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SPIRE_EMA_CORPUS

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ValueError
Message:      Invalid string class label SPIRE_EMA_CORPUS@55f21628de95514e3ff22eaccc75e1547d181297
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1537, in _prepare_split_single
                  example = self.info.features.encode_example(record) if self.info.features is not None else record
                            ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label SPIRE_EMA_CORPUS@55f21628de95514e3ff22eaccc75e1547d181297
              
              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 1382, 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 1560, 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

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End of preview.

This corpus contains paired data of speech, articulatory movements and phonemes. There are 38 speakers in the corpus, each with 460 utterances.

The raw audio files are in audios.zip. The ema data and preprocessed data is stored in processed.zip. The processed data can be loaded with pytorch and has the following keys -

  • ema_raw : The raw ema data
  • ema_clipped : The ema data after trimming using being-end time stamps
  • ema_trimmed_and_normalised_with_6_articulators: The ema data after trimming using being-end time stamps, followed by articulatory specifc standardisation
  • mfcc: 13-dim MFCC computed on trimmed audio
  • phonemes: The phonemes uttered for the audio
  • durations: Duration values for each phoneme
  • begin_end: Begin end time stamps to trim the audio / raw ema
To use this data for tasks such as acoustic to articulatory inversion (AAI), you can use ema_trimmed_and_normalised_with_6_articulators and mfcc as the data.

If you have used this dataset in your work, use the following refrence to cite the dataset -

Bandekar, J., Udupa, S., Ghosh, P.K. (2024) Articulatory synthesis using representations learnt through phonetic label-aware contrastive loss. Proc. Interspeech 2024, 427-431, doi: 10.21437/Interspeech.2024-1756
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