import subprocess, torch, os, traceback, sys, warnings, shutil
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
import threading
from time import sleep
from subprocess import Popen
import faiss
import json, datetime, requests
from gtts import gTTS
now_dir = os.getcwd()
sys.path.append(now_dir)
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
os.environ["TEMP"] = tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
from i18n import I18nAuto
import edge_tts, asyncio
from ilariatts import tts_order_voice
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
import soundfile as sf
from fairseq import checkpoint_utils
import gradio as gr
import logging
from pipeline import VC
from config import Config
from utils import *
config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("fairseq").setLevel(logging.WARNING)
logging.getLogger("PIL").setLevel(logging.WARNING)
logging.getLogger("imageio").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)
hubert_model = None
language_dict = tts_order_voice
ilariavoices = language_dict.keys()
import signal
import math
from utils import load_audio, CSVutil
global DoFormant, Quefrency, Timbre
if not os.path.isdir('csvdb/'):
os.makedirs('csvdb')
frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w')
frmnt.close()
stp.close()
try:
DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting')
DoFormant = (
lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant)
)(DoFormant)
except (ValueError, TypeError, IndexError):
DoFormant, Quefrency, Timbre = False, 1.0, 1.0
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre)
def download_models():
# Download hubert base model if not present
if not os.path.isfile('./hubert_base.pt'):
response = requests.get('https://huggingface.co/buckets/R-Kentaren/rvcmodels-bucket/resolve/main/hubert_base.pt?download=true')
if response.status_code == 200:
with open('./hubert_base.pt', 'wb') as f:
f.write(response.content)
print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.")
else:
raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".")
# Download rmvpe model if not present
if not os.path.isfile('./rmvpe.pt'):
response = requests.get('https://huggingface.co/buckets/R-Kentaren/rvcmodels-bucket/resolve/main/rmvpe.pt?download=true')
if response.status_code == 200:
with open('./rmvpe.pt', 'wb') as f:
f.write(response.content)
print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.")
else:
raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".")
download_models()
print("\n-------------------------------\nRVC v2 Easy GUI\n-------------------------------\n")
def formant_apply(qfrency, tmbre):
Quefrency = qfrency
Timbre = tmbre
DoFormant = True
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"})
def get_fshift_presets():
fshift_presets_list = []
for dirpath, _, filenames in os.walk("./formantshiftcfg/"):
for filename in filenames:
if filename.endswith(".txt"):
fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/'))
if len(fshift_presets_list) > 0:
return fshift_presets_list
else:
return ''
def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button):
if (cbox):
DoFormant = True
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
return (
{"value": True, "__type__": "update"},
{"visible": True, "__type__": "update"},
{"visible": True, "__type__": "update"},
{"visible": True, "__type__": "update"},
{"visible": True, "__type__": "update"},
{"visible": True, "__type__": "update"},
)
else:
DoFormant = False
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
return (
{"value": False, "__type__": "update"},
{"visible": False, "__type__": "update"},
{"visible": False, "__type__": "update"},
{"visible": False, "__type__": "update"},
{"visible": False, "__type__": "update"},
{"visible": False, "__type__": "update"},
{"visible": False, "__type__": "update"},
)
def preset_apply(preset, qfer, tmbr):
if str(preset) != '':
with open(str(preset), 'r') as p:
content = p.readlines()
qfer, tmbr = content[0].split('\n')[0], content[1]
formant_apply(qfer, tmbr)
else:
pass
return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"})
def update_fshift_presets(preset, qfrency, tmbre):
qfrency, tmbre = preset_apply(preset, qfrency, tmbre)
if (str(preset) != ''):
with open(str(preset), 'r') as p:
content = p.readlines()
qfrency, tmbre = content[0].split('\n')[0], content[1]
formant_apply(qfrency, tmbre)
else:
pass
return (
{"choices": get_fshift_presets(), "__type__": "update"},
{"value": qfrency, "__type__": "update"},
{"value": tmbre, "__type__": "update"},
)
i18n = I18nAuto(language="pt_BR")
ngpu = torch.cuda.device_count()
gpu_infos = []
mem = []
if (not torch.cuda.is_available()) or ngpu == 0:
if_gpu_ok = False
else:
if_gpu_ok = False
for i in range(ngpu):
gpu_name = torch.cuda.get_device_name(i)
if (
"10" in gpu_name
or "16" in gpu_name
or "20" in gpu_name
or "30" in gpu_name
or "40" in gpu_name
or "A2" in gpu_name.upper()
or "A3" in gpu_name.upper()
or "A4" in gpu_name.upper()
or "P4" in gpu_name.upper()
or "A50" in gpu_name.upper()
or "A60" in gpu_name.upper()
or "70" in gpu_name
or "80" in gpu_name
or "90" in gpu_name
or "M4" in gpu_name.upper()
or "T4" in gpu_name.upper()
or "TITAN" in gpu_name.upper()
):
if_gpu_ok = True
gpu_infos.append("%s\t%s" % (i, gpu_name))
mem.append(
int(
torch.cuda.get_device_properties(i).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
)
if if_gpu_ok == True and len(gpu_infos) > 0:
gpu_info = "\n".join(gpu_infos)
default_batch_size = min(mem) // 2
else:
gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])
def load_hubert():
global hubert_model
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()
weight_root = "weights"
index_root = "logs"
names = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_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))
@spaces.GPU
def vc_single(
sid,
input_audio_path,
f0_up_key,
f0_file,
f0_method,
file_index,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
crepe_hop_length,
):
global tgt_sr, net_g, vc, hubert_model, version
if input_audio_path is None:
return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
try:
audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre)
audio_max = abs(audio).max() / 0.95
if audio_max > 1:
audio /= audio_max
times = [0, 0, 0]
if hubert_model == None:
load_hubert()
if_f0 = cpt.get("f0", 1)
file_index = (
(
file_index.strip(" ")
.strip('"')
.strip("\n")
.strip('"')
.strip(" ")
.replace("trained", "added")
)
)
audio_opt = vc.pipeline(
hubert_model,
net_g,
sid,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
crepe_hop_length,
f0_file=f0_file,
)
if resample_sr >= 16000 and tgt_sr != resample_sr:
tgt_sr = resample_sr
index_info = (
"Using index:%s." % file_index
if os.path.exists(file_index)
else "Index not used."
)
return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
index_info,
times[0],
times[1],
times[2],
), (tgt_sr, audio_opt)
except:
info = traceback.format_exc()
print(info)
return info, (None, None)
def get_vc(sid):
global n_spk, tgt_sr, net_g, vc, cpt, version
if sid == "" or sid == []:
global hubert_model
if hubert_model != 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"}
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)
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": False, "maximum": n_spk, "__type__": "update"}
def change_choices():
names = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_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))
return {"choices": sorted(names), "__type__": "update"}, {
"choices": sorted(index_paths),
"__type__": "update",
}
def clean():
return {"value": "", "__type__": "update"}
sr_dict = {
"32k": 32000,
"40k": 40000,
"48k": 48000,
}
def whethercrepeornah(radio):
mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False
return ({"visible": mango, "__type__": "update"})
from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
def export_onnx(ModelPath, ExportedPath, MoeVS=True):
cpt = torch.load(ModelPath, map_location="cpu")
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768
test_phone = torch.rand(1, 200, hidden_channels)
test_phone_lengths = torch.tensor([200]).long()
test_pitch = torch.randint(size=(1, 200), low=5, high=255)
test_pitchf = torch.rand(1, 200)
test_ds = torch.LongTensor([0])
test_rnd = torch.rand(1, 192, 200)
device = "cpu"
net_g = SynthesizerTrnMsNSFsidM(
*cpt["config"], is_half=False,version=cpt.get("version","v1")
)
net_g.load_state_dict(cpt["weight"], strict=False)
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
output_names = [
"audio",
]
torch.onnx.export(
net_g,
(
test_phone.to(device),
test_phone_lengths.to(device),
test_pitch.to(device),
test_pitchf.to(device),
test_ds.to(device),
test_rnd.to(device),
),
ExportedPath,
dynamic_axes={
"phone": [1],
"pitch": [1],
"pitchf": [1],
"rnd": [2],
},
do_constant_folding=False,
opset_version=16,
verbose=False,
input_names=input_names,
output_names=output_names,
)
return "Finished"
def change_choices2():
audio_files=[]
for filename in os.listdir("./audios"):
if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"}
audio_files=[]
for filename in os.listdir("./audios"):
if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
def get_index():
if check_for_name() != '':
chosen_model=sorted(names)[0].split(".")[0]
logs_path="./logs/"+chosen_model
if os.path.exists(logs_path):
for file in os.listdir(logs_path):
if file.endswith(".index"):
return os.path.join(logs_path, file)
return ''
else:
return ''
def get_indexes():
indexes_list=[]
for dirpath, dirnames, filenames in os.walk("./logs/"):
for filename in filenames:
if filename.endswith(".index"):
indexes_list.append(os.path.join(dirpath,filename))
if len(indexes_list) > 0:
return indexes_list
else:
return ''
def get_name():
if len(audio_files) > 0:
return sorted(audio_files)[0]
else:
return ''
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_path
def save_to_wav2(dropbox):
file_path=dropbox.name
shutil.move(file_path,'./audios')
return os.path.join('./audios',os.path.basename(file_path))
def match_index(sid0):
folder=sid0.split(".")[0]
parent_dir="./logs/"+folder
if os.path.exists(parent_dir):
for filename in os.listdir(parent_dir):
if filename.endswith(".index"):
index_path=os.path.join(parent_dir,filename)
return index_path
else:
return ''
def check_for_name():
if len(names) > 0:
return sorted(names)[0]
else:
return ''
def ilariaTTS(text, ttsvoice):
vo=language_dict[ttsvoice]
asyncio.run(edge_tts.Communicate(text, vo).save("./temp_ilaria.mp3"))
aud_path = save_to_wav('./temp_ilaria.mp3')
return aud_path, aud_path
def download_from_url(url, model):
if not url:
return "The URL cannot be empty."
if not model:
return "You need to name your model. For example: My model"
url = url.strip()
zip_dirs = ["zips", "unzips"]
for directory in zip_dirs:
if os.path.exists(directory):
shutil.rmtree(directory)
os.makedirs("zips", exist_ok=True)
os.makedirs("unzips", exist_ok=True)
zipfile = model + '.zip'
zipfile_path = './zips/' + zipfile
try:
if "drive.google.com" in url:
subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path])
else:
subprocess.run(["wget", url, "-O", zipfile_path])
for filename in os.listdir("./zips"):
if filename.endswith(".zip"):
zipfile_path = os.path.join("./zips/", filename)
shutil.unpack_archive(zipfile_path, "./unzips", 'zip')
else:
return "No zipfile found."
for root, dirs, files in os.walk('./unzips'):
for file in files:
file_path = os.path.join(root, file)
if file.endswith(".index"):
os.mkdir(f'./logs/{model}')
shutil.copy2(file_path, f'./logs/{model}')
elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
shutil.copy(file_path, f'./weights/{model}.pth')
shutil.rmtree("zips")
shutil.rmtree("unzips")
return "Model downloaded, you can return to the inference page!"
except:
return "ERROR - download failed. Check if the link is valid."
with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="red"), title="RVC - AsukaDev", css=css) as app:
gr.Markdown(badges)
gr.Markdown(description)
gr.HTML(" Easy GUI
")
with gr.Tabs():
with gr.TabItem("Inference"):
with gr.Row():
sid0 = gr.Dropdown(label="1. Choose your model", choices=sorted(names), value=check_for_name())
refresh_button = gr.Button("To update", variant="primary")
if check_for_name() != '':
get_vc(sorted(names)[0])
vc_transform0 = gr.Number(label="Change the tone here. If the voice is of the same sex, it is not necessary to change (12 if it is Male to Female, -12 if it is the other way around.", value=0)
spk_item = gr.Slider(
minimum=0,
maximum=2333,
step=1,
label=i18n("请选择说话人id"),
value=0,
visible=False,
interactive=True,
)
sid0.change(
fn=get_vc,
inputs=[sid0],
outputs=[spk_item],
)
but0 = gr.Button("Converter", variant="primary")
with gr.Row():
with gr.Column():
with gr.Row():
dropbox = gr.File(label="Drag your audio file and click update.")
with gr.Row():
record_button=gr.Audio(sources="microphone", label="Or you can use your microphone!", type="filepath")
with gr.Row():
input_audio0 = gr.Dropdown(
label="2.Choose the audio file",
value="./audios/Poema-do-Cume-Arnold",
choices=audio_files
)
dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0])
refresh_button2 = gr.Button("To update", variant="primary", size='sm')
record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0])
with gr.Column():
vc_output2 = gr.Audio(
label="Final result! (Click on the three dots to download the audio)",
type='filepath',
interactive=False,
)
with gr.Accordion('Edge-TTS', open=True):
with gr.Column():
ilariaid=gr.Dropdown(label="Voice:", choices=ilariavoices, value="Brazilian-Antonio- (Male)")
ilariatext = gr.Textbox(label="Digite o seu Texto", interactive=True, value="Isso é um teste.")
ilariatts_button = gr.Button(value="To speak")
ilariatts_button.click(fn=ilariaTTS, inputs=[ilariatext, ilariaid], outputs=[record_button, input_audio0])
with gr.Accordion("Index Configuration", open=False):
file_index1 = gr.Dropdown(
label="3. Choose the index file (if it was not found automatically).",
choices=get_indexes(),
value=get_index(),
interactive=True,
)
sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1])
refresh_button.click(
fn=change_choices, inputs=[], outputs=[sid0, file_index1]
)
index_rate1 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("Search Feature Ratio"),
value=0.66,
interactive=True,
)
with gr.Accordion("advanced options", open=False):
f0method0 = gr.Radio(
label="Optional: Change the pitch extraction algorithm. Extraction methods are ranked from “worst quality” to “best quality”. If you don't know what you're doing, quit rmvpe.",
choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"],
value="rmvpe",
interactive=True,
)
crepe_hop_length = gr.Slider(
minimum=1,
maximum=512,
step=1,
label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.",
value=120,
interactive=True,
visible=False,
)
f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length])
filter_radius0 = gr.Slider(
minimum=0,
maximum=7,
label=i18n(">=3, use median filtering on the harvest tone recognition result, the value is the filter radius, which may weaken the muted sound."),
value=3,
step=1,
interactive=True,
)
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
value=0,
step=1,
interactive=True,
visible=False
)
rms_mix_rate0 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("The volume envelope of the input source overrides the fusing rate of the output volume envelope. The closer it is to 1, the more output envelope is used."),
value=0.21,
interactive=True,
)
protect0 = gr.Slider(
minimum=0,
maximum=0.5,
label=i18n("Protect voiceless consonants and breath sounds to avoid artifacts such as electronic sound breakdown. Do not activate when it reaches 0.5. Lower it to increase protection, but may reduce the indexing effect."),
value=0.33,
step=0.01,
interactive=True,
)
formanting = gr.Checkbox(
value=bool(DoFormant),
label="[EXPERIMENTAL] Formant shift inference audio",
info="Used for conversions from male to female and vice versa",
interactive=True,
visible=True,
)
formant_preset = gr.Dropdown(
value='',
choices=get_fshift_presets(),
label="browse presets for formanting",
visible=bool(DoFormant),
)
formant_refresh_button = gr.Button(
value='\U0001f504',
visible=bool(DoFormant),
variant='primary',
)
qfrency = gr.Slider(
value=Quefrency,
info="Default value is 1.0",
label="Quefrency for formant shifting",
minimum=0.0,
maximum=16.0,
step=0.1,
visible=bool(DoFormant),
interactive=True,
)
tmbre = gr.Slider(
value=Timbre,
info="Default value is 1.0",
label="Timbre for formant shifting",
minimum=0.0,
maximum=16.0,
step=0.1,
visible=bool(DoFormant),
interactive=True,
)
formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre])
frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant))
formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button])
frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre])
formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre])
with gr.Row():
vc_output1 = gr.Textbox("")
f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False)
but0.click(
vc_single,
[
spk_item,
input_audio0,
vc_transform0,
f0_file,
f0method0,
file_index1,
index_rate1,
filter_radius0,
resample_sr0,
rms_mix_rate0,
protect0,
crepe_hop_length
],
[vc_output1, vc_output2],
)
with gr.TabItem("Download new templates"):
with gr.Row():
url=gr.Textbox(label="Huggingface Link:")
with gr.Row():
model = gr.Textbox(label="Models Name:")
download_button=gr.Button("Dowoad")
with gr.Row():
status_bar=gr.Textbox(label="Status download")
download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar])
gr.Markdown(" Easy GUI (English)
")
gr.Markdown(
"""
original by Rejekts improved by AsukaDev
"""
)
app.launch(share=True, quiet=True, debug=False)