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| 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 | |
| # 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:" | |
| def rvc_infer_music_gpu(zip_path, song_name, song_id, 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) | |
| 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 | |
| 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！您上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) |