app.py

import io
import shlex
from io import BytesIO
import gradio as gr
import librosa
import numpy as np
import soundfile
from inference import slicer
from inference.infer_tool import Svc
import logging
from logmmse import logmmse
from typing import Tuple
import time
import requests
import os,json
from subprocess import getoutput

from urllib.parse import quote

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

model_sing = "./G_2000.pth"
#model_talk = "logs/32k/talk1.pth"
config_name = "./config.json"

sid_map ={"chiya":"./chiya.pth","koyomi":"./koyomi.pth","yuki":"./yuki.pth","plw":"./plw.pth","vik":"./vik.pth"}

os.system('chmod +x ./pget')
class YukieGradio:
    def __init__(self):
        self.UI = gr.Blocks()
        with self.UI:
            with gr.Tabs():
                with gr.TabItem("Basic"):
                    gr.Markdown(value="""
                       偷的界面，参考LICENSE                        """)
                    self.sid = gr.Dropdown(label="音色", choices=["chiya","koyomi","yuki","plw","vik"], value="yuki", interactive=True)
                    self.dev = gr.Dropdown(label="设备（云端一般请勿切换，使用默认值即可）", choices=[
                                           "cuda", "cpu"], value="cpu", interactive=True)
                    self.inMic = gr.Textbox(label='url(@start)')
                    self.inAudio = gr.Audio(label="or 上传音频")
                    self.needLogmmse = gr.Checkbox(label="是否使用自带降噪")
                    self.slice_db = gr.Slider(label="切片阈值(较嘈杂时-30，保留呼吸声时-50，一般默认-40)",
                                              maximum=0, minimum=-60, step=1, value=-40)
                    self.vcTransform = gr.Number(
                        label="升降调（整数，可以正负，半音数量，升高八度就是12）", value=0)
                    self.vcSubmit = gr.Button("转换", variant="primary")
                    self.outVcText = gr.Textbox(
                        label="音高平均偏差半音数量，体现转换音频的跑调情况（一般小于0.5）")
                    self.outAudio = gr.Audio(
                        type="numpy", label="Output Audio")
                    self.f0_image = gr.Image(
                        label="f0曲线，蓝色为输入音高，橙色为合成音频的音高（代码有误差）")
                    gr.Markdown(value="""
                        ## 注意
                        如果要在本地使用该demo，请使用  `git lfs clone https://huggingface.co/spaces/yukie/yukie-sovits3`克隆该仓库([简单教程](https://huggingface.co/spaces/yukie/yukie-sovits3/edit/main/local.md))
                    """)
                    self.vcSubmit.click(infer, inputs=[self.inMic, self.inAudio, self.vcTransform, self.slice_db, self.needLogmmse, self.sid, self.dev], outputs=[
                        self.outVcText, self.outAudio, self.f0_image],api_name="go")

def download_audio(url):
    # 下载音频数据
    response = requests.get(url)
    audio_bytes = BytesIO(response.content)

    # 转换音频格式为wav
    y, sr = librosa.load(audio_bytes, sr=None)
    with BytesIO() as wav_bytes:
        soundfile.write(wav_bytes, y, sr, format='wav')
        wav_bytes.seek(0)

        # 读取wav文件
        data, sr = soundfile.read(wav_bytes)
        # 转换数据类型为int16
        data = np.asarray(data * 32767, dtype=np.int16)
        return sr, data

def downloadTubeUpload(query):
    pquery=shlex.quote(query.split('@')[0])
    proxy=os.environ['proxy']
    os.system('chmod +x ./yt-dlp')
    os.system(f'./yt-dlp -f worstaudio* -o "temp.mp4" --force-overwrites --no-playlist --concurrent-fragments 4 --proxy "{proxy}" {pquery}')
    upload_url = "https://lalal.ai/api/upload/"
    headers = {
    "Content-Disposition": f"attachment; filename=video_id.mp4"
}
   
    

    result = os.popen('ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 temp.mp4')
    duration = float(result.read().strip())
    
    # 计算需要截取的时间区间
    start_time = max(0, (duration) / 2)
    if len(query.split('@'))==2:
        start_time=int(query.split('@')[-1])
    end_time = start_time + 60

    
    # 使用ffmpeg进行截取
    os.system(f'ffmpeg -i temp.mp4 -ss {start_time} -t 60 -c copy output.mp4')
    
    command= f'curl --url https://www.lalal.ai/api/upload/ --data-binary @output.mp4 --header "Content-Disposition: attachment; filename=output.mp4" -s'
    moutput=getoutput(command)
    print(moutput)
    upload_response=json.loads(moutput)
    return upload_response.get("id")
    
def split_file(file_id):
    command = f'rm temp.mp4'
    os.system(command)
    command = f'rm output.mp4'
    os.system(command)
    url_for_split = "https://www.lalal.ai/api/preview/" 
    headers = {
  'accept': 'application/json, text/plain, */*',
  'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
  'dnt': '1',
  'origin': 'https://www.lalal.ai',
  'priority': 'u=1, i',
  'referer': 'https://www.lalal.ai/',
  'sec-ch-ua': '"Not/A)Brand";v="8", "Chromium";v="126", "Microsoft Edge";v="126"',
  'sec-ch-ua-mobile': '?0',
  'sec-ch-ua-platform': '"Windows"',
  'sec-fetch-dest': 'empty',
  'sec-fetch-mode': 'cors',
  'sec-fetch-site': 'same-origin',
  'sentry-trace': 'efee9c07725645dc896a8be5ace08ba4-87568d216d25918a-0',
  'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0',
  'x-csrftoken': 'ytk6iENZ6uT71lFQ6NgAPBGvwUt6A2Xi',
  'x-request-id': 'lalalai'
}

    query_args = {'id': file_id, 'stem': "vocals",'dereverb_enabled':True}
    response = requests.post(url_for_split,  data=query_args,headers=headers)#
    split_result = response.json()
    if split_result["status"] == "error":
        print(split_result["error"])
        raise RuntimeError('split err')

def check_file(file_id):
    url_for_check = "https://www.lalal.ai/api/check/"
    query_args = {'id': file_id}

    is_queueup = False

    while True:
        response = requests.get(url_for_check, params=query_args)
        check_result = response.json()

        if check_result["status"] == "error":
            raise RuntimeError(check_result["error"])

        task_state = check_result["task"]["state"]

        if task_state == "error":
            raise RuntimeError(check_result["task"]["error"])

        if task_state == "progress":
            progress = int(check_result["task"]["progress"])
            if progress == 0 and not is_queueup:
                print("Queue up...")
                is_queueup = True
            elif progress > 0:
                print(f"Progress: {progress}%")

        if task_state == "success":
            stem_track_url = check_result["preview"]["stem_track"]
            back_track_url = check_result["preview"]["back_track"]
            return stem_track_url, back_track_url

        time.sleep(30)
def infer(inMic, inAudio, transform, slice_db, lm, sid, dev):
    if inAudio != None:
        sampling_rate, inaudio = inAudio
    else:
        if inMic != None:
            id=downloadTubeUpload(inMic)
            split_file(id)
            sampling_rate, inaudio=download_audio(check_file(id)[0])

        else:
            return "请上传一段音频后再次尝试", None
    print("start inference")
    start_time = time.time()
    # 预处理，重编码
    inaudio = (inaudio / np.iinfo(inaudio.dtype).max).astype(np.float32)
    if len(inaudio.shape) > 1:
        inaudio = librosa.to_mono(inaudio.transpose(1, 0))
    if sampling_rate != 32000:
        inaudio = librosa.resample(
            inaudio, orig_sr=sampling_rate, target_sr=32000)
    if lm:
        inaudio = logmmse(inaudio, 32000)

    ori_wav_path = "tmp_ori.wav"
    soundfile.write(ori_wav_path, inaudio, 32000, format="wav")
    chunks = slicer.cut(ori_wav_path, db_thresh=slice_db)
    audio_data, audio_sr = slicer.chunks2audio(ori_wav_path, chunks)

    audio = []
    sid = sid_map[sid]
    if sid!=None:
        svc_model = Svc(sid, config_name, dev=dev)
#sid is model path now
    for (slice_tag, data) in audio_data:
        length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
        raw_path = io.BytesIO()
        soundfile.write(raw_path, data, audio_sr, format="wav")
        raw_path.seek(0)
        if slice_tag:
            _audio = np.zeros(length)
        else:
            out_audio, out_str = svc_model.infer(0, transform, raw_path)
            _audio = out_audio.cpu().numpy()
        audio.extend(list(_audio))
    audio = (np.array(audio) * 32768.0).astype('int16')
    used_time = time.time() - start_time

    out_wav_path = "tmp.wav"
    soundfile.write(out_wav_path, audio, 32000, format="wav")

    mistake, var = svc_model.calc_error(ori_wav_path, out_wav_path, transform)
    out_picture = svc_model.f0_plt(ori_wav_path, out_wav_path, transform)
    out_str = ("Success! total use time:{}s\n半音偏差:{}\n半音方差:{}".format(
        used_time, mistake, var))

    return out_str, (32000, audio), "temp.jpg"


if __name__ == "__main__":
    app = YukieGradio()
    app.UI.launch()