import io

import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from inference.infer_tool import Svc
import logging

logging.getLogger('numba').setLevel(logging.WARNING)

model_name = "logs/32k/G_38000.pth"
config_name = "configs/config.json"

svc_model = Svc(model_name, config_name)
sid_map = {
    "桃李": "taoli"
}


def vc_fn(sid, input_audio, vc_transform):
    if input_audio is None:
        return "You need to upload an audio", None
    sampling_rate, audio = input_audio
    # print(audio.shape,sampling_rate)
    duration = audio.shape[0] / sampling_rate
    if duration > 45:
        return "请上传小于45s的音频，需要转换长音频请本地进行转换", 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)
    print(audio.shape)
    out_wav_path = io.BytesIO()
    soundfile.write(out_wav_path, audio, 16000, format="wav")
    out_wav_path.seek(0)

    sid = sid_map[sid]
    out_audio, out_sr = svc_model.infer(sid, vc_transform, out_wav_path)
    _audio = out_audio.cpu().numpy()
    return "Success", (32000, _audio)


app = gr.Blocks()
with app:
    with gr.Tabs():
        with gr.TabItem("Basic"):
            gr.Markdown(value="""
               
                
                i7000如果要在本地使用该demo，请使用git lfs clone 该仓库，安装requirements.txt后运行app.py即可
                
                项目改写基于 https://huggingface.co/spaces/innnky/nyaru-svc-3.0
                
                本地合成可以删除26、27两行代码以解除合成45s长度限制""")
            sid = gr.Dropdown(label="音色", choices=["桃李"], value="taoli")
            vc_input3 = gr.Audio(label="上传音频（长度小于45秒）")
            vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
            vc_submit = gr.Button("转换", variant="primary")
            vc_output1 = gr.Textbox(label="Output Message")
            vc_output2 = gr.Audio(label="Output Audio")
        vc_submit.click(vc_fn, [sid, vc_input3, vc_transform], [vc_output1, vc_output2])

    app.launch()