| import torch | |
| import torchaudio | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import streamlit as st | |
| text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased") | |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
| whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") | |
| whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") | |
| def transcribe(audio_path): | |
| waveform, sample_rate = torchaudio.load(audio_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] | |
| 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_path, text): | |
| transcription = transcribe(audio_path) | |
| text_input = text if text else transcription | |
| prediction = extract_text_features(text_input) | |
| return "Hate Speech" if prediction == 1 else "Not Hate Speech" | |
| st.title("Hate Speech Detector with Audio and Text") | |
| audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "flac"]) | |
| text_input = st.text_input("Optional text input") | |
| if st.button("Predict"): | |
| if audio_file is not None: | |
| with open("temp_audio.wav", "wb") as f: | |
| f.write(audio_file.read()) | |
| prediction = predict_hate_speech("temp_audio.wav", text_input) | |
| st.success(prediction) | |
| else: | |
| st.warning("Please upload an audio file.") | |