humandotlearning/speech_enhancement — explained in plain English
Analysis updated 2026-05-18
Compare six different audio enhancement pipelines on the same source video.
Produce a cleaned up MP4 that keeps the original video but swaps in enhanced audio.
Experiment with speech enhancement tools like DeepFilterNet and Resemble Enhance without installing anything locally.
Validate notebook structure and setup with the included test scripts.
| humandotlearning/speech_enhancement | agostynah/distributed-vector-memory-routing | akashsingh3031/python-libraries | |
|---|---|---|---|
| Stars | 0 | 0 | — |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Last pushed | — | — | 2020-12-03 |
| Maintenance | — | — | Dormant |
| Setup difficulty | easy | moderate | easy |
| Complexity | 2/5 | 3/5 | 1/5 |
| Audience | researcher | researcher | vibe coder |
Figures from each repo's GitHub metadata at analysis time.
Runs entirely in Google Colab on a GPU runtime, no local installation needed beyond a source video to upload.
This project is a set of Google Colab notebooks for cleaning up noisy audio in a video, then putting the cleaned audio back onto the original footage. Colab is Google's free service for running code notebooks in the browser with access to a GPU, so nothing needs to be installed on the user's own computer. There are two notebooks with different jobs. The first is an experiment notebook that runs six different audio cleanup pipelines on the same source video and produces a comparison for each one, mixing tools like DeepFilterNet, Resemble Enhance, LavaSR, and FFmpeg's loudness normalization in different orders. Each successful pipeline produces its own comparison audio file, and the ones that also rebuild a video produce a matching video file too, so a person can listen to each version side by side and judge which sounds best. The second notebook is a simpler, focused version that skips the comparison step and just runs the one combination the author settled on, DeepFilterNet followed by Resemble Enhance, then writes a new video file that keeps the original picture but swaps in the enhanced audio track. To use either notebook, a person opens it in Google Colab, switches to a GPU runtime, runs the setup cells to install the needed libraries, then uploads a video file or points the notebook at a file already stored in Google Drive. After the cleanup runs, the notebook shows a table for comparing the results and lets the results be played back directly in the browser before downloading them as a zip file. The repository also includes a couple of small test scripts that check the notebooks are still structured correctly, plus documentation describing the design notes and plan used while building the project. Any downloaded videos or generated audio files are deliberately excluded from the repository itself, so only the code and notebooks are kept in version control.
Google Colab notebooks that clean up noisy audio in a video using DeepFilterNet and Resemble Enhance, then remux the cleaned audio back onto the video.
Mainly Jupyter Notebook. The stack also includes Google Colab, DeepFilterNet, Resemble Enhance.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
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