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updated results (#165)

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@ -40,22 +40,22 @@ python main.py --help
## Results
After training with the `kwt1` architecture for 10 epochs, you
After training with the `kwt1` architecture for 100 epochs, you
should see the following results:
```
Epoch: 9 | avg. Train loss 0.519 | avg. Train acc 0.857 | Throughput: 661.28 samples/sec
Epoch: 9 | Val acc 0.861 | Throughput: 2976.54 samples/sec
Testing best model from epoch 9
Test acc -> 0.841
Epoch: 99 | avg. Train loss 0.018 | avg. Train acc 0.996 | Throughput: 662.51 samples/sec
Epoch: 99 | Val acc 0.893 | Throughput: 3091.26 samples/sec
Testing best model from epoch 97
Test acc -> 0.882
```
For the `kwt2` model, you should see:
```
Epoch: 9 | avg. Train loss 0.374 | avg. Train acc 0.895 | Throughput: 395.26 samples/sec
Epoch: 9 | Val acc 0.879 | Throughput: 1542.44 samples/sec
Testing best model from epoch 9
Test acc -> 0.861
Epoch: 99 | avg. Train loss 0.003 | avg. Train acc 1.000 | Throughput: 396.53 samples/sec
Epoch: 99 | Val acc 0.901 | Throughput: 1543.48 samples/sec
Testing best model from epoch 94
Test acc -> 0.893
```
Note that this was run on an M1 Macbook Pro with 16GB RAM.
@ -65,5 +65,5 @@ schedules, which is used along with the AdamW optimizer in the official
implementation. We intend to update this example once these features are added,
as well as with appropriate data augmentations.
[^1]: Based one the paper [Keyword Transformer: A Self-Attention Model for Keyword Spotting](https://www.isca-speech.org/archive/interspeech_2021/berg21_interspeech.html)
[^2]: We use version 0.02. See the [paper]((https://arxiv.org/abs/1804.03209) for more details.
[^1]: Based on the paper [Keyword Transformer: A Self-Attention Model for Keyword Spotting](https://www.isca-speech.org/archive/interspeech_2021/berg21_interspeech.html)
[^2]: We use version 0.02. See the [paper](https://arxiv.org/abs/1804.03209) for more details.