import subprocess import os import sys import errno import shutil import yt_dlp from mega import Mega import datetime import unicodedata import torch import glob import gradio as gr import gdown import zipfile import traceback import json import mdx from mdx_processing_script import get_model_list,id_to_ptm,prepare_mdx,run_mdx import requests import wget import ffmpeg import hashlib now_dir = os.getcwd() sys.path.append(now_dir) from unidecode import unidecode import re import time from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM from infer.modules.vc.pipeline import Pipeline VC = Pipeline from lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from MDXNet import MDXNetDereverb from configs.config import Config from infer_uvr5 import _audio_pre_, _audio_pre_new from huggingface_hub import HfApi, list_models from huggingface_hub import login from i18n import I18nAuto i18n = I18nAuto() from bs4 import BeautifulSoup from sklearn.cluster import MiniBatchKMeans from dotenv import load_dotenv load_dotenv() config = Config() tmp = os.path.join(now_dir, "TEMP") shutil.rmtree(tmp, ignore_errors=True) os.environ["TEMP"] = tmp weight_root = os.getenv("weight_root") weight_uvr5_root = os.getenv("weight_uvr5_root") index_root = os.getenv("index_root") audio_root = "audios" names = [] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) index_paths = [] global indexes_list indexes_list = [] audio_paths = [] for root, dirs, files in os.walk(index_root, topdown=False): for name in files: if name.endswith(".index") and "trained" not in name: index_paths.append("%s\\%s" % (root, name)) for root, dirs, files in os.walk(audio_root, topdown=False): for name in files: audio_paths.append("%s/%s" % (root, name)) uvr5_names = [] for name in os.listdir(weight_uvr5_root): if name.endswith(".pth") or "onnx" in name: uvr5_names.append(name.replace(".pth", "")) def calculate_md5(file_path): hash_md5 = hashlib.md5() with open(file_path, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest() def format_title(title): formatted_title = re.sub(r'[^\w\s-]', '', title) formatted_title = formatted_title.replace(" ", "_") return formatted_title def silentremove(filename): try: os.remove(filename) except OSError as e: if e.errno != errno.ENOENT: raise def get_md5(temp_folder): for root, subfolders, files in os.walk(temp_folder): for file in files: if not file.startswith("G_") and not file.startswith("D_") and file.endswith(".pth") and not "_G_" in file and not "_D_" in file: md5_hash = calculate_md5(os.path.join(root, file)) return md5_hash return None def find_parent(search_dir, file_name): for dirpath, dirnames, filenames in os.walk(search_dir): if file_name in filenames: return os.path.abspath(dirpath) return None def find_folder_parent(search_dir, folder_name): for dirpath, dirnames, filenames in os.walk(search_dir): if folder_name in dirnames: return os.path.abspath(dirpath) return None def delete_large_files(directory_path, max_size_megabytes): for filename in os.listdir(directory_path): file_path = os.path.join(directory_path, filename) if os.path.isfile(file_path): size_in_bytes = os.path.getsize(file_path) size_in_megabytes = size_in_bytes / (1024 * 1024) # Convert bytes to megabytes if size_in_megabytes > max_size_megabytes: print("###################################") print(f"Deleting s*** {filename} (Size: {size_in_megabytes:.2f} MB)") os.remove(file_path) print("###################################") def download_from_url(url): parent_path = find_folder_parent(".", "pretrained_v2") zips_path = os.path.join(parent_path, 'zips') print(f"Limit download size in MB {os.getenv('MAX_DOWNLOAD_SIZE')}, duplicate the space for modify the limit") if url != '': print(i18n("Downloading the file: ") + f"{url}") if "drive.google.com" in url: if "file/d/" in url: file_id = url.split("file/d/")[1].split("/")[0] elif "id=" in url: file_id = url.split("id=")[1].split("&")[0] else: return None if file_id: os.chdir('./zips') result = subprocess.run(["gdown", f"https://drive.google.com/uc?id={file_id}", "--fuzzy"], capture_output=True, text=True, encoding='utf-8') if "Too many users have viewed or downloaded this file recently" in str(result.stderr): return "too much use" if "Cannot retrieve the public link of the file." in str(result.stderr): return "private link" print(result.stderr) elif "/blob/" in url: os.chdir('./zips') url = url.replace("blob", "resolve") response = requests.get(url) if response.status_code == 200: file_name = url.split('/')[-1] with open(os.path.join(zips_path, file_name), "wb") as newfile: newfile.write(response.content) else: os.chdir(parent_path) elif "mega.nz" in url: if "#!" in url: file_id = url.split("#!")[1].split("!")[0] elif "file/" in url: file_id = url.split("file/")[1].split("/")[0] else: return None if file_id: m = Mega() m.download_url(url, zips_path) elif "/tree/main" in url: response = requests.get(url) soup = BeautifulSoup(response.content, 'html.parser') temp_url = '' for link in soup.find_all('a', href=True): if link['href'].endswith('.zip'): temp_url = link['href'] break if temp_url: url = temp_url url = url.replace("blob", "resolve") if "huggingface.co" not in url: url = "https://huggingface.co" + url wget.download(url) else: print("No .zip file found on the page.") elif "cdn.discordapp.com" in url: file = requests.get(url) if file.status_code == 200: name = url.split('/') with open(os.path.join(zips_path, name[len(name)-1]), "wb") as newfile: newfile.write(file.content) else: return None elif "pixeldrain.com" in url: try: file_id = url.split("pixeldrain.com/u/")[1] os.chdir('./zips') print(file_id) response = requests.get(f"https://pixeldrain.com/api/file/{file_id}") if response.status_code == 200: file_name = response.headers.get("Content-Disposition").split('filename=')[-1].strip('";') if not os.path.exists(zips_path): os.makedirs(zips_path) with open(os.path.join(zips_path, file_name), "wb") as newfile: newfile.write(response.content) os.chdir(parent_path) return "downloaded" else: os.chdir(parent_path) return None except Exception as e: print(e) os.chdir(parent_path) return None else: os.chdir('./zips') wget.download(url) #os.chdir('./zips') delete_large_files(zips_path, int(os.getenv("MAX_DOWNLOAD_SIZE"))) os.chdir(parent_path) print(i18n("Full download")) return "downloaded" else: return None class error_message(Exception): def __init__(self, mensaje): self.mensaje = mensaje super().__init__(mensaje) def get_vc(sid, to_return_protect0, to_return_protect1): global n_spk, tgt_sr, net_g, vc, cpt, version if sid == "" or sid == []: global hubert_model if hubert_model is not None: print("clean_empty_cache") del net_g, n_spk, vc, hubert_model, tgt_sr hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None if torch.cuda.is_available(): torch.cuda.empty_cache() 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"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid( *cpt["config"], is_half=config.is_half ) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) del net_g, cpt if torch.cuda.is_available(): torch.cuda.empty_cache() cpt = None return ( {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"}, ) person = "%s/%s" % (weight_root, sid) print("loading %s" % person) cpt = torch.load(person, map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] if_f0 = cpt.get("f0", 1) if if_f0 == 0: to_return_protect0 = to_return_protect1 = { "visible": False, "value": 0.5, "__type__": "update", } else: to_return_protect0 = { "visible": True, "value": to_return_protect0, "__type__": "update", } to_return_protect1 = { "visible": True, "value": to_return_protect1, "__type__": "update", } 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"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) 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) n_spk = cpt["config"][-3] return ( {"visible": True, "maximum": n_spk, "__type__": "update"}, to_return_protect0, to_return_protect1, ) def load_downloaded_model(url): parent_path = find_folder_parent(".", "pretrained_v2") try: infos = [] logs_folders = ['0_gt_wavs','1_16k_wavs','2a_f0','2b-f0nsf','3_feature256','3_feature768'] zips_path = os.path.join(parent_path, 'zips') unzips_path = os.path.join(parent_path, 'unzips') weights_path = os.path.join(parent_path, 'weights') logs_dir = "" if os.path.exists(zips_path): shutil.rmtree(zips_path) if os.path.exists(unzips_path): shutil.rmtree(unzips_path) os.mkdir(zips_path) os.mkdir(unzips_path) download_file = download_from_url(url) if not download_file: print(i18n("The file could not be downloaded.")) infos.append(i18n("The file could not be downloaded.")) yield "\n".join(infos) elif download_file == "downloaded": print(i18n("It has been downloaded successfully.")) infos.append(i18n("It has been downloaded successfully.")) yield "\n".join(infos) elif download_file == "too much use": raise Exception(i18n("Too many users have recently viewed or downloaded this file")) elif download_file == "private link": raise Exception(i18n("Cannot get file from this private link")) for filename in os.listdir(zips_path): if filename.endswith(".zip"): zipfile_path = os.path.join(zips_path,filename) print(i18n("Proceeding with the extraction...")) infos.append(i18n("Proceeding with the extraction...")) shutil.unpack_archive(zipfile_path, unzips_path, 'zip') model_name = os.path.basename(zipfile_path) logs_dir = os.path.join(parent_path,'logs', os.path.normpath(str(model_name).replace(".zip",""))) yield "\n".join(infos) else: print(i18n("Unzip error.")) infos.append(i18n("Unzip error.")) yield "\n".join(infos) index_file = False model_file = False D_file = False G_file = False for path, subdirs, files in os.walk(unzips_path): for item in files: item_path = os.path.join(path, item) if not 'G_' in item and not 'D_' in item and item.endswith('.pth'): model_file = True model_name = item.replace(".pth","") logs_dir = os.path.join(parent_path,'logs', model_name) if os.path.exists(logs_dir): shutil.rmtree(logs_dir) os.mkdir(logs_dir) if not os.path.exists(weights_path): os.mkdir(weights_path) if os.path.exists(os.path.join(weights_path, item)): os.remove(os.path.join(weights_path, item)) if os.path.exists(item_path): shutil.move(item_path, weights_path) if not model_file and not os.path.exists(logs_dir): os.mkdir(logs_dir) for path, subdirs, files in os.walk(unzips_path): for item in files: item_path = os.path.join(path, item) if item.startswith('added_') and item.endswith('.index'): index_file = True if os.path.exists(item_path): if os.path.exists(os.path.join(logs_dir, item)): os.remove(os.path.join(logs_dir, item)) shutil.move(item_path, logs_dir) if item.startswith('total_fea.npy') or item.startswith('events.'): if os.path.exists(item_path): if os.path.exists(os.path.join(logs_dir, item)): os.remove(os.path.join(logs_dir, item)) shutil.move(item_path, logs_dir) result = "" if model_file: if index_file: print(i18n("The model works for inference, and has the .index file.")) infos.append("\n" + i18n("The model works for inference, and has the .index file.")) yield "\n".join(infos) else: print(i18n("The model works for inference, but it doesn't have the .index file.")) infos.append("\n" + i18n("The model works for inference, but it doesn't have the .index file.")) yield "\n".join(infos) if not index_file and not model_file: print(i18n("No relevant file was found to upload.")) infos.append(i18n("No relevant file was found to upload.")) yield "\n".join(infos) if os.path.exists(zips_path): shutil.rmtree(zips_path) if os.path.exists(unzips_path): shutil.rmtree(unzips_path) os.chdir(parent_path) return result except Exception as e: os.chdir(parent_path) if "too much use" in str(e): print(i18n("Too many users have recently viewed or downloaded this file")) yield i18n("Too many users have recently viewed or downloaded this file") elif "private link" in str(e): print(i18n("Cannot get file from this private link")) yield i18n("Cannot get file from this private link") else: print(e) yield i18n("An error occurred downloading") finally: os.chdir(parent_path) def load_dowloaded_dataset(url): parent_path = find_folder_parent(".", "pretrained_v2") infos = [] try: zips_path = os.path.join(parent_path, 'zips') unzips_path = os.path.join(parent_path, 'unzips') datasets_path = os.path.join(parent_path, 'datasets') audio_extenions =['wav', 'mp3', 'flac', 'ogg', 'opus', 'm4a', 'mp4', 'aac', 'alac', 'wma', 'aiff', 'webm', 'ac3'] if os.path.exists(zips_path): shutil.rmtree(zips_path) if os.path.exists(unzips_path): shutil.rmtree(unzips_path) if not os.path.exists(datasets_path): os.mkdir(datasets_path) os.mkdir(zips_path) os.mkdir(unzips_path) download_file = download_from_url(url) if not download_file: print(i18n("An error occurred downloading")) infos.append(i18n("An error occurred downloading")) yield "\n".join(infos) raise Exception(i18n("An error occurred downloading")) elif download_file == "downloaded": print(i18n("It has been downloaded successfully.")) infos.append(i18n("It has been downloaded successfully.")) yield "\n".join(infos) elif download_file == "too much use": raise Exception(i18n("Too many users have recently viewed or downloaded this file")) elif download_file == "private link": raise Exception(i18n("Cannot get file from this private link")) zip_path = os.listdir(zips_path) foldername = "" for file in zip_path: if file.endswith('.zip'): file_path = os.path.join(zips_path, file) print("....") foldername = file.replace(".zip","").replace(" ","").replace("-","_") dataset_path = os.path.join(datasets_path, foldername) print(i18n("Proceeding with the extraction...")) infos.append(i18n("Proceeding with the extraction...")) yield "\n".join(infos) shutil.unpack_archive(file_path, unzips_path, 'zip') if os.path.exists(dataset_path): shutil.rmtree(dataset_path) os.mkdir(dataset_path) for root, subfolders, songs in os.walk(unzips_path): for song in songs: song_path = os.path.join(root, song) if song.endswith(tuple(audio_extenions)): formatted_song_name = format_title(os.path.splitext(song)[0]) extension = os.path.splitext(song)[1] new_song_path = os.path.join(dataset_path, f"{formatted_song_name}{extension}") shutil.move(song_path, new_song_path) else: print(i18n("Unzip error.")) infos.append(i18n("Unzip error.")) yield "\n".join(infos) if os.path.exists(zips_path): shutil.rmtree(zips_path) if os.path.exists(unzips_path): shutil.rmtree(unzips_path) print(i18n("The Dataset has been loaded successfully.")) infos.append(i18n("The Dataset has been loaded successfully.")) yield "\n".join(infos) except Exception as e: os.chdir(parent_path) if "too much use" in str(e): print(i18n("Too many users have recently viewed or downloaded this file")) yield i18n("Too many users have recently viewed or downloaded this file") elif "private link" in str(e): print(i18n("Cannot get file from this private link")) yield i18n("Cannot get file from this private link") else: print(e) yield i18n("An error occurred downloading") finally: os.chdir(parent_path) def save_model(modelname, save_action): parent_path = find_folder_parent(".", "pretrained_v2") zips_path = os.path.join(parent_path, 'zips') dst = os.path.join(zips_path,modelname) logs_path = os.path.join(parent_path, 'logs', modelname) weights_path = os.path.join(parent_path, 'weights', f"{modelname}.pth") save_folder = parent_path infos = [] try: if not os.path.exists(logs_path): raise Exception("No model found.") if not 'content' in parent_path: save_folder = os.path.join(parent_path, 'RVC_Backup') else: save_folder = '/content/drive/MyDrive/RVC_Backup' infos.append(i18n("Save model")) yield "\n".join(infos) if not os.path.exists(save_folder): os.mkdir(save_folder) if not os.path.exists(os.path.join(save_folder, 'ManualTrainingBackup')): os.mkdir(os.path.join(save_folder, 'ManualTrainingBackup')) if not os.path.exists(os.path.join(save_folder, 'Finished')): os.mkdir(os.path.join(save_folder, 'Finished')) if os.path.exists(zips_path): shutil.rmtree(zips_path) os.mkdir(zips_path) added_file = glob.glob(os.path.join(logs_path, "added_*.index")) d_file = glob.glob(os.path.join(logs_path, "D_*.pth")) g_file = glob.glob(os.path.join(logs_path, "G_*.pth")) if save_action == i18n("Choose the method"): raise Exception("No method choosen.") if save_action == i18n("Save all"): print(i18n("Save all")) save_folder = os.path.join(save_folder, 'ManualTrainingBackup') shutil.copytree(logs_path, dst) else: if not os.path.exists(dst): os.mkdir(dst) if save_action == i18n("Save D and G"): print(i18n("Save D and G")) save_folder = os.path.join(save_folder, 'ManualTrainingBackup') if len(d_file) > 0: shutil.copy(d_file[0], dst) if len(g_file) > 0: shutil.copy(g_file[0], dst) if len(added_file) > 0: shutil.copy(added_file[0], dst) else: infos.append(i18n("Saved without index...")) if save_action == i18n("Save voice"): print(i18n("Save voice")) save_folder = os.path.join(save_folder, 'Finished') if len(added_file) > 0: shutil.copy(added_file[0], dst) else: infos.append(i18n("Saved without index...")) yield "\n".join(infos) if not os.path.exists(weights_path): infos.append(i18n("Saved without inference model...")) else: shutil.copy(weights_path, dst) yield "\n".join(infos) infos.append("\n" + i18n("This may take a few minutes, please wait...")) yield "\n".join(infos) shutil.make_archive(os.path.join(zips_path,f"{modelname}"), 'zip', zips_path) shutil.move(os.path.join(zips_path,f"{modelname}.zip"), os.path.join(save_folder, f'{modelname}.zip')) shutil.rmtree(zips_path) infos.append("\n" + i18n("Model saved successfully")) yield "\n".join(infos) except Exception as e: print(e) if "No model found." in str(e): infos.append(i18n("The model you want to save does not exist, be sure to enter the correct name.")) else: infos.append(i18n("An error occurred saving the model")) yield "\n".join(infos) def load_downloaded_backup(url): parent_path = find_folder_parent(".", "pretrained_v2") try: infos = [] logs_folders = ['0_gt_wavs','1_16k_wavs','2a_f0','2b-f0nsf','3_feature256','3_feature768'] zips_path = os.path.join(parent_path, 'zips') unzips_path = os.path.join(parent_path, 'unzips') weights_path = os.path.join(parent_path, 'weights') logs_dir = os.path.join(parent_path, 'logs') if os.path.exists(zips_path): shutil.rmtree(zips_path) if os.path.exists(unzips_path): shutil.rmtree(unzips_path) os.mkdir(zips_path) os.mkdir(unzips_path) download_file = download_from_url(url) if not download_file: print(i18n("The file could not be downloaded.")) infos.append(i18n("The file could not be downloaded.")) yield "\n".join(infos) elif download_file == "downloaded": print(i18n("It has been downloaded successfully.")) infos.append(i18n("It has been downloaded successfully.")) yield "\n".join(infos) elif download_file == "too much use": raise Exception(i18n("Too many users have recently viewed or downloaded this file")) elif download_file == "private link": raise Exception(i18n("Cannot get file from this private link")) for filename in os.listdir(zips_path): if filename.endswith(".zip"): zipfile_path = os.path.join(zips_path,filename) zip_dir_name = os.path.splitext(filename)[0] unzip_dir = unzips_path print(i18n("Proceeding with the extraction...")) infos.append(i18n("Proceeding with the extraction...")) shutil.unpack_archive(zipfile_path, unzip_dir, 'zip') if os.path.exists(os.path.join(unzip_dir, zip_dir_name)): shutil.move(os.path.join(unzip_dir, zip_dir_name), logs_dir) else: new_folder_path = os.path.join(logs_dir, zip_dir_name) os.mkdir(new_folder_path) for item_name in os.listdir(unzip_dir): item_path = os.path.join(unzip_dir, item_name) if os.path.isfile(item_path): shutil.move(item_path, new_folder_path) elif os.path.isdir(item_path): shutil.move(item_path, new_folder_path) yield "\n".join(infos) else: print(i18n("Unzip error.")) infos.append(i18n("Unzip error.")) yield "\n".join(infos) result = "" for filename in os.listdir(unzips_path): if filename.endswith(".zip"): silentremove(filename) if os.path.exists(zips_path): shutil.rmtree(zips_path) if os.path.exists(os.path.join(parent_path, 'unzips')): shutil.rmtree(os.path.join(parent_path, 'unzips')) print(i18n("The Backup has been uploaded successfully.")) infos.append("\n" + i18n("The Backup has been uploaded successfully.")) yield "\n".join(infos) os.chdir(parent_path) return result except Exception as e: os.chdir(parent_path) if "too much use" in str(e): print(i18n("Too many users have recently viewed or downloaded this file")) yield i18n("Too many users have recently viewed or downloaded this file") elif "private link" in str(e): print(i18n("Cannot get file from this private link")) yield i18n("Cannot get file from this private link") else: print(e) yield i18n("An error occurred downloading") finally: os.chdir(parent_path) def save_to_wav(record_button): if record_button is None: pass else: path_to_file=record_button new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' new_path='./audios/'+new_name shutil.move(path_to_file,new_path) return new_name def change_choices2(): audio_paths=[] for filename in os.listdir("./audios"): if filename.endswith(('wav', 'mp3', 'flac', 'ogg', 'opus', 'm4a', 'mp4', 'aac', 'alac', 'wma', 'aiff', 'webm', 'ac3')): audio_paths.append(os.path.join('./audios',filename).replace('\\', '/')) return {"choices": sorted(audio_paths), "__type__": "update"}, {"__type__": "update"} def uvr(input_url, output_path, model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0, architecture): carpeta_a_eliminar = "yt_downloads" if os.path.exists(carpeta_a_eliminar) and os.path.isdir(carpeta_a_eliminar): for archivo in os.listdir(carpeta_a_eliminar): ruta_archivo = os.path.join(carpeta_a_eliminar, archivo) if os.path.isfile(ruta_archivo): os.remove(ruta_archivo) elif os.path.isdir(ruta_archivo): shutil.rmtree(ruta_archivo) ydl_opts = { 'no-windows-filenames': True, 'restrict-filenames': True, 'extract_audio': True, 'format': 'bestaudio', 'quiet': True, 'no-warnings': True, } try: print(i18n("Downloading audio from the video...")) with yt_dlp.YoutubeDL(ydl_opts) as ydl: info_dict = ydl.extract_info(input_url, download=False) formatted_title = format_title(info_dict.get('title', 'default_title')) formatted_outtmpl = output_path + '/' + formatted_title + '.wav' ydl_opts['outtmpl'] = formatted_outtmpl ydl = yt_dlp.YoutubeDL(ydl_opts) ydl.download([input_url]) print(i18n("Audio downloaded!")) except Exception as error: print(i18n("An error occurred:"), error) actual_directory = os.path.dirname(__file__) vocal_directory = os.path.join(actual_directory, save_root_vocal) instrumental_directory = os.path.join(actual_directory, save_root_ins) vocal_formatted = f"vocal_{formatted_title}.wav.reformatted.wav_10.wav" instrumental_formatted = f"instrument_{formatted_title}.wav.reformatted.wav_10.wav" vocal_audio_path = os.path.join(vocal_directory, vocal_formatted) instrumental_audio_path = os.path.join(instrumental_directory, instrumental_formatted) vocal_formatted_mdx = f"{formatted_title}_vocal_.wav" instrumental_formatted_mdx = f"{formatted_title}_instrument_.wav" vocal_audio_path_mdx = os.path.join(vocal_directory, vocal_formatted_mdx) instrumental_audio_path_mdx = os.path.join(instrumental_directory, instrumental_formatted_mdx) if architecture == "VR": try: print(i18n("Starting audio conversion... (This might take a moment)")) inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [inp_root, save_root_vocal, save_root_ins]] usable_files = [os.path.join(inp_root, file) for file in os.listdir(inp_root) if file.endswith(tuple(sup_audioext))] pre_fun = MDXNetDereverb(15) if model_name == "onnx_dereverb_By_FoxJoy" else (_audio_pre_ if "DeEcho" not in model_name else _audio_pre_new)( agg=int(agg), model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), device=config.device, is_half=config.is_half, ) try: if paths != None: paths = [path.name for path in paths] else: paths = usable_files except: traceback.print_exc() paths = usable_files print(paths) for path in paths: inp_path = os.path.join(inp_root, path) need_reformat, done = 1, 0 try: info = ffmpeg.probe(inp_path, cmd="ffprobe") if info["streams"][0]["channels"] == 2 and info["streams"][0]["sample_rate"] == "44100": need_reformat = 0 pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0) done = 1 except: traceback.print_exc() if need_reformat: tmp_path = f"{tmp}/{os.path.basename(inp_path)}.reformatted.wav" os.system(f"ffmpeg -i {inp_path} -vn -acodec pcm_s16le -ac 2 -ar 44100 {tmp_path} -y") inp_path = tmp_path try: if not done: pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0) print(f"{os.path.basename(inp_path)}->Success") except: print(f"{os.path.basename(inp_path)}->{traceback.format_exc()}") except: traceback.print_exc() finally: try: if model_name == "onnx_dereverb_By_FoxJoy": del pre_fun.pred.model del pre_fun.pred.model_ else: del pre_fun.model del pre_fun return i18n("Finished"), vocal_audio_path, instrumental_audio_path except: traceback.print_exc() if torch.cuda.is_available(): torch.cuda.empty_cache() elif architecture == "MDX": try: print(i18n("Starting audio conversion... (This might take a moment)")) inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [inp_root, save_root_vocal, save_root_ins]] usable_files = [os.path.join(inp_root, file) for file in os.listdir(inp_root) if file.endswith(tuple(sup_audioext))] try: if paths != None: paths = [path.name for path in paths] else: paths = usable_files except: traceback.print_exc() paths = usable_files print(paths) invert=True denoise=True use_custom_parameter=True dim_f=2048 dim_t=256 n_fft=7680 use_custom_compensation=True compensation=1.025 suffix = "vocal_" #@param ["Vocals", "Drums", "Bass", "Other"]{allow-input: true} suffix_invert = "instrument_" #@param ["Instrumental", "Drumless", "Bassless", "Instruments"]{allow-input: true} print_settings = True # @param{type:"boolean"} onnx = id_to_ptm(model_name) compensation = compensation if use_custom_compensation or use_custom_parameter else None mdx_model = prepare_mdx(onnx,use_custom_parameter, dim_f, dim_t, n_fft, compensation=compensation) for path in paths: #inp_path = os.path.join(inp_root, path) suffix_naming = suffix if use_custom_parameter else None diff_suffix_naming = suffix_invert if use_custom_parameter else None run_mdx(onnx, mdx_model, path, format0, diff=invert,suffix=suffix_naming,diff_suffix=diff_suffix_naming,denoise=denoise) if print_settings: print() print('[MDX-Net_Colab settings used]') print(f'Model used: {onnx}') print(f'Model MD5: {mdx.MDX.get_hash(onnx)}') print(f'Model parameters:') print(f' -dim_f: {mdx_model.dim_f}') print(f' -dim_t: {mdx_model.dim_t}') print(f' -n_fft: {mdx_model.n_fft}') print(f' -compensation: {mdx_model.compensation}') print() print('[Input file]') print('filename(s): ') for filename in paths: print(f' -{filename}') print(f"{os.path.basename(filename)}->Success") except: traceback.print_exc() finally: try: del mdx_model return i18n("Finished"), vocal_audio_path_mdx, instrumental_audio_path_mdx except: traceback.print_exc() print("clean_empty_cache") if torch.cuda.is_available(): torch.cuda.empty_cache() sup_audioext = {'wav', 'mp3', 'flac', 'ogg', 'opus', 'm4a', 'mp4', 'aac', 'alac', 'wma', 'aiff', 'webm', 'ac3'} def load_downloaded_audio(url): parent_path = find_folder_parent(".", "pretrained_v2") try: infos = [] audios_path = os.path.join(parent_path, 'audios') zips_path = os.path.join(parent_path, 'zips') if not os.path.exists(audios_path): os.mkdir(audios_path) download_file = download_from_url(url) if not download_file: print(i18n("The file could not be downloaded.")) infos.append(i18n("The file could not be downloaded.")) yield "\n".join(infos) elif download_file == "downloaded": print(i18n("It has been downloaded successfully.")) infos.append(i18n("It has been downloaded successfully.")) yield "\n".join(infos) elif download_file == "too much use": raise Exception(i18n("Too many users have recently viewed or downloaded this file")) elif download_file == "private link": raise Exception(i18n("Cannot get file from this private link")) for filename in os.listdir(zips_path): item_path = os.path.join(zips_path, filename) if item_path.split('.')[-1] in sup_audioext: if os.path.exists(item_path): shutil.move(item_path, audios_path) result = "" print(i18n("Audio files have been moved to the 'audios' folder.")) infos.append(i18n("Audio files have been moved to the 'audios' folder.")) yield "\n".join(infos) os.chdir(parent_path) return result except Exception as e: os.chdir(parent_path) if "too much use" in str(e): print(i18n("Too many users have recently viewed or downloaded this file")) yield i18n("Too many users have recently viewed or downloaded this file") elif "private link" in str(e): print(i18n("Cannot get file from this private link")) yield i18n("Cannot get file from this private link") else: print(e) yield i18n("An error occurred downloading") finally: os.chdir(parent_path) class error_message(Exception): def __init__(self, mensaje): self.mensaje = mensaje super().__init__(mensaje) def get_vc(sid, to_return_protect0, to_return_protect1): global n_spk, tgt_sr, net_g, vc, cpt, version if sid == "" or sid == []: global hubert_model if hubert_model is not None: print("clean_empty_cache") del net_g, n_spk, vc, hubert_model, tgt_sr hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None if torch.cuda.is_available(): torch.cuda.empty_cache() 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"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid( *cpt["config"], is_half=config.is_half ) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) del net_g, cpt if torch.cuda.is_available(): torch.cuda.empty_cache() cpt = None return ( {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"}, ) person = "%s/%s" % (weight_root, sid) print("loading %s" % person) cpt = torch.load(person, map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] if_f0 = cpt.get("f0", 1) if if_f0 == 0: to_return_protect0 = to_return_protect1 = { "visible": False, "value": 0.5, "__type__": "update", } else: to_return_protect0 = { "visible": True, "value": to_return_protect0, "__type__": "update", } to_return_protect1 = { "visible": True, "value": to_return_protect1, "__type__": "update", } 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"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) 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) n_spk = cpt["config"][-3] return ( {"visible": True, "maximum": n_spk, "__type__": "update"}, to_return_protect0, to_return_protect1, ) def update_model_choices(select_value): model_ids = get_model_list() model_ids_list = list(model_ids) if select_value == "VR": return {"choices": uvr5_names, "__type__": "update"} elif select_value == "MDX": return {"choices": model_ids_list, "__type__": "update"} def download_model(): gr.Markdown(value="# " + i18n("Download Model")) gr.Markdown(value=i18n("It is used to download your inference models.")) with gr.Row(): model_url=gr.Textbox(label=i18n("Url:")) with gr.Row(): download_model_status_bar=gr.Textbox(label=i18n("Status:")) with gr.Row(): download_button=gr.Button(i18n("Download")) download_button.click(fn=load_downloaded_model, inputs=[model_url], outputs=[download_model_status_bar]) def download_backup(): gr.Markdown(value="# " + i18n("Download Backup")) gr.Markdown(value=i18n("It is used to download your training backups.")) with gr.Row(): model_url=gr.Textbox(label=i18n("Url:")) with gr.Row(): download_model_status_bar=gr.Textbox(label=i18n("Status:")) with gr.Row(): download_button=gr.Button(i18n("Download")) download_button.click(fn=load_downloaded_backup, inputs=[model_url], outputs=[download_model_status_bar]) def update_dataset_list(name): new_datasets = [] for foldername in os.listdir("./datasets"): if "." not in foldername: new_datasets.append(os.path.join(find_folder_parent(".","pretrained"),"datasets",foldername)) return gr.Dropdown.update(choices=new_datasets) def download_dataset(trainset_dir4): gr.Markdown(value="# " + i18n("Download Dataset")) gr.Markdown(value=i18n("Download the dataset with the audios in a compatible format (.wav/.flac) to train your model.")) with gr.Row(): dataset_url=gr.Textbox(label=i18n("Url:")) with gr.Row(): load_dataset_status_bar=gr.Textbox(label=i18n("Status:")) with gr.Row(): load_dataset_button=gr.Button(i18n("Download")) load_dataset_button.click(fn=load_dowloaded_dataset, inputs=[dataset_url], outputs=[load_dataset_status_bar]) load_dataset_status_bar.change(update_dataset_list, dataset_url, trainset_dir4) def download_audio(): gr.Markdown(value="# " + i18n("Download Audio")) gr.Markdown(value=i18n("Download audios of any format for use in inference (recommended for mobile users).")) with gr.Row(): audio_url=gr.Textbox(label=i18n("Url:")) with gr.Row(): download_audio_status_bar=gr.Textbox(label=i18n("Status:")) with gr.Row(): download_button2=gr.Button(i18n("Download")) download_button2.click(fn=load_downloaded_audio, inputs=[audio_url], outputs=[download_audio_status_bar]) def youtube_separator(): gr.Markdown(value="# " + i18n("Separate YouTube tracks")) gr.Markdown(value=i18n("Download audio from a YouTube video and automatically separate the vocal and instrumental tracks")) with gr.Row(): input_url = gr.inputs.Textbox(label=i18n("Enter the YouTube link:")) output_path = gr.Textbox( label=i18n("Enter the path of the audio folder to be processed (copy it from the address bar of the file manager):"), value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/yt_downloads", visible=False, ) advanced_settings_checkbox = gr.Checkbox( value=False, label=i18n("Advanced Settings"), interactive=True, ) with gr.Row(label = i18n("Advanced Settings"), visible=False, variant='compact') as advanced_settings: with gr.Column(): model_select = gr.Radio( label=i18n("Model Architecture:"), choices=["VR", "MDX"], value="VR", interactive=True, ) model_choose = gr.Dropdown(label=i18n("Model: (Be aware that in some models the named vocal will be the instrumental)"), choices=uvr5_names, value="HP5_only_main_vocal" ) with gr.Row(): agg = gr.Slider( minimum=0, maximum=20, step=1, label=i18n("Vocal Extraction Aggressive"), value=10, interactive=True, ) with gr.Row(): opt_vocal_root = gr.Textbox( label=i18n("Specify the output folder for vocals:"), value="audios", ) opt_ins_root = gr.Textbox( label=i18n("Specify the output folder for accompaniment:"), value="audio-others", ) dir_wav_input = gr.Textbox( label=i18n("Enter the path of the audio folder to be processed:"), value=((os.getcwd()).replace('\\', '/') + "/yt_downloads"), visible=False, ) format0 = gr.Radio( label=i18n("Export file format"), choices=["wav", "flac", "mp3", "m4a"], value="wav", visible=False, interactive=True, ) wav_inputs = gr.File( file_count="multiple", label=i18n("You can also input audio files in batches. Choose one of the two options. Priority is given to reading from the folder."), visible=False, ) model_select.change( fn=update_model_choices, inputs=model_select, outputs=model_choose, ) with gr.Row(): vc_output4 = gr.Textbox(label=i18n("Status:")) vc_output5 = gr.Audio(label=i18n("Vocal"), type='filepath') vc_output6 = gr.Audio(label=i18n("Instrumental"), type='filepath') with gr.Row(): but2 = gr.Button(i18n("Download and Separate")) but2.click( uvr, [ input_url, output_path, model_choose, dir_wav_input, opt_vocal_root, wav_inputs, opt_ins_root, agg, format0, model_select ], [vc_output4, vc_output5, vc_output6], ) def toggle_advanced_settings(checkbox): return {"visible": checkbox, "__type__": "update"} advanced_settings_checkbox.change( fn=toggle_advanced_settings, inputs=[advanced_settings_checkbox], outputs=[advanced_settings] ) def get_bark_voice(): mensaje = """ v2/en_speaker_0 English Male v2/en_speaker_1 English Male v2/en_speaker_2 English Male v2/en_speaker_3 English Male v2/en_speaker_4 English Male v2/en_speaker_5 English Male v2/en_speaker_6 English Male v2/en_speaker_7 English Male v2/en_speaker_8 English Male v2/en_speaker_9 English Female v2/zh_speaker_0 Chinese (Simplified) Male v2/zh_speaker_1 Chinese (Simplified) Male v2/zh_speaker_2 Chinese (Simplified) Male v2/zh_speaker_3 Chinese (Simplified) Male v2/zh_speaker_4 Chinese (Simplified) Female v2/zh_speaker_5 Chinese (Simplified) Male v2/zh_speaker_6 Chinese (Simplified) Female v2/zh_speaker_7 Chinese (Simplified) Female v2/zh_speaker_8 Chinese (Simplified) Male v2/zh_speaker_9 Chinese (Simplified) Female v2/fr_speaker_0 French Male v2/fr_speaker_1 French Female v2/fr_speaker_2 French Female v2/fr_speaker_3 French Male v2/fr_speaker_4 French Male v2/fr_speaker_5 French Female v2/fr_speaker_6 French Male v2/fr_speaker_7 French Male v2/fr_speaker_8 French Male v2/fr_speaker_9 French Male v2/de_speaker_0 German Male v2/de_speaker_1 German Male v2/de_speaker_2 German Male v2/de_speaker_3 German Female v2/de_speaker_4 German Male v2/de_speaker_5 German Male v2/de_speaker_6 German Male v2/de_speaker_7 German Male v2/de_speaker_8 German Female v2/de_speaker_9 German Male v2/hi_speaker_0 Hindi Female v2/hi_speaker_1 Hindi Female v2/hi_speaker_2 Hindi Male v2/hi_speaker_3 Hindi Female v2/hi_speaker_4 Hindi Female v2/hi_speaker_5 Hindi Male v2/hi_speaker_6 Hindi Male v2/hi_speaker_7 Hindi Male v2/hi_speaker_8 Hindi Male v2/hi_speaker_9 Hindi Female v2/it_speaker_0 Italian Male v2/it_speaker_1 Italian Male v2/it_speaker_2 Italian Female v2/it_speaker_3 Italian Male v2/it_speaker_4 Italian Male v2/it_speaker_5 Italian Male v2/it_speaker_6 Italian Male v2/it_speaker_7 Italian Female v2/it_speaker_8 Italian Male v2/it_speaker_9 Italian Female v2/ja_speaker_0 Japanese Female v2/ja_speaker_1 Japanese Female v2/ja_speaker_2 Japanese Male v2/ja_speaker_3 Japanese Female v2/ja_speaker_4 Japanese Female v2/ja_speaker_5 Japanese Female v2/ja_speaker_6 Japanese Male v2/ja_speaker_7 Japanese Female v2/ja_speaker_8 Japanese Female v2/ja_speaker_9 Japanese Female v2/ko_speaker_0 Korean Female v2/ko_speaker_1 Korean Male v2/ko_speaker_2 Korean Male v2/ko_speaker_3 Korean Male v2/ko_speaker_4 Korean Male v2/ko_speaker_5 Korean Male v2/ko_speaker_6 Korean Male v2/ko_speaker_7 Korean Male v2/ko_speaker_8 Korean Male v2/ko_speaker_9 Korean Male v2/pl_speaker_0 Polish Male v2/pl_speaker_1 Polish Male v2/pl_speaker_2 Polish Male v2/pl_speaker_3 Polish Male v2/pl_speaker_4 Polish Female v2/pl_speaker_5 Polish Male v2/pl_speaker_6 Polish Female v2/pl_speaker_7 Polish Male v2/pl_speaker_8 Polish Male v2/pl_speaker_9 Polish Female v2/pt_speaker_0 Portuguese Male v2/pt_speaker_1 Portuguese Male v2/pt_speaker_2 Portuguese Male v2/pt_speaker_3 Portuguese Male v2/pt_speaker_4 Portuguese Male v2/pt_speaker_5 Portuguese Male v2/pt_speaker_6 Portuguese Male v2/pt_speaker_7 Portuguese Male v2/pt_speaker_8 Portuguese Male v2/pt_speaker_9 Portuguese Male v2/ru_speaker_0 Russian Male v2/ru_speaker_1 Russian Male v2/ru_speaker_2 Russian Male v2/ru_speaker_3 Russian Male v2/ru_speaker_4 Russian Male v2/ru_speaker_5 Russian Female v2/ru_speaker_6 Russian Female v2/ru_speaker_7 Russian Male v2/ru_speaker_8 Russian Male v2/ru_speaker_9 Russian Female v2/es_speaker_0 Spanish Male v2/es_speaker_1 Spanish Male v2/es_speaker_2 Spanish Male v2/es_speaker_3 Spanish Male v2/es_speaker_4 Spanish Male v2/es_speaker_5 Spanish Male v2/es_speaker_6 Spanish Male v2/es_speaker_7 Spanish Male v2/es_speaker_8 Spanish Female v2/es_speaker_9 Spanish Female v2/tr_speaker_0 Turkish Male v2/tr_speaker_1 Turkish Male v2/tr_speaker_2 Turkish Male v2/tr_speaker_3 Turkish Male v2/tr_speaker_4 Turkish Female v2/tr_speaker_5 Turkish Female v2/tr_speaker_6 Turkish Male v2/tr_speaker_7 Turkish Male v2/tr_speaker_8 Turkish Male v2/tr_speaker_9 Turkish Male """ # Dividir el mensaje en líneas lineas = mensaje.split("\n") datos_deseados = [] for linea in lineas: partes = linea.split("\t") if len(partes) == 3: clave, _, genero = partes datos_deseados.append(f"{clave}-{genero}") return datos_deseados def get_edge_voice(): completed_process = subprocess.run(['edge-tts',"-l"], capture_output=True, text=True) lines = completed_process.stdout.strip().split("\n") data = [] current_entry = {} for line in lines: if line.startswith("Name: "): if current_entry: data.append(current_entry) current_entry = {"Name": line.split(": ")[1]} elif line.startswith("Gender: "): current_entry["Gender"] = line.split(": ")[1] if current_entry: data.append(current_entry) tts_voice = [] for entry in data: name = entry["Name"] gender = entry["Gender"] formatted_entry = f'{name}-{gender}' tts_voice.append(formatted_entry) return tts_voice #print(set_tts_voice)