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utils.py
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#! /usr/bin/python
# -*- encoding: utf-8 -*-
import yaml
import sys
import torch
import argparse
import torch.nn.functional as F
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
class PreEmphasis(torch.nn.Module):
def __init__(self, coef: float = 0.97):
super().__init__()
self.coef = coef
# make kernel
# In pytorch, the convolution operation uses cross-correlation. So, filter is flipped.
self.register_buffer(
'flipped_filter', torch.FloatTensor(
[-self.coef, 1.]).unsqueeze(0).unsqueeze(0)
)
def forward(self, input: torch.tensor) -> torch.tensor:
assert len(
input.size()) == 2, 'The number of dimensions of input tensor must be 2!'
# reflect padding to match lengths of in/out
input = input.unsqueeze(1)
input = F.pad(input, (1, 0), 'reflect')
return F.conv1d(input, self.flipped_filter).squeeze(1)
def get_args():
parser = argparse.ArgumentParser(description='SpeakerNet')
parser.add_argument('--config', type=str, default=None,
help='Config YAML file')
# DataLoader
parser.add_argument('--max_frames', type=int, default=200,
help='Input length to the network for training')
parser.add_argument('--eval_frames', type=int, default=300,
help='Input length to the network for testing 0 uses the whole files')
parser.add_argument('--batch_size', type=int, default=200,
help='Batch size, number of speakers per batch')
parser.add_argument('--max_seg_per_spk', type=int, default=500,
help='Maximum number of utterances per speaker per epoch')
parser.add_argument('--nDataLoaderThread', type=int,
default=5, help='Number of loader threads')
parser.add_argument('--augment', type=bool, default=10,
help='Seed for the random number generator')
parser.add_argument('--seed', type=int, default=10,
help='Seed for the random number generator')
# Training details
parser.add_argument('--test_interval', type=int, default=10,
help='Test and save every [test_interval] epochs')
parser.add_argument('--max-epoch', type=int, default=500,
help='Maximum number of epochs')
parser.add_argument('--trainfunc', type=str,
default='', help='loss function')
parser.add_argument('--supervised_loss', type=str, default='',
help='supervised loss, used only for joint training')
parser.add_argument('--ssl_loss', type=str, default='',
help='ssl loss, used only for joint training')
parser.add_argument('--training_mode', default='ssl', type=str,
help='training mode. available: ssl, joint, supervised')
parser.add_argument('--ssl_path', default='./data/cnceleb/data', type=str,
help='only for joint training. data for ssl')
parser.add_argument('--sup_path', default='./data/voxceleb2', type=str,
help='only for joint training. data for sup')
parser.add_argument('--ssl_list', default='./data/train_list_cnceleb.txt', type=str,
help='only for joint training. train list for ssl')
parser.add_argument('--sup_list', default='./data/train_list.txt', type=str,
help='only for joint training. train list for sup')
# optimizer
parser.add_argument('--optimizer', type=str,
default='adam', help='sgd or adam')
parser.add_argument('--scheduler', type=str, default='steplr',
help='learning rate scheduler')
parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')
parser.add_argument('--lr_decay', type=float, default=0.95,
help='learning rate decay every [test_interval] epochs')
parser.add_argument('--weight_decay', type=float, default=0,
help='weight decay in the optimizer')
# loss function
parser.add_argument('--hard_prob', type=float, default=0.5,
help='Hard negative mining prob, otherwise random, only for some loss functions')
parser.add_argument('--hard_rank', type=int, default=10,
help='Hard negative mining rank in the batch, only for some loss functions')
parser.add_argument('--margin', type=float, default=0.1,
help='Loss margin, only for some loss functions')
parser.add_argument('--scale', type=float, default=30,
help='loss scale, only for some loss functions')
parser.add_argument('--nPerSpeaker', type=int, default=1,
help='Number of utterances per speaker per batch, only for metric learning based losses')
parser.add_argument('--nClasses', type=int, default=5994,
help='Number of speakers in the softmax layer, only for softmax-based losses')
# evaluation parameters
parser.add_argument('--dcf_p_target', type=float, default=0.05,
help='A priori prob of the specified target speaker')
parser.add_argument('--dcf_c_miss', type=float, default=1,
help='Cost of a missed detection')
parser.add_argument('--dcf_c_fa', type=float, default=1,
help='Cost of a spurious detection')
# load and save
parser.add_argument('--initial_model', type=str,
default='', help='initial model weights')
parser.add_argument('--save_path', type=str,
default='exps/exp1', help='Path for model and logs')
# training and test data
parser.add_argument('--train_list', type=str,
default='data/train_list.txt', help='Train list')
parser.add_argument('--test_list', type=str,
default='data/test_list.txt', help='Evaluation list')
parser.add_argument('--train_path', type=str,
default='data/voxceleb2', help='Absolute path to the train set')
parser.add_argument('--test_path', type=str,
default='data/voxceleb1', help='Absolute path to the test set')
parser.add_argument('--musan_path', type=str, default='data/musan_split',
help='Absolute path to the musan data')
parser.add_argument('--rir_path', type=str, default='data/RIRS_NOISES/simulated_rirs',
help='Absolute path to the rir data')
# model definition
parser.add_argument('--n_mels', type=int, default=40,
help='Number of mel filterbanks')
parser.add_argument('--log_input', type=bool,
default=False, help='log input features')
parser.add_argument('--model', type=str, default='',
help='Name of model definition')
parser.add_argument('--encoder_type', type=str,
default='SAP', help='Type of encoder')
parser.add_argument('--nOut', type=int, default=512,
help='Embedding size in the last FC layer')
parser.add_argument('--sinc_stride', type=int, default=10,
help='Stride size of the first analytic filterbank layer of RawNet3')
# for test only
parser.add_argument('--eval', dest='eval',
action='store_true', help='Eval only')
parser.add_argument('--experiment_name', type=str,
help='experiment name', dest='experiment_name')
args = parser.parse_args()
# parse YAML
def find_option_type(key, parser):
for opt in parser._get_optional_actions():
if ('--' + key) in opt.option_strings:
return opt.type
raise ValueError
if args.config is not None:
with open(args.config, 'r') as f:
yml_config = yaml.load(f, Loader=yaml.FullLoader)
for k, v in yml_config.items():
if k in args.__dict__:
typ = find_option_type(k, parser)
args.__dict__[k] = typ(v)
else:
sys.stderr.write(
"Ignored unknown parameter {} in yaml. \n".format(k))
return args