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app.py

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+import streamlit as st
+import datetime
+from transformers import pipeline
+import gradio as gr
+
+
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+
+def transcribe(audio):
+    text = asr(audio)["text"]
+    return text
+
+classifier = pipeline(
+    "text-classification",
+    model="bhadresh-savani/distilbert-base-uncased-emotion")
+
+def speech_to_text(speech):
+    text = asr(speech)["text"]
+    return text
+
+def text_to_sentiment(text):
+    sentiment = classifier(text)[0]["label"]
+    return sentiment 
+    
+demo = gr.Blocks()
+
+with demo:
+    #audio_file = gr.Audio(type="filepath")
+    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
+    text = gr.Textbox()
+    label = gr.Label()
+    saved = gr.Textbox()
+    savedAll = gr.Textbox()
+    
+    b1 = gr.Button("Recognize Speech")
+    b2 = gr.Button("Classify Sentiment")
+
+    b1.click(speech_to_text, inputs=audio_file, outputs=text)
+    b2.click(text_to_sentiment, inputs=text, outputs=label)
+    
+demo.launch(share=True)