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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
__key__ string | __url__ string | md unknown |
|---|---|---|
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avqa_full/hf_snapshot/.cache/huggingface/download/train/data-00067-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00000.tar | null |
avqa_full/hf_snapshot/.cache/huggingface/download/train/data-00068-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00000.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/00 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00001.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/01 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/02 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/03 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/04 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/05 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/06 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/07 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/08 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/09 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/10 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/11 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/12 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/13 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/14 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/.ignored_incomplete_audit/15 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/dataset_dict | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/train/data-00000-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/train/data-00001-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/train/data-00002-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/train/data-00003-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00002.tar | null |
avqa_full/hf_snapshot/train/data-00004-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00003.tar | null |
avqa_full/hf_snapshot/train/data-00005-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00003.tar | null |
avqa_full/hf_snapshot/train/data-00006-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00003.tar | null |
avqa_full/hf_snapshot/train/data-00007-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00003.tar | null |
avqa_full/hf_snapshot/train/data-00008-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00004.tar | null |
avqa_full/hf_snapshot/train/data-00009-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00004.tar | null |
avqa_full/hf_snapshot/train/data-00010-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00004.tar | null |
avqa_full/hf_snapshot/train/data-00011-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00004.tar | null |
avqa_full/hf_snapshot/train/data-00012-of-00078 | hf://datasets/penguinfish1688/D2-training-data-archive@c82d4f0787c2a40e7d26682ca4f6a875f201367c/archives/avqa_full/part-00005.tar | null |
End of preview.
D2 training data and preparation archive
This repository preserves prepared speech data and source snapshots used in the Dialogue-to-Duplex research workflow. It includes inputs for causal audio-encoder training (stage 1) and dialogue fine-tuning (stage 2), together with additional prepared datasets and their provenance. Original file bytes and relative paths are retained in independently extractable tar shards, normally about 2 GiB each.
Stage 1 uses avqa_full and librispeech_cached_pair_35245. Stage 2 uses
instructs2s_kokoro_zero_trimmed, its _dynamic companion,
soda_kokoro_400h_v1, and prepared avqa_small/sft_async_v3 audio.
The InstructS2S source index records approximately 150,000 accepted rows and
source_complete: false; this is a preserved partial source, not a claim that
all upstream examples were regenerated. SODA's completion record reports
41,776 accepted rows and about 407 audio hours.
| Component | Source GiB | Role |
|---|---|---|
_root |
0.00 | Additional prepared data / provenance |
avqa_full |
34.61 | D2 training input |
avqa_small |
48.46 | D2 training input |
duplex_chat |
0.03 | Additional prepared data / provenance |
instructs2s_100k_dynamic_12p5hz |
3.56 | Additional prepared data / provenance |
instructs2s_100k_multiturn_12p5hz |
1.66 | Additional prepared data / provenance |
instructs2s_200k_qwen_rollout |
0.65 | Additional prepared data / provenance |
instructs2s_dynamic |
0.30 | Additional prepared data / provenance |
instructs2s_kokoro_zero_trimmed |
197.35 | D2 training input |
instructs2s_kokoro_zero_trimmed_dynamic |
1.56 | D2 training input |
lang_rollout_dynamic_qwen_v2_10m |
0.69 | Additional prepared data / provenance |
librispeech_cached_pair_35245 |
32.06 | D2 training input |
soda_kokoro_400h_v1 |
54.02 | D2 training input |
topical_chat_kokoro_400h_v1 |
3.21 | Additional prepared data / provenance |
Restore and verify
Download the selected archives/<component>/part-*.tar files. Check their
SHA-256 values against archive-index.json, then extract each tar into the same
destination directory. All shards use paths relative to the original dataset
root; original paths and per-file SHA-256 values are recorded in
file-manifest.jsonl.gz. directories.json preserves empty-directory names.
Some original manifests contain absolute research paths; configure the D2
dataset links to your restored location. This archive does not silently rewrite
provenance or change the source formats. PyTorch .pt files remain in their
original serialization; do not load untrusted pickle data.
Sources and terms
Source identities and pinned revisions are retained in the original manifests. Sources include ICTNLP/InstructS2S-200K, gijs/avqa-processed, openslr/librispeech_asr, SODA, and Topical-Chat. Kokoro-generated speech is identified in the preparation metadata. Each component retains its original source terms; this archive does not grant a new blanket license over third-party data.
Environment installations, local restore tooling, and Git internals are omitted.
archive-index.json is published only after all listed shards have been uploaded
and their remote sizes and SHA-256 identifiers verified anonymously.
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