"""
Speech Recognition Module
Supports multiple ASR models including Whisper and Parakeet
Handles audio preprocessing and transcription
"""

import logging
import numpy as np
import os
from abc import ABC, abstractmethod

logger = logging.getLogger(__name__)

from faster_whisper import WhisperModel as FasterWhisperModel
from pydub import AudioSegment

class ASRModel(ABC):
    """Base class for ASR models"""
    
    @abstractmethod
    def load_model(self):
        """Load the ASR model"""
        pass
    
    @abstractmethod
    def transcribe(self, audio_path):
        """Transcribe audio to text"""
        pass
    
    def preprocess_audio(self, audio_path):
        """Convert audio to required format"""
        logger.info("Converting audio format")
        audio = AudioSegment.from_file(audio_path)
        processed_audio = audio.set_frame_rate(16000).set_channels(1)
        wav_path = audio_path.replace(".mp3", ".wav") if audio_path.endswith(".mp3") else audio_path
        if not wav_path.endswith(".wav"):
            wav_path = f"{os.path.splitext(wav_path)[0]}.wav"
        processed_audio.export(wav_path, format="wav")
        logger.info(f"Audio converted to: {wav_path}")
        return wav_path


class WhisperModel(ASRModel):
    """Faster Whisper ASR model implementation"""
    
    def __init__(self):
        self.model = None
        # Check for CUDA availability without torch dependency
        try:
            import torch
            self.device = "cuda" if torch.cuda.is_available() else "cpu"
        except ImportError:
            # Fallback to CPU if torch is not available
            self.device = "cpu"
        self.compute_type = "float16" if self.device == "cuda" else "int8"
        
    def load_model(self):
        """Load Faster Whisper model"""
        logger.info("Loading Faster Whisper model")
        logger.info(f"Using device: {self.device}")
        logger.info(f"Using compute type: {self.compute_type}")
        
        # Use large-v3 model with appropriate compute type based on device
        self.model = FasterWhisperModel(
            "large-v3",
            device=self.device,
            compute_type=self.compute_type
        )
        logger.info("Faster Whisper model loaded successfully")
    
    def transcribe(self, audio_path):
        """Transcribe audio using Faster Whisper"""
        if self.model is None:
            self.load_model()
            
        wav_path = self.preprocess_audio(audio_path)
        
        # Transcription with Faster Whisper
        logger.info("Generating transcription with Faster Whisper")
        segments, info = self.model.transcribe(
            wav_path,
            beam_size=5,
            language="en",
            task="transcribe"
        )
        
        logger.info(f"Detected language '{info.language}' with probability {info.language_probability}")
        
        # Collect all segments into a single text
        result_text = ""
        for segment in segments:
            result_text += segment.text + " "
            logger.debug(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
        
        result = result_text.strip()
        logger.info(f"Transcription completed successfully")
        return result


class ParakeetModel(ASRModel):
    """Parakeet ASR model implementation"""
    
    def __init__(self):
        self.model = None
        
    def load_model(self):
        """Load Parakeet model"""
        try:
            import nemo.collections.asr as nemo_asr
            logger.info("Loading Parakeet model")
            self.model = nemo_asr.models.ASRModel.from_pretrained(model_name="nvidia/parakeet-tdt-0.6b-v2")
            logger.info("Parakeet model loaded successfully")
        except ImportError:
            logger.error("Failed to import nemo_toolkit. Please install with: pip install -U 'nemo_toolkit[asr]'")
            raise
    
    def transcribe(self, audio_path):
        """Transcribe audio using Parakeet"""
        if self.model is None:
            self.load_model()
            
        wav_path = self.preprocess_audio(audio_path)
        
        # Transcription
        logger.info("Generating transcription with Parakeet")
        output = self.model.transcribe([wav_path])
        result = output[0].text
        logger.info(f"Transcription completed successfully")
        return result


class ASRFactory:
    """Factory for creating ASR model instances"""
    
    @staticmethod
    def get_model(model_name="parakeet"):
        """
        Get ASR model by name
        Args:
            model_name: Name of the model to use (whisper or parakeet)
        Returns:
            ASR model instance
        """
        if model_name.lower() == "whisper":
            return WhisperModel()
        elif model_name.lower() == "parakeet":
            return ParakeetModel()
        else:
            logger.warning(f"Unknown model: {model_name}, falling back to Whisper")
            return WhisperModel()


def transcribe_audio(audio_path, model_name="parakeet"):
    """
    Convert audio file to text using specified ASR model
    Args:
        audio_path: Path to input audio file
        model_name: Name of the ASR model to use (whisper or parakeet)
    Returns:
        Transcribed English text
    """
    logger.info(f"Starting transcription for: {audio_path} using {model_name} model")
    
    try:
        # Get the appropriate model
        asr_model = ASRFactory.get_model(model_name)
        
        # Transcribe audio
        result = asr_model.transcribe(audio_path)
        logger.info(f"transcription: %s" % result)
        return result

    except Exception as e:
        logger.error(f"Transcription failed: {str(e)}", exc_info=True)
        raise