ZipL-Dialog slashes GPU memory 11x for long-form AI speech synthesis
New method compresses speech to 25Hz latent space for minute-long dialog without chunking
Long-form spoken dialog synthesis has been bottlenecked by memory: generating minute-scale audio from dense mel-spectrograms forces unnatural chunked processing. ZipL-Dialog solves this by compressing the time dimension 4x into a 25 Hz latent space using a deterministic mel autoencoder with auxiliary mel-domain supervision. This allows flow-matching to run in a compact latent representation, drastically lowering memory footprint.
Benchmarks show peak GPU memory reduced by 11.22x and inference speed improved 2.23x over the baseline zero-shot TTS system, all while preserving perceptual naturalness. The method uses ZipFormer's hierarchical downsampling schedule to maintain acoustic fidelity under compression. Accepted to Interspeech 2026, ZipL-Dialog clears the path for realistic, long-duration voice assistants, audiobooks, and conversational AI without memory crashes.
- Compresses speech time dimension 4x into a 25 Hz latent space via deterministic mel autoencoder
- Reduces peak GPU memory by 11.22x and speeds up inference by 2.23x over baseline
- Accepted to Interspeech 2026; enables single-pass multi-minute dialog synthesis without chunking
Why It Matters
Enables realistic, long-form AI voice conversations without memory crashes, advancing voice assistants and audiobooks.