Create app_dropdown.py

app_dropdown.py

@@ -0,0 +1,30 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+import numpy as np
+
+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):
+    sr, y = grabacion
+    # Pasamos el array de muestras a tipo NumPy de 32 bits
+    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,
+                    inputs=[
+                            gr.Dropdown(
+                                ["Base", "Small"], label="Modelo", info="Modelos de Lara entrenados"
+                            ),
+                            gr.Audio()
+                    ],
+                    outputs="text")
+demo.launch()