Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts
Abstract
Audio generation has made significant progress, yet synthesizing unified audio where speech and sounds are naturally composited remains a challenge. Current methods either rely on disjoint pipelines, which fail to capture fine-grained interactions, or require structured inputs and external text rewriting, which limits the flexibility of free-form text prompts. In this paper, we introduce a new task: Free-Form-Text-Prompt-to-Unified-Audio generation, which aims to directly synthesize unified audio containing speech, sound, and their composites from unconstrained natural language. To address this task, we propose PlanAudio, a unified, autoregressive LLM-based framework. First, it simplifies the model architecture by leveraging intrinsic LLM reasoning capability instead of traditional text encoders. Second, it introduces a semantic latent chain-of-thought mechanism, an implicit planning mechanism that bridges high-level semantic understanding and low-level acoustic synthesis. Furthermore, we create PlanAudio-Bench, a specialized benchmark for evaluating composite audio scenarios. We perform evaluations in the scenarios of speech, sound, and their composites. The results demonstrate that PlanAudio generally outperforms the existing pipeline and unified baselines, while staying competitive with models designed for a single scenario. Our analysis further reveals the superiority of semantic latent CoT over other CoT mechanisms and highlights the importance of continuous multi-scenario training curricula.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of PlanAudio, a unified framework for generating complex audio compositions from free-form text prompts, which significantly advances the state-of-the-art in audio synthesis by integrating semantic understanding with acoustic generation. The methodology is innovative, the experiments are rigorous, and the potential applications are broad, marking a meaningful contribution to the field of machine learning and audio generation.
Methodology
The proposed methodology, PlanAudio, introduces a novel framework for generating unified audio from free-form text prompts, leveraging an autoregressive LLM architecture and a semantic latent Chain-of-Thought (CoT) mechanism. This approach is innovative as it avoids traditional text encoders and explicit text rewriting, which are common in existing models. The integration of semantic planning in the latent space before audio synthesis is a significant advancement, allowing for better alignment between high-level semantics and low-level audio generation. The methodology is well-structured, with clear phases for semantic planning and acoustic generation, which enhances the model's ability to produce coherent audio outputs.
Results & evaluation
The experiments are comprehensive, evaluating PlanAudio across multiple scenarios (sound, speech, and composite) using both objective metrics (FAD, KL divergence, WER) and subjective assessments (human ratings on acoustic quality, temporal correctness, etc.). The results demonstrate that PlanAudio outperforms existing pipeline and unified models, showcasing its versatility and effectiveness. The creation of PlanAudio-Bench as a specialized benchmark for composite audio scenarios adds value to the evaluation process, providing a structured way to assess the model's performance in real-world applications.
Reproducibility
The paper provides detailed implementation details, including the datasets used, training procedures, and evaluation metrics. However, the lack of a publicly available demo or project URL limits the reproducibility of the results. While the methodology is clearly described, access to the code and trained models would enhance the ability of other researchers to replicate the findings.
Limitations
One limitation is the potential for the model to struggle with highly complex prompts that require intricate audio interactions, as indicated by the slight performance drop in speech generation compared to specialized models. Additionally, the reliance on the quality of the training data and the inherent challenges in synthesizing audio from free-form text prompts may introduce variability in performance across different contexts.
Broader impact
The implications of this research are significant for various applications, including content creation, game development, and assistive technologies for individuals with speech impairments. By enabling the generation of coherent audio from natural language prompts, this work could facilitate new forms of human-computer interaction and enhance multimedia experiences. The main contribution of this paper is the introduction of PlanAudio, a unified framework for generating complex audio compositions from free-form text prompts, which significantly advances the state-of-the-art in audio synthesis by integrating semantic understanding with acoustic generation. The methodology is innovative, the experiments are rigorous, and the potential applications are broad, marking a meaningful contribution to the field of machine learning and audio generation.
Author of this paper? Send a correction.