Initial commit: ShortGenerator project setup
- Add shorts_generator.py and shorts_generator2.py (main scripts) - Add README.md with project documentation - Add requirements.txt with Python dependencies - Add .gitignore to exclude video files and other unnecessary files
This commit is contained in:
klop51
commit
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.gitignore
vendored
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61
.gitignore
vendored
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# Video files (exclude large video files from version control)
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*.mp4
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*.avi
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*.mov
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*.mkv
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*.wmv
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*.flv
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# Virtual environments
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venv/
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env/
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ENV/
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env.bak/
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venv.bak/
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# OS
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.DS_Store
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Thumbs.db
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# Output folders
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shorts/
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output/
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temp/
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# Font zip files
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*.zip
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# Logs
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*.log
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83
README.md
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README.md
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# ShortGenerator
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A Python-based tool for automatically generating short-form videos from longer video content, optimized for platforms like TikTok, Instagram Reels, and YouTube Shorts.
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## Features
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- **Automatic loud moment detection**: Identifies exciting parts of videos using audio analysis
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- **AI-powered transcription**: Uses Whisper for accurate speech-to-text conversion
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- **Dynamic subtitles**: Creates engaging, word-by-word highlighted subtitles in the style of popular content creators
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- **Vertical format optimization**: Automatically converts landscape videos to 9:16 aspect ratio
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- **Batch processing**: Generate multiple shorts from a single video
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## Requirements
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- Python 3.8+
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- FFmpeg
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- Required Python packages (install with `pip install -r requirements.txt`):
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- moviepy
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- faster-whisper
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- numpy
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## Installation
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1. Clone the repository:
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```bash
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git clone https://git.ptbox.org/klop51/ShortGenerator.git
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cd ShortGenerator
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Ensure FFmpeg is installed and accessible from your PATH
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## Usage
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### Basic usage:
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```bash
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python shorts_generator2.py your_video.mp4
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```
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This will:
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1. Analyze your video for loud/exciting moments
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2. Generate up to 3 short clips (5 seconds each by default)
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3. Add dynamic subtitles with word-by-word highlighting
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4. Save the shorts in the `shorts/` folder
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### Customization
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You can modify the script parameters:
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- `max_clips`: Number of shorts to generate
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- `chunk_duration`: Length of each short clip
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- `threshold_db`: Audio loudness threshold for moment detection
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- `output_folder`: Where to save generated shorts
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## Output
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Generated shorts will be:
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- 1080x1920 resolution (9:16 aspect ratio)
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- MP4 format with H.264 codec
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- Include dynamic subtitles with highlighting effects
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- Automatically cropped and centered from the original video
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## File Structure
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```
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ShortGenerator/
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├── shorts_generator2.py # Main script (latest version)
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├── shorts_generator.py # Legacy version
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├── fonts/ # Font files for subtitles
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├── shorts/ # Generated short videos (gitignored)
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└── README.md # This file
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```
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## Contributing
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Feel free to submit issues and enhancement requests!
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## License
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This project is open source. Please check the license file for details.
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3
requirements.txt
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requirements.txt
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moviepy>=1.0.3
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faster-whisper>=0.10.0
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numpy>=1.21.0
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157
shorts_generator.py
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shorts_generator.py
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import os
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import numpy as np
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from moviepy import VideoFileClip, TextClip, CompositeVideoClip
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from faster_whisper import WhisperModel
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def detect_loud_moments(video_path, chunk_duration=5, threshold_db=10):
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print("🔍 Analyzing audio...")
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clip = VideoFileClip(video_path)
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audio = clip.audio.to_soundarray(fps=44100)
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volume = np.linalg.norm(audio, axis=1)
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chunk_size = int(chunk_duration * 44100)
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loud_chunks = []
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max_db = -float('inf')
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for i in range(0, len(volume), chunk_size):
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chunk = volume[i:i+chunk_size]
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db = 20 * np.log10(np.mean(chunk) + 1e-10)
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max_db = max(max_db, db)
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if db > threshold_db:
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start = i / 44100
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loud_chunks.append((start, min(start + chunk_duration, clip.duration)))
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print(f"🔊 Max volume found: {max_db:.2f} dB, threshold: {threshold_db} dB")
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print(f"📈 Found {len(loud_chunks)} loud moments")
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clip.close()
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return loud_chunks
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def transcribe_and_extract_subtitles(video_path, start, end):
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print(f"🗣️ Transcribing audio from {start:.2f}s to {end:.2f}s...")
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model = WhisperModel("base", device="cpu", compute_type="int8")
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segments, _ = model.transcribe(video_path, beam_size=5, language="en", vad_filter=True)
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subtitles = []
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for segment in segments:
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if start <= segment.start <= end:
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subtitles.append((segment.start - start, segment.end - start, segment.text))
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return subtitles
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def create_short_clip(video_path, start, end, subtitles, output_path):
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print(f"🎬 Creating short: {output_path}")
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clip = VideoFileClip(video_path).subclipped(start, end)
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video_duration = clip.duration
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print(f"📏 Video clip duration: {video_duration:.2f}s")
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# Convert to vertical 9:16
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vertical_clip = clip.resized(height=1920).cropped(width=1080, x_center=clip.w / 2)
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clips = [vertical_clip]
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for (s, e, text) in subtitles:
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try:
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# Ensure subtitle timing doesn't exceed video duration
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subtitle_start = max(0, s)
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subtitle_end = min(e, video_duration)
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if subtitle_start >= video_duration or subtitle_end <= subtitle_start:
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print(f"⚠️ Skipping subtitle outside video duration: {text[:30]}...")
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continue
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# Opus Clip style professional subtitles
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words = text.strip().split()
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if not words:
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continue
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# Break text into smaller chunks for better readability (max 3-4 words per line)
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chunks = []
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current_chunk = []
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for word in words:
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current_chunk.append(word)
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if len(current_chunk) >= 3 or len(' '.join(current_chunk)) > 25:
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chunks.append(' '.join(current_chunk))
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current_chunk = []
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if current_chunk:
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chunks.append(' '.join(current_chunk))
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# Position subtitles in the center-bottom area (Opus style)
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subtitle_position = 0.75
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# Create subtitle for each chunk with smooth transitions
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chunk_duration = (subtitle_end - subtitle_start) / len(chunks)
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for chunk_idx, chunk_text in enumerate(chunks):
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chunk_start = subtitle_start + (chunk_idx * chunk_duration)
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chunk_end = min(chunk_start + chunk_duration, subtitle_end)
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chunk_words = chunk_text.split()
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# Base subtitle with Opus-style design (bold white text with strong outline)
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base_subtitle = TextClip(
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text=chunk_text.upper(),
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font='C:/Windows/Fonts/LatoWeb-Bold.ttf', # Lato Bold - excellent for subtitles
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font_size=65, # Larger, chunkier text
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color='white',
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stroke_color='black',
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stroke_width=5 # Thicker outline for better readability
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)
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base_subtitle = base_subtitle.with_start(chunk_start).with_end(chunk_end).with_position(('center', subtitle_position), relative=True)
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clips.append(base_subtitle)
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# Opus-style word-by-word highlighting (yellow/orange like Opus)
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word_duration = chunk_duration / len(chunk_words)
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for i, word in enumerate(chunk_words):
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word_start = chunk_start + (i * word_duration)
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word_end = min(word_start + word_duration * 0.8, chunk_end)
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# Opus-style highlighted word (vibrant yellow/orange)
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highlighted_word = TextClip(
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text=word.upper(),
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font='C:/Windows/Fonts/LatoWeb-Bold.ttf', # Lato Bold for consistency
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font_size=68, # Slightly larger for highlight effect
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color='#FFD700', # Gold/yellow like Opus Clip
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stroke_color='#FF6B35', # Orange outline for pop
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stroke_width=5
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)
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# Calculate precise word positioning within the chunk
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words_before = chunk_words[:i]
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chars_before = sum(len(w) for w in words_before) + len(words_before)
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# More accurate character width calculation for Arial Bold
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char_width = 35 # Adjusted for larger, bolder font
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total_chunk_width = len(chunk_text) * char_width
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word_x_offset = (chars_before * char_width) - (total_chunk_width // 2)
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highlighted_word = highlighted_word.with_start(word_start).with_end(word_end).with_position((540 + word_x_offset, subtitle_position), relative=(False, True))
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clips.append(highlighted_word)
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print(f"✅ Added Opus-style subtitle ({subtitle_start:.1f}s-{subtitle_end:.1f}s): {text[:30]}...")
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except Exception as e:
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print(f"⚠️ Subtitle error: {e}, skipping subtitle: {text[:50]}...")
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continue
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final = CompositeVideoClip(clips, size=(1080, 1920))
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final.write_videofile(output_path, codec="libx264", audio_codec="aac", threads=1)
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# 💥 Force close to avoid Windows pipe errors
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clip.reader.close()
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if clip.audio:
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clip.audio.reader.close()
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final.close()
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def generate_shorts(video_path, max_clips=3, output_folder="shorts"):
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os.makedirs(output_folder, exist_ok=True)
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best_moments = detect_loud_moments(video_path, threshold_db=-30)
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selected = best_moments[:max_clips]
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for i, (start, end) in enumerate(selected):
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subtitles = transcribe_and_extract_subtitles(video_path, start, end)
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out_path = os.path.join(output_folder, f"short_{i+1}.mp4")
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create_short_clip(video_path, start, end, subtitles, out_path)
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if __name__ == "__main__":
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import sys
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if len(sys.argv) < 2:
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print("Usage: python shorts_generator.py your_video.mp4")
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else:
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generate_shorts(sys.argv[1])
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shorts_generator2.py
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shorts_generator2.py
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import os
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import numpy as np
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from moviepy import VideoFileClip, TextClip, CompositeVideoClip
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from faster_whisper import WhisperModel
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def detect_loud_moments(video_path, chunk_duration=5, threshold_db=10):
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print("🔍 Analyzing audio...")
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clip = VideoFileClip(video_path)
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audio = clip.audio.to_soundarray(fps=44100)
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volume = np.linalg.norm(audio, axis=1)
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chunk_size = int(chunk_duration * 44100)
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loud_chunks = []
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max_db = -float('inf')
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for i in range(0, len(volume), chunk_size):
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chunk = volume[i:i+chunk_size]
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db = 20 * np.log10(np.mean(chunk) + 1e-10)
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max_db = max(max_db, db)
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if db > threshold_db:
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start = i / 44100
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loud_chunks.append((start, min(start + chunk_duration, clip.duration)))
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print(f"🔊 Max volume found: {max_db:.2f} dB, threshold: {threshold_db} dB")
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print(f"📈 Found {len(loud_chunks)} loud moments")
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clip.close()
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return loud_chunks
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def transcribe_and_extract_subtitles(video_path, start, end):
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print(f"🗣️ Transcribing audio from {start:.2f}s to {end:.2f}s...")
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model = WhisperModel("base", device="cpu", compute_type="int8")
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segments, _ = model.transcribe(video_path, beam_size=5, language="en", vad_filter=True)
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subtitles = []
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for segment in segments:
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if start <= segment.start <= end:
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subtitles.append((segment.start - start, segment.end - start, segment.text))
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return subtitles
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def create_short_clip(video_path, start, end, subtitles, output_path):
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print(f"🎬 Creating short: {output_path}")
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clip = VideoFileClip(video_path).subclipped(start, end)
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video_duration = clip.duration
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print(f"📏 Video clip duration: {video_duration:.2f}s")
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vertical_clip = clip.resized(height=1920).cropped(width=1080, x_center=clip.w / 2)
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clips = [vertical_clip]
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subtitle_y_px = 1550 # Fixed Y position for subtitles
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for (s, e, text) in subtitles:
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try:
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subtitle_start = max(0, s)
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subtitle_end = min(e, video_duration)
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if subtitle_start >= video_duration or subtitle_end <= subtitle_start:
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print(f"⚠️ Skipping subtitle outside video duration: {text[:30]}...")
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continue
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words = text.strip().split()
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if not words:
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continue
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# Split into small readable chunks (max ~3-4 words)
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chunks = []
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current_chunk = []
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for word in words:
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current_chunk.append(word)
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if len(current_chunk) >= 2 or len(' '.join(current_chunk)) > 25:
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chunks.append(' '.join(current_chunk))
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current_chunk = []
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if current_chunk:
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chunks.append(' '.join(current_chunk))
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chunk_duration = (subtitle_end - subtitle_start) / len(chunks)
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for chunk_idx, chunk_text in enumerate(chunks):
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chunk_start = subtitle_start + (chunk_idx * chunk_duration)
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chunk_end = min(chunk_start + chunk_duration, subtitle_end)
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chunk_words = chunk_text.split()
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# Base subtitle
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base_subtitle = TextClip(
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text=chunk_text.upper(),
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font='C:/Windows/Fonts/LatoWeb-Bold.ttf',
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font_size=65,
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color='white',
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stroke_color='black',
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stroke_width=5
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||||
)
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text_width, _ = base_subtitle.size
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base_subtitle = base_subtitle.with_start(chunk_start).with_end(chunk_end).with_position(('center', subtitle_y_px))
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clips.append(base_subtitle)
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# Highlighted words (perfectly aligned)
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word_duration = chunk_duration / len(chunk_words)
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current_x = 540 - (text_width / 2) # 540 is center X of 1080px width
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for i, word in enumerate(chunk_words):
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word_start = chunk_start + (i * word_duration)
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word_end = min(word_start + word_duration * 0.8, chunk_end)
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highlighted_word = TextClip(
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text=word.upper(),
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font='C:/Windows/Fonts/LatoWeb-Bold.ttf',
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font_size=68,
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color='#FFD700',
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stroke_color='#FF6B35',
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stroke_width=5
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)
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word_width, _ = highlighted_word.size
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word_x = current_x + (word_width / 2)
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highlighted_word = highlighted_word.with_start(word_start).with_end(word_end).with_position((word_x -8, subtitle_y_px))
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clips.append(highlighted_word)
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current_x += word_width + 20 # Add spacing between words
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print(f"✅ Added Opus-style subtitle ({subtitle_start:.1f}s-{subtitle_end:.1f}s): {text[:30]}...")
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except Exception as e:
|
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print(f"⚠️ Subtitle error: {e}, skipping subtitle: {text[:50]}...")
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continue
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||||
final = CompositeVideoClip(clips, size=(1080, 1920))
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final.write_videofile(output_path, codec="libx264", audio_codec="aac", threads=1)
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clip.reader.close()
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if clip.audio:
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||||
clip.audio.reader.close()
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final.close()
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def generate_shorts(video_path, max_clips=3, output_folder="shorts"):
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||||
os.makedirs(output_folder, exist_ok=True)
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||||
best_moments = detect_loud_moments(video_path, threshold_db=-30)
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selected = best_moments[:max_clips]
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for i, (start, end) in enumerate(selected):
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||||
subtitles = transcribe_and_extract_subtitles(video_path, start, end)
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||||
out_path = os.path.join(output_folder, f"short_{i+1}.mp4")
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||||
create_short_clip(video_path, start, end, subtitles, out_path)
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||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python shorts_generator.py your_video.mp4")
|
||||
else:
|
||||
generate_shorts(sys.argv[1])
|
||||
|
||||
Loading…
Reference in New Issue
Block a user