Datasets:

aistocrat
/

c5-vctk-volume-scale

License:
Dataset Viewer
Split (1)
train
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
accent: string
acoustic_frac: double
age: int64
audio_path: string
content_match_unit: int64
crop_end_sample_24k: int64
crop_index: int64
crop_start_sample_24k: int64
dataset_license: string
gender: string
id: string
is_shared_prompt: bool
mic: string
mimi_codes_path: string
mimi_frames: int64
n_frames: int64
orig_duration_s: double
prompt_text_policy: string
region: string
source_path: string
speaker: string
speaker_gender: string
speaker_id: string
speaker_supply_postfilter: int64
splits: struct<dev: list<item: string>, heldout_replay: bool, heldout_unseen_speaker: bool, train: list<item (... 10 chars omitted)
  child 0, dev: list<item: string>
      child 0, item: string
  child 1, heldout_replay: bool
  child 2, heldout_unseen_speaker: bool
  child 3, train: list<item: string>
      child 0, item: string
target_sr: int64
text_id: string
window_id: string
label: string
model: string
hparams: struct<seed: int64, volumes: list<item: int64>, fixed_train_speakers: int64, schedule: struct<min_ep (... 1414 chars omitted)
  child 0, seed: int64
  child 1, volumes: list<item: int64>
      child 0, item: int64
  child 2, fixed_train_speakers: int64
  child 3, schedule: struct<min_epochs_before_patience: double, max_epochs: double, absolute_max_updates_per_size: int64, (... 1211 chars omitted)
      child 0, min_epochs_before_patience: double
      child 1, max_epochs: double
      child 2, absolute_max_updates_per_size: int64
      child 3, patience_evals: int64
      child 4, eval
...
eval_every_divisor: int64
              child 7, min_updates: int64
              child 8, max_updates: int64
              child 9, eval_interval: int64
              child 10, max_by_epochs: int64
              child 11, cap_is_absolute_update_ceiling: bool
              child 12, max_sample_exposures: int64
              child 13, max_coverage_epochs: double
          child 2, 8000: struct<train_size: int64, grad_accum: int64, min_epochs_before_patience: double, max_epochs: double, (... 268 chars omitted)
              child 0, train_size: int64
              child 1, grad_accum: int64
              child 2, min_epochs_before_patience: double
              child 3, max_epochs: double
              child 4, absolute_max_updates_per_size: int64
              child 5, patience_evals: int64
              child 6, eval_every_divisor: int64
              child 7, min_updates: int64
              child 8, max_updates: int64
              child 9, eval_interval: int64
              child 10, max_by_epochs: int64
              child 11, cap_is_absolute_update_ceiling: bool
              child 12, max_sample_exposures: int64
              child 13, max_coverage_epochs: double
      child 3, grad_accum: int64
  child 10, hf_hub_offline: string
  child 11, transformers_offline: string
  child 12, local_files_only: bool
  child 13, no_generation: bool
  child 14, no_sampling: bool
  child 15, no_tts: bool
  child 16, no_speaker_identification: bool
  child 17, no_gemma_checkpoint: bool
to
{'label': Value('string'), 'status': Value('string'), 'timestamp': Value('timestamp[s]'), 'commit': Value('string'), 'script_path': Value('string'), 'script_sha256': Value('string'), 'model': Value('string'), 'plan_dir': Value('string'), 'vctk_root': Value('string'), 'guardrails': {'transcript_free_enforced': Value('bool'), 'prompt_sha256_required': Value('string'), 'fixed_optimizer_hparams_all_points': Value('bool'), 'epoch_normalized_convergence_schedule': Value('bool'), 'one_variable_sweep': Value('string'), 'frozen_eval_assertion_required': Value('bool'), 'reporting_per_point': Value('string'), 'integrity_each_point': Value('string'), 'always_measure_each_point': Value('bool'), 'cost_bounded': {'points': List(Value('int64')), 'fixed_train_speakers': Value('int64'), 'schedule_per_volume': {'2000': {'train_size': Value('int64'), 'grad_accum': Value('int64'), 'min_epochs_before_patience': Value('float64'), 'max_epochs': Value('float64'), 'absolute_max_updates_per_size': Value('int64'), 'patience_evals': Value('int64'), 'eval_every_divisor': Value('int64'), 'min_updates': Value('int64'), 'max_updates': Value('int64'), 'eval_interval': Value('int64'), 'max_by_epochs': Value('int64'), 'cap_is_absolute_update_ceiling': Value('bool'), 'max_sample_exposures': Value('int64'), 'max_coverage_epochs': Value('float64')}, '4000': {'train_size': Value('int64'), 'grad_accum': Value('int64'), 'min_epochs_before_patience': Value('float64'), 'max_epochs': Value('float64'), 'absolute_max_upda
...
'cb0_exact': Value('bool'), 'diff_entries': Value('int64'), 'total_entries': Value('int64'), 'pass': Value('bool'), 'kind': Value('string'), 'id': Value('string'), 'audio_path': Value('string'), 'cpu_stored_bit_for_bit': Value('bool'), 'speaker': Value('string'), 'gender': Value('string'), 'accent': Value('string')}), 'gate_pass': Value('bool'), 'note': Value('string')}}, 'timestamp_completed': Value('timestamp[s]'), 'summary': {'status': Value('string'), 'mimi_device': Value('string'), 'codec_backend_check': {'target_device': Value('string'), 'cpu_helper_matches_public_encode_file_bit_for_bit': Value('bool'), 'scale50_cpu_reference_matches_stored_bit_for_bit': Value('bool'), 'agreement_min_overall': Value('float64'), 'requires_cb0_exact': Value('bool'), 'checks': List({'shape_match': Value('bool'), 'shape': List(Value('int64')), 'overall': Value('float64'), 'per_codebook': {'cb0': Value('float64'), 'cb1': Value('float64'), 'cb2': Value('float64'), 'cb3': Value('float64'), 'cb4': Value('float64'), 'cb5': Value('float64'), 'cb6': Value('float64'), 'cb7': Value('float64')}, 'cb0_exact': Value('bool'), 'diff_entries': Value('int64'), 'total_entries': Value('int64'), 'pass': Value('bool'), 'kind': Value('string'), 'id': Value('string'), 'audio_path': Value('string'), 'cpu_stored_bit_for_bit': Value('bool'), 'speaker': Value('string'), 'gender': Value('string'), 'accent': Value('string')}), 'gate_pass': Value('bool'), 'note': Value('string')}, 'total_runtime_s': Value('float64')}}
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
              accent: string
              acoustic_frac: double
              age: int64
              audio_path: string
              content_match_unit: int64
              crop_end_sample_24k: int64
              crop_index: int64
              crop_start_sample_24k: int64
              dataset_license: string
              gender: string
              id: string
              is_shared_prompt: bool
              mic: string
              mimi_codes_path: string
              mimi_frames: int64
              n_frames: int64
              orig_duration_s: double
              prompt_text_policy: string
              region: string
              source_path: string
              speaker: string
              speaker_gender: string
              speaker_id: string
              speaker_supply_postfilter: int64
              splits: struct<dev: list<item: string>, heldout_replay: bool, heldout_unseen_speaker: bool, train: list<item (... 10 chars omitted)
                child 0, dev: list<item: string>
                    child 0, item: string
                child 1, heldout_replay: bool
                child 2, heldout_unseen_speaker: bool
                child 3, train: list<item: string>
                    child 0, item: string
              target_sr: int64
              text_id: string
              window_id: string
              label: string
              model: string
              hparams: struct<seed: int64, volumes: list<item: int64>, fixed_train_speakers: int64, schedule: struct<min_ep (... 1414 chars omitted)
                child 0, seed: int64
                child 1, volumes: list<item: int64>
                    child 0, item: int64
                child 2, fixed_train_speakers: int64
                child 3, schedule: struct<min_epochs_before_patience: double, max_epochs: double, absolute_max_updates_per_size: int64, (... 1211 chars omitted)
                    child 0, min_epochs_before_patience: double
                    child 1, max_epochs: double
                    child 2, absolute_max_updates_per_size: int64
                    child 3, patience_evals: int64
                    child 4, eval
              ...
              eval_every_divisor: int64
                            child 7, min_updates: int64
                            child 8, max_updates: int64
                            child 9, eval_interval: int64
                            child 10, max_by_epochs: int64
                            child 11, cap_is_absolute_update_ceiling: bool
                            child 12, max_sample_exposures: int64
                            child 13, max_coverage_epochs: double
                        child 2, 8000: struct<train_size: int64, grad_accum: int64, min_epochs_before_patience: double, max_epochs: double, (... 268 chars omitted)
                            child 0, train_size: int64
                            child 1, grad_accum: int64
                            child 2, min_epochs_before_patience: double
                            child 3, max_epochs: double
                            child 4, absolute_max_updates_per_size: int64
                            child 5, patience_evals: int64
                            child 6, eval_every_divisor: int64
                            child 7, min_updates: int64
                            child 8, max_updates: int64
                            child 9, eval_interval: int64
                            child 10, max_by_epochs: int64
                            child 11, cap_is_absolute_update_ceiling: bool
                            child 12, max_sample_exposures: int64
                            child 13, max_coverage_epochs: double
                    child 3, grad_accum: int64
                child 10, hf_hub_offline: string
                child 11, transformers_offline: string
                child 12, local_files_only: bool
                child 13, no_generation: bool
                child 14, no_sampling: bool
                child 15, no_tts: bool
                child 16, no_speaker_identification: bool
                child 17, no_gemma_checkpoint: bool
              to
              {'label': Value('string'), 'status': Value('string'), 'timestamp': Value('timestamp[s]'), 'commit': Value('string'), 'script_path': Value('string'), 'script_sha256': Value('string'), 'model': Value('string'), 'plan_dir': Value('string'), 'vctk_root': Value('string'), 'guardrails': {'transcript_free_enforced': Value('bool'), 'prompt_sha256_required': Value('string'), 'fixed_optimizer_hparams_all_points': Value('bool'), 'epoch_normalized_convergence_schedule': Value('bool'), 'one_variable_sweep': Value('string'), 'frozen_eval_assertion_required': Value('bool'), 'reporting_per_point': Value('string'), 'integrity_each_point': Value('string'), 'always_measure_each_point': Value('bool'), 'cost_bounded': {'points': List(Value('int64')), 'fixed_train_speakers': Value('int64'), 'schedule_per_volume': {'2000': {'train_size': Value('int64'), 'grad_accum': Value('int64'), 'min_epochs_before_patience': Value('float64'), 'max_epochs': Value('float64'), 'absolute_max_updates_per_size': Value('int64'), 'patience_evals': Value('int64'), 'eval_every_divisor': Value('int64'), 'min_updates': Value('int64'), 'max_updates': Value('int64'), 'eval_interval': Value('int64'), 'max_by_epochs': Value('int64'), 'cap_is_absolute_update_ceiling': Value('bool'), 'max_sample_exposures': Value('int64'), 'max_coverage_epochs': Value('float64')}, '4000': {'train_size': Value('int64'), 'grad_accum': Value('int64'), 'min_epochs_before_patience': Value('float64'), 'max_epochs': Value('float64'), 'absolute_max_upda
              ...
              'cb0_exact': Value('bool'), 'diff_entries': Value('int64'), 'total_entries': Value('int64'), 'pass': Value('bool'), 'kind': Value('string'), 'id': Value('string'), 'audio_path': Value('string'), 'cpu_stored_bit_for_bit': Value('bool'), 'speaker': Value('string'), 'gender': Value('string'), 'accent': Value('string')}), 'gate_pass': Value('bool'), 'note': Value('string')}}, 'timestamp_completed': Value('timestamp[s]'), 'summary': {'status': Value('string'), 'mimi_device': Value('string'), 'codec_backend_check': {'target_device': Value('string'), 'cpu_helper_matches_public_encode_file_bit_for_bit': Value('bool'), 'scale50_cpu_reference_matches_stored_bit_for_bit': Value('bool'), 'agreement_min_overall': Value('float64'), 'requires_cb0_exact': Value('bool'), 'checks': List({'shape_match': Value('bool'), 'shape': List(Value('int64')), 'overall': Value('float64'), 'per_codebook': {'cb0': Value('float64'), 'cb1': Value('float64'), 'cb2': Value('float64'), 'cb3': Value('float64'), 'cb4': Value('float64'), 'cb5': Value('float64'), 'cb6': Value('float64'), 'cb7': Value('float64')}, 'cb0_exact': Value('bool'), 'diff_entries': Value('int64'), 'total_entries': Value('int64'), 'pass': Value('bool'), 'kind': Value('string'), 'id': Value('string'), 'audio_path': Value('string'), 'cpu_stored_bit_for_bit': Value('bool'), 'speaker': Value('string'), 'gender': Value('string'), 'accent': Value('string')}), 'gate_pass': Value('bool'), 'note': Value('string')}, 'total_runtime_s': Value('float64')}}
              because column names don't match
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C5 VCTK Fixed-64 Volume Scale

Seam-3 C5 VCTK fixed-64 volume-scaling Stage-B results.
Sweep points

v2000: 2000 windows, 64 speakers, 3996 updates
v4000: 4000 windows, 64 speakers, 7326 updates
v8000: 8000 windows, 64 speakers, 8000 updates (cap-bound)
Commit

repo: khakimov/gemmatalks
commit: 6bfbb1c
runner SHA: a220737e4855649d40e77bd36deba76e301c2d1a9b1a7492b34ccda5c2f7e4d6
Artifacts

full_cpu/report.json — full binding report
full_cpu/checkpoints/ — AF+DT-only checkpoints per volume
codec_check_cpu/ — CPU Mimi backend verification
Downloads last month: 2,313
Total file size:
856 MB