espnet3.components.dataloader.DataLoaderBuilder

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class espnet3.components.dataloader.DataLoaderBuilder(dataset, config, collate_fn, num_device: int, epoch: int)

Bases: object

Builder class for constructing training and validation DataLoaders in ESPnet3.

This class provides a unified interface for setting up PyTorch or ESPnet-specific DataLoaders based on the configuration. It supports advanced features such as:

• Custom collate functions (e.g., CommonCollateFn)
• Sharded sampling using {shard_idx}-formatted shape files
• Sequence-based batch sampling with batch_bins or batch_size
• Dynamic handling of DDP-compatible iteration strategies

Typically used by LitESPnetModel to instantiate training and validation DataLoaders with consistent behavior across single-GPU, multi-GPU, and distributed training.

• Parameters:
  • dataset (torch.utils.data.Dataset) – Dataset instance wrapped by DataLoader.
  • config (DictConfig) – Full training configuration (e.g., from OmegaConf).
  • collate_fn (Callable) – Function to collate individual samples into mini-batches.
  • num_device (int) – Number of devices in use (e.g., GPUs or nodes).
  • epoch (int) – Current epoch number. Used to reseed samplers deterministically.

Example

builder = DataLoaderBuilder( : dataset=train_dataset, config=config, collate_fn=collate_fn, num_device=4, epoch=3

) train_loader = builder.build(mode=”train”)

build(mode: str)

Build and return a DataLoader for the specified mode (“train” or “valid”).

This method supports two modes of operation depending on the configuration: (1) ESPnet-style iterator: Custom sampling and iteration logic. (2) Standard PyTorch DataLoader: Simpler use-case with fixed batch size.

The selection depends on whether config.dataloader.<mode>.iter_factory is defined.

• Parameters:mode (str) – One of {“train”, “valid”} indicating which dataloader to build.
• Returns: Configured DataLoader instance for training or : validation.
• Return type: torch.utils.data.DataLoader

Config-driven branching logic: : Case 1: ESPnet SequenceIterFactory is used Example config: <br/>

``

<br/> ` dataloader: train: <br/>

iter_factory: _target_: espnet2.iterators.sequence_iter_factory.SequenceIterFactory shuffle: true collate_fn: ${dataloader.collate_fn} batches: <br/>

_target_: espnet2.samplers.build_batch_sampler.build_batch_sampler type: numel shape_files:

• stats/train/split.12/speech_shape.{shard_idx} batch_bins: 4000000 min_batch_size: 2 <br/>

``

<br/>

`

<br/> Case 2: Standard PyTorch DataLoader is used Example config: <br/>

``

<br/> ` dataloader: train: <br/>

iter_factory: # Set iter_factory to None batch_size: 4 num_workers: 2 shuffle: true <br/>

``

<br/>

`

Notes

• For Case 1, all placeholder strings like “{shard_idx}” are resolved

when initializing shards. (e.g., via update_shard()).

• Raises:ValueError – If the provided mode is neither “train” nor “valid”.