[doc] update paper link in README.md (#124)

* [doc] update paper link in README.md

* [wekws] remove unused variables in wekws/utils/executor.py

README.md

@@ -71,7 +71,7 @@ Please scan the QR code on the right to join the chat group.
 
 * Mining Effective Negative Training Samples for Keyword Spotting
   ([github]( https://github.com/jingyonghou/KWS_Max-pooling_RHE),
-   [paper](http://lxie.nwpu-aslp.org/papers/2020ICASSP_HJY.pdf))
+   [paper](https://www.microsoft.com/en-us/research/uploads/prod/2020/04/ICASSP2020_Max_pooling_KWS.pdf))
 * Max-pooling Loss Training of Long Short-term Memory Networks for Small-footprint Keyword Spotting
   ([paper](https://arxiv.org/pdf/1705.02411.pdf))
 * A depthwise separable convolutional neural network for keyword spotting on an embedded system

wekws/utils/executor.py

@@ -33,8 +33,6 @@ def train(self, model, optimizer, data_loader, device, writer, args):
         epoch = args.get('epoch', 0)
         min_duration = args.get('min_duration', 0)
 
-        num_total_batch = 0
-        total_loss = 0.0
         for batch_idx, batch in enumerate(data_loader):
             key, feats, target, feats_lengths = batch
             feats = feats.to(device)