mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-10 10:32:17 +00:00
Fix for style check
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parent
fcd60160be
commit
322f954aa4
@ -27,7 +27,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--world-size 4 \
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--world-size 4 \
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_0.85_hard_1.py \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_hard_1.py \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 300
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--max-duration 300
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@ -38,7 +38,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--use-fp16 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_0.85_hard_1.py \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_hard_1.py \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 750
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--max-duration 750
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"""
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"""
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@ -697,7 +697,9 @@ def compute_loss(
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)
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)
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# Works with a BPE model
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# Works with a BPE model
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decoding_graph = k2.fast_ctc_graph(token_ids, modified=False, device=device, max_repeat=1)
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decoding_graph = k2.fast_ctc_graph(
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token_ids, modified=False, device=device, max_repeat=1
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)
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dense_fsa_vec = k2.DenseFsaVec(
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dense_fsa_vec = k2.DenseFsaVec(
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ctc_output,
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ctc_output,
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supervision_segments,
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supervision_segments,
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@ -27,7 +27,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--world-size 4 \
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--world-size 4 \
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_0.85_hard_2.py \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_hard_2.py \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 300
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--max-duration 300
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@ -38,7 +38,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--use-fp16 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_0.85_hard_2.py \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_hard_2.py \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 750
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--max-duration 750
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"""
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"""
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@ -697,7 +697,9 @@ def compute_loss(
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)
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)
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# Works with a BPE model
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# Works with a BPE model
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decoding_graph = k2.fast_ctc_graph(token_ids, modified=False, device=device, max_repeat=2)
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decoding_graph = k2.fast_ctc_graph(
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token_ids, modified=False, device=device, max_repeat=2
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)
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dense_fsa_vec = k2.DenseFsaVec(
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dense_fsa_vec = k2.DenseFsaVec(
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ctc_output,
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ctc_output,
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supervision_segments,
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supervision_segments,
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@ -27,7 +27,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--world-size 4 \
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--world-size 4 \
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_0.03 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_0.03 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 300
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--max-duration 300
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@ -38,7 +38,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--use-fp16 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_0.03 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_0.03 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 750
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--max-duration 750
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"""
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"""
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@ -705,10 +705,12 @@ def compute_loss(
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)
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)
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# non-blank arcs self-loop penalty
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# non-blank arcs self-loop penalty
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all_self_blanks_idx = decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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all_self_blanks_idx = (
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decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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)
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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decoding_graph.scores[all_self_blanks_idx] = -0.03
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decoding_graph.scores[all_self_blanks_idx] = -0.03
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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ctc_loss = k2.ctc_loss(
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ctc_loss = k2.ctc_loss(
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decoding_graph=decoding_graph,
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decoding_graph=decoding_graph,
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@ -27,7 +27,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--world-size 4 \
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--world-size 4 \
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_0.04 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_0.04 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 300
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--max-duration 300
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@ -38,7 +38,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--use-fp16 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_0.04 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_0.04 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 750
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--max-duration 750
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"""
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"""
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@ -705,10 +705,12 @@ def compute_loss(
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)
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)
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# non-blank arcs self-loop penalty
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# non-blank arcs self-loop penalty
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all_self_blanks_idx = decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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all_self_blanks_idx = (
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decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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)
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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decoding_graph.scores[all_self_blanks_idx] = -0.04
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decoding_graph.scores[all_self_blanks_idx] = -0.04
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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ctc_loss = k2.ctc_loss(
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ctc_loss = k2.ctc_loss(
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decoding_graph=decoding_graph,
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decoding_graph=decoding_graph,
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@ -27,7 +27,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--world-size 4 \
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--world-size 4 \
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_0.05 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_0.05 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 300
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--max-duration 300
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@ -38,7 +38,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--use-fp16 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_0.05 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_0.05 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 750
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--max-duration 750
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"""
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"""
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@ -705,10 +705,12 @@ def compute_loss(
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)
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)
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# non-blank arcs self-loop penalty
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# non-blank arcs self-loop penalty
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all_self_blanks_idx = decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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all_self_blanks_idx = (
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decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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)
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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decoding_graph.scores[all_self_blanks_idx] = -0.05
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decoding_graph.scores[all_self_blanks_idx] = -0.05
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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ctc_loss = k2.ctc_loss(
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ctc_loss = k2.ctc_loss(
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decoding_graph=decoding_graph,
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decoding_graph=decoding_graph,
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@ -27,7 +27,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--world-size 4 \
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--world-size 4 \
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_5 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_5 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 300
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--max-duration 300
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@ -38,7 +38,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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--num-epochs 40 \
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--num-epochs 40 \
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--start-epoch 21 \
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--start-epoch 21 \
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--use-fp16 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_soft_5 \
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--exp-dir pruned_transducer_stateless4_ctc_bs_withoutlconv/exp_960_21to40_soft_5 \
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--full-libri 1 \
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--full-libri 1 \
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--max-duration 750
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--max-duration 750
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"""
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"""
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@ -705,10 +705,12 @@ def compute_loss(
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)
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)
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# non-blank arcs self-loop penalty
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# non-blank arcs self-loop penalty
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all_self_blanks_idx = decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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all_self_blanks_idx = (
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decoding_graph.arcs.values()[:, 0] == decoding_graph.arcs.values()[:, 1]
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)
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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blank_self_loops_idx = decoding_graph.arcs.values()[:, 2] == 0
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decoding_graph.scores[all_self_blanks_idx] = -5.00
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decoding_graph.scores[all_self_blanks_idx] = -5.00
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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decoding_graph.scores[blank_self_loops_idx] = 0.0
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ctc_loss = k2.ctc_loss(
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ctc_loss = k2.ctc_loss(
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decoding_graph=decoding_graph,
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decoding_graph=decoding_graph,
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