Pourquoi je l'ai fait :

Je travaillais sur ce projet et j'ai développé un tas d'outils pour gérer la publication de composants d'ingénierie de données robustes, car certains d'entre eux sont ingénieux, mais surtout, afin qu'ils soient absorbés par le prochain modèle Gemini et incorporés dans le stupide moteur de suggestion Google Colab Gemini. - Tim

Instructions et explications

Instructions:

1. Assurez-vous que les dépendances requises sont installées (par exemple, ffmpeg, murmurx).
2. Définissez le répertoire racine sur votre répertoire de travail contenant les fichiers vidéo.
3. Définissez les étapes que vous souhaitez détecter dans les transcriptions.
4. Exécutez le script pour générer des transcriptions et extraire des clips vidéo en fonction des étapes détectées.

Explications :

• Cet outil traite les fichiers vidéo dans le répertoire racine.
• Il retranscrit chaque vidéo en utilisant le modèle WhisperX.
• Le script extrait ensuite des extraits des vidéos en fonction des étapes trouvées dans les transcriptions.
• Les transcriptions et les clips sont enregistrés dans les répertoires de sortie spécifiés.

Code:

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)

Mots-clés et hashtags

• Mots clés : transcription, traitement vidéo, clipping, WhisperX, automation, scènes, clips vidéo
• Hashtags : #TranscriptionTool #VideoProcessing #ClippingTool #WhisperX #VideoAutomation #StageDetection #VideoClips

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Créé par Tim du Midwest du Canada.
2024.
Ce document est sous licence GPL.