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num_units: 1750
tokenizer: !new:sentencepiece.SentencePieceProcessor
# Feature parameters
sample_rate: 16000
n_fft: 400
n_mels: 40
# Model parameters
activation: !name:torch.nn.LeakyReLU
dropout: 0.15
cnn_blocks: 2
cnn_channels: (64, 128)
inter_layer_pooling_size: (2, 2)
cnn_kernelsize: (3, 3)
time_pooling_size: 4
rnn_class: !name:speechbrain.nnet.RNN.LSTM
rnn_layers: 3
rnn_neurons: 512
rnn_bidirectional: True
dnn_blocks: 1
dnn_neurons: 512
emb_size: 128
dec_neurons: 512
dec_layers: 1
output_neurons: !ref <num_units> #Number of tokens (same as LM)
blank_index: 0
bos_index: 0
eos_index: 0
unk_index: 0
min_decode_ratio: 0.0
max_decode_ratio: 1.0
valid_beam_size: 4
test_beam_size: 12
eos_threshold: 1.2
using_max_attn_shift: False
max_attn_shift: 240
ctc_weight_decode: 0.0
coverage_penalty: 3.0
temperature: 2.0
# Feature extraction
compute_features: !new:speechbrain.lobes.features.Fbank
sample_rate: !ref <sample_rate>
n_fft: !ref <n_fft>
n_mels: !ref <n_mels>
# Feature normalization (mean and std)
normalize: !new:speechbrain.processing.features.InputNormalization
norm_type: global
# The CRDNN model is an encoder that combines CNNs, RNNs, and DNNs.
encoder: !new:speechbrain.lobes.models.CRDNN.CRDNN
input_shape: [null, null, !ref <n_mels>]
activation: !ref <activation>
dropout: !ref <dropout>
cnn_blocks: !ref <cnn_blocks>
cnn_channels: !ref <cnn_channels>
cnn_kernelsize: !ref <cnn_kernelsize>
inter_layer_pooling_size: !ref <inter_layer_pooling_size>
time_pooling: True
using_2d_pooling: False
time_pooling_size: !ref <time_pooling_size>
rnn_class: !ref <rnn_class>
rnn_layers: !ref <rnn_layers>
rnn_neurons: !ref <rnn_neurons>
rnn_bidirectional: !ref <rnn_bidirectional>
rnn_re_init: True
dnn_blocks: !ref <dnn_blocks>
dnn_neurons: !ref <dnn_neurons>
use_rnnp: False
# Embedding (from indexes to an embedding space of dimension emb_size).
embedding: !new:speechbrain.nnet.embedding.Embedding
num_embeddings: !ref <output_neurons>
embedding_dim: !ref <emb_size>
# Attention-based RNN decoder.
decoder: !new:speechbrain.nnet.RNN.AttentionalRNNDecoder
enc_dim: !ref <dnn_neurons>
input_size: !ref <emb_size>
rnn_type: gru
attn_type: location
hidden_size: !ref <dec_neurons>
attn_dim: 1024
num_layers: !ref <dec_layers>
scaling: 1.0
channels: 10
kernel_size: 100
re_init: True
dropout: !ref <dropout>
# Linear transformation on the top of the decoder.
seq_lin: !new:speechbrain.nnet.linear.Linear
input_size: !ref <dec_neurons>
n_neurons: !ref <output_neurons>
# Final softmax (for log posteriors computation).
log_softmax: !new:speechbrain.nnet.activations.Softmax
apply_log: True
# Gathering all the submodels in a single model object.
model: !new:torch.nn.ModuleList
- - !ref <encoder>
- !ref <embedding>
- !ref <decoder>
- !ref <ctc_lin>
- !ref <seq_lin>
test_search: !new:speechbrain.decoders.S2SRNNBeamSearcher
embedding: !ref <embedding>
decoder: !ref <decoder>
linear: !ref <seq_lin>
bos_index: !ref <bos_index>
eos_index: !ref <eos_index>
blank_index: !ref <blank_index>
min_decode_ratio: !ref <min_decode_ratio>
max_decode_ratio: !ref <max_decode_ratio>
beam_size: !ref <test_beam_size>
eos_threshold: !ref <eos_threshold>
using_max_attn_shift: !ref <using_max_attn_shift>
max_attn_shift: !ref <max_attn_shift>
coverage_penalty: !ref <coverage_penalty>
ctc_weight: !ref <ctc_weight_decode>
temperature: !ref <temperature>
# Objects in "modules" dict will have their parameters moved to the correct
# device, as well as having train()/eval() called on them by the Brain class
modules:
encoder: !ref <encoder>
embedding: !ref <embedding>
dec: !ref <decoder>
decoder: !ref <test_search>
seq_lin: !ref <seq_lin>
normalize: !ref <normalize>
pretrainer !new:speechbrain.utils.parameter_transfer
loadables:
model: !ref <model>
normalizer: !ref <normalize>
tokenizer: !ref <tokenizer>
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