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from typing import List
import pytorch_lightning as pl
import torch.nn as nn
def get_callbacks(
task_name: str,
config_yaml: str,
workspace: str,
checkpoints_dir: str,
statistics_path: str,
logger: pl.loggers.TensorBoardLogger,
model: nn.Module,
evaluate_device: str,
) -> List[pl.Callback]:
r"""Get callbacks of a task and config yaml file.
Args:
task_name: str
config_yaml: str
dataset_dir: str
workspace: str, containing useful files such as audios for evaluation
checkpoints_dir: str, directory to save checkpoints
statistics_dir: str, directory to save statistics
logger: pl.loggers.TensorBoardLogger
model: nn.Module
evaluate_device: str
Return:
callbacks: List[pl.Callback]
"""
if task_name == 'musdb18':
from bytesep.callbacks.musdb18 import get_musdb18_callbacks
return get_musdb18_callbacks(
config_yaml=config_yaml,
workspace=workspace,
checkpoints_dir=checkpoints_dir,
statistics_path=statistics_path,
logger=logger,
model=model,
evaluate_device=evaluate_device,
)
elif task_name == 'voicebank-demand':
from bytesep.callbacks.voicebank_demand import get_voicebank_demand_callbacks
return get_voicebank_demand_callbacks(
config_yaml=config_yaml,
workspace=workspace,
checkpoints_dir=checkpoints_dir,
statistics_path=statistics_path,
logger=logger,
model=model,
evaluate_device=evaluate_device,
)
elif task_name in ['vctk-musdb18', 'violin-piano', 'piano-symphony']:
from bytesep.callbacks.instruments_callbacks import get_instruments_callbacks
return get_instruments_callbacks(
config_yaml=config_yaml,
workspace=workspace,
checkpoints_dir=checkpoints_dir,
statistics_path=statistics_path,
logger=logger,
model=model,
evaluate_device=evaluate_device,
)
else:
raise NotImplementedError
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