fnaith Video to Text
Extracts speech text from video files using OpenAI Whisper and outputs a transcript with timestamps.
When to Use This Skill
- You need to extract spoken words from a video file
- You want a timestamped transcript for subtitles or analysis
- You need to convert video speech to text for further processing
- You have a video file and want to generate a text transcript
Prerequisites
- Python 3.8+
- ffmpeg installed and available in PATH
- Internet connection (for downloading Whisper models on first run)
Usage
The tool is located at tools/video-to-text/video-to-text.py.
Command Line Arguments
python video-to-text.py <VIDEO_PATH> <OUTPUT_DIR>
VIDEO_PATH: Path to the input video file (supports most formats)OUTPUT_DIR: Directory where the transcript text file will be saved
Example
python tools/video-to-text/video-to-text.py "path/to/video.mp4" "output/folder"
The output file will be named after the video file (e.g., video.txt) and placed in the output directory.
Features
- Automatically creates output directory if it doesn't exist
- Extracts audio from video using ffmpeg (mono, 16kHz WAV)
- Transcribes using OpenAI Whisper (base model by default)
- Outputs timestamps in
HH:mm:ssformat - Handles various video formats supported by ffmpeg
- Clean error messages and progress indicators
Output Format
Each line in the output text file has the format:
[HH:mm:ss] Transcribed text here
Example:
[00:00:00] Hello world
[00:00:02] This is a test
Troubleshooting
Unicode Path Issues on Windows
If the video file path contains non-ASCII characters (e.g., Japanese, Chinese), you may encounter encoding issues on Windows command prompt. Consider:
- Renaming the file to use ASCII characters
- Using short paths (8.3 format)
- Running from PowerShell or WSL
ffmpeg Not Found
Ensure ffmpeg is installed and available in your PATH. You can test with:
ffmpeg -version
Whisper Model Download Failures
The first run downloads the Whisper model (base, ~150MB). Ensure you have internet connectivity and sufficient disk space.
Performance
Transcription speed depends on video length and CPU. For long videos, consider using a smaller model (edit script to use "tiny" or "small").
Customization
You can modify the script to use different Whisper models by changing the line:
model = whisper.load_model("base") # Change to "tiny", "small", "medium", "large"