app.py

import os
import io
import gradio as gr
import librosa
import numpy as np
import utils
from inference.infer_tool import Svc
import logging
import soundfile
import asyncio
import argparse
import edge_tts
import gradio.processing_utils as gr_processing_utils

logging.getLogger('numba').setLevel(logging.WARNING)
logging.getLogger('markdown_it').setLevel(logging.WARNING)
logging.getLogger('urllib3').setLevel(logging.WARNING)
logging.getLogger('matplotlib').setLevel(logging.WARNING)

limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces

audio_postprocess_ori = gr.Audio.postprocess


def audio_postprocess(self, y):
    data = audio_postprocess_ori(self, y)
    if data is None:
        return None
    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])


gr.Audio.postprocess = audio_postprocess


def create_vc_fn(model, sid):
    def vc_fn(input_audio, vc_transform, auto_f0):
        if input_audio is None:
            return "请先上传音频", None
        sampling_rate, audio = input_audio
        duration = audio.shape[0] / sampling_rate
        if duration > 20 and limitation:
            return "请上传小于20秒的音频，或点击右上角裁剪", None
        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
        if len(audio.shape) > 1:
            audio = librosa.to_mono(audio.transpose(1, 0))
        if sampling_rate != 16000:
            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
        raw_path = io.BytesIO()
        soundfile.write(raw_path, audio, 16000, format="wav")
        raw_path.seek(0)
        out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
                                        auto_predict_f0=auto_f0,
                                        )
        return "Success", (44100, out_audio.cpu().numpy())

    return vc_fn


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--api', action="store_true", default=False)
    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
    args = parser.parse_args()
    hubert_model = utils.get_hubert_model().to(args.device)
    models = []
    voices = []
    for f in os.listdir("models"):
        name = f
        model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config_{f}.json", device=args.device)
        cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None
        models.append((name, cover, create_vc_fn(model, name)))
    with gr.Blocks() as app:
        gr.Markdown(
            "# <center> GTASA人物SOVITS\n"
            "## <center> 作者：B站Cyber蝈蝈总\n"
        )
        with gr.Tabs():
            for (name, cover, vc_fn) in models:
                with gr.TabItem(name):
                    with gr.Row():
                        with gr.Column():
                            gr.Markdown(
                                '<div align="center">'
                                f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
                                                                                                           '</div>'
                            )
                        with gr.Column():
                            vc_input = gr.Audio(label="输入干声" + ' (小于 20 秒)' if limitation else '')
                            vc_transform = gr.Number(label="音高调整(支持正负半音，12为一个八度)", value=0)
                            auto_f0 = gr.Checkbox(label="自动音高预测（非唱歌音频）", value=False)
                            vc_submit = gr.Button("生成", variant="primary")
                        with gr.Column():
                            vc_output1 = gr.Textbox(label="输出信息")
                            vc_output2 = gr.Audio(label="输出音频")
                vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0], [vc_output1, vc_output2])
        app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)