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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 5 new columns ({'recording', 'duration', 'channel', 'start', 'supervisions'}) and 1 missing columns ({'tracks'}).
This happened while the json dataset builder was generating data using
gzip://lsheavymix_cuts_dev_2spk_snr_aug_mono.jsonl::hf://datasets/zrjin/LibriheavyMix-dev@812a2129f070acaeab2b9cda15e1e1392b2823e7/dev-lhotse/lsheavymix_cuts_dev_2spk_snr_aug_mono.jsonl.gz
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: string
start: double
duration: double
channel: int64
supervisions: list<item: struct<id: string, recording_id: string, start: double, duration: double, channel: int64, language: string, speaker: string, custom: struct<texts: list<item: string>, pre_texts: list<item: string>, begin_byte: int64, end_byte: int64>>>
child 0, item: struct<id: string, recording_id: string, start: double, duration: double, channel: int64, language: string, speaker: string, custom: struct<texts: list<item: string>, pre_texts: list<item: string>, begin_byte: int64, end_byte: int64>>
child 0, id: string
child 1, recording_id: string
child 2, start: double
child 3, duration: double
child 4, channel: int64
child 5, language: string
child 6, speaker: string
child 7, custom: struct<texts: list<item: string>, pre_texts: list<item: string>, begin_byte: int64, end_byte: int64>
child 0, texts: list<item: string>
child 0, item: string
child 1, pre_texts: list<item: string>
child 0, item: string
child 2, begin_byte: int64
child 3, end_byte: int64
recording: struct<id: string, sources: list<item: struct<type: string, channels: list<item: int64>, source: string>>, sampling_rate: int64, num_samples: int64, duration: double, channel_ids: list<item: int64>>
child 0, id: string
child 1, sources: list<item: struct<type: string, channels: list<item: int64>, source: string>>
child 0, item: struct<type: string, channels: list<item: int64>, source: string>
child 0, type: string
child 1, channels: list<item: int64>
child 0, item: int64
child 2, source: string
child 2, sampling_rate: int64
child 3, num_samples: int64
child 4, duration: double
child 5, channel_ids: list<item: int64>
child 0, item: int64
type: string
to
{'id': Value(dtype='string', id=None), 'tracks': [{'cut': {'id': Value(dtype='string', id=None), 'start': Value(dtype='float64', id=None), 'duration': Value(dtype='float64', id=None), 'channel': Value(dtype='int64', id=None), 'supervisions': [{'id': Value(dtype='string', id=None), 'recording_id': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'duration': Value(dtype='float64', id=None), 'channel': Value(dtype='int64', id=None), 'language': Value(dtype='string', id=None), 'speaker': Value(dtype='string', id=None), 'custom': {'texts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'pre_texts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'begin_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}}], 'features': {'type': Value(dtype='string', id=None), 'num_frames': Value(dtype='int64', id=None), 'num_features': Value(dtype='int64', id=None), 'frame_shift': Value(dtype='float64', id=None), 'sampling_rate': Value(dtype='int64', id=None), 'start': Value(dtype='float64', id=None), 'duration': Value(dtype='float64', id=None), 'storage_type': Value(dtype='string', id=None), 'storage_path': Value(dtype='string', id=None), 'storage_key': Value(dtype='string', id=None), 'recording_id': Value(dtype='string', id=None), 'channels': Value(dtype='int64', id=None)}, 'recording': {'id': Value(dtype='string', id=None), 'sources': [{'type': Value(dtype='string', id=None), 'channels': Sequence(fea
...
Value(dtype='int64', id=None), 'num_features': Value(dtype='int64', id=None), 'frame_shift': Value(dtype='float64', id=None), 'sampling_rate': Value(dtype='int64', id=None), 'start': Value(dtype='float64', id=None), 'duration': Value(dtype='float64', id=None), 'storage_type': Value(dtype='string', id=None), 'storage_path': Value(dtype='string', id=None), 'storage_key': Value(dtype='string', id=None), 'recording_id': Value(dtype='string', id=None), 'channels': Value(dtype='int64', id=None)}, 'recording': {'id': Value(dtype='string', id=None), 'sources': [{'type': Value(dtype='string', id=None), 'channels': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'source': Value(dtype='string', id=None)}], 'sampling_rate': Value(dtype='int64', id=None), 'num_samples': Value(dtype='int64', id=None), 'duration': Value(dtype='float64', id=None), 'channel_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, 'custom': {'text_path': Value(dtype='string', id=None)}, 'sampling_rate': Value(dtype='int64', id=None), 'feat_value': Value(dtype='float64', id=None), 'num_frames': Value(dtype='int64', id=None), 'num_features': Value(dtype='int64', id=None), 'frame_shift': Value(dtype='float64', id=None), 'num_samples': Value(dtype='int64', id=None)}, 'type': Value(dtype='string', id=None), 'offset': Value(dtype='float64', id=None)}]}, 'type': Value(dtype='string', id=None), 'offset': Value(dtype='float64', id=None)}], 'type': Value(dtype='string', id=None)}
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 1572, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1136, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
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 5 new columns ({'recording', 'duration', 'channel', 'start', 'supervisions'}) and 1 missing columns ({'tracks'}).
This happened while the json dataset builder was generating data using
gzip://lsheavymix_cuts_dev_2spk_snr_aug_mono.jsonl::hf://datasets/zrjin/LibriheavyMix-dev@812a2129f070acaeab2b9cda15e1e1392b2823e7/dev-lhotse/lsheavymix_cuts_dev_2spk_snr_aug_mono.jsonl.gz
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.
id string | tracks list | type string |
|---|---|---|
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"duration": 20.119,
"channel": 0,
"supervisions": [
{
"id": "medium/54/short_story_007_64kb_mp3/nighingale_andersen_sdw_64kb_29",
"recording_i... | MixedCut |
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{
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{
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{
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{
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{
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{
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"recording_id": ... | MixedCut |
End of preview.