Automatically generate viral-ready vertical short clips from long-form gameplay footage using AI-powered scene analysis, GPU-accelerated rendering, and optional AI voiceovers.

AutoShorts analyzes your gameplay videos to identify the most engaging moments—action sequences, funny fails, or highlight achievements—then automatically crops, renders, and adds subtitles or AI voiceovers to create ready-to-upload short-form content.

Here are some shorts automatically generated from gameplay footage:

Sample 1	Sample 2	Sample 3	Sample 4

• Multi-Provider Support: Choose between OpenAI (GPT-5-mini, GPT-4o) or Google Gemini for scene analysis
• Semantic Analysis Modes:
  • action — Focus on intense combat/action moments
  • funny — Detect fail compilations and humorous moments
  • highlight — Find memorable achievements and clutch plays
  • mixed — Auto-detect the best category for each clip (recommended)

• Speech Mode: Uses OpenAI Whisper to transcribe voice/commentary
• AI Captions Mode: AI-generated contextual captions for gameplay without voice
• Caption Styles: gaming, dramatic, funny, minimal, or auto
• PyCaps Integration: Multiple visual templates including hype, retro-gaming, neo-minimal
• AI Enhancement: Semantic tagging and emoji suggestions (e.g., "HEADSHOT! 💀🔥")

• Local TTS Generation: No cloud API needed for voice synthesis
• Emotion Control: Adjustable emotion/exaggeration levels for English
• Multilingual Support: 20+ languages including Japanese, Korean, Chinese, Spanish, French, and more
• Voice Cloning: Optional reference audio for custom voice styles
• Smart Mixing: Automatic ducking of game audio when voiceover plays

• Scene Detection: Custom implementation using decord + PyTorch on GPU
• Audio Analysis: torchaudio on GPU for fast RMS and spectral flux calculation
• Video Analysis: GPU streaming via decord for stable motion estimation
• Image Processing: cupy (CUDA-accelerated NumPy) for blur and transforms
• Rendering: PyTorch + NVENC hardware encoder for ultra-fast rendering

• Scenes ranked by combined action score (audio 0.6 + video 0.4 weights)
• Configurable aspect ratio (default 9:16 for TikTok/Shorts/Reels)
• Smart cropping with optional blurred background for non-vertical footage
• Retry logic during rendering to avoid spurious failures

AutoShorts is designed to work even when optimal components fail:

• NVIDIA GPU with CUDA support (RTX series recommended for NVENC + TTS)
• NVIDIA Drivers compatible with CUDA 12.x

• Python 3.10
• FFmpeg 4.4.2 (for Decord compatibility)
• CUDA Toolkit with nvcc (for building Decord from source)
• System libraries: libgl1, libglib2.0-0

The Makefile handles everything automatically—environment creation, dependency installation, and building Decord with CUDA support.

git clone https://github.com/divyaprakash0426/autoshorts.git
cd autoshorts

# Run the installer (uses conda/micromamba automatically)
make install

# Activate the environment
overlay use .venv/bin/activate.nu    # For Nushell
# OR
source .venv/bin/activate            # For Bash/Zsh

The Makefile will:

1. Download micromamba if conda/mamba is not found
2. Create a Python 3.10 environment with FFmpeg 4.4.2
3. Install NV Codec Headers for NVENC support
4. Build Decord from source with CUDA enabled
5. Install all pip requirements

Prerequisite: NVIDIA Container Toolkit must be installed.

# Build the image
docker build -t autoshorts .

# Run with GPU access
docker run --rm \
    --gpus all \
    -v $(pwd)/gameplay:/app/gameplay \
    -v $(pwd)/generated:/app/generated \
    --env-file .env \
    autoshorts

Note: The --gpus all flag is essential for NVENC and CUDA acceleration.

Copy .env.example to .env and configure:

cp .env.example .env

See .env.example for the complete list with detailed descriptions.

1. Place source videos in the gameplay/ directory

2. Run the script:

   python run.py

3. Generated clips are saved to generated/

generated/
├── video_name scene-0.mp4          # Rendered short clip
├── video_name scene-0_sub.json     # Subtitle data
├── video_name scene-0.ffmpeg.log   # Render log
├── video_name scene-1.mp4
└── ...

pip install ruff
ruff check .

pytest -q

Tests mock GPU availability and can run in standard CI environments.

For faster iteration during development, you can skip expensive steps using these environment variables in your .env:

Example workflow for testing subtitles only:

# In .env
DEBUG_SKIP_ANALYSIS=1
DEBUG_SKIP_RENDER=1
DEBUG_RENDERED_CLIPS="generated/test_clip.mp4:action"

This project builds upon the excellent work of:

• artryazanov/shorts-maker-gpu — Heuristics-based shorts maker
• Binary-Bytes/Auto-YouTube-Shorts-Maker — Original concept and inspiration

This project is licensed under the MIT License.