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import gradio as gr |
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from transformers import pipeline |
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transcripcion = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish") |
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clasificador = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis") |
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def audio_a_texto(audio): |
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texto = transcripcion(audio)["text"] |
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return texto, texto |
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def texto_a_sentimiento(texto): |
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sentimiento = clasificador(texto)[0]["label"] |
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return sentimiento |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("## Transcribe audio2text and sentimental classification - Spanish") |
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with gr.Tabs(): |
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with gr.TabItem("Transcribe"): |
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with gr.Row(): |
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audio_input = gr.Audio(sources=["microphone"], type="filepath") |
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texto_output = gr.Textbox(label="Audio to text") |
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with gr.Row(): |
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b1 = gr.Button("Transcribe 🎙️✍🏻") |
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with gr.TabItem("Sentimental Classification"): |
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with gr.Row(): |
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texto_input = gr.Textbox(label="Text to sentimental") |
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sentimiento_output = gr.Label() |
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with gr.Row(): |
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b2 = gr.Button("Sentimental Classification 🤖") |
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b1.click(audio_a_texto, inputs=audio_input, outputs=[texto_output,texto_input]) |
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b2.click(texto_a_sentimiento, inputs=texto_input, outputs=sentimiento_output) |
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demo.launch() |
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