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import gradio as gr |
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from transformers import pipeline |
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trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish") |
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classificador = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis") |
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def audio_a_texto(audio): |
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text = trans(audio)['text'] |
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return text |
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def texto_a_sentimiento(text): |
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return classificador(text)[0]["label"] |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("Second demo with Blocks") |
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with gr.Tabs(): |
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with gr.TabItem("Transcribe audio"): |
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with gr.Row(): |
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audio = gr.Audio(source="microphone", type = "filepath") |
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transcription = gr.Textbox() |
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b1 = gr.Button("Transcribe Audio") |
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with gr.TabItem("Sentiment Analysis"): |
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with gr.Row(): |
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text = gr.Textbox() |
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label = gr.Label() |
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b2 = gr.Button('Classify') |
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b1.click(audio_a_texto, inputs = audio, outputs = transcription) |
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b2.click(texto_a_sentimiento, inputs = text, outputs = label) |
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demo.launch() |
