Hugging Face's logo

Definite
/
hwaja_insic

Model card Files
xet
Community
hwaja_insic
File size: 6,964 Bytes 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
f3588ae
 
 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
 
 
 
 
 
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
f3588ae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b72ec2c
 
 
 
 
 
 
 
 
 
 
 
 
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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
 
# Generated 2024-06-04 from:
# /content/speechbrain/recipes/VoxCeleb/SpeakerRec/hparams/train_x_vectors.yaml
# yamllint disable
# ################################
# Model: Speaker identification with ECAPA
# Authors: Hwidong Na & Mirco Ravanelli
# ################################

# Basic parameters
seed: 1986
__set_seed: !apply:torch.manual_seed [1986]
output_folder: results/xvect_augment/1986
save_folder: results/xvect_augment/1986/save
pretrained_path: Definite/hwaja_insic
train_log: results/xvect_augment/1986/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: ./Voxceleb  # e.g. /path/to/Voxceleb
data_folder_noise: ./Voxceleb/noise         # The noisy sequences for data augmentation will automatically be downloaded here.
data_folder_rir: ./Voxceleb/rir         # The impulse responses used for data augmentation will automatically be downloaded here.
train_annotation: results/xvect_augment/1986/save/train.csv
valid_annotation: results/xvect_augment/1986/save/dev.csv
noise_annotation: results/xvect_augment/1986/save/noise.csv
rir_annotation: results/xvect_augment/1986/save/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: ./Voxceleb/save/veri_test.txt

split_ratio: [90, 10]
skip_prep: true
ckpt_interval_minutes: 15 # save checkpoint every N min

# Training parameters
number_of_epochs: 1
batch_size: 16
lr: 0.001
lr_final: 0.0001

sample_rate: 16000
sentence_len: 3.0 # seconds
shuffle: true
random_chunk: false

# Feature parameters
n_mels: 24
left_frames: 0
right_frames: 0
deltas: false

# Number of speakers
out_n_neurons: 1349 #1211 for vox1  # 5994 for vox2, 7205 for vox1+vox2
emb_dim: 512

num_workers: 4
dataloader_options:
  batch_size: 16
  shuffle: true
  num_workers: 4

# Functions
compute_features: &id005 !new:speechbrain.lobes.features.Fbank
  n_mels: 24
  left_frames: 0
  right_frames: 0
  deltas: false

embedding_model: &id006 !new:speechbrain.lobes.models.Xvector.Xvector
  in_channels: 24
  activation: !name:torch.nn.LeakyReLU
  tdnn_blocks: 5
  tdnn_channels: [512, 512, 512, 512, 1500]
  tdnn_kernel_sizes: [5, 3, 3, 1, 1]
  tdnn_dilations: [1, 2, 3, 1, 1]
  lin_neurons: 512

classifier: &id007 !new:speechbrain.lobes.models.Xvector.Classifier
  input_shape: [null, null, 512]
  activation: !name:torch.nn.LeakyReLU
  lin_blocks: 1
  lin_neurons: 512
  out_neurons: 1349

epoch_counter: &id009 !new:speechbrain.utils.epoch_loop.EpochCounter
  limit: 1

############################## Augmentations ###################################

# Download and prepare the dataset of noisy sequences for augmentation
prepare_noise_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
  URL: 
    https://www.dropbox.com/scl/fi/a09pj97s5ifan81dqhi4n/noises.zip?rlkey=j8b0n9kdjdr32o1f06t0cw5b7&dl=1
  dest_folder: ./Voxceleb/noise
  ext: wav
  csv_file: results/xvect_augment/1986/save/noise.csv


# Add noise to input signal
add_noise: &id001 !new:speechbrain.augment.time_domain.AddNoise
  csv_file: results/xvect_augment/1986/save/noise.csv
  snr_low: 0
  snr_high: 15
  noise_sample_rate: 16000
  clean_sample_rate: 16000
  num_workers: 4

# Download and prepare the dataset of room impulse responses for augmentation
prepare_rir_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
  URL: 
    https://www.dropbox.com/scl/fi/linhy77c36mu10965a836/RIRs.zip?rlkey=pg9cu8vrpn2u173vhiqyu743u&dl=1
  dest_folder: ./Voxceleb/rir
  ext: wav
  csv_file: results/xvect_augment/1986/save/rir.csv

# Add reverberation to input signal
add_reverb: &id002 !new:speechbrain.augment.time_domain.AddReverb
  csv_file: results/xvect_augment/1986/save/rir.csv
  reverb_sample_rate: 16000
  clean_sample_rate: 16000
  num_workers: 4

# Frequency drop: randomly drops a number of frequency bands to zero.
drop_freq: &id003 !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: &id004 !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: [*id001, *id002, *id003, *id004]

mean_var_norm: &id008 !new:speechbrain.processing.features.InputNormalization

# Cost + optimization
  norm_type: sentence
  std_norm: false

mean_var_norm_emb: !new:speechbrain.processing.features.InputNormalization
    norm_type: global
    std_norm: False

label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder

modules:
  compute_features: *id005
  embedding_model: *id006
  classifier: *id007
  mean_var_norm: *id008
compute_cost: !name:speechbrain.nnet.losses.nll_loss
# compute_error: !name:speechbrain.nnet.losses.classification_error

opt_class: !name:torch.optim.Adam
  lr: 0.001
  weight_decay: 0.000002

lr_annealing: !new:speechbrain.nnet.schedulers.LinearScheduler
  initial_value: 0.001
  final_value: 0.0001
  epoch_count: 1

# Logging + checkpoints
train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
  save_file: results/xvect_augment/1986/train_log.txt

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: results/xvect_augment/1986/save
  recoverables:
    embedding_model: *id006
    classifier: *id007
    normalizer: *id008
    counter: *id009

pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
    loadables:
        embedding_model: *id006
        mean_var_norm: *id008
        classifier: *id007
        label_encoder: !ref <label_encoder>
    paths:
        embedding_model: !ref <pretrained_path>/embedding_model.ckpt
        mean_var_norm: !ref <pretrained_path>/normalizer.ckpt
        classifier: !ref <pretrained_path>/classifier.ckpt
        label_encoder: !ref <pretrained_path>/label_encoder.txt