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