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Ameli2
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import io
import os

# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
import gradio as gr
import librosa
import numpy as np
import soundfile
from inference.infer_tool import Svc
import logging

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)

config_path = "config.json"

model = Svc("G_754.pth", "config.json")

# model = Svc("E:/Items/so-vits-svc/models/Arknights/G_10400.pth", "E:/Items/so-vits-svc/models/Arknights/config.json")


# def vc_fn(sid,input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
#     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 > 90:
#         return "请上传小于90s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 = "temp.wav"
#     soundfile.write(out_wav_path, audio, 16000, format="wav")
#     # print( cluster_ratio, auto_f0, noise_scale)
#     _audio = model.slice_inference(out_wav_path,sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
#     return "Success", (44100, _audio)


# app = gr.Blocks()
# with app:
#     with gr.Tabs():
#         with gr.TabItem("Basic"):
#             gr.Markdown(value="""
#                 sovits Ameli
#                 Upload your audio
#                 """)
#             spks = list(model.spk2id.keys())
#             # sid = gr.Dropdown(label="Voice Tone", choices=spks, value=spks[0])
#             vc_input3 = gr.Audio(label="Upload your audio(under 90s length)")
#             vc_submit = gr.Button("Run", variant="primary")
#             vc_output1 = gr.Textbox(label="Output Message")
#             vc_output2 = gr.Audio(label="Output Audio")
#         vc_submit.click(vc_fn, [vc_input3], [vc_output1, vc_output2])
#         # vc_submit.click(vc_fn, [vc_input3,0,-40,0,False,0.4], [vc_output1, vc_output2])

#     app.launch()




def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
    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 > 90:
        return "请上传小于90s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 = "temp.wav"
    soundfile.write(out_wav_path, audio, 16000, format="wav")
    print( cluster_ratio, auto_f0, noise_scale)
    _audio = model.slice_inference(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
    return "Success", (_audio)


app = gr.Blocks()
with app:
    with gr.Tabs():
        with gr.TabItem("Basic"):
            gr.Markdown(value="""
                sovits Ameli
                Upload your audio
                """)
            spks = list(model.spk2id.keys())
            sid = gr.Dropdown(label="音色", choices=spks, value=spks[0])
            vc_input3 = gr.Audio(label="上传音频（长度小于90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)
            cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
            auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False)
            slice_db = gr.Number(label="切片阈值", value=-40)
            noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
            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,auto_f0,cluster_ratio, slice_db, noise_scale], [vc_output1, vc_output2])

    app.launch()