SEW-D-base-plus-400k-ft-ls100h
| Property | Value |
|---|---|
| Author | ASAPP Research |
| Paper | Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition |
| Word Error Rate (Clean) | 4.34% |
| Word Error Rate (Other) | 9.45% |
What is sew-d-base-plus-400k-ft-ls100h?
SEW-D-base-plus is an advanced speech recognition model developed by ASAPP Research that represents a significant improvement in the efficiency-performance trade-off compared to wav2vec 2.0. Pre-trained on 16kHz sampled speech audio, this model achieves a 1.9x inference speedup while reducing word error rates by 13.5% relative to its predecessor.
Implementation Details
The model utilizes the Squeezed and Efficient Wav2vec (SEW) architecture, specifically optimized for automatic speech recognition tasks. It requires 16kHz audio input and can be easily integrated using the Transformers library.
- Pre-trained on high-quality 16kHz audio data
- Implements CTC-based speech recognition
- Optimized for inference speed without compromising accuracy
- Fine-tuned on LibriSpeech dataset
Core Capabilities
- Automatic Speech Recognition (ASR)
- Speaker Identification
- Intent Classification
- Emotion Recognition
- Real-time transcription support
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its optimal balance between performance and efficiency, achieving significantly faster inference speeds while maintaining high accuracy. It demonstrates a 13.5% reduction in word error rate compared to wav2vec 2.0 while being 1.9x faster.
Q: What are the recommended use cases?
This model is ideal for production environments where both accuracy and speed are crucial. It's particularly well-suited for ASR tasks, speaker identification, intent classification, and emotion recognition applications. The model requires fine-tuning for specific downstream tasks.
