federatedSpeechCommands
Speech recognition with federated learning
Convolutional neural networks for Google speech commands data set with PyTorch.
With federated Learning
Run federated_train_speech_commands_gpu.py for simulating federated training with multiple clients, for example
python federated_train_speech_commands.py --model=vgg11 --optim=sgd --lr-scheduler=plateau --learning-rate=0.01 --lr-scheduler-patience=5 --max-epochs=1 --batch-size=156 --clients=2 --matrix-size=500
CUDA_VISIBLE_DEVICES=0,1 python federated_train_speech_commands_cpu_v5_collect_gradient.py --model=conv --optim=sgd --lr-scheduler=plateau --learning-rate=0.01 --lr-scheduler-patience=5 --max-epochs=1 --batch-size=128 --clients=3 --matrix-size=100 --num-threads=10
General
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.
Features
-
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
Results
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.
Results with Mixup
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.