forked from speechbrain/speechbrain
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain_ecapa_tdnn.yaml
190 lines (159 loc) · 6.32 KB
/
train_ecapa_tdnn.yaml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
# ################################
# Model: Speaker identification with ECAPA
# Authors: Hwidong Na & Mirco Ravanelli
# ################################
# Basic parameters
seed: 1986
__set_seed: !apply:speechbrain.utils.seed_everything [!ref <seed>]
output_folder: !ref results/ecapa_augment/<seed>
save_folder: !ref <output_folder>/save
train_log: !ref <output_folder>/train_log.txt
# Data for augmentation
NOISE_DATASET_URL: https://www.dropbox.com/scl/fi/a09pj97s5ifan81dqhi4n/noises.zip?rlkey=j8b0n9kdjdr32o1f06t0cw5b7&dl=1
RIR_DATASET_URL: https://www.dropbox.com/scl/fi/linhy77c36mu10965a836/RIRs.zip?rlkey=pg9cu8vrpn2u173vhiqyu743u&dl=1
# Data files
data_folder: !PLACEHOLDER # e.g. /path/to/Voxceleb
data_folder_noise: !ref <data_folder>/noise # The noisy sequences for data augmentation will automatically be downloaded here.
data_folder_rir: !ref <data_folder>/rir # The impulse responses used for data augmentation will automatically be downloaded here.
train_annotation: !ref <save_folder>/train.csv
valid_annotation: !ref <save_folder>/dev.csv
noise_annotation: !ref <save_folder>/noise.csv
rir_annotation: !ref <save_folder>/rir.csv
# Use the following links for the official voxceleb splits:
# VoxCeleb1 (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt
# VoxCeleb1-H (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/list_test_hard2.txt
# VoxCeleb1-E (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/list_test_all2.txt.
# VoxCeleb1-E and VoxCeleb1-H lists are drawn from the VoxCeleb1 training set.
# Therefore you cannot use any files in VoxCeleb1 for training if you are using these lists for testing.
verification_file: https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt
split_ratio: [90, 10]
skip_prep: False
ckpt_interval_minutes: 15 # save checkpoint every N min
# Training parameters
number_of_epochs: 10
batch_size: 32
lr: 0.001
base_lr: 0.00000001
max_lr: !ref <lr>
step_size: 65000
sample_rate: 16000
sentence_len: 3.0 # seconds
shuffle: True
random_chunk: True
# Feature parameters
n_mels: 80
left_frames: 0
right_frames: 0
deltas: False
# Number of speakers
out_n_neurons: 7205 #1211 for vox1 # 5994 for vox2, 7205 for vox1+vox2
num_workers: 4
dataloader_options:
batch_size: !ref <batch_size>
shuffle: !ref <shuffle>
num_workers: !ref <num_workers>
# Functions
compute_features: !new:speechbrain.lobes.features.Fbank
n_mels: !ref <n_mels>
left_frames: !ref <left_frames>
right_frames: !ref <right_frames>
deltas: !ref <deltas>
embedding_model: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
input_size: !ref <n_mels>
channels: [1024, 1024, 1024, 1024, 3072]
kernel_sizes: [5, 3, 3, 3, 1]
dilations: [1, 2, 3, 4, 1]
groups: [1, 1, 1, 1, 1]
attention_channels: 128
lin_neurons: 192
classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
input_size: 192
out_neurons: !ref <out_n_neurons>
epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
limit: !ref <number_of_epochs>
############################## Augmentations ###################################
# Download and prepare the dataset of noisy sequences for augmentation
prepare_noise_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
URL: !ref <NOISE_DATASET_URL>
dest_folder: !ref <data_folder_noise>
ext: wav
csv_file: !ref <noise_annotation>
# Add noise to input signal
add_noise: !new:speechbrain.augment.time_domain.AddNoise
csv_file: !ref <noise_annotation>
snr_low: 0
snr_high: 15
noise_sample_rate: !ref <sample_rate>
clean_sample_rate: !ref <sample_rate>
num_workers: !ref <num_workers>
# Download and prepare the dataset of room impulse responses for augmentation
prepare_rir_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
URL: !ref <RIR_DATASET_URL>
dest_folder: !ref <data_folder_rir>
ext: wav
csv_file: !ref <rir_annotation>
# Add reverberation to input signal
add_reverb: !new:speechbrain.augment.time_domain.AddReverb
csv_file: !ref <rir_annotation>
reverb_sample_rate: !ref <sample_rate>
clean_sample_rate: !ref <sample_rate>
num_workers: !ref <num_workers>
# Frequency drop: randomly drops a number of frequency bands to zero.
drop_freq: !new:speechbrain.augment.time_domain.DropFreq
drop_freq_low: 0
drop_freq_high: 1
drop_freq_count_low: 1
drop_freq_count_high: 3
drop_freq_width: 0.05
# Time drop: randomly drops a number of temporal chunks.
drop_chunk: !new:speechbrain.augment.time_domain.DropChunk
drop_length_low: 1000
drop_length_high: 2000
drop_count_low: 1
drop_count_high: 5
# Augmenter: Combines previously defined augmentations to perform data augmentation
wav_augment: !new:speechbrain.augment.augmenter.Augmenter
parallel_augment: True
concat_original: True
min_augmentations: 4
max_augmentations: 4
augment_prob: 1.0
augmentations: [
!ref <add_noise>,
!ref <add_reverb>,
!ref <drop_freq>,
!ref <drop_chunk>]
mean_var_norm: !new:speechbrain.processing.features.InputNormalization
norm_type: sentence
std_norm: False
modules:
compute_features: !ref <compute_features>
embedding_model: !ref <embedding_model>
classifier: !ref <classifier>
mean_var_norm: !ref <mean_var_norm>
compute_cost: !new:speechbrain.nnet.losses.LogSoftmaxWrapper
loss_fn: !new:speechbrain.nnet.losses.AdditiveAngularMargin
margin: 0.2
scale: 30
# compute_error: !name:speechbrain.nnet.losses.classification_error
opt_class: !name:torch.optim.Adam
lr: !ref <lr>
weight_decay: 0.000002
lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler
base_lr: !ref <base_lr>
max_lr: !ref <max_lr>
step_size: !ref <step_size>
# Logging + checkpoints
train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
save_file: !ref <train_log>
error_stats: !name:speechbrain.utils.metric_stats.MetricStats
metric: !name:speechbrain.nnet.losses.classification_error
reduction: batch
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
checkpoints_dir: !ref <save_folder>
recoverables:
embedding_model: !ref <embedding_model>
classifier: !ref <classifier>
normalizer: !ref <mean_var_norm>
counter: !ref <epoch_counter>
lr_annealing: !ref <lr_annealing>