from typing import Any
import librosa
import numpy as np
from logger import logger
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

model_ids = {
    "Distil-Whisper": [
        "distil-whisper/distil-large-v2",
        "distil-whisper/distil-medium.en",
        "distil-whisper/distil-small.en"
    ],
    "Whisper": [
        "openai/whisper-large-v3",
        "openai/whisper-large-v2",
        "openai/whisper-large",
        "openai/whisper-medium",
        "openai/whisper-small",
        "openai/whisper-base",
        "openai/whisper-tiny",
        "openai/whisper-medium.en",
        "openai/whisper-small.en",
        "openai/whisper-base.en",
        "openai/whisper-tiny.en",
    ]
}

model_type = model_ids["Distil-Whisper"]

logger.info(model_type)
model_id = model_type[0]


processor: Any = AutoProcessor.from_pretrained(pretrained_model_name_or_path=model_id)  # type: ignore

pt_model: Any = AutoModelForSpeechSeq2Seq.from_pretrained(pretrained_model_name_or_path=model_id)  # type: ignore
pt_model.eval()  # type: ignore


def extract_input_features(audio_array: np.ndarray, sampling_rate: int):
    """Extract input features from audio array and sampling rate."""
    input_features = processor(
        audio_array,
        sampling_rate=sampling_rate,
        return_tensors="pt",
    ).input_features
    return input_features


def load_audio_file(file_path: str) -> tuple[np.ndarray, int]:
    """Load audio file from disk and return audio array and sampling rate."""
    try:
        # Load audio file using librosa
        audio_array, sampling_rate = librosa.load(file_path, sr=None)
        logger.info(f"Loaded audio file: {file_path}, duration: {len(audio_array)/sampling_rate:.2f}s, sample rate: {sampling_rate}Hz")
        return audio_array, int(sampling_rate)  # Ensure sampling_rate is int
    except Exception as e:
        logger.error(f"Error loading audio file {file_path}: {e}")
        raise


# Example usage - replace with your audio file path
audio_file_path = "/voicebot/F_0818_15y11m_1.wav"

# Load audio from file instead of dataset
try:
    audio_array, sampling_rate = load_audio_file(audio_file_path)
    input_features = extract_input_features(audio_array, sampling_rate)
    
    predicted_ids = pt_model.generate(input_features)  # type: ignore
    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)  # type: ignore
    
    print(f"Audio file: {audio_file_path}")
    print(f"Transcription: {transcription[0]}")
    
except FileNotFoundError:
    logger.error(f"Audio file not found: {audio_file_path}")
    print("Please update the audio_file_path variable with a valid path to your wav file")
except Exception as e:
    logger.error(f"Error processing audio: {e}")
    print(f"Error: {e}")