Roshan S Sharma
Roshan S Sharma
OOMs can be handled during model training in ESPNet2 by skipping the problematic batch or reducing the batch size dynamically as done in Fairseq here: https://github.com/pytorch/fairseq/blob/50a671f78d0c8de0392f924180db72ac9b41b801/fairseq/trainer.py#L188 This feature would be...
ESP-summ PR
## What? - Building the ESPNet-SUMM toolkit to support more summarization recipes, model architectures and evaluation metrics ## TODO: Code Additions - [x] Truncating feature inputs - [x] FNet Encoder...
## What? Added recipe for asr2, sum2 on How2-2000h Extras: - [x] Added segmentation support for KMeans based Discretization over long input recordings - this is needed for SSL feature...
Is there a way to obtain utterance level and corpus level metrics simultaneously by default? I want utterance-level metrics to do fine-grained analysis afterwards. Would appreciate any pointers - thanks...
We are examining non-NLP applications of the cosformer self-attention, and would need to use attention masking for the padded tokens in the batch. Is there a way to incorporate this...
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