Datasets:

LapisLa2uli
/

ALIGN_Dataset

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x93 in position 14: invalid start byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from 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 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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ALIGN Dataset

Synthetic Bb clarinet practice bundles for ALIGN / MusicEval: a clean MusicXML score paired with a SoundFont performance that contains planted faults. Gold labels are score-part melodies (schema 1.2), not only wall-clock intervals.

The clean score stays correct. Only the synthesized performance is wrong.

Splits

Samples are packed into zip shards (250 clips each) so the Hub stays within file-count limits.

Folder	Samples	Source scores
`output/part_XXXX.zip`	12,000	Procedural original clarinet scores
`output_2k_rawdata/part_XXXX.zip`	2,104	Random measure windows from uploaded `RawData/Score` MusicXML

manifest.json lists which sample ids sit in each shard.

After unzipping a shard you get one directory per clip, for example synth_gen_0042/:

File	Role
`verified_score.musicxml`	Clean notation (ground truth)
`performance_score.musicxml`	Errored (and possibly repeated) render source
`reference_audio.wav`	Clarinet render of the clean score
`performance_audio.wav`	Clarinet render with planted errors
`reference_audio.mid` / `performance_audio.mid`	MIDI sent to the SoundFont
`labels.json`	Synthetic gold (`source: synthetic`, schema 1.2)
`candidates.json`	Auto alignment candidates
`alignment.npz`	DTW alignment artifacts
`performance_mel.npy` / `reference_mel.npy`	Log-mel features (22.05 kHz, 128 mels)
`metadata.json`	Render settings, error types, seed

Audio is sounding pitch (sounding_transpose: -2): written C sounds Bb. MusicXML and labels.json pitches stay written.

Planted errors

A clip draws 1–8 non-overlapping content errors from five equally weighted types, then often repeats the affected measure(s):

Type	What changes in the performance
`wrong_note`	±1 or ±2 semitones, or a squeak (MIDI C6–A7)
`missed_note`	Written note replaced by a rest
`extra_note`	Split a note; insert a neighbor or squeak
`intonation_error`	Pitch bend 40–80 cents on 1–4 notes
`rhythm_error`	Late/early start or end, tempo change, or uneven durations

Repetition is applied after content errors (usual case) or as a standalone replay of a sounding measure. A silent 0.2–1.0 s gap is inserted before the copy. Repeated-pass copies of content labels are not gold.

Melody gold is a contiguous run of clean-score notes (score_part + pitches) with 1–2 notes of pad on each side.

Loading

from huggingface_hub import hf_hub_download
from zipfile import ZipFile
from pathlib import Path
import json

zip_path = hf_hub_download(
    repo_id="LapisLa2uli/ALIGN_Dataset",
    filename="output/part_0000.zip",
    repo_type="dataset",
)
out = Path("align_samples")
with ZipFile(zip_path) as zf:
    zf.extractall(out)
sample = next(p for p in out.iterdir() if p.is_dir())
labels = json.loads((sample / "labels.json").read_text(encoding="utf-8"))
print(sample.name, len(labels["labels"]))

Notes

Render: tinysoundfont + FreePats clarinet SoundFont; MIDI via music21.
Official synth eval in ALIGN is contiguous pitch-list containment F1, not timestamp IoU.
pipeline.log files are omitted from the upload (they contain local machine paths).

Downloads last month: 85

Total file size:

104 GB