Skip to content
This repository has been archived by the owner on Dec 8, 2023. It is now read-only.

Latest commit

 

History

History
184 lines (135 loc) · 6.09 KB

tutorial.md

File metadata and controls

184 lines (135 loc) · 6.09 KB
Raw

Muskits

Recipes using Muskits

You can find the recipes in egs which follows the structure of ESPnet2 style:

muskit/  # Python modules of muskit
egs/     # corresponding recipes

The usage of recipes is almost the same as that of ESPnet2.

  1. Change directory to the base directory

    # e.g.
cd egs/kiritan/svs1/

    kiritan is one of the Japanese singing voice database. The KIRITAN data should be downloaded from https://zunko.jp/kiridev/login.php You can perform any other recipes as the same way. e.g. csd, natsume, and etc.

    Keep in mind that all scripts should be ran at the level of egs/*/svs1.

    # Doesn't work
cd egs/kiritan/
./svs1/run.sh
./svs1/scripts/<some-script>.sh

# Doesn't work
cd egs/kiritan//svs1/local/
./data.sh

# Work
cd egs/kiritan//svs1
./run.sh
./scripts/<some-script>.sh

  2. Change the configuration Describing the directory structure as follows:

    egs2/kiritan/svs1/
 - conf/      # Configuration files for training, inference, etc.
 - scripts/   # Bash utilities of muskit
 - pyscripts/ # Python utilities of muskit
 - utils/     # From Kaldi utilities
 - db.sh      # The directory path of each corpora
 - path.sh    # Setup script for environment variables
 - cmd.sh     # Configuration for your backend of job scheduler
 - run.sh     # Entry point
 - svs.sh     # Invoked by run.sh
    • You need to modify db.sh for specifying your corpus before executing run.sh. For example, when you touch the recipe of egs/kiritan, you need to change the paths of KIRITANin db.sh.
    • Some corpora can be freely obtained from the WEB and they are written as "downloads/" at the initial state. You can also change them to your corpus path if it's already downloaded.
    • path.sh is used to set up the environment for run.sh. Note that the Python interpreter used for ESPnet is not the current Python of your terminal, but it's the Python which was installed at tools/. Thus you need to source path.sh to use this Python. 
      . path.sh
python
    • cmd.sh is used for specifying the backend of the job scheduler. If you don't have such a system in your local machine environment, you don't need to change anything about this file. See Using Job scheduling system

  3. Run run.sh

    ./run.sh

    run.sh is an example script, which we often call as "recipe", to run all stages related to DNN experiments; data-preparation, training, and evaluation.

See training status

Show the log file

% tail -f exp/*_train_*/train.log
[host] 2020-04-05 16:34:54,278 (trainer:192) INFO: 2/40epoch started. Estimated time to finish: 7 minutes and 58.63 seconds
[host] 2020-04-05 16:34:56,315 (trainer:453) INFO: 2epoch:train:1-10batch: iter_time=0.006, forward_time=0.076, loss=50.873, los
s_att=35.801, loss_ctc=65.945, acc=0.471, backward_time=0.072, optim_step_time=0.006, lr_0=1.000, train_time=0.203
[host] 2020-04-05 16:34:58,046 (trainer:453) INFO: 2epoch:train:11-20batch: iter_time=4.280e-05, forward_time=0.068, loss=44.369
, loss_att=28.776, loss_ctc=59.962, acc=0.506, backward_time=0.055, optim_step_time=0.006, lr_0=1.000, train_time=0.173

Show the training status in a image file

# Accuracy plot
# (eog is Eye of GNOME Image Viewer)
eog exp/*_train_*/images/acc.img
# Attention plot
eog exp/*_train_*/att_ws/<sample-id>/<param-name>.img

Use tensorboard

tensorboard --logdir exp/*_train_*/tensorboard/

Instruction for run.sh

How to parse command-line arguments in shell scripts?

All shell scripts in muskit depend on utils/parse_options.sh to parase command line arguments.

e.g. If the script has ngpu option

#!/usr/bin/env bash
# run.sh
ngpu=1
. utils/parse_options.sh
echo ${ngpu}

Then you can change the value as follows:

$ ./run.sh --ngpu 2
echo 2

You can also show the help message:

./run.sh --help

Start from a specified stage and stop at a specified stage

The procedures in run.sh can be divided into some stages, e.g. data preparation, training, and evaluation. You can specify the starting stage and the stopping stage.

./run.sh --stage 2 --stop-stage 6

There are also some altenative otpions to skip specified stages:

run.sh --skip_data_prep true  # Skip data preparation stages.
run.sh --skip_train true      # Skip training stages.
run.sh --skip_eval true       # Skip decoding and evaluation stages.
run.sh --skip_upload false    # Enable packing and uploading stages.

Note that skip_upload is true by default. Please change it to false when uploading your model.

Change the configuration for training

Please keep in mind that run.sh is a wrapper script of several tools including DNN training command. You need to do one of the following two ways to change the training configuration.

# Give a configuration file
./run.sh --svs_train_config conf/train_svs.yaml
# Give arguments to "muskit/bin/svs_train.py" directly
./run.sh --svs_args "--foo arg --bar arg2"

See Change the configuration for training for more detail about the usage of training tools.

Change the number of parallel jobs

./run.sh --nj 10             # Chnage the number of parallels for data preparation stages.
./run.sh --inference_nj 10   # Chnage the number of parallels for inference jobs.

We also support submitting jobs to multiple hosts to accelerate your experiment: See Using Job scheduling system

Use specified experiment directory for evaluation

If you already have trained a model, you may wonder how to give it to run.sh when you'll evaluate it later. By default the directory name is determined according to given options, svs_args, or etc. You can overwrite it by --svs_exp.

# For SVS recipe
./run.sh --skip_data_prep true --skip_train true --svs_exp <your_svs_exp_directory>