Create app.py

app.py

@@ -0,0 +1,33 @@
+from transformers import pipeline
+import numpy as np
+import gradio as gr
+
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base")
+tts = pipeline("text-to-speech", model="suno/bark-small")
+
+
+def transcribe(audio):
+    sr, y = audio
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    text_generated = transcriber({"sampling_rate": sr, "raw": y})["text"]
+    audio_generated = tts(text_generated)
+
+    audio_returned = audio_generated["sampling_rate"],audio_generated["audio"][0]
+
+    return [text_generated, audio_returned]
+
+
+demo = gr.Interface(
+    transcribe,
+    inputs=gr.Audio(sources=["microphone"]),
+    outputs=[
+        gr.Text(label="texto generado"),
+        gr.Audio(label="audio generado")
+    ],
+    title="De audio a Whisper y TTS",
+    description="Transcribe el audio y luego sintetiza el texto en audio"
+)
+
+demo.launch()