File size: 2,116 Bytes
36b8f27 118e098 962579e 375c831 36b8f27 80d83c0 526e201 93dfcec e702a33 16d93a1 57bb8a9 118e098 375c831 36b8f27 16d93a1 36b8f27 16d93a1 57bb8a9 16d93a1 375c831 16d93a1 962579e 16d93a1 57bb8a9 0474c84 16d93a1 375c831 16d93a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
import torch
import os
import streamlit as st
from pydub import AudioSegment
import numpy as np
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForSequenceClassification
os.environ["TRANSFORMERS_CACHE"] = "/app/.cache/huggingface"
os.environ["HF_HOME"] = "/app/.cache/huggingface"
os.environ["TORCH_HOME"] = "/app/.cache/torch"
hf_token = os.getenv("HateSpeechMujtabatoken")
whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny", token=hf_token)
whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", token=hf_token)
text_model = AutoModelForSequenceClassification.from_pretrained("GroNLP/hateBERT", token=hf_token)
tokenizer = AutoTokenizer.from_pretrained("GroNLP/hateBERT", token=hf_token)
def transcribe(audio_path):
audio = AudioSegment.from_file(audio_path, format="opus")
audio = audio.set_channels(1).set_frame_rate(16000)
samples = np.array(audio.get_array_of_samples()).astype(np.float32) / (2**15)
input_features = whisper_processor(samples, sampling_rate=16000, 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)
predicted_class = outputs.logits.argmax(dim=1).item()
return "Hate Speech" if predicted_class >= 1 else "Not Hate Speech"
def predict(text_input):
audio_path = "input.opus"
transcribed_text = transcribe(audio_path)
prediction = extract_text_features(text_input or transcribed_text)
if text_input:
return f"Predicted: {prediction}"
else:
return f"Predicted: {prediction} \n\n(Transcribed: {transcribed_text})"
st.title("Hate Speech Detector")
text_input = st.text_input("Enter text (optional):")
if st.button("Run Prediction"):
result = predict(text_input)
st.success(result)
|