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Datasets:
Fhrozen
/
CABankSakuraCHJP

Tasks:
Audio Classification
Automatic Speech Recognition
Sub-tasks:
speaker-identification
Languages:
Japanese
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
found
Tags:
speech-recognition
License:
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    DatasetGenerationError
Message:      An error occurred while generating the dataset
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1766, in _prepare_split_single
                  writer.write(example, key)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 500, in write
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 458, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 572, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 587, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2280, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2154, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 276, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3205, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3645, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1775, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 599, in finalize
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 458, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 572, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 587, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2280, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2154, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 276, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3205, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3645, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              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 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1789, in _download_and_prepare
                  super()._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1627, 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 1784, 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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audio
audio
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CABank Japanese CallHome Corpus

Citation information

Some citation here. In accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.

Project Description

This is the Japanese portion of CallHome.

Speakers were solicited by the LDC to participate in this telephone speech collection effort via the internet, publications (advertisements), and personal contacts. A total of 200 call originators were found, each of whom placed a telephone call via a toll-free robot operator maintained by the LDC. Access to the robot operator was possible via a unique Personal Identification Number (PIN) issued by the recruiting staff at the LDC when the caller enrolled in the project. The participants were made aware that their telephone call would be recorded, as were the call recipients. The call was allowed only if both parties agreed to being recorded. Each caller was allowed to talk up to 30 minutes. Upon successful completion of the call, the caller was paid $20 (in addition to making a free long-distance telephone call). Each caller was allowed to place only one telephone call.

Although the goal of the call collection effort was to have unique speakers in all calls, a handful of repeat speakers are included in the corpus. In all, 200 calls were transcribed. Of these, 80 have been designated as training calls, 20 as development test calls, and 100 as evaluation test calls. For each of the training and development test calls, a contiguous 10-minute region was selected for transcription; for the evaluation test calls, a 5-minute region was transcribed. For the present publication, only 20 of the evaluation test calls are being released; the remaining 80 test calls are being held in reserve for future LVCSR benchmark tests.

After a successful call was completed, a human audit of each telephone call was conducted to verify that the proper language was spoken, to check the quality of the recording, and to select and describe the region to be transcribed. The description of the transcribed region provides information about channel quality, number of speakers, their gender, and other attributes.

Acknowledgements

Andrew Yankes reformatted this corpus into accord with current versions of CHAT.

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