MamTra: A Hybrid Mamba-Transformer Backbone for Speech Synthesis
MamTra combines Mamba and Transformer, reducing inference VRAM usage by 34% with only 2% training data.
Key Findings
Methodology
MamTra is an interleaved Mamba-Transformer framework leveraging Mamba's linear-time efficiency and Transformer’s global modeling capabilities. Novel knowledge transfer strategies integrate pretrained Transformer insights into the hybrid architecture, avoiding high costs of training from scratch.
Key Results
- MamTra reduces inference VRAM usage by 34% while maintaining speech quality, even when trained on only 2% of the original dataset.
- Experiments on the LibriTTS dataset show MamTra maintains naturalness with only a 0.25% absolute increase in WER.
- Through multi-level distillation, MamTra successfully recovers teacher model performance, reducing 1.4e11 FLOPs per token.
Significance
MamTra holds significant academic and industrial impact, addressing computational bottlenecks in autoregressive Transformers for long-sequence generation, offering an efficient and scalable speech synthesis solution.
Technical Contribution
MamTra introduces fundamental differences from SOTA methods by replacing attention mechanisms with selective state space models, enhancing inference efficiency and introducing new engineering possibilities.
Novelty
MamTra is the first systematic study of Mamba-Transformer hybrid architecture in speech synthesis, avoiding high pretraining costs and offering an innovative architectural design.
Limitations
- MamTra may show performance degradation under extreme long-sequence conditions due to limited local modeling capabilities of Mamba layers.
- In complex speech scenarios, more Transformer layers may be needed to ensure rich speech expressiveness.
Future Work
Future work could explore more complex hybrid architecture designs to further optimize the integration of Mamba and Transformer for improved long-sequence speech synthesis performance.
AI Executive Summary
MamTra is an innovative speech synthesis framework combining Mamba's efficiency and Transformer’s modeling capabilities. Existing autoregressive Transformers face computational bottlenecks in long-sequence generation, while MamTra successfully reduces inference VRAM usage and maintains speech quality.
By employing knowledge transfer strategies, MamTra avoids high costs of training from scratch, utilizing pretrained Transformer weights to initialize Mamba layers, ensuring rapid convergence and performance recovery. Experimental results demonstrate MamTra’s excellence on the LibriTTS dataset, reducing inference VRAM usage by 34% and achieving only a 0.25% absolute increase in WER.
While MamTra performs well under long-sequence conditions, it may require further architectural optimization for extreme long-sequence or complex speech scenarios. Future research could explore more complex hybrid architectures to enhance long-sequence speech synthesis performance.
Deep Analysis
Background
Recent advancements in LLM-based text-to-speech systems have achieved significant progress, especially in expressive and multi-speaker speech generation. However, these systems rely on autoregressive Transformers, leading to quadratic computational complexity, limiting practical applications.
Core Problem
Autoregressive Transformers face computational bottlenecks in long-sequence generation, resulting in high latency and large cache memory, making deployment on edge devices impractical. Existing optimization methods fail to fundamentally address this issue.
Innovation
MamTra combines Mamba and Transformer in an interleaved manner, leveraging Mamba's linear-time efficiency and Transformer’s global modeling capabilities, providing an efficient speech synthesis solution. Knowledge transfer strategies avoid high costs of training from scratch.
Methodology
- �� MamTra framework interleaves Mamba and Transformer layers
- �� Utilizes pretrained Transformer weights to initialize Mamba layers
- �� Applies multi-level distillation strategy to recover teacher model performance
- �� Conducts experiments on the LibriTTS dataset for validation
Experiments
Experiments use the LibriTTS dataset for training and evaluate on the Seed-TTS-eval test set. Baselines include CosyVoice 2 and Llasa-1B. Evaluation metrics include WER, VRAM usage, and FLOPs.
Results
MamTra reduces inference VRAM usage by 34% while maintaining speech quality. Absolute WER increases by only 0.25%, and reduces 1.4e11 FLOPs per token.
Applications
MamTra can be used for long-sequence speech synthesis, such as podcasts, audiobooks, and streaming dialogue agents, providing an efficient speech generation solution.
Limitations & Outlook
MamTra may show performance degradation under extreme long-sequence conditions. Future work could explore more complex hybrid architecture designs to enhance long-sequence speech synthesis performance.
Plain Language Accessible to non-experts
Imagine a kitchen where MamTra is like a chef using two different cooking tools: one is a knife for quick chopping (Mamba), and the other is a seasoning bottle for precise flavoring (Transformer). This chef alternates between these tools, ensuring each dish is prepared quickly and tastes rich. Similarly, MamTra not only generates speech efficiently but also maintains naturalness and expressiveness.
ELI14 Explained like you're 14
Imagine you're playing a game with two characters: one runs fast but can only see what's right in front (Mamba), and the other runs slow but sees the whole map (Transformer). MamTra is like getting these two characters to work together, quickly running through the map while finding all hidden treasures. That's what MamTra does in speech synthesis — it's efficient and creates natural speech!
Glossary
Transformer
A sequence modeling architecture capable of capturing long-range dependencies.
Used for global semantic reasoning in MamTra.
Mamba
An architecture based on state space models with linear time complexity.
Used for local temporal modeling in MamTra.
Knowledge Transfer
A technique to apply knowledge from a pretrained model to a new model.
Used for MamTra's initialization and performance recovery.
Autoregressive
A sequence generation method where current output depends on previous outputs.
Application of Transformer in speech synthesis.
Distillation
A model compression technique where a teacher model guides a student model.
Used for MamTra's performance recovery.
Open Questions Unanswered questions from this research
- 1 How to further optimize MamTra's performance under extreme long-sequence conditions?
- 2 How to overcome MamTra's limitations in complex speech scenarios?
Applications
Immediate Applications
Podcast Generation
MamTra can be used for efficiently generating long-form podcast content, reducing computational resource demands.
Audiobooks
MamTra can be used to generate natural audiobooks, enhancing user experience.
Long-term Vision
Streaming Dialogue Agents
MamTra can be used for real-time streaming dialogue agents, providing natural speech interaction experiences.
Abstract
Despite the remarkable quality of LLM-based text-to-speech systems, their reliance on autoregressive Transformers leads to quadratic computational complexity, which severely limits practical applications. Linear-time alternatives, notably Mamba, offer a potential remedy; however, they often sacrifice the global context essential for expressive synthesis. In this paper, we propose MamTra, an interleaved Mamba-Transformer framework designed to leverage the advantages of Mamba's efficiency and Transformers' modeling capability. We also introduce novel knowledge transfer strategies to distill insights from a pretrained Transformer into our hybrid architecture, thereby bypassing the prohibitive costs of training from scratch. Systematic experiments identify the optimal hybrid configuration, and demonstrate that MamTra reduces inference VRAM usage by up to 34% without compromising speech fidelity - even trained on only 2% of the original training dataset. Audio samples are available at https://mamtratts.github.io.