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# ############################################################################
# Model: Small CRDNN model for Voice Activity Detection
# Author: Mirco Ravanelli, 2021
# ############################################################################
# Feature parameters
sample_rate: 16000
time_resolution: 0.01 # in seconds (e.g,, 0.01 = 10 ms)
n_fft: 400
n_mels: 40
# Model parameters
activation: !name:torch.nn.LeakyReLU
dropout: 0.15
cnn_blocks: 2
cnn_channels: (16, 32)
cnn_kernelsize: (3, 3)
rnn_layers: 2
rnn_neurons: 32
rnn_bidirectional: True
dnn_blocks: 1
dnn_neurons: 16
output_neurons: 1
device: 'cpu' # set 'cuda:0' for gpu
# Feature/Model objects
compute_features: !new:speechbrain.lobes.features.Fbank
sample_rate: !ref <sample_rate>
n_fft: !ref <n_fft>
n_mels: !ref <n_mels>
hop_length: !ref <time_resolution> * 1000 # in ms
mean_var_norm: !new:speechbrain.processing.features.InputNormalization
norm_type: sentence
cnn: !new:speechbrain.nnet.containers.Sequential
input_shape: [null, null, !ref <n_mels>]
norm1: !name:speechbrain.nnet.normalization.LayerNorm
cnn1: !name:speechbrain.lobes.models.CRDNN.CNN_Block
channels: 16
kernel_size: (3, 3)
cnn2: !name:speechbrain.lobes.models.CRDNN.CNN_Block
channels: 32
kernel_size: (3, 3)
rnn: !new:speechbrain.nnet.RNN.GRU
input_shape: [null, null, 320]
hidden_size: !ref <rnn_neurons>
num_layers: !ref <rnn_layers>
bidirectional: True
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>
bias: False
model: !new:torch.nn.ModuleList
- [!ref <cnn>, !ref <rnn>, !ref <dnn>]
modules:
compute_features: !ref <compute_features>
model: !ref <model>
cnn: !ref <cnn>
rnn: !ref <rnn>
dnn: !ref <dnn>
mean_var_norm: !ref <mean_var_norm>
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
model: !ref <model>
mean_var_norm: !ref <mean_var_norm>
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