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Create app.py
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app.py
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import gradio as gr
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import torch
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from transformers import pipeline
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import numpy as np
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import time
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pipe_base = pipeline("automatic-speech-recognition", model="aitor-medrano/lara-base-pushed")
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pipe_small = pipeline("automatic-speech-recognition", model="aitor-medrano/whisper-small-lara")
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def greet(grabacion, modelo="base"):
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inicio = time.time()
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sr, y = grabacion
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# Pasamos el array de muestras a tipo NumPy de 32 bits
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y = y.astype(np.float32)
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y /= np.max(np.abs(y))
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if modelo is not None and modelo == "base":
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pipe = pipe_base
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else:
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modelo = "small"
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pipe = pipe_small
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result = modelo + ":" + pipe({"sampling_rate": sr, "raw": y})["text"]
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fin = time.time()
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return result, fin - inicio
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demo = gr.Interface(fn=greet,
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inputs=[
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gr.Audio(),
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gr.Dropdown(
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["base", "small"], label="Modelo", info="Modelos de Lara entrenados"
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)
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],
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outputs=[
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gr.Text(label="Salida"),
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gr.Number(label="Tiempo")
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])
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demo.launch()
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