actualización con 4to modelo medium

app.py

@@ -7,7 +7,8 @@ import time
 # Utilizamos los tres modelos entrenados
 pipe_model_1 = pipeline("automatic-speech-recognition", model="IABDs8a/AfinandoElEntrenamiento")
 pipe_model_2 = pipeline("automatic-speech-recognition", model="IABDs8a/whisper-base-full")
-pipe_model_3 = pipeline("automatic-speech-recognition", model="IABDs8a/whisper-tiny-top3")
+pipe_model_3 = pipeline("automatic-speech-recognition", model="IABDs8a/whisper-medium-asfe")
+pipe_model_4 = pipeline("automatic-speech-recognition", model="IABDs8a/whisper-tiny-top3")
 
 def transcribe(audio, model_choice):
     inicio = time.time()
@@ -16,8 +17,10 @@ def transcribe(audio, model_choice):
         pipe = pipe_model_1
     elif model_choice == "Whisper Base Full":
         pipe = pipe_model_2
-    else:
+    elif model_choice == "Whisper Medium Asfe":
         pipe = pipe_model_3
+    else:
+        pipe = pipe_model_4
 
     # Leer el archivo de audio
     y, sr = librosa.load(audio, sr=16000)
@@ -43,7 +46,7 @@ demo = gr.Interface(
     fn=transcribe,
     inputs=[
         gr.Audio(type="filepath", label="Sube un archivo de audio o graba desde el micrófono"),
-        gr.Dropdown(choices=["AfinandoElEntrenamiento", "Whisper Base Full", "Whisper Tiny Top 3"], label="Selecciona el modelo", value="Whisper Base Full")
+        gr.Dropdown(choices=["AfinandoElEntrenamiento", "Whisper Base Full","Whisper Medium Asfe","Whisper Tiny Top 3"], label="Selecciona el modelo", value="Whisper Base Full")
     ],
     outputs=[
         gr.Text(label="Salida"),