The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: AttributeError
Message: 'str' object has no attribute 'items'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1182, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1157, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 692, in get_module
config_name: DatasetInfo.from_dict(dataset_info_dict)
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'items'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
🔬 Notes on Dialogue Audio Text data
Overview
A documented Dialogue data-preparation workflow for Audio Text records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.
Intended use
The repository is suitable for testing the data pipeline, adapting it to a documented source, and preparing controlled research splits. Release status: metadata sample; full source data not bundled. The sample is for schema inspection only and should not be reported as a full training corpus.
Contact
Found a mistake? Open an issue — patches and pointers both welcome.
Acknowledgments
This repo borrows ideas freely from public reference implementations; credit to their authors.
Risks and limitations
The loader cannot guarantee that an external source is representative, correctly licensed, or free of sensitive information. Users remain responsible for source review, privacy checks, and bias analysis before training or redistribution.
Validation checklist
Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows.
Included material
clean.py— loading, cleaning and split preparation.dataset_infos.json— schema and split metadata.metadata_sample.jsonl— readable records for schema checks.
Processing choices
- Storage format: npy sharded
- Preprocessing: progressive
- Augmentation: none
- Split strategy: leave one out
- Sampling: contrastive
- Quality checks: adaptive
- Labeling: manual
Files
clean.py— primary artifactREADME.md— this documentationdataset_infos.json— schema metadatametadata_sample.jsonl— schema sampleexamples/sample_usage.py— usage sketch
License
Released under cc-by-4.0. Review the source-data terms separately when this repository is used with external datasets.
- Downloads last month
- -