RedbeardNZ / Vevo

Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement

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.
• Vevo-Style : It can conduct style conversion, such as accent conversion and emotion conversion .
• Vevo-Voice : It can conduct style-converted voice conversion.
• Vevo-TTS : It can conduct style and timbre controllable TTS.

Besides, we also release the content tokenizer and content-style tokenizer proposed by Vevo. Notably, all these pre-trained models are trained on Emilia , containing 101k hours of speech data among six languages (English, Chinese, German, French, Japanese, and Korean).

Model Introduction

We provide the following pre-trained models:

You can download all pretrained checkpoints from HuggingFace or use huggingface API.

Usage

You can refer to our recipe at GitHub for more usage details. For example, to use Vevo-TTS, after you clone the Amphion github repository, you can use the script like:

import os
from huggingface_hub import snapshot_download

from models.vc.vevo.vevo_utils import *


def vevo_tts(
    src_text,
    ref_wav_path,
    timbre_ref_wav_path=None,
    output_path=None,
    ref_text=None,
    src_language="en",
    ref_language="en",
):
    if timbre_ref_wav_path is None:
        timbre_ref_wav_path = ref_wav_path

    gen_audio = inference_pipeline.inference_ar_and_fm(
        src_wav_path=None,
        src_text=src_text,
        style_ref_wav_path=ref_wav_path,
        timbre_ref_wav_path=timbre_ref_wav_path,
        style_ref_wav_text=ref_text,
        src_text_language=src_language,
        style_ref_wav_text_language=ref_language,
    )

    assert output_path is not None
    save_audio(gen_audio, output_path=output_path)


if __name__ == "__main__":
    # ===== Device =====
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    # ===== Content-Style Tokenizer =====
    local_dir = snapshot_download(
        repo_id="amphion/Vevo",
        repo_type="model",
        cache_dir="./ckpts/Vevo",
        allow_patterns=["tokenizer/vq8192/*"],
    )

    content_style_tokenizer_ckpt_path = os.path.join(local_dir, "tokenizer/vq8192")

    # ===== Autoregressive Transformer =====
    local_dir = snapshot_download(
        repo_id="amphion/Vevo",
        repo_type="model",
        cache_dir="./ckpts/Vevo",
        allow_patterns=["contentstyle_modeling/PhoneToVq8192/*"],
    )

    ar_cfg_path = "./models/vc/vevo/config/PhoneToVq8192.json"
    ar_ckpt_path = os.path.join(local_dir, "contentstyle_modeling/PhoneToVq8192")

    # ===== Flow Matching Transformer =====
    local_dir = snapshot_download(
        repo_id="amphion/Vevo",
        repo_type="model",
        cache_dir="./ckpts/Vevo",
        allow_patterns=["acoustic_modeling/Vq8192ToMels/*"],
    )

    fmt_cfg_path = "./models/vc/vevo/config/Vq8192ToMels.json"
    fmt_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vq8192ToMels")

    # ===== Vocoder =====
    local_dir = snapshot_download(
        repo_id="amphion/Vevo",
        repo_type="model",
        cache_dir="./ckpts/Vevo",
        allow_patterns=["acoustic_modeling/Vocoder/*"],
    )

    vocoder_cfg_path = "./models/vc/vevo/config/Vocoder.json"
    vocoder_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vocoder")

    # ===== Inference =====
    inference_pipeline = VevoInferencePipeline(
        content_style_tokenizer_ckpt_path=content_style_tokenizer_ckpt_path,
        ar_cfg_path=ar_cfg_path,
        ar_ckpt_path=ar_ckpt_path,
        fmt_cfg_path=fmt_cfg_path,
        fmt_ckpt_path=fmt_ckpt_path,
        vocoder_cfg_path=vocoder_cfg_path,
        vocoder_ckpt_path=vocoder_ckpt_path,
        device=device,
    )

    src_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences."

    ref_wav_path = "./models/vc/vevo/wav/arabic_male.wav"
    ref_text = "Flip stood undecided, his ears strained to catch the slightest sound."

    # 1. Zero-Shot TTS (the style reference and timbre reference are same)
    vevo_tts(
        src_text,
        ref_wav_path,
        output_path="./models/vc/vevo/wav/output_vevotts1.wav",
        ref_text=ref_text,
        src_language="en",
        ref_language="en",
    )

    # 2. Style and Timbre Controllable Zero-Shot TTS (the style reference and timbre reference are different)
    vevo_tts(
        src_text,
        ref_wav_path,
        timbre_ref_wav_path="./models/vc/vevo/wav/mandarin_female.wav",
        output_path="./models/vc/vevo/wav/output_vevotts2.wav",
        ref_text=ref_text,
        src_language="en",
        ref_language="en",
    )

Citation

If you use Vevo in your research, please cite the following papers:

@inproceedings{vevo,
  author       = {Xueyao Zhang and Xiaohui Zhang and Kainan Peng and Zhenyu Tang and Vimal Manohar and Yingru Liu and Jeff Hwang and Dangna Li and Yuhao Wang and Julian Chan and Yuan Huang and Zhizheng Wu and Mingbo Ma},
  title        = {Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement},
  booktitle    = {{ICLR}},
  publisher    = {OpenReview.net},
  year         = {2025}
}

@article{amphion_v0.2,
  title        = {Overview of the Amphion Toolkit (v0.2)},
  author       = {Jiaqi Li and Xueyao Zhang and Yuancheng Wang and Haorui He and Chaoren Wang and Li Wang and Huan Liao and Junyi Ao and Zeyu Xie and Yiqiao Huang and Junan Zhang and Zhizheng Wu},
  year         = {2025},
  journal      = {arXiv preprint arXiv:2501.15442},
}

@inproceedings{amphion,
    author={Xueyao Zhang and Liumeng Xue and Yicheng Gu and Yuancheng Wang and Jiaqi Li and Haorui He and Chaoren Wang and Ting Song and Xi Chen and Zihao Fang and Haopeng Chen and Junan Zhang and Tze Ying Tang and Lexiao Zou and Mingxuan Wang and Jun Han and Kai Chen and Haizhou Li and Zhizheng Wu},
    title={Amphion: An Open-Source Audio, Music and Speech Generation Toolkit},
    booktitle={{IEEE} Spoken Language Technology Workshop, {SLT} 2024},
    year={2024}
}