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import streamlit as st |
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import datetime |
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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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def transcribe(audio): |
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text = asr(audio)["text"] |
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return text |
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classifier = pipeline( |
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"text-classification", |
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model="bhadresh-savani/distilbert-base-uncased-emotion") |
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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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sentiment = classifier(text)[0]["label"] |
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return sentiment |
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demo = gr.Blocks() |
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with demo: |
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audio_file = gr.inputs.Audio(source="microphone", type="filepath") |
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text = gr.Textbox() |
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label = gr.Label() |
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saved = gr.Textbox() |
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savedAll = gr.Textbox() |
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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=audio_file, outputs=text) |
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b2.click(text_to_sentiment, inputs=text, outputs=label) |
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demo.launch(share=True) |
