import mlx_whisper
import numpy as np
import sounddevice as sd
import queue
import sys
import torch
from silero_vad import load_silero_vad, get_speech_timestamps
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Parameters
WHISPER_MODEL = "mlx-community/whisper-small.en-mlx"
TRANSLATE_MODEL = "facebook/nllb-200-distilled-600M"
TARGET_LANG = "spa_Latn" # Spanish (Latin America) - change as needed
CHANNELS = 1
SAMPLERATE = 16000
BLOCK_SIZE = 512
VAD_THRESHOLD = 0.5 
BUFFER_LIMIT = SAMPLERATE * 30 
MIN_SILENCE_DURATION_MS = 500 

audio_queue = queue.Queue()

def callback(indata, frames, time, status):
    if status:
        print(status, file=sys.stderr)
    audio_queue.put(indata.copy())

def main():
    # Set device for translation model (using MPS for Mac M chips)
    device = "mps" if torch.backends.mps.is_available() else "cpu"
    print(f"Using device: {device}")

    print(f"Loading Whisper model '{WHISPER_MODEL}'...")
    # Whisper MLX runs on its own optimized path
    
    print(f"Loading translation model '{TRANSLATE_MODEL}'...")
    tokenizer = AutoTokenizer.from_pretrained(TRANSLATE_MODEL)
    model = AutoModelForSeq2SeqLM.from_pretrained(TRANSLATE_MODEL).to(device)
    translator = pipeline("translation", model=model, tokenizer=tokenizer, 
                          src_lang="eng_Latn", tgt_lang=TARGET_LANG, device=device)

    print("Loading Silero VAD model...")
    vad_model = load_silero_vad()
    
    print("Models loaded.")

    print(f"\nStarting live transcription & translation to {TARGET_LANG}... (Press Ctrl+C to stop)")
    
    audio_buffer = []
    speech_started = False

    try:
        with sd.InputStream(samplerate=SAMPLERATE, channels=CHANNELS, callback=callback, blocksize=BLOCK_SIZE):
            while True:
                while not audio_queue.empty():
                    data = audio_queue.get()
                    audio_buffer.append(data.flatten())

                if len(audio_buffer) > 0:
                    current_audio = np.concatenate(audio_buffer)
                    audio_tensor = torch.from_numpy(current_audio)
                    
                    speech_timestamps = get_speech_timestamps(
                        audio_tensor, 
                        vad_model, 
                        sampling_rate=SAMPLERATE,
                        threshold=VAD_THRESHOLD,
                        min_silence_duration_ms=MIN_SILENCE_DURATION_MS
                    )

                    if len(speech_timestamps) > 0:
                        speech_started = True
                        last_end = speech_timestamps[-1]['end']
                        buffer_len_samples = len(current_audio)
                        
                        if (buffer_len_samples - last_end) > (SAMPLERATE * MIN_SILENCE_DURATION_MS / 1000) or buffer_len_samples > BUFFER_LIMIT:
                            
                            # 1. Transcribe (English)
                            result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=WHISPER_MODEL)
                            original_text = result['text'].strip()
                            
                            if original_text:
                                # 2. Translate
                                translation = translator(original_text)
                                translated_text = translation[0]['translation_text']
                                
                                print(f"\nEN: {original_text}")
                                print(f"ES: {translated_text}")
                            
                            audio_buffer = []
                            speech_started = False
                    
                    elif not speech_started and len(current_audio) > SAMPLERATE * 2:
                        audio_buffer = []

    except KeyboardInterrupt:
        print("\nStopped by user.")
    except Exception as e:
        print(f"\nError: {e}")

if __name__ == "__main__":
    main()