Descript Research

We develop our own AI models for audio and video generation, editing, and understanding. They power Descript's video editing workflows.

What we work on

Audio editing by latent inpainting

A two-stage system — a continuous neural codec plus a flow-matching transformer — that regenerates a masked span of speech conditioned on surrounding audio and target text, zero-shot, with inaudible boundaries.

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Anchored tree sampling beats autoregressive drift

A training-free, inference-time scheduler that replaces left-to-right rollout with anchor-bounded tree imputation — converting horizon-compounding drift into bounded drift, and running 5.3× faster than autoregressive baselines.

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Publications

Goodbye Drift: Anchored Tree Sampling for Long-Horizon Video-to-Video Generation

May 19, 2026

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PoDAR: Power-Disentangled Audio Representation for Generative Modeling

May 11, 2026

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High-Fidelity Audio Compression with Improved RVQGAN

October 26, 2023

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Wav2CLIP: Learning Robust Audio Representations From CLIP

October 21, 2021

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Chunked Autoregressive GAN for Conditional Waveform Synthesis

October 19, 2021

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NU-GAN: High resolution neural upsampling with GAN

October 22, 2020

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MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

December 2019

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About Descript Research

Alexandre de Brébisson, Kundan Kumar, and Jose Sotelo founded Lyrebird in 2017, while studying under Yoshua Bengio at Mila. In 2019, after pioneering research in AI-generated speech and media synthesis, Lyrebird was acquired by Descript. While the team has evolved, its focus remains locked on developing technologies that power creative tools and advance the state of generative media.

Most of this is still hard. That’s the job.