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import evaluate
from evaluate.utils import launch_gradio_widget
import gradio as gr
from transformers import AutoModelForSequenceClassification, pipeline, RobertaForSequenceClassification, RobertaTokenizer, AutoTokenizer
import tempfile
tmp = tempfile.NamedTemporaryFile()
# Define the list of available models
available_models = {
"mskov/roberta-base-toxicity": "Roberta Finetuned Model"
}
# Create a Gradio interface with audio file and text inputs
def classify_toxicity(audio_file, text_file, selected_model):
# Transcribe the audio file using Whisper ASR
if audio_file != None:
whisper_module = evaluate.load("whisper")
transcription_results = whisper_module.compute(uploaded=audio_file)
# Extract the transcribed text
transcribed_text = transcription_results["transcription"]
else:
with open(tmp.text_file) as f:
for line in f:
transcribed_text.append(line)
# Load the selected toxicity classification model
toxicity_module = evaluate.load("toxicity", selected_model)
toxicity_results = toxicity_module.compute(predictions=[transcribed_text])
toxicity_score = toxicity_results["toxicity"][0]
return transcribed_text, f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
iface = gr.Interface(
fn=classify_toxicity,
inputs=[
gr.Audio(source="upload", type="filepath", label="Upload Audio File"), gr.File(label="Upload Text File"),
gr.Radio(available_models, type="value", label="Select Model")
],
outputs="text",
live=True,
title="Toxicity Classifier with ASR",
description="Upload an audio file or enter text to classify its toxicity using the selected model.",
)
iface.launch() |