icefall/egs/mucs/ASR/RESULTS.md
2023-06-26 15:08:40 +05:30

74 lines
2.0 KiB
Markdown

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