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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")
classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
def transcribe(audio):
text = asr(audio)["text"]
return text
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.inputs.Audio(source="microphone", type="filepath")
b1 = gr.Button("Recognize Speech")
b1.click(speech_to_text, inputs=audio_file, outputs=text)
text = gr.Textbox()
b2 = gr.Button("Classify Sentiment")
b2.click(text_to_sentiment, inputs=text, outputs=label)
label = gr.Label()
demo.launch(share=True) |