lb-whisper in the browser
🎙
1
Luxembourgish speech to text, computed in your browser
SitrakaForler
SitrakaForler
·
AI & ML interests
NLP; Navier-Stokes; Fluid Mechanics
Recent Activity
liked a Space 11 days ago
SitrakaForler/lb-whisper-demo updated a Space 18 days ago
SitrakaForler/lb-whisper-demo published a Space 18 days ago
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Hugging Face Changelog
External Collaborators from Repo Settings
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upvoted an article 8 months ago
Article
Introducing Storage Buckets on the Hugging Face Hub
- +10
Wauplin, coyotte508, XciD, victor, julien-c, lhoestq, pierric, Sylvestre, hlarcher, rajatarya, seanses, assafvayner
• • 199 liked a
model 12 months ago
liked a
Space about 1 year ago
reacted to yeonseok-zeticai's post with 🚀 about 1 year ago
Post
3,741
🎯 RetinaFace On-Device Deployment Study: NPU Acceleration Breakthrough!
(Check details at :https://mlange.zetic.ai/p/Steve/RetinaFace)
TL;DR: Successfully deployed RetinaFace with ZETIC.MLange achieving 1.43ms inference on mobile NPU!
🔍 Complete Performance Analysis:
Latency Comparison:
- NPU: 1.43ms (Winner! 🏆)
- GPU: 3.75ms
- CPU: 21.42ms
Accuracy Metrics - SNR:
- FP16: 56.98 dB
- Integer Quantized: 48.03 dB
(Precision-Performance: Excellent trade-off maintained)
Memory Footprint:
- Model Size: 2.00 MB (highly compressed)
- Runtime Memory: 14.58 MB peak
- Deployment Ready: ✅ Production optimized
🛠 Technical Implementation:
(Runnable with Copy & Paste at the MLange link!)
📊 Device Compatibility Matrix:
Tested on 50+ devices including Samsung Galaxy series, Google Pixel lineup, and Xiaomi devices, iPhones and iPads.
Consistent sub-5ms performance across the board!
🚀 Applications Unlocked:
- Real-time AR/VR face tracking
- Privacy-preserving edge authentication
- Live video processing pipelines
- Mobile security applications
- Interactive camera filters
The democratization of high-performance computer vision on mobile devices is happening NOW! This study proves that complex CV models can run efficiently on consumer hardware without compromising accuracy.
Want to reproduce these results? Check out the benchmark methodology and implementation guide!
(Check details at :https://mlange.zetic.ai/p/Steve/RetinaFace)
TL;DR: Successfully deployed RetinaFace with ZETIC.MLange achieving 1.43ms inference on mobile NPU!
🔍 Complete Performance Analysis:
Latency Comparison:
- NPU: 1.43ms (Winner! 🏆)
- GPU: 3.75ms
- CPU: 21.42ms
Accuracy Metrics - SNR:
- FP16: 56.98 dB
- Integer Quantized: 48.03 dB
(Precision-Performance: Excellent trade-off maintained)
Memory Footprint:
- Model Size: 2.00 MB (highly compressed)
- Runtime Memory: 14.58 MB peak
- Deployment Ready: ✅ Production optimized
🛠 Technical Implementation:
(Runnable with Copy & Paste at the MLange link!)
📊 Device Compatibility Matrix:
Tested on 50+ devices including Samsung Galaxy series, Google Pixel lineup, and Xiaomi devices, iPhones and iPads.
Consistent sub-5ms performance across the board!
🚀 Applications Unlocked:
- Real-time AR/VR face tracking
- Privacy-preserving edge authentication
- Live video processing pipelines
- Mobile security applications
- Interactive camera filters
The democratization of high-performance computer vision on mobile devices is happening NOW! This study proves that complex CV models can run efficiently on consumer hardware without compromising accuracy.
Want to reproduce these results? Check out the benchmark methodology and implementation guide!
upvoted an article over 1 year ago
Article
Introducing smolagents: simple agents that write actions in code.
- +1
m-ric, merve, thomwolf
• • 1.21k updated a
Space over 1 year ago
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