Data Set: LibriSpeech
Reference paper: Deep Speaker: an End-to-End Neural Speaker Embedding System
Reference code : https://github.com/philipperemy/deep-speaker (Thanks to Philippe Rémy)
This code was trained on librispeech-train-clean dataset, tested on librispeech-test-clean dataset. In my code, librispeech dataset shows ~5% EER with CNN model.
train.py
This is the main file, contains training, evaluation and save-model function
models.py
The neural network used for the experiment. This file contains three models, CNN model (same with the paper’s CNN), GRU model (same with the paper's GRU), simple_cnn model. simple_cnn model has similar performance with the original CNN model, but the number of trained parameter dropped from 24M to 7M.
select_batch.py
Choose the optimal batch feed to the network. This is one of the cores of this experiment.
triplet_loss.py
This is a code to calculate triplet-loss for network training. Implementation is the same as paper.
test_model.py
This is a code that evaluates (test) the model, in terms of EER...
eval_matrics.py
For calculating equal error rate, f-measure, accuracy, and other metrics
pretaining.py
This is for pre-training on softmax classification loss.
pre_process.py
Load the utterance, filter out the mute, extract the fbank feature and save the module in .npy format.
This code was trained on librispeech-train-clean dataset, tested on librispeech-test-clean dataset. In my code, librispeech dataset shows ~5% EER with CNN model.
If you want to know more details, please read deep_speaker_report.pdf (English) or deep_speaker实验报告.pdf (中文).
- Preprare data.
I provide the sample data inaudio/LibriSpeechSamples/
or you can download full LibriSpeech data or prepare your own data. - Preprocessing.
Extract feature and preprocessing:python preprocess.py
. - Training.
If you want to train your model with Triplet Loss:python train.py
.
If you want to pretrain with softmax loss first:python pretraining.py
thenpython train.py
.
Note: If you want to pretrain or not, you need to setPRE_TRAIN
(inconstants.py
) flag withTrue
orFalse
.
(The training process inpython train.py
is an endless loop, please add stop-training conditions or kill the training process according to your own requirements.) - Evaluation.
Evaluate the model in terms of EER:test_model.py
.
Note: During training,train.py
also evaluates the model. - Plot loss curve.
Plot loss curve and EER curve withutils.py
.
import constants as c
from utils import plot_loss
loss_file=c.CHECKPOINT_FOLDER+'/losses.txt' # loss file path
plot_loss(loss_file)