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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Mask must be a pyarrow.Array of type boolean
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1586, in _prepare_split_single
writer.write(example, key)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 553, in write
self.write_examples_on_file()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, 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 631, 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 646, in write_table
pa_table = embed_table_storage(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2248, in embed_table_storage
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2249, in <listcomp>
embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, 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 1795, 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 2124, in embed_array_storage
return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 289, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 3697, 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 1595, 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 658, in finalize
self.write_examples_on_file()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, 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 631, 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 646, in write_table
pa_table = embed_table_storage(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2248, in embed_table_storage
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2249, in <listcomp>
embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, 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 1795, 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 2124, in embed_array_storage
return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 289, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 3697, 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 1451, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1447, 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 1604, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
audio
audio |
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End of preview.
GAPS Dataset (Guitar-Aligned Performance Scores)
300 solo guitar performances with accurately aligned MIDI and musicxml scores.
Update: version 1.1
- audio now included
- fixed scorehash case sensitive clashes (thanks Daniel Wägner for pointing this out)
Abstract
We introduce GAPS (Guitar-Aligned Performance Scores), a new dataset of classical guitar performances, and a benchmark guitar transcription model that achieves state-of-the-art performance on GuitarSet in both supervised and zero-shot settings. GAPS is the largest dataset of real guitar audio, containing 14 hours of freely available audio-score aligned pairs, recorded in diverse conditions by over 200 performers, together with high-resolution note-level MIDI alignments and performance videos. These enable us to train a state-of-the-art model for automatic transcription of solo guitar recordings which can generalise well to real world audio that is unseen during training.
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