audio.py

"""
Audio Transcription and NFL Commentary Processing Module.

This module handles:
1. Whisper-based speech-to-text transcription optimized for NFL broadcasts
2. NFL-specific vocabulary enhancement and corrections
3. Audio preprocessing with noise filtering and normalization
4. Sports-specific text post-processing and standardization

Key Components:
- AudioTranscriber: Main class for speech recognition
- NFLTextCorrector: Sports-specific text correction and enhancement
- AudioPreprocessor: Audio loading and filtering utilities
"""

import re
import subprocess
from typing import Dict, List, Optional, Tuple

import numpy as np
from transformers import pipeline

from config import (
    AUDIO_MODEL_NAME, AUDIO_DEVICE, AUDIO_SAMPLE_RATE, AUDIO_MIN_DURATION, 
    AUDIO_MIN_AMPLITUDE, AUDIO_HIGHPASS_FREQ, AUDIO_LOWPASS_FREQ,
    WHISPER_GENERATION_PARAMS, NFL_SPORTS_CONTEXT, NFL_TEAMS, NFL_POSITIONS,
    ENABLE_DEBUG_PRINTS
)


class AudioPreprocessor:
    """
    Audio preprocessing utilities for enhanced transcription quality.
    
    Handles audio loading, filtering, and normalization to optimize
    Whisper transcription for NFL broadcast content.
    """
    
    def __init__(self, 
                 sample_rate: int = AUDIO_SAMPLE_RATE,
                 highpass_freq: int = AUDIO_HIGHPASS_FREQ,
                 lowpass_freq: int = AUDIO_LOWPASS_FREQ):
        """
        Initialize audio preprocessor.
        
        Args:
            sample_rate: Target sample rate for audio
            highpass_freq: High-pass filter frequency (removes low rumble)
            lowpass_freq: Low-pass filter frequency (removes high noise)
        """
        self.sample_rate = sample_rate
        self.highpass_freq = highpass_freq
        self.lowpass_freq = lowpass_freq
    
    def load_audio(self, path: str) -> Tuple[np.ndarray, int]:
        """
        Load and preprocess audio from video file using FFmpeg.
        
        Applies noise filtering and normalization for optimal transcription.
        
        Args:
            path: Path to video/audio file
            
        Returns:
            Tuple of (audio_array, sample_rate)
        """
        # Build FFmpeg command with audio filtering
        cmd = [
            "ffmpeg", "-i", path,
            "-af", f"highpass=f={self.highpass_freq},lowpass=f={self.lowpass_freq},loudnorm",
            "-f", "s16le",              # 16-bit signed little-endian
            "-acodec", "pcm_s16le",     # PCM codec
            "-ac", "1",                 # Mono channel
            "-ar", str(self.sample_rate), # Sample rate
            "pipe:1"                    # Output to stdout
        ]
        
        try:
            # Run FFmpeg and capture audio data
            process = subprocess.Popen(
                cmd, 
                stdout=subprocess.PIPE, 
                stderr=subprocess.DEVNULL
            )
            raw_audio = process.stdout.read()
            
            # Convert to numpy array and normalize to [-1, 1]
            audio = np.frombuffer(raw_audio, np.int16).astype(np.float32) / 32768.0
            
            # Apply additional normalization if audio is present
            if len(audio) > 0:
                max_amplitude = np.max(np.abs(audio))
                if max_amplitude > 0:
                    audio = audio / (max_amplitude + 1e-8)  # Prevent division by zero
            
            return audio, self.sample_rate
            
        except Exception as e:
            if ENABLE_DEBUG_PRINTS:
                print(f"[ERROR] Audio loading failed for {path}: {e}")
            return np.array([]), self.sample_rate
    
    def is_valid_audio(self, audio: np.ndarray) -> bool:
        """
        Check if audio meets minimum quality requirements for transcription.
        
        Args:
            audio: Audio array to validate
            
        Returns:
            True if audio is suitable for transcription
        """
        if len(audio) == 0:
            return False
        
        # Check minimum duration
        duration = len(audio) / self.sample_rate
        if duration < AUDIO_MIN_DURATION:
            return False
        
        # Check minimum amplitude (not too quiet)
        max_amplitude = np.max(np.abs(audio))
        if max_amplitude < AUDIO_MIN_AMPLITUDE:
            return False
        
        return True


class NFLTextCorrector:
    """
    NFL-specific text correction and enhancement system.
    
    Applies sports vocabulary corrections, standardizes terminology,
    and fixes common transcription errors in NFL commentary.
    """
    
    def __init__(self):
        """Initialize NFL text corrector with sports-specific rules."""
        self.nfl_teams = NFL_TEAMS
        self.nfl_positions = NFL_POSITIONS
        self.sports_context = NFL_SPORTS_CONTEXT
        
        # Build correction dictionaries
        self._build_correction_patterns()
    
    def _build_correction_patterns(self) -> None:
        """Build regex patterns for common NFL terminology corrections."""
        self.basic_corrections = {
            # Position corrections
            r'\bqb\b': "QB",
            r'\bquarter back\b': "quarterback", 
            r'\bwide receiver\b': "wide receiver",
            r'\btight end\b': "tight end",
            r'\brunning back\b': "running back",
            r'\bline backer\b': "linebacker",
            r'\bcorner back\b': "cornerback",
            
            # Play corrections
            r'\btouch down\b': "touchdown",
            r'\bfield goal\b': "field goal", 
            r'\bfirst down\b': "first down",
            r'\bsecond down\b': "second down",
            r'\bthird down\b': "third down",
            r'\bfourth down\b': "fourth down",
            r'\byard line\b': "yard line",
            r'\bend zone\b': "end zone",
            r'\bred zone\b': "red zone",
            r'\btwo minute warning\b': "two minute warning",
            
            # Common misheard words
            r'\bfourty\b': "forty",
            r'\bfourty yard\b': "forty yard",
            r'\btwenny\b': "twenty",
            r'\bthirty yard\b': "thirty yard",
            
            # Numbers/downs that are often misheard
            r'\b1st\b': "first",
            r'\b2nd\b': "second", 
            r'\b3rd\b': "third",
            r'\b4th\b': "fourth",
            r'\b1st and 10\b': "first and ten",
            r'\b2nd and long\b': "second and long",
            r'\b3rd and short\b': "third and short",
            
            # Team name corrections (common mishears)
            r'\bforty niners\b': "49ers",
            r'\bforty-niners\b': "49ers", 
            r'\bsan francisco\b': "49ers",
            r'\bnew england\b': "Patriots",
            r'\bgreen bay\b': "Packers",
            r'\bkansas city\b': "Chiefs",
            r'\bnew york giants\b': "Giants",
            r'\bnew york jets\b': "Jets",
            r'\blos angeles rams\b': "Rams",
            r'\blos angeles chargers\b': "Chargers",
            
            # Yard markers and positions
            r'\b10 yard line\b': "ten yard line",
            r'\b20 yard line\b': "twenty yard line",
            r'\b30 yard line\b': "thirty yard line",
            r'\b40 yard line\b': "forty yard line", 
            r'\b50 yard line\b': "fifty yard line",
            r'\bmid field\b': "midfield",
            r'\bgoal line\b': "goal line",
            
            # Penalties and flags
            r'\bfalse start\b': "false start",
            r'\boff side\b': "offside",
            r'\bpass interference\b': "pass interference",
            r'\broughing the passer\b': "roughing the passer",
            r'\bdelay of game\b': "delay of game",
            
            # Common play calls
            r'\bplay action\b': "play action",
            r'\bscreen pass\b': "screen pass",
            r'\bdraw play\b': "draw play",
            r'\bboot leg\b': "bootleg",
            r'\broll out\b': "rollout",
            r'\bshot gun\b': "shotgun",
            r'\bno huddle\b': "no huddle"
        }
        
        self.fuzzy_corrections = {
            # Team names with common misspellings
            r'\bpatriots?\b': "Patriots",
            r'\bcowboys?\b': "Cowboys", 
            r'\bpackers?\b': "Packers",
            r'\bchief\b': "Chiefs",
            r'\bchiefs?\b': "Chiefs",
            r'\beagles?\b': "Eagles",
            r'\bgiants?\b': "Giants",
            r'\brams?\b': "Rams",
            r'\bsaints?\b': "Saints",
            
            # Positions with common variations
            r'\bquarterbacks?\b': "quarterback",
            r'\brunning backs?\b': "running back",
            r'\bwide receivers?\b': "wide receiver",
            r'\btight ends?\b': "tight end",
            r'\blinebackers?\b': "linebacker",
            r'\bcornerbacks?\b': "cornerback",
            r'\bsafety\b': "safety",
            r'\bsafeties\b': "safety",
            
            # Play terms
            r'\btouchdowns?\b': "touchdown",
            r'\bfield goals?\b': "field goal",
            r'\binterceptions?\b': "interception",
            r'\bfumbles?\b': "fumble",
            r'\bsacks?\b': "sack",
            r'\bpunt\b': "punt",
            r'\bpunts?\b': "punt",
            
            # Numbers and yards
            r'\byards?\b': "yard",
            r'\byards? line\b': "yard line",
            r'\byard lines?\b': "yard line"
        }
        
        # Terms that should always be capitalized
        self.capitalization_terms = [
            "NFL", "QB", "Patriots", "Cowboys", "Chiefs", "Packers", "49ers", 
            "Eagles", "Giants", "Rams", "Saints", "Bills", "Ravens", "Steelers"
        ]
    
    def correct_text(self, text: str) -> str:
        """
        Apply comprehensive NFL-specific text corrections.
        
        Args:
            text: Raw transcription text
            
        Returns:
            Corrected and standardized text
        """
        if not text:
            return text
        
        corrected = text
        
        # Apply basic corrections
        corrected = self._apply_corrections(corrected, self.basic_corrections)
        
        # Apply fuzzy corrections
        corrected = self._apply_corrections(corrected, self.fuzzy_corrections)
        
        # Apply capitalization rules
        corrected = self._apply_capitalization(corrected)
        
        return corrected.strip()
    
    def _apply_corrections(self, text: str, corrections: Dict[str, str]) -> str:
        """Apply a set of regex corrections to text."""
        corrected = text
        for pattern, replacement in corrections.items():
            corrected = re.sub(pattern, replacement, corrected, flags=re.IGNORECASE)
        return corrected
    
    def _apply_capitalization(self, text: str) -> str:
        """Apply proper capitalization for NFL terms."""
        corrected = text
        for term in self.capitalization_terms:
            pattern = r'\b' + re.escape(term.lower()) + r'\b'
            corrected = re.sub(pattern, term, corrected, flags=re.IGNORECASE)
        return corrected


class AudioTranscriber:
    """
    Whisper-based audio transcriber optimized for NFL broadcasts.
    
    Combines Whisper speech recognition with NFL-specific enhancements
    for high-quality sports commentary transcription.
    """
    
    def __init__(self, 
                 model_name: str = AUDIO_MODEL_NAME,
                 device: int = AUDIO_DEVICE):
        """
        Initialize audio transcriber.
        
        Args:
            model_name: Whisper model name (base, medium, large)
            device: Device for inference (-1 for CPU, 0+ for GPU)
        """
        self.model_name = model_name
        self.device = device
        self.preprocessor = AudioPreprocessor()
        self.corrector = NFLTextCorrector()
        
        # Initialize Whisper pipeline
        self._initialize_pipeline()
    
    def _initialize_pipeline(self) -> None:
        """Initialize the Whisper transcription pipeline."""
        if ENABLE_DEBUG_PRINTS:
            print(f"Initializing Whisper pipeline: {self.model_name}")
        
        self.pipeline = pipeline(
            "automatic-speech-recognition",
            model=self.model_name,
            device=self.device,
            generate_kwargs=WHISPER_GENERATION_PARAMS
        )
        
        if ENABLE_DEBUG_PRINTS:
            print("Whisper pipeline ready")
    
    def transcribe_clip(self, video_path: str) -> str:
        """
        Transcribe audio from a video clip with NFL-specific enhancements.
        
        Args:
            video_path: Path to video file
            
        Returns:
            Transcribed and corrected text
        """
        # Load and preprocess audio
        audio, sample_rate = self.preprocessor.load_audio(video_path)
        
        # Validate audio quality
        if not self.preprocessor.is_valid_audio(audio):
            return ""
        
        try:
            # Run Whisper transcription
            result = self.pipeline(audio, generate_kwargs=WHISPER_GENERATION_PARAMS)
            text = result.get("text", "").strip()
            
            # Clean up common Whisper artifacts
            text = self._clean_whisper_artifacts(text)
            
            # Apply NFL-specific corrections
            text = self.corrector.correct_text(text)
            
            return text
            
        except Exception as e:
            if ENABLE_DEBUG_PRINTS:
                print(f"[WARN] Transcription failed for {video_path}: {e}")
            return ""
    
    def _clean_whisper_artifacts(self, text: str) -> str:
        """Remove common Whisper transcription artifacts."""
        # Remove placeholder text
        text = text.replace("[BLANK_AUDIO]", "")
        text = text.replace("♪", "")  # Remove music notes
        text = text.replace("(music)", "")
        text = text.replace("[music]", "")
        
        # Clean up extra whitespace
        text = re.sub(r'\s+', ' ', text)
        
        return text.strip()


# ============================================================================
# CONVENIENCE FUNCTIONS FOR BACKWARD COMPATIBILITY
# ============================================================================

# Global instance for backward compatibility
_audio_transcriber = None

def get_audio_transcriber() -> AudioTranscriber:
    """Get global audio transcriber instance (lazy initialization)."""
    global _audio_transcriber
    if _audio_transcriber is None:
        _audio_transcriber = AudioTranscriber()
    return _audio_transcriber

def transcribe_clip(path: str) -> str:
    """
    Backward compatibility function for audio transcription.
    
    Args:
        path: Path to video file
        
    Returns:
        Transcribed text
    """
    transcriber = get_audio_transcriber()
    return transcriber.transcribe_clip(path)

def load_audio(path: str, sr: int = AUDIO_SAMPLE_RATE) -> Tuple[np.ndarray, int]:
    """
    Backward compatibility function for audio loading.
    
    Args:
        path: Path to audio/video file
        sr: Sample rate (kept for compatibility)
        
    Returns:
        Tuple of (audio_array, sample_rate)
    """
    preprocessor = AudioPreprocessor(sample_rate=sr)
    return preprocessor.load_audio(path)

def apply_sports_corrections(text: str) -> str:
    """
    Backward compatibility function for text corrections.
    
    Args:
        text: Text to correct
        
    Returns:
        Corrected text
    """
    corrector = NFLTextCorrector()
    return corrector.correct_text(text)

def fuzzy_sports_corrections(text: str) -> str:
    """
    Backward compatibility function (now handled within NFLTextCorrector).
    
    Args:
        text: Text to correct
        
    Returns:
        Corrected text
    """
    return apply_sports_corrections(text)