Script caso 1 Whisper (app.py)

app.py

@@ -0,0 +1,25 @@
+import torch
+from transformers import pipeline
+import numpy as np
+
+pipe = pipeline(
+    "automatic-speech-recognition", model="openai/whisper-base"
+)
+
+def transcribe(audio):
+    sr, y = audio
+    # Pasamos el array de muestras a tipo NumPy de 32 bits
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    return pipe({"sampling_rate": sr, "raw": y})["text"]
+
+import gradio as gr
+
+demo = gr.Interface(
+    transcribe,
+    gr.Audio(sources=["microphone"]),
+    "text",
+)
+
+demo.launch(share=True)