This directory contains reference implementations for source separations. For the detail of each model, please checkout the followings.
To training a model, you can use lightning_train.py. This script takes the form of
lightning_train.py [parameters]
```
python lightning_train.py \
[--data-dir DATA_DIR] \
[--num-gpu NUM_GPU] \
[--num-workers NUM_WORKERS] \
...
# For the detail of the parameter values, use;
python lightning_train.py --help
```
This script runs training in PyTorch-Lightning framework with Distributed Data Parallel (DDP) backend.
Example scripts for running the training on SLURM cluster
- launch_job.sh
#!/bin/bash
#SBATCH --job-name=source_separation
#SBATCH --output=/checkpoint/%u/jobs/%x/%j.out
#SBATCH --error=/checkpoint/%u/jobs/%x/%j.err
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=2
#SBATCH --cpus-per-task=8
#SBATCH --mem-per-cpu=16G
#SBATCH --gpus-per-node=2
#srun env
srun wrapper.sh $@- wrapper.sh
#!/bin/bash
num_speakers=2
this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
exp_dir="/checkpoint/${USER}/exp/"
root_dir="/dataset/" # The directory where the directory ``Libri2Mix`` or ``Libri3Mix`` is stored.
num_gpu=2 # The number of GPUs used on one node.
num_node=1 # The number of nodes used on the cluster.
batch_size=6 # The batch size per GPU.
mkdir -p "${exp_dir}"
python -u \
"${this_dir}/lightning_train.py" \
--num-speakers "${num_speakers}" \
--sample-rate 8000 \
--root-dir "${root_dir}" \
--exp-dir "${exp_dir}" \
--num-gpu ${num_gpu} \
--num-node ${num_node} \
--batch-size ${batch_size} \