We present the performance of Vevo-Style in the zero-shot style imitation task, focusing on widely studied styles such as accent and emotion. Notably, Vevo-Style employs a zero-shot manner (i.e., using just a few seconds of speech) to achieve style imitation, which is …

We present our reproduction of Vevo, a versatile zero-shot voice imitation framework with controllable timbre and style. We invite you to explore the audio samples to experience Vevo's capabilities firsthand. We have included the following pre-trained Vevo models at Amphion: Vevo-Timbre: It can conduct style-preserved voice conversion.

2025年2月11日 · However, existing methods rely heavily on annotated data, and struggle with effectively disentangling timbre and style, leading to challenges in achieving controllable generation, especially in zero-shot scenarios. To address these issues, we propose Vevo, a versatile zero-shot voice imitation framework with controllable timbre and style.

We present our reproduction of Vevo, a versatile zero-shot voice imitation framework with controllable timbre and style. We invite you to explore the audio samples to experience Vevo's capabilities firsthand. We have included the following pre-trained Vevo models at Amphion: Vevo-Timbre: It can conduct style-preserved voice conversion.

2025年1月22日 · TL;DR: We propose a versatile zero-shot voice imitation framework, with controllable timbre and style. The imitation of voice, targeted on specific speech attributes such as timbre and speaking style, is crucial in speech generation.

2022年1月26日 · Mara Sattei dal vivo con “Shot” in un’esclusiva performance per Vevo.

2024年12月6日 · Experiments demonstrate that our proposed StableVC outperforms state-of-the-art baseline systems in zero-shot VC and achieves flexible control over timbre and style from different unseen speakers. Moreover, StableVC offers approximately 25x and 1.65x faster sampling compared to autoregressive and diffusion-based baselines.

2025年3月7日 · However, existing methods relyheavily on annotated data, and struggle with effectively disentangling timbre andstyle, leading to challenges in achieving controllable generation, especially inzero-shot scenarios. To address these issues, we propose Vevo, a versatile zero-shot voice imitation framework with controllable timbre and style.

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We introduce Vevo, a versatile zero-shot voice imitation framework featuring controllable timbre and style. Vevo contains of two primary stages: content-style modeling via an autoregressive transformer, and acoustic modeling via a flow matching transformer.