Create app_colab.py

app_colab.py

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+import re, os
+import requests
+import json
+import torch
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+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[^"]*'
+
+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
+
+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']
+  video_url = json_data['data']['dash']['video'][0]['baseUrl']
+  return audio_url, video_url
+
+def save_audio(title, html_url):
+  audio_url = get_video_info(html_url)[0]
+  #video_url = get_video_info(html_url)[1]
+
+  audio_content = get_response(audio_url).content
+  #video_content = get_response(video_url).content
+
+  with open(title + '.mp3', mode='wb') as f:
+    f.write(audio_content)
+  print("音乐内容保存完成")
+  #with open(title + '.mp4', mode='wb') as f:
+  #  f.write(video_content)
+  #print("视频内容保存完成"
+
+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=device,
+  is_half=True,
+)
+
+pre_fun_hp5 = func(
+  agg=int(10),
+  model_path=os.path.join(weight_uvr5_root, "UVR-HP5.pth"),
+  device=device,
+  is_half=True,
+)
+
+import webrtcvad
+from pydub import AudioSegment
+from pydub.utils import make_chunks
+
+import os
+import librosa
+import soundfile
+import gradio as gr
+
+
+def vad(audio_name):
+  audio = AudioSegment.from_file(audio_name, format="wav")
+  # Set the desired sample rate (WebRTC VAD supports only 8000, 16000, 32000, or 48000 Hz)
+  audio = audio.set_frame_rate(48000)
+  # Set single channel (mono)
+  audio = audio.set_channels(1)
+
+  # Initialize VAD
+  vad = webrtcvad.Vad()
+  # Set aggressiveness mode (an integer between 0 and 3, 3 is the most aggressive)
+  vad.set_mode(3)
+
+  # Convert pydub audio to bytes
+  frame_duration = 30  # Duration of a frame in ms
+  frame_width = int(audio.frame_rate * frame_duration / 1000)  # width of a frame in samples
+  frames = make_chunks(audio, frame_duration)
+
+  # Perform voice activity detection
+  voiced_frames = []
+  for frame in frames:
+      if len(frame.raw_data) < frame_width * 2:  # Ensure frame is correct length
+          break
+      is_speech = vad.is_speech(frame.raw_data, audio.frame_rate)
+      if is_speech:
+          voiced_frames.append(frame)
+
+  # Combine voiced frames back to an audio segment
+  voiced_audio = sum(voiced_frames, AudioSegment.silent(duration=0))
+
+  voiced_audio.export("voiced_audio.wav", format="wav")
+
+
+
+
+def youtube_downloader(
+    video_identifier,
+    filename,
+    split_model,
+    start_time
+):
+    print(video_identifier)
+    video_info = get_video_info(video_identifier)[0]
+    print(video_info)
+    audio_content = get_response(video_info).content
+    with open(filename.strip() + ".wav", mode="wb") as f:
+        f.write(audio_content)
+    audio_path = filename.strip() + ".wav"
+    start_ms = start_time * 1000
+    end_ms = start_ms + 45000
+      # 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
+
+    audio_orig = AudioSegment.from_file(audio_path)
+    if len(audio_orig) > end_ms:
+
+      # Extract the segment
+      segment = audio_orig[start_ms:end_ms]
+      segment.export(filename.strip() + ".wav", format="wav")
+      pre_fun._path_audio_(filename.strip() + ".wav", f"./output/{split_model}/{filename}/", f"./output/{split_model}/{filename}/", "wav")
+      os.remove(filename.strip()+".wav")
+    else:
+      segment = audio_orig[start_ms:len(audio_orig)]
+      segment.export(filename.strip() + ".wav", format="wav")
+      pre_fun._path_audio_(filename.strip() + ".wav", 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"
+
+
+def youtube_downloader_100s(
+    video_identifier,
+    filename,
+    split_model
+):
+    print(video_identifier)
+    video_info = get_video_info(video_identifier)[0]
+    print(video_info)
+    audio_content = get_response(video_info).content
+    with open(filename.strip() + ".wav", mode="wb") as f:
+        f.write(audio_content)
+    audio_path = filename.strip() + ".wav"
+    if split_model=="UVR-HP2":
+        pre_fun = pre_fun_hp2
+    else:
+        pre_fun = pre_fun_hp5
+
+    os.makedirs("output", exist_ok=True)
+    audio_orig = AudioSegment.from_file(audio_path)
+
+    if len(audio_orig) > 120000:
+      start_ms = 10000
+      end_ms = start_ms + 110000
+
+      # Extract the segment
+
+      segment = audio_orig[start_ms:end_ms]
+
+      segment.export(filename.strip() + ".wav", format="wav")
+
+      pre_fun._path_audio_(filename.strip() + ".wav", f"./output/{split_model}/{filename}/", f"./output/{split_model}/{filename}/", "wav")
+      os.remove(filename.strip()+".wav")
+    else:
+      pre_fun._path_audio_(filename.strip() + ".wav", 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"
+
+
+def convert(start_time, song_name_src, song_name_ref, check_song, key_shift, vocal_vol, inst_vol):
+  split_model = "UVR-HP5"
+  song_name_ref = song_name_ref.strip().replace(" ", "")
+  video_identifier = search_bilibili(song_name_ref)
+  song_id = get_bilibili_video_id(video_identifier)
+
+  song_name_src = song_name_src.strip().replace(" ", "")
+  video_identifier_src = search_bilibili(song_name_src)
+  song_id_src = get_bilibili_video_id(video_identifier_src)
+
+  if os.path.isdir(f"./output/{split_model}/{song_id}")==False:
+    audio, sr = librosa.load(youtube_downloader_100s(video_identifier, song_id, split_model)[0], sr=24000, mono=True)
+    soundfile.write("audio_ref.wav", audio, sr)
+  else:
+    audio, sr = librosa.load(f"./output/{split_model}/{song_id}/vocal_{song_id}.wav_10.wav", sr=24000, mono=True)
+    soundfile.write("audio_ref.wav", audio, sr)
+
+  vad("audio_ref.wav")
+
+  #if os.path.isdir(f"./output/{split_model}/{song_id_src}")==False:
+  audio_src, sr_src = librosa.load(youtube_downloader(video_identifier_src, song_id_src, split_model, start_time)[0], sr=24000, mono=True)
+  soundfile.write("audio_src.wav", audio_src, sr_src)
+  #else:
+  #  audio_src, sr_src = librosa.load(f"./output/{split_model}/{song_id_src}/vocal_{song_id_src}.wav_10.wav", sr=24000, mono=True)
+  #  soundfile.write("audio_src.wav", audio_src, sr_src)
+  if os.path.isfile("output_svc/NeuCoSVCv2.wav"):
+    os.remove("output_svc/NeuCoSVCv2.wav")
+
+  if check_song == True:
+    os.system(f"python inference.py --src_wav_path audio_src.wav --ref_wav_path voiced_audio.wav --key_shift {key_shift}")
+  else:
+    os.system(f"python inference.py --src_wav_path audio_src.wav --ref_wav_path voiced_audio.wav --key_shift {key_shift} --speech_enroll")
+
+  audio_vocal = AudioSegment.from_file("output_svc/NeuCoSVCv2.wav", format="wav")
+
+  # Load the second audio file
+  audio_inst = AudioSegment.from_file(f"output/{split_model}/{song_id_src}/instrument_{song_id_src}.wav_10.wav", format="wav")
+
+  audio_vocal = audio_vocal + vocal_vol  # Increase volume of the first audio by 5 dB
+  audio_inst = audio_inst + inst_vol  # Decrease volume of the second audio by 5 dB
+
+  # Concatenate audio files
+  combined_audio = audio_vocal.overlay(audio_inst)
+
+  # Export the concatenated audio to a new file
+  combined_audio.export(f"{song_name_src}-AI翻唱.wav", format="wav")
+
+  return f"{song_name_src}-AI翻唱.wav"
+
+
+
+app = gr.Blocks()
+
+
+with app:
+  gr.Markdown("# <center>🥳💕🎶 NeuCoSVC v2 AI歌手全明星，无需训练、一键翻唱、重磅更新！</center>")
+  gr.Markdown("## <center>🌟 只需 1 个歌曲名，一键翻唱任意歌手的任意歌曲，支持说话语音翻唱，随时随地，听你想听！</center>")
+  gr.Markdown("### <center>🌊 NeuCoSVC v2 先享版 Powered by Tencent ARC Lab & Tsinghua University 💕</center>")
+  with gr.Row():
+    with gr.Column():
+      with gr.Row():
+        inp1 = gr.Textbox(label="请填写想要AI翻唱的歌曲或BV号", info="直接填写BV号的得到的歌曲最匹配，也可以选择填写“歌曲名+歌手名”")
+        inp2 = gr.Textbox(label="请填写含有目标音色的歌曲或BV号", info="例如您希望使用AI周杰伦的音色，就在此处填写周杰伦的任意一首歌")
+      with gr.Row():
+        inp0 = gr.Number(value=0, label="起始时间 (秒)", info="此程序将自动从起始时间开始提取45秒的翻唱歌曲")
+        inp3 = gr.Checkbox(label="参考音频是否为歌曲演唱，默认为是", info="如果参考音频为正常说话语音，请取消打勾", value=True)
+        inp4 = gr.Slider(minimum=-12, maximum=12, value=0, step=1, label="歌曲人声升降调", info="默认为0，+2为升高2个key，以此类推")
+      with gr.Row():
+        inp5 = gr.Slider(minimum=-3, maximum=3, value=0, step=1, label="调节人声音量，默认为0")
+        inp6 = gr.Slider(minimum=-3, maximum=3, value=0, step=1, label="调节伴奏音量，默认为0")
+      btn = gr.Button("一键开启AI翻唱之旅吧💕", variant="primary")
+    with gr.Column():
+      out = gr.Audio(label="AI歌手为您倾情演唱的歌曲", type="filepath", interactive=True)
+
+  btn.click(convert, [inp0, inp1, inp2, inp3, inp4, inp5, inp6], out)
+
+  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。</center>")
+  gr.HTML('''
+      <div class="footer">
+                  <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
+                  </p>
+      </div>
+  ''')
+
+app.queue().launch(share=True, show_error=True)