Deepstory
Turn text into a talking-head video with open-source research code.
Deepstory is a fascinating research prototype for developers who want to understand the internals of talking-head generation. If you're comfortable with Python, PyTorch, and command-line tools, you'll appreciate the full pipeline transparency. However, non-technical users seeking a plug-and-play video generator should look at Synthesia or D-ID instead.
- AI researchers exploring generative video and animation pipelines
- Hobbyists building custom talking-head applications from scratch
- Developers integrating TTS with facial animation in open-source projects
- Content creators experimenting with deepfake storytelling
- Non-technical users seeking a one-click video generator
- Commercial video production requiring artifact-free output
- Users needing a polished product with customer support
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Skip Deepstory if you want a polished, one-click video generator or if you are not comfortable with Python, PyTorch, and command-line debugging.
Deepstory is free and open-source, making it the cheapest option for learning about talking-head generation. Commercial tools like Synthesia ($30+/mo) are better for production but cost. Deepstory's cost is your time to set up and debug.
In short
Deepstory — Turn text into a talking-head video with open-source research code. Best for AI researchers exploring generative video and animation pipelines, Hobbyists building custom talking-head applications from scratch, Developers integrating TTS with facial animation in open-source projects. Free to use.
What's new in Deepstory
Checked 12 days agoAcross the latest 5 updates: 5 changelog entries.
Deepstory – major update
A major update to the Deepstory Google Colab notebook and codebase.
Deepstory – major update
Another major update to the Deepstory project, likely refining the pipeline.
Waiting for Godot
A blog post about the development process, possibly a status update.
Deepstory – memory issue
Addressed a memory issue in the Deepstory pipeline.
Deepstory – Web interface and Flask
Added a Flask web interface for easier local use.
Viability Score
How likely is Deepstory to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Text-to-speech synthesis with customizable voice
- Speech-driven facial animation (VOCA model)
- First-order motion model for still image animation
- Audio preprocessing and silence normalization
- Text normalization pipeline
- Integration with GPT-2 for text generation
- Web interface using Flask and jQuery
- Google Colab notebook for cloud execution
- Supports multiple audio formats via preprocessing
- Data packing with HDF5, h5py, Zarr
- Audio segmentation and manual verification tools
About Deepstory
Deepstory is an experimental research project by Siu King Wai that converts written text (or AI-generated text via GPT-2) into a talking-head video. It combines text-to-speech (TTS), speech-driven facial animation (VOCA), and first-order motion models to animate a still image, creating a lifelike video where a character speaks the provided text. The tool includes a web interface (Flask) and a Google Colab notebook for easy experimentation. Key capabilities include customizable TTS voice, audio preprocessing and silence normalization, GPT-2 integration for text generation, and efficient data packing with HDF5/h5py/Zarr. This is not a polished commercial product but a research prototype—ideal for developers, researchers, and hobbyists interested in generative media and deepfake technology. Compared to commercial solutions like Synthesia or D-ID, Deepstory offers full pipeline transparency and customizability but lacks production-grade quality, support, and ease of use.
Behind the Verdict
Deepstory is a capstone project (SM4701) that documents a journey through modern generative AI components. The code is all on GitHub and the blog posts explain each step—from text normalization to first-order motion animation. This makes it a great learning resource for anyone wanting to build a talking-head pipeline from scratch. Strengths: end-to-end transparency, customizable TTS voice, integration with GPT-2, and cloud execution via Colab. Weaknesses: the output quality is highly dependent on input image and audio preprocessing; there's no official support; documentation is minimal; and the project appears to be from 2020 with no recent updates. It is not suitable for commercial production or non-technical users. If you're a researcher or hobbyist, you'll enjoy tweaking the pipeline. If you need a reliable video generation tool, choose a commercial alternative.
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Real-world workflow fit
Concrete scenarios for the personas Deepstory actually fits — and what changes day-one when you adopt it.
Clone the GitHub repo, run the Colab notebook with a portrait photo and text input, and examine how VOCA and first-order model animate the face.
Outcome: A rendered talking-head video demonstrating the full pipeline, with all code and parameters accessible for modification.
Feed a script into GPT-2, configure TTS voice parameters, preprocess the audio, then animate a still image using the Flask web interface locally.
Outcome: A custom video narration for a storytelling project, created without any recurring subscription cost.
Use Cases
- Create a talking-head video from a written story using a cloned voice.
- Animate a still portrait to speak AI-generated text from GPT-2.
- Experiment with speech-driven facial animation for research or art.
- Build a custom video narration tool for educational content.
- Preprocess audio datasets for training speech animation models.
Models Under the Hood
as of 2026-07-05
Limitations
- Deepstory is an experimental research project with minimal documentation.
- Users must be comfortable with Python, PyTorch, and command-line tools.
- Output quality depends heavily on the input image and audio preprocessing.
- There is no official support and the project appears unmaintained since 2020.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Deepstory tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Researchers, developers, and hobbyists who want full source code access and are comfortable with self-hosting and debugging.
What this tier adds
This is the only tier; it provides the complete project source code, Colab notebook, and Flask web interface at no cost.
Where the pricing makes sense
The company stage and team size where Deepstory's pricing actually pencils out — and where peers do it cheaper.
Deepstory is free and open-source, making it the cheapest option for learning about talking-head generation. Commercial tools like Synthesia ($30+/mo) are better for production but cost. Deepstory's cost is your time to set up and debug.
Setup time & first value
How long it actually takes to get something useful out of Deepstory — broken out by persona, not the marketing-page minute.
For a researcher familiar with Python and ML frameworks: roughly 1-2 hours to clone, install dependencies, and run the Colab notebook. For a hobbyist new to such tools: expect half a day to a full day to understand the pipeline and troubleshoot.
Resources & Guides
Official links
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Common stack mates teams adopt alongside Deepstory, with the specific reason each pairing earns its keep.
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