| import os | |
| os.environ["TRANSFORMERS_CACHE"] = "/app/.cache" | |
| os.environ["HF_HOME"] = "/app/.cache" | |
| 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") | |