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--- |
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title: Video Accent Analyzer |
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emoji: 🎧 |
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colorFrom: green |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 5.33.1 |
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app_file: app.py |
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pinned: false |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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# 🎧 Video Accent Analyzer |
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A sophisticated Python-based tool that analyzes English accents in videos using advanced machine learning techniques. Supports multiple video sources and provides detailed accent analysis with interactive visualizations. |
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## 🌟 Features |
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### Video Support |
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- YouTube videos with browser cookie integration |
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- Loom recordings |
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- Direct MP4 video links |
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- Local video file uploads |
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### Accent Analysis |
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- Detects 6 distinct English accents: |
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- 🇬🇧 British English |
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- 🇺🇸 American English |
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- 🇦🇺 Australian English |
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- 🇨🇦 Canadian English |
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- 🇮🇳 Indian English |
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- 🌐 Neutral English |
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- Provides confidence scores for accent detection |
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- Assesses English proficiency level |
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- Analyzes audio quality metrics |
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### Technical Capabilities |
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- Automatic video download and processing |
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- Audio extraction and preprocessing |
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- Multi-chunk analysis for improved accuracy |
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- Real-time progress tracking |
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- Interactive Plotly visualizations |
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- Batch processing support |
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## 🚀 Installation |
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```bash |
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pip install -r requirements.txt |
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``` |
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# 💻 Usage |
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Web Interface |
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Start the application: |
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python app.py |
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Open the provided URL in your browser |
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Enter a video URL or upload a video file |
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Adjust the maximum duration (10-120 seconds) |
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Click "Analyze Video" |
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# Python API |
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```from video_accent_analyzer import VideoAccentAnalyzer |
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# Initialize analyzer |
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analyzer = VideoAccentAnalyzer() |
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# Analyze video |
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results = analyzer.analyze_video_url("your_video_url", max_duration=30) |
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# Display results |
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analyzer.display_results(results) |
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``` |
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# ⚙️ Technical Requirements |
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Python 3.7+ |
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FFmpeg installed on system |
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Required Python packages: |
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gradio ≥4.0.0 |
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plotly ≥5.0.0 |
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torch |
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transformers |
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librosa |
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soundfile |
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yt-dlp |
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browser-cookie3 ≥0.19.1 |
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## 📊 Results |
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🎯 Best Practices |
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- Use videos with clear audio and minimal background noise |
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For Best Results |
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Use videos under 2 minutes in length |
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Ensure clear audio quality |
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Single speaker per analysis |
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Continuous speech segments |
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Minimum background noise |
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Known Limitations |
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Multiple speakers may affect accuracy |
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Heavy background noise can impact results |
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Very short speech segments (<10s) may be less accurate |
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Some region-restricted videos might not be accessible |
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# 🔧 Development |
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The project consists of three main components: |
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video_accent_analyzer.py: Core analysis engine |
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app.py: Gradio web interface |
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Supporting utilities for video processing |
