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74 lines
2.0 KiB
Markdown
74 lines
2.0 KiB
Markdown
# Results for mucs hi-en and bn-en
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This page shows the WERs for the code switched test corpus of MUCS hi-en and bn-en.
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## using conformer ctc
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The following results are obtained with run.sh
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Specify the language through dataset arg (hi-en or bn-en)
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LM is trained using kenlm, with the training corpus
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Here are the results with different decoding methods
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bn-en
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|-------------------------|-------|
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| ctc decoding | 31.72 |
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| 1best | 28.05 |
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| nbest | 27.92 |
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| nbest-rescoring | 27.22 |
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| whole-lattice-rescoring | 27.24 |
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| attention-decoder | 26.46 |
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hi-en
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| | test |
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|-------------------------|-------|
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| ctc decoding | 31.43 |
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| 1best | 28.48 |
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| nbest | 28.55 |
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| nbest-rescoring | 28.23 |
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| whole-lattice-rescoring | 28.77 |
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| attention-decoder | 28.16 |
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The training command for reproducing is given below:
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```bash
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cd egs/mucs/ASR/
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./prepare.sh
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dataset="hi-en" #hi-en or bn-en
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bpe=400
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datadir=data_"$dataset"
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./conformer_ctc/train.py \
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--num-epochs 60 \
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--max-duration 300 \
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--exp-dir ./conformer_ctc/exp_"$dataset"_bpe"$bpe" \
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--manifest-dir $datadir/fbank \
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--lang-dir $datadir/lang_bpe_"$bpe" \
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--enable-musan False \
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```
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The decoding command is given below:
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```bash
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dataset="hi-en" #hi-en or bn-en
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bpe=400
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datadir=data_"$dataset"
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num_paths=10
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max_duration=10
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decode_methods="attention-decoder 1best nbest nbest-rescoring ctc-decoding whole-lattice-rescoring"
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for decode_method in $decode_methods;
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do
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./conformer_ctc/decode.py \
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--epoch 59 \
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--avg 10 \
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--manifest-dir $datadir/fbank \
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--exp-dir ./conformer_ctc/exp_"$dataset"_bpe"$bpe" \
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--max-duration $max_duration \
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--lang-dir $datadir/lang_bpe_"$bpe" \
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--lm-dir $datadir/"lm" \
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--method $decode_method \
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--num-paths $num_paths \
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done
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``` |