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:

Model Name	Description
Content Tokenizer	Converting speech to content tokens. It is a single codebook VQ-VAE with a vocabulary size of 32. The frame rate is 50Hz.
Content-Style Tokenizer	Converting speech to content-style tokens. It is a single codebook VQ-VAE with a vocabulary size of 8192. The frame rate is 50Hz.
Vq32ToVq8192	Predicting content-style tokens from content tokens with an auto-regressive transformer (480M).
PhoneToVq8192	Predicting content-style tokens from phone tokens with an auto-regressive transformer (740M).
Vq8192ToMels	Predicting mel-spectrogram from content-style tokens with a flow-matching transformer (330M).
Vocoder	Predicting audio from mel-spectrogram with a Vocos-based vocoder (250M).

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:

@article{vevo,
  title={Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement},
  journal={OpenReview},
  year={2024}
}

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