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Update app.py
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app.py
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import gradio as gr
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import gradio as gr
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, pipeline
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# Cargar el modelo Wav2Vec2 para transcripción de audio
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
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model_wav2vec2 = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
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# Cargar el modelo BART para generación de texto
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generator = pipeline('text2text-generation', model='facebook/bart-large')
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# Función para transcribir audio y evaluar fluidez
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def transcribe_and_evaluate(audio):
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input_values = processor(audio.read(), return_tensors="pt").input_values
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logits = model_wav2vec2(input_values).logits
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transcription = processor.batch_decode(torch.argmax(logits, dim=-1))
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# Evaluar la fluidez del texto generado
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fluency_score = evaluate_fluency(transcription)
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return transcription, fluency_score
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# Función para evaluar la fluidez del texto
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def evaluate_fluency(text):
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# Puedes implementar métricas más sofisticadas aquí si es necesario
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fluency_score = len(text.split()) # Ejemplo simple: contar palabras
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return fluency_score
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# Configurar la interfaz de Gradio
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audio_input = gr.inputs.Audio(source="upload", type="file")
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output_text = gr.outputs.Textbox()
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output_fluency = gr.outputs.Textbox()
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# Definir la función de Gradio
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iface = gr.Interface(
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fn=transcribe_and_evaluate,
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inputs=audio_input,
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outputs=[output_text, output_fluency],
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title="Transcripción de Audio y Evaluación de Fluidez",
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description="Carga un archivo de audio y obtén la transcripción junto con el puntaje de fluidez del texto generado."
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)
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# Ejecutar la interfaz de Gradio
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iface.launch()
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