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# Model:
# Author:
# ############################################################################
# Feature parameters
sample_rate: 16000
time_resolution: 0.01 # in seconds (e.g,, 0.01 = 10 ms)
n_fft: 400
n_mels_vad: 40
batch_size: 512
# VAD parameters
cnn1_channels: 16
cnn2_channels: 32
cnn_kernelsize: (3, 3)
rnn_layers: 2
rnn_neurons: 32
rnn_bidirectional: True
dnn_blocks: 1
dnn_neurons: 16
output_neurons_vad: 1
# ECAPA_TDNN
n_mels_ecapa: 80
out_neurons_ecapa: 7205
emb_dim: 192
dataloader_opts:
batch_size: !ref <batch_size>
# VAD objects
compute_fbank_vad: !new:speechbrain.lobes.features.Fbank
sample_rate: !ref <sample_rate>
n_fft: !ref <n_fft>
n_mels: !ref <n_mels_vad>
hop_length: !ref <time_resolution> * 1000 # in ms
mean_var_norm_vad: !new:speechbrain.processing.features.InputNormalization
norm_type: sentence
cnn: !new:speechbrain.nnet.containers.Sequential
input_shape: [null, null, !ref <n_mels_vad>]
norm1: !name:speechbrain.nnet.normalization.LayerNorm
cnn1: !name:speechbrain.lobes.models.CRDNN.CNN_Block
channels: !ref <cnn1_channels>
kernel_size: !ref <cnn_kernelsize>
cnn2: !name:speechbrain.lobes.models.CRDNN.CNN_Block
channels: !ref <cnn2_channels>
kernel_size: !ref <cnn_kernelsize>
rnn: !new:speechbrain.nnet.RNN.GRU
input_shape: [null, null, 320]
hidden_size: !ref <rnn_neurons>
num_layers: !ref <rnn_layers>
bidirectional: !ref <rnn_bidirectional>
dnn: !new:speechbrain.nnet.containers.Sequential
input_shape: [null, null, !ref <rnn_neurons> * 2]
dnn1: !name:speechbrain.lobes.models.CRDNN.DNN_Block
neurons: !ref <dnn_neurons>
dnn2: !name:speechbrain.lobes.models.CRDNN.DNN_Block
neurons: !ref <dnn_neurons>
lin: !name:speechbrain.nnet.linear.Linear
n_neurons: !ref <output_neurons_vad>
bias: False
##########################################################
# ECAPA_TDNN objects
compute_fbank_ecapa: !new:speechbrain.lobes.features.Fbank
n_mels: !ref <n_mels_ecapa>
mean_var_norm_ecapa: !new:speechbrain.processing.features.InputNormalization
norm_type: sentence
std_norm: False
embedding_model: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
input_size: !ref <n_mels_ecapa>
channels: [1024, 1024, 1024, 1024, 3072]
kernel_sizes: [5, 3, 3, 3, 1]
dilations: [1, 2, 3, 4, 1]
attention_channels: 128
lin_neurons: 192
mean_var_norm_emb: !new:speechbrain.processing.features.InputNormalization
norm_type: global
std_norm: False
#####################
vad: !new:torch.nn.ModuleList
- [!ref <cnn>, !ref <rnn>, !ref <dnn>]
#####################
modules:
compute_fbank_vad: !ref <compute_fbank_vad>
compute_fbank_ecapa: !ref <compute_fbank_ecapa>
cnn: !ref <cnn>
rnn: !ref <rnn>
dnn: !ref <dnn>
mean_var_norm_vad: !ref <mean_var_norm_vad>
mean_var_norm_ecapa: !ref <mean_var_norm_ecapa>
embedding_model: !ref <embedding_model>
mean_var_norm_emb: !ref <mean_var_norm_emb>
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
vad: !ref <vad>
embedding_model: !ref <embedding_model>
mean_var_norm_vad: !ref <mean_var_norm_vad>
mean_var_norm_emb: !ref <mean_var_norm_emb>
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