Update app.py

app.py

@@ -1,4 +1,653 @@
+import spaces
 import os
+import glob
+import json
+import traceback
+import logging
+import gradio as gr
+import numpy as np
+import librosa
+import torch
+import asyncio
+import ffmpeg
+import subprocess
+import sys
+import io
+import wave
+from datetime import datetime
+import urllib.request
+import zipfile
+import shutil
+import gradio as gr
+from textwrap import dedent
+import pprint
+import time
 
+import re
+import requests
+import subprocess
+from pathlib import Path
+from scipy.io.wavfile import write
+from scipy.io import wavfile
+import soundfile as sf
 
-exec(os.environ.get('CODE'))
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from vc_infer_pipeline import VC
+from config import Config
+config = Config()
+logging.getLogger("numba").setLevel(logging.WARNING)
+spaces_hf = True #os.getenv("SYSTEM") == "spaces"
+force_support = True
+
+audio_mode = []
+f0method_mode = []
+f0method_info = ""
+
+headers = {
+    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36"
+}
+pattern = r'//www\.bilibili\.com/video[^"]*'
+
+# Download models
+
+#urllib.request.urlretrieve("https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/hubert_base", "hubert_base.pt")
+#urllib.request.urlretrieve("https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/rmvpe", "rmvpe.pt")
+
+# Get zip name
+
+pattern_zip = r"/([^/]+)\.zip$"
+
+def get_file_name(url):
+  match = re.search(pattern_zip, url)
+  if match:
+      extracted_string = match.group(1)
+      return extracted_string
+  else:
+      raise Exception("没有找到AI歌手模型的zip压缩包。")
+
+# Get RVC models
+
+def extract_zip(extraction_folder, zip_name):
+    os.makedirs(extraction_folder)
+    with zipfile.ZipFile(zip_name, 'r') as zip_ref:
+        zip_ref.extractall(extraction_folder)
+    os.remove(zip_name)
+
+    index_filepath, model_filepath = None, None
+    for root, dirs, files in os.walk(extraction_folder):
+        for name in files:
+            if name.endswith('.index') and os.stat(os.path.join(root, name)).st_size > 1024 * 100:
+                index_filepath = os.path.join(root, name)
+
+            if name.endswith('.pth') and os.stat(os.path.join(root, name)).st_size > 1024 * 1024 * 40:
+                model_filepath = os.path.join(root, name)
+
+    if not model_filepath:
+        raise Exception(f'No .pth model file was found in the extracted zip. Please check {extraction_folder}.')
+
+    # move model and index file to extraction folder
+    os.rename(model_filepath, os.path.join(extraction_folder, os.path.basename(model_filepath)))
+    if index_filepath:
+        os.rename(index_filepath, os.path.join(extraction_folder, os.path.basename(index_filepath)))
+
+    # remove any unnecessary nested folders
+    for filepath in os.listdir(extraction_folder):
+        if os.path.isdir(os.path.join(extraction_folder, filepath)):
+            shutil.rmtree(os.path.join(extraction_folder, filepath))
+
+# Get username in OpenXLab
+
+def get_username(url):
+    match_username = re.search(r'models/(.*?)/', url)
+    if match_username:
+        result = match_username.group(1)
+        return result
+
+# Get username in Hugging Face
+
+def get_username_hf(url):
+    match_username = re.search(r'huggingface.co/(.*?)/', url)
+    if match_username:
+        result = match_username.group(1)
+        return result
+
+def download_online_model(url, dir_name):
+    if url.startswith('https://download.openxlab.org.cn/models/'):
+        zip_path = get_username(url) + "-" + get_file_name(url)
+    elif url.startswith('https://huggingface.co/'):
+        zip_path = get_username_hf(url) + "-" + get_file_name(url)
+    else:
+        zip_path = get_file_name(url)
+    if not os.path.exists(zip_path):
+      print("P.S. AI歌手模型还未下载")
+      try:
+          zip_name = url.split('/')[-1]
+          extraction_folder = os.path.join(zip_path, dir_name)
+          if os.path.exists(extraction_folder):
+              raise Exception(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')
+
+          if 'pixeldrain.com' in url:
+              url = f'https://pixeldrain.com/api/file/{zip_name}'
+
+          urllib.request.urlretrieve(url, zip_name)
+
+          extract_zip(extraction_folder, zip_name)
+          #return f'[√] {dir_name} Model successfully downloaded!'
+
+      except Exception as e:
+          raise Exception(str(e))
+    else:
+      print("P.S. AI歌手模型之前已经下载")
+
+#Get bilibili BV id
+
+def get_bilibili_video_id(url):
+    match = re.search(r'/video/([a-zA-Z0-9]+)/', url)
+    extracted_value = match.group(1)
+    return extracted_value
+
+# Get bilibili audio
+def find_first_appearance_with_neighborhood(text, pattern):
+    match = re.search(pattern, text)
+
+    if match:
+        return match.group()
+    else:
+        return None
+
+def search_bilibili(keyword):
+    if keyword.startswith("BV"):
+        req = requests.get("https://search.bilibili.com/all?keyword={}&duration=1".format(keyword), headers=headers).text
+    else:
+        req = requests.get("https://search.bilibili.com/all?keyword={}&duration=1&tids=3&page=1".format(keyword), headers=headers).text
+
+    video_link = "https:" + find_first_appearance_with_neighborhood(req, pattern)
+
+    return video_link
+
+# Save bilibili audio
+
+def get_response(html_url):
+  headers = {
+      "referer": "https://www.bilibili.com/",
+      "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36"
+  }
+  response = requests.get(html_url, headers=headers)
+  return response
+
+def get_video_info(html_url):
+  response = get_response(html_url)
+  html_data = re.findall('<script>window.__playinfo__=(.*?)</script>', response.text)[0]
+  json_data = json.loads(html_data)
+  if json_data['data']['dash']['audio'][0]['backupUrl']!=None:
+    audio_url = json_data['data']['dash']['audio'][0]['backupUrl'][0]
+  else:
+    audio_url = json_data['data']['dash']['audio'][0]['baseUrl']
+  return audio_url
+
+def save_audio(title, audio_url):
+  audio_content = get_response(audio_url).content
+  with open(title + '.wav', mode='wb') as f:
+    f.write(audio_content)
+  print("音乐内容保存完成")
+
+
+# Use UVR-HP5/2
+
+urllib.request.urlretrieve("https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/UVR-HP2.pth", "uvr5/uvr_model/UVR-HP2.pth")
+urllib.request.urlretrieve("https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/UVR-HP5.pth", "uvr5/uvr_model/UVR-HP5.pth")
+#urllib.request.urlretrieve("https://huggingface.co/fastrolling/uvr/resolve/main/Main_Models/5_HP-Karaoke-UVR.pth", "uvr5/uvr_model/UVR-HP5.pth")
+
+from uvr5.vr import AudioPre
+weight_uvr5_root = "uvr5/uvr_model"
+uvr5_names = []
+for name in os.listdir(weight_uvr5_root):
+    if name.endswith(".pth") or "onnx" in name:
+        uvr5_names.append(name.replace(".pth", ""))
+
+func = AudioPre
+pre_fun_hp2 = func(
+  agg=int(10),
+  model_path=os.path.join(weight_uvr5_root, "UVR-HP2.pth"),
+  device="cuda",
+  is_half=True,
+)
+
+pre_fun_hp5 = func(
+  agg=int(10),
+  model_path=os.path.join(weight_uvr5_root, "UVR-HP5.pth"),
+  device="cuda",
+  is_half=True,
+)
+
+# Separate vocals
+
+def youtube_downloader(
+    filename,
+    split_model,
+):
+
+    audio_path = filename.strip() + ".wav"
+
+    # make dir output
+    os.makedirs("output", exist_ok=True)
+
+    if split_model=="UVR-HP2":
+        pre_fun = pre_fun_hp2
+    else:
+        pre_fun = pre_fun_hp5
+
+    pre_fun._path_audio_(audio_path, f"./output/{split_model}/{filename}/", f"./output/{split_model}/{filename}/", "wav")
+    os.remove(filename.strip()+".wav")
+    
+    return f"./output/{split_model}/{filename}/vocal_{filename}.wav_10.wav", f"./output/{split_model}/{filename}/instrument_{filename}.wav_10.wav"
+
+# get duration
+
+import wave
+def get_duration_wave(file_path):
+   with wave.open(file_path, 'r') as audio_file:
+      frame_rate = audio_file.getframerate()
+      n_frames = audio_file.getnframes()
+      duration = n_frames / float(frame_rate)
+      return duration
+
+# Original code
+
+if force_support is False or spaces_hf is True:
+    if spaces_hf is True:
+        audio_mode = ["Upload audio", "TTS Audio"]
+    else:
+        audio_mode = ["Input path", "Upload audio", "TTS Audio"]
+    f0method_mode = ["pm", "harvest"]
+    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)"
+else:
+    audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
+    f0method_mode = ["pm", "harvest", "crepe"]
+    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"
+
+if os.path.isfile("rmvpe.pt"):
+    f0method_mode.insert(2, "rmvpe")
+
+def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
+    def vc_fn(
+        vc_audio_mode,
+        vc_input,
+        vc_upload,
+        tts_text,
+        tts_voice,
+        f0_up_key,
+        f0_method,
+        index_rate,
+        filter_radius,
+        resample_sr,
+        rms_mix_rate,
+        protect,
+    ):
+        try:
+            logs = []
+            print(f"Converting using {model_name}...")
+            logs.append(f"Converting using {model_name}...")
+            yield "\n".join(logs), None
+            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
+                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
+            elif vc_audio_mode == "Upload audio":
+                if vc_upload is None:
+                    return "You need to upload an audio", None
+                sampling_rate, audio = vc_upload
+                duration = audio.shape[0] / sampling_rate
+                if duration > 20 and spaces_hf:
+                    return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", 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)
+            times = [0, 0, 0]
+            f0_up_key = int(f0_up_key)
+            audio_opt = vc.pipeline(
+                hubert_model,
+                net_g,
+                0,
+                audio,
+                vc_input,
+                times,
+                f0_up_key,
+                f0_method,
+                file_index,
+                # file_big_npy,
+                index_rate,
+                if_f0,
+                filter_radius,
+                tgt_sr,
+                resample_sr,
+                rms_mix_rate,
+                version,
+                protect,
+                f0_file=None,
+            )
+            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
+            print(f"{model_name} | {info}")
+            logs.append(f"Successfully Convert {model_name}\n{info}")
+            yield "\n".join(logs), (tgt_sr, audio_opt)
+        except Exception as err:
+            info = traceback.format_exc()
+            print(info)
+            print(f"Error when using {model_name}.\n{str(err)}")
+            yield info, None
+    return vc_fn
+
+def combine_vocal_and_inst(model_name, song_name, song_id, split_model, cover_song, vocal_volume, inst_volume):
+    #samplerate, data = wavfile.read(cover_song)
+    vocal_path = cover_song #f"output/{split_model}/{song_id}/vocal_{song_id}.wav_10.wav"
+    output_path = song_name.strip() + "-AI-" + ''.join(os.listdir(f"{model_name}")).strip() + "翻唱版.mp3"
+    inst_path = f"output/{split_model}/{song_id}/instrument_{song_id}.wav_10.wav"
+    #with wave.open(vocal_path, "w") as wave_file:
+        #wave_file.setnchannels(1)
+        #wave_file.setsampwidth(2)
+        #wave_file.setframerate(samplerate)
+        #wave_file.writeframes(data.tobytes())
+    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [0:a]volume={inst_volume}[i];[1:a]volume={vocal_volume}[v];[i][v]amix=inputs=2:duration=longest[a] -map [a] -b:a 320k -c:a libmp3lame {output_path}'
+    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
+    print(result.stdout.decode())
+    return output_path
+
+def rvc_models(model_name):
+  global vc, net_g, index_files, tgt_sr, version
+  categories = []
+  models = []
+  for w_root, w_dirs, _ in os.walk(f"{model_name}"):
+      model_count = 1
+      for sub_dir in w_dirs:
+          pth_files = glob.glob(f"{model_name}/{sub_dir}/*.pth")
+          index_files = glob.glob(f"{model_name}/{sub_dir}/*.index")
+          if pth_files == []:
+              print(f"Model [{model_count}/{len(w_dirs)}]: No Model file detected, skipping...")
+              continue
+          cpt = torch.load(pth_files[0])
+          tgt_sr = cpt["config"][-1]
+          cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+          if_f0 = cpt.get("f0", 1)
+          version = cpt.get("version", "v1")
+          if version == "v1":
+              if if_f0 == 1:
+                  net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+              else:
+                  net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+              model_version = "V1"
+          elif version == "v2":
+              if if_f0 == 1:
+                  net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+              else:
+                  net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+              model_version = "V2"
+          del net_g.enc_q
+          print(net_g.load_state_dict(cpt["weight"], strict=False))
+          net_g.eval().to(config.device)
+          if config.is_half:
+              net_g = net_g.half()
+          else:
+              net_g = net_g.float()
+          vc = VC(tgt_sr, config)
+          if index_files == []:
+              print("Warning: No Index file detected!")
+              index_info = "None"
+              model_index = ""
+          else:
+              index_info = index_files[0]
+              model_index = index_files[0]
+          print(f"Model loaded [{model_count}/{len(w_dirs)}]: {index_files[0]} / {index_info} | ({model_version})")
+          model_count += 1
+          models.append((index_files[0][:-4], index_files[0][:-4], "", "", model_version, create_vc_fn(index_files[0], tgt_sr, net_g, vc, if_f0, version, model_index)))
+  categories.append(["Models", "", models])
+  return vc, net_g, index_files, tgt_sr, version
+
+singers="您的专属AI歌手阵容:"
+
+@spaces.GPU(duration=120)
+def rvc_infer_music_gpu(zip_path, song_name, song_id, split_model, f0_up_key, vocal_volume, inst_volume):
+  print("3.1.开��加载HuBert模型...")
+  from fairseq import checkpoint_utils
+  models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+      ["hubert_base.pt"],
+      suffix="",
+  )
+  hubert_model = models[0]
+  hubert_model = hubert_model.to(config.device)
+  if config.is_half:
+      hubert_model = hubert_model.half()
+  else:
+      hubert_model = hubert_model.float()
+  hubert_model.eval()
+  print("3.2.开始加载AI歌手模型参数...")
+  rvc_models(zip_path)
+  if os.path.isdir(f"./output/{split_model}/{song_id}")==True:
+      print("4.直接开始推理（BGM之前已经去除）...")
+      audio, sr = librosa.load(f"./output/{split_model}/{song_id}/vocal_{song_id}.wav_10.wav", sr=16000, mono=True)
+      song_infer = vc.pipeline(
+              hubert_model,
+              net_g,
+              0,
+              audio,
+              "",
+              [0, 0, 0],
+              f0_up_key,
+              "rmvpe",
+              index_files[0],
+              0.7,
+              1,
+              3,
+              tgt_sr,
+              0,
+              0.25,
+              version,
+              0.33,
+              f0_file=None,
+      )
+  else:
+      print("4.1.开始去除BGM...")
+      audio, sr = librosa.load(youtube_downloader(song_id, split_model)[0], sr=16000, mono=True)
+      print("4.2.开始推理...")
+      song_infer = vc.pipeline(
+              hubert_model,
+              net_g,
+              0,
+              audio,
+              "",
+              [0, 0, 0],
+              f0_up_key,
+              "rmvpe",
+              index_files[0],
+              0.7,
+              1,
+              3,
+              tgt_sr,
+              0,
+              0.25,
+              version,
+              0.33,
+              f0_file=None,
+      )
+  sf.write(song_name.strip()+zip_path+"AI翻唱.wav", song_infer, tgt_sr)
+  output_full_song = combine_vocal_and_inst(zip_path, song_name.strip(), song_id, split_model, song_name.strip()+zip_path+"AI翻唱.wav", vocal_volume, inst_volume)
+  os.remove(song_name.strip()+zip_path+"AI翻唱.wav")
+  return output_full_song
+
+@spaces.GPU(duration=30)
+def rvc_infer_upload_audio_gpu(zip_path, upload_audio, split_model, f0_up_key, vocal_volume, inst_volume):
+  print("3.1.开始加载HuBert模型...")
+  from fairseq import checkpoint_utils
+  models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+      ["hubert_base.pt"],
+      suffix="",
+  )
+  hubert_model = models[0]
+  hubert_model = hubert_model.to(config.device)
+  if config.is_half:
+      hubert_model = hubert_model.half()
+  else:
+      hubert_model = hubert_model.float()
+  hubert_model.eval()
+  print("3.2.开始加载AI歌手模型参数...")
+  rvc_models(zip_path)
+  print("4.开始推理用户上传的歌曲...")
+  audio, sr = librosa.load(upload_audio, sr=16000, mono=True)
+  song_infer = vc.pipeline(
+          hubert_model,
+          net_g,
+          0,
+          audio,
+          "",
+          [0, 0, 0],
+          f0_up_key,
+          "rmvpe",
+          index_files[0],
+          0.7,
+          1,
+          3,
+          tgt_sr,
+          0,
+          0.25,
+          version,
+          0.33,
+          f0_file=None,
+  )
+  sf.write("AI" + ''.join(os.listdir(f"{zip_path}")).strip() + "翻唱歌曲.wav", song_infer, tgt_sr)
+  return "AI" + ''.join(os.listdir(f"{zip_path}")).strip() + "翻唱歌曲.wav"
+
+def rvc_infer_music(url, model_name, song_name, upload_audio, split_model, f0_up_key, vocal_volume, inst_volume):
+  url = url.strip().replace(" ", "")
+  model_name = model_name.strip().replace(" ", "")
+  if url.startswith('https://download.openxlab.org.cn/models/'):
+      zip_path = get_username(url) + "-" + get_file_name(url)
+  elif url.startswith('https://huggingface.co/'):
+      zip_path = get_username_hf(url) + "-" + get_file_name(url)
+  else:
+      zip_path = get_file_name(url)
+  global singers
+  if model_name not in singers:
+    singers = singers+ '   '+ model_name
+  print("1.开始下载AI歌手模型...")
+  download_online_model(url, model_name)
+  if upload_audio is None:
+      video_identifier = search_bilibili(song_name.strip())
+      song_name = song_name.strip().replace(" ", "")
+      song_id = get_bilibili_video_id(video_identifier)
+      print(video_identifier)
+      video_info = get_video_info(video_identifier)
+      print(video_info)
+      audio_content = get_response(video_info).content
+      print("2.开始下载AI翻唱歌曲...")
+      with open(song_id.strip() + ".wav", mode="wb") as f:
+          f.write(audio_content)
+      output_full_song = rvc_infer_music_gpu(zip_path, song_name, song_id, split_model, f0_up_key, vocal_volume, inst_volume)
+      return output_full_song, singers
+  else:
+      song_duration = get_duration_wave(upload_audio)
+      if song_duration < 480:
+          print(f"上传歌曲时长：{song_duration}秒")
+          output_full_song = rvc_infer_upload_audio_gpu(zip_path, upload_audio, split_model, f0_up_key, vocal_volume, inst_volume)
+      else:
+          raise Exception('抱歉！您��传的歌曲时长超过了8分钟，请上传短于8分钟的歌曲。')
+      return output_full_song, singers
+      
+app = gr.Blocks(theme="JohnSmith9982/small_and_pretty")
+with app:
+    with gr.Tab("中文版"):
+      gr.Markdown("# <center>🌊💕🎶 滔滔AI，您的专属AI全明星乐团</center>")
+      gr.Markdown("## <center>🌟 只需一个歌曲名，全网AI歌手任您选择！随时随地，听我想听！</center>")
+      gr.Markdown("### <center>🤗 更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；相关问题欢迎在我们的[B站](https://space.bilibili.com/501495851)账号交流！滔滔AI，为爱滔滔！💕</center>")
+      with gr.Accordion("💡 一些AI歌手模型链接及使用说明（建议阅读）：您若在一段时间内达到GPU使用限额，可在另一台设备上访问滔滔AI官网并继续使用此程序", open=False):
+          _ = f""" 任何能够在线下载的zip压缩包的链接都可以哦（zip压缩包只需包括AI歌手模型的.pth和.index文件，zip压缩包的链接需要以.zip作为后缀）:
+              * Taylor Swift: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/taylor.zip
+              * Blackpink Lisa: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/Lisa.zip
+              * AI派蒙: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/paimon.zip
+              * AI孙燕姿: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/syz.zip
+              * AI[一清清清](https://www.bilibili.com/video/BV1wV411u74P)（推荐使用 [Hugging Face](https://huggingface.co/new) 存放模型zip压缩包）: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/yiqing.zip\n
+              说明1：点击“一键开启AI翻唱之旅吧！”按钮即可使用！✨\n
+              说明2：一般情况下，男声演唱的歌曲转换成AI女声演唱需要升调，反之则需要降调；在“歌曲人声升降调”模块可以调整\n
+              说明3：对于同一个AI歌手模型或者同一首歌曲，第一次的运行时间会比较长（大约1分钟），请您耐心等待；之后的运行时间会大大缩短哦！\n
+              说明4：您之前下载过的模型会在“已下载的AI歌手全明星阵容”模块出现\n
+              说明5：此程序使用 [RVC](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) AI歌手模型，感谢[作者](https://space.bilibili.com/5760446)的开源！RVC模型训练教程参见[视频](https://www.bilibili.com/video/BV1mX4y1C7w4)\n
+              🤗 我们正在创建一个完全开源、共建共享的AI歌手模型社区，让更多的人感受到AI音乐的乐趣与魅力！请关注我们的[B站](https://space.bilibili.com/501495851)账号，了解社区的最新进展！合作联系：talktalkai.kevin@gmail.com
+              """
+          gr.Markdown(dedent(_))
+    
+      with gr.Row():
+        with gr.Column():
+          inp1 = gr.Textbox(label="请输入AI歌手模型链接", info="模型需要是含有.pth和.index文件的zip压缩包，推荐使用Hugging Face链接", lines=2, value="https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/taylor.zip", placeholder="https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/taylor.zip")
+          with gr.Accordion("🎶 从本地上传歌曲文件", open=False):
+              inp_upload = gr.Audio(label="请上传一首您喜欢的歌曲，需要是无伴奏的人声", type="filepath")
+        with gr.Column():
+          inp2 = gr.Textbox(label="请给您的AI歌手起一个昵称吧", info="可自定义名称，但名称中不能有特殊符号", lines=1, value="AI Taylor", placeholder="AI Taylor")
+          inp3 = gr.Textbox(label="请输入您需要AI翻唱的歌曲名", info="1. 如果您对搜索结果不满意，可在歌曲名后加上“无损”或“歌手的名字”等关键词，歌曲名中不能有特殊符号 2. 如果您希望通过歌曲名上传歌曲，请勿在程序左侧上传歌曲文件", lines=1, value="小幸运", placeholder="小幸运")
+      with gr.Row():
+        inp4 = gr.Dropdown(label="请选择用于分离伴奏的模型", choices=["UVR-HP2", "UVR-HP5"], value="UVR-HP5", visible=False)
+        inp5 = gr.Slider(label="歌曲人声升降调", info="默认为0，+2为升高2个key，以此类推", minimum=-12, maximum=12, value=0, step=1)
+        inp6 = gr.Slider(label="歌曲人声音量调节", info="默认为1，等于0时为静音", minimum=0, maximum=3, value=1, step=0.2)
+        inp7 = gr.Slider(label="歌曲伴奏音量调节", info="默认为1，等于0时为静音", minimum=0, maximum=3, value=1, step=0.2)
+        btn = gr.Button("一键开启AI翻唱之旅吧！💕", variant="primary")
+      with gr.Row():
+        output_song = gr.Audio(label="AI歌手为您倾情演绎")
+        singer_list = gr.Textbox(label="已下载的AI歌手全明星阵容")
+    
+      btn.click(fn=rvc_infer_music, inputs=[inp1, inp2, inp3, inp_upload, inp4, inp5, inp6, inp7], outputs=[output_song, singer_list])
+    
+      gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。请自觉合规使用此程序，程序开发者不负有任何责任。</center>")
+      gr.HTML('''
+          <div class="footer">
+                      <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
+                      </p>
+          </div>
+      ''')
+    with gr.Tab("EN"):
+      gr.Markdown("# <center>🌊💕🎶 TalkTalkAI - Best AI song cover generator ever</center>")
+      gr.Markdown("## <center>🌟 Provide the name of a song and our application running on A100 will handle everything else!</center>")
+      gr.Markdown("### <center>🤗 [TalkTalkAI](http://www.talktalkai.com/), let everyone enjoy a better life through human-centered AI💕</center>")
+      with gr.Accordion("💡 Some AI singers you can play with", open=False):
+          _ = f""" Any Zip file that you can download online will be fine (The Zip file should contain .pth and .index files):
+              * AI Taylor Swift: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/taylor.zip
+              * AI Blackpink Lisa: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/Lisa.zip
+              * AI Paimon: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/paimon.zip
+              * AI Stefanie Sun: https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/syz.zip
+              * AI[一清清清](https://www.bilibili.com/video/BV1wV411u74P): https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/yiqing.zip\n
+              """
+          gr.Markdown(dedent(_))
+    
+      with gr.Row():
+        with gr.Column():
+          inp1_en = gr.Textbox(label="The Zip file of an AI singer", info="The Zip file should contain .pth and .index files", lines=2, value="https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/taylor.zip", placeholder="https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/taylor.zip")
+          with gr.Accordion("🎶 Upload a song yourself", open=False):
+              inp_upload_en = gr.Audio(label="Please upload a song you like (vocal only)", type="filepath")
+        with gr.Column():
+          inp2_en = gr.Textbox(label="The name of your AI singer", lines=1, value="AI Taylor", placeholder="AI Taylor")
+          inp3_en = gr.Textbox(label="The name of a song", lines=1, value="Hotel California Eagles", placeholder="Hotel California Eagles")
+      with gr.Row():
+        inp4_en = gr.Dropdown(label="UVR models", choices=["UVR-HP2", "UVR-HP5"], value="UVR-HP5", visible=False)
+        inp5_en = gr.Slider(label="Transpose", info="0 from man to man (or woman to woman); 12 from man to woman and -12 from woman to man.", minimum=-12, maximum=12, value=0, step=1)
+        inp6_en = gr.Slider(label="Vocal volume", info="Adjust vocal volume (Default: 1)", minimum=0, maximum=3, value=1, step=0.2)
+        inp7_en = gr.Slider(label="Instrument volume", info="Adjust instrument volume (Default: 1)", minimum=0, maximum=3, value=1, step=0.2)
+        btn_en = gr.Button("Convert💕", variant="primary")
+      with gr.Row():
+        output_song_en = gr.Audio(label="AI song cover")
+        singer_list_en = gr.Textbox(label="The AI singers you have")
+    
+      btn_en.click(fn=rvc_infer_music, inputs=[inp1_en, inp2_en, inp3_en, inp_upload_en, inp4_en, inp5_en, inp6_en, inp7_en], outputs=[output_song_en, singer_list_en])
+    
+
+      gr.HTML('''
+          <div class="footer">
+                      <p>🤗 - Stay tuned! The best is yet to come.
+                      </p>
+                      <p>📧 - Contact us: talktalkai.kevin@gmail.com
+                      </p>
+          </div>
+      ''')    
+
+app.queue(max_size=40, api_open=False)
+app.launch(max_threads=400, show_error=True)