arXiv:2606.09019v1 Announce Type: cross Abstract: Codec-based autoregressive (AR) speech language models have achieved strong text-to-speech (TTS) quality by modeling speech as sequences of discrete audio tokens with large pretrained backbones. However, this token-level formulation creates a structural efficiency bottleneck: speech-token sequences are much longer than text sequences, requiring the AR backbone to perform causal computation at every token position and maintain a KV cache that grows with the sequence length. We introduce TLDR, a patch-based autoregressive framework that accelerat
The proliferation of codec-based autoregressive models for text-to-speech has exposed efficiency bottlenecks, driving innovation in compression and processing of audio tokens.
Improving the efficiency of text-to-speech models reduces compute requirements and latency, making advanced AI voice generation more accessible and scalable across many applications.
Current token-level autoregressive models will become less dominant as more efficient patch-based frameworks emerge, leading to faster and cheaper high-quality speech synthesis.
- · AI compute providers
- · Developers leveraging TTS
- · Cloud service providers
- · Speech interface companies
- · Inefficient TTS models
- · Companies with high TTS operational costs
More widespread adoption of real-time, high-fidelity AI-generated speech across industries.
Reduced latency and cost could enable new types of conversational AI agents and interactive experiences.
The increased realism and availability of synthetic speech could accelerate the development of more sophisticated deepfake detection and authentication methods.
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Read at arXiv cs.AI