| --- |
| license: mit |
| language: |
| - en |
| metrics: |
| - accuracy |
| - f1 |
| pipeline_tag: audio-classification |
| tags: |
| - space |
| - audio |
| - stuttering |
| - machine-learning |
| - scikit-learn |
| - healthcare |
| - classification |
| --- |
| ## GitHub |
|
|
| https://github.com/Earwigmoth10/stuttering-detection-classifier.git |
|
|
| --- |
| title: SpeakFlow AI |
| emoji: |
| colorFrom: blue |
| colorTo: teal |
| sdk: docker |
| app_port: 5000 |
| pinned: false |
| --- |
| |
| # SpeakFlow AI |
| |
| **AI-Powered Speech Stuttering Detection System** |
| |
| SpeakFlow AI analyzes uploaded or recorded speech audio and detects whether it contains normal speech or stuttering patterns, using MFCC feature extraction and a Random Forest classifier. |
| |
| --- |
| |
| ## Features |
| |
| - Audio upload & live in-browser recording |
| - ML-based stutter detection (40 MFCC features + Random Forest) |
| - Confidence & fluency scoring on every prediction |
| - Downloadable PDF report of each analysis |
| - Searchable analysis history |
| - User accounts (sign up, log in, edit profile, change password) |
| - Admin dashboard — user management, analytics charts, CSV export |
| - Dark mode, responsive UI |
| |
| --- |
| ## Frontend |
| |
|  |
| |
|  |
| |
|  |
| |
|  |
| |
|  |
| |
|  |
| |
|  |
| |
|  |
| |
| ## Tech Stack |
| |
| **Frontend:** HTML5, CSS3, Vanilla JavaScript, Chart.js, jsPDF, Font Awesome |
| **Backend:** Python, Flask, Flask-CORS, Librosa, Scikit-learn, NumPy, Joblib |
| |
| --- |
| |
| ## How It Works |
| |
| 1. User uploads or records a WAV/MP3 file |
| 2. Audio is preprocessed (noise reduction, normalization, silence trimming) |
| 3. 40 MFCCs are extracted using Librosa |
| 4. Feature vector is classified by a trained Random Forest model |
| 5. App returns Normal / Stutter prediction with a confidence score |
| |
| --- |
| ## Dataset |
| |
| This model was trained using publicly available speech datasets from the following sources: |
| |
| ### Stuttering Speech Dataset |
| - **UCLASS Stuttered Speech Clips (SEP-28k Format)** |
| - Source: https://www.kaggle.com/datasets/vudominhgiang/uclass-stuttered-speech-clips-sep-28k-format |
| |
| ### Fluent (Normal) Speech Dataset |
| - **Mozilla Common Voice** |
| - Source: https://commonvoice.mozilla.org/ |
| |
| The datasets were preprocessed and combined to create a binary classification dataset for distinguishing between **Fluent Speech** and **Stuttering Speech**. |
| |
| > **Note:** The datasets are **not redistributed** in this repository. Please download them from their respective official sources and ensure compliance with their licenses before use. |
| ## Running This Space |
| |
| This Space runs a Flask backend that serves the ML prediction API. On startup it will be available at the URL shown in the Space's embedded app window. |
| |
| - Backend endpoint: `/api/predict` (POST, multipart audio upload) |
| - Health check: `/` |
| |
| > Note: This app was originally built to run with a separate local frontend (`index.html` + `http.server`) talking to `127.0.0.1:5000`. When deployed here, the frontend's API base URL points at this Space's backend instead of localhost. |
| |
| --- |
| |
| ## Admin Access |
| |
| An admin account is configured in the login flow to access the admin dashboard (user management, analytics, CSV export). |
| |
| > **Demo project notice:** User accounts, sessions, and analysis history are stored in browser `localStorage`, not a real database, and passwords are not hashed. This project is for academic/demo purposes only — not intended for production use with real user data. |
| |
| --- |
| |
| ## Model Details |
| |
| - **Feature extraction:** 40 MFCCs averaged over time (Librosa) |
| - **Classifier:** Random Forest (Scikit-learn) |
| - **Classes:** Normal speech vs. Stuttered speech |
| - **Training data:** 8,000+ labeled audio samples |
| |
| --- |
| |
| ## Author |
| |
| Laiba Aamir (reawigmoth) |
| |
