import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np
from mega import Mega
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
import threading
from time import time
from subprocess import Popen
import datetime, requests
now_dir = os.getcwd()
sys.path.append(now_dir)
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=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, "weights"), exist_ok=True)
os.environ["TEMP"] = tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
from i18n import I18nAuto
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 vc_infer_pipeline import VC
from config import Config
from utils import load_audio, CSVutil
import demucs.separate
import scipy.io.wavfile as wav
from pydub import AudioSegment
DoFormant = False
Quefrency = 1.0
Timbre = 1.0
f0_method = 'rmvpe'
f0_up_key = 0
crepe_hop_length = 120
filter_radius = 3
resample_sr = 1
rms_mix_rate = 0.21
protect = 0.33
index_rate = 0.66
sr_dict = {
"32k": 32000,
"40k": 40000,
"48k": 48000,
}
# essa parte excluir dps
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/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt')
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/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt')
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()
# Check if we're in a Google Colab environment
if os.path.exists('/content/'):
print("\n-------------------------------\nRVC v2 Easy GUI (Colab Edition)\n-------------------------------\n")
print("-------------------------------")
# Check if the file exists at the specified path
if os.path.exists('/content/Mangio-RVC-Fork/hubert_base.pt'):
# If the file exists, print a statement saying so
print("File /content/Mangio-RVC-Fork/hubert_base.pt already exists. No need to download.")
else:
# If the file doesn't exist, print a statement saying it's downloading
print("File /content/Mangio-RVC-Fork/hubert_base.pt does not exist. Starting download.")
# Make a request to the URL
response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt')
# Ensure the request was successful
if response.status_code == 200:
# If the response was a success, save the content to the specified file path
with open('/content/Mangio-RVC-Fork/hubert_base.pt', 'wb') as f:
f.write(response.content)
print("Download complete. File saved to /content/Mangio-RVC-Fork/hubert_base.pt.")
else:
# If the response was a failure, print an error message
print("Failed to download file. Status code: " + str(response.status_code) + ".")
else:
print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n")
print("-------------------------------\nNot running on Google Colab, skipping download.")
i18n = I18nAuto()
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()
): # 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 == 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])
config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
hubert_model = None
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))
def vc_single(
input_audio,
separate_vocals_bool,
progress = gr.Progress()
):
progress(0, desc="Preparando áudio...")
overlay_audios_bool = False
input_audio_path = input_audio
global tgt_sr, net_g, vc, hubert_model, version
if input_audio_path is None:
return "You need to upload an audio", None
try:
t1 = 0
t2 = 0
if (separate_vocals_bool):
t1 = time()
progress(0.1, desc="Separando vocais...")
path_to_separated_vocals = separate_vocals(input_audio_path)
if (path_to_separated_vocals):
input_audio_path = path_to_separated_vocals
overlay_audios_bool = True
t2 = time()
progress(0.2, desc="Carregando áudio...")
audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre)
audio_max = np.abs(audio).max() / 0.95
if audio_max > 1:
audio /= audio_max
times = [0, 0, 0, t2 - t1, 0]
if hubert_model == None:
load_hubert()
if_f0 = cpt.get("f0", 1)
file_index = get_index()
file_index = (
(
file_index.strip(" ")
.strip('"')
.strip("\n")
.strip('"')
.strip(" ")
.replace("trained", "added")
)
)
progress(0.3, desc="Gerando áudio...")
audio_opt = vc.pipeline(
hubert_model,
net_g,
0,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
index_rate,
if_f0,
filter_radius,
40000,
resample_sr,
rms_mix_rate,
version,
protect,
crepe_hop_length,
progress,
f0_file=None,
)
progress(0.8, desc="Áudio convertido...")
if resample_sr >= 16000 and tgt_sr != resample_sr:
tgt_sr = resample_sr
if (overlay_audios_bool):
t1 = time()
progress(0.9, desc="Juntando vocal e instrumental...")
(tgt_sr, audio_opt) = overlay_audios(40000, audio_opt, input_audio_path.replace("vocals", "no_vocals"))
remove_separated_files(input_audio_path)
t2 = time()
times[4] = t2 - t1
return {"visible": True, "__type__": "update", "value": "Áudio convertido com sucesso!\nTempo: %1fs" % (
sum(times),
)}, (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 # ,cpt
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] # n_spk
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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]
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"}
def update_dropdowns():
return [change_choices(), change_choices2()]
#region RVC WebUI App
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"}
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 ''
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 check_for_name():
if len(names) > 0:
return sorted(names)[0]
else:
return ''
def download_from_url(url, model):
if url == '':
return "URL cannot be left empty."
if 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])
elif "mega.nz" in url:
m = Mega()
m.download_url(url, './zips')
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 "Success."
except:
return "There's been an error."
def download_from_youtube(url):
if url == '':
pass
filename = subprocess.getoutput(f'yt-dlp --print filename {url} --format m4a -o "./audios/%(title)s.%(ext)s"')
subprocess.getoutput(f'yt-dlp {url} --format m4a -o "./audios/%(title)s.%(ext)s"')
if os.path.exists(filename):
return filename
def find_vocals(root_directory, target_folder_name, file_name='vocals.wav'):
for root, dirs, files in os.walk(root_directory):
if target_folder_name in dirs:
folder_path = os.path.join(root, target_folder_name)
vocals_path = os.path.join(folder_path, file_name)
if os.path.exists(vocals_path):
return vocals_path
return None
def separate_vocals(audio_path):
audio_name = audio_path[9:-4]
if (os.path.exists(audio_path) and audio_name):
demucs.separate.main(["--two-stems", "vocals", audio_path, "-o", './audios'])
vocals_path = find_vocals('./audios', audio_name)
if vocals_path:
return vocals_path
return None
def pydub_to_np(audio):
return audio.frame_rate, np.array(audio.get_array_of_samples(), dtype=np.float32).reshape((-1, audio.channels)) / (
1 << (8 * audio.sample_width - 1))
def overlay_audios(sample_rate, np_array, accompaniment_path):
if (not os.path.exists(accompaniment_path)):
return (sample_rate, np_array)
converted_vocals_path = accompaniment_path.replace('no_vocals', 'converted_vocals')
wav.write(converted_vocals_path, sample_rate, np_array)
sound1 = AudioSegment.from_file(accompaniment_path)
sound2 = AudioSegment.from_file(converted_vocals_path)
combined = sound2.overlay(sound1)
sample_rate, np_array = pydub_to_np(combined)
return (sample_rate, np_array)
def remove_separated_files(vocals_path):
parent_dir = os.path.dirname(vocals_path)
try:
shutil.rmtree(parent_dir)
print(f"Deleted {parent_dir} folder and its contents")
except FileNotFoundError:
print(f"{parent_dir} folder not found")
except Exception as e:
print(f"An error occurred: {str(e)}")
def hide_output_text():
return {"visible": False, "__type__": "update", "value": ""}
def show_selected_audio(input_audio_path):
return input_audio_path
css = """
.padding {padding-left: 15px; padding-top: 5px;}
"""
with gr.Blocks(theme = gr.themes.Base(), title="Vocais da Loirinha 👱🏻♀️", css=css) as app:
gr.HTML("Vocais da Loirinha 👱🏻♀️
")
gr.Markdown("""[Repositório no Github](https://github.com/aliceoq/Mangio-RVC-Fork/tree/feat/new-gui) - [Colab](https://colab.research.google.com/drive/1FeIVwiOY2NApKtqlTtMfQGBBmWnZ7pz1?usp=sharing) - [Hugging Face Space](https://huggingface.co/spaces/aliceoq/vozes-da-loirinha)""")
with gr.Tabs():
with gr.TabItem("Inferência"):
with gr.Row().style(equal_height=True):
with gr.Column():
with gr.Row():
model_dropdown = gr.Dropdown(label="1. Selecione a voz:", choices=sorted(names), value=check_for_name())
if check_for_name() != '':
get_vc(sorted(names)[0])
model_dropdown.change(
fn=get_vc,
inputs=[model_dropdown],
outputs=[],
)
gr.HTML("2. Adicione um arquivo de áudio
", elem_classes="padding")
yt_link_textbox = gr.Textbox(label="Insira um link para uma música no Youtube:")
download_yt_button = gr.Button("Baixar áudio do vídeo")
dropbox = gr.File(label="OU selecione um arquivo:")
record_button = gr.Audio(source="microphone", label="OU grave o áudio:", type="filepath")
with gr.Column():
with gr.Row():
audio_dropdown = gr.Dropdown(
label="3. Selecione o áudio",
value="",
choices=audio_files,
scale=1
)
refresh_button = gr.Button("Atualizar listas de vozes e áudios", variant="primary", scale=0)
# Events
download_yt_button.click(fn=download_from_youtube, inputs=[yt_link_textbox], outputs=[audio_dropdown])
dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[audio_dropdown])
dropbox.upload(fn=change_choices2, inputs=[], outputs=[audio_dropdown])
record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[audio_dropdown])
record_button.change(fn=change_choices2, inputs=[], outputs=[audio_dropdown])
refresh_button.click(fn=update_dropdowns, inputs=[], outputs=[model_dropdown, audio_dropdown])
selected_audio = gr.Audio(label="Áudio selecionado", interactive=False)
audio_dropdown.select(show_selected_audio, inputs=[audio_dropdown], outputs=[selected_audio])
separate_checkbox = gr.Checkbox(label="Separar vocais e instrumental",
info="Marque esta opção quando o áudio selecionado NÃO tiver a voz isolada. Os vocais serão extraídos para a conversão e depois reintegrados ao áudio final com os instrumentais. ⚠️ O tempo de conversão pode aumentar significamente com essa opção ativada.")
convert_button = gr.Button("Gerar áudio", variant="primary")
output_audio = gr.Audio(
label="Áudio convertido (Clique nos três pontos para fazer o download)",
type='filepath',
interactive=False,
)
output_audio_textbox = gr.Textbox(label="Resultado", interactive=False, visible=True, placeholder="Nenhum áudio gerado.")
convert_button.click(hide_output_text, outputs=[output_audio_textbox]).then(vc_single, [audio_dropdown, separate_checkbox], [output_audio_textbox, output_audio])
with gr.TabItem("Adicione uma voz"):
with gr.Column():
model_link_textbox = gr.Textbox(label="1. Insira o link para o modelo:", info="A URL inserida deve ser o link no Hugging Face para o download de um arquivo zip que contém o arquivo .pth. Como por exemplo: https://huggingface.co/yaya2169/folkloretaylor/resolve/main/folkloretaylor.zip")
model_name_textbox = gr.Textbox(label="2. Escolha um nome para identificar o modelo:", info="Esse nome deve ser diferente do nome dos modelos (vozes) já existentes!")
download_button = gr.Button("Baixar modelo")
output_download_textbox = gr.Textbox(label="Resultado", interactive=False, placeholder="Nenhum modelo baixado.")
download_button.click(fn=download_from_url, inputs=[model_link_textbox, model_name_textbox], outputs=[output_download_textbox])
with gr.Row():
gr.Markdown(
"""
Original RVC: https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
Mangio's RVC Fork: https://github.com/Mangio621/Mangio-RVC-Fork
Easy GUI: https://paypal.me/lesantillan
Made with ❤️ by [Alice Oliveira](https://github.com/aliceoq) | Hosted with ❤️ by [Mateus Elias](https://github.com/mateuseap)
"""
)
if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE)
app.queue(concurrency_count=511, max_size=1022).launch(share=True, quiet=True)
else:
app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=True)
#endregion