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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 1405, 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 982, 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 dataset

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.

mp3
audio
__key__
string
__url__
string
accc_acting_challenge_1_seed00_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed00_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed00_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed01_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed01_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed01_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed02_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed02_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed02_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed03_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed03_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed03_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed04_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed04_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed04_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed05_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed05_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed05_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed06_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed06_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed06_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed07_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed07_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed07_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed08_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed08_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed08_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed09_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed09_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed09_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed10_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed10_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed10_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed11_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed11_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed11_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed12_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed12_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed12_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed13_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed13_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed13_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed14_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed14_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed14_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed15_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed15_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed15_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed16_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed16_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed16_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed17_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed17_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed17_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed18_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed18_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed18_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed19_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed19_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed19_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed20_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed20_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed20_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed21_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed21_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed21_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed22_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed22_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed22_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed23_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed23_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed23_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed24_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed24_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_1_seed24_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed00_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed00_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed00_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed01_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed01_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed01_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed02_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed02_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed02_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed03_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed03_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed03_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed04_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed04_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed04_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed05_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed05_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed05_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed06_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed06_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed06_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed07_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed07_part1
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed07_part2
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
accc_acting_challenge_de_1_seed08_full
hf://datasets/laion/voice-acting-data-annotated@ba40ca8a10e4b014e487bedf93cb755045079bb3/data/batch_000000.tar
End of preview.

Voice Acting Data - Annotated

Post-processed version of laion/voice-acting-data.

Processing Pipeline

  1. RE-USE Speech Enhancement (nvidia/RE-USE) - Applied to non-singing samples for noise reduction
  2. LavaSR Super Resolution (YatharthS/LavaSR) - Audio bandwidth extension to 48kHz
  3. Whisper Turbo ASR - Full transcript with word-level timestamps
  4. Scene Split - Audio split at CUT TO: transition into two parts (Part 1 + Part 2)
  5. VoiceCLAP Large Embeddings (laion/voiceclap-large) - 3584-dim voice embeddings per part

Purpose

Each sample contains two emotional scenes from the same speaker separated by "CUT TO:". By splitting into parts, we create paired clips of the same voice identity with different emotions, enabling training of voice acting systems that maintain speaker consistency across emotional transitions.

Format

  • MP3 files: 256kbps mono 48kHz
  • Annotation JSON per sample with ASR, timestamps, embeddings, and metadata
  • Packed in tar files matching the source dataset structure

Files per sample

  • {prompt_id}_seed{NN}_part1.mp3 - Scene 1 audio
  • {prompt_id}_seed{NN}_part2.mp3 - Scene 2 audio
  • {prompt_id}_seed{NN}.json - Full annotations

Models Used

License

CC BY 4.0.


The audio in this dataset was generated with a voice-acting AI model.

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