Advances in Singing Voice Synthesis Enabled by 1.7B Parameter Speech Language Models
Summarize this article with:
Recent advances in speech language models (SLMs) present a unified approach to diverse speech tasks, yet their potential for singing voice synthesis remains largely untapped. Yiwen Zhao, Jiatong Shi, and Jinchuan Tian, from Carnegie Mellon University, along with colleagues including Yuxun Tang from Renmin University of China and Jiarui Hai from Johns Hopkins University, now demonstrate that a pre-trained SLM readily adapts to generate singing voices.
The team achieves this by leveraging a relatively small dataset of synthetic singing and employing a novel adaptation recipe built upon existing speech technology. This work establishes that large-scale SLMs possess a remarkable capacity to generalise beyond conventional speech, paving the way for more realistic and accessible singing voice synthesis systems and challenging existing discrete token-based approaches.
Singing Voice Synthesis via Language Model Adaptation Researchers have achieved a breakthrough in singing voice synthesis (SVS) by adapting a large language model, initially developed for text-to-speech, to generate singing from musical scores. This innovative approach demonstrates the potential of transferring knowledge from general speech synthesis to the specialized domain of singing, even with limited training data, and achieves performance comparable to leading discrete SVS systems.
The team built upon the established Espnet-SpeechLM framework using a 1. 7 billion parameter language model fine-tuned with a 135-hour synthetic singing corpus. The core of this work involves representing both musical scores and singing waveforms as sequences of discrete tokens, allowing the model to learn the relationships between musical notation and acoustic realization. Initial experiments revealed challenges with waveform discontinuities at token boundaries, prompting the researchers to implement a conditional flow matching model that transforms random noise into a mel spectrogram, conditioned on the input tokens, to refine the synthesis process and enhance pitch accuracy. To further improve expressiveness, the team strengthened the conditioning of pitch information within the flow matching process. The system employs a unique data tokenization pipeline, introducing a modality that combines frame-level pitch, duration, and phoneme information, with duration implicitly represented by repeating the (phoneme, pitch) tuple based on the timing information within the music score. The model utilizes a multi-stream discrete audio representation, concatenating audio codec tokens and SSL tokens to balance prediction accuracy and acoustic reconstruction. Finally, a mel-to-wave vocoder converts the generated mel-spectrograms into audible waveforms, completing the SVS pipeline. This innovative approach enables high-fidelity singing synthesis with limited training data, representing a significant advancement in the field of SVS. The results confirm the potential of adapting pretrained language models to complex audio tasks, opening new avenues for research and development in music technology. Future research directions may involve exploring multi-task learning strategies to further enhance the model’s capabilities and reduce the need for task-specific training data. 👉 More information 🗞 Adapting Speech Language Model to Singing Voice Synthesis 🧠 ArXiv: https://arxiv.org/abs/2512.14657 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Strain Engineering Achieves Tunable Spin Qubits in Graphene P-n Junctions December 18, 2025 Venus Atmosphere Modeling Achieves High Fidelity with 32 k-Terms and Vertical Resolution December 18, 2025 Electron-nuclear Entanglement Advances Quantum Memories Using One-Tangles December 18, 2025