Spaces:
Runtime error
Runtime error
File size: 5,931 Bytes
fb1f7ba 44b7476 fb1f7ba 44b7476 fb1f7ba 3f8919a 44b7476 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
import gradio as gr
from gradio_client import Client
import os
import tempfile
# Crea il client Gradio per l'inferenza
inference_client = Client("http://127.0.0.1:6969/")
# Cartella di output in assets
output_folder = "assets/generated"
os.makedirs(output_folder, exist_ok=True)
def process_audio(audio_file):
if audio_file is None:
return None, "Nessun file audio caricato."
# Ottieni il percorso temporaneo del file caricato
input_file = audio_file.name
try:
# Prepara il percorso di output
output_filename = f"generated_{os.path.basename(input_file)}"
output_path = os.path.join(output_folder, output_filename)
# Esegui la predizione
result = inference_client.predict(
-24, # Pece
0, # Raggio del filtro
0, # Rapporto feature di ricerca
0, # Inviluppo del volume
0, # Proteggi le consonanti sorde
1, # Lunghezza del luppolo
"pm", # Algoritmo di estrazione del passo
input_file, # Percorso del file audio di input
output_path, # Percorso del file audio di output
"logs/master/master.pth", # Modello vocale
"logs/master/added_IVF124_Flat_nprobe_1_master-v2_v2.index", # File di indice
True, # Dividere l'audio
True, # Sintonizzazione automatica
True, # Audio pulito
0, # Forza pulita
"WAV", # Export Format
api_name="/run_infer_script"
)
return output_path, f"Elaborazione completata. File salvato in {output_path}"
except Exception as e:
return None, f"Errore durante l'elaborazione: {str(e)}"
# Creazione dell'interfaccia Gradio
iface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="filepath", label="Carica file audio (WAV o MP3)"),
outputs=[
gr.Audio(type="filepath", label="Audio elaborato"),
gr.Textbox(label="Messaggio")
],
title="Elaborazione Audio con Applio",
description="Carica un file audio WAV o MP3 per elaborarlo con Applio."
)
# Avvio dell'interfaccia
iface.launch()
# import gradio as gr
# import sys
# import os
# import logging
# now_dir = os.getcwd()
# sys.path.append(now_dir)
# # Tabs
# from tabs.inference.inference import inference_tab
# from tabs.train.train import train_tab
# from tabs.extra.extra import extra_tab
# from tabs.report.report import report_tab
# from tabs.download.download import download_tab
# from tabs.tts.tts import tts_tab
# from tabs.voice_blender.voice_blender import voice_blender_tab
# from tabs.settings.presence import presence_tab, load_config_presence
# from tabs.settings.flask_server import flask_server_tab
# from tabs.settings.fake_gpu import fake_gpu_tab, gpu_available, load_fake_gpu
# from tabs.settings.themes import theme_tab
# from tabs.plugins.plugins import plugins_tab
# from tabs.settings.version import version_tab
# from tabs.settings.lang import lang_tab
# from tabs.settings.restart import restart_tab
# # Assets
# import assets.themes.loadThemes as loadThemes
# from assets.i18n.i18n import I18nAuto
# import assets.installation_checker as installation_checker
# from assets.discord_presence import RPCManager
# from assets.flask.server import start_flask, load_config_flask
# from core import run_prerequisites_script
# run_prerequisites_script("False", "True", "True", "True")
# i18n = I18nAuto()
# if load_config_presence() == True:
# RPCManager.start_presence()
# installation_checker.check_installation()
# logging.getLogger("uvicorn").disabled = True
# logging.getLogger("fairseq").disabled = True
# if load_config_flask() == True:
# print("Starting Flask server")
# start_flask()
# my_applio = loadThemes.load_json()
# if my_applio:
# pass
# else:
# my_applio = "ParityError/Interstellar"
# with gr.Blocks(theme=my_applio, title="Applio") as Applio:
# gr.Markdown("# Applio")
# gr.Markdown(
# i18n(
# "Ultimate voice cloning tool, meticulously optimized for unrivaled power, modularity, and user-friendly experience."
# )
# )
# gr.Markdown(
# i18n(
# "[Support](https://discord.gg/IAHispano) — [Discord Bot](https://discord.com/oauth2/authorize?client_id=1144714449563955302&permissions=1376674695271&scope=bot%20applications.commands) — [Find Voices](https://applio.org/models) — [GitHub](https://github.com/IAHispano/Applio)"
# )
# )
# with gr.Tab(i18n("Inference")):
# inference_tab()
# with gr.Tab(i18n("Train")):
# if gpu_available() or load_fake_gpu():
# train_tab()
# else:
# gr.Markdown(
# i18n(
# "Currently, training is unsupported due to the absence of a GPU. If you have a PC with a GPU and wish to train a model, please refer to our installation guide here: [Applio Installation Guide](https://docs.applio.org/get-started/installation/). For those without a GPU-enabled PC, explore alternative options here: [Applio Alternatives](https://docs.applio.org/get-started/alternatives/)."
# )
# )
# with gr.Tab(i18n("TTS")):
# tts_tab()
# # with gr.Tab(i18n("Voice Blender")):
# # voice_blender_tab()
# # with gr.Tab(i18n("Plugins")):
# # plugins_tab()
# with gr.Tab(i18n("Download")):
# download_tab()
# with gr.Tab(i18n("Report a Bug")):
# report_tab()
# with gr.Tab(i18n("Extra")):
# extra_tab()
# # with gr.Tab(i18n("Settings")):
# # presence_tab()
# # flask_server_tab()
# # if not gpu_available():
# # fake_gpu_tab()
# # theme_tab()
# # version_tab()
# # lang_tab()
# # restart_tab()
# def launch_gradio():
# Applio.launch()
# if __name__ == "__main__":
# launch_gradio() |