Por qué lo hice:

Estaba trabajando en este proyecto y desarrollé un montón de herramientas para superar la publicación de componentes de ingeniería de datos pesados ​​porque algunos de ellos son ingeniosos, pero en su mayoría, para que sean utilizados por el próximo modelo Gemini y se incorporen al Estúpido motor de sugerencias Google Colab Gemini. -Tim

Instrucciones y explicaciones

Instrucciones:

1. Asegúrese de tener instaladas las dependencias requeridas (por ejemplo, ffmpeg, Whisperx).
2. Establezca el directorio raíz en su directorio de trabajo que contiene los archivos de video.
3. Define las etapas que deseas detectar en las transcripciones.
4. Ejecute el script para generar transcripciones y extraer videoclips según las etapas detectadas.

Explicaciones:

• Esta herramienta procesa archivos de video en el directorio raíz.
• Transcribe cada video usando el modelo WhisperX.
• El guión luego extrae clips de los videos según las etapas que se encuentran en las transcripciones.
• Las transcripciones y los clips se guardan en los directorios de salida especificados.

Código:

import os
import shutil
import cv2
import numpy as np
import json
from PIL import Image
import random
import string
from rembg import remove
import ffmpeg
from datetime import timedelta
from ultralytics import YOLO
import whisperx
import gc
gc.collect()

# Define paths to directories
root = '/

workspace/'
stages = ['apple', 'banana', 'car', 'dog']

transcript_dir = root   'transcripts'
clip_output_dir = root   'stage1'
stage1_clips_dir = clip_output_dir

# Ensure the output directory exists
os.makedirs(transcript_dir, exist_ok=True)
os.makedirs(clip_output_dir, exist_ok=True)

def log_and_print(message):
    print(message)

def convert_time_to_seconds(time_str):
    hours, minutes, seconds_milliseconds = time_str.split(':')
    seconds, milliseconds = seconds_milliseconds.split(',')
    total_seconds = int(hours) * 3600   int(minutes) * 60   int(seconds)   int(milliseconds) / 1000
    return total_seconds

def transcribe_video(video_path):
    """Transcribe the video using Whisper model and return the transcript."""
    compute_type = "float32"
    model = whisperx.load_model("large-v2", device='cpu', compute_type=compute_type)
    audio = whisperx.load_audio(video_path)
    result = model.transcribe(audio, batch_size=4, language="en")
    model_a, metadata = whisperx.load_align_model(language_code=result["language"], device='cpu')
    aligned_result = whisperx.align(result["segments"], model_a, metadata, audio, 'cpu', return_char_alignments=False)
    segments = aligned_result["segments"]
    transcript = []
    for index, segment in enumerate(segments):
        start_time = str(0)   str(timedelta(seconds=int(segment['start'])))   ',000'
        end_time = str(0)   str(timedelta(seconds=int(segment['end'])))   ',000'
        text = segment['text']
        segment_text = {
            "index": index   1,
            "start_time": start_time,
            "end_time": end_time,
            "text": text.strip(),
        }
        transcript.append(segment_text)
    return transcript

def extract_clips(video_path, transcript, stages):
    """Extract clips from the video based on the transcript and stages."""
    base_filename = os.path.splitext(os.path.basename(video_path))[0]
    clip_index = 0
    current_stage = None
    start_time = None
    partial_transcript = []

    for segment in transcript:
        segment_text = segment["text"].lower()
        for stage in stages:
            if stage in segment_text:
                if current_stage is not None:
                    end_time = convert_time_to_seconds(segment["start_time"])
                    output_clip_filename = f"{base_filename}.{current_stage}.mp4"
                    output_clip = os.path.join(clip_output_dir, output_clip_filename)
                    if not os.path.exists(output_clip):
                        try:
                            ffmpeg.input(video_path, ss=start_time, to=end_time).output(output_clip, loglevel='error', q='100', s='1920x1080', vcodec='libx264',  pix_fmt='yuv420p').run(overwrite_output=True)
                            log_and_print(f"Extracted clip for {current_stage} from {start_time} to {end_time}. Saved: {output_clip}")
                        except ffmpeg.Error as e:
                            log_and_print(f"Error extracting clip: {e}")

                        transcript_text = "\n".join([f"{seg['start_time']} --> {seg['end_time']}\n{seg['text']}" for seg in partial_transcript])
                        transcript_path = os.path.join(clip_output_dir, f"{base_filename}.{current_stage}.json")
                        with open(transcript_path, 'w', encoding='utf-8') as f:
                            json.dump(transcript_text, f, ensure_ascii=False, indent=4)
                        log_and_print(f"Saved partial transcript to {transcript_path}")

                        partial_transcript = []

                current_stage = stage
                start_time = convert_time_to_seconds(segment["start_time"])
            partial_transcript.append(segment)

    if current_stage is not None:
        end_time = convert_time_to_seconds(transcript[-1]["end_time"])
        output_clip_filename = f"{base_filename}.{current_stage}.mp4"
        output_clip = os.path.join(clip_output_dir, output_clip_filename)
        if not os.path.exists(output_clip):
            try:
                ffmpeg.input(video_path, ss=start_time, to=end_time).output(output_clip, loglevel='error', q='100', s='1920x1080', vcodec='libx264',  pix_fmt='yuv420p').run(overwrite_output=True)
                log_and_print(f"Extracted clip for {current_stage} from {start_time} to {end_time}. Saved: {output_clip}")
            except ffmpeg.Error as e:
                log_and_print(f"Error extracting clip: {e}")

            transcript_text = "\n".join([f"{seg['start_time']} --> {seg['end_time']}\n{seg['text']}" for seg in partial_transcript])
            transcript_path = os.path.join(clip_output_dir, f"{base_filename}.{current_stage}.json")
            with open(transcript_path, 'w', encoding='utf-8') as f:
                json.dump(transcript_text, f, ensure_ascii=False, indent=4)
            log_and_print(f"Saved partial transcript to {transcript_path}")

def process_transcripts(input_dir, transcript_dir, stages):
    """Process each video file to generate transcripts and extract clips."""
    video_files = [f for f in os.listdir(input_dir) if f.endswith('.mp4') or f.endswith('.MOV') or f.endswith('.mov')]

    for video_file in video_files:
        video_path = os.path.join(input_dir, video_file)
        transcript_path = os.path.join(transcript_dir, os.path.splitext(video_file)[0]   ".json")

        if not os.path.exists(transcript_path):
            transcript = transcribe_video(video_path)
            with open(transcript_path, 'w', encoding='utf-8') as f:
                json.dump(transcript, f, ensure_ascii=False, indent=4)
            log_and_print(f"Created transcript for {video_path}")
        else:
            with open(transcript_path, 'r', encoding='utf-8') as f:
                transcript = json.load(f)

        extract_clips(video_path, transcript, stages)

process_transcripts(root, transcript_dir, stages)

Palabras clave y hashtags

• Palabras clave: transcripción, procesamiento de vídeo, clipping, WhisperX, automatización, escenarios, videoclips
• Hashtags: #TranscriptionTool #VideoProcessing #ClippingTool #WhisperX #VideoAutomation #StageDetection #VideoClips

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Creado por Tim del Medio Oeste de Canadá.
2024.
Este documento tiene licencia GPL.