Convolutional neural networks for Google speech commands data set with PyTorch.
We, xuyuan and tugstugi, have participated in the Kaggle competition TensorFlow Speech Recognition Challenge and reached the 10-th place. This repository contains a simplified and cleaned up version of our team's code.
1x32x32mel-spectrogram as network input- single network implementation both for CIFAR10 and Google speech commands data sets
- faster audio data augmentation on STFT
- Kaggle private LB scores evaluated on 150.000+ audio files
Due to time limit of the competition, we have trained most of the nets with sgd using ReduceLROnPlateau for 70 epochs.
For the training parameters and dependencies, see TRAINING.md. Earlier stopping the train process will sometimes produce a better score in Kaggle.
| Model | CIFAR10 test set accuracy |
Speech Commands test set accuracy |
Speech Commands test set accuracy with crop |
Speech Commands Kaggle private LB score |
Speech Commands Kaggle private LB score with crop |
Remarks |
|---|---|---|---|---|---|---|
| VGG19 BN | 93.56% | 97.337235% | 97.527432% | 0.87454 | 0.88030 | |
| ResNet32 | - | 96.181419% | 96.196050% | 0.87078 | 0.87419 | |
| WRN-28-10 | - | 97.937089% | 97.922458% | 0.88546 | 0.88699 | |
| WRN-28-10-dropout | 96.22% | 97.702999% | 97.717630% | 0.89580 | 0.89568 | |
| WRN-52-10 | - | 98.039503% | 97.980980% | 0.88159 | 0.88323 | another trained model has 97.52%/0.89322 |
| ResNext29 8x64 | - | 97.190929% | 97.161668% | 0.89533 | 0.89733 | our best model during competition |
| DPN92 | - | 97.190929% | 97.249451% | 0.89075 | 0.89286 | |
| DenseNet-BC (L=100, k=12) | 95.52% | 97.161668% | 97.147037% | 0.88946 | 0.89134 | |
| DenseNet-BC (L=190, k=40) | - | 97.117776% | 97.147037% | 0.89369 | 0.89521 |
After the competition, some of the networks were retrained using mixup: Beyond Empirical Risk Minimization by Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin and David Lopez-Paz.
| Model | CIFAR10 test set accuracy |
Speech Commands test set accuracy |
Speech Commands test set accuracy with crop |
Speech Commands Kaggle private LB score |
Speech Commands Kaggle private LB score with crop |
Remarks |
|---|---|---|---|---|---|---|
| VGG19 BN | - | 97.483541% | 97.542063% | 0.89521 | 0.89839 | |
| WRN-52-10 | - | 97.454279% | 97.498171% | 0.90273 | 0.90355 | same score as the 16-th place in Kaggle |