---
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

![Capture](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/XqTL13_tJzbqqIkqX1-jN.png)

![2](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/8EKs21scwFQSF6p08Flkj.png)

![4](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/2X5lMk1X487_dSMC47ExA.png)

![5](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/1rowa7Lj3iXiae6LGNMmN.png)

![6](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/JDBsgkxDUFSFYI-ls_prs.png)

![11](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/QhbazTOzS7tj7M_XI_iI2.png)

![13](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/twmyT3mY3M-1EvLcKm0G6.png)

![10](https://cdn-uploads.huggingface.co/production/uploads/6a64d94c3ea93aefcffbbd53/OxBaZTkCC1DTdhGoKmnsw.png)

## 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)