from threading import Thread
from pathlib import Path
import gradio as gr
import subprocess
import shutil
import time
import copy
import glob
import json
import os
CURRENT_DIR = Path(__file__).resolve().parent
MODELOS = CURRENT_DIR / "modelos"
INFERENCE_OUTPUT_DIRNAME = CURRENT_DIR / "inference_output"
def get_container_format(filename):
command = ["ffprobe", "-v", "error", "-select_streams", "v:0", "-show_entries", "format=format_name", "-of", "default=noprint_wrappers=1:nokey=1", filename]
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
output, error = process.communicate()
if error:
raise ValueError(f"Error: {error.decode()}")
return output.decode().strip()
def cleanup_dirs():
dir_path = Path(INFERENCE_OUTPUT_DIRNAME)
if dir_path.exists():
shutil.rmtree(dir_path)
def get_speakers():
global speakers
speakers = []
for _, dirs, _ in os.walk(MODELOS):
for folder in dirs:
cur_speaker = {}
g = glob.glob(os.path.join(MODELOS, folder, 'G_*.pth'))
if not len(g):
continue
cur_speaker["model_path"] = g[0]
cur_speaker["model_folder"] = folder
cur_speaker["cluster_path"] = ""
cfg = glob.glob(os.path.join(MODELOS, folder, '*.json'))
if not len(cfg):
continue
cur_speaker["cfg_path"] = cfg[0]
with open(cur_speaker["cfg_path"]) as f:
try:
cfg_json = json.loads(f.read())
except Exception as e:
print("Archivo json malformado en" + folder)
for name, i in cfg_json["spk"].items():
cur_speaker["name"] = name
cur_speaker["id"] = i
if not name.startswith('.'):
speakers.append(copy.copy(cur_speaker))
return sorted(speakers, key=lambda x: x["name"].lower())
def run_inference(speaker, path, f0_method, transpose, noise_scale, cluster_ratio):
model_path = speaker["model_path"]
config_path = speaker["cfg_path"]
cluster_path = speaker["cluster_path"]
cluster_args = f"-k {cluster_path} -r {cluster_ratio}" if cluster_path and cluster_ratio > 0 else ""
inference_cmd = f"svc infer {path.absolute()} -m {model_path} -c {config_path} {cluster_args} -t {transpose} --f0-method crepe -n 0.4 -o {INFERENCE_OUTPUT_DIRNAME}/{path.name} --no-auto-predict-f0"
result = subprocess.run(inference_cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if result.stderr:
if "AttributeError" in result.stderr:
return None, gr.Textbox.update("⚠️ Modelo SVC incompatible.")
if not list(Path(INFERENCE_OUTPUT_DIRNAME).glob("*")):
return None, gr.Textbox.update("⚠️ Error.")
def convert(speaker_box, audio):
speaker = next((x for x in speakers if x["name"] == speaker_box), None)
if not speaker:
return None, gr.Textbox.update("⚠️ Selecciona un modelo.")
if not audio:
return None, gr.Textbox.update("⚠️ Sube un audio.")
file_path = os.path.join(os.getcwd(), str(audio))
transpose = 0
cluster_ratio = 0
if os.path.exists(INFERENCE_OUTPUT_DIRNAME):
cleanup_dirs()
os.makedirs("inference_output", exist_ok=True)
ts0 = time.time()
run_inference(speaker, Path(file_path), 0, 0, 0.4, 0)
final_filename = f"output{Path(file_path).suffix}"
shutil.move(Path(INFERENCE_OUTPUT_DIRNAME, Path(file_path).name), Path(final_filename))
cleanup_dirs()
os.remove(file_path)
ts1 = time.time()
tiempo1 = int(ts1 - ts0)
return final_filename, gr.Textbox.update("👌 ¡Voz cambiada!", label=f"Tiempo total: {tiempo1} segundos")
def clear():
shutil.rmtree(INFERENCE_OUTPUT_DIRNAME, ignore_errors=True)
tmp_files = glob.glob("*.tmp")
for f in tmp_files:
os.remove(f)
return gr.Dropdown.update(value="Elige un modelo de voz"), None, gr.Textbox.update("🗑️ Datos borrados.", label=f"Información")
css = """
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
footer {
visibility: hidden;
display: none;
}
.center-container {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
}
"""
with gr.Blocks(
css=css,
title="VoiceIt! - Pavloh",
theme=gr.themes.Soft(
primary_hue="cyan",
secondary_hue="blue",
radius_size="lg",
text_size="lg"
).set(loader_color="#0B0F19", shadow_drop='*shadow_drop_lg', block_border_width="3px")
) as pavloh:
gr.HTML(
"""
🗣️ VoiceIt! - Un proyecto de Pavloh
Cambia la voz de audios utilizando modelos pre-entrenados de streamers.
"""
)
with gr.Row(elem_id="1").style(equal_height=True):
with gr.Column():
d1 = gr.Dropdown([x["name"] for x in get_speakers()], label="📦 Selecciona un modelo", value="Elige un modelo de voz")
audio = gr.Audio(label="🗣️ Sube un audio", type="filepath")
with gr.Column():
a2 = gr.Audio(label="🔊 Resultado", type="filepath")
t1 = gr.Textbox(type="text", label="📄 Información", value="Elige un modelo y un audio para cambiar la voz.")
with gr.Row():
b0 = gr.Button("🗑️ Borrar")
b1 = gr.Button("🎤 Cambiar voz",variant="primary")
b0.click(clear, outputs=[d1, audio, t1])
b1.click(convert, inputs=[d1, audio], outputs=[a2, t1])
with gr.Row():
with gr.Accordion(label="Información importante", open=False):
gr.HTML("""
Ten en cuenta que los audios deben contener solamente una voz y estar libres de ruido o música de fondo.
Asegúrate de que el nombre del archivo no contenga espacios ni símbolos raros, utilizando solo caracteres alfanuméricos y guiones bajos (_) para separar palabras si es necesario.
Al utilizar este sitio web, aceptas la licencia y condiciones de uso.
""")
if __name__ == "__main__": pavloh.launch(enable_queue=True) 
