| | |
| | | from funasr.samplers.unsorted_batch_sampler import UnsortedBatchSampler |
| | | from funasr.schedulers.noam_lr import NoamLR |
| | | from funasr.schedulers.warmup_lr import WarmupLR |
| | | from funasr.schedulers.tri_stage_scheduler import TriStageLR |
| | | from funasr.torch_utils.load_pretrained_model import load_pretrained_model |
| | | from funasr.torch_utils.model_summary import model_summary |
| | | from funasr.torch_utils.pytorch_version import pytorch_cudnn_version |
| | |
| | | CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR, |
| | | noamlr=NoamLR, |
| | | warmuplr=WarmupLR, |
| | | tri_stage=TriStageLR, |
| | | cycliclr=torch.optim.lr_scheduler.CyclicLR, |
| | | onecyclelr=torch.optim.lr_scheduler.OneCycleLR, |
| | | CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts, |
| | |
| | | collate_fn, |
| | | key_file: str = None, |
| | | batch_size: int = 1, |
| | | fs: dict = None, |
| | | dtype: str = np.float32, |
| | | num_workers: int = 1, |
| | | allow_variable_data_keys: bool = False, |
| | |
| | | dataset = IterableESPnetDataset( |
| | | data_path_and_name_and_type, |
| | | float_dtype=dtype, |
| | | fs=fs, |
| | | preprocess=preprocess_fn, |
| | | key_file=key_file, |
| | | ) |