#!/opt/homebrew/bin/python3.11
import sys
import json
import tempfile
import os
from faster_whisper import WhisperModel


def run_asr(video_path, output_path):
    print(f"ASR_START", file=sys.stderr)
    print(f"Loading Whisper model...", file=sys.stderr)

    model = WhisperModel("tiny", device="cpu", compute_type="int8")

    print(f"Transcribing: {video_path}", file=sys.stderr)
    segments, info = model.transcribe(video_path, beam_size=5)

    print(f"ASR_LANGUAGE:{info.language}", file=sys.stderr)
    print(
        f"Detected language: {info.language} (probability: {info.language_probability:.2f})",
        file=sys.stderr,
    )

    results = []
    total_segments = 0

    for segment in segments:
        results.append(
            {"start": segment.start, "end": segment.end, "text": segment.text.strip()}
        )
        total_segments += 1
        if total_segments % 100 == 0:
            print(f"ASR_PROGRESS:{total_segments}", file=sys.stderr)

    output = {
        "language": info.language,
        "language_probability": info.language_probability,
        "segments": results,
    }

    with open(output_path, "w") as f:
        json.dump(output, f, indent=2)

    print(f"ASR_COMPLETE:{total_segments}", file=sys.stderr)
    print(f"ASR complete. {len(results)} segments.", file=sys.stderr)


if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: asr_processor.py <video_path> <output_json_path>")
        sys.exit(1)

    run_asr(sys.argv[1], sys.argv[2])