metadata

title: English Accent Detector
emoji: 🎤
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8501
tags:
  - streamlit
  - audio
  - accent-detection
  - hiring
pinned: false
short_description: Detect and analyze English accents from videos
license: mit

🎤 English Accent Detection Tool

This app analyzes a speaker's English accent from video URLs or audio uploads, providing detailed insights for hiring evaluation purposes.

Features

Video URL Processing: Accept and analyze videos from Loom, YouTube, or direct MP4 links
Audio Upload Support: Directly upload audio files for analysis
English Accent Classification: Identify specific English accents (American, British, Australian, etc.)
Confidence Scoring: Get detailed confidence scores for English proficiency
Detailed Analysis: Receive expert-like explanations about accent characteristics
Visual Feedback: View audio waveforms and listen to the processed audio

Usage

Via Video URL:
- Enter a public video URL (Loom, YouTube, direct MP4, etc.)
- Click "Analyze Video"
- View the accent classification, confidence scores, and analysis
Via Audio Upload:
- Upload an audio file (WAV, MP3, M4A, OGG)
- Click "Analyze Audio"
- View the results

Troubleshooting YouTube Authentication Issues

If you encounter errors like Sign in to confirm you're not a bot when using YouTube videos:

Use a different video source:
- Try using Loom, Vimeo, or direct MP4 links instead of YouTube
Using cookies for YouTube authentication:
- The app supports uploading a cookies.txt file for YouTube authentication
- You can export cookies from your browser using browser extensions like "Get cookies.txt"
- Or use yt-dlp's built-in browser cookie extraction:
```
# Install yt-dlp if you don't have it
pip install yt-dlp

# Extract cookies from your browser (replace chrome with your browser)
yt-dlp --cookies-from-browser chrome -o cookies.txt https://youtube.com
```
- Upload the generated cookies file in the app interface

Technology Stack

Audio Processing: FFmpeg, Librosa
ML Models: SpeechBrain, Transformers
UI: Streamlit
Deployment: Docker

Requirements

Python 3.9+
FFmpeg
See requirements.txt for Python dependencies

Deployment

The app is containerized with Docker for easy deployment. Use the included Dockerfile to build and run:

docker build -t accent-detector .
docker run -p 8501:8501 accent-detector

Spaces:

amirjamali
/

accent-detector

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