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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 13 new columns ({'notes', 'coder_id', 'youtube_id', 'advertiser_id', 'wmp_office', 'end_sec', 'duration_sec', 'party_collapsed_modal', 'wmp_creative_id', 'advertiser_name', 'sponsor_type_collapsed_modal', 'start_sec', 'segment_idx'})
This happened while the csv dataset builder was generating data using
hf://datasets/haohxin/measuring-political-sound-events/data/annotations/test_10s/individual_annotations.csv (at revision 8bc355fe6ff545821c18f6c11c205e69593e06cb), ['hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_10s/ground_truth.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_10s/individual_annotations.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_5s/ground_truth.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_5s/individual_annotations.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/clap/clap_ms/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/gemini_3_5_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/gemini_3_5_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/qwen3_5_omni_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/qwen3_5_omni_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/clap/clap_ms/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/gemini_3_5_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/gemini_3_5_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/qwen3_5_omni_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/qwen3_5_omni_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_10s/test_set/ast/index.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_10s/test_set/wav2vec2/index.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_5s/test_set/ast/index.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_5s/test_set/wav2vec2/index.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
coder_id: string
coder_index: int64
wmp_creative_id: string
segment_idx: int64
start_sec: int64
end_sec: int64
duration_sec: int64
party_collapsed_modal: string
sponsor_type_collapsed_modal: string
advertiser_id: string
advertiser_name: string
wmp_office: string
youtube_id: string
music: int64
music_scary: int64
music_uplifting: int64
speech: int64
male_speech: int64
female_speech: int64
narration: int64
processed_voiceover: int64
notes: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2968
to
{'coder_index': Value('int64'), 'music': Value('int64'), 'music_scary': Value('int64'), 'music_uplifting': Value('int64'), 'speech': Value('int64'), 'male_speech': Value('int64'), 'female_speech': Value('int64'), 'narration': Value('int64'), 'processed_voiceover': Value('int64')}
because column names don't match
During handling of the above exception, another exception occurred:
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 1694, 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 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 13 new columns ({'notes', 'coder_id', 'youtube_id', 'advertiser_id', 'wmp_office', 'end_sec', 'duration_sec', 'party_collapsed_modal', 'wmp_creative_id', 'advertiser_name', 'sponsor_type_collapsed_modal', 'start_sec', 'segment_idx'})
This happened while the csv dataset builder was generating data using
hf://datasets/haohxin/measuring-political-sound-events/data/annotations/test_10s/individual_annotations.csv (at revision 8bc355fe6ff545821c18f6c11c205e69593e06cb), ['hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_10s/ground_truth.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_10s/individual_annotations.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_5s/ground_truth.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/annotations/test_5s/individual_annotations.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/clap/clap_ms/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/gemini_3_5_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/gemini_3_5_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/qwen3_5_omni_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_10s/qwen3_5_omni_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/clap/clap_ms/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/gemini_3_5_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/gemini_3_5_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/qwen3_5_omni_flash/present.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/candidate_scores/test_5s/qwen3_5_omni_flash/targets.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_10s/test_set/ast/index.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_10s/test_set/wav2vec2/index.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_5s/test_set/ast/index.csv', 'hf://datasets/haohxin/measuring-political-sound-events@8bc355fe6ff545821c18f6c11c205e69593e06cb/data/results/embeddings/benchmark_5s/test_set/wav2vec2/index.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
coder_index int64 | music int64 | music_scary int64 | music_uplifting int64 | speech int64 | male_speech int64 | female_speech int64 | narration int64 | processed_voiceover int64 |
|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
2 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
3 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
4 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
5 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
6 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
7 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 |
8 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 |
9 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
10 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
11 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
12 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 |
13 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
14 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
15 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 |
16 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
17 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
18 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
19 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
20 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
21 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
22 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
23 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
24 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
25 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
26 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
27 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
28 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
29 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
30 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
31 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
32 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 |
33 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
34 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
35 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
36 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
37 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
38 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
39 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
40 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
41 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
42 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
43 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
44 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
45 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
46 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
47 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
48 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
49 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
50 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
51 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
52 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
53 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
54 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
55 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 |
56 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
57 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
58 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
59 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
60 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
61 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 |
62 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
63 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
64 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
65 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
66 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
67 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
68 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
69 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
70 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
71 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 |
72 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
73 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
74 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
75 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
76 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
77 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
78 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
79 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
80 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
81 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 |
82 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
83 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
84 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
85 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
86 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
87 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
88 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 |
89 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
90 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
91 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
92 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 |
93 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
94 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
95 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
96 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
97 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
98 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
99 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 |
100 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
End of preview.
Measuring Political Sound Events
This dataset is the derived-data companion to the replication repository for “Measuring Sound Events in Political Communication: A Computational Guide” by Yingdan Lu, Haohang Xin, Annie Chu, Yilang Peng, and Cuihua Shen.
It contains the human annotations, segment and sampling metadata, model scores, audio embeddings, selected-model outputs, and intermediate analysis files needed to reproduce the article’s model evaluation, political-advertising application, and 5-second segmentation robustness analysis. It does not contain raw audio or video.
Contents
data/annotations/: finalized development and held-out-test labels, plus the available anonymized individual coding records.data/manifests/: identifiers, segment boundaries, benchmark draws, analytic sample metadata, and filtering fields.data/results/embeddings/: AST and Wav2Vec2 embeddings used by the linear probes.data/results/tagger_scores/: PANNs and YAMNet AudioSet scores.data/results/candidate_scores/: MS-CLAP scores, Gemini/Qwen probabilities and native presence flags, and fitted-probe outputs.data/results/deployments/: selected-model scores, binary event calls, and analysis-ready segment metadata.- Files directly under
data/results/: evaluation, robustness, statistical, and provenance artifacts generated by the replication workflow.
The core join key is (wmp_creative_id, segment_idx). Human-coding benchmarks
also use coder_index, an anonymized row identifier. The eight reported events
are speech, music, male speech, female speech, narration, processed voiceover,
scary music, and uplifting music.
Sample summary
- 1,045 political advertisements.
- 2,799 raw 10-second segmentation rows and 5,216 raw 5-second segmentation rows, including trailing partial segments.
- 500 development and 200 advertisement-disjoint held-out-test segments at each segmentation length.
- 2,444 retained 10-second segments in the primary application.
- 4,359 retained 5-second segments in the robustness application after the duration filter and six duration-valid Qwen refusals.
Use with the replication code
The public code and full file documentation are available at ottoxin/measuring-political-sound-events. After cloning that repository, run:
python code/python/download_data.py
./run_analysis.sh
The standard workflow uses the stored derived outputs and does not call an API, download model weights, or require the source audio.
Source media
The advertisements originate from Political Advertising on Google: 2022 U.S. Midterm Elections, available from the original authors at doi:10.25438/wes02.26124355.
Raw audiovisual advertisements are not redistributed here because they may be subject to third-party copyright or redistribution restrictions. Researchers who want to reconstruct the audio-to-model-output pipeline should obtain the source materials independently and comply with the applicable data-use and copyright requirements.
Citation
Please cite the associated article and replication repository. Publication metadata will be added when available.
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