Fix noisereduce, UI progress, remove spaces.GPU for L4, and add train_cli.py

app.py

@@ -121,6 +121,10 @@ def train_voice_model(audio_file, model_name, progress=gr.Progress()):
     except Exception as e:
         return f"Error: {str(e)}", None
 
+def train_rvc_model_ui(audio_path, model_name, epochs, batch_size, f0_method, save_every, progress=gr.Progress()):
+    return train_rvc_model(audio_path, model_name, epochs, batch_size, f0_method, save_every, progress=progress)
+
+
 def get_model_choices():
     models = list_models()
     if not models:
@@ -291,7 +295,7 @@ with gr.Blocks(title="Voice Clone RVC", theme=gr.themes.Soft()) as app:
             )
 
             rvc_train_btn.click(
-                fn=train_rvc_model,
+                fn=train_rvc_model_ui,
                 inputs=[rvc_audio, rvc_model_name, rvc_epochs, rvc_batch_size, rvc_f0_method, rvc_save_every],
                 outputs=[rvc_status, rvc_download],
             )

pipeline/rvc_training.py

@@ -44,7 +44,7 @@ except Exception as e:
     RVC_IMPORT_ERROR = str(e)
     RVC_LOGIC_AVAILABLE = False
 
-@spaces.GPU(duration=1000)
+# Removing spaces.GPU decorator so it doesn't limit execution on L4
 def train_rvc_model(audio_path, model_name, epochs=100, batch_size=4, f0_method="rmvpe", save_every=10, progress=None):
     if not RVC_LOGIC_AVAILABLE:
         return f"Error: rvc_logic module failed to load. Reason: {RVC_IMPORT_ERROR}", None

requirements.txt

@@ -42,3 +42,5 @@ tensorboardX
 wget
 static-ffmpeg
 pydantic
+noisereduce
+

train_cli.py

@@ -0,0 +1,38 @@
+import argparse
+import sys
+import os
+
+def main():
+    parser = argparse.ArgumentParser(description="Entrenamiento RVC por Consola")
+    parser.add_argument("--audio", required=True, help="Ruta al archivo de audio (WAV/MP3)")
+    parser.add_argument("--name", required=True, help="Nombre del modelo (sin espacios)")
+    parser.add_argument("--epochs", type=int, default=100, help="Número de epochs (defecto: 100)")
+    parser.add_argument("--batch_size", type=int, default=8, help="Tamaño de lote (defecto: 8 para L4)")
+    parser.add_argument("--f0_method", type=str, default="rmvpe", choices=["rmvpe", "crepe", "fcpe"], help="Método F0")
+    parser.add_argument("--save_every", type=int, default=10, help="Guardar checkpoint cada X epochs")
+
+    args = parser.parse_args()
+
+    # Import locally
+    from pipeline.rvc_training import train_rvc_model
+
+    print(f"\n🚀 Iniciando entrenamiento por consola para el modelo: {args.name}")
+    print(f"📊 Parámetros: Epochs={args.epochs}, Batch={args.batch_size}, F0={args.f0_method}\n")
+
+    status, download_path = train_rvc_model(
+        audio_path=args.audio,
+        model_name=args.name,
+        epochs=args.epochs,
+        batch_size=args.batch_size,
+        f0_method=args.f0_method,
+        save_every=args.save_every,
+        progress=None
+    )
+
+    print("\n✅ ESTADO FINAL:")
+    print(status)
+    if download_path:
+        print(f"📁 Archivo generado en: {download_path}")
+
+if __name__ == "__main__":
+    main()