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Minor fixes after review.

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Fangjun Kuang 2021-08-12 10:33:50 +08:00
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commit f0ee6cf0dc
2 changed files with 3 additions and 27 deletions
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24
README.md
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@ -22,30 +22,6 @@ Please refer to [k2's installation documentation][k2-install] to install k2.
If you have any issues about installing k2, please open an issue at If you have any issues about installing k2, please open an issue at
<https://github.com/k2-fsa/k2/issues>. <https://github.com/k2-fsa/k2/issues>.
The following shows the minimal commands needed to install k2 from source:
```bash
mkdir $HOME/open-source
cd $HOME/open-source
git clone https://github.com/k2-fsa/k2.git
cd k2
mkdir build_release
cd build_release
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j _k2
export PYTHONPATH=$HOME/open-source/k2/k2/python:$PYTHONPATH
export PYTHONPATH=$HOME/open-source/k2/build_release/lib:$PYTHONPATH
```
To check that k2 is installed successfully, please run
```bash
python3 -m k2.version
```
It should show the information about the environment in which
k2 was built.
### Install lhotse ### Install lhotse
Please refer to [lhotse's installation documentation][lhotse-install] to install Please refer to [lhotse's installation documentation][lhotse-install] to install

6
egs/librispeech/ASR/README.md
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@ -20,7 +20,7 @@ please run
The script `./prepare.sh` prepares features, lexicon, LMs, etc. The script `./prepare.sh` prepares features, lexicon, LMs, etc.
All generated files are saved in the folder `./data`. All generated files are saved in the folder `./data`.
HINT: `./prepare.sh` support options `--stage` and `--stop-stage`. **HINT:** `./prepare.sh` supports options `--stage` and `--stop-stage`.
## TDNN-LSTM CTC training ## TDNN-LSTM CTC training
@ -31,7 +31,7 @@ Pre-configured parameters for training and decoding are set in the function
`get_params()` within `tdnn_lstm_ctc/train.py` `get_params()` within `tdnn_lstm_ctc/train.py`
and `tdnn_lstm_ctc/decode.py`. and `tdnn_lstm_ctc/decode.py`.
Parameters that can be passed from the commandline can be found by Parameters that can be passed from the command-line can be found by
``` ```
./tdnn_lstm_ctc/train.py --help ./tdnn_lstm_ctc/train.py --help
@ -61,4 +61,4 @@ If you want to decode by averaging checkpoints `epoch-8.pt`,
The folder `conformer-ctc` contains scripts for CTC training The folder `conformer-ctc` contains scripts for CTC training
with conformer models. The steps of running the training and with conformer models. The steps of running the training and
decoding are similar as `tdnn_lstm_ctc`. decoding are similar to `tdnn_lstm_ctc`.