import os, sys
os.system("pip install pyworld") # ==0.3.3
os.system("python -m pip install --upgrade --no-deps --force-reinstall torchcrepe")
now_dir = os.getcwd()
sys.path.append(now_dir)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
# Download models
shell_script = './tools/dlmodels.sh'
os.system(f'chmod +x {shell_script}')
os.system('apt install git-lfs')
os.system('git lfs install')
os.system('apt-get -y install aria2')
os.system('aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d . -o hubert_base.pt')
try:
return_code = os.system(shell_script)
if return_code == 0:
print("Shell script executed successfully.")
else:
print(f"Shell script failed with return code {return_code}")
except Exception as e:
print(f"An error occurred: {e}")
import logging
import shutil
import threading
import lib.globals.globals as rvc_globals
from LazyImport import lazyload
import mdx
from mdx_processing_script import get_model_list,id_to_ptm,prepare_mdx,run_mdx
math = lazyload('math')
import traceback
import warnings
tensorlowest = lazyload('tensorlowest')
from random import shuffle
from subprocess import Popen
from time import sleep
import json
import pathlib
import fairseq
logging.getLogger("faiss").setLevel(logging.WARNING)
import faiss
gr = lazyload("gradio")
np = lazyload("numpy")
torch = lazyload('torch')
re = lazyload('regex')
SF = lazyload("soundfile")
SFWrite = SF.write
from dotenv import load_dotenv
from sklearn.cluster import MiniBatchKMeans
import datetime
from glob import glob1
import signal
from signal import SIGTERM
import librosa
from configs.config import Config
from i18n import I18nAuto
from infer.lib.train.process_ckpt import (
change_info,
extract_small_model,
merge,
show_info,
)
#from infer.modules.uvr5.modules import uvr
from infer.modules.vc.modules import VC
from infer.modules.vc.utils import *
from infer.modules.vc.pipeline import Pipeline
import lib.globals.globals as rvc_globals
math = lazyload('math')
ffmpeg = lazyload('ffmpeg')
import nltk
nltk.download('punkt', quiet=True)
from nltk.tokenize import sent_tokenize
from bark import SAMPLE_RATE
import easy_infer
import audioEffects
from infer.lib.csvutil import CSVutil
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
from infer_uvr5 import _audio_pre_, _audio_pre_new
from MDXNet import MDXNetDereverb
from infer.lib.audio import load_audio
from sklearn.cluster import MiniBatchKMeans
import time
import csv
from shlex import quote as SQuote
RQuote = lambda val: SQuote(str(val))
tmp = os.path.join(now_dir, "TEMP")
runtime_dir = os.path.join(now_dir, "runtime/Lib/site-packages")
directories = ['logs', 'audios', 'datasets', 'weights', 'audio-others' , 'audio-outputs']
shutil.rmtree(tmp, ignore_errors=True)
shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
for folder in directories:
os.makedirs(os.path.join(now_dir, folder), exist_ok=True)
os.makedirs(tmp, exist_ok=True)
os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
os.makedirs(os.path.join(now_dir, "assets/weights"), exist_ok=True)
os.environ["TEMP"] = tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
logging.getLogger("numba").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
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()
global DoFormant, Quefrency, Timbre
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)
load_dotenv()
config = Config()
vc = VC(config)
if config.dml == True:
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
return res
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
i18n = I18nAuto()
i18n.print()
# 判断是否有能用来训练和加速推理的N卡
ngpu = torch.cuda.device_count()
gpu_infos = []
mem = []
if_gpu_ok = False
isinterrupted = 0
if torch.cuda.is_available() or ngpu != 0:
for i in range(ngpu):
gpu_name = torch.cuda.get_device_name(i)
if any(
value in gpu_name.upper()
for value in [
"10",
"16",
"20",
"30",
"40",
"A2",
"A3",
"A4",
"P4",
"A50",
"500",
"A60",
"70",
"80",
"90",
"M4",
"T4",
"TITAN",
]
):
# A10#A100#V100#A40#P40#M40#K80#A4500
if_gpu_ok = True # 至少有一张能用的N卡
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 and len(gpu_infos) > 0:
gpu_info = "\n".join(gpu_infos)
default_batch_size = min(mem) // 2
else:
gpu_info = "Unfortunately, there is no compatible GPU available to support your training."
default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])
class ToolButton(gr.Button, gr.components.FormComponent):
"""Small button with single emoji as text, fits inside gradio forms"""
def __init__(self, **kwargs):
super().__init__(variant="tool", **kwargs)
def get_block_name(self):
return "button"
hubert_model = None
weight_root = os.getenv("weight_root")
weight_uvr5_root = os.getenv("weight_uvr5_root")
index_root = os.getenv("index_root")
datasets_root = "datasets"
fshift_root = "formantshiftcfg"
audio_root = "audios"
audio_others_root = "audio-others"
sup_audioext = {'wav', 'mp3', 'flac', 'ogg', 'opus',
'm4a', 'mp4', 'aac', 'alac', 'wma',
'aiff', 'webm', 'ac3'}
names = [os.path.join(root, file)
for root, _, files in os.walk(weight_root)
for file in files
if file.endswith((".pth", ".onnx"))]
indexes_list = [os.path.join(root, name)
for root, _, files in os.walk(index_root, topdown=False)
for name in files
if name.endswith(".index") and "trained" not in name]
audio_paths = [os.path.join(root, name)
for root, _, files in os.walk(audio_root, topdown=False)
for name in files
if name.endswith(tuple(sup_audioext))]
audio_others_paths = [os.path.join(root, name)
for root, _, files in os.walk(audio_others_root, topdown=False)
for name in files
if name.endswith(tuple(sup_audioext))]
uvr5_names = [name.replace(".pth", "")
for name in os.listdir(weight_uvr5_root)
if name.endswith(".pth") or "onnx" in name]
check_for_name = lambda: sorted(names)[0] if names else ''
datasets=[]
for foldername in os.listdir(os.path.join(now_dir, datasets_root)):
if "." not in foldername:
datasets.append(os.path.join(easy_infer.find_folder_parent(".","pretrained"),"datasets",foldername))
def get_dataset():
if len(datasets) > 0:
return sorted(datasets)[0]
else:
return ''
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"}
set_bark_voice = easy_infer.get_bark_voice()
set_edge_voice = easy_infer.get_edge_voice()
def update_tts_methods_voice(select_value):
#["Edge-tts", "RVG-tts", "Bark-tts"]
if select_value == "Edge-tts":
return {"choices": set_edge_voice, "value": "", "__type__": "update"}
elif select_value == "Bark-tts":
return {"choices": set_bark_voice, "value": "", "__type__": "update"}
def update_dataset_list(name):
new_datasets = []
for foldername in os.listdir(os.path.join(now_dir, datasets_root)):
if "." not in foldername:
new_datasets.append(os.path.join(easy_infer.find_folder_parent(".","pretrained"),"datasets",foldername))
return gr.Dropdown.update(choices=new_datasets)
def get_indexes():
indexes_list = [
os.path.join(dirpath, filename)
for dirpath, _, filenames in os.walk(index_root)
for filename in filenames
if filename.endswith(".index") and "trained" not in filename
]
return indexes_list if indexes_list else ''
def get_fshift_presets():
fshift_presets_list = [
os.path.join(dirpath, filename)
for dirpath, _, filenames in os.walk(fshift_root)
for filename in filenames
if filename.endswith(".txt")
]
return fshift_presets_list if fshift_presets_list else ''
import soundfile as sf
def generate_output_path(output_folder, base_name, extension):
# Generar un nombre único para el archivo de salida
index = 1
while True:
output_path = os.path.join(output_folder, f"{base_name}_{index}.{extension}")
if not os.path.exists(output_path):
return output_path
index += 1
def combine_and_save_audios(audio1_path, audio2_path, output_path, volume_factor_audio1, volume_factor_audio2):
audio1, sr1 = librosa.load(audio1_path, sr=None)
audio2, sr2 = librosa.load(audio2_path, sr=None)
# Alinear las tasas de muestreo
if sr1 != sr2:
if sr1 > sr2:
audio2 = librosa.resample(audio2, orig_sr=sr2, target_sr=sr1)
else:
audio1 = librosa.resample(audio1, orig_sr=sr1, target_sr=sr2)
# Ajustar los audios para que tengan la misma longitud
target_length = min(len(audio1), len(audio2))
audio1 = librosa.util.fix_length(audio1, target_length)
audio2 = librosa.util.fix_length(audio2, target_length)
# Ajustar el volumen de los audios multiplicando por el factor de ganancia
if volume_factor_audio1 != 1.0:
audio1 *= volume_factor_audio1
if volume_factor_audio2 != 1.0:
audio2 *= volume_factor_audio2
# Combinar los audios
combined_audio = audio1 + audio2
sf.write(output_path, combined_audio, sr1)
# Resto de tu código...
# Define función de conversión llamada por el botón
def audio_combined(audio1_path, audio2_path, volume_factor_audio1=1.0, volume_factor_audio2=1.0, reverb_enabled=False, compressor_enabled=False, noise_gate_enabled=False):
output_folder = os.path.join(now_dir, "audio-outputs")
os.makedirs(output_folder, exist_ok=True)
# Generar nombres únicos para los archivos de salida
base_name = "combined_audio"
extension = "wav"
output_path = generate_output_path(output_folder, base_name, extension)
print(reverb_enabled)
print(compressor_enabled)
print(noise_gate_enabled)
if reverb_enabled or compressor_enabled or noise_gate_enabled:
# Procesa el primer audio con los efectos habilitados
base_name = "effect_audio"
output_path = generate_output_path(output_folder, base_name, extension)
processed_audio_path = audioEffects.process_audio(audio2_path, output_path, reverb_enabled, compressor_enabled, noise_gate_enabled)
base_name = "combined_audio"
output_path = generate_output_path(output_folder, base_name, extension)
# Combina el audio procesado con el segundo audio usando audio_combined
combine_and_save_audios(audio1_path, processed_audio_path, output_path, volume_factor_audio1, volume_factor_audio2)
return i18n("Conversion complete!"), output_path
else:
base_name = "combined_audio"
output_path = generate_output_path(output_folder, base_name, extension)
# No hay efectos habilitados, combina directamente los audios sin procesar
combine_and_save_audios(audio1_path, audio2_path, output_path, volume_factor_audio1, volume_factor_audio2)
return i18n("Conversion complete!"), output_path
def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0,architecture):
infos = []
if architecture == "VR":
try:
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(RQuote(inp_path))}.reformatted.wav"
os.system(f"ffmpeg -i {RQuote(inp_path)} -vn -acodec pcm_s16le -ac 2 -ar 44100 {RQuote(tmp_path)} -y")
inp_path = tmp_path
try:
if not done:
pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0)
infos.append(f"{os.path.basename(inp_path)}->Success")
yield "\n".join(infos)
except:
infos.append(f"{os.path.basename(inp_path)}->{traceback.format_exc()}")
yield "\n".join(infos)
except:
infos.append(traceback.format_exc())
yield "\n".join(infos)
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
except: traceback.print_exc()
print("clean_empty_cache")
if torch.cuda.is_available(): torch.cuda.empty_cache()
yield "\n".join(infos)
elif architecture == "MDX":
try:
infos.append(i18n("Starting audio conversion... (This might take a moment)"))
yield "\n".join(infos)
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=3072
dim_t=256
n_fft=7680
use_custom_compensation=True
compensation=1.025
suffix = "Vocals_custom" #@param ["Vocals", "Drums", "Bass", "Other"]{allow-input: true}
suffix_invert = "Instrumental_custom" #@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}')
infos.append(f"{os.path.basename(filename)}->Success")
yield "\n".join(infos)
except:
infos.append(traceback.format_exc())
yield "\n".join(infos)
finally:
try:
del mdx_model
except: traceback.print_exc()
print("clean_empty_cache")
if torch.cuda.is_available(): torch.cuda.empty_cache()
def change_choices():
names = [os.path.join(root, file)
for root, _, files in os.walk(weight_root)
for file in files
if file.endswith((".pth", ".onnx"))]
indexes_list = [os.path.join(root, name) for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name]
audio_paths = [os.path.join(audio_root, file) for file in os.listdir(os.path.join(now_dir, "audios")) + os.path.join(audio_root, file) for file in os.listdir(os.path.join(now_dir, "audio-outputs"))]
return (
{"choices": sorted(names), "__type__": "update"},
{"choices": sorted(indexes_list), "__type__": "update"},
{"choices": sorted(audio_paths), "__type__": "update"}
)
def change_choices2():
names = [os.path.join(root, file)
for root, _, files in os.walk(weight_root)
for file in files
if file.endswith((".pth", ".onnx"))]
indexes_list = [os.path.join(root, name) for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name]
return (
{"choices": sorted(names), "__type__": "update"},
{"choices": sorted(indexes_list), "__type__": "update"},
)
def change_choices3():
audio_paths = [os.path.join(audio_root, file) for file in os.listdir(os.path.join(now_dir, "audios"))]
audio_others_paths = [os.path.join(audio_others_root, file) for file in os.listdir(os.path.join(now_dir, "audio-others"))]
return (
{"choices": sorted(audio_others_paths), "__type__": "update"},
{"choices": sorted(audio_paths), "__type__": "update"}
)
def clean():
return {"value": "", "__type__": "update"}
def export_onnx():
from infer.modules.onnx.export import export_onnx as eo
eo()
sr_dict = {
"32k": 32000,
"40k": 40000,
"48k": 48000,
}
def if_done(done, p):
while 1:
if p.poll() is None:
sleep(0.5)
else:
break
done[0] = True
def if_done_multi(done, ps):
while 1:
# poll==None代表进程未结束
# 只要有一个进程未结束都不停
flag = 1
for p in ps:
if p.poll() is None:
flag = 0
sleep(0.5)
break
if flag == 1:
break
done[0] = True
def formant_enabled(
cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button
):
if cbox:
DoFormant = True
CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, qfrency, tmbre)
# print(f"is checked? - {cbox}\ngot {DoFormant}")
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)
# print(f"is checked? - {cbox}\ngot {DoFormant}")
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 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 update_fshift_presets(preset, qfrency, tmbre):
if preset:
with open(preset, 'r') as p:
content = p.readlines()
qfrency, tmbre = content[0].strip(), content[1]
formant_apply(qfrency, tmbre)
else:
qfrency, tmbre = preset_apply(preset, qfrency, tmbre)
return (
{"choices": get_fshift_presets(), "__type__": "update"},
{"value": qfrency, "__type__": "update"},
{"value": tmbre, "__type__": "update"},
)
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
sr = sr_dict[sr]
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
f.close()
per = 3.0 if config.is_half else 3.7
cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
config.python_cmd,
trainset_dir,
sr,
n_p,
now_dir,
exp_dir,
config.noparallel,
per,
)
logger.info(cmd)
p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
done = [False]
threading.Thread(
target=if_done,
args=(
done,
p,
),
).start()
while 1:
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
logger.info(log)
yield log
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl, gpus_rmvpe):
gpus = gpus.split("-")
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
f.close()
if if_f0:
if f0method != "rmvpe_gpu":
cmd = (
'"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
% (
config.python_cmd,
now_dir,
exp_dir,
n_p,
f0method,
echl,
)
)
logger.info(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
done = [False]
threading.Thread(
target=if_done,
args=(
done,
p,
),
).start()
else:
if gpus_rmvpe != "-":
gpus_rmvpe = gpus_rmvpe.split("-")
leng = len(gpus_rmvpe)
ps = []
for idx, n_g in enumerate(gpus_rmvpe):
cmd = (
'"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
% (
config.python_cmd,
leng,
idx,
n_g,
now_dir,
exp_dir,
config.is_half,
)
)
logger.info(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
ps.append(p)
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
done = [False]
threading.Thread(
target=if_done_multi, #
args=(
done,
ps,
),
).start()
else:
cmd = (
config.python_cmd
+ ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
% (
now_dir,
exp_dir,
)
)
logger.info(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
p.wait()
done = [True]
while 1:
with open(
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
) as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
logger.info(log)
yield log
####对不同part分别开多进程
"""
n_part=int(sys.argv[1])
i_part=int(sys.argv[2])
i_gpu=sys.argv[3]
exp_dir=sys.argv[4]
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
"""
leng = len(gpus)
ps = []
for idx, n_g in enumerate(gpus):
cmd = (
'"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s'
% (
config.python_cmd,
config.device,
leng,
idx,
n_g,
now_dir,
exp_dir,
version19,
)
)
logger.info(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
ps.append(p)
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
done = [False]
threading.Thread(
target=if_done_multi,
args=(
done,
ps,
),
).start()
while 1:
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
logger.info(log)
yield log
def get_pretrained_models(path_str, f0_str, sr2):
if_pretrained_generator_exist = os.access(
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if_pretrained_discriminator_exist = os.access(
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if not if_pretrained_generator_exist:
logger.warn(
"assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
path_str,
f0_str,
sr2,
)
if not if_pretrained_discriminator_exist:
logger.warn(
"assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
path_str,
f0_str,
sr2,
)
return (
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_generator_exist
else "",
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_discriminator_exist
else "",
)
def change_sr2(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
f0_str = "f0" if if_f0_3 else ""
return get_pretrained_models(path_str, f0_str, sr2)
def change_version19(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
if sr2 == "32k" and version19 == "v1":
sr2 = "40k"
to_return_sr2 = (
{"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
if version19 == "v1"
else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
)
f0_str = "f0" if if_f0_3 else ""
return (
*get_pretrained_models(path_str, f0_str, sr2),
to_return_sr2,
)
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
path_str = "" if version19 == "v1" else "_v2"
return (
{"visible": if_f0_3, "__type__": "update"},
*get_pretrained_models(path_str, "f0", sr2),
)
global log_interval
def set_log_interval(exp_dir, batch_size12):
log_interval = 1
folder_path = os.path.join(exp_dir, "1_16k_wavs")
if os.path.isdir(folder_path):
wav_files_num = len(glob1(folder_path,"*.wav"))
if wav_files_num > 0:
log_interval = math.ceil(wav_files_num / batch_size12)
if log_interval > 1:
log_interval += 1
return log_interval
global PID, PROCESS
def click_train(
exp_dir1,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
):
CSVutil("csvdb/stop.csv", "w+", "formanting", False)
# 生成filelist
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
os.makedirs(exp_dir, exist_ok=True)
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
feature_dir = (
"%s/3_feature256" % (exp_dir)
if version19 == "v1"
else "%s/3_feature768" % (exp_dir)
)
if if_f0_3:
f0_dir = "%s/2a_f0" % (exp_dir)
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
names = (
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
)
else:
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
[name.split(".")[0] for name in os.listdir(feature_dir)]
)
opt = []
for name in names:
if if_f0_3:
opt.append(
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
f0_dir.replace("\\", "\\\\"),
name,
f0nsf_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
else:
opt.append(
"%s/%s.wav|%s/%s.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
fea_dim = 256 if version19 == "v1" else 768
if if_f0_3:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
)
else:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
f.write("\n".join(opt))
logger.debug("Write filelist done")
# 生成config#无需生成config
# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
logger.info("Use gpus: %s", str(gpus16))
if pretrained_G14 == "":
logger.info("No pretrained Generator")
if pretrained_D15 == "":
logger.info("No pretrained Discriminator")
if version19 == "v1" or sr2 == "40k":
config_path = "v1/%s.json" % sr2
else:
config_path = "v2/%s.json" % sr2
config_save_path = os.path.join(exp_dir, "config.json")
if not pathlib.Path(config_save_path).exists():
with open(config_save_path, "w", encoding="utf-8") as f:
json.dump(
config.json_config[config_path],
f,
ensure_ascii=False,
indent=4,
sort_keys=True,
)
f.write("\n")
if gpus16:
cmd = (
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
% (
config.python_cmd,
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
gpus16,
total_epoch11,
save_epoch10,
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
1 if if_save_latest13 == True else 0,
1 if if_cache_gpu17 == True else 0,
1 if if_save_every_weights18 == True else 0,
version19,
)
)
else:
cmd = (
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
% (
config.python_cmd,
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
total_epoch11,
save_epoch10,
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
1 if if_save_latest13 == True else 0,
1 if if_cache_gpu17 == True else 0,
1 if if_save_every_weights18 == True else 0,
version19,
)
)
logger.info(cmd)
global p
p = Popen(cmd, shell=True, cwd=now_dir)
global PID
PID = p.pid
p.wait()
return i18n("Training is done, check train.log"), {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"}
def train_index(exp_dir1, version19):
# exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
exp_dir = "logs/%s" % (exp_dir1)
os.makedirs(exp_dir, exist_ok=True)
feature_dir = (
"%s/3_feature256" % (exp_dir)
if version19 == "v1"
else "%s/3_feature768" % (exp_dir)
)
if not os.path.exists(feature_dir):
return "请先进行特征提取!"
listdir_res = list(os.listdir(feature_dir))
if len(listdir_res) == 0:
return "请先进行特征提取！"
infos = []
npys = []
for name in sorted(listdir_res):
phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone)
big_npy = np.concatenate(npys, 0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
if big_npy.shape[0] > 2e5:
infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
yield "\n".join(infos)
try:
big_npy = (
MiniBatchKMeans(
n_clusters=10000,
verbose=True,
batch_size=256 * config.n_cpu,
compute_labels=False,
init="random",
)
.fit(big_npy)
.cluster_centers_
)
except:
info = traceback.format_exc()
logger.info(info)
infos.append(info)
yield "\n".join(infos)
np.save("%s/total_fea.npy" % exp_dir, big_npy)
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
infos.append("%s,%s" % (big_npy.shape, n_ivf))
yield "\n".join(infos)
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
# index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
infos.append("training")
yield "\n".join(infos)
index_ivf = faiss.extract_index_ivf(index) #
index_ivf.nprobe = 1
index.train(big_npy)
faiss.write_index(
index,
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
infos.append("adding")
yield "\n".join(infos)
batch_size_add = 8192
for i in range(0, big_npy.shape[0], batch_size_add):
index.add(big_npy[i : i + batch_size_add])
faiss.write_index(
index,
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
infos.append(
"Successful Index Construction，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
)
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
yield "\n".join(infos)
def change_info_(ckpt_path):
if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
try:
with open(
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
) as f:
info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
sr, f0 = info["sample_rate"], info["if_f0"]
version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
return sr, str(f0), version
except:
traceback.print_exc()
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
F0GPUVisible = config.dml == False
def change_f0_method(f0method8):
if f0method8 == "rmvpe_gpu":
visible = F0GPUVisible
else:
visible = False
return {"visible": visible, "__type__": "update"}
def export_onnx(model_path, exported_path):
device = torch.device("cpu")
checkpoint = torch.load(model_path, map_location=device)
vec_channels = 256 if checkpoint.get("version", "v1") == "v1" else 768
test_inputs = {
"phone": torch.rand(1, 200, vec_channels),
"phone_lengths": torch.LongTensor([200]),
"pitch": torch.randint(5, 255, (1, 200)),
"pitchf": torch.rand(1, 200),
"ds": torch.zeros(1).long(),
"rnd": torch.rand(1, 192, 200)
}
checkpoint["config"][-3] = checkpoint["weight"]["emb_g.weight"].shape[0]
net_g = SynthesizerTrnMsNSFsidM(*checkpoint["config"], is_half=False, version=checkpoint.get("version", "v1"))
net_g.load_state_dict(checkpoint["weight"], strict=False)
net_g = net_g.to(device)
dynamic_axes = {"phone": [1], "pitch": [1], "pitchf": [1], "rnd": [2]}
torch.onnx.export(
net_g,
tuple(value.to(device) for value in test_inputs.values()),
exported_path,
dynamic_axes=dynamic_axes,
do_constant_folding=False,
opset_version=13,
verbose=False,
input_names=list(test_inputs.keys()),
output_names=["audio"],
)
return "Finished"
import re as regex
import scipy.io.wavfile as wavfile
cli_current_page = "HOME"
def cli_split_command(com):
exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)'
split_array = regex.findall(exp, com)
split_array = [group[0] if group[0] else group[1] for group in split_array]
return split_array
def execute_generator_function(genObject):
for _ in genObject:
pass
def cli_infer(com):
# get VC first
com = cli_split_command(com)
model_name = com[0]
source_audio_path = com[1]
output_file_name = com[2]
feature_index_path = com[3]
f0_file = None # Not Implemented Yet
# Get parameters for inference
speaker_id = int(com[4])
transposition = float(com[5])
f0_method = com[6]
crepe_hop_length = int(com[7])
harvest_median_filter = int(com[8])
resample = int(com[9])
mix = float(com[10])
feature_ratio = float(com[11])
protection_amnt = float(com[12])
protect1 = 0.5
if com[14] == "False" or com[14] == "false":
DoFormant = False
Quefrency = 0.0
Timbre = 0.0
CSVutil(
"csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre
)
else:
DoFormant = True
Quefrency = float(com[15])
Timbre = float(com[16])
CSVutil(
"csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre
)
print("Mangio-RVC-Fork Infer-CLI: Starting the inference...")
vc_data = vc.get_vc(model_name, protection_amnt, protect1)
print(vc_data)
print("Mangio-RVC-Fork Infer-CLI: Performing inference...")
conversion_data = vc.vc_single(
speaker_id,
source_audio_path,
source_audio_path,
transposition,
f0_file,
f0_method,
feature_index_path,
feature_index_path,
feature_ratio,
harvest_median_filter,
resample,
mix,
protection_amnt,
crepe_hop_length,
)
if "Success." in conversion_data[0]:
print(
"Mangio-RVC-Fork Infer-CLI: Inference succeeded. Writing to %s/%s..."
% ("audio-outputs", output_file_name)
)
wavfile.write(
"%s/%s" % ("audio-outputs", output_file_name),
conversion_data[1][0],
conversion_data[1][1],
)
print(
"Mangio-RVC-Fork Infer-CLI: Finished! Saved output to %s/%s"
% ("audio-outputs", output_file_name)
)
else:
print("Mangio-RVC-Fork Infer-CLI: Inference failed. Here's the traceback: ")
print(conversion_data[0])
def cli_pre_process(com):
com = cli_split_command(com)
model_name = com[0]
trainset_directory = com[1]
sample_rate = com[2]
num_processes = int(com[3])
print("Mangio-RVC-Fork Pre-process: Starting...")
generator = preprocess_dataset(
trainset_directory, model_name, sample_rate, num_processes
)
execute_generator_function(generator)
print("Mangio-RVC-Fork Pre-process: Finished")
def cli_extract_feature(com):
com = cli_split_command(com)
model_name = com[0]
gpus = com[1]
num_processes = int(com[2])
has_pitch_guidance = True if (int(com[3]) == 1) else False
f0_method = com[4]
crepe_hop_length = int(com[5])
version = com[6] # v1 or v2
print("Mangio-RVC-CLI: Extract Feature Has Pitch: " + str(has_pitch_guidance))
print("Mangio-RVC-CLI: Extract Feature Version: " + str(version))
print("Mangio-RVC-Fork Feature Extraction: Starting...")
generator = extract_f0_feature(
gpus,
num_processes,
f0_method,
has_pitch_guidance,
model_name,
version,
crepe_hop_length,
)
execute_generator_function(generator)
print("Mangio-RVC-Fork Feature Extraction: Finished")
def cli_train(com):
com = cli_split_command(com)
model_name = com[0]
sample_rate = com[1]
has_pitch_guidance = True if (int(com[2]) == 1) else False
speaker_id = int(com[3])
save_epoch_iteration = int(com[4])
total_epoch = int(com[5]) # 10000
batch_size = int(com[6])
gpu_card_slot_numbers = com[7]
if_save_latest = True if (int(com[8]) == 1) else False
if_cache_gpu = True if (int(com[9]) == 1) else False
if_save_every_weight = True if (int(com[10]) == 1) else False
version = com[11]
pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/"
g_pretrained_path = "%sf0G%s.pth" % (pretrained_base, sample_rate)
d_pretrained_path = "%sf0D%s.pth" % (pretrained_base, sample_rate)
print("Mangio-RVC-Fork Train-CLI: Training...")
click_train(
model_name,
sample_rate,
has_pitch_guidance,
speaker_id,
save_epoch_iteration,
total_epoch,
batch_size,
if_save_latest,
g_pretrained_path,
d_pretrained_path,
gpu_card_slot_numbers,
if_cache_gpu,
if_save_every_weight,
version,
)
def cli_train_feature(com):
com = cli_split_command(com)
model_name = com[0]
version = com[1]
print("Mangio-RVC-Fork Train Feature Index-CLI: Training... Please wait")
generator = train_index(model_name, version)
execute_generator_function(generator)
print("Mangio-RVC-Fork Train Feature Index-CLI: Done!")
def cli_extract_model(com):
com = cli_split_command(com)
model_path = com[0]
save_name = com[1]
sample_rate = com[2]
has_pitch_guidance = com[3]
info = com[4]
version = com[5]
extract_small_model_process = extract_small_model(
model_path, save_name, sample_rate, has_pitch_guidance, info, version
)
if extract_small_model_process == "Success.":
print("Mangio-RVC-Fork Extract Small Model: Success!")
else:
print(str(extract_small_model_process))
print("Mangio-RVC-Fork Extract Small Model: Failed!")
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 print_page_details():
if cli_current_page == "HOME":
print(
"\n go home : Takes you back to home with a navigation list."
"\n go infer : Takes you to inference command execution."
"\n go pre-process : Takes you to training step.1) pre-process command execution."
"\n go extract-feature : Takes you to training step.2) extract-feature command execution."
"\n go train : Takes you to training step.3) being or continue training command execution."
"\n go train-feature : Takes you to the train feature index command execution."
"\n go extract-model : Takes you to the extract small model command execution."
)
elif cli_current_page == "INFER":
print(
"\n arg 1) model name with .pth in ./weights: mi-test.pth"
"\n arg 2) source audio path: myFolder\\MySource.wav"
"\n arg 3) output file name to be placed in './audio-outputs': MyTest.wav"
"\n arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index"
"\n arg 5) speaker id: 0"
"\n arg 6) transposition: 0"
"\n arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny, rmvpe)"
"\n arg 8) crepe hop length: 160"
"\n arg 9) harvest median filter radius: 3 (0-7)"
"\n arg 10) post resample rate: 0"
"\n arg 11) mix volume envelope: 1"
"\n arg 12) feature index ratio: 0.78 (0-1)"
"\n arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.)"
"\n arg 14) Whether to formant shift the inference audio before conversion: False (if set to false, you can ignore setting the quefrency and timbre values for formanting)"
"\n arg 15)* Quefrency for formanting: 8.0 (no need to set if arg14 is False/false)"
"\n arg 16)* Timbre for formanting: 1.2 (no need to set if arg14 is False/false) \n"
"\nExample: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33 0.45 True 8.0 1.2"
)
elif cli_current_page == "PRE-PROCESS":
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Trainset directory: mydataset (or) E:\\my-data-set"
"\n arg 3) Sample rate: 40k (32k, 40k, 48k)"
"\n arg 4) Number of CPU threads to use: 8 \n"
"\nExample: mi-test mydataset 40k 24"
)
elif cli_current_page == "EXTRACT-FEATURE":
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)"
"\n arg 3) Number of CPU threads to use: 8"
"\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
"\n arg 5) f0 Method: harvest (pm, harvest, dio, crepe)"
"\n arg 6) Crepe hop length: 128"
"\n arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n"
"\nExample: mi-test 0 24 1 harvest 128 v2"
)
elif cli_current_page == "TRAIN":
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Sample rate: 40k (32k, 40k, 48k)"
"\n arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
"\n arg 4) speaker id: 0"
"\n arg 5) Save epoch iteration: 50"
"\n arg 6) Total epochs: 10000"
"\n arg 7) Batch size: 8"
"\n arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)"
"\n arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)"
"\n arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)"
"\n arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)"
"\n arg 12) Model architecture version: v2 (use either v1 or v2)\n"
"\nExample: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2"
)
elif cli_current_page == "TRAIN-FEATURE":
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Model architecture version: v2 (use either v1 or v2)\n"
"\nExample: mi-test v2"
)
elif cli_current_page == "EXTRACT-MODEL":
print(
"\n arg 1) Model Path: logs/mi-test/G_168000.pth"
"\n arg 2) Model save name: MyModel"
"\n arg 3) Sample rate: 40k (32k, 40k, 48k)"
"\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
'\n arg 5) Model information: "My Model"'
"\n arg 6) Model architecture version: v2 (use either v1 or v2)\n"
'\nExample: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2'
)
def change_page(page):
global cli_current_page
cli_current_page = page
return 0
def execute_command(com):
if com == "go home":
return change_page("HOME")
elif com == "go infer":
return change_page("INFER")
elif com == "go pre-process":
return change_page("PRE-PROCESS")
elif com == "go extract-feature":
return change_page("EXTRACT-FEATURE")
elif com == "go train":
return change_page("TRAIN")
elif com == "go train-feature":
return change_page("TRAIN-FEATURE")
elif com == "go extract-model":
return change_page("EXTRACT-MODEL")
else:
if com[:3] == "go ":
print("page '%s' does not exist!" % com[3:])
return 0
if cli_current_page == "INFER":
cli_infer(com)
elif cli_current_page == "PRE-PROCESS":
cli_pre_process(com)
elif cli_current_page == "EXTRACT-FEATURE":
cli_extract_feature(com)
elif cli_current_page == "TRAIN":
cli_train(com)
elif cli_current_page == "TRAIN-FEATURE":
cli_train_feature(com)
elif cli_current_page == "EXTRACT-MODEL":
cli_extract_model(com)
def cli_navigation_loop():
while True:
print("\nYou are currently in '%s':" % cli_current_page)
print_page_details()
command = input("%s: " % cli_current_page)
try:
execute_command(command)
except:
print(traceback.format_exc())
if config.is_cli:
print("\n\nMangio-RVC-Fork v2 CLI App!\n")
print(
"Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n"
)
cli_navigation_loop()
def switch_pitch_controls(f0method0):
is_visible = f0method0 != 'rmvpe'
if rvc_globals.NotesOrHertz:
return (
{"visible": False, "__type__": "update"},
{"visible": is_visible, "__type__": "update"},
{"visible": False, "__type__": "update"},
{"visible": is_visible, "__type__": "update"}
)
else:
return (
{"visible": is_visible, "__type__": "update"},
{"visible": False, "__type__": "update"},
{"visible": is_visible, "__type__": "update"},
{"visible": False, "__type__": "update"}
)
def match_index(sid0):
picked = False
# folder = sid0.split('.')[0]
# folder = re.split(r'. |_', sid0)[0]
folder = sid0.split(".")[0].split("_")[0]
# folder_test = sid0.split('.')[0].split('_')[0].split('-')[0]
parent_dir = "./logs/" + folder
# print(parent_dir)
if os.path.exists(parent_dir):
# print('path exists')
for filename in os.listdir(parent_dir.replace("\\", "/")):
if filename.endswith(".index"):
for i in range(len(indexes_list)):
if indexes_list[i] == (
os.path.join(("./logs/" + folder), filename).replace("\\", "/")
):
# print('regular index found')
break
else:
if indexes_list[i] == (
os.path.join(
("./logs/" + folder.lower()), filename
).replace("\\", "/")
):
# print('lowered index found')
parent_dir = "./logs/" + folder.lower()
break
# elif (indexes_list[i]).casefold() == ((os.path.join(("./logs/" + folder), filename).replace('\\','/')).casefold()):
# print('8')
# parent_dir = "./logs/" + folder.casefold()
# break
# elif (indexes_list[i]) == ((os.path.join(("./logs/" + folder_test), filename).replace('\\','/'))):
# parent_dir = "./logs/" + folder_test
# print(parent_dir)
# break
# elif (indexes_list[i]) == (os.path.join(("./logs/" + folder_test.lower()), filename).replace('\\','/')):
# parent_dir = "./logs/" + folder_test
# print(parent_dir)
# break
# else:
# #print('couldnt find index')
# continue
# print('all done')
index_path = os.path.join(
parent_dir.replace("\\", "/"), filename.replace("\\", "/")
).replace("\\", "/")
# print(index_path)
return (index_path, index_path)
else:
# print('nothing found')
return ("", "")
def stoptraining(mim):
if int(mim) == 1:
CSVutil("csvdb/stop.csv", "w+", "stop", "True")
# p.terminate()
# p.kill()
try:
os.kill(PID, signal.SIGTERM)
except Exception as e:
print(f"Couldn't click due to {e}")
pass
else:
pass
return (
{"visible": False, "__type__": "update"},
{"visible": True, "__type__": "update"},
)
weights_dir = 'weights/'
def note_to_hz(note_name):
SEMITONES = {'C': -9, 'C#': -8, 'D': -7, 'D#': -6, 'E': -5, 'F': -4, 'F#': -3, 'G': -2, 'G#': -1, 'A': 0, 'A#': 1, 'B': 2}
pitch_class, octave = note_name[:-1], int(note_name[-1])
semitone = SEMITONES[pitch_class]
note_number = 12 * (octave - 4) + semitone
frequency = 440.0 * (2.0 ** (1.0/12)) ** note_number
return frequency
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 save_to_wav2_edited(dropbox):
if dropbox is None:
pass
else:
file_path = dropbox.name
target_path = os.path.join('audios', os.path.basename(file_path))
if os.path.exists(target_path):
os.remove(target_path)
print('Replacing old dropdown file...')
shutil.move(file_path, target_path)
return
def save_to_wav2(dropbox):
file_path = dropbox.name
target_path = os.path.join('audios', os.path.basename(file_path))
if os.path.exists(target_path):
os.remove(target_path)
print('Replacing old dropdown file...')
shutil.move(file_path, target_path)
return target_path
from gtts import gTTS
import edge_tts
import asyncio
def custom_voice(
_values, # filter indices
audio_files, # all audio files
model_voice_path='',
transpose=0,
f0method='pm',
index_rate_=float(0.66),
crepe_hop_length_=float(64),
f0_autotune=False,
file_index='',
file_index2='',
):
vc.get_vc(model_voice_path)
for _value_item in _values:
filename = "audio2/"+audio_files[_value_item] if _value_item != "converted_tts" else audio_files[0]
#filename = "audio2/"+audio_files[_value_item]
try:
print(audio_files[_value_item], model_voice_path)
except:
pass
info_, (sample_, audio_output_) = vc.vc_single_dont_save(
sid=0,
input_audio_path0=filename, #f"audio2/{filename}",
input_audio_path1=filename, #f"audio2/{filename}",
f0_up_key=transpose, # transpose for m to f and reverse 0 12
f0_file=None,
f0_method= f0method,
file_index= file_index, # dir pwd?
file_index2= file_index2,
# file_big_npy1,
index_rate= index_rate_,
filter_radius= int(3),
resample_sr= int(0),
rms_mix_rate= float(0.25),
protect= float(0.33),
crepe_hop_length= crepe_hop_length_,
f0_autotune=f0_autotune,
f0_min=50,
note_min=50,
f0_max=1100,
note_max=1100
)
sf.write(
file= filename, #f"audio2/{filename}",
samplerate=sample_,
data=audio_output_
)
def cast_to_device(tensor, device):
try:
return tensor.to(device)
except Exception as e:
print(e)
return tensor
def __bark__(text, voice_preset):
os.makedirs(os.path.join(now_dir,"tts"), exist_ok=True)
from transformers import AutoProcessor, BarkModel
device = "cuda:0" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if "cpu" in device else torch.float16
bark_processor = AutoProcessor.from_pretrained(
"suno/bark",
cache_dir=os.path.join(now_dir,"tts","suno/bark"),
torch_dtype=dtype)
bark_model = BarkModel.from_pretrained(
"suno/bark",
cache_dir=os.path.join(now_dir,"tts","suno/bark"),
torch_dtype=dtype).to(device)
# bark_model.enable_cpu_offload()
inputs = bark_processor(
text=[text],
return_tensors="pt",
voice_preset=voice_preset
)
tensor_dict = {k: cast_to_device(v,device) if hasattr(v,"to") else v for k, v in inputs.items()}
speech_values = bark_model.generate(**tensor_dict, do_sample=True)
sampling_rate = bark_model.generation_config.sample_rate
speech = speech_values.cpu().numpy().squeeze()
return speech, sampling_rate
def make_test(
tts_text,
tts_voice,
model_path,
index_path,
transpose,
f0_method,
index_rate,
crepe_hop_length,
f0_autotune,
tts_method
):
if tts_voice == None:
return
filename = os.path.join(now_dir, "audio-outputs", "converted_tts.wav")
if "SET_LIMIT" == os.getenv("DEMO"):
if len(tts_text) > 60:
tts_text = tts_text[:60]
print("DEMO; limit to 60 characters")
language = tts_voice[:2]
if tts_method == "Edge-tts":
try:
#nest_asyncio.apply() # gradio;not
asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(filename))
except:
try:
tts = gTTS(tts_text, lang=language)
tts.save(filename)
tts.save
print(f'No audio was received. Please change the tts voice for {tts_voice}. USING gTTS.')
except:
tts = gTTS('a', lang=language)
tts.save(filename)
print('Error: Audio will be replaced.')
os.system("cp audio-outputs/converted_tts.wav audio-outputs/real_tts.wav")
custom_voice(
["converted_tts"], # filter indices
["audio-outputs/converted_tts.wav"], # all audio files
model_voice_path=model_path,
transpose=transpose,
f0method=f0_method,
index_rate_=index_rate,
crepe_hop_length_=crepe_hop_length,
f0_autotune=f0_autotune,
file_index='',
file_index2=index_path,
)
return os.path.join(now_dir, "audio-outputs", "converted_tts.wav"), os.path.join(now_dir, "audio-outputs", "real_tts.wav")
elif tts_method == "Bark-tts":
try:
script = tts_text.replace("\n", " ").strip()
sentences = sent_tokenize(script)
print(sentences)
silence = np.zeros(int(0.25 * SAMPLE_RATE))
pieces = []
nombre_archivo = os.path.join(now_dir, "audio-outputs", "bark_out.wav")
for sentence in sentences:
audio_array , _ = __bark__(sentence, tts_voice.split("-")[0])
pieces += [audio_array, silence.copy()]
sf.write(
file= nombre_archivo,
samplerate=SAMPLE_RATE,
data=np.concatenate(pieces)
)
vc.get_vc(model_path)
info_, (sample_, audio_output_) = vc.vc_single_dont_save(
sid=0,
input_audio_path0=os.path.join(now_dir, "audio-outputs", "bark_out.wav"), #f"audio2/{filename}",
input_audio_path1=os.path.join(now_dir, "audio-outputs", "bark_out.wav"), #f"audio2/{filename}",
f0_up_key=transpose, # transpose for m to f and reverse 0 12
f0_file=None,
f0_method=f0_method,
file_index= '', # dir pwd?
file_index2= index_path,
# file_big_npy1,
index_rate= index_rate,
filter_radius= int(3),
resample_sr= int(0),
rms_mix_rate= float(0.25),
protect= float(0.33),
crepe_hop_length= crepe_hop_length,
f0_autotune=f0_autotune,
f0_min=50,
note_min=50,
f0_max=1100,
note_max=1100
)
wavfile.write(os.path.join(now_dir, "audio-outputs", "converted_bark.wav"), rate=sample_, data=audio_output_)
return os.path.join(now_dir, "audio-outputs", "converted_bark.wav"), nombre_archivo
except Exception as e:
print(f"{e}")
return None, None
def GradioSetup(UTheme=gr.themes.Soft()):
default_weight = names[0] if names else ''
with gr.Blocks(theme='JohnSmith9982/small_and_pretty', title="Applio") as app:
gr.HTML(" 🍏 Applio (Mangio-RVC-Fork HF)
")
gr.HTML(" The current space only uses CPU, so it's only for inference. If you have issues with the queue, I recommend duplicating the space.
")
gr.Markdown(
"[](https://huggingface.co/spaces/r3gm/RVC_HF?duplicate=true)\n\n"
)
with gr.Tabs():
with gr.TabItem(i18n("Model Inference")):
with gr.Row():
sid0 = gr.Dropdown(label=i18n("Inferencing voice:"), choices=sorted(names), value=default_weight)
refresh_button = gr.Button(i18n("Refresh"), variant="primary")
clean_button = gr.Button(i18n("Unload voice to save GPU memory"), variant="primary")
clean_button.click(fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0])
with gr.TabItem(i18n("Single")):
with gr.Row():
spk_item = gr.Slider(
minimum=0,
maximum=2333,
step=1,
label=i18n("Select Speaker/Singer ID:"),
value=0,
visible=False,
interactive=True,
)
with gr.Group():
with gr.Row():
with gr.Column(): # First column for audio-related inputs
dropbox = gr.File(label=i18n("Drag your audio here:"))
record_button=gr.Audio(source="microphone", label=i18n("Or record an audio:"), type="filepath")
input_audio0 = gr.Textbox(
label=i18n("Manual path to the audio file to be processed"),
value=os.path.join(now_dir, "audios", "someguy.mp3"),
visible=False
)
input_audio1 = gr.Dropdown(
label=i18n("Auto detect audio path and select from the dropdown:"),
choices=sorted(audio_paths),
value='',
interactive=True,
)
input_audio1.select(fn=lambda:'',inputs=[],outputs=[input_audio0])
input_audio0.input(fn=lambda:'',inputs=[],outputs=[input_audio1])
dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
dropbox.upload(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1])
record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
record_button.change(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1])
best_match_index_path1 = match_index(sid0.value) # Get initial index from default sid0 (first voice model in list)
with gr.Column(): # Second column for pitch shift and other options
file_index2 = gr.Dropdown(
label=i18n("Auto-detect index path and select from the dropdown:"),
choices=get_indexes(),
value=best_match_index_path1,
interactive=True,
allow_custom_value=True,
)
index_rate1 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("Search feature ratio:"),
value=0.75,
interactive=True,
)
refresh_button.click(
fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1]
)
with gr.Column():
vc_transform0 = gr.Number(
label=i18n("Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12):"), value=0
)
# Create a checkbox for advanced settings
advanced_settings_checkbox = gr.Checkbox(
value=False,
label=i18n("Advanced Settings"),
interactive=True,
)
# Advanced settings container
with gr.Column(visible=False) as advanced_settings: # Initially hidden
with gr.Row(label = i18n("Advanced Settings"), open = False):
with gr.Column():
f0method0 = gr.Radio(
label=i18n(
"Select the pitch extraction algorithm:"
),
choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe", "rmvpe+"],
value="rmvpe+",
interactive=True,
)
f0_autotune = gr.Checkbox(
label="Enable autotune",
interactive=True
)
crepe_hop_length = gr.Slider(
minimum=1,
maximum=512,
step=1,
label=i18n("Mangio-Crepe Hop Length (Only applies to mangio-crepe): Hop length refers to the time it takes for the speaker to jump to a dramatic pitch. Lower hop lengths take more time to infer but are more pitch accurate."),
value=120,
interactive=True,
visible=False,
)
filter_radius0 = gr.Slider(
minimum=0,
maximum=7,
label=i18n("If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."),
value=3,
step=1,
interactive=True,
)
minpitch_slider = gr.Slider(
label = i18n("Min pitch:"),
info = i18n("Specify minimal pitch for inference [HZ]"),
step = 0.1,
minimum = 1,
scale = 0,
value = 50,
maximum = 16000,
interactive = True,
visible = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
)
minpitch_txtbox = gr.Textbox(
label = i18n("Min pitch:"),
info = i18n("Specify minimal pitch for inference [NOTE][OCTAVE]"),
placeholder = "C5",
visible = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
interactive = True,
)
maxpitch_slider = gr.Slider(
label = i18n("Max pitch:"),
info = i18n("Specify max pitch for inference [HZ]"),
step = 0.1,
minimum = 1,
scale = 0,
value = 1100,
maximum = 16000,
interactive = True,
visible = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
)
maxpitch_txtbox = gr.Textbox(
label = i18n("Max pitch:"),
info = i18n("Specify max pitch for inference [NOTE][OCTAVE]"),
placeholder = "C6",
visible = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
interactive = True,
)
with gr.Column():
file_index1 = gr.Textbox(
label=i18n("Feature search database file path:"),
value="",
interactive=True,
)
with gr.Accordion(label = i18n("Custom f0 [Root pitch] File"), open = False):
f0_file = gr.File(label=i18n("F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation:"))
f0method0.change(
fn=lambda radio: (
{
"visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'],
"__type__": "update"
}
),
inputs=[f0method0],
outputs=[crepe_hop_length]
)
f0method0.change(
fn=switch_pitch_controls,
inputs=[f0method0],
outputs=[minpitch_slider, minpitch_txtbox,
maxpitch_slider, maxpitch_txtbox]
)
with gr.Column():
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n("Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling:"),
value=0,
step=1,
interactive=True,
)
rms_mix_rate0 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used:"),
value=0.25,
interactive=True,
)
protect0 = gr.Slider(
minimum=0,
maximum=0.5,
label=i18n(
"Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy:"
),
value=0.33,
step=0.01,
interactive=True,
)
formanting = gr.Checkbox(
value=bool(DoFormant),
label=i18n("Formant shift inference audio"),
info=i18n("Used for male to female and vice-versa conversions"),
interactive=True,
visible=True,
)
formant_preset = gr.Dropdown(
value='',
choices=get_fshift_presets(),
label=i18n("Browse presets for formanting"),
info=i18n("Presets are located in formantshiftcfg/ folder"),
visible=bool(DoFormant),
)
formant_refresh_button = gr.Button(
value='\U0001f504',
visible=bool(DoFormant),
variant='primary',
)
qfrency = gr.Slider(
value=Quefrency,
info=i18n("Default value is 1.0"),
label=i18n("Quefrency for formant shifting"),
minimum=0.0,
maximum=16.0,
step=0.1,
visible=bool(DoFormant),
interactive=True,
)
tmbre = gr.Slider(
value=Timbre,
info=i18n("Default value is 1.0"),
label=i18n("Timbre for formant shifting"),
minimum=0.0,
maximum=16.0,
step=0.1,
visible=bool(DoFormant),
interactive=True,
)
frmntbut = gr.Button(
"Apply", variant="primary", visible=bool(DoFormant)
)
formant_preset.change(
fn=preset_apply,
inputs=[formant_preset, qfrency, tmbre],
outputs=[qfrency, tmbre],
)
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],
)
# Function to toggle advanced settings
def toggle_advanced_settings(checkbox):
return {"visible": checkbox, "__type__": "update"}
# Attach the change event
advanced_settings_checkbox.change(
fn=toggle_advanced_settings,
inputs=[advanced_settings_checkbox],
outputs=[advanced_settings]
)
but0 = gr.Button(i18n("Convert"), variant="primary").style(full_width=True)
with gr.Row(): # Defines output info + output audio download after conversion
vc_output1 = gr.Textbox(label=i18n("Output information:"))
vc_output2 = gr.Audio(label=i18n("Export audio (click on the three dots in the lower right corner to download)"))
with gr.Group(): # I think this defines the big convert button
with gr.Row():
but0.click(
vc.vc_single,
[
spk_item,
input_audio0,
input_audio1,
vc_transform0,
f0_file,
f0method0,
file_index1,
file_index2,
index_rate1,
filter_radius0,
resample_sr0,
rms_mix_rate0,
protect0,
crepe_hop_length,
minpitch_slider, minpitch_txtbox,
maxpitch_slider, maxpitch_txtbox,
f0_autotune
],
[vc_output1, vc_output2],
)
with gr.TabItem(i18n("Batch")): # Dont Change
with gr.Group(): # Markdown explanation of batch inference
gr.Markdown(
value=i18n("Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').")
)
with gr.Row():
with gr.Column():
vc_transform1 = gr.Number(
label=i18n("Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12):"), value=0
)
opt_input = gr.Textbox(label=i18n("Specify output folder:"), value="opt")
with gr.Column():
file_index4 = gr.Dropdown(
label=i18n("Auto-detect index path and select from the dropdown:"),
choices=get_indexes(),
value=best_match_index_path1,
interactive=True,
)
sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4])
refresh_button.click(
fn=lambda: change_choices()[1],
inputs=[],
outputs=file_index4,
)
index_rate2 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("Search feature ratio:"),
value=0.75,
interactive=True,
)
with gr.Row():
dir_input = 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.join(now_dir, "audios"),
)
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.")
)
with gr.Row():
with gr.Column():
# Create a checkbox for advanced batch settings
advanced_settings_batch_checkbox = gr.Checkbox(
value=False,
label=i18n("Advanced Settings"),
interactive=True,
)
# Advanced batch settings container
with gr.Row(visible=False) as advanced_settings_batch: # Initially hidden
with gr.Row(label = i18n("Advanced Settings"), open = False):
with gr.Column():
file_index3 = gr.Textbox(
label=i18n("Feature search database file path:"),
value="",
interactive=True,
)
f0method1 = gr.Radio(
label=i18n(
"Select the pitch extraction algorithm:"
),
choices=["pm", "harvest", "crepe", "rmvpe"],
value="rmvpe",
interactive=True,
)
f0_autotune = gr.Checkbox(
label="Enable autotune",
interactive=True
)
filter_radius1 = gr.Slider(
minimum=0,
maximum=7,
label=i18n("If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."),
value=3,
step=1,
interactive=True,
)
with gr.Row():
format1 = gr.Radio(
label=i18n("Export file format"),
choices=["wav", "flac", "mp3", "m4a"],
value="wav",
interactive=True,
)
with gr.Column():
resample_sr1 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n("Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling:"),
value=0,
step=1,
interactive=True,
)
rms_mix_rate1 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used:"),
value=1,
interactive=True,
)
protect1 = gr.Slider(
minimum=0,
maximum=0.5,
label=i18n(
"Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy:"
),
value=0.33,
step=0.01,
interactive=True,
)
vc_output3 = gr.Textbox(label=i18n("Output information:"))
but1 = gr.Button(i18n("Convert"), variant="primary")
but1.click(
vc.vc_multi,
[
spk_item,
dir_input,
opt_input,
inputs,
vc_transform1,
f0method1,
file_index3,
file_index4,
index_rate2,
filter_radius1,
resample_sr1,
rms_mix_rate1,
protect1,
format1,
crepe_hop_length,
minpitch_slider if (not rvc_globals.NotesOrHertz) else minpitch_txtbox,
maxpitch_slider if (not rvc_globals.NotesOrHertz) else maxpitch_txtbox,
f0_autotune
],
[vc_output3],
)
sid0.change(
fn=vc.get_vc,
inputs=[sid0, protect0, protect1],
outputs=[spk_item, protect0, protect1],
)
if not sid0.value == '':
spk_item, protect0, protect1 = vc.get_vc(sid0.value, protect0, protect1)
#spk_item, protect0, protect1 = vc.get_vc(sid0.value, protect0, protect1)
# Function to toggle advanced settings
def toggle_advanced_settings_batch(checkbox):
return {"visible": checkbox, "__type__": "update"}
# Attach the change event
advanced_settings_batch_checkbox.change(
fn=toggle_advanced_settings_batch,
inputs=[advanced_settings_batch_checkbox],
outputs=[advanced_settings_batch]
)
with gr.TabItem(i18n("Train")):
gr.Markdown("Training and All in One Inference Without UI/Gradio, Prevent Banning")
gr.Markdown("[Repository](https://github.com/ardha27/AI-Song-Cover-RVC)")
with gr.Accordion(label=i18n("Step 1: Processing data")):
with gr.Row():
exp_dir1 = gr.Textbox(label=i18n("Enter the model name:"), value=i18n("Model_Name"))
sr2 = gr.Radio(
label=i18n("Target sample rate:"),
choices=["40k", "48k", "32k"],
value="40k",
interactive=True,
)
if_f0_3 = gr.Checkbox(
label=i18n("Whether the model has pitch guidance."),
value=True,
interactive=True,
)
version19 = gr.Radio(
label=i18n("Version:"),
choices=["v1", "v2"],
value="v2",
interactive=True,
visible=True,
)
np7 = gr.Slider(
minimum=0,
maximum=config.n_cpu,
step=1,
label=i18n("Number of CPU processes:"),
value=int(np.ceil(config.n_cpu / 1.5)),
interactive=True,
)
with gr.Group():
with gr.Accordion(label=i18n("Step 2: Skipping pitch extraction")):
with gr.Row():
# trainset_dir4 = gr.Textbox(
# label=i18n("Enter the path of the training folder:"), value=os.path.join(now_dir, datasets_root)
# )
with gr.Column():
trainset_dir4 = gr.Dropdown(choices=sorted(datasets), label=i18n("Select your dataset:"), value=get_dataset())
btn_update_dataset_list = gr.Button(i18n("Update list"), variant="primary")
spk_id5 = gr.Slider(
minimum=0,
maximum=4,
step=1,
label=i18n("Specify the model ID:"),
value=0,
interactive=True,
)
btn_update_dataset_list.click(
easy_infer.update_dataset_list, [spk_id5], trainset_dir4
)
but1 = gr.Button(i18n("Process data"), variant="primary")
info1 = gr.Textbox(label=i18n("Output information:"), value="")
but1.click(
preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
)
with gr.Group():
with gr.Accordion(label=i18n("Step 3: Extracting features")):
with gr.Row():
with gr.Column():
gpus6 = gr.Textbox(
label=i18n("Provide the GPU index(es) separated by '-', like 0-1-2 for using GPUs 0, 1, and 2:"),
value=gpus,
interactive=True,
)
gpu_info9 = gr.Textbox(
label=i18n("GPU Information:"), value=gpu_info, visible=F0GPUVisible
)
with gr.Column():
f0method8 = gr.Radio(
label=i18n(
"Select the pitch extraction algorithm:"
),
choices=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe", "rmvpe_gpu"],
# [ MANGIO ]: Fork feature: Crepe on f0 extraction for training.
value="rmvpe",
interactive=True,
)
gpus_rmvpe = gr.Textbox(
label=i18n(
"rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程"
),
value="%s-%s" % (gpus, gpus),
interactive=True,
visible=F0GPUVisible,
)
extraction_crepe_hop_length = gr.Slider(
minimum=1,
maximum=512,
step=1,
label=i18n("Mangio-Crepe Hop Length (Only applies to mangio-crepe): Hop length refers to the time it takes for the speaker to jump to a dramatic pitch. Lower hop lengths take more time to infer but are more pitch accurate."),
value=64,
interactive=True,
visible=False,
)
f0method8.change(
fn=lambda radio: (
{
"visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'],
"__type__": "update"
}
),
inputs=[f0method8],
outputs=[extraction_crepe_hop_length]
)
f0method8.change(
fn=change_f0_method,
inputs=[f0method8],
outputs=[gpus_rmvpe],
)
but2 = gr.Button(i18n("Feature extraction"), variant="primary")
info2 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8, interactive=False)
but2.click(
extract_f0_feature,
[gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length, gpus_rmvpe,],
[info2],
)
with gr.Group():
with gr.Row():
with gr.Accordion(label=i18n("Step 4: Model training started")):
with gr.Row():
save_epoch10 = gr.Slider(
minimum=1,
maximum=100,
step=1,
label=i18n("Save frequency:"),
value=10,
interactive=True,
visible=True,
)
total_epoch11 = gr.Slider(
minimum=1,
maximum=10000,
step=2,
label=i18n("Training epochs:"),
value=750,
interactive=True,
)
batch_size12 = gr.Slider(
minimum=1,
maximum=50,
step=1,
label=i18n("Batch size per GPU:"),
value=default_batch_size,
#value=20,
interactive=True,
)
with gr.Row():
if_save_latest13 = gr.Checkbox(
label=i18n("Whether to save only the latest .ckpt file to save hard drive space"),
value=True,
interactive=True,
)
if_cache_gpu17 = gr.Checkbox(
label=i18n("Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training"),
value=False,
interactive=True,
)
if_save_every_weights18 = gr.Checkbox(
label=i18n("Save a small final model to the 'weights' folder at each save point"),
value=True,
interactive=True,
)
with gr.Row():
pretrained_G14 = gr.Textbox(
lines=4,
label=i18n("Load pre-trained base model G path:"),
value="assets/pretrained_v2/f0G40k.pth",
interactive=True,
)
pretrained_D15 = gr.Textbox(
lines=4,
label=i18n("Load pre-trained base model D path:"),
value="assets/pretrained_v2/f0D40k.pth",
interactive=True,
)
gpus16 = gr.Textbox(
label=i18n("Provide the GPU index(es) separated by '-', like 0-1-2 for using GPUs 0, 1, and 2:"),
value=gpus,
interactive=True,
)
sr2.change(
change_sr2,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15],
)
version19.change(
change_version19,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15, sr2],
)
if_f0_3.change(
fn=change_f0,
inputs=[if_f0_3, sr2, version19],
outputs=[f0method8, pretrained_G14, pretrained_D15],
)
if_f0_3.change(fn=lambda radio: (
{
"visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'],
"__type__": "update"
}
), inputs=[f0method8], outputs=[extraction_crepe_hop_length])
butstop = gr.Button(i18n("Stop training"),
variant='primary',
visible=False,
)
but3 = gr.Button(i18n("Train model"), variant="primary", visible=True)
but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop])
butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[but3, butstop])
with gr.Column():
info3 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=4)
save_action = gr.Dropdown(label=i18n("Save type"), choices=[i18n("Save all"),i18n("Save D and G"),i18n("Save voice")], value=i18n("Choose the method"), interactive=True)
but7 = gr.Button(i18n("Save model"), variant="primary")
but4 = gr.Button(i18n("Train feature index"), variant="primary")
if_save_every_weights18.change(
fn=lambda if_save_every_weights: (
{
"visible": if_save_every_weights,
"__type__": "update"
}
),
inputs=[if_save_every_weights18],
outputs=[save_epoch10]
)
but3.click(
click_train,
[
exp_dir1,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
],
[info3, butstop, but3],
)
but4.click(train_index, [exp_dir1, version19], info3)
but7.click(easy_infer.save_model, [exp_dir1, save_action], info3)
with gr.Group():
with gr.Row():
with gr.Accordion(label=i18n("Step 5: Export lowest points on a graph of the model")):
lowestval_weight_dir = gr.Textbox(visible=False)
ds = gr.Textbox(visible=False)
weights_dir1 = gr.Textbox(visible=False, value=weights_dir)
with gr.Row():
amntlastmdls = gr.Slider(
minimum=1,
maximum=25,
label=i18n('How many lowest points to save:'),
value=3,
step=1,
interactive=True,
)
lpexport = gr.Button(
value=i18n('Export lowest points of a model'),
variant='primary',
)
lw_mdls = gr.File(
file_count="multiple",
label=i18n("Output models:"),
interactive=False,
) #####
with gr.Row():
infolpex = gr.Textbox(label=i18n("Output information:"), value="", max_lines=10)
mdlbl = gr.Dataframe(label=i18n('Stats of selected models:'), datatype='number', type='pandas')
lpexport.click(
lambda model_name: os.path.join("logs", model_name, "lowestvals"),
inputs=[exp_dir1],
outputs=[lowestval_weight_dir]
)
lpexport.click(fn=tensorlowest.main, inputs=[exp_dir1, save_epoch10, amntlastmdls], outputs=[ds])
ds.change(
fn=tensorlowest.selectweights,
inputs=[exp_dir1, ds, weights_dir1, lowestval_weight_dir],
outputs=[infolpex, lw_mdls, mdlbl],
)
with gr.TabItem(i18n("UVR5")): # UVR section
with gr.Group():
with gr.Row():
with gr.Column():
model_select = gr.Radio(
label=i18n("Model Architecture:"),
choices=["VR", "MDX"],
value="VR",
interactive=True,
)
dir_wav_input = gr.Textbox(
label=i18n("Enter the path of the audio folder to be processed:"),
value=os.path.join(now_dir, "audios")
)
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.")
)
with gr.Column():
model_choose = gr.Dropdown(label=i18n("Model:"), choices=uvr5_names)
agg = gr.Slider(
minimum=0,
maximum=20,
step=1,
label="Vocal Extraction Aggressive",
value=10,
interactive=True,
visible=False,
)
opt_vocal_root = gr.Textbox(
label=i18n("Specify the output folder for vocals:"), value="opt"
)
opt_ins_root = gr.Textbox(
label=i18n("Specify the output folder for accompaniment:"), value="opt"
)
format0 = gr.Radio(
label=i18n("Export file format:"),
choices=["wav", "flac", "mp3", "m4a"],
value="flac",
interactive=True,
)
model_select.change(
fn=update_model_choices,
inputs=model_select,
outputs=model_choose,
)
but2 = gr.Button(i18n("Convert"), variant="primary")
vc_output4 = gr.Textbox(label=i18n("Output information:"))
#wav_inputs.upload(fn=save_to_wav2_edited, inputs=[wav_inputs], outputs=[])
but2.click(
uvr,
[
model_choose,
dir_wav_input,
opt_vocal_root,
wav_inputs,
opt_ins_root,
agg,
format0,
model_select
],
[vc_output4],
)
with gr.TabItem(i18n("TTS")):
with gr.Group():
with gr.Column():
text_test = gr.Textbox(label=i18n("Text:"), placeholder=i18n("Enter the text you want to convert to voice..."), lines=6)
with gr.Group():
with gr.Row():
with gr.Column():
tts_methods_voice = ["Edge-tts", "Bark-tts"]
ttsmethod_test = gr.Dropdown(tts_methods_voice, value='Edge-tts', label = i18n('TTS Method:'), visible=True)
tts_test = gr.Dropdown(set_edge_voice, label = i18n('TTS Model:'), visible=True)
ttsmethod_test.change(
fn=update_tts_methods_voice,
inputs=ttsmethod_test,
outputs=tts_test,
)
with gr.Column():
model_voice_path07 = gr.Dropdown(label=i18n('RVC Model:'), choices=sorted(names), value=default_weight)
best_match_index_path1 = match_index(model_voice_path07.value)
file_index2_07 = gr.Dropdown(
label=i18n('Select the .index file:'),
choices=get_indexes(),
value=best_match_index_path1,
interactive=True,
allow_custom_value=True,
)
#transpose_test = gr.Number(label = i18n('Transpose (integer, number Fof semitones, raise by an octave: 12, lower by an octave: -12):'), value=0, visible=True, interactive= True)
with gr.Row():
refresh_button_ = gr.Button(i18n("Refresh"), variant="primary")
refresh_button_.click(fn=change_choices2, inputs=[], outputs=[model_voice_path07, file_index2_07])
with gr.Row():
original_ttsvoice = gr.Audio(label=i18n('Audio TTS:'))
ttsvoice = gr.Audio(label=i18n('Audio RVC:'))
with gr.Row():
button_test = gr.Button(i18n("Convert"), variant="primary")
button_test.click(make_test, inputs=[
text_test,
tts_test,
model_voice_path07,
file_index2_07,
#transpose_test,
vc_transform0,
f0method8,
index_rate1,
crepe_hop_length,
f0_autotune,
ttsmethod_test
], outputs=[ttsvoice, original_ttsvoice])
with gr.TabItem(i18n("Resources")):
easy_infer.download_model()
easy_infer.download_backup()
easy_infer.download_dataset(trainset_dir4)
easy_infer.download_audio()
easy_infer.youtube_separator()
with gr.TabItem(i18n("Extra")):
gr.Markdown(
value=i18n("This section contains some extra utilities that often may be in experimental phases")
)
with gr.TabItem(i18n("Merge Audios")):
with gr.Group():
gr.Markdown(
value="## " + i18n("Merge your generated audios with the instrumental")
)
gr.Markdown(value=".",visible=True)
gr.Markdown(value=".",visible=True)
with gr.Row():
with gr.Column():
dropbox = gr.File(label=i18n("Drag your audio here:"))
gr.Markdown(value=i18n("### Instrumental settings:"))
input_audio1 = gr.Dropdown(
label=i18n("Choose your instrumental:"),
choices=sorted(audio_others_paths),
value='',
interactive=True,
)
input_audio1_scale = gr.Slider(
minimum=0,
maximum=10,
label=i18n("Volume of the instrumental audio:"),
value=1.00,
interactive=True,
)
gr.Markdown(value=i18n("### Audio settings:"))
input_audio3 = gr.Dropdown(
label=i18n("Select the generated audio"),
choices=sorted(audio_paths),
value='',
interactive=True,
)
with gr.Row():
input_audio3_scale = gr.Slider(
minimum=0,
maximum=10,
label=i18n("Volume of the generated audio:"),
value=1.00,
interactive=True,
)
gr.Markdown(value=i18n("### Add the effects:"))
reverb_ = gr.Checkbox(
label=i18n("Reverb"),
value=False,
interactive=True,
)
compressor_ = gr.Checkbox(
label=i18n("Compressor"),
value=False,
interactive=True,
)
noise_gate_ = gr.Checkbox(
label=i18n("Noise Gate"),
value=False,
interactive=True,
)
butnone = gr.Button(i18n("Merge"), variant="primary").style(full_width=True)
vc_output1 = gr.Textbox(label=i18n("Output information:"))
vc_output2 = gr.Audio(label=i18n("Export audio (click on the three dots in the lower right corner to download)"), type='filepath')
dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio1])
dropbox.upload(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1])
refresh_button.click(
fn=lambda: change_choices3(),
inputs=[],
outputs=[input_audio1, input_audio3],
)
butnone.click(
fn=audio_combined,
inputs=[input_audio1, input_audio3,input_audio1_scale,input_audio3_scale,reverb_,compressor_,noise_gate_],
outputs=[vc_output1, vc_output2]
)
with gr.TabItem(i18n("Processing")):
with gr.Group():
with gr.Accordion(label=i18n("Model fusion, can be used to test timbre fusion")):
with gr.Row():
with gr.Column():
name_to_save0 = gr.Textbox(
label=i18n("Name:"),
value="",
max_lines=1,
interactive=True,
placeholder=i18n("Name for saving")
)
alpha_a = gr.Slider(
minimum=0,
maximum=1,
label=i18n("Weight for Model A:"),
value=0.5,
interactive=True,
)
if_f0_ = gr.Checkbox(
label=i18n("Whether the model has pitch guidance."),
value=True,
interactive=True,
)
version_2 = gr.Radio(
label=i18n("Model architecture version:"),
choices=["v1", "v2"],
value="v2",
interactive=True,
)
sr_ = gr.Radio(
label=i18n("Target sample rate:"),
choices=["40k", "48k"],
value="40k",
interactive=True,
)
with gr.Column():
ckpt_a = gr.Textbox(label=i18n("Path to Model A:"), value="", interactive=True, placeholder=i18n("Path to model"))
ckpt_b = gr.Textbox(label=i18n("Path to Model B:"), value="", interactive=True, placeholder=i18n("Path to model"))
info__ = gr.Textbox(
label=i18n("Model information to be placed:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed")
)
info4 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)
but6 = gr.Button(i18n("Fusion"), variant="primary")
but6.click(
merge,
[
ckpt_a,
ckpt_b,
alpha_a,
sr_,
if_f0_,
info__,
name_to_save0,
version_2,
],
info4,
) # def merge(path1,path2,alpha1,sr,f0,info):
with gr.Group():
with gr.Accordion(label=i18n("Modify model information")):
with gr.Row(): ######
with gr.Column():
ckpt_path0 = gr.Textbox(
label=i18n("Path to Model:"), value="", interactive=True, placeholder=i18n("Path to model")
)
info_ = gr.Textbox(
label=i18n("Model information to be modified:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed")
)
with gr.Column():
name_to_save1 = gr.Textbox(
label=i18n("Save file name:"),
placeholder=i18n("Name for saving"),
value="",
max_lines=8,
interactive=True,
)
info5 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)
but7 = gr.Button(i18n("Modify"), variant="primary")
but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5)
with gr.Group():
with gr.Accordion(label=i18n("View model information")):
with gr.Row():
with gr.Column():
ckpt_path1 = gr.Textbox(
label=i18n("Path to Model:"), value="", interactive=True, placeholder=i18n("Path to model")
)
info6 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)
but8 = gr.Button(i18n("View"), variant="primary")
but8.click(show_info, [ckpt_path1], info6)
with gr.Group():
with gr.Accordion(label=i18n("Model extraction")):
with gr.Row():
with gr.Column():
save_name = gr.Textbox(
label=i18n("Name:"), value="", interactive=True, placeholder=i18n("Name for saving")
)
if_f0__ = gr.Checkbox(
label=i18n("Whether the model has pitch guidance."),
value=True,
interactive=True,
)
version_1 = gr.Radio(
label=i18n("Model architecture version:"),
choices=["v1", "v2"],
value="v2",
interactive=True,
)
sr__ = gr.Radio(
label=i18n("Target sample rate:"),
choices=["32k", "40k", "48k"],
value="40k",
interactive=True,
)
with gr.Column():
ckpt_path2 = gr.Textbox(
label=i18n("Path to Model:"),
placeholder=i18n("Path to model"),
interactive=True,
)
info___ = gr.Textbox(
label=i18n("Model information to be placed:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed")
)
info7 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)
with gr.Row():
but9 = gr.Button(i18n("Extract"), variant="primary")
ckpt_path2.change(
change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
)
but9.click(
extract_small_model,
[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
info7,
)
with gr.TabItem(i18n("Settings")):
with gr.Row():
gr.Markdown(value=
i18n("Pitch settings")
)
noteshertz = gr.Checkbox(
label = i18n("Whether to use note names instead of their hertz value. E.G. [C5, D6] instead of [523.25, 1174.66]Hz"),
value = rvc_globals.NotesOrHertz,
interactive = True,
)
noteshertz.change(fn=lambda nhertz: rvc_globals.__setattr__('NotesOrHertz', nhertz), inputs=[noteshertz], outputs=[])
noteshertz.change(
fn=switch_pitch_controls,
inputs=[f0method0],
outputs=[
minpitch_slider, minpitch_txtbox,
maxpitch_slider, maxpitch_txtbox,]
)
return app
def GradioRun(app):
share_gradio_link = config.iscolab or config.paperspace
concurrency_count = 511
max_size = 1022
if (
config.iscolab or config.paperspace
):
app.queue(concurrency_count=concurrency_count, max_size=max_size).launch(
favicon_path="./images/icon.png",
)
else:
app.queue(concurrency_count=concurrency_count, max_size=max_size).launch(
favicon_path=".\images\icon.png",
)
if __name__ == "__main__":
if os.name == 'nt':
print(i18n("Any ConnectionResetErrors post-conversion are irrelevant and purely visual; they can be ignored.\n"))
app = GradioSetup(UTheme=config.grtheme)
GradioRun(app)