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import os
import torch
import torchaudio
import tempfile
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import streamlit as st
whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
def transcribe(audio_bytes):
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
tmp.write(audio_bytes)
tmp_path = tmp.name
waveform, sample_rate = torchaudio.load(tmp_path)
input_features = whisper_processor(waveform.squeeze().numpy(), sampling_rate=sample_rate, return_tensors="pt").input_features
predicted_ids = whisper_model.generate(input_features)
transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
os.remove(tmp_path)
return transcription
def extract_text_features(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = text_model(**inputs)
return outputs.logits.argmax(dim=1).item()
def predict_hate_speech(audio_bytes, text):
if audio_bytes:
transcription = transcribe(audio_bytes)
text_input = text if text else transcription
elif text:
text_input = text
else:
return "Please provide audio or text"
prediction = extract_text_features(text_input)
return "Hate Speech" if prediction == 1 else "Not Hate Speech"
st.title("Hate Speech Detection")
audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3", "flac", "ogg", "opus"])
text_input = st.text_input("Or enter text")
if st.button("Predict"):
if audio_file is not None or text_input:
audio_bytes = audio_file.read() if audio_file else None
result = predict_hate_speech(audio_bytes, text_input)
st.success(result)
else:
st.warning("Please provide either audio or text input")
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