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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)
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