Add Urdu Intent Extractor with audio processing capabilities
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helpers/audio_analysis.py
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helpers/audio_analysis.py
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import whisper
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import torch
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import argparse
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import os
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from typing import Dict, Tuple, Optional
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import warnings
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warnings.filterwarnings('ignore')
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class UrduIntentExtractor:
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def __init__(self, model_size: str = "large-v3"):
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"""
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Initialize Urdu intent extractor using Whisper
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Args:
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model_size: Whisper model size (tiny, base, small, medium, large)
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"""
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print(f"Loading Whisper {model_size} model...")
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self.model = whisper.load_model(model_size)
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# Comprehensive intent mapping for Urdu and English
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self.intent_keywords = {
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"greeting": {
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"urdu": ["سلام", "السلام علیکم", "ہیلو", "آداب", "صبح بخیر", "شام بخیر"],
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"english": ["hello", "hi", "greetings", "good morning", "good evening", "assalam"]
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},
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"question": {
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"urdu": ["کیا", "کب", "کیوں", "کسے", "کہاں", "کس طرح", "کتنا", "کیسے"],
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"english": ["what", "when", "why", "who", "where", "how", "how much", "which"]
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},
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"request": {
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"urdu": ["براہ کرم", "مہربانی", "چاہتا ہوں", "چاہتی ہوں", "درکار ہے", "مدد چاہیے"],
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"english": ["please", "kindly", "want", "need", "require", "help", "could you", "would you"]
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},
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"command": {
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"urdu": ["کرو", "کریں", "لاؤ", "دیں", "بناؤ", "روکو", "جاؤ", "آؤ"],
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"english": ["do", "make", "bring", "give", "create", "stop", "go", "come"]
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},
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"complaint": {
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"urdu": ["شکایت", "مسئلہ", "پریشانی", "غلط", "خراب", "نقص", "برا"],
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"english": ["complaint", "problem", "issue", "wrong", "bad", "fault", "error"]
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},
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"information": {
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"urdu": ["بتائیں", "جانیں", "معلوم", "تفصیل", "رہنمائی", "بتاؤ"],
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"english": ["tell", "know", "information", "details", "guide", "explain"]
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},
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"emergency": {
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"urdu": ["حادثہ", "ایمرجنسی", "تباہی", "بچاؤ", "جلدی", "فوری", "خطرہ"],
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"english": ["accident", "emergency", "help", "urgent", "quick", "danger", "dangerous"]
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},
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"appointment": {
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"urdu": ["ملاقات", "اپائنٹمنٹ", "ٹائم", "تاریخ", "وقت", "دن"],
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"english": ["meeting", "appointment", "time", "date", "schedule", "day"]
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},
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"farewell": {
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"urdu": ["اللہ حافظ", "خدا حافظ", "بای", "اختتام", "ختم", "اگلی بار"],
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"english": ["goodbye", "bye", "farewell", "end", "see you", "next time"]
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},
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"thanks": {
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"urdu": ["شکریہ", "مہربانی", "آپ کا بہت شکریہ", "تھینکس"],
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"english": ["thank", "thanks", "grateful", "appreciate"]
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}
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}
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def transcribe_and_translate(self, audio_path: str) -> Dict[str, str]:
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"""
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Transcribe Urdu audio and translate to English using Whisper
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Args:
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audio_path: Path to audio file
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Returns:
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Dictionary containing Urdu transcription and English translation
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"""
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print(f"\nProcessing audio file: {os.path.basename(audio_path)}")
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# First, transcribe in Urdu
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print("Transcribing in Urdu...")
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urdu_result = self.model.transcribe(
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audio_path,
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language="ur", # Force Urdu language
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task="transcribe",
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fp16=torch.cuda.is_available()
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)
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urdu_text = urdu_result["text"].strip()
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# Then, translate to English
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print("Translating to English...")
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english_result = self.model.transcribe(
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audio_path,
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language="ur", # Source language is Urdu
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task="translate", # This tells Whisper to translate
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fp16=torch.cuda.is_available()
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)
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english_text = english_result["text"].strip()
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return {
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"urdu": urdu_text,
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"english": english_text,
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"urdu_segments": urdu_result.get("segments", []),
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"english_segments": english_result.get("segments", [])
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}
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def extract_intent(self, urdu_text: str, english_text: str) -> Tuple[str, float, Dict]:
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"""
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Extract main intent from both Urdu and English texts
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Args:
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urdu_text: Original Urdu transcription
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english_text: Translated English text
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Returns:
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Tuple of (intent, confidence, details)
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"""
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print("\nAnalyzing intent...")
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# Prepare text for analysis
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urdu_lower = urdu_text.lower()
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english_lower = english_text.lower()
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# Calculate intent scores
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intent_scores = {}
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intent_details = {}
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for intent, keywords in self.intent_keywords.items():
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# Count Urdu keyword matches
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urdu_matches = []
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for keyword in keywords["urdu"]:
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if keyword in urdu_lower:
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urdu_matches.append(keyword)
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# Count English keyword matches
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english_matches = []
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for keyword in keywords["english"]:
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if keyword.lower() in english_lower:
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english_matches.append(keyword)
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# Calculate scores
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urdu_score = len(urdu_matches)
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english_score = len(english_matches)
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total_score = urdu_score + english_score
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if total_score > 0:
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intent_scores[intent] = total_score
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intent_details[intent] = {
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"urdu_matches": urdu_matches,
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"english_matches": english_matches,
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"urdu_score": urdu_score,
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"english_score": english_score,
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"total_score": total_score
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}
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# Determine main intent
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if intent_scores:
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# Get intent with highest score
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main_intent = max(intent_scores, key=intent_scores.get)
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# Calculate confidence based on multiple factors
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total_words = len(english_lower.split()) + len(urdu_lower.split())
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base_confidence = intent_scores[main_intent] / max(1, total_words / 5)
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# Boost confidence if matches found in both languages
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if (intent_details[main_intent]["urdu_score"] > 0 and
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intent_details[main_intent]["english_score"] > 0):
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base_confidence *= 1.5
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confidence = min(base_confidence, 1.0)
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else:
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main_intent = "general_conversation"
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confidence = 0.3
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intent_details[main_intent] = {
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"urdu_matches": [],
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"english_matches": [],
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"urdu_score": 0,
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"english_score": 0,
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"total_score": 0
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}
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return main_intent, confidence, intent_details[main_intent]
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def get_intent_description(self, intent: str) -> str:
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"""
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Get human-readable description for intent
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Args:
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intent: Detected intent
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Returns:
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Description string
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"""
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descriptions = {
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"greeting": "👋 Greeting or starting a conversation",
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"question": "❓ Asking a question or seeking clarification",
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"request": "🙏 Making a request or asking for something",
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"command": "⚡ Giving a command or instruction",
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"complaint": "😠 Expressing a complaint or dissatisfaction",
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"information": "ℹ️ Seeking or providing information",
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"emergency": "🚨 Emergency situation requiring immediate attention",
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"appointment": "📅 Scheduling or inquiring about a meeting/appointment",
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"farewell": "👋 Ending the conversation",
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"thanks": "🙏 Expressing gratitude or thanks",
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"general_conversation": "💬 General conversation without specific intent"
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}
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return descriptions.get(intent, "💭 Unknown or general conversation")
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def analyze_sentiment(self, english_text: str) -> Tuple[str, float]:
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"""
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Basic sentiment analysis based on keywords
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Args:
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english_text: English translated text
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Returns:
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Tuple of (sentiment, confidence)
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"""
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positive_words = ["good", "great", "excellent", "happy", "thanks", "thank", "please",
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"wonderful", "nice", "helpful", "appreciate", "love", "like"]
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negative_words = ["bad", "wrong", "problem", "issue", "complaint", "angry", "upset",
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"terrible", "horrible", "hate", "not working", "broken", "failed"]
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text_lower = english_text.lower()
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positive_count = sum(1 for word in positive_words if word in text_lower)
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negative_count = sum(1 for word in negative_words if word in text_lower)
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if positive_count > negative_count:
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return "positive", positive_count / max(1, (positive_count + negative_count))
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elif negative_count > positive_count:
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return "negative", negative_count / max(1, (positive_count + negative_count))
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else:
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return "neutral", 0.5
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def process_audio_file(self, audio_path: str, verbose: bool = True) -> Dict:
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"""
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Main function to process audio file and extract intent
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Args:
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audio_path: Path to audio file
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verbose: Whether to print detailed output
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Returns:
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Dictionary with all analysis results
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"""
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# Validate file
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if not os.path.exists(audio_path):
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raise FileNotFoundError(f"Audio file not found: {audio_path}")
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# Transcribe and translate
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results = self.transcribe_and_translate(audio_path)
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# Extract intent
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intent, confidence, intent_details = self.extract_intent(
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results["urdu"],
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results["english"]
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)
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# Analyze sentiment
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sentiment, sentiment_confidence = self.analyze_sentiment(results["english"])
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# Prepare final results
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final_results = {
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"file": os.path.basename(audio_path),
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"transcription": {
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"urdu": results["urdu"],
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"english": results["english"]
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},
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"intent": {
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"type": intent,
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"confidence": confidence,
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"description": self.get_intent_description(intent),
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"details": intent_details
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},
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"sentiment": {
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"type": sentiment,
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"confidence": sentiment_confidence
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},
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"segments": {
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"urdu": results.get("urdu_segments", []),
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"english": results.get("english_segments", [])
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}
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}
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# Print results if verbose
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if verbose:
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self.print_results(final_results)
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return final_results
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def print_results(self, results: Dict):
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"""
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Print analysis results in a formatted way
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"""
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print("\n" + "="*70)
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print("URDU SPEECH INTENT ANALYSIS RESULTS")
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print("="*70)
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print(f"\n📁 File: {results['file']}")
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print(f"\n🗣️ URDU TRANSCRIPTION:")
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print(f" {results['transcription']['urdu']}")
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print(f"\n🌐 ENGLISH TRANSLATION:")
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print(f" {results['transcription']['english']}")
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print(f"\n🎯 DETECTED INTENT:")
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print(f" {results['intent']['description']}")
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print(f" Confidence: {results['intent']['confidence']:.1%}")
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if results['intent']['details']['urdu_matches']:
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print(f" Urdu keywords found: {', '.join(results['intent']['details']['urdu_matches'])}")
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if results['intent']['details']['english_matches']:
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print(f" English keywords found: {', '.join(results['intent']['details']['english_matches'])}")
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print(f"\n😊 SENTIMENT:")
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print(f" {results['sentiment']['type'].upper()}")
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print(f" Confidence: {results['sentiment']['confidence']:.1%}")
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print("\n" + "="*70)
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