial scene model takes a set of stems and proposes where each one should live in three-dimensional space, expressed as structured metadata a renderer can use. This lesson shows how you would build such a model yourself by adapting an open language model: designing a dataset of stems paired with scene descriptions, fine-tuning efficiently with LoRA, aligning outputs with preference data, and guaranteeing valid JSON. It closes with how to evaluate spatial predictions honestly, from schema checks to listening tests.

From stems to scene descriptions

The task looks simple when written down. The input is a collection of stems (lead vocal, kick, bass, pads, backing vocals, effects returns) plus optional intent such as genre, target format or a short creative brief. The output is a scene description: for each stem, a position (azimuth, elevation, distance), perhaps a spread or size, and global properties such as the target layout. Object-based formats like ADM (ITU-R BS.2076) already separate audio essence from positional metadata in this way, which makes the scene description a natural prediction target. See Immersive Audio Formats for how those formats differ.

Framing the output as structured text has three advantages:

  • Language models are strong structured-text generators. JSON is just a token sequence, and pretrained models have seen a great deal of it.
  • The output is inspectable and editable. An engineer can read, adjust and version a scene file in a way they cannot with an opaque latent.
  • Rendering is decoupled from prediction. The same scene can be rendered to binaural, 5.1.4 or Ambisonics by a downstream renderer, consistent with the ideas in Open Formats Philosophy.

The hard parts are elsewhere: assembling data that reflects good spatial decisions, teaching a model those decisions without retraining billions of parameters, keeping outputs valid, and measuring whether placements are actually good. This lesson walks through each. The conceptual background on how a scene model learns is in How GravityLLM Learns; everything here is a general, independent recipe, not a description of any production system.

The rest of this lesson, its lab and self-check are part of Academy Advanced, a paid course. See who has access.

Key readings

  • Hu, E. J., et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. International Conference on Learning Representations (ICLR).
  • Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. Advances in Neural Information Processing Systems (NeurIPS).
  • Rafailov, R., et al. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. Advances in Neural Information Processing Systems (NeurIPS).
  • Zheng, Z., et al. (2024). BAT: Learning to Reason about Spatial Sounds with Large Language Models. International Conference on Machine Learning (ICML).
  • Gebru, T., et al. (2021). Datasheets for Datasets. Communications of the ACM.