melee-ranked-replays

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              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/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Melee Ranked Replays

Anonymized Slippi ranked replays (platinum+) from Super Smash Bros. Melee, sharded by character and rank pair. Built for behavior-cloning and other replay-driven ML work on Melee — notably MIMIC.

Raw .slp files grouped into tarballs by (character, rank_pair, source_archive), organized into per-character folders:

{CHAR}/
  {CHAR}_{rank_pair}_a{N}.tar.gz
metadata/
  metadata_a{N}.json

Characters (25): BOWSER, CPTFALCON, DK, DOC, FALCO, FOX, GAMEANDWATCH, GANONDORF, ICE_CLIMBERS, JIGGLYPUFF, KIRBY, LINK, LUIGI, MARIO, MARTH, MEWTWO, NESS, PEACH, PICHU, PIKACHU, ROY, SAMUS, YLINK, YOSHI, ZELDA_SHEIK (ZELDA and SHEIK collapsed; POPO and NANA collapsed to ICE_CLIMBERS)
Rank pairs: diamond-diamond, diamond-platinum, master-diamond, master-master, master-platinum, platinum-platinum (6 combos, higher rank first in mixed pairs)
Source archives: a1..a6, corresponding to the 6 original anonymized ranked dumps. Archive suffix exists so incremental uploads don't collide; if you want "everything Fox at master-master" you pull every FOX/FOX_master-master_a*.tar.gz.

Each shard holds the raw .slp files — no preprocessing, normalization, or tensorization applied. Use peppi-py, py-slippi, or libmelee to parse.

Duplication

Each replay is placed into both players' character buckets (unless it's a ditto). A MARTH vs FALCO diamond-platinum replay appears in both MARTH/MARTH_diamond-platinum_aN.tar.gz and FALCO/FALCO_diamond-platinum_aN.tar.gz. A FOX ditto only appears once in FOX/FOX_diamond-diamond_aN.tar.gz. This means downloading "all Marth games at master-master" needs only the MARTH/ folder (not a join across 25 per-player files), at the cost of ~90% duplication on the full dataset.

Metadata

metadata/metadata_a{N}.json is a flat JSON list. One entry per replay (not per bucket), schema:

{
  "filename": "diamond-diamond-6cf8c1ee745993cefe0c88db.slp",
  "p1": "NESS",
  "p2": "JIGGLYPUFF",
  "rank": "diamond-diamond",
  "archive": "3"
}

p1 and p2 use the same collapsed character names as the folder/bucket filenames. archive is a string.

Build pipeline

Source: six anonymized ranked archives covering ~850k total replays at platinum+ rank. Each archive is processed independently by tools/shard_and_upload_ranked.py in the MIMIC repo.

Per-file work (parallel, one worker per CPU)

For each .slp file in an archive:

Read header only via peppi_py.read_slippi(path, skip_frames=True) — skipping frames makes it fast (ms per file) since we only need the Start event, not the ~10k frames of gameplay.
Pull the 2 players out of game.start.players, reject if not exactly 2.
Map each player's character int to a name via a lookup built from melee.Character enum, with two collapses:
- ZELDA (19) and SHEIK (7) → ZELDA_SHEIK (same fighter mid-match)
- POPO (10) and NANA (11) → ICE_CLIMBERS (two climbers are one unit)
Reject junk characters: WIREFRAME_MALE/FEMALE, GIGA_BOWSER, SANDBAG, UNKNOWN — not legal tournament characters; replays featuring them are debug/test files.
Parse rank from filename via regex — the {rank1}-{rank2} prefix.

Per-file output: (filename, p1_name, p2_name, rank_pair, error_or_None).

Bucketing

Each successful replay enters up to two buckets keyed by (character, rank_pair):

One for player 1's character
One for player 2's character (skipped if same char — no double-counting dittos)

Metadata is a flat list of {filename, p1, p2, rank, archive} entries, one row per replay.

Tar + upload

Buckets are compressed one at a time (tarfile w:gz, compresslevel=6), uploaded via huggingface_hub.HfApi.upload_file, then the local tar is deleted. Already-uploaded paths are skipped, so the tool is resume-safe.

Intended use

Training behavior-cloning models on human gameplay (MIMIC, HAL, and similar projects), replay analysis, frame-data research. If you're building something on this, drop me a line.

License

MIT. Replays were originally published by the Slippi/ranked community in anonymized form; this is a re-sharded redistribution for ML convenience.

Downloads last month: -

Total file size:

1.43 TB