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YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone

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arxiv 2112.02418 v4 pith:IWFDLVVW submitted 2021-12-04 cs.SD cs.CLeess.AS

classification cs.SDcs.CLeess.AS
keywords zero-shotmulti-speakervoiceresultsconversionyourttsapproachdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results comparable to SOTA in zero-shot voice conversion on the VCTK dataset. Additionally, our approach achieves promising results in a target language with a single-speaker dataset, opening possibilities for zero-shot multi-speaker TTS and zero-shot voice conversion systems in low-resource languages. Finally, it is possible to fine-tune the YourTTS model with less than 1 minute of speech and achieve state-of-the-art results in voice similarity and with reasonable quality. This is important to allow synthesis for speakers with a very different voice or recording characteristics from those seen during training.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tell me Habibi, is it Real or Fake?

    cs.CV 2025-05 conditional novelty 7.0 of 10

    ArEnAV, the first large-scale Arabic-English code-switched audio-visual deepfake dataset, makes current state-of-the-art detectors fail much more than on monolingual data.

  2. KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features

    eess.AS 2025-08 conditional novelty 4.0 of 10

    A challenge entry combining Wav2Vec-AASIST audio scores with lightweight handcrafted-feature video scores via calibration and maxout reports 92.78% AUC on AV-Deepfake1M++ testA.

  3. EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion

    cs.SD 2025-05 reject novelty 4.0 of 10

    EZ-VC combines discrete units from a multilingual self-supervised encoder (Xeus) with an F5-TTS flow-matching decoder to achieve zero-shot any-to-any voice conversion, without text labels or multiple disentangling encoders.

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