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Datasets:
isaacnetero
/
polyvox-intent-commands

Tasks:
Text Classification
Languages:
English
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
0: string
1: string
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total_samples: int64
fallback_label: int64
label_contract: string
seed: int64
output_csv: string
fallback_name: string
required_examples: struct<1: list<item: string>, 2: list<item: string>, 3: list<item: string>, 4: list<item: string>, 5 (... 331 chars omitted)
  child 0, 1: list<item: string>
      child 0, item: string
  child 1, 2: list<item: string>
      child 0, item: string
  child 2, 3: list<item: string>
      child 0, item: string
  child 3, 4: list<item: string>
      child 0, item: string
  child 4, 5: list<item: string>
      child 0, item: string
  child 5, 6: list<item: string>
      child 0, item: string
  child 6, 9: list<item: string>
      child 0, item: string
  child 7, 10: list<item: string>
      child 0, item: string
  child 8, 28: list<item: string>
      child 0, item: string
  child 9, 50: list<item: string>
      child 0, item: string
  child 10, 51: list<item: string>
      child 0, item: string
  child 11, 52: list<item: string>
      child 0, item: string
  child 12, 53: list<item: string>
      child 0, item: string
  child 13, 54: list<item: string>
      child 0, item: string
  child 14, 55: list<item: string>
      child 0, item: string
  child 15, 56: list<item: string>
      child 0, item: string
  child 16, 57: list<item: string>
      child 0, item: string
  child 17, 58: list<item: string>
      child 0, item: string
samples_per_intent: int64
schema: string
intent_map: string
num_labels: int64
to
{'schema': Value('string'), 'output_csv': Value('string'), 'intent_map': Value('string'), 'samples_per_intent': Value('int64'), 'total_samples': Value('int64'), 'num_labels': Value('int64'), 'seed': Value('int64'), 'fallback_label': Value('int64'), 'fallback_name': Value('string'), 'required_examples': {'1': List(Value('string')), '2': List(Value('string')), '3': List(Value('string')), '4': List(Value('string')), '5': List(Value('string')), '6': List(Value('string')), '9': List(Value('string')), '10': List(Value('string')), '28': List(Value('string')), '50': List(Value('string')), '51': List(Value('string')), '52': List(Value('string')), '53': List(Value('string')), '54': List(Value('string')), '55': List(Value('string')), '56': List(Value('string')), '57': List(Value('string')), '58': List(Value('string'))}, 'label_contract': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              0: string
              1: string
              2: string
              3: string
              4: string
              5: string
              6: string
              7: string
              8: string
              9: string
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              50: string
              51: string
              52: string
              53: string
              54: string
              55: string
              56: string
              57: string
              58: string
              total_samples: int64
              fallback_label: int64
              label_contract: string
              seed: int64
              output_csv: string
              fallback_name: string
              required_examples: struct<1: list<item: string>, 2: list<item: string>, 3: list<item: string>, 4: list<item: string>, 5 (... 331 chars omitted)
                child 0, 1: list<item: string>
                    child 0, item: string
                child 1, 2: list<item: string>
                    child 0, item: string
                child 2, 3: list<item: string>
                    child 0, item: string
                child 3, 4: list<item: string>
                    child 0, item: string
                child 4, 5: list<item: string>
                    child 0, item: string
                child 5, 6: list<item: string>
                    child 0, item: string
                child 6, 9: list<item: string>
                    child 0, item: string
                child 7, 10: list<item: string>
                    child 0, item: string
                child 8, 28: list<item: string>
                    child 0, item: string
                child 9, 50: list<item: string>
                    child 0, item: string
                child 10, 51: list<item: string>
                    child 0, item: string
                child 11, 52: list<item: string>
                    child 0, item: string
                child 12, 53: list<item: string>
                    child 0, item: string
                child 13, 54: list<item: string>
                    child 0, item: string
                child 14, 55: list<item: string>
                    child 0, item: string
                child 15, 56: list<item: string>
                    child 0, item: string
                child 16, 57: list<item: string>
                    child 0, item: string
                child 17, 58: list<item: string>
                    child 0, item: string
              samples_per_intent: int64
              schema: string
              intent_map: string
              num_labels: int64
              to
              {'schema': Value('string'), 'output_csv': Value('string'), 'intent_map': Value('string'), 'samples_per_intent': Value('int64'), 'total_samples': Value('int64'), 'num_labels': Value('int64'), 'seed': Value('int64'), 'fallback_label': Value('int64'), 'fallback_name': Value('string'), 'required_examples': {'1': List(Value('string')), '2': List(Value('string')), '3': List(Value('string')), '4': List(Value('string')), '5': List(Value('string')), '6': List(Value('string')), '9': List(Value('string')), '10': List(Value('string')), '28': List(Value('string')), '50': List(Value('string')), '51': List(Value('string')), '52': List(Value('string')), '53': List(Value('string')), '54': List(Value('string')), '55': List(Value('string')), '56': List(Value('string')), '57': List(Value('string')), '58': List(Value('string'))}, 'label_contract': Value('string')}
              because column names don't match

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PolyVox Intent Commands Dataset

This dataset contains generated smart-home command utterances for PolyVox, a realtime multi-user smart assistant developed for Problem Statement 11: Real-Time Multi-User Smart Assistant for Dynamic and Noisy Smart Environments.

The dataset supports the intent-classification stage of the PolyVox pipeline. It is used to train the lightweight FastText intent classifier that maps recognized speech transcripts to smart-home command intents.

Contents

  • comprehensive_intents_compliant.csv
  • comprehensive_intents_compliant_metadata.json
  • intent_map_compliant.json

Dataset Summary

The default generated dataset contains:

  • 59 intent labels
  • 11,800 generated utterances
  • 200 samples per intent label

Intended Use

This dataset is intended for:

  • Training lightweight smart-home intent classifiers.
  • Evaluating command recognition for realtime smart-assistant pipelines.
  • Reproducing the PolyVox FastText intent-classifier training flow.

Not Intended For

This dataset is not intended for general open-domain conversational AI. It is scoped to smart-home command recognition.

Source And Generation Method

The dataset is generated from command templates, slot expansion, and smart-home action categories used by the PolyVox runtime. It includes command forms for lighting, HVAC, device control, garage control, and related smart-home actions.

The generation script is available in the PolyVox repository: https://github.com/da24s002/polyvox

src/models/intent_classifier_training/create_comprehensive_dataset_compliant.py
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