demo.py

import torch
import sys

import os
from huggingface_hub import snapshot_download

sys.path.insert(0,'/apdcephfs_nj7/share_303172353/ggyzhang/projects/Amphion')

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",
    )

exit(0)


a = torch.rand((3,4,5))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
a = a.to(device)
print(a)