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FSPEN

Paper[1]
Since there's no official implementation, we faithfully re-implemented the model architecture following the paper and configured the training settings identically to FastEnhancer for fair comparison.

[1]: L. Yang, W. Liu, R. Meng, G. Lee, S. Baek, and H.-G. Moon, “Fspen: an ultra-lightweight network for real time speech enahncment,” in Proc. IEEE ICASSP, 2024, pp. 10671–10675.

Training

  • Model: FSPEN
  • Dataset: Voicebank-Demand at 16kHz sampling rate.
  • Number of GPUs: 1
  • Batch size: 64
  • Mixed-precision training with fp16: False
  • Path to save config, tensorboard logs, and checkpoints: logs/vbd/16khz/fspen
CUDA_VISIBLE_DEVICES=0 python train.py \
  -n vbd/16khz/fspen \
  -c configs/others/fspen.yaml \
  -p train.batch_size=64 valid.batch_size=64 \
  -f

or

CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nproc_per_node=1 \
  train_torchrun.py \
  -n vbd/16khz/fspen \
  -c configs/others/fspen.yaml \
  -p train.batch_size=64 valid.batch_size=64 \
  -f

Options:
- -n (Required): Base directory to save configuration, tensorboard logs, and checkpoints.
- -c (Optional): Path to configuration file. If not given, the configuration file in the base directory will be used.
- -p (Optional): Parameters after this will update the configuration.
- -f (Optional): If the base directory already exists and -c flag is given, an exception will be raised to avoid overwriting config file. However, enabling this option will force overwriting config file.

Resume Training

Suppose you stopped the training.
To load the saved checkpoint at logs/vbd/16khz/fspen and resume the training, use the code below:

CUDA_VISIBLE_DEVICES=0 python train.py \
  -n vbd/16khz/fspen

or

CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nproc_per_node=1 \
  train_torchrun.py \
  -n vbd/16khz/fspen

Test the training code

Before you start training, we recommend to run the following test code:

CUDA_VISIBLE_DEVICES=0 python train.py \
  -n delete_it \
  -c configs/others/fspen.yaml \
  -p train.test=True pesq.interval=1 \
  -f

By setting train.test=True, it will execute a training code for only 10 steps. Then it will execute a validation code. Finally, by setting pesq.interval=1, it will execute a code for calculating objective metrics and terminate. If the code runs well, you are ready to begin training. You can delete logs/delete_it directory after the test.

About optimizer_groups

In configs/fastenhancer/l.yaml, take a look at train.optimizer_groups. There are only two scale-invariant parameters in FSPEN, and we manually set them.