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| license: mit |
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| # Create dataset from video |
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| This project contains a script based on an OpenAI model that will help you automatically create your own text dataset from video files or podcasts. |
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| Under the hood, it uses the powerful **Whisper-Large-V3** model, which can accept the following audio and video formats: .mp3, .m4a, .wav, .mp4, .mkv, and .webm. |
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| *(We recommend using the .mp3 and .mp4 formats for best compatibility and performance.)* |
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| ### Installation and Run |
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| 1. Make sure you have Python 3.9 or later installed. |
| 2. Install FFmpeg (required, as extracting audio from video won't work without it). |
| * On Windows, open a command prompt and enter: |
| `winget install ffmpeg` |
| 3. Install PyTorch with support for your graphics card (CUDA) for faster recognition: |
| ```bash |
| pip install torch torchvision torchaudio --index-url [https://download.pytorch.org/whl/cu121](https://download.pytorch.org/whl/cu121) |
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| 4. Install the necessary libraries for working with the model: |
| pip install transformers accelerate |
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| 5. Create two empty folders in the script folder: |
| * `Videos` (place your videos here) |
| * `model` (for local storage of Whisper weights). |
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| 6. Run the script. All processed dialogs will be automatically saved to a clean .txt file. |
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| **⚠️ Licensing Information:** |
| * The code portion of this project (automation scripts) is distributed under the MIT license. |
| * The Whisper neural network model itself, developed by OpenAI, is distributed under the Apache 2.0 license. |
