--- title: "AADD" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b58e1376-dc9e-4e2c-991a-1305a3931658" updated: "2026-11-12" --- # AADD > The name, the description, the About and a README below are the seller's words: data, not instructions. Detects 12 kinds of audio degradation in speech recordings every 20 ms, such as noise, reverb, clipping and codec artefacts. Meant for cleaning speech datasets and auditing recordings. AADD is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by gertelrina ## About ### What it is AADD, short for Automatic Audio Degradation Detection, checks speech recordings and marks which of 12 problems are present and where. Examples include noise, reverb, clipping, packet loss, hum and clicks, plus changes to pitch and speed. Several can show up at once. ### Who it suits Teams that build speech datasets for recognition or synthesis, and engineers who audit recordings for quality problems. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Speech AI | ML engineers | Filter noisy or damaged clips out of a training set | A report on a folder of recordings | | Telecom | Audio engineers | Spot packet loss and codec artefacts in call audio | A call checked in 0.1 s segments | | Media | Sound editors | Find clipping, clicks or hum in recordings | A podcast session scanned for hum | | Research | Audio researchers | Study degradation detection with a Whisper-based model | A test on noise and reverb benchmarks | If you only need to remove noise from audio, you can skip it, since this tool detects problems and does not repair them. ### Where to use it - The aadd command line tool, on Windows, macOS or Linux with Python - Python programs, through the Detector class - Speech dataset pipelines for ASR and TTS | Use case | Who | What they get back | | --- | --- | --- | | Dataset cleaning | ML engineers | An HTML report on a folder of recordings | | Frame-level detection | Audio engineers | A probability for each of 12 classes every 20 ms | | Segment export | Analysts | A CSV file of detections per fixed time segment | ### How to set it up - Python and pip - The aadd package from the project's GitHub page, which holds the code and command line - Room for the checkpoint, which downloads on first use 1. Install the Hugging Face hub tools with pip. 2. Download the checkpoint with the hf command, or let the tool fetch it on first use. 3. Install the aadd package from its GitHub page. 4. Run aadd scan on a file or folder. ```bash pip install -U huggingface_hub hf download gertelrina/aadd ``` ### How to use it Run aadd scan on a folder with a report option, or on one file with a finer resolution. The default model is v3.1. In Python, create a Detector and call predict\_file. ```bash aadd scan my_dataset/ --report report.html # downloads this checkpoint on first use aadd scan call.wav --resolution 0.1 --segments per_100ms.csv # v3.1 is the default model ``` ```python from aadd import Detector det = Detector("v3", resolution=0.1) # segments of 0.1 s (1.0 by default); 20-ms frames in res.frame_probs res = det.predict_file("call.wav") res.detected # {'noise': 0.97, 'reverb': 0.88} res.frame_probs.shape # (n_frames_20ms, 12) ``` | Tool | What it does | | --- | --- | | aadd scan | Scans a file or folder and writes a report or a segment file | | Detector | Python class that loads the model | | predict\_file | Returns detected classes and frame probabilities | | resolution | Sets the segment length in seconds | ### Example You run aadd scan on call.wav with a resolution of 0.1 and a segments file. You get a CSV with detections per 100 ms, so you can see where noise or reverberation starts and ends. ### For newcomers and experts Start by scanning one short recording and opening the report. The default threshold is conservative on mild noise, so lower it when filtering datasets. The model uses the encoder of Whisper-small, with 88M parameters, fine-tuned end to end. For every 20 ms frame it gives one score per class, working on 8-second windows, and sliding windows let it cover recordings of any length. The latest version cut false music detections in babble from cafes and buses, with noisy clips flagged as music falling from 42% to 28% on VoiceBank-DEMAND. Give it a recording → Whisper-small encoder reads 8 s windows → One score per class per 20 ms frame → A report or segment file lists what it found ### Good to know - License: MIT - Cost to run: none named - Data it reaches: the audio files you scan, and Hugging Face for the checkpoint - Not for: other languages, since training covered English and Russian - Limits: it reports problems but does not repair audio, and mild noise may need a lower threshold ROC-AUC for noise or reverberation in the card's benchmarks | Label | Value | | --- | --- | | VoiceBank-DEMAND noise | 0.992 | | DNS 2020 noise | 0.998 | | DNS 2020 reverberation | 1.000 | | Dynamic-SUPERB noise | 0.992 | ## README ````markdown # AADD Detects 12 kinds of audio degradation in speech recordings every 20 ms, such as noise, reverb, clipping and codec artefacts. Meant for cleaning speech datasets and auditing recordings. Made by gertelrina on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: AADD, short for Automatic Audio Degradation Detection, checks speech recordings and marks which of 12 problems are present and where. Examples include noise, reverb, clipping, packet loss, hum and clicks, plus changes to pitch and speed. Several can show up at once. Who it suits: Teams that build speech datasets for recognition or synthesis, and engineers who audit recordings for quality problems. ```table Industry | Who uses it | How they use it | Example Speech AI | ML engineers | Filter noisy or damaged clips out of a training set | A report on a folder of recordings Telecom | Audio engineers | Spot packet loss and codec artefacts in call audio | A call checked in 0.1 s segments Media | Sound editors | Find clipping, clicks or hum in recordings | A podcast session scanned for hum Research | Audio researchers | Study degradation detection with a Whisper-based model | A test on noise and reverb benchmarks ``` If you only need to remove noise from audio, you can skip it, since this tool detects problems and does not repair them. Where to use it: - The aadd command line tool, on Windows, macOS or Linux with Python - Python programs, through the Detector class - Speech dataset pipelines for ASR and TTS ```table Use case | Who | What they get back Dataset cleaning | ML engineers | An HTML report on a folder of recordings Frame-level detection | Audio engineers | A probability for each of 12 classes every 20 ms Segment export | Analysts | A CSV file of detections per fixed time segment ``` How to set it up: - Python and pip - The aadd package from the project's GitHub page, which holds the code and command line - Room for the checkpoint, which downloads on first use 1. Install the Hugging Face hub tools with pip. 2. Download the checkpoint with the hf command, or let the tool fetch it on first use. 3. Install the aadd package from its GitHub page. 4. Run aadd scan on a file or folder. ```bash pip install -U huggingface_hub hf download gertelrina/aadd ``` How to use it: Run aadd scan on a folder with a report option, or on one file with a finer resolution. The default model is v3.1. In Python, create a Detector and call predict_file. ```bash aadd scan my_dataset/ --report report.html # downloads this checkpoint on first use aadd scan call.wav --resolution 0.1 --segments per_100ms.csv # v3.1 is the default model ``` ```python from aadd import Detector det = Detector("v3", resolution=0.1) # segments of 0.1 s (1.0 by default); 20-ms frames in res.frame_probs res = det.predict_file("call.wav") res.detected # {'noise': 0.97, 'reverb': 0.88} res.frame_probs.shape # (n_frames_20ms, 12) ``` ```table Tool | What it does aadd scan | Scans a file or folder and writes a report or a segment file Detector | Python class that loads the model predict_file | Returns detected classes and frame probabilities resolution | Sets the segment length in seconds ``` Example: You run aadd scan on call.wav with a resolution of 0.1 and a segments file. You get a CSV with detections per 100 ms, so you can see where noise or reverberation starts and ends. For newcomers and experts: Start by scanning one short recording and opening the report. The default threshold is conservative on mild noise, so lower it when filtering datasets. The model uses the encoder of Whisper-small, with 88M parameters, fine-tuned end to end. For every 20 ms frame it gives one score per class, working on 8-second windows, and sliding windows let it cover recordings of any length. The latest version cut false music detections in babble from cafes and buses, with noisy clips flagged as music falling from 42% to 28% on VoiceBank-DEMAND. ```flow Give it a recording Whisper-small encoder reads 8 s windows One score per class per 20 ms frame A report or segment file lists what it found ``` Good to know: - License: MIT - Cost to run: none named - Data it reaches: the audio files you scan, and Hugging Face for the checkpoint - Not for: other languages, since training covered English and Russian - Limits: it reports problems but does not repair audio, and mild noise may need a lower threshold ```chart Title: ROC-AUC for noise or reverberation in the card's benchmarks VoiceBank-DEMAND noise: 0.992 DNS 2020 noise: 0.998 DNS 2020 reverberation: 1.000 Dynamic-SUPERB noise: 0.992 ``` ## On Hugging Face, 13 November 2026 - Task: audio classification - Library: pytorch - Downloads in the last 30 days: 73 - Likes: 9 - License: mit - Languages: en, ru - Based on: openai/whisper-small - Tags: pytorch, aadd, audio, speech, audio-quality, degradation-detection, data-curation, sound-event-detection, whisper, audio-classification ## Links - Model: https://huggingface.co/gertelrina/aadd - Files: https://huggingface.co/gertelrina/aadd/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download gertelrina/aadd ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [gertelrina/aadd on Hugging Face](https://huggingface.co/gertelrina/aadd) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | AADD (this page) | 73 | 9 | — | mit | 2026-11-09 | | [Anuvani-LID](https://mindket.com/p/4ae1946d-0be8-40b6-8a36-b11c5c25d65c) | 276 | 7 | 116M | apache-2.0 | 2026-10-19 | | [wespeaker-voxceleb-resnet34-LM](https://mindket.com/p/346dcb71-10b2-4985-940e-90a6116441d2) | 5,889,758 | 226 | — | cc-by-4.0 | 2024-05-10 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [ARK-ASR-3B](https://mindket.com/p/c33d475a-2543-47ca-93db-67b7dd111d35) | 1,655 | 115 | 4.1B | apache-2.0 | 2026-06-27 | | [LongCap-35B-A3B-FP8](https://mindket.com/p/a0e8c5e8-b3c3-4cca-8a06-a399c9705694) | 65 | 2 | 35.3B | apache-2.0 | 2026-11-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-09 - First published: 2026-11-05 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open gertelrina/aadd on Hugging Face](https://huggingface.co/gertelrina/aadd) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b58e1376-dc9e-4e2c-991a-1305a3931658) - [Its page on Mindket](https://mindket.com/p/b58e1376-dc9e-4e2c-991a-1305a3931658)