You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
105 lines
3.0 KiB
105 lines
3.0 KiB
# https://yaml.org/type/float.html
|
|
data:
|
|
train_manifest: data/manifest.train
|
|
dev_manifest: data/manifest.dev
|
|
test_manifest: data/manifest.test-clean
|
|
|
|
collator:
|
|
vocab_filepath: data/lang_char/train_960_unigram5000_units.txt
|
|
unit_type: spm
|
|
spm_model_prefix: data/lang_char/train_960_unigram5000
|
|
feat_dim: 83
|
|
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: 30
|
|
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
|
|
augmentation_config: conf/augmentation.json
|
|
num_workers: 0
|
|
subsampling_factor: 1
|
|
num_encs: 1
|
|
|
|
|
|
# network architecture
|
|
model:
|
|
cmvn_file:
|
|
cmvn_file_type: "json"
|
|
# encoder related
|
|
encoder: transformer
|
|
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
|
|
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
|
|
|
|
# decoder related
|
|
decoder: transformer
|
|
decoder_conf:
|
|
attention_heads: 4
|
|
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
|
|
ctc_dropoutrate: 0.0
|
|
ctc_grad_norm_type: batch
|
|
lsm_weight: 0.1 # label smoothing option
|
|
length_normalized_loss: false
|
|
|
|
|
|
training:
|
|
n_epoch: 120
|
|
accum_grad: 2
|
|
log_interval: 100
|
|
checkpoint:
|
|
kbest_n: 50
|
|
latest_n: 5
|
|
|
|
optim: adam
|
|
optim_conf:
|
|
global_grad_clip: 5.0
|
|
weight_decay: 1.0e-06
|
|
scheduler: warmuplr # pytorch v1.1.0+ required
|
|
scheduler_conf:
|
|
lr: 0.004
|
|
warmup_steps: 25000
|
|
lr_decay: 1.0
|
|
|
|
decoding:
|
|
batch_size: 1
|
|
error_rate_type: wer
|
|
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
|
|
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
|
|
alpha: 2.5
|
|
beta: 0.3
|
|
beam_size: 10
|
|
cutoff_prob: 1.0
|
|
cutoff_top_n: 0
|
|
num_proc_bsearch: 8
|
|
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
|
|
decoding_chunk_size: -1 # 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: False # simulate streaming inference. Defaults to False.
|
|
|
|
|