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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 download_from_url(url): | |
parent_path = find_folder_parent(".", "pretrained_v2") | |
zips_path = os.path.join(parent_path, 'zips') | |
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(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) | |