EmojiVidGen is a fun tool that creates videos from text files. It takes input in the form of plain text files containing a script (similar to a story or dialogue). It then transforms this script into a stunning video. EmojiVidGen is based on a plugin system, which allows for experimenting with different models and languages. All you need is some imagination and typing skills !
Key Features
- Converts text files into visually appealing videos
- Automatically generates narration, images, and audio effects
- Designed to run smoothly on computers with 8 GB of memory, offering reasonable processing speeds even without GPUs
- Utilizes various Generative AI models for its tasks
- Built on a powerful plugin system, allowing for easy extensibility
- Switch between different models and spoken languages.
While initially intended for entertainment with GenAI, EmojiVidGen holds significant potential for producing engaging and cool content, especially in capable hands. This project is experimental and primarily crafted for educational purposes, exploring the possibilities of AI-powered video creation.
This software is intended solely for educational purposes. It is used at your own discretion and risk. Please be aware that the AI models utilized in this code may have restrictions against commercial usage.
sudo apt update
sudo apt install espeak ffmpeg
git clone https://github.com/code2k13/emoji_vid_gen
cd emoji_vid_gen
wget https://github.com/googlefonts/noto-emoji/raw/main/fonts/NotoColorEmoji.ttfpip install -r requirements.txtNote: A script should always start with a
Image:directive
Image: Cartoon illustration showing a beautiful landscape with mountains and a road.
Audio: Tranquil calm music occasional chirping of birds.
Title: EmojiVidGen
🐼: Emoji vid gen is a tool to create videos from text files using AI.python generate_video.py stories/hello.txt hello.mp4Image: A single trophy kept on table. comic book style.
Audio: Upbeat introduction music for cartoon show.
Title: Emoji Quiz Showdown
🎤: "Welcome to the Emoji Quiz Showdown! Are you ready to test your knowledge?"
🐱: "Meow! I'm ready!"
🐶: "Woof! Let's do this!"
Image: Cartoon illustration of the Eiffel Tower.
🎤: "First question What is the capital of France?"
Audio: suspenseful music playing.
🐱: "Paris!"
Audio: people applauding sound
Image: Cartoon illustration of Mount Everest.
🎤: "Correct! One point for the cat! Next question What is the tallest mountain in the world?"
Audio: suspenseful music playing.
🐶: "Mount Everest!"
Audio: people applauding sound
Image: Cartoon illustration of a water molecule.
🎤: "Right again! One point for the dog! Next question What is the chemical symbol for water?"
Audio: suspenseful music playing.
🐱: "H2O!"
Audio: people applauding sound
Image: Cartoon illustration of a globe with seven continents.
🎤: "Correct! Another point for the cat! Last question How many continents are there on Earth?"
Audio: suspenseful music playing.
🐶: "Seven!"
Audio: people applauding sound
🎤: "Correct! It's a tie! You both did great! Thanks for playing the Emoji Quiz Showdown!"The emoji 🎙️ is reserved as narrator. Using it at start of line will cause the system to only generated sound and not output any image on background.
If you've followed the earlier instructions for video generation, you might have noticed that the default setup uses espeak as the text-to-speech engine, resulting in a robotic-sounding output. EmojiVidGen is built with an internal structure comprising of plugins, each capable of modifying how a task is executed or which model is used.
For instance, you can designate a specific plugin for each type of generation task—be it text-to-image, text-to-audio, or text-to-speech. Because each plugin operates with its unique model and method, configuring these settings individually can be overwhelming. To simplify this process, I've introduced the concept of presets. You can apply a preset by supplying the --preset option to the generate_video.py file.
For example the below preset uses a preset called local_medium.
python generate_video.py stories/hello.txt hello.mp4 --preset local_mediumAll presets are stored in ./presets folder. To create a new preset (say custom_preset), just create a new custom_preset.yaml file in `./presets' folder and start using it like this
python generate_video.py stories/hello.txt hello.mp4 --preset custom_presetNote that the voices used in characters section should be supported by the selected text_to_speech provider. Images should ideally be PNG files with square aspect ration and transparent background.
| Preset Name | Description |
|---|---|
| openai_basic | Uses OpenAI for text to speech (standard) and image generation (DALL-E 2 @ 512x512). Needs OPENAI_API_KEY environment variable to be populated |
| openai_medium | Similar to openai_basic but uses (DALL-E 3 @ 1024x1024). Needs OPENAI_API_KEY environment variable to be populated |
| local_basic | Uses Huggingface's Stable Diffusion pipeline with stabilityai/sd-turbo model for text to image. Uses espeak for text to speech and Huggingface's AudioLDM pipeline for text to audio. |
| local_basic_gpu | Same as local_basic, but with cuda support enabled. |
| local_medium | Similar to local_basic but uses brave as text to speech engine and stabilityai/sdxl-turbo model for text to image |
| local_medium | Same as local_medium, but with cuda support is enabled. |
| eleven_medium | Same as local_medium, but uses ElevenLabs text to speech API support is enabled. Needs internet and ELEVEN_API_KEY variable to be defined in .env file. Needs internet and ElevenLabs account. |
| parler_medium | Same as local_medium, but uses parler text to speech API support is enabled. |
Sometimes you may not want to use emojis as characters in your video or use a different voice for each character. This can now be achieved using the characters section in preset yaml files. Given below is an example of how such a section might look like:
global:
width: 512
height: 512
use_cuda: "false"
characters:
- name: "🎤"
voice: "fable"
- name: "🐱"
image: "/workspace/emoji_vid_gen/cat.png"
voice: "alloy"
- name: "🐶"
image: "/workspace/emoji_vid_gen/dog.png"
voice: "echo"
text_to_speech:
provider: openai
voice: NovaWIP
EmojiVidGen utilizes a cache mechanism to retain assets produced during video creation, each associated with the specific 'prompt' used. This feature proves highly beneficial, especially when iteratively refining videos, eliminating the need to regenerate assets repetitively. However, please be aware that the .cache directory is not automatically cleared. It's advisable to clear it upon completing a video project and beginning another.
Tip: To force re-creation of cached assets make minorinor alterations to the 'prompt' such as adding a space or punctuation
Ensure that asset files are present in .cache folder. Create the script in this manner
Image: .cache/existing_background_hd.png
Audio: Funny opening music jingle.
Title: EmojiVidGen
🐼: .cache/existing_speech.wavCopy a suitable preset file and modify following lines:
global:
width: 1152
height: 896Note: This setting does affect the output of stable diffusion. Not all resolutions work that well. For more information checkout this https://replicate.com/guides/stable-diffusion/how-to-use/ . Stable Diffusion seems to work well with square aspect ratios.
You will see this error message when using espeak text to speech provider.
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/pyttsx3/drivers/espeak.py", line 171, in _onSynth
self._proxy.notify('finished-utterance', completed=True)
ReferenceError: weakly-referenced object no longer existsIgnore this error for now as it does not affect the output.
If you receive the below error, delete the .cache directory
File "plyvel/_plyvel.pyx", line 247, in plyvel._plyvel.DB.__init__
File "plyvel/_plyvel.pyx", line 88, in plyvel._plyvel.raise_for_status
plyvel._plyvel.IOError: b'IO error: lock .cache/asset/LOCK: Resource temporarily unavailable'@misc{lacombe-etal-2024-parler-tts,
author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
title = {Parler-TTS},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/parler-tts}}
}
@misc{lyth2024natural,
title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
author={Dan Lyth and Simon King},
year={2024},
eprint={2402.01912},
archivePrefix={arXiv},
primaryClass={cs.SD}
}