我正在研究這個項目,並開發了一堆工具來完成重型數據工程組件的發布,因為其中一些工具很巧妙,但大多數都是這樣,以便它們被下一個Gemini 模型取代並納入到愚蠢的Google Colab Gemini 建議引擎。 - 提姆
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)
-----------EOF------------
由來自加拿大中西部的 Tim 創建。
2024 年。
本文檔已獲得 GPL 許可。
以上是自訂轉錄和剪輯管道的詳細內容。更多資訊請關注PHP中文網其他相關文章!