🚀 Building an AI-Powered Call Intelligence System: A Developer's Epic Journey

Welcome aboard, brave code warriors! Today, we're embarking on an epic quest to build, test, and evolve a call intelligence system that turns boring audio files into goldmines of insights. Grab your favourite caffeinated beverage, fire up Google Colab, and let's dive into this adventure!

🏰 The Quest Begins: Basic Setup

First, let's gather our magical ingredients:

!pip install openai  # Your trusty spell book

🔐 Securing Your Magic Keys

def setup_api_key():
    """Your secret API key vault 🔐"""
    api_key = input("Enter your OpenAI API key (we promise to keep it safe!): ")
    os.environ['OPENAI_API_KEY'] = api_key
    return "API key secured! 🎉"

settings = setup_api_key()

🧙‍♂️ Building Our Magic Powers

🎙️ The Speech Whisperer

class AudioWizard:
    """🎙️ Turning speech into text since 2024"""

    def __init__(self, api_key):
        self.client = OpenAI(api_key=api_key)
        print("AudioWizard initialized and ready for action! 🚀")

    @retry(tries=3, delay=2)  # Magic shield against API hiccups
    async def transcribe_audio(self, audio_file_path):
        try:
            print("🎧 Listening to your audio...")
            with open(audio_file_path, "rb") as audio_file:
                transcription = await self.client.audio.transcriptions.create(
                    model="whisper-1",
                    file=audio_file,
                    response_format="verbose_json"
                )
            print("✨ Translation complete!")
            return {
                "success": True,
                "text": transcription.text,
                "confidence": transcription.confidence
            }
        except Exception as e:
            print(f"🚨 Oh no! A wild error appeared: {str(e)}")
            return {"success": False, "error": str(e)}

🎭 The Script Enchanter

class ScriptEnchanter:
    """🎬 Making your transcripts ready for Broadway"""

    def __init__(self, api_key):
        self.client = OpenAI(api_key=api_key)

    async def format_script(self, text):
        try:
            response = await self.client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": """
                    🎭 You are a master screenplay formatter who:
                    - Names speakers like a casting director
                    - Adds emotions and actions in (parentheses)
                    - Makes conversations flow like poetry
                    """},
                    {"role": "user", "content": f"Transform this into a masterpiece:\n\n{text}"}
                ],
                temperature=0.3
            )
            return response.choices[0].message.content
        except Exception as e:
            return f"🎭 Stage fright! Error: {str(e)}"

🧪 Testing Our Creation

🎯 The Test Master

@dataclass
class TestResults:
    """📊 Keeping score of our system's performance"""
    accuracy: float
    wer: float  # Word Error Rate
    processing_time: float
    confidence_score: float

class TestMaster:
    """🧪 Making sure our magic actually works"""

    def __init__(self):
        self.test_cases = [
            {
                "name": "Clear Speech Test",
                "file": "test_files/clear_speech.wav",
                "expected": "test_files/clear_speech.txt",
                "difficulty": "easy"
            },
            {
                "name": "Noisy Battle Test",
                "file": "test_files/noisy.wav",
                "expected": "test_files/noisy.txt",
                "difficulty": "hard"
            }
        ]
        self.results = {}

    async def run_tests(self):
        """🎯 Running our test gauntlet"""
        for test in self.test_cases:
            print(f"🏃‍♂️ Running {test['name']}...")

            # Time the spell casting
            start_time = time.time()

            # Cast our transcription spell
            result = await AudioWizard().transcribe_audio(test['file'])

            # Calculate our accuracy scores
            metrics = self.calculate_metrics(
                result['text'],
                self.load_expected_text(test['expected']),
                time.time() - start_time
            )

            self.results[test['name']] = metrics

            print(f"✅ Test completed with {metrics.accuracy:.1f}% accuracy!")

    def calculate_metrics(self, result, expected, time_taken):
        """📐 Measuring our magical accuracy"""
        return TestResults(
            accuracy=self.calculate_accuracy(result, expected),
            wer=self.calculate_wer(result, expected),
            processing_time=time_taken,
            confidence_score=self.calculate_confidence(result)
        )

📊 The Results Visualizer

class ResultsVisualizer:
    """📈 Making our test results look magical"""

    def visualize_results(self, results: Dict[str, TestResults]):
        """Create a fancy visualization of our test results"""
        plt.figure(figsize=(12, 6))

        # Plot accuracy scores
        tests = list(results.keys())
        accuracies = [r.accuracy for r in results.values()]

        plt.bar(tests, accuracies, color='skyblue')
        plt.title("🎯 Accuracy Scores", fontsize=15)
        plt.ylabel("Accuracy (%)")

        # Add some sparkle ✨
        for i, v in enumerate(accuracies):
            plt.text(i, v + 1, f"{v:.1f}%", 
                    ha='center', fontsize=12)

        plt.show()

🆙 Leveling Up: Advanced Features

🌟 The Feature Expander

class FeatureExpander:
    """🌟 Adding new magical powers"""

    async def add_speaker_diarization(self, audio):
        """👥 Identifying different speakers"""
        try:
            # Your speaker diarization code here
            print("🎭 Identifying speakers...")
        except Exception as e:
            print(f"🚨 Speaker identification failed: {str(e)}")

    async def add_sentiment_analysis(self, text):
        """😊 Understanding emotional tones"""
        try:
            response = await self.client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": 
                     "Analyze the emotional tone of this conversation"},
                    {"role": "user", "content": text}
                ]
            )
            return response.choices[0].message.content
        except Exception as e:
            return f"😕 Emotion detection failed: {str(e)}"

🧪 Testing New Features

class FeatureTester:
    """🧪 Testing our new magical powers"""

    async def test_new_feature(self, feature_name: str, test_data: Dict):
        print(f"🧪 Testing {feature_name}...")

        start_time = time.time()
        try:
            # Run the feature
            result = await self.run_feature(feature_name, test_data)

            # Validate the results
            validation = self.validate_feature(feature_name, result)

            # Calculate metrics
            metrics = {
                "success": validation["success"],
                "processing_time": time.time() - start_time,
                "accuracy": validation["accuracy"]
            }

            return {
                "status": "✅ Test passed!" if validation["success"] 
                         else "❌ Test failed!",
                "metrics": metrics
            }

        except Exception as e:
            return {
                "status": "💥 Test crashed!",
                "error": str(e)
            }

⚔️ Battle-Testing in Production

🏆 Performance Testing

class PerformanceTester:
    """⚡ Making sure our system is speed-champion"""

    async def run_performance_test(self, test_duration: int = 3600):
        """🏃‍♂️ Running performance marathon"""
        print("🏁 Starting performance test...")

        metrics = {
            "processed_files": 0,
            "average_processing_time": 0,
            "error_rate": 0,
            "memory_usage": []
        }

        start_time = time.time()
        while time.time() - start_time < test_duration:
            # Process test file
            result = await self.process_test_file()

            # Update metrics
            metrics["processed_files"] += 1
            metrics["average_processing_time"] = (
                (metrics["average_processing_time"] * 
                 (metrics["processed_files"] - 1) + 
                 result["processing_time"]) / 
                metrics["processed_files"]
            )

            # Monitor memory
            metrics["memory_usage"].append(
                psutil.Process().memory_info().rss / 1024 / 1024
            )

        return metrics

📈 Results Dashboard

class ResultsDashboard:
    """📊 Creating our magical mission control center"""

    def create_dashboard(self, metrics: Dict):
        """📊 Building a fancy dashboard"""
        fig = plt.figure(figsize=(15, 10))

        # Processing time graph
        plt.subplot(2, 2, 1)
        plt.plot(metrics["processing_times"])
        plt.title("⚡ Processing Speed")

        # Accuracy graph
        plt.subplot(2, 2, 2)
        plt.bar(["WER", "Accuracy"], 
               [metrics["wer"], metrics["accuracy"]])
        plt.title("🎯 Accuracy Metrics")

        # Memory usage
        plt.subplot(2, 2, 3)
        plt.plot(metrics["memory_usage"])
        plt.title("💾 Memory Usage")

        # Error rates
        plt.subplot(2, 2, 4)
        plt.pie([metrics["success_rate"], 
                100 - metrics["success_rate"]], 
               labels=["Success", "Errors"])
        plt.title("✅ Success Rate")

        plt.tight_layout()
        plt.show()

🎮 Putting It All Together

async def run_complete_system():
    """🎮 Running our complete magical system"""

    # Initialize our system
    print("🧙‍♂️ Initializing magical systems...")
    audio_wizard = AudioWizard(os.getenv('OPENAI_API_KEY'))
    test_master = TestMaster()
    feature_expander = FeatureExpander()

    # Run core functionality tests
    print("\n🧪 Testing core magic...")
    await test_master.run_tests()

    # Test new features
    print("\n🌟 Testing new powers...")
    feature_results = await FeatureTester().test_new_feature(
        "sentiment_analysis",
        {"text": "Sample conversation"}
    )

    # Run performance tests
    print("\n⚡ Running speed tests...")
    performance_metrics = await PerformanceTester().run_performance_test(
        test_duration=300  # 5 minutes test
    )

    # Create dashboard
    print("\n📊 Creating magical dashboard...")
    ResultsDashboard().create_dashboard({
        **test_master.results,
        **performance_metrics
    })

    print("\n✨ All tests completed! ✨")

# Run everything!
await run_complete_system()

🎓 Pro Tips for the Advanced Wizard

🔧 Optimization Spells

   # Pre-process audio for better results
   audio = (AudioProcessor(file)
           .reduce_noise()
           .normalize_volume()
           .export())

🛡️ Error Handling Shield

   # Protect against API timeouts
   @retry(tries=3, delay=2, backoff=2)
   async def protected_api_call():
       # Your API calls here
       pass

⚡ Speed Enchantments

   # Batch processing for multiple files
   async def process_batch(files):
       return await asyncio.gather(
           *[process_file(f) for f in files]
       )

🎯 Future Quests

Ready to take your system to the next level? Here are some epic quests to pursue:

🌈 Multilingual Magic
- Add support for multiple languages
- Implement cross-language translation
- Add cultural context awareness
🎭 Advanced Character Recognition
- Improve speaker diarization
- Add emotion detection
- Implement personality insights
⚡ Performance Boosters
- Implement caching
- Add distributed processing
- Optimize memory usage

🏆 Victory Lap

Congratulations, brave developer! You've built a powerful call intelligence system, complete with:

🎯 Robust testing
📊 Performance monitoring
🌟 Advanced features
🛡️ Error handling

Remember: The best code is like magic - it works reliably, but there's always room for more enchantments!

Now go forth and build amazing things! 🚀✨

P.S. If you find any bugs, they're not bugs - they're just unexpected features taking a vacation! 😉

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