Hugging Face's logo

Datasets:
Mothersuperior
/
yue2-minted-corpus

Tasks:
Audio-to-Audio
Text-to-Audio
Tags:
music
yue2
self-distillation
semantic-tokens
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
request: struct<style: string, lyrics: string, cot: string, seed: int64, abc: null, cfg_scale: null, id: stri (... 3 chars omitted)
  child 0, style: string
  child 1, lyrics: string
  child 2, cot: string
  child 3, seed: int64
  child 4, abc: null
  child 5, cfg_scale: null
  child 6, id: string
timing: struct<seconds: double, prefill_seconds: double, ttft_seconds: double, output_tokens: int64, content (... 116 chars omitted)
  child 0, seconds: double
  child 1, prefill_seconds: double
  child 2, ttft_seconds: double
  child 3, output_tokens: int64
  child 4, content_tokens: int64
  child 5, output_tps: double
  child 6, prefix_tokens: int64
  child 7, cfg_branches: int64
  child 8, execution: string
  child 9, attention: string
truncated: bool
prefix: list<item: int64>
  child 0, item: int64
abc_ids: list<item: int64>
  child 0, item: int64
abc: string
seconds: double
family: string
seed: int64
genre: string
abc_chars: int64
bpm: int64
pid: string
audio_seconds: double
target_duration_s: int64
latent_shape: list<item: int64>
  child 0, item: int64
semantic_tokens: int64
caption: string
style: string
instrumental: bool
lyrics: string
to
{'pid': Value('string'), 'seed': Value('int64'), 'genre': Value('string'), 'family': Value('string'), 'bpm': Value('int64'), 'instrumental': Value('bool'), 'target_duration_s': Value('int64'), 'caption': Value('string'), 'style': Value('string'), 'lyrics': Value('string'), 'seconds': Value('float64'), 'audio_seconds': Value('float64'), 'semantic_tokens': Value('int64'), 'latent_shape': List(Value('int64')), 'abc_chars': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in 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 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              request: struct<style: string, lyrics: string, cot: string, seed: int64, abc: null, cfg_scale: null, id: stri (... 3 chars omitted)
                child 0, style: string
                child 1, lyrics: string
                child 2, cot: string
                child 3, seed: int64
                child 4, abc: null
                child 5, cfg_scale: null
                child 6, id: string
              timing: struct<seconds: double, prefill_seconds: double, ttft_seconds: double, output_tokens: int64, content (... 116 chars omitted)
                child 0, seconds: double
                child 1, prefill_seconds: double
                child 2, ttft_seconds: double
                child 3, output_tokens: int64
                child 4, content_tokens: int64
                child 5, output_tps: double
                child 6, prefix_tokens: int64
                child 7, cfg_branches: int64
                child 8, execution: string
                child 9, attention: string
              truncated: bool
              prefix: list<item: int64>
                child 0, item: int64
              abc_ids: list<item: int64>
                child 0, item: int64
              abc: string
              seconds: double
              family: string
              seed: int64
              genre: string
              abc_chars: int64
              bpm: int64
              pid: string
              audio_seconds: double
              target_duration_s: int64
              latent_shape: list<item: int64>
                child 0, item: int64
              semantic_tokens: int64
              caption: string
              style: string
              instrumental: bool
              lyrics: string
              to
              {'pid': Value('string'), 'seed': Value('int64'), 'genre': Value('string'), 'family': Value('string'), 'bpm': Value('int64'), 'instrumental': Value('bool'), 'target_duration_s': Value('int64'), 'caption': Value('string'), 'style': Value('string'), 'lyrics': Value('string'), 'seconds': Value('float64'), 'audio_seconds': Value('float64'), 'semantic_tokens': Value('int64'), 'latent_shape': List(Value('int64')), 'abc_chars': Value('int64')}
              because column names don't match

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.

YuE2 minted corpus

Songs generated by m-a-p/YuE2-3B (with YuE2-Vae) from MusicForge plan_50k prompts, queued genre-round-robin (355 genres, ~35% instrumental), seeds from the plan. Every track keeps the exact intermediate the model produced, so the set is a labelled corpus for training an audio → semantic-token encoder (the piece YuE2 does not ship) and a grammar regularizer for AR fine-tunes.

tracks/<pid>/

file content
audio.flac 48 kHz stereo 24-bit
semantic.npy int32, YuE2 semantic codes (0..32767), 25 per second — the labels
latent.npy float32 [T,64] acoustic latents, frame-aligned with semantic.npy
score.abc, abc_tokens.npy, prefix.npy, plan.json, plan_manifest.json the AR's score and prompt tokens
request.json / config.json / result.json style, lyrics, seed, cot; generation config; hashes + timing
item.json queue item (genre, family, bpm, instrumental flag, caption) + wall time

Regularization pack

regularizer/minted_regularizer_pack.pt — a torch list of {name, src, style, lyrics, codec} for the minted songs (true semantic tokens, no audio; ~60 MB). Mix it 50/50 with an artist's tokenised songs when LoRA-tuning YuE2's AR: it keeps the model's token grammar intact so a small artist set cannot collapse it. src == "minted_val" (5% by pid hash) is a held-out set whose next-token loss should stay flat during training.

import torch; pack = torch.load("regularizer/minted_regularizer_pack.pt", weights_only=False)

Generated with YuE2-3B whose weights are CC BY-NC 4.0; this corpus inherits that license. Growing: uploads are incremental.
Downloads last month
1,500
Total file size:
166 GB

Models trained or fine-tuned on Mothersuperior/yue2-minted-corpus