ESPnet Speech Translation Model
| Property | Value |
|---|---|
| License | CC-BY-4.0 |
| Framework | ESPnet |
| Paper | ESPnet: End-to-End Speech Processing Toolkit |
| BLEU Score | 13.9 |
What is brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug?
This is a speech translation model trained using the ESPnet toolkit on the IWSLT22 dialect dataset. It implements a Conformer-based architecture with specialized configurations for dialect translation tasks. The model achieves a BLEU score of 13.9 on the validation set, demonstrating its capability in handling dialectal speech translation.
Implementation Details
The model utilizes a Conformer encoder with 12 blocks, 256 output size, and 4 attention heads. It employs a sophisticated specaugment configuration for data augmentation and uses a combination of CTC and attention-based approaches with a CTC weight of 0.3. The decoder is based on the Transformer architecture with 6 blocks and 2048 linear units.
- Conformer encoder with 12 blocks and CNN module kernel size 31
- Transformer decoder with 6 blocks and 4 attention heads
- Learning rate of 0.002 with 15000 warmup steps
- Specaugment with time warping and masking for robust training
Core Capabilities
- End-to-end speech translation
- Dialect-aware processing
- Robust against speech variations through specaugment
- Combined CTC and attention-based translation
Frequently Asked Questions
Q: What makes this model unique?
The model combines Conformer and Transformer architectures with specialized specaugment techniques, specifically designed for handling dialect translation tasks in the IWSLT22 dataset. The use of CTC weight 0.3 provides a balance between alignment and translation quality.
Q: What are the recommended use cases?
This model is best suited for translating dialectal speech in scenarios where handling regional variations is important. It's particularly effective for end-to-end speech translation tasks that require robust handling of speech variations and dialectal features.
