180 lines
4.4 KiB
Python
180 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""Procesamiento de vídeo: extrae audio, transcribe/traduce y
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quema subtítulos.
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Flujo:
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- Extrae audio con ffmpeg (WAV 16k mono)
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- Transcribe con faster-whisper o openai-whisper
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(opción task='translate')
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- Escribe SRT y lo incrusta en el vídeo con ffmpeg
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Nota: requiere ffmpeg instalado y, para modelos, faster-whisper
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o openai-whisper.
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"""
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import argparse
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import subprocess
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import tempfile
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from pathlib import Path
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import sys
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from transcribe import write_srt
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def extract_audio(video_path: str, out_audio: str):
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cmd = [
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"ffmpeg",
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"-y",
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"-i",
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video_path,
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"-vn",
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"-acodec",
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"pcm_s16le",
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"-ar",
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"16000",
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"-ac",
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"1",
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out_audio,
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]
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subprocess.run(cmd, check=True)
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def burn_subtitles(video_path: str, srt_path: str, out_video: str):
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# Usar filtro subtitles de ffmpeg
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cmd = [
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"ffmpeg",
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"-y",
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"-i",
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video_path,
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"-vf",
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f"subtitles={srt_path}",
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"-c:a",
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"copy",
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out_video,
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]
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subprocess.run(cmd, check=True)
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def transcribe_and_translate_faster(audio_path: str, model: str, target: str):
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from faster_whisper import WhisperModel
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wm = WhisperModel(model, device="cpu", compute_type="int8")
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segments, info = wm.transcribe(
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audio_path, beam_size=5, task="translate", language=target
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)
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return segments
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def transcribe_and_translate_openai(audio_path: str, model: str, target: str):
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import whisper
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m = whisper.load_model(model, device="cpu")
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result = m.transcribe(
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audio_path, fp16=False, task="translate", language=target
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)
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return result.get("segments", None)
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def main():
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parser = argparse.ArgumentParser(
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description=(
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"Extraer, transcribir/traducir y quemar subtítulos en vídeo"
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" (offline)"
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)
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)
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parser.add_argument(
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"--video", "-v", required=True, help="Ruta del archivo de vídeo"
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)
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parser.add_argument(
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"--backend",
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"-b",
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choices=["faster-whisper", "openai-whisper"],
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default="faster-whisper",
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)
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parser.add_argument(
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"--model",
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"-m",
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default="base",
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help="Modelo de whisper a usar (tiny, base, etc.)",
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)
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parser.add_argument(
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"--to", "-t", default="es", help="Idioma de destino para traducción"
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)
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parser.add_argument(
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"--out",
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"-o",
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default=None,
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help=(
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"Ruta del vídeo de salida (si no se especifica,"
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" se usa input_burned.mp4)"
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),
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)
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parser.add_argument(
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"--srt",
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default=None,
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help=(
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"Ruta SRT a escribir (si no se especifica,"
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" se usa input.srt)"
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),
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)
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args = parser.parse_args()
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video = Path(args.video)
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if not video.exists():
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print("Vídeo no encontrado", file=sys.stderr)
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sys.exit(2)
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out_video = (
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args.out
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if args.out
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else str(video.with_name(video.stem + "_burned.mp4"))
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)
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srt_path = args.srt if args.srt else str(video.with_suffix('.srt'))
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
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audio_path = tmp.name
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try:
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print("Extrayendo audio con ffmpeg...")
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extract_audio(str(video), audio_path)
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print(
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f"Transcribiendo y traduciendo a '{args.to}'"
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f" usando {args.backend}..."
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)
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if args.backend == "faster-whisper":
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segments = transcribe_and_translate_faster(
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audio_path, args.model, args.to
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)
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else:
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segments = transcribe_and_translate_openai(
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audio_path, args.model, args.to
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)
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if not segments:
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print(
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"No se obtuvieron segmentos de la transcripción",
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file=sys.stderr,
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)
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sys.exit(3)
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print(f"Escribiendo SRT en {srt_path}...")
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write_srt(segments, srt_path)
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print(
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f"Quemando subtítulos en el vídeo -> {out_video}"
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f" (esto puede tardar)..."
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)
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burn_subtitles(str(video), srt_path, out_video)
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print("Proceso completado.")
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finally:
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try:
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Path(audio_path).unlink()
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except Exception:
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pass
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if __name__ == "__main__":
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main()
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