Convert the below python code into a Gradio app that will keep the AI models loaded and have fast inference, the Gradio UI needs an audio file select and a text box for inference. I want you to provide me the full script fixed and keep in mind that this is going to run in WSL if that info is needed to help with any possible filepath errors.

# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

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",
    )