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[doc&tools] librispeech recipe #30
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examples/librispeech/s0/README.md
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test clean | ||
| decoding mode/chunk size | full | | ||
|--------------------------|------| | ||
| attention rescoring | 2,85 | |
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2.85
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# decoder related | ||
decoder: transformer | ||
decoder_conf: |
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whether the output size should be modified to 512 for a large conformer?
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collate_conf: | ||
spec_aug: true | ||
norm_mean: true # if true, must specify cmvn file in cmd line |
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what's the default configure in espnet for cmvn, spec augmentation?
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# network architecture |
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remove this if you don't have any experiment result on this recipe
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# network architecture |
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remove this
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# network architecture |
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remove this, add it later
datadir=/nfsa/diwu/open-dir | ||
nj=16 | ||
dict=data/dict/lang_char.txt | ||
# Optional train_config |
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refine this comment or remove this
examples/librispeech/s0/run.sh
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# use your own data path | ||
datadir=/nfsa/diwu/open-dir | ||
nj=16 | ||
dict=data/dict/lang_char.txt |
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conflicts with the dict declared below
test_dir=$dir/${test}_${mode} | ||
mkdir -p $test_dir | ||
python wenet/bin/recognize.py --gpu 0 \ | ||
--mode $mode \ |
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indent here
@@ -2,3 +2,5 @@ flake8 | |||
pyyaml | |||
tensorboardX | |||
typeguard | |||
Pillow |
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where Pillow is used?
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spec aug time warp, will be added
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#!/usr/bin/env python |
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what's the benefits of wrapping spm_encode/spm_decode/spm_train?
If just for easier input output interface, the raw spm_* are recommended.
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[tools] specaugmentation config
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[tools] specaugmentation config
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