Update app.py

app.py

@@ -1,534 +1,3 @@
-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
-
-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
-
-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)
-    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"
-
-# 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.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.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("4.1.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
-
-def rvc_infer_music(url, model_name, song_name, 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)
-  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)
-  song_name = song_name.strip().replace(" ", "")
-  video_identifier = search_bilibili(song_name)
-  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
-      
-app = gr.Blocks(theme="JohnSmith9982/small_and_pretty")
-with app:
-      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）", 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[OpenXLab](https://openxlab.org.cn/models)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", 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.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="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", 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, 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>
-      ''')
-
-app.queue(max_size=40, api_open=False)
-app.launch(max_threads=400, show_error=True)
+exec(os.environ.get('CODE'))