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Kaldi-style all-in-one recipes

This repository provides Kaldi-style recipes, as the same as ESPnet.
Currently, the following recipes are supported.

  • LJSpeech: English female speaker
  • JSUT: Japanese female speaker
  • CSMSC: Mandarin female speaker
  • CMU Arctic: English speakers
  • JNAS: Japanese multi-speaker
  • VCTK: English multi-speaker
  • LibriTTS: English multi-speaker
  • YesNo: English speaker (For debugging)

How to run the recipe

# Let us move on the recipe directory
$ cd egs/ljspeech/voc1

# Run the recipe from scratch
$ ./run.sh

# You can change config via command line
$ ./run.sh --conf <your_customized_yaml_config>

# You can select the stage to start and stop
$ ./run.sh --stage 2 --stop_stage 2

# If you want to specify the gpu
$ CUDA_VISIBLE_DEVICES=1 ./run.sh --stage 2

# If you want to resume training from 10000 steps checkpoint
$ ./run.sh --stage 2 --resume <path>/<to>/checkpoint-10000steps.pkl

You can check the command line options in run.sh.

The integration with job schedulers such as slurm can be done via cmd.sh and conf/slurm.conf.
If you want to use it, please check this page.

All of the hyperparameters are written in a single yaml format configuration file.
Please check this example in ljspeech recipe.

You can monitor the training progress via tensorboard.

$ tensorboard --logdir exp

If you want to accelerate the training, you can try distributed multi-gpu training based on apex.
You need to install apex for distributed training. Please make sure you already installed it.
Then you can run distributed multi-gpu training via following command:

# in the case of the number of gpus = 8
$ CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" ./run.sh --stage 2 --n_gpus 8

In the case of distributed training, the batch size will be automatically multiplied by the number of gpus.
Please be careful.

How to make the recipe for your own dateset

Here, I will show how to make the recipe for your own dataset.

  1. Setup your dataset to be the following structure.

    # For single-speaker case
$ tree /path/to/databse
/path/to/database
├── utt_1.wav
├── utt_2.wav
│   ...
└── utt_N.wav
# The directory can be nested, but each filename must be unique

# For multi-speaker case
$ tree /path/to/databse
/path/to/database
├── spk_1
│   ├── utt1.wav
├── spk_2
│   ├── utt1.wav
│   ...
└── spk_N
    ├── utt1.wav
    ...
# The directory under each speaker can be nested, but each filename in each speaker directory must be unique

  2. Copy the template directory.

    cd egs

# For single speaker case
cp -r template_single_spk <your_dataset_name>

# For multi speaker case
cp -r template_multi_spk <your_dataset_name>

# Move on your recipe
cd egs/<your_dataset_name>/voc1

  3. Modify the options in run.sh.
    What you need to change at least in run.sh is as follows:

    • db_root: Root path of the database.
    • num_dev: The number of utterances for development set.
    • num_eval: The number of utterances for evaluation set.

  4. Modify the hyperpameters in conf/parallel_wavegan.v1.yaml.
    What you need to change at least in config is as follows:

    • sampling_rate: If you can specify the lower sampling rate, the audio will be downsampled by sox.

  5. (Optional) Change command backend in cmd.sh.
    If you are not familiar with kaldi and run in your local env, you do not need to change.
    See more info on https://kaldi-asr.org/doc/queue.html.

  6. Run your recipe.

    # Run all stages from the first stage
./run.sh

# If you want to specify CUDA device
CUDA_VISIBLE_DEVICES=0 ./run.sh

If you want to try the other advanced model, please check the config files in egs/ljspeech/voc1/conf.