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-# salam_bot
+# 🎤 Urdu Speech Intent Recognition using Whisper
 
+A Python tool that transcribes Urdu speech, translates it to English, and extracts the main intent from the conversation. Built with OpenAI's Whisper model for accurate speech recognition and translation.
+
+## ✨ Features
+
+- **🎙️ Urdu Speech Transcription**: Accurate transcription of Urdu audio using Whisper
+- **🌐 Built-in Translation**: Direct Urdu-to-English translation using Whisper's translation capability
+- **🎯 Intent Detection**: Identifies user intent from conversation (questions, requests, commands, etc.)
+- **😊 Sentiment Analysis**: Basic sentiment detection (positive/negative/neutral)
+- **📊 Confidence Scoring**: Provides confidence scores for both transcription and intent detection
+- **🔧 Multiple Model Sizes**: Support for tiny, base, small, medium, and large Whisper models
+- **💾 JSON Export**: Option to save results in structured JSON format
+- **🎵 Multi-format Support**: Works with MP3, WAV, M4A, FLAC, and other common audio formats
+
+## 📋 Supported Intents
+
+The system can detect the following intents:
+
+| Intent | Description | Example Keywords |
+|--------|-------------|------------------|
+| **greeting** | Starting a conversation | "سلام", "ہیلو", "السلام علیکم" |
+| **question** | Asking questions | "کیا", "کب", "کیوں", "کسے" |
+| **request** | Making requests | "براہ کرم", "مہربانی", "مدد چاہیے" |
+| **command** | Giving commands | "کرو", "لاؤ", "دیں", "بناؤ" |
+| **complaint** | Expressing complaints | "شکایت", "مسئلہ", "پریشانی" |
+| **information** | Seeking information | "بتائیں", "جانیں", "تفصیل" |
+| **emergency** | Emergency situations | "حادثہ", "ایمرجنسی", "فوری" |
+| **appointment** | Scheduling meetings | "ملاقات", "اپائنٹمنٹ", "تاریخ" |
+| **farewell** | Ending conversations | "اللہ حافظ", "خدا حافظ", "اختتام" |
+| **thanks** | Expressing gratitude | "شکریہ", "آپ کا بہت شکریہ" |
+
+## 🚀 Quick Start
+
+### Installation
+
+1. **Clone the repository:**
+```bash
+git clone https://github.com/yourusername/urdu-intent-recognition.git
+cd urdu-intent-recognition
+```
+
+2. **Install required packages:**
+```bash
+pip install openai-whisper torch torchaudio
+```
+
+3. **Install FFmpeg (required for audio processing):**
+   - **Ubuntu/Debian:** `sudo apt-get install ffmpeg`
+   - **macOS:** `brew install ffmpeg`
+   - **Windows:** Download from [ffmpeg.org](https://ffmpeg.org/download.html)
+
+### Basic Usage
+
+```bash
+# Process an Urdu audio file
+python urdu_intent_extractor.py path/to/your/audio.mp3
+
+# Use a larger model for better accuracy
+python urdu_intent_extractor.py audio.mp3 --model medium
+
+# Save results to JSON file
+python urdu_intent_extractor.py audio.mp3 --output results.json
+```
+
+## 📖 Detailed Usage
+
+### Command Line Arguments
+
+```bash
+python urdu_intent_extractor.py AUDIO_FILE [OPTIONS]
+```
+
+**Arguments:**
+- `AUDIO_FILE`: Path to the audio file to process (required)
+
+**Options:**
+- `--model`: Whisper model size (default: "base")
+  - Choices: `tiny`, `base`, `small`, `medium`, `large`
+  - Larger models are more accurate but slower
+- `--output`: Save results to JSON file
+- `--quiet`: Minimal console output
+- `--help`: Show help message
+
+### Python API Usage
+
+You can also use the tool programmatically:
+
+```python
+from urdu_intent_extractor import UrduIntentExtractor
+
+# Initialize the extractor
+extractor = UrduIntentExtractor(model_size="base")
+
+# Process an audio file
+results = extractor.process_audio_file("path/to/audio.mp3")
+
+# Access results
+print(f"Urdu Transcription: {results['transcription']['urdu']}")
+print(f"English Translation: {results['transcription']['english']}")
+print(f"Detected Intent: {results['intent']['type']}")
+print(f"Intent Confidence: {results['intent']['confidence']:.1%}")
+print(f"Sentiment: {results['sentiment']['type']}")
+```
+
+### Example Output
+
+```
+==============================================================
+URDU SPEECH INTENT ANALYSIS RESULTS
+==============================================================
+
+📁 File: conversation.mp3
+
+🗣️ URDU TRANSCRIPTION:
+   السلام علیکم، میں آپ سے ایک سوال پوچھنا چاہتا ہوں
+
+🌐 ENGLISH TRANSLATION:
+   Hello, I want to ask you a question
+
+🎯 DETECTED INTENT:
+   ❓ Asking a question or seeking clarification
+   Confidence: 85.0%
+   Urdu keywords found: سوال
+   English keywords found: question
+
+😊 SENTIMENT:
+   NEUTRAL
+   Confidence: 50.0%
+
+==============================================================
+```
+
+## 🏗️ Architecture
+
+The system works in three main steps:
+
+1. **Transcription**: Whisper transcribes the Urdu audio to Urdu text
+2. **Translation**: Whisper translates the Urdu text to English (using `task="translate"`)
+3. **Intent Analysis**: Analyzes both Urdu and English text for intent keywords
+
+### Intent Detection Algorithm
+
+1. **Bilingual Keyword Matching**: Checks for intent keywords in both Urdu and English text
+2. **Scoring System**: Assigns scores based on keyword matches
+3. **Confidence Calculation**: Calculates confidence based on match frequency and text length
+4. **Sentiment Analysis**: Basic sentiment detection using positive/negative keywords
+
+## 📊 Model Performance
+
+| Model Size | Speed | Accuracy | GPU Memory | Best Use Case |
+|------------|-------|----------|------------|---------------|
+| **base**   | ⚡ Fast | 🟡 Moderate | ~1GB | Quick prototyping |
+| **medium**   | 🚀 Good | 🟢 Good | ~1GB | General purpose |
+| **large-v1**  | 🐢 Moderate | 🟢🟢 Better | ~2GB | Better accuracy needed |
+| **large-v2** | 🐌 Slow | 🟢🟢🟢 Very Good | ~5GB | High accuracy required |
+| **large-v3**  | 🐌🐌 Very Slow | 🟢🟢🟢🟢 Excellent | ~10GB | Research/production |
+
+## 🔧 Advanced Configuration
+
+### Custom Intent Keywords
+
+You can extend or modify the intent keywords by editing the `intent_keywords` dictionary in the code:
+
+```python
+self.intent_keywords = {
+    "custom_intent": {
+        "urdu": ["کلیدی لفظ", "دوسرا لفظ"],
+        "english": ["keyword1", "keyword2"]
+    },
+    # ... existing intents
+}
+```
+
+### GPU Acceleration
+
+The tool automatically uses GPU if available. To force CPU usage:
+
+```python
+# In the code, remove fp16 parameter:
+result = self.model.transcribe(
+    audio_path,
+    language="ur",
+    task="translate"
+    # Remove: fp16=torch.cuda.is_available()
+)
+```
+
+## 📝 Example Use Cases
+
+1. **Customer Service**: Automatically categorize customer calls
+2. **Voice Assistants**: Understand user commands in Urdu
+3. **Healthcare**: Triage patient concerns based on urgency
+4. **Education**: Analyze student questions in online learning
+5. **Business Analytics**: Understand customer feedback from calls
+
+## 🐛 Troubleshooting
+
+### Common Issues
+
+1. **"Audio file not found"**
+   - Ensure the file path is correct
+   - Check file permissions
+
+2. **Poor transcription quality**
+   - Try a larger Whisper model (`--model medium`)
+   - Ensure clear audio quality
+   - Check if audio contains Urdu speech
+
+3. **Slow processing**
+   - Use smaller model (`--model tiny` or `--model base`)
+   - Ensure GPU is available and properly configured
+   - Reduce audio file size or duration
+
+4. **FFmpeg errors**
+   - Reinstall FFmpeg
+   - Ensure FFmpeg is in system PATH
+
+### Debug Mode
+
+For debugging, you can enable more verbose output by modifying the code:
+
+```python
+# Set verbose=True in transcribe calls
+result = self.model.transcribe(
+    audio_path,
+    language="ur",
+    task="translate",
+    verbose=True,  # Add this line
+    fp16=torch.cuda.is_available()
+)
+```
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