add chunk conformer config from release model

pull/2212/head
Hui Zhang 2 years ago
parent 3a8869fba4
commit 67709155e9

@ -0,0 +1,99 @@
############################################
# Network Architecture #
############################################
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 512 # dimension of attention
attention_heads: 8
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
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
use_cnn_module: True
cnn_module_kernel: 15
activation_type: swish
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
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
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
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
length_normalized_loss: false
init_type: 'kaiming_uniform'
# https://yaml.org/type/float.html
###########################################
# Data #
###########################################
train_manifest: data/train_l/data.list
dev_manifest: data/dev/data.list
test_manifest: data/test_meeting/data.list
###########################################
# Dataloader #
###########################################
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
spm_model_prefix: ''
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
do_filter: True
maxlen_in: 1200 # if do_filter == False && input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 100 # if do_filter == False && output length > maxlen-out, batchsize is automatically reduced
minlen_in: 10
minlen_out: 0
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 0
subsampling_factor: 1
num_encs: 1
###########################################
# Training #
###########################################
n_epoch: 26
accum_grad: 32
global_grad_clip: 5.0
dist_sampler: True
log_interval: 1
checkpoint:
kbest_n: 50
latest_n: 5
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-6
scheduler: warmuplr
scheduler_conf:
warmup_steps: 5000
lr_decay: 1.0

@ -5,7 +5,7 @@ process:
n_mels: 80 n_mels: 80
n_shift: 160 n_shift: 160
win_length: 400 win_length: 400
dither: 0.1 dither: 1.0
- type: cmvn_json - type: cmvn_json
cmvn_path: data/mean_std.json cmvn_path: data/mean_std.json
# these three processes are a.k.a. SpecAugument # these three processes are a.k.a. SpecAugument

@ -0,0 +1,11 @@
beam_size: 10
decode_batch_size: 128
error_rate_type: cer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: 16 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: True # simulate streaming inference. Defaults to False.
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