if models

app.py

@@ -3,8 +3,11 @@ import torch
 from transformers import pipeline
 import numpy as np
 
-pipe = pipeline(
-    "automatic-speech-recognition", model="openai/whisper-base"
+pipe_base = pipeline(
+    "automatic-speech-recognition", model="aitor-medrano/whisper-base-lara"    
+)
+pipe_small = pipeline(
+    "automatic-speech-recognition", model="aitor-medrano/whisper-small-lara"    
 )
 
 def greet(modelo, grabacion):
@@ -13,6 +16,11 @@ def greet(modelo, grabacion):
     y = y.astype(np.float32)
     y /= np.max(np.abs(y))
 
+    if modelo == "Base":
+        pipe = pipe_base
+    else:
+        pipe = pipe_small
+
     return modelo + ":" + pipe({"sampling_rate": sr, "raw": y})["text"]
 
 demo = gr.Interface(fn=greet,