audio_processors/raw_spectrogram.py

import numpy as np
import logging
from .base_processor import AudioProcessor

logger = logging.getLogger(__name__)

class RawSpectrogramProcessor(AudioProcessor):
    """
    Raw Spectrogram processor using STFT (Short-Time Fourier Transform).
    
    Future implementation will:
    - Apply STFT to audio data for time-frequency representation
    - Use CNN classifier trained on spectrogram images
    - Process raw frequency domain features without mel scaling
    
    Currently returns placeholder '00' for testing UI functionality.
    """
    
    def __init__(self):
        super().__init__("Raw Spectrogram")
        logger.info("Raw Spectrogram processor initialized (PLACEHOLDER MODE)")
    
    def process_audio(self, audio_data: bytes) -> str:
        """
        Process audio using raw spectrogram analysis.
        
        PLACEHOLDER IMPLEMENTATION:
        Currently returns '00' for UI testing purposes.
        
        Future implementation will:
        1. Convert audio bytes to numpy array
        2. Apply STFT with appropriate window size and overlap
        3. Create time-frequency representation
        4. Normalize spectrogram values
        5. Feed to trained CNN model
        6. Return predicted digit
        
        Args:
            audio_data: Raw audio bytes
            
        Returns:
            Predicted digit as string (currently '00')
        """
        logger.debug("Processing audio with Raw Spectrogram (placeholder)")
        
        # Simulate processing time
        import time
        time.sleep(0.1)
        
        # TODO: Implement actual STFT-based processing:
        # 1. audio_array = np.frombuffer(audio_data, dtype=np.float32)
        # 2. stft_result = np.abs(librosa.stft(audio_array, n_fft=2048, hop_length=512))
        # 3. spectrogram = librosa.amplitude_to_db(stft_result, ref=np.max)
        # 4. prediction = self.cnn_model.predict(spectrogram)
        # 5. return str(np.argmax(prediction))
        
        return '00'
    
    def get_model_info(self) -> dict:
        """Get information about the raw spectrogram model."""
        return {
            'method': 'Raw Spectrogram (STFT)',
            'status': 'PLACEHOLDER',
            'features': 'Time-frequency representation',
            'classifier': 'CNN (not implemented)',
            'window_size': 2048,
            'hop_length': 512,
            'expected_inference_time': '<1s'
        }