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import gradio as gr
from inference import Inference
import os
import zipfile
import hashlib
from utils.model import model_downloader, get_model
import requests
import json
api_url = "https://rvc-models-api.onrender.com/uploadfile/"
zips_folder = "./zips"
unzips_folder = "./unzips"
if not os.path.exists(zips_folder):
os.mkdir(zips_folder)
if not os.path.exists(unzips_folder):
os.mkdir(unzips_folder)
def calculate_md5(file_path):
hash_md5 = hashlib.md5()
with open(file_path, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_md5.update(chunk)
return hash_md5.hexdigest()
def compress(modelname, files):
file_path = os.path.join(zips_folder, f"{modelname}.zip")
# Select the compression mode ZIP_DEFLATED for compression
# or zipfile.ZIP_STORED to just store the file
compression = zipfile.ZIP_DEFLATED
# Comprueba si el archivo ZIP ya existe
if not os.path.exists(file_path):
# Si no existe, crea el archivo ZIP
with zipfile.ZipFile(file_path, mode="w") as zf:
try:
for file in files:
if file:
# Agrega el archivo al archivo ZIP
zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression)
except FileNotFoundError as fnf:
print("An error occurred", fnf)
else:
# Si el archivo ZIP ya existe, agrega los archivos a un archivo ZIP existente
with zipfile.ZipFile(file_path, mode="a") as zf:
try:
for file in files:
if file:
# Agrega el archivo al archivo ZIP
zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression)
except FileNotFoundError as fnf:
print("An error occurred", fnf)
return file_path
def infer(model, f0_method, audio_file):
print("****", audio_file)
inference = Inference(
model_name=model,
f0_method=f0_method,
source_audio_path=audio_file,
output_file_name=os.path.join("./audio-outputs", os.path.basename(audio_file))
)
output = inference.run()
if 'success' in output and output['success']:
return output, output['file']
else:
return
def post_model(name, model_url, version, creator):
modelname = model_downloader(model_url, zips_folder, unzips_folder)
model_files = get_model(unzips_folder, modelname)
if not model_files:
return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde."
if not model_files.get('pth'):
return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde."
md5_hash = calculate_md5(os.path.join(unzips_folder,model_files['pth']))
zipfile = compress(modelname, list(model_files.values()))
file_to_upload = open(zipfile, "rb")
data = {
"name": name,
"version": version,
"creator": creator,
"hash": md5_hash
}
print("Subiendo archivo...")
# Realizar la solicitud POST
response = requests.post(api_url, files={"file": file_to_upload}, data=data)
# Comprobar la respuesta
if response.status_code == 200:
result = response.json()
return json.dumps(result, indent=4)
else:
print("Error al cargar el archivo:", response.status_code)
return result
def search_model(name):
web_service_url = "https://script.google.com/macros/s/AKfycbzfIOiwmPj-q8-hEyvjRQfgLtO7ESolmtsQmnNheCujwnitDApBSjgTecdfXb8f2twT/exec"
response = requests.post(web_service_url, json={
'type': 'search_by_filename',
'name': name
})
result = []
response.raise_for_status() # Lanza una excepción en caso de error
json_response = response.json()
cont = 0
if json_response.get('ok', None):
for model in json_response['ocurrences']:
if cont < 20:
model_name = model.get('name', 'N/A')
model_url = model.get('url', 'N/A')
result.append(f"**Nombre del modelo: {model_name}**</br>{model_url}</br>")
yield "</br>".join(result)
cont += 1
with gr.Blocks() as app:
gr.HTML("<h1> Simple RVC Inference - by Juuxn 💻 </h1>")
with gr.Tab("Inferencia"):
model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Url del modelo", show_label=True)
audio_path = gr.Audio(label="Archivo de audio", show_label=True, type="filepath", )
f0_method = gr.Dropdown(choices=["harvest", "pm", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"],
value="rmvpe",
label="Algoritmo", show_label=True)
# Salida
with gr.Row():
vc_output1 = gr.Textbox(label="Salida")
vc_output2 = gr.Audio(label="Audio de salida")
btn = gr.Button(value="Convertir")
btn.click(infer, inputs=[model_url, f0_method, audio_path], outputs=[vc_output1, vc_output2])
with gr.Tab("Recursos"):
gr.HTML("<h4>Buscar modelos</h4>")
search_name = gr.Textbox(placeholder="Billie Eillish (RVC v2 - 100 epoch)", label="Nombre", show_label=True)
# Salida
with gr.Row():
sarch_output = gr.Markdown(label="Salida")
btn_search_model = gr.Button(value="Buscar")
btn_search_model.click(fn=search_model, inputs=[search_name], outputs=[sarch_output])
gr.HTML("<h4>Publica tu modelo</h4>")
post_name = gr.Textbox(placeholder="Billie Eillish (RVC v2 - 100 epoch)", label="Nombre", show_label=True)
post_model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Url del modelo", show_label=True)
post_creator = gr.Textbox(placeholder="ID de discord o enlace al perfil del creador", label="Creador", show_label=True)
post_version = gr.Dropdown(choices=["RVC v1", "RVC v2"], value="RVC v1", label="Versión", show_label=True)
# Salida
with gr.Row():
post_output = gr.Markdown(label="Salida")
btn_post_model = gr.Button(value="Publicar")
btn_post_model.click(fn=post_model, inputs=[post_name, post_model_url, post_version, post_creator], outputs=[post_output])
app.queue(concurrency_count=511, max_size=1022).launch(share=True) |