- Login:
address_hidden - Create Private Folder shared
- Create Session
- Select Environments:
pytorch-espnet - Select Version:
210325 - Mount Folders:
a1003andshared - Max
CPU,RAM,Shared Memory,GPU - 1
Session
- Select Environments:
* (unblock popup)
* Try **copy and paste**
mkdir -p /home/work/logs
ln -s /home/work/a1003/models/ref1x /home/work/logs/ref1x
Visual Studio CodeConsoleTensorboard
git clone https://github.com/pkyoung/a1003.git ./train.1
cd train.1
cd data && tar xvzf ks.tgz && cd ..
ln -s /opt/kaldi/egs/wsj/s5/steps .
ln -s /opt/kaldi/egs/wsj/s5/utils .
cat data/ks/uttid.01 data/ks/uttid.03 data/ks/uttid.05 > data/ks/uttid.train
cat data/ks/uttid.01 data/ks/uttid.03 > data/ks/uttid.train
cat data/ks/uttid.01 > data/ks/uttid.train
source path.sh
mkdir -p data/train data/test data/dev
filter_scp.pl data/ks/uttid.test data/ks/wav.scp > data/test/wav.scp
filter_scp.pl data/ks/uttid.dev data/ks/wav.scp > data/dev/wav.scp
filter_scp.pl data/ks/uttid.train data/ks/wav.scp > data/train/wav.scp
filter_scp.pl data/ks/uttid.test data/ks/text > data/test/text
filter_scp.pl data/ks/uttid.dev data/ks/text > data/dev/text
filter_scp.pl data/ks/uttid.train data/ks/text > data/train/text
awk '{print $1 " " $1}' data/test/wav.scp > data/test/spk2utt
awk '{print $1 " " $1}' data/dev/wav.scp > data/dev/spk2utt
awk '{print $1 " " $1}' data/train/wav.scp > data/train/spk2utt
cp data/test/spk2utt data/test/utt2spk
cp data/dev/spk2utt data/dev/utt2spk
cp data/train/spk2utt data/train/utt2spk
./steps/make_fbank.sh data/test
./steps/make_fbank.sh data/dev
./steps/make_fbank.sh data/train
bash stage3-5.sh
bash stage9.sh
bash stage10.sh
## testing
bash stage11.sh
cd /home/work
mkdir -p logs
## tensorboard 위치는 환경에 맞게
## train.1 은 아무 이름이나 구분되는 이름으로
ln -s /home/work/train.1/exp/exp01a/tensorboard ./logs/train.1
- Prepare files
wav.scp,text,spk2utt,utt2spkindata/mydata - If you don't have your data,
mkdir data/mydata
cp /home/work/a1003/db/navidlg/* data/mydata
## ignore cp -r ... message
-
Edit inference.sh and run it
./inference.sh
-
Measure WER and CER
python local/uttwer.py data/mydata/text result/text python local/uttcer.py data/mydata/text result/text
- 훈련데이터
| model | training data | hours | elapsed |
|---|---|---|---|
| 01 | 01 | 173.9h | 8h 8m |
| 03 | 03 | 192.3h | 8h 55m |
| 2x | 01 + 03 | 366.3h | 16h 4m |
| 3x | 01 + 03 + 05 | 563.7h | 40h 50m (1080ti x1) |
- 모델 위치
/home/work/a1003/models/ref01/valid.acc.ave_10best.pth
/home/work/a1003/models/ref03/valid.acc.ave_10best.pth
/home/work/a1003/models/ref2x/valid.acc.ave_10best.pth
/home/work/a1003/models/ref3x/valid.acc.ave_10best.pth
- tensorboard
ln -s /home/work/a1003/models/ref1x/exp01a/tensorboard /home/work/logs/ref1x
ln -s /home/work/a1003/models/ref2x/exp01a/tensorboard /home/work/logs/ref2x
ln -s /home/work/a1003/models/ref3x/exp01a/tensorboard /home/work/logs/ref3x
ln -s /home/work/a1003/models/ref3x_sa/exp01a/tensorboard /home/work/logs/ref3x_sa
ln -s /home/work/a1003/models/ref1x_lr5/exp01a/tensorboard /home/work/logs/ref1x_lr5
- Evaluation results: data/test
| model | CER | WER |
|---|---|---|
| ref1x | ||
| ref2x | ||
| ref3x | ||
| ref3x+sa |
- Edit inference.sh and run it
model=/home/work/a1003/models/ref01/valid.acc.ave_10best.pth
model=/home/work/a1003/models/ref03/valid.acc.ave_10best.pth
model=/home/work/a1003/models/ref2x/valid.acc.ave_10best.pth
model=/home/work/a1003/models/ref3x/valid.acc.ave_10best.pth
bash inference.sh
- Measure CER/WER
python local/uttcer.py data/mydata/text result/text
import torch
torch.__version__
/opt/conda/bin/python3 -m pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch==1.9.0
pip install git+https://github.com/openai/whisper.git
git clone https://github.com/openai/whisper
cd whisper
whisper tests/jfk.flac --model tiny
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent