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conformer based end-to-end model for VKW challenge

Conformer without speed perpurb and lm

config: conf/train_train_vkw_bidirect_12conformer_hs2048_output256_att4_conv2d_char.yaml
beam: 10
num of gpu: 8
num of averaged model: 5
ctc weight (used for attention rescoring): 0.5

dev set results trained only with training set (785 keywords, 1505 hour train set)

scenario	Precision	Recall	F1	ATWV
lgv	0.9281	0.6420	0.7590	0.5183
liv	0.8886	0.6515	0.7518	0.6050
stv	0.9120	0.7471	0.8213	0.6256

dev set results trained with training set and finetune set (785 keywords, 1505 hour train set + 15 hour finetune set)

scenario	Precision	Recall	F1	ATWV
lgv	0.9478	0.7311	0.8255	0.6352
liv	0.9177	0.8398	0.8770	0.7412
stv	0.9320	0.8207	0.8729	0.7120

test set results trained only with training set (384 keywords, 1505 hour train set)

scenario	Precision	Recall	F1	ATWV
lgv	0.6262	0.5648	0.5939	0.5825
liv	0.8797	0.6282	0.7330	0.6061
stv	0.9102	0.7221	0.8053	0.6682

test set results trained with training set and finetune set (384 keywords, 1505 hour train set + 15 hour finetune set)

scenario	Precision	Recall	F1	ATWV
lgv	0.6469	0.6276	0.6371	0.6116
liv	0.9278	0.7560	0.8331	0.6927
stv	0.9434	0.8061	0.8693	0.7275