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Commit e7625a34 by xuchen
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accu update

parent 45bb3195
正在显示 包含 3938 行增加 和 279 行删除
egs/mustc/st/conf/aipa.yaml
......@@ -14,7 +14,7 @@ ctc-mixup-consistent-weight: 0
inter-ctc-mixup-consistent-weight: 0
mixup-consistent-weight: 0
cal-mixup-loss: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
......
egs/mustc/st/conf/aipa_kd.yaml
......@@ -14,7 +14,7 @@ ctc-mixup-consistent-weight: 0.15
inter-ctc-mixup-consistent-weight: 0.1
mixup-consistent-weight: 0.5
cal-mixup-loss: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
......
egs/mustc/st/conf/base.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/basis.yaml
......@@ -25,5 +25,6 @@ num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
label-smoothing: 0.1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
\ No newline at end of file
egs/mustc/st/conf/big.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/ctc_aug_base.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/ctc_aug_big.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/ctc_aug_pds_big.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/dual.yaml
......@@ -26,10 +26,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: join_speech_and_text_loss
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/ipa.yaml
......@@ -13,7 +13,7 @@ ctc-mixup-consistent-weight: 0
inter-ctc-mixup-consistent-weight: 0
mixup-consistent-weight: 0
cal-mixup-loss: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
......
egs/mustc/st/conf/multibranch.yaml
......@@ -45,10 +45,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
subsampling-type: conv1d
subsampling-layers: 2
......
egs/mustc/st/conf/nast_base.yaml
......@@ -13,7 +13,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/nast_big.yaml
......@@ -13,7 +13,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/nast_pds_big.yaml
......@@ -13,7 +13,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/pds_base_16.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/pds_base_32.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/pds_base_4_444.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/pds_base_8.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/pds_base_8_444.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/pds_big_16.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/mustc/st/conf/pds_big_32.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/mustc/st/conf/pds_big_8.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/mustc/st/conf/pds_big_deep_16.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/mustc/st/conf/pds_big_deep_32.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/mustc/st/conf/pds_big_deep_8.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/mustc/st/conf/pds_deep_16.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/pds_deep_32.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/pds_deep_8.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/mustc/st/conf/reproduction_aipa_bilctc_kd.yaml 0 → 100644
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# Bilingual CTC
share-ctc-and-embed: True
share-xctc-and-embed: True
ctc-weight: 0.2
xctc-weight: 0.1
# InterCTC
inter-ctc-weight: 0.1
inter-ctc-layers: 6,9
share-inter-ctc: True
inter-xctc-weight: 0.05
inter-xctc-layers: 6,9
# Prediction-aware encoding
ctc-pae: inter_league
xctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.1
inter-ctc-mixup-consistent-weight: 0.05
xctc-mixup-consistent-weight: 0.05
inter-xctc-mixup-consistent-weight: 0.25
mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/mustc/st/conf/reproduction_aipa_bilctc_kd_nohardloss.yaml 0 → 100644
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: True
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# Bilingual CTC
share-ctc-and-embed: True
share-xctc-and-embed: True
ctc-weight: 0.2
xctc-weight: 0.1
# InterCTC
inter-ctc-weight: 0.1
inter-ctc-layers: 6,9
share-inter-ctc: True
inter-xctc-weight: 0.05
inter-xctc-layers: 6,9
# Prediction-aware encoding
ctc-pae: inter_league
xctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.1
inter-ctc-mixup-consistent-weight: 0.05
xctc-mixup-consistent-weight: 0.05
inter-xctc-mixup-consistent-weight: 0.25
mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/mustc/st/conf/reproduction_aipa_kd.yaml
......@@ -10,7 +10,7 @@ inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
cal-mixup-loss: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
......
egs/mustc/st/conf/reproduction_aipa_kd_womixuploss.yaml → egs/mustc/st/conf/reproduction_aipa_kd_nohardloss.yaml
......@@ -10,7 +10,7 @@ inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
cal-mixup-loss: False
mixup-no-hard-loss: True
no-specaugment: False
layer-out-norm: False
......
egs/mustc/st/conf/reproduction_bil_ctc_progressive.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/reproduction_bil_ctc_progressive2.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/reproduction_bil_ctc_synchronous.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/reproduction_ctc_aug.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/reproduction_nast.yaml
......@@ -15,7 +15,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/reproduction_nast_bilctc_syn.yaml 0 → 100644
arch: s2t_ctc
encoder-type: transformer
criterion: ctc
zero_infinity: True
xctc-weight: 1.0
ctc-weight: 1.0
share-ctc-and-embed: True
share-xctc-and-embed: True
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam-betas: (0.9,0.98)
encoder-normalize-before: True
decoder-normalize-before: True
encoder-embed-norm: True
encoder-no-scale-embedding: True
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 2048
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.15
activation-fn: relu
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 18
encoder-attention-heads: 8
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
# InterCTC
share-inter-ctc: True
inter-ctc-weight: 1.0
inter-ctc-layers: 6,9,12,15
inter-xctc-weight: 1.0
inter-xctc-layers: 6,9,12,15
# Prediction-aware encoding
ctc-pae: inter_league
xctc-pae: inter_league
pae-unnorm-input: True
# Cross-layer attn
# xctc-cross-attn: True
# cross-attn-start-layer: 4
# cross-attn-layer: 3
# cross-attn-collaboration-mode: serial
# cross-attn-league-drop-net: True
# cross-attn-league-drop-net-prob: 0.1
# Curriculum learning mixing
xctc-pae-ground-truth-ratio: 0.8
xctc-pae-ground-truth-only-mistake: True
pae-oracle-smooth: True
egs/mustc/st/conf/reproduction_sate.yaml
......@@ -6,12 +6,11 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
ctc-weight: 0.3
share-ctc-and-embed: True
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/sate.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/sate_big.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/sate_big_pds.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/sate_deep.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
......
egs/mustc/st/conf/sate_pds_16.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/sate_pds_8.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/sate_pds_8_444.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/sate_pds_base_8.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/sate_pds_deep_8.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/conf/w2v2.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/mustc/st/decode.sh
......@@ -11,7 +11,7 @@ if [ "$#" -eq 1 ]; then
fi
sacrebleu=1
ctc_infer=0
ctc_infer=1
n_average=10
beam_size=5
infer_ctc_weight=0
......
egs/mustc/st/run.sh
......@@ -354,8 +354,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
mv tmp.log $log
log=${model_dir}/train.log
# cmd="${cmd} 2>&1 | tee -a ${log}"
cmd="${cmd} >> ${log} 2>&1 "
cmd="${cmd} 2>&1 | tee -a ${log}"
#cmd="${cmd} >> ${log} 2>&1 "
if [[ $eval -eq 1 ]]; then
# tensorboard
port=6666
......
egs/talcs/asr/binary.sh 0 → 100755
set -e
eval=1
lcrm=0
tokenizer=0
root_dir=~/st/Fairseq-S2T
data_dir=~/st/data/test
vocab_dir=~/st/data/mustc/st/en-de
asr_vocab_prefix=spm_unigram10000_st_share
src_lang=en
tgt_lang=de
subsets=(2019)
cp -r ${vocab_dir}/${asr_vocab_prefix}.* ${data_dir}/${src_lang}-${tgt_lang}
rm -rf ${data_dir}/${src_lang}-${tgt_lang}/fbank80.zip
splits=$(echo ${subsets[*]} | sed 's/ /,/g')
cmd="python ${root_dir}/examples/speech_to_text/prep_st_data.py
--data-root ${data_dir}
--output-root ${data_dir}
--splits ${splits}
--task asr
--src-lang ${src_lang}
--tgt-lang ${tgt_lang}
--add-src
--share
--asr-prefix ${asr_vocab_prefix}
--cmvn-type utterance"
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
if [[ ${tokenizer} -eq 1 ]]; then
cmd="$cmd
--tokenizer"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
egs/talcs/asr/conf/base.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/talcs/asr/conf/base_nonorm.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/talcs/asr/conf/basis.yaml 0 → 100644
train-subset: train
valid-subset: dev
max-epoch: 100
max-update: 100000
patience: 20
post-process: sentencepiece
# best-checkpoint-metric: loss
# maximize-best-checkpoint-metric: False
eval-wer: True
eval-wer-args: {"beam": 1, "lenpen": 1.0}
eval-wer-tok-args: {"wer_remove_punct": true, "wer_lowercase": true, "wer_char_level": true}
eval-wer-remove-bpe: sentencepiece
eval-wer-print-samples: True
best_checkpoint_metric: dec_wer
maximize_best_checkpoint_metric: False
no-epoch-checkpoints: True
# keep-last-epochs: 10
keep-best-checkpoints: 10
num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
label-smoothing: 0.1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
\ No newline at end of file
egs/talcs/asr/conf/big.yaml 0 → 100644
arch: s2t_transformer_m
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 2048
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.15
activation-fn: relu
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
\ No newline at end of file
egs/talcs/asr/conf/big_wenet.yaml 0 → 100644
arch: s2t_transformer_m
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv2d
subsampling-layers: 2
subsampling-filter: 512
subsampling-kernel: 3
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: relu
dropout: 0.15
activation-fn: relu
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
cnn-module-norm: layer_norm
load-pretrained-encoder-from: /home/xuchen/after.pt
load-pretrained-decoder-from: /home/xuchen/after.pt
#load-pretrained-decoder-from:
egs/talcs/asr/conf/conformer.yaml 0 → 100644
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/talcs/asr/conf/ctc.yaml 0 → 100644
ctc-weight: 0.3
\ No newline at end of file
egs/talcs/asr/conf/dlcl.yaml 0 → 100644
use-enc-dlcl: True
use-dec-dlcl: True
egs/talcs/asr/conf/dynamic.yaml 0 → 100644
compression-metric: threshold
compression-mode: create
compression-layers: 6,9
compression-threshold: 0.95
compression-norm: True
compression-pos: True
\ No newline at end of file
egs/talcs/asr/conf/inter.yaml 0 → 100644
inter-ctc-weight: 0.2
inter-ctc-layers: 6,9
inter-ctc-drop-prob: 0
share-inter-ctc: True
ctc-pae: none
# ctc-pae: inter_league
# ctc-pae-ground-truth-ratio: 0.1
# pae-gumbel: True
# pae-distribution-hard: True
# pae-drop-prob: 0.0
# pae-distribution-cutoff: 10
# share-pae-and-ctc: True
# pae-embed-norm: True
# pae-out-norm: True
# ctc-self-distill-weight: 1
# target-ctc-self-distill-weight: 1
# ctc-self-distill-prob: 0.1
# cal-all-ctc: True
\ No newline at end of file
egs/talcs/asr/conf/ipa.yaml 0 → 100644
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: False
inter-mixup-decoder-emb: False
ctc-mixup-consistent-weight: 0
inter-ctc-mixup-consistent-weight: 0
mixup-consistent-weight: 0
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
\ No newline at end of file
egs/talcs/asr/conf/mlo.yaml 0 → 100644
inter-ctc-mlo: 1:2:3
\ No newline at end of file
egs/talcs/asr/conf/norm.yaml 0 → 100644
encoder-embed-norm: True
encoder-no-scale-embedding: True
\ No newline at end of file
egs/talcs/asr/conf/pds_base.yaml 0 → 100644
arch: pdss2t_transformer_s_8
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/talcs/asr/conf/pds_base_16.yaml 0 → 100644
arch: pdss2t_transformer_s_16
encoder-embed-dim: 256
pds-stages: 4
pds-layers: 2_2_6_2
pds-ratios: 2_2_2_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/talcs/asr/conf/pds_base_32.yaml 0 → 100644
arch: pdss2t_transformer_s_32
encoder-embed-dim: 256
pds-stages: 5
pds-layers: 2_2_3_3_2
pds-ratios: 2_2_2_2_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 256_256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1_1
pds-kernel-sizes: 5_5_5_5_5
pds-ffn-ratios: 8_8_8_8_8
pds-attn-heads: 4_4_4_4_4
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/talcs/asr/conf/pds_base_8.yaml 0 → 100644
arch: pdss2t_transformer_s_8
encoder-embed-dim: 256
pds-stages: 4
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/talcs/asr/conf/pds_big_8.yaml 0 → 100644
arch: pdss2t_transformer_m_8
encoder-embed-dim: 512
pds-stages: 4
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 512_512_512_512
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 4_4_4_4
pds-attn-heads: 8_8_8_8
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.15
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
egs/talcs/asr/conf/purectc.yaml 0 → 100644
arch: s2t_ctc
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
ctc-weight: 1.0
encoder-normalize-before: True
decoder-normalize-before: True
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 18
encoder-attention-heads: 4
\ No newline at end of file
egs/talcs/asr/conf/rpr.yaml 0 → 100644
encoder-attention-type: rel_pos
#encoder-attention-type: rel_pos_legacy
#encoder-attention-type: rel_selfattn
#encoder-attention-type: relative
#decoder-attention-type: relative
#max-encoder-relative-length: 100
#max-decoder-relative-length: 20
egs/talcs/asr/conf/xctc.yaml 0 → 100644
xctc-weight: 0.3
share-xctc-and-embed: True
\ No newline at end of file
egs/talcs/asr/conf/xinter.yaml 0 → 100644
inter-xctc-weight: 0.2
inter-xctc-layers: 6,9
xctc-pae: none
# xctc-pae: inter_league
xctc-cross-attn: False
cross-attn-start-layer: 7
cross-attn-layer: 6
cross-attn-collaboration-mode: parallel
cross-attn-league-s1-ratio: 0.5
cross-attn-league-s2-ratio: 0.5
cross-attn-league-out-norm: False
cross-attn-league-gated: False
cross-attn-league-drop-net: False
cross-attn-league-drop-net-prob: 0.2
cross-attn-league-drop-net-mix: False
# xctc-pae-ground-truth-ratio: 0.1
# xctc-pae-ground-truth-ratio-adaptive: True
# xctc-pae-ground-truth-only-mistake: True
# pae-oracle-smooth: True
# pae-gumbel: True
# pae-distribution-hard: True
# pae-drop-prob: 0.0
# pae-distribution-cutoff: 10
# share-pae-and-xctc: True
# pae-embed-norm: True
# pae-out-norm: True
# ctc-self-distill-weight: 1
# target-ctc-self-distill-weight: 1
# ctc-self-distill-prob: 0.1
# cal-all-ctc: True
\ No newline at end of file
egs/talcs/asr/decode.sh 0 → 100755
#!/usr/bin/env bash
gpu_num=1
data_tag=asr
test_subset=(dev test)
exp_name=
if [ "$#" -eq 1 ]; then
exp_name=$1
fi
cer=1
ctc_infer=1
n_average=10
beam_size=5
infer_ctc_weight=0.1
len_penalty=1.0
max_tokens=50000
batch_size=1
infer_debug=0
dec_model=checkpoint_best.pt
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--n_average ${n_average}
--cer ${cer}
--ctc_infer ${ctc_infer}
--beam_size ${beam_size}
--len_penalty ${len_penalty}
--batch_size ${batch_size}
--max_tokens ${max_tokens}
--dec_model ${dec_model}
--ctc_infer ${ctc_infer}
--infer_ctc_weight ${infer_ctc_weight}
--infer_debug ${infer_debug}
"
if [[ -n ${data_tag} ]]; then
cmd="$cmd --data_tag ${data_tag}"
fi
if [[ ${#test_subset[@]} -ne 0 ]]; then
subsets=$(echo ${test_subset[*]} | sed 's/ /,/g')
cmd="$cmd --test_subset ${subsets}"
fi
echo $cmd
eval $cmd
egs/talcs/asr/local/cal_bleu.sh 0 → 100755
#!/usr/bin/env bash
set -e
ref=$1
gen=$2
tokenizer=$3
lang=$4
lang_pair=en-${lang}
record=$(mktemp -t temp.record.XXXXXX)
if [[ ${tokenizer} -eq 1 ]]; then
echo "MultiBLEU" > ${record}
cmd="multi-bleu.perl ${ref} < ${gen}"
eval $cmd | head -n 1 >> ${record}
cmd="detokenizer.perl -q -l ${lang} --threads 32 < ${ref} > ${ref}.detok"
eval $cmd
cmd="detokenizer.perl -q -l ${lang} --threads 32 < ${gen} > ${gen}.detok"
eval $cmd
ref=${ref}.detok
gen=${gen}.detok
fi
echo "SacreBLEU" >> ${record}
cmd="cat ${gen} | sacrebleu ${ref} -m bleu -w 4 -l ${lang_pair}"
eval $cmd >> ${record}
cat ${record}
rm ${record}
\ No newline at end of file
egs/talcs/asr/local/cal_ctc_bleu.sh 0 → 100755
#!/usr/bin/env bash
set -e
infer_dir=$1
tag=$2
s2s_infer_file=${infer_dir}/$3
org_ctc_infer_file=${infer_dir}/$4
ref=$5
tokenizer=$6
lang=$7
idx=${infer_dir}/${tag}_idx
ctc_infer=${infer_dir}/${tag}_ctc_infer
ctc_infer_sort=${infer_dir}/${tag}_ctc_infer_sort
if [[ ! -f ${ctc_infer_sort} ]]; then
cut -f1 ${s2s_infer_file} > ${idx}
paste ${idx} ${org_ctc_infer_file} > ${ctc_infer}
sort -n -t $'\t' ${ctc_infer} | cut -f2 > ${ctc_infer_sort}
fi
gen=${ctc_infer_sort}
./cal_bleu.sh ${ref} ${gen} ${tokenizer} ${lang}
\ No newline at end of file
egs/talcs/asr/local/cal_wer.py 0 → 100644
import unicodedata
import re
import jiwer
import jiwer.transforms as tr
import sys
ref_file = sys.argv[1]
hyp_file = sys.argv[2]
wer_standardize = tr.Compose(
[
tr.SubstituteRegexes({r"<<unk>>": r"@"}),
tr.ToLowerCase(),
tr.RemovePunctuation(),
tr.ExpandCommonEnglishContractions(),
tr.RemoveKaldiNonWords(),
tr.RemoveWhiteSpace(replace_by_space=True),
tr.ReduceToListOfListOfWords(),
]
)
cer_standardize = tr.Compose(
[
tr.SubstituteRegexes({r"<<unk>>": r"@"}),
tr.ToLowerCase(),
tr.RemovePunctuation(),
tr.Strip(),
tr.ReduceToListOfListOfChars(),
]
)
def process_text(text):
# 将中文字符和英文字符间加空格
text = re.sub(r'([\u4e00-\u9fa5])([a-zA-Z0-9])', r'\1 \2', text)
text = re.sub(r'([a-zA-Z0-9])([\u4e00-\u9fa5])', r'\1 \2', text)
# 将中文字符间加空格
text = re.sub(r'([\u4e00-\u9fa5])', r'\1 ', text)
# 去掉多余的空格
text = re.sub(r'\s+', ' ', text).strip()
return text
ref_lines = open(ref_file, "r").readlines()
hyp_lines = open(hyp_file, "r").readlines()
ref_lines = [process_text(line) for line in ref_lines]
hyp_lines = [process_text(line) for line in hyp_lines]
print(hyp_lines[:10])
wer = jiwer.wer(ref_lines, hyp_lines,
truth_transform=wer_standardize,
hypothesis_transform=wer_standardize,
)
cer = jiwer.cer(ref_lines, hyp_lines,
truth_transform=cer_standardize,
hypothesis_transform=cer_standardize,
)
print("WER: %.4f" % wer)
print("CER: %.4f" % cer)
egs/talcs/asr/local/cal_wer.sh 0 → 100755
#!/usr/bin/env bash
set -e
infer_dir=$1
tag=$2
s2s_infer_file=${infer_dir}/$3
org_ctc_infer_file=${infer_dir}/$4
ref=$5
idx=${infer_dir}/${tag}_idx
ctc_infer=${infer_dir}/${tag}_ctc_infer
ctc_infer_sort=${infer_dir}/${tag}_ctc_infer_sort
cut -f1 ${s2s_infer_file} > ${idx}
paste ${idx} ${org_ctc_infer_file} > ${ctc_infer}
sort -n -t $'\t' ${ctc_infer} | cut -f2 > ${ctc_infer_sort}
python3 ./cal_wer.py ${ref} ${ctc_infer_sort}
\ No newline at end of file
egs/talcs/asr/local/extract_txt_from_tsv.py 0 → 100644
import sys
import csv
tsv_file = sys.argv[1]
out_file = sys.argv[2]
extract_item = sys.argv[3]
with open(tsv_file) as f:
reader = csv.DictReader(
f,
delimiter="\t",
quotechar=None,
doublequote=False,
lineterminator="\n",
quoting=csv.QUOTE_NONE,
)
samples = [dict(e) for e in reader]
fw = open(out_file, "w", encoding="utf-8")
for s in samples:
if extract_item in s:
fw.write("%s\n" % s[extract_item])
else:
print("Error in sample: ")
print(s)
exit()
egs/talcs/asr/local/monitor.sh 0 → 100755
#!/usr/bin/env bash
gpu_num=4
cmd="sh train.sh"
while :
do
record=$(mktemp -t temp.record.XXXXXX)
gpustat > $record
all_devices=$(seq 0 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((count + 1))
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
sleep 60s
else
echo "Run $cmd"
eval $cmd
sleep 10s
exit
fi
done
egs/talcs/asr/local/parse_options.sh 0 → 100755
#!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# Parse command-line options.
# To be sourced by another script (as in ". parse_options.sh").
# Option format is: --option-name arg
# and shell variable "option_name" gets set to value "arg."
# The exception is --help, which takes no arguments, but prints the
# $help_message variable (if defined).
###
### The --config file options have lower priority to command line
### options, so we need to import them first...
###
# Now import all the configs specified by command-line, in left-to-right order
for ((argpos=1; argpos<$#; argpos++)); do
if [ "${!argpos}" == "--config" ]; then
argpos_plus1=$((argpos+1))
config=${!argpos_plus1}
[ ! -r $config ] && echo "$0: missing config '$config'" && exit 1
. $config # source the config file.
fi
done
###
### Now we process the command line options
###
i=1
argv="$@"
while true; do
key=${!i}
j=$(($i + 1))
value=${!j}
[ -z "${!i:-}" ] && break; # break if there are no arguments
case "${key}" in
# If the enclosing script is called with --help option, print the help
# message and exit. Scripts should put help messages in $help_message
--help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2;
else printf "$help_message\n" 1>&2 ; fi;
exit 0 ;;
--*=*) echo "$0: options to scripts must be of the form --name value, got '${key}}'"
exit 1 ;;
# If the first command-line argument begins with "--" (e.g. --foo-bar),
# then work out the variable name as $name, which will equal "foo_bar".
--*) name=`echo "${key}" | sed s/^--// | sed s/-/_/g`;
# Next we test whether the variable in question is undefned-- if so it's
# an invalid option and we die. Note: $0 evaluates to the name of the
# enclosing script.
# The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar
# is undefined. We then have to wrap this test inside "eval" because
# foo_bar is itself inside a variable ($name).
#eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
oldval="`eval echo \\$$name`";
# Work out whether we seem to be expecting a Boolean argument.
if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then
was_bool=true;
else
was_bool=false;
fi
# Set the variable to the right value-- the escaped quotes make it work if
# the option had spaces, like --cmd "queue.pl -sync y"
# echo $name
eval $name=\"${value}\";
# Check that Boolean-valued arguments are really Boolean.
if $was_bool && [[ "${value}" != "true" && "${value}" != "false" ]]; then
echo "$0: expected \"true\" or \"false\": ${key} ${value}" 1>&2
exit 1;
fi
# shift 2;
i=$(($i + 2))
;;
*) break;
esac
done
# Check for an empty argument to the --cmd option, which can easily occur as a
# result of scripting errors.
[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1;
true; # so this script returns exit code 0.
egs/talcs/asr/local/utils.sh 0 → 100755
get_devices(){
gpu_num=$1
use_cpu=$2
device=()
while :
do
record=$(mktemp -t temp.record.XXXXXX)
gpustat > $record
all_devices=$(seq 0 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 1000 ]]; then
device[$count]=$dev
count=$((count + 1))
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
if [[ $use_cpu -eq 1 ]]; then
device=(-1)
else
sleep 60s
fi
else
break
fi
done
echo ${device[*]} | sed 's/ /,/g'
return $?
}
egs/talcs/asr/run.sh 0 → 100755
#!/usr/bin/env bash
# Processing AIShell ASR Datasets
# Copyright 2021 Chen Xu (xuchennlp@outlook.com)
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
#set -u
set -o pipefail
export PYTHONIOENCODING=UTF-8
eval=1
time=$(date "+%m%d_%H%M")
stage=1
stop_stage=2
######## Hardware ########
# Devices
device=(0)
gpu_num=2
update_freq=1
max_tokens=100000
pwd_dir=$PWD
root_dir=${ST_ROOT}
data_root_dir=${root_dir}
code_dir=${root_dir}/S2T
# Dataset
src_lang=zh_en.tok
lang=${src_lang}
dataset=talcs
data_tag=asr
task=speech_to_text
vocab_type=unigram
vocab_size=10000
speed_perturb=0
lcrm=0
tokenizer=0
use_raw_audio=0
. ./local/parse_options.sh || exit 1;
use_specific_dict=0
specific_prefix=st
specific_dir=${root_dir}/data/mustc/st
asr_vocab_prefix=spm_unigram10000_st_share
data_model_subfix=${dataset}/${data_tag}
org_data_dir=${data_root_dir}/data/${dataset}
data_dir=${data_root_dir}/data/${data_model_subfix}
train_split=train
valid_split=dev
test_split=test
test_subset=dev,test
# exp
sub_tag=
exp_prefix=$(date "+%m%d")
extra_tag=
extra_parameter=
exp_tag=baseline
exp_name=
# Training Settings
train_config=base,ctc
fp16=1
step_valid=0
# Decoding Settings
dec_model=checkpoint_best.pt
cer=1
ctc_infer=0
infer_ctc_weight=0
ctc_self_ensemble=0
ctc_inter_logit=0
n_average=10
batch_size=0
beam_size=5
len_penalty=1.0
single=0
epoch_ensemble=0
best_ensemble=1
infer_debug=0
infer_score=0
# infer_parameters="--cal-monotonic-cross-attn-weights --cal-localness --localness-window 0.1 --cal-topk-cross-attn-weights --topk-cross-attn-weights 15 --cal-entropy"
data_config=config.yaml
# Parsing Options
if [[ ${speed_perturb} -eq 1 ]]; then
data_dir=${data_dir}_sp
exp_prefix=${exp_prefix}_sp
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
data_dir=${data_dir}_${specific_prefix}
exp_prefix=${exp_prefix}_${specific_prefix}
fi
if [[ ${tokenizer} -eq 1 ]]; then
data_dir=${data_dir}_tok
exp_prefix=${exp_prefix}_tok
fi
if [[ ${use_raw_audio} -eq 1 ]]; then
data_dir=${data_dir}_raw
exp_prefix=${exp_prefix}_raw
fi
if [[ "${vocab_type}" == "char" ]]; then
data_dir=${data_dir}_char
exp_prefix=${exp_prefix}_char
fi
# setup nccl envs
export NCCL_IB_DISABLE=0
export NCCL_IB_HCA=$ARNOLD_RDMA_DEVICE:1
export NCCL_IB_GID_INDEX=3
export NCCL_SOCKET_IFNAME=eth0
export PATH=$PATH:${code_dir}/scripts
. ./local/parse_options.sh || exit 1;
if [[ -z ${exp_name} ]]; then
config_string=${train_config//,/_}
exp_name=${exp_prefix}_${config_string}_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
if [[ -n ${exp_subfix} ]]; then
exp_name=${exp_name}_${exp_subfix}
fi
fi
ckpt_dir=${root_dir}/checkpoints/
model_dir=${root_dir}/checkpoints/${data_model_subfix}/${sub_tag}/${exp_name}
# Start
cd ${code_dir}
echo "Start Stage: $stage"
echo "Stop Stage: $stop_stage"
if [[ `pip list | grep fairseq | wc -l` -eq 0 ]]; then
echo "Default Stage: env configure"
pip3 install -e ${code_dir}
fi
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "Stage -1: Data Download"
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
### Task dependent. You have to make data the following preparation part by yourself.
echo "Stage 0: Data Preparation"
if [[ ! -e ${data_dir} ]]; then
mkdir -p ${data_dir}
fi
cmd="python3 ${code_dir}/examples/speech_to_text/prep_audio_data.py
--data-root ${org_data_dir}
--output-root ${data_dir}
--task asr
--src-lang ${src_lang}
--splits ${valid_split},${test_split},${train_split}
--add-src
--share
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ ${use_raw_audio} -eq 1 ]]; then
cmd="$cmd
--raw"
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
cp -r ${specific_dir}/${asr_vocab_prefix}.* ${data_dir}
cmd="$cmd
--asr-prefix ${asr_vocab_prefix}"
fi
if [[ ${speed_perturb} -eq 1 ]]; then
cmd="$cmd
--speed-perturb"
fi
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
if [[ ${tokenizer} -eq 1 ]]; then
cmd="$cmd
--tokenizer"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Stage 1: Network Training"
[[ ! -d ${data_dir} ]] && echo "The data dir ${data_dir} is not existing!" && exit 1;
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=""
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
export CUDA_VISIBLE_DEVICES=${device}
fi
echo -e "data=${data_dir} model=${model_dir}"
if [[ ! -d ${model_dir} ]]; then
mkdir -p ${model_dir}
else
echo "${model_dir} exists."
fi
cp -f ${pwd_dir}/`basename ${BASH_SOURCE[0]}` ${model_dir}
cp -f ${pwd_dir}/train.sh ${model_dir}
extra_parameter="${extra_parameter}
--train-config ${pwd_dir}/conf/basis.yaml"
cp -f ${pwd_dir}/conf/basis.yaml ${model_dir}
config_list="${train_config//,/ }"
idx=1
for config in ${config_list[@]}
do
config_path=${pwd_dir}/conf/${config}.yaml
if [[ ! -f ${config_path} ]]; then
echo "No config file ${config_path}"
exit
fi
cp -f ${config_path} ${model_dir}
extra_parameter="${extra_parameter}
--train-config${idx} ${config_path}"
idx=$((idx + 1))
done
cmd="python3 -u ${code_dir}/fairseq_cli/train.py
${data_dir}
--config-yaml ${data_config}
--task ${task}
--max-tokens ${max_tokens}
--skip-invalid-size-inputs-valid-test
--update-freq ${update_freq}
--log-interval 100
--save-dir ${model_dir}
--tensorboard-logdir ${model_dir}"
if [[ -n ${extra_parameter} ]]; then
cmd="${cmd}
${extra_parameter}"
fi
if [[ ${gpu_num} -gt 0 ]]; then
cmd="${cmd}
--distributed-world-size $gpu_num
--ddp-backend no_c10d"
fi
if [[ $fp16 -eq 1 ]]; then
cmd="${cmd}
--fp16"
fi
if [[ $step_valid -eq 1 ]]; then
validate_interval=1
save_interval=1
no_epoch_checkpoints=0
save_interval_updates=500
keep_interval_updates=10
fi
if [[ -n $no_epoch_checkpoints && $no_epoch_checkpoints -eq 1 ]]; then
cmd="$cmd
--no-epoch-checkpoints"
fi
if [[ -n $validate_interval ]]; then
cmd="${cmd}
--validate-interval $validate_interval "
fi
if [[ -n $save_interval ]]; then
cmd="${cmd}
--save-interval $save_interval "
fi
if [[ -n $save_interval_updates ]]; then
cmd="${cmd}
--save-interval-updates $save_interval_updates"
if [[ -n $keep_interval_updates ]]; then
cmd="${cmd}
--keep-interval-updates $keep_interval_updates"
fi
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
# save info
log=${ckpt_dir}/history.log
echo "${time} | ${data_dir} | ${exp_name} | ${model_dir} " >> $log
tail -n 50 ${log} > tmp.log
mv tmp.log $log
log=${model_dir}/train.log
cmd="${cmd} 2>&1 | tee -a ${log}"
#cmd="${cmd} >> ${log} 2>&1 "
if [[ $eval -eq 1 ]]; then
# tensorboard
port=6666
tensorboard --logdir ${model_dir} --port ${port} --bind_all &
echo "${cmd}" > ${model_dir}/cmd
eval $cmd
#sleep 2s
#tail -n "$(wc -l ${log} | awk '{print $1+1}')" -f ${log}
fi
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "Stage 2: Decoding"
dec_models=
if [[ ${n_average} -eq 1 ]]; then
dec_models=${dec_model}
fi
if [[ ${n_average} -ne 1 ]]; then
# Average models
if [[ ${epoch_ensemble} -eq 1 ]]; then
avg_model=avg_epoch${n_average}_checkpoint.pt
if [[ ! -f ${model_dir}/${avg_model} ]]; then
cmd="python3 ${code_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-epoch-checkpoints ${n_average}
--output ${model_dir}/${avg_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
fi
dec_models+=(${avg_model})
fi
if [[ ${best_ensemble} -eq 1 ]]; then
avg_model=avg_best${n_average}_checkpoint.pt
if [[ ! -f ${model_dir}/${avg_model} ]]; then
cmd="python3 ${code_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-best-checkpoints ${n_average}
--output ${model_dir}/${avg_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
fi
dec_models+=(${avg_model})
fi
fi
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=""
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
export CUDA_VISIBLE_DEVICES=${device}
fi
for dec_model in ${dec_models[@]}; do
suffix=alpha${len_penalty}
model_str=`echo $dec_model | sed -e "s#checkpoint##" | sed "s#.pt##"`
suffix=${suffix}_${model_str}
if [[ -n ${cer} && ${cer} -eq 1 ]]; then
suffix=${suffix}_cer
else
suffix=${suffix}_wer
fi
suffix=${suffix}_beam${beam_size}
if [[ ${batch_size} -ne 0 ]]; then
suffix=${suffix}_batch${batch_size}
else
suffix=${suffix}_tokens${max_tokens}
fi
if [[ ${ctc_infer} -eq 1 ]]; then
suffix=${suffix}_ctc
fi
if [[ ${ctc_self_ensemble} -eq 1 ]]; then
suffix=${suffix}_ensemble
fi
if [[ ${ctc_inter_logit} -ne 0 ]]; then
suffix=${suffix}_logit${ctc_inter_logit}
fi
if (( $(echo "${infer_ctc_weight} > 0" | bc -l) )); then
suffix=${suffix}_ctc${infer_ctc_weight}
fi
if [[ ${infer_score} -eq 1 ]]; then
suffix=${suffix}_score
fi
suffix=`echo $suffix | sed -e "s#__#_#"`
result_file=${model_dir}/decode_result_${suffix}
[[ -f ${result_file} ]] && rm ${result_file}
test_subset=${test_subset//,/ }
for subset in ${test_subset[@]}; do
subset=${subset}
if [[ ${infer_debug} -ne 0 ]]; then
cmd="python3 -m debugpy --listen 0.0.0.0:5678 --wait-for-client"
else
cmd="python3 "
fi
cmd="$cmd ${code_dir}/fairseq_cli/generate.py
${data_dir}
--config-yaml ${data_config}
--gen-subset ${subset}
--task speech_to_text
--path ${model_dir}/${dec_model}
--results-path ${model_dir}
--batch-size ${batch_size}
--max-tokens ${max_tokens}
--beam ${beam_size}
--lenpen ${len_penalty}
--infer-ctc-weight ${infer_ctc_weight}
--scoring wer"
if [[ ${cer} -eq 1 ]]; then
cmd="${cmd}
--wer-char-level"
fi
if [[ ${ctc_infer} -eq 1 ]]; then
cmd="${cmd}
--ctc-infer"
fi
if [[ ${ctc_self_ensemble} -eq 1 ]]; then
cmd="${cmd}
--ctc-self-ensemble"
fi
if [[ ${ctc_inter_logit} -ne 0 ]]; then
cmd="${cmd}
--ctc-inter-logit ${ctc_inter_logit}"
fi
if [[ ${infer_score} -eq 1 ]]; then
cmd="${cmd}
--score-reference"
fi
if [[ -n ${infer_parameters} ]]; then
cmd="${cmd}
${infer_parameters}"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
cd ${code_dir}
if [[ $eval -eq 1 ]]; then
ctc_file=translation-${subset}.ctc
if [[ -f ${model_dir}/${ctc_file} ]]; then
rm ${model_dir}/${ctc_file}
fi
eval $cmd
echo "" >> ${result_file}
tail -n 2 ${model_dir}/generate-${subset}.txt >> ${result_file}
mv ${model_dir}/generate-${subset}.txt ${model_dir}/generate-${subset}-${suffix}.txt
mv ${model_dir}/translation-${subset}.txt ${model_dir}/translation-${subset}-${suffix}.txt
cd ${pwd_dir}
if [[ -f ${model_dir}/enc_dump ]]; then
mv ${model_dir}/enc_dump ${model_dir}/dump-${subset}-enc-${suffix}
fi
if [[ -f ${model_dir}/dec_dump ]]; then
mv ${model_dir}/dec_dump ${model_dir}/dump-${subset}-dec-${suffix}
fi
trans_file=translation-${subset}-${suffix}.txt
if [[ ${ctc_infer} -eq 1 && -f ${model_dir}/${ctc_file} ]]; then
ref_file=${model_dir}/${subset}.${src_lang}
if [[ ! -f ${ref_file} ]]; then
python3 ./local/extract_txt_from_tsv.py ${data_dir}/${subset}.tsv ${ref_file} "tgt_text"
fi
if [[ -f ${ref_file} ]]; then
ctc=$(mktemp -t temp.record.XXXXXX)
cd ./local
cmd="./cal_wer.sh ${model_dir} ${subset} ${trans_file} ${ctc_file} ${ref_file} > ${ctc}"
#echo $cmd
eval $cmd
cd ..
echo "CTC WER" >> ${result_file}
tail -n 2 ${ctc} >> ${result_file}
src_bleu=$(mktemp -t temp.record.XXXXXX)
cd local
./cal_ctc_bleu.sh ${model_dir} ${subset} ${trans_file} ${ctc_file} ${ref_file} ${tokenizer} ${src_lang} > ${src_bleu}
cd ..
cat ${src_bleu} >> ${result_file}
rm ${ctc} ${src_bleu}
else
echo "No reference for source language."
fi
fi
fi
done
echo
cat ${result_file}
done
fi
egs/talcs/asr/train.sh 0 → 100755
#!/usr/bin/env bash
# training the model
gpu_num=1
update_freq=1
max_tokens=100000
extra_tag=
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
exp_tag=
# CTC
config_list=(purectc)
# Transformer
config_list=(base ctc)
# Conformer
#config_list=(base conformer ctc)
# PDS
config_list=(purectc_pds_base_8)
config_list=(pds_base_8)
# exp full name
exp_name=
train_config=$(echo ${config_list[*]} | sed 's/ /,/g')
cmd="./run.sh
--stage 1
--stop_stage 2
--gpu_num ${gpu_num}
--update_freq ${update_freq}
--train_config ${train_config}
--max_tokens ${max_tokens}
"
if [[ -n ${exp_name} ]]; then
cmd="$cmd --exp_name ${exp_name}"
fi
if [[ -n ${exp_tag} ]]; then
cmd="$cmd --exp_tag ${exp_tag}"
fi
if [[ -n ${extra_tag} ]]; then
cmd="$cmd --extra_tag ${extra_tag}"
fi
if [[ -n ${extra_parameter} ]]; then
cmd="$cmd --extra_parameter \"${extra_parameter}\""
fi
echo ${cmd}
eval ${cmd}
egs/tedlium2/asr/conf/base.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/tedlium2/asr/conf/base_nonorm.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
subsampling-type: conv1d
subsampling-layers: 2
......
egs/tedlium2/asr/conf/basis.yaml
......@@ -25,5 +25,6 @@ num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
label-smoothing: 0.1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
\ No newline at end of file
egs/tedlium2/asr/conf/big.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-embed-norm: True
encoder-no-scale-embedding: True
......
egs/tedlium2/asr/conf/big_wenet.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
subsampling-type: conv2d
subsampling-layers: 2
......
egs/tedlium2/asr/conf/mixup.yaml
......@@ -8,6 +8,6 @@ inter-mixup-keep-org: False
inter-mixup-decoder-emb: False
ctc-mixup-consistent-weight: 0
mixup-consistent-weight: 0
cal-mixup-loss: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
\ No newline at end of file
egs/tedlium2/asr/conf/pds_base.yaml
......@@ -10,10 +10,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_16.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_16_growth.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_16_growth_fusion320.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_32.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_4.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_8.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_8_444.yaml
......@@ -23,10 +23,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_8_growth.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_base_8_growth_fusion256.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_big.yaml
......@@ -12,10 +12,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_big_16.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_big_32.yaml
......@@ -21,10 +21,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_big_8.yaml
......@@ -21,10 +21,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep.yaml
......@@ -12,10 +12,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep18_16.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep18_32.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep18_8.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep18_big_8.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.15
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep30_16.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep30_32.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep30_8.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep_16.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep_32.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/pds_deep_8.yaml
......@@ -22,10 +22,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
......
egs/tedlium2/asr/conf/purectc.yaml
......@@ -5,7 +5,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
......
egs/tedlium2/asr/conf/purectc_base.yaml
......@@ -7,7 +7,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.002
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_base_16.yaml
......@@ -29,7 +29,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
......
egs/tedlium2/asr/conf/purectc_pds_base_16_growth.yaml
......@@ -23,7 +23,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_base_16_growth_fusion256.yaml
......@@ -23,7 +23,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_base_16_growth_fusion320.yaml
......@@ -22,7 +22,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_base_16_growth_fusion360.yaml
......@@ -22,7 +22,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_base_8.yaml
......@@ -30,7 +30,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
......
egs/tedlium2/asr/conf/purectc_pds_base_8_666.yaml
......@@ -29,7 +29,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
......
egs/tedlium2/asr/conf/purectc_pds_base_8_666_growth360.yaml
......@@ -29,7 +29,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
......
egs/tedlium2/asr/conf/purectc_pds_base_8_growth.yaml
......@@ -23,7 +23,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_base_8_growth360.yaml
......@@ -22,7 +22,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_base_8_growth_fusion256.yaml
......@@ -22,7 +22,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
ctc-weight: 1.0
......
egs/tedlium2/asr/conf/purectc_pds_big_16.yaml
......@@ -29,7 +29,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
......
egs/tedlium2/asr/conf/purectc_pds_big_8.yaml
......@@ -23,7 +23,7 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
......
egs/tedlium2/asr/run.sh
......@@ -2,8 +2,7 @@
# Processing TED-LIUM2 ASR Datasets
# Copyright 2023 Natural Language Processing Laboratory
# Xu Chen (xuchenneu@163.com)
# Copyright 2021 Chen Xu (xuchennlp@outlook.com)
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
......@@ -16,22 +15,22 @@ eval=1
time=$(date "+%m%d_%H%M")
stage=1
stop_stage=4
stop_stage=2
######## hardware ########
# devices
######## Hardware ########
# Devices
device=(0)
gpu_num=2
update_freq=1
max_tokens=100000
root_dir=/opt/tiger
# data_root_dir=/mnt/bd/data-model
data_root_dir=/mnt/bn/nas-xc-1
code_dir=${root_dir}/s2t
pwd_dir=$PWD
root_dir=${ST_ROOT}
data_root_dir=${root_dir}
# dataset
code_dir=${root_dir}/S2T
# Dataset
src_lang=en
lang=${src_lang}
dataset=tedlium2
......@@ -40,7 +39,7 @@ data_tag=asr
task=speech_to_text
vocab_type=unigram
vocab_size=10000
speed_perturb=1
speed_perturb=0
lcrm=0
tokenizer=0
use_raw_audio=0
......@@ -49,7 +48,7 @@ use_raw_audio=0
use_specific_dict=0
specific_prefix=st
specific_dir=${root_dir}/data/mustc/st
specific_dir=${data_root_dir}/data/must_c/en-de/st
asr_vocab_prefix=spm_unigram10000_st_share
data_model_subfix=${dataset}/${data_tag}
......@@ -63,35 +62,37 @@ test_subset=dev,test
# exp
sub_tag=
exp_prefix=$(date "+%m%d")
# exp_subfix=${ARNOLD_JOB_ID}_${ARNOLD_TASK_ID}_${ARNOLD_TRIAL_ID}
extra_tag=
extra_parameter=
exp_tag=baseline
exp_name=
# config
train_config=base,ctc
data_config=config.yaml
# training setting
# Training Settings
train_config=base
fp16=1
max_tokens=100000
step_valid=0
# decoding setting
# Decoding Settings
dec_model=checkpoint_best.pt
cer=0
ctc_infer=0
ctc_infer=1
infer_ctc_weight=0
ctc_self_ensemble=0
ctc_inter_logit=0
n_average=10
batch_size=0
beam_size=5
len_penalty=1.0
single=0
epoch_ensemble=0
best_ensemble=1
infer_parameters=
infer_debug=0
infer_score=0
#infer_parameters="--cal-monotonic-cross-attn-weights --cal-localness --localness-window 0.1 --cal-topk-cross-attn-weights --topk-cross-attn-weights 15 --cal-entropy"
data_config=config.yaml
# Parsing Options
if [[ ${speed_perturb} -eq 1 ]]; then
data_dir=${data_dir}_sp
exp_prefix=${exp_prefix}_sp
......@@ -116,13 +117,6 @@ if [[ "${vocab_type}" == "char" ]]; then
data_dir=${data_dir}_char
exp_prefix=${exp_prefix}_char
fi
if [[ ! -d /mnt/bd/data-model && -d /mnt/bd/data-model2 ]]; then
sudo ln -s /mnt/bd/data-model2/ /mnt/bd/data-model
fi
if [[ ! -d ${data_dir} ]]; then
echo "No feature dir ${data_dir}"
exit
fi
# setup nccl envs
export NCCL_IB_DISABLE=0
......@@ -130,13 +124,6 @@ export NCCL_IB_HCA=$ARNOLD_RDMA_DEVICE:1
export NCCL_IB_GID_INDEX=3
export NCCL_SOCKET_IFNAME=eth0
HOSTS=$ARNOLD_WORKER_HOSTS
HOST=(${HOSTS//,/ })
HOST_SPLIT=(${HOST//:/ })
PORT=${HOST_SPLIT[1]}
INIT_METHOD="tcp://${ARNOLD_WORKER_0_HOST}:${ARNOLD_WORKER_0_PORT}"
DIST_RANK=$((ARNOLD_ID * ARNOLD_WORKER_GPU))
export PATH=$PATH:${code_dir}/scripts
. ./local/parse_options.sh || exit 1;
......@@ -150,21 +137,28 @@ if [[ -z ${exp_name} ]]; then
exp_name=${exp_name}_${exp_subfix}
fi
fi
model_dir=${code_dir}/checkpoints/${data_model_subfix}/${sub_tag}/${exp_name}
echo "stage: $stage"
echo "stop_stage: $stop_stage"
ckpt_dir=${root_dir}/checkpoints/
model_dir=${root_dir}/checkpoints/${data_model_subfix}/${sub_tag}/${exp_name}
# Start
cd ${code_dir}
echo "Start Stage: $stage"
echo "Stop Stage: $stop_stage"
if [[ `pip list | grep fairseq | wc -l` -eq 0 ]]; then
echo "Default Stage: env configure"
pip3 install -e ${code_dir}
fi
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
# pass
echo "Stage -1: Data Download"
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
### Task dependent. You have to make data the following preparation part by yourself.
### But you can utilize Kaldi recipes in most cases
echo "stage 0: ASR Data Preparation"
echo "Stage 0: Data Preparation"
if [[ ! -e ${data_dir} ]]; then
mkdir -p ${data_dir}
fi
......@@ -205,31 +199,8 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
[[ $eval -eq 1 ]] && eval ${cmd}
fi
echo "stage 1: env configure"
if [[ `pip list | grep fairseq | wc -l` -eq 0 ]]; then
pip3 install -e ${code_dir} -i https://bytedpypi.byted.org/simple --no-build-isolation --default-timeout=10000
fi
if [[ -d /mnt/bn/nas-xc-1/checkpoints && ! -d ${code_dir}/checkpoints ]]; then
ln -s /mnt/bn/nas-xc-1/checkpoints ${code_dir}
fi
# if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# ln_data_dir=`echo ${data_dir} | sed -e "s#${data_root_dir}#${code_dir}#"`
# echo ${ln_data_dir}
# mkdir -p ${ln_data_dir}
# ln -s ${data_dir}/../* ${ln_data_dir}
# rm -r ${ln_data_dir}
# hdfs_path=`echo ${data_dir} | sed -e "s#${data_root_dir}#hdfs://haruna/home/byte_arnold_lq_mlnlc/user/xuchen/#"`
# hdfs dfs -get ${hdfs_path} ${ln_data_dir}
# sed -i -e "s#${data_root_dir}#${code_dir}#" ${ln_data_dir}/config*
# data_dir=${ln_data_dir}
# fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "stage 2: ASR Network Training"
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Stage 1: Network Training"
[[ ! -d ${data_dir} ]] && echo "The data dir ${data_dir} is not existing!" && exit 1;
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
......@@ -239,6 +210,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
export CUDA_VISIBLE_DEVICES=${device}
fi
echo -e "data=${data_dir} model=${model_dir}"
......@@ -252,11 +224,9 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
cp -f ${pwd_dir}/`basename ${BASH_SOURCE[0]}` ${model_dir}
cp -f ${pwd_dir}/train.sh ${model_dir}
extra_parameter="${extra_parameter}
--train-config ${pwd_dir}/conf/basis.yaml"
cp -f ${pwd_dir}/conf/basis.yaml ${model_dir}
train_config=basis,${train_config}
config_list="${train_config//,/ }"
idx=1
idx=0
for config in ${config_list[@]}
do
config_path=${pwd_dir}/conf/${config}.yaml
......@@ -266,8 +236,13 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
fi
cp -f ${config_path} ${model_dir}
if [[ $idx -eq 0 ]]; then
extra_parameter="${extra_parameter}
--train-config ${config_path}"
else
extra_parameter="${extra_parameter}
--train-config${idx} ${config_path}"
fi
idx=$((idx + 1))
done
......@@ -290,8 +265,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
cmd="${cmd}
--distributed-world-size $gpu_num
--ddp-backend no_c10d"
#--distributed-init-method ${INIT_METHOD}
#--distributed-rank ${DIST_RANK}"
fi
if [[ $fp16 -eq 1 ]]; then
cmd="${cmd}
......@@ -328,35 +301,28 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo -e "\033[34mRun command: \n${cmd} \033[0m"
# save info
log=./history.log
log=${ckpt_dir}/history.log
echo "${time} | ${data_dir} | ${exp_name} | ${model_dir} " >> $log
tail -n 50 ${log} > tmp.log
mv tmp.log $log
# export CUDA_VISIBLE_DEVICES=${device}
log=${model_dir}/train.log
cmd="${cmd} 2>&1 | tee -a ${log}"
#cmd="nohup ${cmd} >> ${log} 2>&1 &"
#cmd="${cmd} >> ${log} 2>&1 "
if [[ $eval -eq 1 ]]; then
# tensorboard
if [[ -z ${ARNOLD_TENSORBOARD_CURRENT_PORT} ]]; then
port=6666
else
port=${ARNOLD_TENSORBOARD_CURRENT_PORT}
fi
tensorboard --logdir ${model_dir} --port ${port} --bind_all &
echo "${cmd}" > ${model_dir}/cmd
eval $cmd
#sleep 2s
#tail -n "$(wc -l ${log} | awk '{print $1+1}')" -f ${log}
fi
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "stage 3: ASR Decoding"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "Stage 2: Decoding"
dec_models=
if [[ ${single} -eq 1 ]]; then
if [[ ${n_average} -eq 1 ]]; then
dec_models=${dec_model}
fi
if [[ ${n_average} -ne 1 ]]; then
......@@ -396,11 +362,11 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
export CUDA_VISIBLE_DEVICES=${device}
fi
# export CUDA_VISIBLE_DEVICES=${device}
for dec_model in ${dec_models[@]}; do
suffix=beam${beam_size}_alpha${len_penalty}_tokens${max_tokens}
suffix=alpha${len_penalty}
model_str=`echo $dec_model | sed -e "s#checkpoint##" | sed "s#.pt##"`
suffix=${suffix}_${model_str}
if [[ -n ${cer} && ${cer} -eq 1 ]]; then
......@@ -408,6 +374,13 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
else
suffix=${suffix}_wer
fi
suffix=${suffix}_beam${beam_size}
if [[ ${batch_size} -ne 0 ]]; then
suffix=${suffix}_batch${batch_size}
else
suffix=${suffix}_tokens${max_tokens}
fi
if [[ ${ctc_infer} -eq 1 ]]; then
suffix=${suffix}_ctc
fi
......@@ -417,6 +390,12 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
if [[ ${ctc_inter_logit} -ne 0 ]]; then
suffix=${suffix}_logit${ctc_inter_logit}
fi
if (( $(echo "${infer_ctc_weight} > 0" | bc -l) )); then
suffix=${suffix}_ctc${infer_ctc_weight}
fi
if [[ ${infer_score} -eq 1 ]]; then
suffix=${suffix}_score
fi
suffix=`echo $suffix | sed -e "s#__#_#"`
result_file=${model_dir}/decode_result_${suffix}
......@@ -425,16 +404,23 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
test_subset=${test_subset//,/ }
for subset in ${test_subset[@]}; do
subset=${subset}
cmd="python3 ${code_dir}/fairseq_cli/generate.py
if [[ ${infer_debug} -ne 0 ]]; then
cmd="python3 -m debugpy --listen 0.0.0.0:5678 --wait-for-client"
else
cmd="python3 "
fi
cmd="$cmd ${code_dir}/fairseq_cli/generate.py
${data_dir}
--config-yaml ${data_config}
--gen-subset ${subset}
--task speech_to_text
--path ${model_dir}/${dec_model}
--results-path ${model_dir}
--batch-size ${batch_size}
--max-tokens ${max_tokens}
--beam ${beam_size}
--lenpen ${len_penalty}
--infer-ctc-weight ${infer_ctc_weight}
--scoring wer"
if [[ ${cer} -eq 1 ]]; then
......@@ -453,6 +439,10 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
cmd="${cmd}
--ctc-inter-logit ${ctc_inter_logit}"
fi
if [[ ${infer_score} -eq 1 ]]; then
cmd="${cmd}
--score-reference"
fi
if [[ -n ${infer_parameters} ]]; then
cmd="${cmd}
${infer_parameters}"
......@@ -460,13 +450,17 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo -e "\033[34mRun command: \n${cmd} \033[0m"
cd ${code_dir}
if [[ $eval -eq 1 ]]; then
src_ctc_file=translation-${subset}.txt.ctc
if [[ -f ${model_dir}/${src_ctc_file} ]]; then
rm ${model_dir}/${src_ctc_file}
ctc_file=translation-${subset}.ctc
xctc_file=translation-${subset}.xctc
if [[ -f ${model_dir}/${ctc_file} ]]; then
rm ${model_dir}/${ctc_file}
fi
if [[ -f ${model_dir}/${xctc_file} ]]; then
rm ${model_dir}/${xctc_file}
fi
cd ${code_dir}
eval $cmd
echo "" >> ${result_file}
tail -n 2 ${model_dir}/generate-${subset}.txt >> ${result_file}
......@@ -474,28 +468,39 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
mv ${model_dir}/translation-${subset}.txt ${model_dir}/translation-${subset}-${suffix}.txt
cd ${pwd_dir}
if [[ -f ${model_dir}/enc_dump ]]; then
mv ${model_dir}/enc_dump ${model_dir}/dump-${subset}-enc-${suffix}
fi
if [[ -f ${model_dir}/dec_dump ]]; then
mv ${model_dir}/dec_dump ${model_dir}/dump-${subset}-dec-${suffix}
fi
trans_file=translation-${subset}-${suffix}.txt
if [[ ${ctc_infer} -eq 1 && -f ${model_dir}/${src_ctc_file} ]]; then
if [[ ! -f ${model_dir}/{ctc_file} && -f ${model_dir}/${xctc_file} ]]; then
ctc_file=${xctc_file}
fi
if [[ ${ctc_infer} -eq 1 && -f ${model_dir}/${ctc_file} ]]; then
ref_file=${model_dir}/${subset}.${src_lang}
if [[ ! -f ${ref_file} ]]; then
python3 ./local/extract_txt_from_tsv.py ${data_dir}/${subset}.tsv ${ref_file} "src_text"
python3 ./local/extract_txt_from_tsv.py ${data_dir}/${subset}.tsv ${ref_file} "tgt_text"
fi
if [[ -f ${ref_file} ]]; then
src_ctc=$(mktemp -t temp.record.XXXXXX)
ctc=$(mktemp -t temp.record.XXXXXX)
cd ./local
./cal_wer.sh ${model_dir} ${subset} ${trans_file} ${src_ctc_file} ${ref_file} > ${src_ctc}
cmd="./cal_wer.sh ${model_dir} ${subset} ${trans_file} ${ctc_file} ${ref_file} > ${ctc}"
eval $cmd
cd ..
echo "CTC WER" >> ${result_file}
tail -n 2 ${src_ctc} >> ${result_file}
tail -n 2 ${ctc} >> ${result_file}
src_bleu=$(mktemp -t temp.record.XXXXXX)
cd local
./cal_ctc_bleu.sh ${model_dir} ${subset} ${trans_file} ${src_ctc_file} ${ref_file} ${tokenizer} ${src_lang} > ${src_bleu}
./cal_ctc_bleu.sh ${model_dir} ${subset} ${trans_file} ${ctc_file} ${ref_file} ${tokenizer} ${src_lang} > ${src_bleu}
cd ..
cat ${src_bleu} >> ${result_file}
rm ${src_ctc} ${src_bleu}
rm ${ctc} ${src_bleu}
else
echo "No reference for source language."
fi
......@@ -506,10 +511,3 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
cat ${result_file}
done
fi
# if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# echo "Stage 4: Upload model and log"
# echo "Path: hdfs://haruna/home/byte_arnold_lq_mlnlc/user/xuchen/s2t/checkpoints/${data_model_subfix}/${exp_name}"
# hdfs dfs -mkdir -p hdfs://haruna/home/byte_arnold_lq_mlnlc/user/xuchen/s2t/checkpoints/${data_model_subfix}
# hdfs dfs -put -f ${model_dir} hdfs://haruna/home/byte_arnold_lq_mlnlc/user/xuchen/s2t/checkpoints/${data_model_subfix}
# fi
egs/wav2vec/conf/basis.yaml
......@@ -10,5 +10,6 @@ num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
label-smoothing: 0.1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
egs/wav2vec/conf/wav2vec2.yaml
......@@ -38,7 +38,7 @@ optimization:
optimizer:
_name: adam
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
adam_eps: 1e-06
weight_decay: 0.01
......
egs/wmt16/mt/conf/base.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 8000
lr: 1e-3
adam_betas: (0.9,0.997)
adam-betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
......
egs/wmt16/mt/conf/base_postnorm.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 4000
lr: 7e-4
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
......
egs/wmt16/mt/conf/basis.yaml
......@@ -25,5 +25,6 @@ num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
label-smoothing: 0.1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
\ No newline at end of file
egs/wmt16/mt/conf/big.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 8000
lr: 7e-4
adam_betas: (0.9,0.997)
adam-betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.3
attention-dropout: 0.1
......
egs/wmt16/mt/conf/big_postnorm.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 4000
lr: 5e-4
adam_betas: (0.9,0.98)
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.3
attention-dropout: 0.1
......
egs/wmt16/mt/conf/deep.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 16000
lr: 2e-3
adam_betas: (0.9,0.997)
adam-betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
......
egs/wmt16/mt/conf/deep_ctc.yaml
......@@ -6,10 +6,9 @@ lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 16000
lr: 2e-3
adam_betas: (0.9,0.997)
adam-betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
......
egs/zerocs/asr/binary.sh 0 → 100755
set -e
eval=1
lcrm=0
tokenizer=0
root_dir=~/st/Fairseq-S2T
data_dir=~/st/data/test
vocab_dir=~/st/data/mustc/st/en-de
asr_vocab_prefix=spm_unigram10000_st_share
src_lang=en
tgt_lang=de
subsets=(2019)
cp -r ${vocab_dir}/${asr_vocab_prefix}.* ${data_dir}/${src_lang}-${tgt_lang}
rm -rf ${data_dir}/${src_lang}-${tgt_lang}/fbank80.zip
splits=$(echo ${subsets[*]} | sed 's/ /,/g')
cmd="python ${root_dir}/examples/speech_to_text/prep_st_data.py
--data-root ${data_dir}
--output-root ${data_dir}
--splits ${splits}
--task asr
--src-lang ${src_lang}
--tgt-lang ${tgt_lang}
--add-src
--share
--asr-prefix ${asr_vocab_prefix}
--cmvn-type utterance"
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
if [[ ${tokenizer} -eq 1 ]]; then
cmd="$cmd
--tokenizer"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
egs/zerocs/asr/conf/base.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/zerocs/asr/conf/base_nonorm.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/zerocs/asr/conf/basis.yaml 0 → 100644
#train-subset: train_zh_en,train_en_zh
train-subset: train_zh,train_en,train_cs
valid-subset: dev_zh,dev_en,dev_cs
max-epoch: 100
max-update: 100000
patience: 20
post-process: sentencepiece
# best-checkpoint-metric: loss
# maximize-best-checkpoint-metric: False
eval-wer: True
eval-wer-args: {"beam": 1, "lenpen": 1.0}
eval-wer-tok-args: {"wer_remove_punct": true, "wer_lowercase": true, "wer_char_level": false}
eval-wer-remove-bpe: sentencepiece
eval-wer-print-samples: True
best_checkpoint_metric: dec_wer
maximize_best_checkpoint_metric: False
no-epoch-checkpoints: True
# keep-last-epochs: 10
keep-best-checkpoints: 10
num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
label-smoothing: 0.1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
egs/zerocs/asr/conf/big.yaml 0 → 100644
arch: s2t_transformer_m
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 2048
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.15
activation-fn: relu
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
\ No newline at end of file
egs/zerocs/asr/conf/big_wenet.yaml 0 → 100644
arch: s2t_transformer_m
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv2d
subsampling-layers: 2
subsampling-filter: 512
subsampling-kernel: 3
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: relu
dropout: 0.15
activation-fn: relu
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
cnn-module-norm: layer_norm
load-pretrained-encoder-from: /home/xuchen/after.pt
load-pretrained-decoder-from: /home/xuchen/after.pt
#load-pretrained-decoder-from:
egs/zerocs/asr/conf/conformer.yaml 0 → 100644
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/zerocs/asr/conf/ctc.yaml 0 → 100644
ctc-weight: 0.3
\ No newline at end of file
egs/zerocs/asr/conf/dlcl.yaml 0 → 100644
use-enc-dlcl: True
use-dec-dlcl: True
egs/zerocs/asr/conf/dynamic.yaml 0 → 100644
compression-metric: threshold
compression-mode: create
compression-layers: 6,9
compression-threshold: 0.95
compression-norm: True
compression-pos: True
\ No newline at end of file
egs/zerocs/asr/conf/inter.yaml 0 → 100644
inter-ctc-weight: 0.2
inter-ctc-layers: 6,9
inter-ctc-drop-prob: 0
share-inter-ctc: True
ctc-pae: none
# ctc-pae: inter_league
# ctc-pae-ground-truth-ratio: 0.1
# pae-gumbel: True
# pae-distribution-hard: True
# pae-drop-prob: 0.0
# pae-distribution-cutoff: 10
# share-pae-and-ctc: True
# pae-embed-norm: True
# pae-out-norm: True
# ctc-self-distill-weight: 1
# target-ctc-self-distill-weight: 1
# ctc-self-distill-prob: 0.1
# cal-all-ctc: True
\ No newline at end of file
egs/zerocs/asr/conf/ipa.yaml 0 → 100644
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: False
inter-mixup-decoder-emb: False
ctc-mixup-consistent-weight: 0
inter-ctc-mixup-consistent-weight: 0
mixup-consistent-weight: 0
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
\ No newline at end of file
egs/zerocs/asr/conf/mlo.yaml 0 → 100644
inter-ctc-mlo: 1:2:3
\ No newline at end of file
egs/zerocs/asr/conf/norm.yaml 0 → 100644
encoder-embed-norm: True
encoder-no-scale-embedding: True
\ No newline at end of file
egs/zerocs/asr/conf/pds_base.yaml 0 → 100644
arch: pdss2t_transformer_s_8
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/zerocs/asr/conf/pds_base_16.yaml 0 → 100644
arch: pdss2t_transformer_s_16
encoder-embed-dim: 256
pds-stages: 4
pds-layers: 2_2_6_2
pds-ratios: 2_2_2_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/zerocs/asr/conf/pds_base_32.yaml 0 → 100644
arch: pdss2t_transformer_s_32
encoder-embed-dim: 256
pds-stages: 5
pds-layers: 2_2_3_3_2
pds-ratios: 2_2_2_2_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 256_256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1_1
pds-kernel-sizes: 5_5_5_5_5
pds-ffn-ratios: 8_8_8_8_8
pds-attn-heads: 4_4_4_4_4
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/zerocs/asr/conf/pds_base_8.yaml 0 → 100644
arch: pdss2t_transformer_s_8
encoder-embed-dim: 256
pds-stages: 4
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.1
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/zerocs/asr/conf/pds_big_8.yaml 0 → 100644
arch: pdss2t_transformer_m_8
encoder-embed-dim: 512
pds-stages: 4
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: False
pds-fusion-method: all_conv2
pds-fusion-layers: 0_1_1_1
pds-fusion-weight: 0.2_0.3_0.5
pds-embed-dims: 512_512_512_512
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 4_4_4_4
pds-attn-heads: 8_8_8_8
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 0.0014
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
dropout: 0.15
activation-fn: relu
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
egs/zerocs/asr/conf/purectc.yaml 0 → 100644
arch: s2t_ctc
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
ctc-weight: 1.0
encoder-normalize-before: True
decoder-normalize-before: True
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 18
encoder-attention-heads: 4
\ No newline at end of file
egs/zerocs/asr/conf/reproduction_bil_ctc_syn.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
weight-decay: 1e-4
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 18
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
# Bilingual CTC
share-ctc-and-embed: True
share-xctc-and-embed: True
ctc-weight: 0.05
xctc-weight: 0.2
# InterCTC
inter-ctc-weight: 0.025
inter-ctc-layers: 6,9,12,15
share-inter-ctc: True
inter-xctc-weight: 0.1
inter-xctc-layers: 6,9,12,15
# Prediction-aware encoding
ctc-pae: inter_league
xctc-pae: inter_league
pae-unnorm-input: True
# Curriculum learning mixing
xctc-pae-ground-truth-ratio: 0.1
xctc-pae-ground-truth-only-mistake: True
pae-oracle-smooth: True
\ No newline at end of file
egs/zerocs/asr/conf/reproduction_encdec_aipa_bilctc_kd.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
encoder-embed-norm: True
encoder-no-scale-embedding: True
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# Bilingual CTC
share-ctc-and-embed: True
share-xctc-and-embed: True
ctc-weight: 0.05
xctc-weight: 0.2
# InterCTC
inter-ctc-weight: 0.025
inter-ctc-layers: 6,9
share-inter-ctc: True
inter-xctc-weight: 0.1
inter-xctc-layers: 6,9
# Prediction-aware encoding
ctc-pae: inter_league
xctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.1
xctc-mixup-consistent-weight: 0.025
inter-ctc-mixup-consistent-weight: 0.05
inter-xctc-mixup-consistent-weight: 0.0125
# mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/zerocs/asr/conf/reproduction_encdec_aipa_kd.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
encoder-embed-norm: True
encoder-no-scale-embedding: True
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# MTL
ctc-weight: 0.3
#inter-ctc-weight: 0.2
#inter-ctc-layers: 6,9
#share-inter-ctc: True
share-ctc-and-embed: True
ctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.15
#inter-ctc-mixup-consistent-weight: 0.1
# mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
egs/zerocs/asr/conf/reproduction_encdec_aipa_kd_nohardloss.yaml 0 → 100644
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy_with_ctc
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
encoder-embed-norm: True
encoder-no-scale-embedding: True
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
attention-dropout: 0.1
activation-dropout: 0.1
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: True
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# MTL
ctc-weight: 0.3
inter-ctc-weight: 0.2
inter-ctc-layers: 6,9
share-inter-ctc: True
share-ctc-and-embed: True
ctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.15
inter-ctc-mixup-consistent-weight: 0.1
mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/zerocs/asr/conf/reproduction_purectc_aipa_bilctc_kd.yaml 0 → 100644
arch: s2t_ctc
encoder-type: transformer
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
encoder-normalize-before: True
decoder-normalize-before: True
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
encoder-embed-norm: True
encoder-no-scale-embedding: True
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 18
encoder-attention-heads: 4
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# Bilingual CTC
share-ctc-and-embed: True
share-xctc-and-embed: True
ctc-weight: 0.2
xctc-weight: 1
# InterCTC
inter-ctc-weight: 0.1
inter-ctc-layers: 6,9,12,15
share-inter-ctc: True
inter-xctc-weight: 1.0
inter-xctc-layers: 6,9,12,15
# Prediction-aware encoding
ctc-pae: inter_league
xctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.1
inter-ctc-mixup-consistent-weight: 0.05
xctc-mixup-consistent-weight: 0.5
xinter-ctc-mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/zerocs/asr/conf/reproduction_purectc_aipa_kd.yaml 0 → 100644
arch: s2t_ctc
encoder-type: transformer
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
ctc-weight: 1.0
encoder-normalize-before: True
decoder-normalize-before: True
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
encoder-embed-norm: True
encoder-no-scale-embedding: True
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 18
encoder-attention-heads: 4
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: False
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# MTL
inter-ctc-weight: 1.0
inter-ctc-layers: 6,9,12,15
share-inter-ctc: True
share-ctc-and-embed: True
ctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.5
inter-ctc-mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/zerocs/asr/conf/reproduction_purectc_aipa_kd_nohardloss.yaml 0 → 100644
arch: s2t_ctc
encoder-type: transformer
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
adam-betas: (0.9,0.98)
criterion: ctc
zero_infinity: True
ctc-weight: 1.0
encoder-normalize-before: True
decoder-normalize-before: True
subsampling-type: conv1d
subsampling-layers: 2
subsampling-filter: 1024
subsampling-kernel: 5
subsampling-stride: 2
subsampling-norm: none
subsampling-activation: glu
encoder-embed-norm: True
encoder-no-scale-embedding: True
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 18
encoder-attention-heads: 4
# Append-based Interpolation Augmentation
inter-mixup: True
inter-mixup-layer: -1
inter-mixup-decoder-layer: 0
inter-mixup-prob: 1.0
inter-mixup-ratio: 1.0
inter-mixup-beta: 0.2
inter-mixup-keep-org: True
inter-mixup-decoder-emb: True
mixup-no-hard-loss: True
no-specaugment: False
layer-out-norm: False
inter-mixup-ratio-decay: False
inter-mixup-ratio-decay-params: 20000,40000,0
# MTL
inter-ctc-weight: 1.0
inter-ctc-layers: 6,9,12,15
share-inter-ctc: True
share-ctc-and-embed: True
ctc-pae: inter_league
pae-unnorm-input: True
ctc-mixup-consistent-weight: 0.5
inter-ctc-mixup-consistent-weight: 0.5
# Conformer
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 15
encoder-attention-type: rel_pos
encoder-activation-fn: swish
layer-padding-mask: True
\ No newline at end of file
egs/zerocs/asr/conf/rpr.yaml 0 → 100644
encoder-attention-type: rel_pos
#encoder-attention-type: rel_pos_legacy
#encoder-attention-type: rel_selfattn
#encoder-attention-type: relative
#decoder-attention-type: relative
#max-encoder-relative-length: 100
#max-decoder-relative-length: 20
egs/zerocs/asr/conf/xctc.yaml 0 → 100644
xctc-weight: 0.3
share-xctc-and-embed: True
\ No newline at end of file
egs/zerocs/asr/conf/xinter.yaml 0 → 100644
inter-xctc-weight: 0.2
inter-xctc-layers: 6,9
xctc-pae: none
# xctc-pae: inter_league
xctc-cross-attn: False
cross-attn-start-layer: 7
cross-attn-layer: 6
cross-attn-collaboration-mode: parallel
cross-attn-league-s1-ratio: 0.5
cross-attn-league-s2-ratio: 0.5
cross-attn-league-out-norm: False
cross-attn-league-gated: False
cross-attn-league-drop-net: False
cross-attn-league-drop-net-prob: 0.2
cross-attn-league-drop-net-mix: False
# xctc-pae-ground-truth-ratio: 0.1
# xctc-pae-ground-truth-ratio-adaptive: True
# xctc-pae-ground-truth-only-mistake: True
# pae-oracle-smooth: True
# pae-gumbel: True
# pae-distribution-hard: True
# pae-drop-prob: 0.0
# pae-distribution-cutoff: 10
# share-pae-and-xctc: True
# pae-embed-norm: True
# pae-out-norm: True
# ctc-self-distill-weight: 1
# target-ctc-self-distill-weight: 1
# ctc-self-distill-prob: 0.1
# cal-all-ctc: True
\ No newline at end of file
egs/zerocs/asr/decode.sh 0 → 100755
#!/usr/bin/env bash
gpu_num=1
data_tag=asr
test_subset=(dev test)
test_subset=(test)
exp_name=
if [ "$#" -eq 1 ]; then
exp_name=$1
fi
cer=1
ctc_infer=1
n_average=10
beam_size=5
infer_ctc_weight=0
len_penalty=1.0
max_tokens=50000
batch_size=0
infer_debug=0
dec_model=checkpoint_best.pt
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--n_average ${n_average}
--cer ${cer}
--ctc_infer ${ctc_infer}
--beam_size ${beam_size}
--len_penalty ${len_penalty}
--batch_size ${batch_size}
--max_tokens ${max_tokens}
--dec_model ${dec_model}
--ctc_infer ${ctc_infer}
--infer_ctc_weight ${infer_ctc_weight}
--infer_debug ${infer_debug}
"
if [[ -n ${data_tag} ]]; then
cmd="$cmd --data_tag ${data_tag}"
fi
if [[ ${#test_subset[@]} -ne 0 ]]; then
subsets=$(echo ${test_subset[*]} | sed 's/ /,/g')
cmd="$cmd --test_subset ${subsets}"
fi
echo $cmd
eval $cmd
egs/zerocs/asr/local/cal_bleu.sh 0 → 100755
#!/usr/bin/env bash
set -e
ref=$1
gen=$2
tokenizer=$3
lang=$4
lang_pair=en-${lang}
record=$(mktemp -t temp.record.XXXXXX)
if [[ ${tokenizer} -eq 1 ]]; then
echo "MultiBLEU" > ${record}
cmd="multi-bleu.perl ${ref} < ${gen}"
eval $cmd | head -n 1 >> ${record}
cmd="detokenizer.perl -q -l ${lang} --threads 32 < ${ref} > ${ref}.detok"
eval $cmd
cmd="detokenizer.perl -q -l ${lang} --threads 32 < ${gen} > ${gen}.detok"
eval $cmd
ref=${ref}.detok
gen=${gen}.detok
fi
echo "SacreBLEU" >> ${record}
cmd="cat ${gen} | sacrebleu ${ref} -m bleu -w 4 -l ${lang_pair}"
eval $cmd >> ${record}
cat ${record}
rm ${record}
\ No newline at end of file
egs/zerocs/asr/local/cal_ctc_bleu.sh 0 → 100755
#!/usr/bin/env bash
set -e
infer_dir=$1
tag=$2
s2s_infer_file=${infer_dir}/$3
org_ctc_infer_file=${infer_dir}/$4
ref=$5
tokenizer=$6
lang=$7
idx=${infer_dir}/${tag}_idx
ctc_infer=${infer_dir}/${tag}_ctc_infer
ctc_infer_sort=${infer_dir}/${tag}_ctc_infer_sort
if [[ ! -f ${ctc_infer_sort} ]]; then
cut -f1 ${s2s_infer_file} > ${idx}
paste ${idx} ${org_ctc_infer_file} > ${ctc_infer}
sort -n -t $'\t' ${ctc_infer} | cut -f2 > ${ctc_infer_sort}
fi
gen=${ctc_infer_sort}
./cal_bleu.sh ${ref} ${gen} ${tokenizer} ${lang}
\ No newline at end of file
egs/zerocs/asr/local/cal_wer.py 0 → 100644
import unicodedata
import jiwer
import jiwer.transforms as tr
import sys
ref_file = sys.argv[1]
hyp_file = sys.argv[2]
wer_standardize = tr.Compose(
[
tr.SubstituteRegexes({r"<<unk>>": r"@"}),
tr.ToLowerCase(),
tr.RemovePunctuation(),
tr.ExpandCommonEnglishContractions(),
tr.RemoveKaldiNonWords(),
tr.RemoveWhiteSpace(replace_by_space=True),
tr.ReduceToListOfListOfWords(),
]
)
cer_standardize = tr.Compose(
[
tr.SubstituteRegexes({r"<<unk>>": r"@"}),
tr.ToLowerCase(),
tr.RemovePunctuation(),
tr.Strip(),
tr.ReduceToListOfListOfChars(),
]
)
ref_lines = open(ref_file, "r").readlines()
hyp_lines = open(hyp_file, "r").readlines()
wer = jiwer.wer(ref_lines, hyp_lines,
truth_transform=wer_standardize,
hypothesis_transform=wer_standardize,
)
cer = jiwer.cer(ref_lines, hyp_lines,
truth_transform=cer_standardize,
hypothesis_transform=cer_standardize,
)
print("WER: %.4f" % wer)
print("CER: %.4f" % cer)
egs/zerocs/asr/local/cal_wer.sh 0 → 100755
#!/usr/bin/env bash
set -e
infer_dir=$1
tag=$2
s2s_infer_file=${infer_dir}/$3
org_ctc_infer_file=${infer_dir}/$4
ref=$5
idx=${infer_dir}/${tag}_idx
ctc_infer=${infer_dir}/${tag}_ctc_infer
ctc_infer_sort=${infer_dir}/${tag}_ctc_infer_sort
cut -f1 ${s2s_infer_file} > ${idx}
paste ${idx} ${org_ctc_infer_file} > ${ctc_infer}
sort -n -t $'\t' ${ctc_infer} | cut -f2 > ${ctc_infer_sort}
python3 ./cal_wer.py ${ref} ${ctc_infer_sort}
\ No newline at end of file
egs/zerocs/asr/local/extract_txt_from_tsv.py 0 → 100644
import sys
import csv
tsv_file = sys.argv[1]
out_file = sys.argv[2]
extract_item = sys.argv[3]
with open(tsv_file) as f:
reader = csv.DictReader(
f,
delimiter="\t",
quotechar=None,
doublequote=False,
lineterminator="\n",
quoting=csv.QUOTE_NONE,
)
samples = [dict(e) for e in reader]
fw = open(out_file, "w", encoding="utf-8")
for s in samples:
if extract_item in s:
fw.write("%s\n" % s[extract_item])
else:
print("Error in sample: ")
print(s)
exit()
egs/zerocs/asr/local/monitor.sh 0 → 100755
#!/usr/bin/env bash
gpu_num=4
cmd="sh train.sh"
while :
do
record=$(mktemp -t temp.record.XXXXXX)
gpustat > $record
all_devices=$(seq 0 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((count + 1))
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
sleep 60s
else
echo "Run $cmd"
eval $cmd
sleep 10s
exit
fi
done
egs/zerocs/asr/local/parse_options.sh 0 → 100755
#!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# Parse command-line options.
# To be sourced by another script (as in ". parse_options.sh").
# Option format is: --option-name arg
# and shell variable "option_name" gets set to value "arg."
# The exception is --help, which takes no arguments, but prints the
# $help_message variable (if defined).
###
### The --config file options have lower priority to command line
### options, so we need to import them first...
###
# Now import all the configs specified by command-line, in left-to-right order
for ((argpos=1; argpos<$#; argpos++)); do
if [ "${!argpos}" == "--config" ]; then
argpos_plus1=$((argpos+1))
config=${!argpos_plus1}
[ ! -r $config ] && echo "$0: missing config '$config'" && exit 1
. $config # source the config file.
fi
done
###
### Now we process the command line options
###
i=1
argv="$@"
while true; do
key=${!i}
j=$(($i + 1))
value=${!j}
[ -z "${!i:-}" ] && break; # break if there are no arguments
case "${key}" in
# If the enclosing script is called with --help option, print the help
# message and exit. Scripts should put help messages in $help_message
--help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2;
else printf "$help_message\n" 1>&2 ; fi;
exit 0 ;;
--*=*) echo "$0: options to scripts must be of the form --name value, got '${key}}'"
exit 1 ;;
# If the first command-line argument begins with "--" (e.g. --foo-bar),
# then work out the variable name as $name, which will equal "foo_bar".
--*) name=`echo "${key}" | sed s/^--// | sed s/-/_/g`;
# Next we test whether the variable in question is undefned-- if so it's
# an invalid option and we die. Note: $0 evaluates to the name of the
# enclosing script.
# The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar
# is undefined. We then have to wrap this test inside "eval" because
# foo_bar is itself inside a variable ($name).
#eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
oldval="`eval echo \\$$name`";
# Work out whether we seem to be expecting a Boolean argument.
if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then
was_bool=true;
else
was_bool=false;
fi
# Set the variable to the right value-- the escaped quotes make it work if
# the option had spaces, like --cmd "queue.pl -sync y"
# echo $name
eval $name=\"${value}\";
# Check that Boolean-valued arguments are really Boolean.
if $was_bool && [[ "${value}" != "true" && "${value}" != "false" ]]; then
echo "$0: expected \"true\" or \"false\": ${key} ${value}" 1>&2
exit 1;
fi
# shift 2;
i=$(($i + 2))
;;
*) break;
esac
done
# Check for an empty argument to the --cmd option, which can easily occur as a
# result of scripting errors.
[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1;
true; # so this script returns exit code 0.
egs/zerocs/asr/local/utils.sh 0 → 100755
get_devices(){
gpu_num=$1
use_cpu=$2
device=()
while :
do
record=$(mktemp -t temp.record.XXXXXX)
gpustat > $record
all_devices=$(seq 0 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 1000 ]]; then
device[$count]=$dev
count=$((count + 1))
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
if [[ $use_cpu -eq 1 ]]; then
device=(-1)
else
sleep 60s
fi
else
break
fi
done
echo ${device[*]} | sed 's/ /,/g'
return $?
}
egs/zerocs/asr/run.sh 0 → 100755
#!/usr/bin/env bash
# Processing AIShell ASR Datasets
# Copyright 2021 Chen Xu (xuchennlp@outlook.com)
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
#set -u
set -o pipefail
export PYTHONIOENCODING=UTF-8
eval=1
time=$(date "+%m%d_%H%M")
stage=1
stop_stage=2
######## Hardware ########
# Devices
device=(0)
gpu_num=2
update_freq=1
max_tokens=50000
pwd_dir=$PWD
root_dir=${ST_ROOT}
data_root_dir=${root_dir}
code_dir=${root_dir}/S2T
# Dataset
src_lang=zh
lang=${src_lang}
dataset=zerocs
data_tag=asr
task=speech_to_text
vocab_type=unigram
vocab_size=32000
speed_perturb=0
lcrm=0
tokenizer=0
use_raw_audio=0
. ./local/parse_options.sh || exit 1;
use_specific_dict=0
specific_prefix=st
specific_dir=${root_dir}/data/mustc/st
asr_vocab_prefix=spm_unigram10000_st_share
data_model_subfix=${dataset}/${data_tag}
org_data_dir=${data_root_dir}/data/${dataset}
data_dir=${data_root_dir}/data/${data_model_subfix}
train_split=train_zh_en,train_en_zh,train_cs
train_split=train_zh,train_en,train_cs
valid_split=dev_zh,dev_en,dev_cs
test_split=test_zh,test_en,test_cs
test_subset=test_zh,test_en,test_cs,test_cs_zh
tgt_langs=zh,en,cs
joint=1
# exp
sub_tag=
exp_prefix=$(date "+%m%d")
extra_tag=
extra_parameter=
exp_tag=baseline
exp_name=
# Training Settings
train_config=base,ctc
fp16=1
step_valid=0
# Decoding Settings
dec_model=checkpoint_best.pt
cer=0
ctc_infer=0
infer_ctc_weight=0
ctc_self_ensemble=0
ctc_inter_logit=0
n_average=10
batch_size=0
beam_size=5
len_penalty=1.0
single=0
epoch_ensemble=0
best_ensemble=1
infer_debug=0
infer_score=0
# infer_parameters="--cal-monotonic-cross-attn-weights --cal-localness --localness-window 0.1 --cal-topk-cross-attn-weights --topk-cross-attn-weights 15 --cal-entropy"
data_config=config.yaml
# Parsing Options
if [[ ${speed_perturb} -eq 1 ]]; then
data_dir=${data_dir}_sp
exp_prefix=${exp_prefix}_sp
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
data_dir=${data_dir}_${specific_prefix}
exp_prefix=${exp_prefix}_${specific_prefix}
fi
if [[ ${tokenizer} -eq 1 ]]; then
data_dir=${data_dir}_tok
exp_prefix=${exp_prefix}_tok
fi
if [[ ${use_raw_audio} -eq 1 ]]; then
data_dir=${data_dir}_raw
exp_prefix=${exp_prefix}_raw
fi
export PATH=$PATH:${code_dir}/scripts
. ./local/parse_options.sh || exit 1;
if [[ -z ${exp_name} ]]; then
config_string=${train_config//,/_}
exp_name=${exp_prefix}_${config_string}_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
if [[ -n ${exp_subfix} ]]; then
exp_name=${exp_name}_${exp_subfix}
fi
fi
ckpt_dir=${root_dir}/checkpoints/
model_dir=${root_dir}/checkpoints/${data_model_subfix}/${sub_tag}/${exp_name}
# Start
cd ${code_dir}
echo "Start Stage: $stage"
echo "Stop Stage: $stop_stage"
if [[ `pip list | grep fairseq | wc -l` -eq 0 ]]; then
echo "Default Stage: env configure"
pip3 install -e ${code_dir}
fi
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "Stage -1: Data Download"
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
### Task dependent. You have to make data the following preparation part by yourself.
echo "Stage 0: Data Preparation"
if [[ ! -e ${data_dir} ]]; then
mkdir -p ${data_dir}
fi
cmd="python3 ${code_dir}/examples/speech_to_text/prep_audio_data.py
--data-root ${org_data_dir}
--output-root ${data_dir}
--task asr
--src-lang ${src_lang}
--splits ${valid_split},${test_split},${train_split}
--add-src
--share
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ ${joint} -eq 1 ]]; then
cmd="$cmd
--add-syms
--tgt-langs ${tgt_langs}"
fi
if [[ ${use_raw_audio} -eq 1 ]]; then
cmd="$cmd
--raw"
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
cp -r ${specific_dir}/${asr_vocab_prefix}.* ${data_dir}
cmd="$cmd
--asr-prefix ${asr_vocab_prefix}"
fi
if [[ ${speed_perturb} -eq 1 ]]; then
cmd="$cmd
--speed-perturb"
fi
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
if [[ ${tokenizer} -eq 1 ]]; then
cmd="$cmd
--tokenizer"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Stage 1: Network Training"
[[ ! -d ${data_dir} ]] && echo "The data dir ${data_dir} is not existing!" && exit 1;
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=""
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
export CUDA_VISIBLE_DEVICES=${device}
fi
echo -e "data=${data_dir} model=${model_dir}"
if [[ ! -d ${model_dir} ]]; then
mkdir -p ${model_dir}
else
echo "${model_dir} exists."
fi
cp -f ${pwd_dir}/`basename ${BASH_SOURCE[0]}` ${model_dir}
cp -f ${pwd_dir}/train.sh ${model_dir}
train_config=basis,${train_config}
config_list="${train_config//,/ }"
idx=0
for config in ${config_list[@]}
do
config_path=${pwd_dir}/conf/${config}.yaml
if [[ ! -f ${config_path} ]]; then
echo "No config file ${config_path}"
exit
fi
cp -f ${config_path} ${model_dir}
if [[ $idx -eq 0 ]]; then
extra_parameter="${extra_parameter}
--train-config ${config_path}"
else
extra_parameter="${extra_parameter}
--train-config${idx} ${config_path}"
fi
idx=$((idx + 1))
done
cmd="python3 -u ${code_dir}/fairseq_cli/train.py
${data_dir}
--config-yaml ${data_config}
--task ${task}
--max-tokens ${max_tokens}
--skip-invalid-size-inputs-valid-test
--update-freq ${update_freq}
--log-interval 100
--save-dir ${model_dir}
--tensorboard-logdir ${model_dir}"
if [[ -n ${extra_parameter} ]]; then
cmd="${cmd}
${extra_parameter}"
fi
if [[ ${gpu_num} -gt 0 ]]; then
cmd="${cmd}
--distributed-world-size $gpu_num
--ddp-backend no_c10d"
fi
if [[ $joint -eq 1 ]]; then
cmd="${cmd}
--prefix-size 1
--ignore-prefix-size 1"
fi
if [[ $fp16 -eq 1 ]]; then
cmd="${cmd}
--fp16"
fi
if [[ $step_valid -eq 1 ]]; then
validate_interval=1
save_interval=1
no_epoch_checkpoints=0
save_interval_updates=500
keep_interval_updates=10
fi
if [[ -n $no_epoch_checkpoints && $no_epoch_checkpoints -eq 1 ]]; then
cmd="$cmd
--no-epoch-checkpoints"
fi
if [[ -n $validate_interval ]]; then
cmd="${cmd}
--validate-interval $validate_interval "
fi
if [[ -n $save_interval ]]; then
cmd="${cmd}
--save-interval $save_interval "
fi
if [[ -n $save_interval_updates ]]; then
cmd="${cmd}
--save-interval-updates $save_interval_updates"
if [[ -n $keep_interval_updates ]]; then
cmd="${cmd}
--keep-interval-updates $keep_interval_updates"
fi
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
# save info
log=${ckpt_dir}/history.log
echo "${time} | ${data_dir} | ${exp_name} | ${model_dir} " >> $log
tail -n 50 ${log} > tmp.log
mv tmp.log $log
log=${model_dir}/train.log
cmd="${cmd} 2>&1 | tee -a ${log}"
#cmd="${cmd} >> ${log} 2>&1 "
if [[ $eval -eq 1 ]]; then
# tensorboard
port=6666
tensorboard --logdir ${model_dir} --port ${port} --bind_all &
echo "${cmd}" > ${model_dir}/cmd
eval $cmd
fi
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "Stage 2: Decoding"
dec_models=
if [[ ${n_average} -eq 1 ]]; then
dec_models=${dec_model}
fi
if [[ ${n_average} -ne 1 ]]; then
# Average models
if [[ ${epoch_ensemble} -eq 1 ]]; then
avg_model=avg_epoch${n_average}_checkpoint.pt
if [[ ! -f ${model_dir}/${avg_model} ]]; then
cmd="python3 ${code_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-epoch-checkpoints ${n_average}
--output ${model_dir}/${avg_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
fi
dec_models+=(${avg_model})
fi
if [[ ${best_ensemble} -eq 1 ]]; then
avg_model=avg_best${n_average}_checkpoint.pt
if [[ ! -f ${model_dir}/${avg_model} ]]; then
cmd="python3 ${code_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-best-checkpoints ${n_average}
--output ${model_dir}/${avg_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
fi
dec_models+=(${avg_model})
fi
fi
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=""
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
export CUDA_VISIBLE_DEVICES=${device}
fi
for dec_model in ${dec_models[@]}; do
suffix=alpha${len_penalty}
model_str=`echo $dec_model | sed -e "s#checkpoint##" | sed "s#.pt##"`
suffix=${suffix}_${model_str}
if [[ -n ${cer} && ${cer} -eq 1 ]]; then
suffix=${suffix}_cer
else
suffix=${suffix}_wer
fi
suffix=${suffix}_beam${beam_size}
if [[ ${batch_size} -ne 0 ]]; then
suffix=${suffix}_batch${batch_size}
else
suffix=${suffix}_tokens${max_tokens}
fi
if [[ ${ctc_infer} -eq 1 ]]; then
suffix=${suffix}_ctc
fi
if [[ ${ctc_self_ensemble} -eq 1 ]]; then
suffix=${suffix}_ensemble
fi
if [[ ${ctc_inter_logit} -ne 0 ]]; then
suffix=${suffix}_logit${ctc_inter_logit}
fi
if (( $(echo "${infer_ctc_weight} > 0" | bc -l) )); then
suffix=${suffix}_ctc${infer_ctc_weight}
fi
if [[ ${infer_score} -eq 1 ]]; then
suffix=${suffix}_score
fi
suffix=`echo $suffix | sed -e "s#__#_#"`
result_file=${model_dir}/decode_result_${suffix}
[[ -f ${result_file} ]] && rm ${result_file}
test_subset=${test_subset//,/ }
for subset in ${test_subset[@]}; do
subset=${subset}
if [[ ${infer_debug} -ne 0 ]]; then
cmd="python3 -m debugpy --listen 0.0.0.0:5678 --wait-for-client"
else
cmd="python3 "
fi
cmd="$cmd ${code_dir}/fairseq_cli/generate.py
${data_dir}
--config-yaml ${data_config}
--gen-subset ${subset}
--task speech_to_text
--path ${model_dir}/${dec_model}
--results-path ${model_dir}
--batch-size ${batch_size}
--max-tokens ${max_tokens}
--beam ${beam_size}
--lenpen ${len_penalty}
--infer-ctc-weight ${infer_ctc_weight}
--scoring wer"
if [[ ${cer} -eq 1 ]]; then
cmd="${cmd}
--wer-char-level"
fi
if [[ ${ctc_infer} -eq 1 ]]; then
cmd="${cmd}
--ctc-infer"
fi
if [[ ${ctc_self_ensemble} -eq 1 ]]; then
cmd="${cmd}
--ctc-self-ensemble"
fi
if [[ ${ctc_inter_logit} -ne 0 ]]; then
cmd="${cmd}
--ctc-inter-logit ${ctc_inter_logit}"
fi
if [[ ${infer_score} -eq 1 ]]; then
cmd="${cmd}
--score-reference"
fi
if [[ -n ${infer_parameters} ]]; then
cmd="${cmd}
${infer_parameters}"
fi
if [[ $joint -eq 1 ]]; then
cmd="${cmd}
--prefix-size 1"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
cd ${code_dir}
if [[ $eval -eq 1 ]]; then
ctc_file=translation-${subset}.ctc
xctc_file=translation-${subset}.xctc
if [[ -f ${model_dir}/${ctc_file} ]]; then
rm ${model_dir}/${ctc_file}
fi
if [[ -f ${model_dir}/${xctc_file} ]]; then
rm ${model_dir}/${xctc_file}
fi
eval $cmd
echo "" >> ${result_file}
tail -n 2 ${model_dir}/generate-${subset}.txt >> ${result_file}
mv ${model_dir}/generate-${subset}.txt ${model_dir}/generate-${subset}-${suffix}.txt
mv ${model_dir}/translation-${subset}.txt ${model_dir}/translation-${subset}-${suffix}.txt
cd ${pwd_dir}
if [[ -f ${model_dir}/enc_dump ]]; then
mv ${model_dir}/enc_dump ${model_dir}/dump-${subset}-enc-${suffix}
fi
if [[ -f ${model_dir}/dec_dump ]]; then
mv ${model_dir}/dec_dump ${model_dir}/dump-${subset}-dec-${suffix}
fi
trans_file=translation-${subset}-${suffix}.txt
if [[ ! -f ${model_dir}/{ctc_file} && -f ${model_dir}/${xctc_file} ]]; then
ctc_file=${xctc_file}
fi
if [[ ${ctc_infer} -eq 1 && -f ${model_dir}/${ctc_file} ]]; then
ref_file=${model_dir}/${subset}.${src_lang}
if [[ ! -f ${ref_file} ]]; then
python3 ./local/extract_txt_from_tsv.py ${data_dir}/${subset}.tsv ${ref_file} "tgt_text"
fi
if [[ -f ${ref_file} ]]; then
ctc=$(mktemp -t temp.record.XXXXXX)
cd ./local
cmd="./cal_wer.sh ${model_dir} ${subset} ${trans_file} ${ctc_file} ${ref_file} > ${ctc}"
eval $cmd
cd ..
echo "CTC WER" >> ${result_file}
tail -n 2 ${ctc} >> ${result_file}
src_bleu=$(mktemp -t temp.record.XXXXXX)
cd local
./cal_ctc_bleu.sh ${model_dir} ${subset} ${trans_file} ${ctc_file} ${ref_file} ${tokenizer} ${src_lang} > ${src_bleu}
cd ..
cat ${src_bleu} >> ${result_file}
rm ${ctc} ${src_bleu}
else
echo "No reference for source language."
fi
fi
fi
done
echo
cat ${result_file}
done
fi
egs/zerocs/asr/train.sh 0 → 100755
#!/usr/bin/env bash
# training the model
gpu_num=2
update_freq=1
max_tokens=100000
extra_tag=
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
exp_tag=
# CTC
config_list=(purectc)
# Transformer
config_list=(base ctc)
# Conformer
#config_list=(base conformer ctc)
# PDS
config_list=(purectc_pds_base_8)
config_list=(pds_base_8)
# exp full name
exp_name=
train_config=$(echo ${config_list[*]} | sed 's/ /,/g')
cmd="./run.sh
--stage 1
--stop_stage 2
--gpu_num ${gpu_num}
--update_freq ${update_freq}
--train_config ${train_config}
--max_tokens ${max_tokens}
"
if [[ -n ${exp_name} ]]; then
cmd="$cmd --exp_name ${exp_name}"
fi
if [[ -n ${exp_tag} ]]; then
cmd="$cmd --exp_tag ${exp_tag}"
fi
if [[ -n ${extra_tag} ]]; then
cmd="$cmd --extra_tag ${extra_tag}"
fi
if [[ -n ${extra_parameter} ]]; then
cmd="$cmd --extra_parameter \"${extra_parameter}\""
fi
echo ${cmd}
eval ${cmd}
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