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import streamlit as st
import speech_recognition as sr
from transformers import pipeline
import re
# Load NLP models
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
def extract_info(text):
candidate_labels = ["project status", "risks", "questions", "administration"]
result = classifier(text, candidate_labels)
return result
def normalize_text(text):
text = text.lower()
text = re.sub(r'\s+', ' ', text)
return text
st.title("Audio to Text Processing and Categorization")
audio_file = st.file_uploader("Upload an audio file", type=["wav"])
if audio_file is not None:
st.audio(audio_file, format='audio/wav')
# Convert audio to text
recognizer = sr.Recognizer()
with sr.AudioFile(audio_file) as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_google(audio_data)
st.write("Transcribed Text:")
st.write(text)
# NLP processing
summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
st.write("Summarized Text:")
st.write(summary[0]['summary_text'])
# Information extraction
extracted_info = extract_info(summary[0]['summary_text'])
st.write("Extracted Information:")
st.write(extracted_info)
# Text normalization
normalized_text = normalize_text(str(extracted_info))
st.write("Normalized Text:")
st.write(normalized_text) |