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import zipfile
import hashlib
from utils.model import model_downloader, get_model
import requests
import json
import torch
import os
from inference import Inference
import gradio as gr
from constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES, zips_folder, unzips_folder
from tts.conversion import tts_infer, ELEVENLABS_VOICES_RAW, ELEVENLABS_VOICES_NAMES
api_url = "https://rvc-models-api.onrender.com/uploadfile/"
if not os.path.exists(zips_folder):
os.mkdir(zips_folder)
if not os.path.exists(unzips_folder):
os.mkdir(unzips_folder)
def get_info(path):
path = os.path.join(unzips_folder, path)
try:
a = torch.load(path, map_location="cpu")
return a
except Exception as e:
print("*****************eeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrr*****")
print(e)
return {
}
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()))
a = get_info(model_files.get('pth'))
file_to_upload = open(zipfile, "rb")
info = a.get("info", "None"),
sr = a.get("sr", "None"),
f0 = a.get("f0", "None"),
data = {
"name": name,
"version": version,
"creator": creator,
"hash": md5_hash,
"info": info,
"sr": sr,
"f0": f0
}
print("Subiendo archivo...")
# Realizar la solicitud POST
response = requests.post(api_url, files={"file": file_to_upload}, data=data)
result = response.json()
# 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/AKfycbyRaNxtcuN8CxUrcA_nHW6Sq9G2QJor8Z2-BJUGnQ2F_CB8klF4kQL--U2r2MhLFZ5J/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
result.append("""| Nombre del modelo | Url | Epoch | Sample Rate |
| ---------------- | -------------- |:------:|:-----------:|
""")
yield "<br />".join(result)
if json_response.get('ok', None):
for model in json_response['ocurrences']:
if cont < 20:
model_name = str(model.get('name', 'N/A')).strip()
model_url = model.get('url', 'N/A')
epoch = model.get('epoch', 'N/A')
sr = model.get('sr', 'N/A')
line = f"""|{model_name}|<a>{model_url}</a>|{epoch}|{sr}|
"""
result.append(line)
yield "".join(result)
cont += 1
def update_tts_methods_voice(select_value):
if select_value == "Edge-tts":
return gr.Dropdown.update(choices=EDGE_VOICES, visible=True, value="es-CO-GonzaloNeural-Male"), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False),gr.Radio.update(visible=False)
elif select_value == "Bark-tts":
return gr.Dropdown.update(choices=BARK_VOICES, visible=True), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False),gr.Radio.update(visible=False)
elif select_value == 'ElevenLabs':
return gr.Dropdown.update(choices=ELEVENLABS_VOICES_NAMES, visible=True, value="Bella"), gr.Markdown.update(visible=True), gr.Textbox.update(visible=True), gr.Radio.update(visible=False)
elif select_value == 'CoquiTTS':
return gr.Dropdown.update(visible=False), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False), gr.Radio.update(visible=True)
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