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PaddleSpeech/examples/aishell/asr1/conf/chunk_roformer.yaml

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3.0 KiB

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############################################
# Network Architecture #
############################################
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1 # sublayer output dropout
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
cnn_module_kernel: 15
use_cnn_module: True
activation_type: 'swish'
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pos_enc_layer_type: 'rope_pos' # abs_pos, rel_pos, rope_pos
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selfattention_layer_type: 'rel_selfattn' # unused
causal: true
use_dynamic_chunk: true
cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
use_dynamic_left_chunk: false
# decoder related
decoder: transformer # transformer, bitransformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
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r_num_blocks: 0 # only for bitransformer
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dropout_rate: 0.1 # sublayer output dropout
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
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reverse_weight: 0.0 # only for bitransformer
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length_normalized_loss: false
init_type: 'kaiming_uniform' # !Warning: need to convergence
###########################################
# Data #
###########################################
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
###########################################
# Dataloader #
###########################################
vocab_filepath: data/lang_char/vocab.txt
spm_model_prefix: ''
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
window_ms: 25.0
sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
batch_size: 32
maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 2
subsampling_factor: 1
num_encs: 1
###########################################
# Training #
###########################################
n_epoch: 240
accum_grad: 1
global_grad_clip: 5.0
dist_sampler: True
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-6
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5