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import gradio as gr
from transformers import pipeline
import torch
# Initialize ASR and classifier pipelines
model_asr = "kairaamilanii/whisper-mind14-enUS"
model_class = "kairaamilanii/RoBERTa-minds14-en"
transcriber = pipeline(
"automatic-speech-recognition",
model=model_asr,
chunk_length_s=30,
device="cuda:0" if torch.cuda.is_available() else "cpu"
)
classifier = pipeline("text-classification", model=model_class)
intent_classes = {
0: 'abroad',
1: 'address',
2: 'app_error',
3: 'atm_limit',
4: 'balance',
5: 'business_loan',
6: 'card_issues',
7: 'cash_deposit',
8: 'direct_debit',
9: 'freeze',
10: 'high_value_payment',
11: 'joint_account',
12: 'latest_transactions',
13: 'pay_bill'
}
# Function to process audio
def process_audio(audio):
# Transcribe the audio
text_asr = transcriber(audio)['text']
# Classify the intent
intent_class = classifier(text_asr)
label_index = int(intent_class[0]['label'].split('_')[1])
intent_name = intent_classes.get(label_index, "Unknown")
return text_asr, intent_name
# Create Gradio interface
iface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="filepath"),
outputs=["text", "text"],
title="ASR and Intent Classification",
description="Upload an audio file to get transcription and intent classification."
)
iface.launch()
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