EraX-WoW-Turbo-V1.0
| Property | Value |
|---|---|
| Model Type | Speech Recognition |
| License | MIT |
| Authors | Nguyễn Anh Nguyên, Phạm Huỳnh Nhật |
| Organization | EraX |
| Model URL | https://huggingface.co/erax-ai/EraX-WoW-Turbo-V1.0 |
What is EraX-WoW-Turbo-V1.0?
EraX-WoW-Turbo-V1.0 is a revolutionary speech recognition model built upon Whisper Large-v3 Turbo, specifically optimized for Vietnamese and 10 other languages. The model delivers exceptional performance with ultra-low latency, processing 30 seconds of audio in approximately 350ms. It has been trained on a diverse dataset of 300,000 samples (roughly 1000 hours) covering real-world audio conditions.
Implementation Details
The model leverages the Whisper Large-v3 Turbo architecture with additional optimizations for speed and accuracy. It can be further enhanced using the CTranslate2 library for up to 2.5x speedup in processing time. The model achieves a Word Error Rate (WER) of approximately 12% across major languages, including challenging Vietnamese dialects.
- Optimized for real-time transcription with minimal latency
- Comprehensive multilingual support for 11 languages
- Robust performance across various audio conditions
- CTranslate2 compatibility for enhanced speed
Core Capabilities
- Ultra-fast speech recognition (350ms per 30s of audio)
- Support for Vietnamese (all 8 regions) and 10 other languages
- 12% WER across major languages
- Noise-resistant performance
- Real-time transcription and captioning
- Voice assistant integration
- Media subtitling capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model combines exceptional speed with high accuracy, particularly for Vietnamese language processing. Its ability to handle multiple languages and regional accents, coupled with ultra-low latency, makes it ideal for real-time applications.
Q: What are the recommended use cases?
The model is perfect for real-time transcription, live captioning, voice assistants, media subtitling, accessibility tools, and language learning applications. However, it's important to note that it's optimized for adult speech and may not perform optimally with infant voices or whispered speech.
