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29 lines
669 B
Python
29 lines
669 B
Python
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from transformers import pipeline
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import gradio as gr
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asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
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classifier = pipeline("text-classification")
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def speech_to_text(speech):
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text = asr(speech)["text"]
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return text
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def text_to_sentiment(text):
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return classifier(text)[0]["label"]
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demo = gr.Blocks()
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with demo:
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m = gr.Audio(type="filepath")
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t = gr.Textbox()
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l = gr.Label()
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b1 = gr.Button("Recognize Speech")
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b2 = gr.Button("Classify Sentiment")
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b1.click(speech_to_text, inputs=m, outputs=t)
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b2.click(text_to_sentiment, inputs=t, outputs=l)
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if __name__ == "__main__":
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demo.launch()
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