app.py

import gradio as gr
import torch
from transformers import pipeline
import numpy as np
import time

pipe_base = pipeline("automatic-speech-recognition", model="aitor-medrano/lara-base-pushed")
pipe_small = pipeline("automatic-speech-recognition", model="aitor-medrano/whisper-small-lara")

def greet(grabacion, modelo="base"):

    inicio = time.time()

    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 is not None and modelo == "base":
        pipe = pipe_base
    else:
        modelo = "small"
        pipe = pipe_small

    result = modelo + ":" + pipe({"sampling_rate": sr, "raw": y})["text"]
    fin = time.time()

    return result, fin - inicio

demo = gr.Interface(fn=greet,
        inputs=[
                gr.Audio(),
                gr.Dropdown(
                    ["base", "small"], label="Modelo", info="Modelos de Lara entrenados"
                )
        ],
        outputs=[
            gr.Text(label="Salida"),
            gr.Number(label="Tiempo")
        ])
demo.launch()