This repo is the implementation of GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION using Pytorch including both Text Dependent and Text Independent Speaker Verification.

When implementing the code, I refer two existing repo: Speaker_Verification and PyTorch_Speaker_Verification. While first one is using tensorflow and second one only implements TI-SV.

There are two datasets I used in my repo:

• For TD-SV, I use keyword-spotting-research-datasets key word dataset. Because there are less than 10 utterances for each speaker, I add noise and stretch audio to do data argumentation.
• For TI-SV, I use similar TIMIT. Attention, WAV file in TIMIT need use sphfile to transform in order to work. Please see reading a WAV file from TIMIT database in pythonfor details.
• There are data preprocessing code inside data folder.

Compare ERR result:

	TD-SV	TI-SV
Paper	0.031	0.406
Const_lr	0.02408	0.04112
Step_lr	0.02425	0.03979
Other_git		0.05742

The following is experiment analysis for TD-SV and TI-SV.

import torch
import numpy
import matplotlib.pyplot as plt

const learning rate with 1500 epochs

TD-SV

TD-SV Loss

loss_log_tdsv = torch.load('./log_const_lr/tdsv/loss.log')

fig, axes = plt.subplots(1, 2, figsize=(20, 5))
axes[0].plot(loss_log_tisv['loss'])
axes[1].plot(loss_log_tisv['total_loss'])

axes[0].set_title("Loss during training")
axes[1].set_title("Avg loss during training")

print("Final Loss: {}; Avg loss: {}".format(loss_log_tisv['loss'][-1], loss_log_tisv['total_loss'][-1]))

Final Loss: 1.35565185546875; Avg loss: 1.268243408203125

TD-SV ERR

EER_log_tdsv = torch.load('./log_const_lr/EER/tdsv/EER.eer')
print("Avg EER for TD-SV is {}".format(EER_log_tdsv["avg_EER_log"][-1]))

Avg EER for TD-SV is 0.02408068842832344

TI-SV

TI-SV Loss

loss_log_tisv = torch.load('./log_const_lr/tisv/loss.log')

fig, axes = plt.subplots(1, 2, figsize=(20, 5))
axes[0].plot(loss_log_tisv['loss'])
axes[1].plot(loss_log_tisv['total_loss'])

axes[0].set_title("Loss during training")
axes[1].set_title("Avg loss during training")

print("Final Loss: {}; Avg loss: {}".format(loss_log_tdsv['loss'][-1], loss_log_tdsv['total_loss'][-1]))

Final Loss: 1.3204524517059326; Avg loss: 2.5933795539700255

TI-SV EER

EER_log_tisv = torch.load('./log_const_lr/EER/tisv/EER.eer')
print("Avg EER for TI-SV is {}".format(EER_log_tisv["avg_EER_log"][-1]))

Avg EER for TI-SV is 0.04112244994945027

Step learning rate with 2000 epochs

Decrease lr to half every 800 epochs

TD-SV

TD-SV Loss

loss_log_tdsv = torch.load('./log/loss/tdsv/loss.loss')

fig, axes = plt.subplots(1, 2, figsize=(20, 5))
axes[0].plot(loss_log_tisv['loss'])
axes[1].plot(loss_log_tisv['total_loss'])

axes[0].set_title("Loss during training")
axes[1].set_title("Avg loss during training")

print("Final Loss: {}; Avg loss: {}".format(loss_log_tisv['loss'][-1], loss_log_tisv['total_loss'][-1]))

Final Loss: 0.2689208984375; Avg loss: 2.120063999720982

TD-SV ERR

EER_log_tdsv = torch.load('./log/EER_final_model/tdsv/EER.eer')
print("Avg EER for TD-SV using final model is {}".format(EER_log_tdsv["avg_EER_log"][-1]))

Avg EER for TD-SV using final model is 0.024537037641965544

EER_log_tdsv = torch.load('./log/EER_optim_model/tdsv/EER.eer')
print("Avg EER for TD-SV using optim model is {}".format(EER_log_tdsv["avg_EER_log"][-1]))

Avg EER for TD-SV using optim model is 0.02425264612323156

TI-SV

TI-SV Loss

loss_log_tisv = torch.load('./log/loss/tisv/loss.loss')

fig, axes = plt.subplots(1, 2, figsize=(20, 5))
axes[0].plot(loss_log_tisv['loss'])
axes[1].plot(loss_log_tisv['total_loss'])

axes[0].set_title("Loss during training")
axes[1].set_title("Avg loss during training")

print("Final Loss: {}; Avg loss: {}".format(loss_log_tdsv['loss'][-1], loss_log_tdsv['total_loss'][-1]))

Final Loss: 1.9604518413543701; Avg loss: 2.488234414859694

TI-SV EER

EER_log_tisv = torch.load('./log/EER_final_model/tisv/EER.eer')
print("Avg EER for TI-SV using final model is {}".format(EER_log_tisv["avg_EER_log"][-1]))

Avg EER for TI-SV using final model is 0.03900510319009689

EER_log_tisv = torch.load('./log/EER_optim_model/tisv/EER.eer')
print("Avg EER for TI-SV using optim model is {}".format(EER_log_tisv["avg_EER_log"][-1]))

Avg EER for TI-SV using optim model is 0.03979591946450196

Other available replication in Github

Only TI-SV is implemented in their repo.

EER_log_tisv = torch.load('./checkpoint_git/EER/tisv/EER.eer')
print("Avg EER for TI-SV is {}".format(EER_log_tisv["avg_EER_log"][-1]))

Avg EER for TI-SV is 0.05742347076801317