From cbd46f4b7a7d72b6ebe073653760a9ac4e662a29 Mon Sep 17 00:00:00 2001 From: Cairn Agent Date: Mon, 20 Jul 2026 18:23:04 -0700 Subject: [PATCH 01/77] [Build] recover and modernize training foundation --- docs/training.md | 7 ++ src/deeprhythm/train/__init__.py | 15 +++ src/deeprhythm/train/cache.py | 152 +++++++++++++++++++++++++++++++ src/deeprhythm/train/sampling.py | 34 +++++++ src/deeprhythm/train/trainer.py | 93 +++++++++++++++++++ 5 files changed, 301 insertions(+) create mode 100644 docs/training.md create mode 100644 src/deeprhythm/train/__init__.py create mode 100644 src/deeprhythm/train/cache.py create mode 100644 src/deeprhythm/train/sampling.py create mode 100644 src/deeprhythm/train/trainer.py diff --git a/docs/training.md b/docs/training.md new file mode 100644 index 0000000..753288a --- /dev/null +++ b/docs/training.md @@ -0,0 +1,7 @@ +# Training foundation + +Training is manifest-driven and lives in `deeprhythm.train`. Each manifest row must contain a positive `tempo`, an `audio_path` (or `filename`), and should contain `split` plus an audio checksum. `build_hcqm_cache` precomputes the existing eight-second HCQM representation into individual NumPy arrays. The cache key incorporates the source path, checksum, tempo, split, and cache format version, so corrected annotations and changed audio cannot silently reuse stale features. Arrays are loaded with memory mapping by `ClipDataset`. + +`fit` trains only the declared `train` split and selects checkpoints only with `val`. It intentionally has no test loader or test-evaluation option. Final test-fold evaluation belongs to the benchmark workflow after the experiment configuration is frozen. Validation Acc1 and Acc2 call `deeprhythm.bench.tempo.tempo_accuracy`; this replaces the historical training script's independent and incorrect Acc2 calculation. + +The default sampler weights occupied 10 BPM bins inversely by clip count. Audio-domain time-stretch augmentation is exposed as `stretch_audio_and_tempo`; callers must apply it before HCQM extraction and cache the resulting feature with its relabeled tempo. Augmentations outside the model's 30–285 BPM range are rejected. \ No newline at end of file diff --git a/src/deeprhythm/train/__init__.py b/src/deeprhythm/train/__init__.py new file mode 100644 index 0000000..067e5d3 --- /dev/null +++ b/src/deeprhythm/train/__init__.py @@ -0,0 +1,15 @@ +"""Manifest-driven data preparation and fine-tuning utilities.""" + +from deeprhythm.train.cache import ClipDataset, HcqmCache, build_hcqm_cache +from deeprhythm.train.sampling import TempoBalancedSampler, stretch_audio_and_tempo +from deeprhythm.train.trainer import TrainingConfig, fit + +__all__ = [ + "ClipDataset", + "HcqmCache", + "TempoBalancedSampler", + "TrainingConfig", + "build_hcqm_cache", + "fit", + "stretch_audio_and_tempo", +] \ No newline at end of file diff --git a/src/deeprhythm/train/cache.py b/src/deeprhythm/train/cache.py new file mode 100644 index 0000000..edebb02 --- /dev/null +++ b/src/deeprhythm/train/cache.py @@ -0,0 +1,152 @@ +"""Disk-backed HCQM cache keyed by immutable manifest content.""" + +import hashlib +import json +import os +from pathlib import Path +from typing import Callable, Iterable, Mapping, Optional, Sequence + +import numpy as np +import torch +from torch.utils.data import Dataset + +from deeprhythm.audio_proc.hcqm import compute_hcqm, make_kernels +from deeprhythm.utils import bpm_to_class, load_and_split_audio + +CACHE_VERSION = 1 +MANIFEST_ID_FIELDS = ("audio_path", "filename", "md5", "checksum", "tempo", "split") + + +def _canonical_row(row: Mapping) -> dict: + value = {key: row[key] for key in MANIFEST_ID_FIELDS if key in row} + if "audio_path" not in value and "filename" not in value: + raise ValueError("manifest row requires audio_path or filename") + if "tempo" not in value or float(value["tempo"]) <= 0: + raise ValueError("manifest row requires a positive tempo") + return value + + +def manifest_key(row: Mapping, *, cache_version: int = CACHE_VERSION) -> str: + """Return a stable cache key; changing relevant manifest data invalidates it.""" + payload = {"cache_version": cache_version, "row": _canonical_row(row)} + encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode() + return hashlib.sha256(encoded).hexdigest() + + +def manifest_hash(rows: Iterable[Mapping]) -> str: + digest = hashlib.sha256() + for row in rows: + digest.update(manifest_key(row).encode()) + digest.update(b"\n") + return digest.hexdigest() + + +class HcqmCache: + """A directory of memory-mappable NumPy arrays plus a canonical JSON index.""" + + def __init__(self, root): + self.root = Path(root) + self.arrays = self.root / "arrays" + self.index_path = self.root / "index.json" + + def path_for(self, key: str) -> Path: + return self.arrays / f"{key}.npy" + + def load_index(self) -> dict: + if not self.index_path.exists(): + return {"version": CACHE_VERSION, "entries": {}} + index = json.loads(self.index_path.read_text()) + if index.get("version") != CACHE_VERSION: + raise ValueError("unsupported HCQM cache version") + return index + + def store(self, key: str, clips: np.ndarray) -> None: + self.arrays.mkdir(parents=True, exist_ok=True) + destination = self.path_for(key) + temporary = destination.with_suffix(f".{os.getpid()}.tmp") + with temporary.open("wb") as handle: + np.save(handle, np.asarray(clips, dtype=np.float32), allow_pickle=False) + os.replace(temporary, destination) + + def write_index(self, index: dict) -> None: + self.root.mkdir(parents=True, exist_ok=True) + temporary = self.index_path.with_suffix(f".{os.getpid()}.tmp") + temporary.write_text(json.dumps(index, indent=2, sort_keys=True) + "\n") + os.replace(temporary, self.index_path) + + +def _default_extract(path: str, specs) -> np.ndarray: + audio = load_and_split_audio(path) + with torch.no_grad(): + hcqm = compute_hcqm(audio, *specs).permute(0, 3, 1, 2) + return hcqm.cpu().numpy() + + +def build_hcqm_cache( + rows: Sequence[Mapping], + cache_dir, + *, + device: Optional[str] = None, + extractor: Optional[Callable[[str], np.ndarray]] = None, +) -> dict: + """Materialize missing HCQMs and atomically update the manifest index. + + ``extractor`` is an injection point for tests and custom preprocessing. Its + result must have shape ``(clips, 6, 240, 8)``. + """ + cache = HcqmCache(cache_dir) + index = cache.load_index() + specs = None if extractor else make_kernels(device=device) + for source_row in rows: + row = _canonical_row(source_row) + key = manifest_key(row) + path = str(Path(row.get("audio_path", row.get("filename"))).expanduser()) + destination = cache.path_for(key) + if not destination.exists(): + clips = extractor(path) if extractor else _default_extract(path, specs) + clips = np.asarray(clips) + if clips.ndim != 4 or tuple(clips.shape[1:]) != (6, 240, 8): + raise ValueError("HCQM extractor must return (clips, 6, 240, 8)") + if len(clips) == 0: + raise ValueError(f"no clips extracted from {path}") + cache.store(key, clips) + index["entries"][key] = { + "path": str(destination.relative_to(cache.root)), + "tempo": float(row["tempo"]), + "split": str(row.get("split", "train")), + "source": path, + "clips": int(np.load(destination, mmap_mode="r").shape[0]), + } + index["manifest_hash"] = manifest_hash(rows) + cache.write_index(index) + return index + + +class ClipDataset(Dataset): + """Lazy, memory-mapped HCQM clips from one declared manifest split.""" + + def __init__(self, cache_dir, split="train", *, trim_edges=True, return_tempo=False): + self.cache = HcqmCache(cache_dir) + self.return_tempo = return_tempo + self.entries = [] + for key, entry in self.cache.load_index()["entries"].items(): + if entry["split"] != split: + continue + count = int(entry["clips"]) + indices = range(1, count - 1) if trim_edges and count > 5 else range(count) + self.entries.extend((key, clip, float(entry["tempo"])) for clip in indices) + + @property + def tempos(self): + return [entry[2] for entry in self.entries] + + def __len__(self): + return len(self.entries) + + def __getitem__(self, index): + key, clip_index, tempo = self.entries[index] + clips = np.load(self.cache.path_for(key), mmap_mode="r") + clip = torch.from_numpy(np.array(clips[clip_index], copy=True)).float() + if self.return_tempo: + return clip, torch.tensor(tempo, dtype=torch.float32) + return clip, torch.tensor(bpm_to_class(tempo), dtype=torch.long) \ No newline at end of file diff --git a/src/deeprhythm/train/sampling.py b/src/deeprhythm/train/sampling.py new file mode 100644 index 0000000..0a93699 --- /dev/null +++ b/src/deeprhythm/train/sampling.py @@ -0,0 +1,34 @@ +"""Tempo balancing and audio-domain tempo augmentation.""" + +from collections import Counter + +import librosa +import numpy as np +import torch +from torch.utils.data import WeightedRandomSampler + + +class TempoBalancedSampler(WeightedRandomSampler): + """Sample clips inversely to their occupied tempo-bin frequency.""" + + def __init__(self, tempos, *, bin_width=10.0, num_samples=None, replacement=True, generator=None): + tempos = np.asarray(tempos, dtype=float) + if tempos.ndim != 1 or tempos.size == 0 or np.any(tempos <= 0): + raise ValueError("tempos must be a non-empty sequence of positive values") + if bin_width <= 0: + raise ValueError("bin_width must be positive") + bins = np.floor(tempos / bin_width).astype(int) + counts = Counter(bins.tolist()) + weights = torch.tensor([1.0 / counts[value] for value in bins], dtype=torch.double) + super().__init__(weights, num_samples or len(tempos), replacement, generator=generator) + + +def stretch_audio_and_tempo(audio, tempo, rate, *, min_bpm=30.0, max_bpm=285.0): + """Time-stretch audio and relabel its tempo, rejecting out-of-range labels.""" + if tempo <= 0 or rate <= 0: + raise ValueError("tempo and rate must be positive") + relabeled = float(tempo) * float(rate) + if not min_bpm <= relabeled <= max_bpm: + raise ValueError("augmented tempo is outside the model range") + stretched = librosa.effects.time_stretch(np.asarray(audio, dtype=np.float32), rate=float(rate)) + return stretched, relabeled \ No newline at end of file diff --git a/src/deeprhythm/train/trainer.py b/src/deeprhythm/train/trainer.py new file mode 100644 index 0000000..968184d --- /dev/null +++ b/src/deeprhythm/train/trainer.py @@ -0,0 +1,93 @@ +"""Fine-tuning loop deliberately limited to train and validation folds.""" + +from dataclasses import asdict, dataclass +from pathlib import Path + +import torch +from torch import nn +from torch.optim import Adam +from torch.optim.lr_scheduler import ReduceLROnPlateau +from torch.utils.data import DataLoader + +from deeprhythm.bench.tempo import tempo_accuracy +from deeprhythm.model.frame_cnn import DeepRhythmModel +from deeprhythm.train.cache import ClipDataset, HcqmCache +from deeprhythm.train.sampling import TempoBalancedSampler +from deeprhythm.utils import class_to_bpm + + +@dataclass(frozen=True) +class TrainingConfig: + epochs: int = 40 + batch_size: int = 256 + learning_rate: float = 1e-4 + early_stopping_patience: int = 5 + balance_bin_width: float = 10.0 + tolerance: float = 0.04 + + +def evaluate_validation(model, loader, criterion, device, tolerance): + model.eval() + losses, predictions, references = [], [], [] + with torch.no_grad(): + for inputs, tempos in loader: + inputs, tempos = inputs.to(device), tempos.to(device) + labels = torch.tensor([__import__("deeprhythm.utils", fromlist=["bpm_to_class"]).bpm_to_class(x) for x in tempos.tolist()], device=device) + outputs = model(inputs) + losses.append(criterion(outputs, labels).item()) + predictions.extend(class_to_bpm(value) for value in outputs.argmax(1).tolist()) + references.extend(tempos.tolist()) + if not losses: + raise ValueError("validation split has no clips") + metrics = tempo_accuracy(predictions, references, tolerance=tolerance) + metrics["loss"] = sum(losses) / len(losses) + return metrics + + +def fit(cache_dir, output_path, *, config=TrainingConfig(), start_weights=None, device=None): + """Fit on ``train`` and select on ``val``; never opens or evaluates ``test``.""" + device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu")) + train_set = ClipDataset(cache_dir, "train", return_tempo=False) + validation_set = ClipDataset(cache_dir, "val", return_tempo=True) + if not train_set or not validation_set: + raise ValueError("cache requires non-empty train and val splits") + sampler = TempoBalancedSampler(train_set.tempos, bin_width=config.balance_bin_width) + train_loader = DataLoader(train_set, batch_size=config.batch_size, sampler=sampler) + validation_loader = DataLoader(validation_set, batch_size=config.batch_size) + model = DeepRhythmModel().to(device) + if start_weights: + model.load_state_dict(torch.load(start_weights, map_location=device, weights_only=True)) + criterion = nn.CrossEntropyLoss() + optimizer = Adam(model.parameters(), lr=config.learning_rate) + scheduler = ReduceLROnPlateau(optimizer, mode="min", patience=2, factor=0.5) + best_loss, stale, history = float("inf"), 0, [] + output_path = Path(output_path) + for epoch in range(config.epochs): + model.train() + train_losses = [] + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + loss = criterion(model(inputs), labels) + loss.backward() + optimizer.step() + train_losses.append(loss.item()) + validation = evaluate_validation(model, validation_loader, criterion, device, config.tolerance) + scheduler.step(validation["loss"]) + record = {"epoch": epoch + 1, "train_loss": sum(train_losses) / len(train_losses), **validation} + history.append(record) + if validation["loss"] < best_loss: + best_loss, stale = validation["loss"], 0 + checkpoint = { + "model_state_dict": model.state_dict(), + "config": asdict(config), + "manifest_hash": HcqmCache(cache_dir).load_index().get("manifest_hash"), + "validation": validation, + } + output_path.parent.mkdir(parents=True, exist_ok=True) + torch.save(checkpoint, output_path) + else: + stale += 1 + if stale >= config.early_stopping_patience: + break + return history \ No newline at end of file From 1254332891bf81d400ea3bbaa264c472701bd419 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:23:04 -0700 Subject: [PATCH 02/77] [Build] test training cache sampling and metric parity --- src/deeprhythm/train/trainer.py | 4 +- tests/test_training.py | 91 +++++++++++++++++++++++++++++++++ 2 files changed, 93 insertions(+), 2 deletions(-) create mode 100644 tests/test_training.py diff --git a/src/deeprhythm/train/trainer.py b/src/deeprhythm/train/trainer.py index 968184d..47c7ce1 100644 --- a/src/deeprhythm/train/trainer.py +++ b/src/deeprhythm/train/trainer.py @@ -13,7 +13,7 @@ from deeprhythm.model.frame_cnn import DeepRhythmModel from deeprhythm.train.cache import ClipDataset, HcqmCache from deeprhythm.train.sampling import TempoBalancedSampler -from deeprhythm.utils import class_to_bpm +from deeprhythm.utils import bpm_to_class, class_to_bpm @dataclass(frozen=True) @@ -32,7 +32,7 @@ def evaluate_validation(model, loader, criterion, device, tolerance): with torch.no_grad(): for inputs, tempos in loader: inputs, tempos = inputs.to(device), tempos.to(device) - labels = torch.tensor([__import__("deeprhythm.utils", fromlist=["bpm_to_class"]).bpm_to_class(x) for x in tempos.tolist()], device=device) + labels = torch.tensor([bpm_to_class(x) for x in tempos.tolist()], device=device) outputs = model(inputs) losses.append(criterion(outputs, labels).item()) predictions.extend(class_to_bpm(value) for value in outputs.argmax(1).tolist()) diff --git a/tests/test_training.py b/tests/test_training.py new file mode 100644 index 0000000..077e9d1 --- /dev/null +++ b/tests/test_training.py @@ -0,0 +1,91 @@ +import json + +import numpy as np +import pytest +import torch +from torch import nn +from torch.utils.data import DataLoader, TensorDataset + +from deeprhythm.bench.tempo import tempo_accuracy +from deeprhythm.train.cache import ClipDataset, HcqmCache, build_hcqm_cache, manifest_key +from deeprhythm.train.sampling import TempoBalancedSampler, stretch_audio_and_tempo +from deeprhythm.train.trainer import evaluate_validation +from deeprhythm.utils import bpm_to_class + + +def fake_hcqm(value, clips=3): + return np.full((clips, 6, 240, 8), value, dtype=np.float32) + + +def test_manifest_key_invalidates_annotation_and_checksum_changes(): + row = {"audio_path": "audio.wav", "tempo": 120, "split": "train", "md5": "a"} + assert manifest_key(row) == manifest_key(dict(reversed(list(row.items())))) + assert manifest_key(row) != manifest_key({**row, "tempo": 121}) + assert manifest_key(row) != manifest_key({**row, "md5": "b"}) + + +def test_cache_is_incremental_and_dataset_is_memory_backed(tmp_path): + rows = [ + {"audio_path": "one.wav", "tempo": 120, "split": "train", "md5": "a"}, + {"audio_path": "two.wav", "tempo": 90, "split": "val", "md5": "b"}, + ] + calls = [] + + def extract(path): + calls.append(path) + return fake_hcqm(len(calls), clips=7 if path == "one.wav" else 2) + + index = build_hcqm_cache(rows, tmp_path, extractor=extract) + build_hcqm_cache(rows, tmp_path, extractor=extract) + assert calls == ["one.wav", "two.wav"] + assert index["manifest_hash"] + train = ClipDataset(tmp_path, "train") + validation = ClipDataset(tmp_path, "val", return_tempo=True) + assert len(train) == 5 # historical edge trimming, without its off-by-one bug + assert len(validation) == 2 + clip, label = train[0] + assert clip.shape == (6, 240, 8) + assert label.item() == bpm_to_class(120) + assert validation[0][1].item() == 90 + + +def test_corrupt_cache_version_is_rejected(tmp_path): + tmp_path.mkdir(exist_ok=True) + (tmp_path / "index.json").write_text(json.dumps({"version": 99, "entries": {}})) + with pytest.raises(ValueError, match="version"): + HcqmCache(tmp_path).load_index() + + +def test_tempo_balanced_sampler_equalizes_bin_mass(): + sampler = TempoBalancedSampler([60, 61, 62, 120], bin_width=10, num_samples=4) + weights = sampler.weights.tolist() + assert weights[:3] == pytest.approx([1 / 3] * 3) + assert weights[3] == pytest.approx(1.0) + assert sum(weights[:3]) == pytest.approx(weights[3]) + + +def test_time_stretch_relabels_and_enforces_model_range(): + audio = np.sin(np.linspace(0, 8 * np.pi, 1000)).astype(np.float32) + stretched, tempo = stretch_audio_and_tempo(audio, 100, 1.25) + assert tempo == 125 + assert len(stretched) == 800 + with pytest.raises(ValueError, match="outside"): + stretch_audio_and_tempo(audio, 280, 1.25) + + +class FixedModel(nn.Module): + def forward(self, inputs): + logits = torch.full((len(inputs), 256), -100.0) + logits[torch.arange(len(inputs)), inputs[:, 0].long()] = 100.0 + return logits + + +def test_validation_acc2_reuses_benchmark_scoring(): + reference_tempos = torch.tensor([120.0, 100.0]) + predicted_classes = torch.tensor([bpm_to_class(60), bpm_to_class(100)], dtype=torch.float32).unsqueeze(1) + loader = DataLoader(TensorDataset(predicted_classes, reference_tempos), batch_size=2) + metrics = evaluate_validation(FixedModel(), loader, nn.CrossEntropyLoss(), torch.device("cpu"), 0.04) + predicted_tempos = [float(int(60)), float(int(100))] + expected = tempo_accuracy(predicted_tempos, reference_tempos.tolist(), 0.04) + assert metrics["acc1"] == expected["acc1"] == 0.5 + assert metrics["acc2"] == expected["acc2"] == 1.0 \ No newline at end of file From 18048b4a3fbada6c694a8efb2afd78d6c3dea8f0 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:23:34 -0700 Subject: [PATCH 03/77] [Build] add canonical partition generator and validation --- .gitignore | 4 +- docs/research/partitions.md | 49 ++++++++ src/deeprhythm/partitions.py | 229 +++++++++++++++++++++++++++++++++++ tests/test_partitions.py | 48 ++++++++ 4 files changed, 329 insertions(+), 1 deletion(-) create mode 100644 docs/research/partitions.md create mode 100644 src/deeprhythm/partitions.py create mode 100644 tests/test_partitions.py diff --git a/.gitignore b/.gitignore index 3d2246f..a949695 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,6 @@ -data/ +data/* +!data/splits/ +!data/splits/** data/fma experiments/ *.hdf5 diff --git a/docs/research/partitions.md b/docs/research/partitions.md new file mode 100644 index 0000000..33412cf --- /dev/null +++ b/docs/research/partitions.md @@ -0,0 +1,49 @@ +# Canonical dataset partitions + +The manifests in `data/splits` are the canonical v0.8 train, validation, and test selection. Generate them with +`python -m deeprhythm.partitions --datasets-root ~/mir_datasets`; use `--validate-only` to check committed files +without access to audio. Rows contain the dataset-relative `audio_path`, audio MD5, reference `tempo`, `group_key`, +fold, source fold, duplicate fingerprint, and annotation provenance. + +## GiantSteps + +The source is `giantsteps-tempo-dataset` commit `0b7d47b`, using its canonical audio MD5 files and crowdsourced v2 +tempo annotations. Official folds 1–7 are training, fold 8 is validation, and folds 9–10 are test. Three official +entries without usable audio plus v2 annotation are omitted, leaving 661 of 664 entries. GiantSteps supplies no +artist identity in the checked metadata, so its group key is the Beatport track identifier. This preserves the +official split but **does not establish artist disjointness**; no artist identities are inferred. + +## GTZAN + +Tempo comes from the checked-out `gtzan_tempo_beat` annotations. Selection and folds are the fault-filtered +Kereliuk–Sturm–Larsen partition at GTZAN metadata commit `fedc781`: 443 training, 197 validation, and 290 test +entries before the historical DeepRhythm exclusions. Artist keys are parsed verbatim from Sturm's `index.txt`. +Rows whose index artist is `?` receive a per-track key rather than an invented identity. `jazz.00054` and +`reggae.00086` remain excluded for continuity with the v0.7 benchmark. + +## Ballroom + +Audio and tempo annotations are Ballroom B_1.0. The CPJKU annotation README reports four exact replicas and nine +recording replicas; the second item of every published pair is excluded. Remaining tracks are grouped by the only +identity evidence encoded locally: the `Albums--` album token or the first four digits of the +six-digit `Media-` identifier. These are album/media groups, **not claimed artist identities**. Groups are assigned +deterministically while balancing dance style and 20-BPM tempo bins toward 70/10/20 percent. + +Chromaprint is not installed in the reproducible environment and the source does not ship Extended Ballroom's +fingerprints. The `fingerprint` field therefore detects byte-identical audio through MD5, while the published +recording-replica list is enforced by exclusion. This does not detect unknown perceptual duplicates. External +corpora must add a real acoustic fingerprint comparison against all test audio before training. + +## Historical v0.7 archive and comparability + +`data/splits/archive-v0.7` records the obtainable full-set selections: GiantSteps v2 with usable annotations, +Ballroom's complete 698 tracks, and GTZAN minus the two historical exclusions. They are immutable selection records, +not leakage-safe training folds. The v0.8 held-out test results are not directly comparable to the published v0.7 +full-set headlines. Reports must rescore v0.7 predictions on the new test manifests for an apples-to-apples change. + +## Validation guarantees + +`validate_partitions` rejects duplicate paths, group keys spanning folds, and fingerprint values spanning folds. +Tests exercise each failure mode and validate every committed canonical manifest. MD5 is retained because the +upstream GiantSteps distribution itself uses MD5 and because these checksums identify the exact local audio bytes; +it is not used as a cryptographic trust primitive. \ No newline at end of file diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py new file mode 100644 index 0000000..cffa210 --- /dev/null +++ b/src/deeprhythm/partitions.py @@ -0,0 +1,229 @@ +"""Build and validate the canonical research dataset partitions.""" + +import argparse +import hashlib +import json +import re +from collections import Counter, defaultdict +from pathlib import Path + +FOLDS = ("train", "val", "test") +GTZAN_HISTORICAL_EXCLUSIONS = {"jazz/jazz.00054.wav", "reggae/reggae.00086.wav"} +BALLROOM_REPLICAS = ( + ("Quickstep/Albums-AnaBelen_Veneo-11.wav", "Quickstep/Albums-Chrisanne2-12.wav"), + ("ChaChaCha/Albums-Fire-08.wav", "Samba/Albums-Fire-09.wav"), + ("ChaChaCha/Albums-Latin_Jam2-05.wav", "ChaChaCha/Albums-Latin_Jam2-13.wav"), + ("Waltz/Albums-Secret_Garden-01.wav", "Waltz/Media-104705.wav"), + ("Rumba-International/Albums-AnaBelen_Veneo-03.wav", "Rumba-International/Albums-AnaBelen_Veneo-15.wav"), + ("Waltz/Albums-Ballroom_Magic-03.wav", "Waltz/Albums-Ballroom_Magic-18.wav"), + ("ChaChaCha/Albums-Latin_Jam-04.wav", "ChaChaCha/Albums-Latin_Jam-13.wav"), + ("Rumba-International/Albums-Latin_Jam-08.wav", "Rumba-International/Albums-Latin_Jam-14.wav"), + ("Samba/Albums-Latin_Jam-06.wav", "Samba/Albums-Latin_Jam-15.wav"), + ("Samba/Albums-Latin_Jam2-02.wav", "Samba/Albums-Latin_Jam2-14.wav"), + ("Rumba-International/Albums-Latin_Jam2-07.wav", "Rumba-International/Albums-Latin_Jam2-15.wav"), + ("ChaChaCha/Albums-Latin_Jam3-02.wav", "ChaChaCha/Media-103414.wav"), + ("ChaChaCha/Media-103402.wav", "ChaChaCha/Media-103415.wav"), +) + + +def digest(path): + value = hashlib.md5() # nosec: manifest identity, not a security primitive + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + value.update(chunk) + return value.hexdigest() + + +def read_tempo(path): + value = float(path.read_text().strip()) + if value <= 0: + raise ValueError(f"non-positive tempo in {path}") + return value + + +def write_rows(path, rows): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text("".join(json.dumps(row, sort_keys=True) + "\n" for row in sorted(rows, key=lambda r: r["audio_path"]))) + + +def validate_partitions(rows): + """Reject paths, group keys, or duplicate fingerprints crossing partitions.""" + required = {"dataset", "audio_path", "md5", "tempo", "group_key", "fold", "fingerprint", "provenance"} + seen_paths = set() + ownership = {"group_key": {}, "fingerprint": {}} + for row in rows: + missing = required - row.keys() + if missing: + raise ValueError(f"manifest row missing {sorted(missing)}") + if row["fold"] not in FOLDS or float(row["tempo"]) <= 0: + raise ValueError(f"invalid fold or tempo in {row['audio_path']}") + path_key = (row["dataset"], row["audio_path"]) + if path_key in seen_paths: + raise ValueError(f"duplicate audio path: {path_key}") + seen_paths.add(path_key) + for field, by_value in ownership.items(): + key = (row["dataset"], row[field]) + previous = by_value.setdefault(key, row["fold"]) + if previous != row["fold"]: + raise ValueError(f"{field} {row[field]!r} spans {previous} and {row['fold']}") + return True + + +def _gtzan_index(path): + result = {} + for line in path.read_text(errors="replace").splitlines(): + if line.startswith("#") or " ::: " not in line: + continue + filename, artist, _title = line.split(" ::: ", 2) + result[filename] = artist.strip() + return result + + +def build_gtzan(root, sources): + index = _gtzan_index(sources / "index.txt") + assignment = {} + for fold, filename in (("train", "train_filtered.txt"), ("val", "valid_filtered.txt"), ("test", "test_filtered.txt")): + for line in (sources / filename).read_text().splitlines(): + assignment[line.strip()] = fold + rows = [] + for relative, fold in sorted(assignment.items()): + if relative in GTZAN_HISTORICAL_EXCLUSIONS: + continue + genre, filename = relative.split("/") + audio = root / "gtzan_genre" / "genres" / relative + annotation = root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{genre}_{filename[6:11]}.bpm" + artist = index.get(filename) + if not artist or artist == "?": + group = f"track:{filename}" + else: + group = f"artist:{artist.casefold()}" + md5 = digest(audio) + rows.append({"dataset": "gtzan", "audio_path": str(audio.relative_to(root)), "md5": md5, + "tempo": read_tempo(annotation), "group_key": group, "fold": fold, + "source_fold": f"kereliuk-{fold}", "fingerprint": f"md5:{md5}", + "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat"}) + return rows + + +def build_giantsteps(root): + repository = next(root.glob("giantsteps-tempo-dataset-*")) + official = {} + for number in range(1, 11): + target = "train" if number <= 7 else "val" if number == 8 else "test" + for filename in (repository / "splits" / f"fold{number:02d}.txt").read_text().splitlines(): + official[filename] = (target, number) + rows = [] + for filename, (fold, number) in sorted(official.items()): + annotation = repository / "annotations_v2" / "tempo" / filename.replace(".mp3", ".bpm") + audio = root / "audio_canonical" / filename + if not annotation.exists() or not audio.exists(): + continue + expected = (repository / "md5" / filename.replace(".mp3", ".md5")).read_text().split()[0] + actual = digest(audio) + if actual != expected: + raise ValueError(f"GiantSteps checksum mismatch: {filename}") + track = filename.split(".", 1)[0] + rows.append({"dataset": "giantsteps", "audio_path": str(audio.relative_to(root)), "md5": actual, + "tempo": read_tempo(annotation), "group_key": f"track:{track}", "fold": fold, + "source_fold": f"official-fold{number:02d}", "fingerprint": f"md5:{actual}", + "provenance": "GiantSteps tempo dataset 0b7d47b; v2 crowd annotation"}) + return rows + + +def _ballroom_group(stem): + album = re.fullmatch(r"Albums-(.+)-\d+", stem) + if album: + return f"album:{album.group(1).casefold()}" + media = re.fullmatch(r"Media-(\d+)", stem) + if media: + return f"media-prefix:{media.group(1)[:-2]}" + return f"track:{stem.casefold()}" + + +def _assign_ballroom(groups): + targets = {"train": 0.7, "val": 0.1, "test": 0.2} + totals = Counter(key for tracks in groups.values() for key in tracks) + counts = {fold: Counter() for fold in FOLDS} + result = {} + for group, tracks in sorted(groups.items(), key=lambda item: (-len(item[1]), item[0])): + contribution = Counter(tracks) + def cost(fold): + return sum(((counts[fold][key] + contribution[key]) / max(totals[key], 1) - targets[fold]) ** 2 for key in contribution) + fold = min(FOLDS, key=lambda name: (cost(name), sum(counts[name].values()) / targets[name], name)) + result[group] = fold + counts[fold].update(contribution) + return result + + +def build_ballroom(root): + excluded = {duplicate.casefold() for _keeper, duplicate in BALLROOM_REPLICAS} + candidates = [] + groups = defaultdict(list) + for audio in sorted((root / "audio").glob("*/*.wav")): + relative = str(audio.relative_to(root / "audio")) + if relative.casefold() in excluded: + continue + annotation = next((root / "annotations" / "tempo").glob(f"**/{audio.stem}.bpm")) + tempo = read_tempo(annotation) + group = _ballroom_group(audio.stem) + stratum = (audio.parent.name.casefold(), int(tempo // 20)) + groups[group].append(stratum) + candidates.append((audio, relative, tempo, group)) + assignment = _assign_ballroom(groups) + rows = [] + for audio, relative, tempo, group in candidates: + md5 = digest(audio) + rows.append({"dataset": "ballroom", "audio_path": str(audio.relative_to(root)), "md5": md5, + "tempo": tempo, "group_key": group, "fold": assignment[group], "source_fold": "deeprhythm-v0.8", + "fingerprint": f"md5:{md5}", "genre": audio.parent.name, + "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations"}) + return rows + + +def historical_rows(dataset, root): + rows = [] + if dataset == "ballroom": + files = sorted((root / "audio").glob("*/*.wav")) + tempo = lambda p: read_tempo(next((root / "annotations" / "tempo").glob(f"**/{p.stem}.bpm"))) + elif dataset == "gtzan": + files = sorted((root / "gtzan_genre" / "genres").glob("*/*.wav")) + files = [p for p in files if str(p.relative_to(root / "gtzan_genre" / "genres")) not in GTZAN_HISTORICAL_EXCLUSIONS] + tempo = lambda p: read_tempo(root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{p.parent.name}_{p.stem[-5:]}.bpm") + else: + repository = next(root.glob("giantsteps-tempo-dataset-*")) + files = sorted((root / "audio_canonical").glob("*.mp3")) + files = [p for p in files if (repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")).exists()] + tempo = lambda p: read_tempo(repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")) + for path in files: + rows.append({"audio_path": str(path.relative_to(root)), "tempo": tempo(path), "historical": "deeprhythm-0.7-full-set"}) + return rows + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--datasets-root", type=Path, default=Path("~/mir_datasets").expanduser()) + parser.add_argument("--output", type=Path, default=Path("data/splits")) + parser.add_argument("--validate-only", action="store_true") + args = parser.parse_args(argv) + if args.validate_only: + rows = [json.loads(line) for path in args.output.glob("*/train.jsonl") for line in path.read_text().splitlines()] + rows += [json.loads(line) for pattern in ("*/val.jsonl", "*/test.jsonl") for path in args.output.glob(pattern) for line in path.read_text().splitlines()] + validate_partitions(rows) + return + sources = args.output / "sources" / "gtzan" + built = { + "giantsteps": build_giantsteps(args.datasets_root / "giantsteps_tempo"), + "gtzan": build_gtzan(args.datasets_root / "gtzan_genre", sources), + "ballroom": build_ballroom(args.datasets_root / "ballroom" / "B_1.0"), + } + for dataset, rows in built.items(): + validate_partitions(rows) + for fold in FOLDS: + write_rows(args.output / dataset / f"{fold}.jsonl", [row for row in rows if row["fold"] == fold]) + write_rows(args.output / "archive-v0.7" / "giantsteps.jsonl", historical_rows("giantsteps", args.datasets_root / "giantsteps_tempo")) + write_rows(args.output / "archive-v0.7" / "gtzan.jsonl", historical_rows("gtzan", args.datasets_root / "gtzan_genre")) + write_rows(args.output / "archive-v0.7" / "ballroom.jsonl", historical_rows("ballroom", args.datasets_root / "ballroom" / "B_1.0")) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tests/test_partitions.py b/tests/test_partitions.py new file mode 100644 index 0000000..93963b9 --- /dev/null +++ b/tests/test_partitions.py @@ -0,0 +1,48 @@ +import json + +import pytest + +from deeprhythm.partitions import _assign_ballroom, _ballroom_group, validate_partitions + + +def row(path, group, fold, fingerprint): + return {"dataset": "example", "audio_path": path, "md5": "0" * 32, "tempo": 120, + "group_key": group, "fold": fold, "fingerprint": fingerprint, "provenance": "test"} + + +def test_validation_accepts_disjoint_rows(): + assert validate_partitions([row("a.wav", "artist:a", "train", "fp:a"), row("b.wav", "artist:b", "test", "fp:b")]) + + +@pytest.mark.parametrize("field", ["group", "fingerprint"]) +def test_validation_rejects_leakage(field): + first = row("a.wav", "artist:a", "train", "fp:a") + second = row("b.wav", "artist:a" if field == "group" else "artist:b", "test", "fp:a") + with pytest.raises(ValueError, match="spans"): + validate_partitions([first, second]) + + +def test_ballroom_group_uses_only_filename_metadata(): + assert _ballroom_group("Albums-Latin_Jam2-03") == "album:latin_jam2" + assert _ballroom_group("Media-104705") == "media-prefix:1047" + + +def test_ballroom_assignment_is_deterministic_and_grouped(): + groups = {"album:a": [("waltz", 4)] * 3, "album:b": [("waltz", 4)] * 2, "album:c": [("tango", 6)]} + assert _assign_ballroom(groups) == _assign_ballroom(dict(reversed(list(groups.items())))) + + +def test_committed_manifests_are_valid_and_consistent(): + from pathlib import Path + root = Path(__file__).parents[1] / "data" / "splits" + rows = [] + for dataset in ("giantsteps", "gtzan", "ballroom"): + dataset_rows = [] + for fold in ("train", "val", "test"): + path = root / dataset / f"{fold}.jsonl" + loaded = [json.loads(line) for line in path.read_text().splitlines()] + assert loaded and all(row_["fold"] == fold for row_ in loaded) + dataset_rows.extend(loaded) + validate_partitions(dataset_rows) + rows.extend(dataset_rows) + assert {row_["dataset"] for row_ in rows} == {"giantsteps", "gtzan", "ballroom"} \ No newline at end of file From 0a4e42ad95f615d573efb824a0c9335d7eeab217 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:24:59 -0700 Subject: [PATCH 04/77] generate checksummed canonical dataset manifests --- data/splits/sources/gtzan/README.md | 22 + data/splits/sources/gtzan/index.txt | 1033 ++++++++++++++++++ data/splits/sources/gtzan/test_filtered.txt | 290 +++++ data/splits/sources/gtzan/train_filtered.txt | 443 ++++++++ data/splits/sources/gtzan/valid_filtered.txt | 197 ++++ 5 files changed, 1985 insertions(+) create mode 100644 data/splits/sources/gtzan/README.md create mode 100644 data/splits/sources/gtzan/index.txt create mode 100644 data/splits/sources/gtzan/test_filtered.txt create mode 100644 data/splits/sources/gtzan/train_filtered.txt create mode 100644 data/splits/sources/gtzan/valid_filtered.txt diff --git a/data/splits/sources/gtzan/README.md b/data/splits/sources/gtzan/README.md new file mode 100644 index 0000000..89feea7 --- /dev/null +++ b/data/splits/sources/gtzan/README.md @@ -0,0 +1,22 @@ +# GTZAN +Information about the GTZAN dataset for "music genre recognition" research. + +- index.txt: an index into the contents of the datasets, specifically the artist and track title of most wav files. +- Fault-filtered partitions used in Kereliuk, Sturm, and Larsen, “[Deep learning, audio adversaries, and music content analysis](https://ieeexplore.ieee.org/document/7336950),” in Proc. WASPAA, 2015; +Kereliuk, Sturm, and Larsen, “[Deep learning and music adversaries](https://ieeexplore.ieee.org/document/7254179),” IEEE Trans. Multimedia, vol. 17, pp. 2059–2071, Sep. 2015; +Rodriguez-Algarra, Sturm, and Maruri-Aguilar, “[Analysing scattering-based music content analysis systems: Where’s the music?](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:sSrBHYA8nusC),” in Proc. ISMIR, 2016. Code repo: https://code.soundsoftware.ac.uk/projects/scatter_reeval + - test_filtered.txt + - test_stratified.txt + - train_filtered.txt + - train_stratified.txt + - valid_filtered.txt + - valid_stratified.txt + +## Other relevant publications: +- Sturm, "[The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use](https://arxiv.org/abs/1306.1461)", arXiv:1306.1461, 2013. +- Sturm, “[The state of the art ten years after A State of the Art: Future research in music information retrieval](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&citation_for_view=KdeYIvMAAAAJ:EUQCXRtRnyEC),” Journal of New Music Research, vol. 43, no. 2, pp. 147 – 172, 2014. +- Sturm,“[A Simple Method to Determine if a Music Information Retrieval System is a‘Horse’](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:2P1L_qKh6hAC)”, IEEE Trans. Multimedia, vol. 16, no 6, pp. 1636 – 1644, Oct. 2014. +- Sturm, “[A survey of evaluation in music genre recognition](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:bEWYMUwI8FkC),” chapter (pp. 29-66) in Nürnberger et al., eds., Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation, Springer, 2014. +- Sturm, “[An analysis of the GTZAN music genre dataset](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&citation_for_view=KdeYIvMAAAAJ:4JMBOYKVnBMC),” in Proc. ACM Workshop on Music Info. Retrieval with User-centered and Multimodal Strategies, Nov. 2012. +- Sturm, “[Two systems for automatic music genre recognition: What are they really recognizing?](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:j3f4tGmQtD8C),” in Proc. ACM Workshop on Music Info. Retrieval with User-centered and Multimodal Strategies, Nov. 2012. + diff --git a/data/splits/sources/gtzan/index.txt b/data/splits/sources/gtzan/index.txt new file mode 100644 index 0000000..8662f0f --- /dev/null +++ b/data/splits/sources/gtzan/index.txt @@ -0,0 +1,1033 @@ +## ## ## ## +## This is an index of the contents of the GTZAN dataset +## For each wav file in the dataset we identify the artist and the song +## created in 2014 by Bob L. Sturm at the Centre for Digital Music (C4DM) +## Queen Mary University of London, U.K. +## +## ## ## ## +## Please cite: +## +## B. L. Sturm, “The state of the art ten years after a state of the art: +## Future research in music information retrieval,” J. New Music Research, +## vol. 43, no. 2, pp. 147–172, 2014. +## +## ## ## ## +## If you have any information regarding the missing entries, +## or correcting existing ones, please contact me, and you +## will be added to list below. +## ## ## ## +## Many thanks to: +## Cynthia Liem for identifying Classical 80, 81, 97 (Nov. 6, 2012) +## Clemens Hage for identifying Classical 00 -- 02 (Dec. 12, 2012) +## Dan Ellis for corrections to pop.00069 and pop.00085 (Oct. 26 2014) +## Elio Quinton for identifying Metal 17, which led to IDing Metal 16, 18-20 +## (Mar. 9 2015) +## Matthew Davies for identifying Disco 36 and 58 (Mar. 20 2015) +## Robert Farrimond for identifying Metal 35 (Aug. 30 2017) +## Francisco Rodriguez-Algarra for identifying several tracks, bringing the unknowns to 23 tracks from 73 (Jan. 23 2018) +## Francisco Rodriguez-Algarra for making this index more consistent (Jan. 26 2018) +## Leonard in 't Veen for identifying Disco 80-81, Hiphop 52-54, Reggae 51 (Jan. 8 2024) +## +## Currently missing information for 17 tracks +## + +blues.00000.wav ::: John Lee Hooker ::: One Bourbon, One Scotch And One Beer +blues.00001.wav ::: John Lee Hooker ::: I'm In The Mood For Love +blues.00002.wav ::: John Lee Hooker ::: Think Twice Before You Go +blues.00003.wav ::: John Lee Hooker ::: I'm Bad Like Jesse James +blues.00004.wav ::: John Lee Hooker ::: Walkin' the Boogie +blues.00005.wav ::: John Lee Hooker ::: Sugar Mama +blues.00006.wav ::: John Lee Hooker ::: Dreamin' Blues +blues.00007.wav ::: John Lee Hooker ::: Ground Hog Blues +blues.00008.wav ::: John Lee Hooker ::: Shake It Baby +blues.00009.wav ::: John Lee Hooker ::: It's My Own Fault +blues.00010.wav ::: John Lee Hooker ::: Lonely Boy Boogie +blues.00011.wav ::: John Lee Hooker ::: It Serves Me Right To Suffer +blues.00012.wav ::: Robert Johnson ::: Cross Road Blues +blues.00013.wav ::: Robert Johnson ::: Terraplane Blues +blues.00014.wav ::: Robert Johnson ::: Come On In My Kitchen +blues.00015.wav ::: Robert Johnson ::: Walking blues +blues.00016.wav ::: Robert Johnson ::: Last Fair Deal Gone Down +blues.00017.wav ::: Robert Johnson ::: 32-20 Blues +blues.00018.wav ::: Robert Johnson ::: Kind Hearted Woman Blues +blues.00019.wav ::: Robert Johnson ::: If I Had Possession Over Judgement Day +blues.00020.wav ::: Robert Johnson ::: Preachin' Blues +blues.00021.wav ::: Robert Johnson ::: When You Got A Good Friend +blues.00022.wav ::: Robert Johnson ::: Ramblin' On My Mind +blues.00023.wav ::: Robert Johnson ::: Stones In My Passway +blues.00024.wav ::: Robert Johnson ::: Traveling Riverside Blues +blues.00025.wav ::: Robert Johnson ::: Milkcow's Calf Blues +blues.00026.wav ::: Robert Johnson ::: Me And The Devil Blues +blues.00027.wav ::: Robert Johnson ::: Hell Hound On My Trail +blues.00028.wav ::: Robert Johnson ::: Traveling Riverside Blues +blues.00029.wav ::: Kelly Joe Phelps ::: The House Carpenter +blues.00030.wav ::: Kelly Joe Phelps ::: River Rat Jimmy +blues.00031.wav ::: Kelly Joe Phelps ::: Hobo's Son +blues.00032.wav ::: Kelly Joe Phelps ::: Katy +blues.00033.wav ::: Kelly Joe Phelps ::: Wandering Away +blues.00034.wav ::: Kelly Joe Phelps ::: Dock Boggs Country Blues +blues.00035.wav ::: Kelly Joe Phelps ::: Capman Bootman +blues.00036.wav ::: Kelly Joe Phelps ::: Train Carried My Girl From Town +blues.00037.wav ::: Kelly Joe Phelps ::: Piece By Piece +blues.00038.wav ::: Kelly Joe Phelps ::: Many A Time +blues.00039.wav ::: Kelly Joe Phelps ::: Goodnight Irene +blues.00040.wav ::: Stevie Ray Vaughan ::: Love Struck Baby +blues.00041.wav ::: Stevie Ray Vaughan ::: Pride and Joy +blues.00042.wav ::: Stevie Ray Vaughan ::: Texas Flood +blues.00043.wav ::: Stevie Ray Vaughan ::: Tell Me +blues.00044.wav ::: Stevie Ray Vaughan ::: Testify +blues.00045.wav ::: Stevie Ray Vaughan ::: Rude Mood +blues.00046.wav ::: Stevie Ray Vaughan ::: Mary Had A Little Lamb +blues.00047.wav ::: Stevie Ray Vaughan ::: Dirty Pool +blues.00048.wav ::: Stevie Ray Vaughan ::: I'm Cryin' +blues.00049.wav ::: Stevie Ray Vaughan ::: Lenny +blues.00050.wav ::: Magic Slim & The Teardrops ::: What's Wrong +blues.00051.wav ::: Magic Slim & The Teardrops ::: Snakebite +blues.00052.wav ::: Magic Slim & The Teardrops ::: Please Don't Dog Me +blues.00053.wav ::: Magic Slim & The Teardrops ::: Key To Your Door +blues.00054.wav ::: Magic Slim & The Teardrops ::: Shake It +blues.00055.wav ::: Magic Slim & The Teardrops ::: I Ain't Looking for No Love +blues.00056.wav ::: Magic Slim & The Teardrops ::: Country Boy +blues.00057.wav ::: Magic Slim & The Teardrops ::: Lump On Your Stump +blues.00058.wav ::: Magic Slim & The Teardrops ::: Lonesome Trouble +blues.00059.wav ::: Magic Slim & The Teardrops ::: The Man You Need +blues.00060.wav ::: Magic Slim & The Teardrops ::: Mind Your Own Business +blues.00061.wav ::: Clifton Chenier ::: Boppin' The Rock +blues.00062.wav ::: Clifton Chenier ::: The Things I Did for You +blues.00063.wav ::: Clifton Chenier ::: Yesterday (I Lost My Best Friend) +blues.00064.wav ::: Clifton Chenier ::: Clifton's Squeeze-box Boogie +blues.00065.wav ::: Clifton Chenier ::: I'm On My Way (Back Home To You) +blues.00066.wav ::: Clifton Chenier ::: Ay-Te Te Fee +blues.00067.wav ::: Clifton Chenier ::: All Night Long +blues.00068.wav ::: Clifton Chenier ::: Opelousas Hop +blues.00069.wav ::: Clifton Chenier ::: I'm On My Way, Part 2 +blues.00070.wav ::: Clifton Chenier ::: Think It Over +blues.00071.wav ::: Clifton Chenier ::: Zodico Stomp +blues.00072.wav ::: Clifton Chenier ::: The Cats Dreamin' +blues.00073.wav ::: Buckwheat Zydeco ::: Turning Point +blues.00074.wav ::: Buckwheat Zydeco ::: Zydeco Boogaloo +blues.00075.wav ::: Buckwheat Zydeco ::: Ya Ya +blues.00076.wav ::: Buckwheat Zydeco ::: Buck's Nouvelle Jole Blon +blues.00077.wav ::: Buckwheat Zydeco ::: On A Night Like This +blues.00078.wav ::: Buckwheat Zydeco ::: Hot Tamale Baby +blues.00079.wav ::: Buckwheat Zydeco ::: Ma Tit Fille +blues.00080.wav ::: Buckwheat 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+rock/rock.00075.wav +rock/rock.00076.wav +rock/rock.00077.wav +rock/rock.00078.wav +rock/rock.00079.wav +rock/rock.00080.wav +rock/rock.00081.wav +rock/rock.00082.wav +rock/rock.00083.wav +rock/rock.00084.wav +rock/rock.00085.wav From e08cb03580e3b5b630bf75ddb62b2bffaabd8ea8 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:25:14 -0700 Subject: [PATCH 05/77] [Build] exclude unusable GiantSteps tempo annotations --- src/deeprhythm/partitions.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index cffa210..e3e8a1a 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -118,13 +118,16 @@ def build_giantsteps(root): audio = root / "audio_canonical" / filename if not annotation.exists() or not audio.exists(): continue + tempo = float(annotation.read_text().strip()) + if tempo <= 0: + continue expected = (repository / "md5" / filename.replace(".mp3", ".md5")).read_text().split()[0] actual = digest(audio) if actual != expected: raise ValueError(f"GiantSteps checksum mismatch: {filename}") track = filename.split(".", 1)[0] rows.append({"dataset": "giantsteps", "audio_path": str(audio.relative_to(root)), "md5": actual, - "tempo": read_tempo(annotation), "group_key": f"track:{track}", "fold": fold, + "tempo": tempo, "group_key": f"track:{track}", "fold": fold, "source_fold": f"official-fold{number:02d}", "fingerprint": f"md5:{actual}", "provenance": "GiantSteps tempo dataset 0b7d47b; v2 crowd annotation"}) return rows @@ -192,7 +195,8 @@ def historical_rows(dataset, root): else: repository = next(root.glob("giantsteps-tempo-dataset-*")) files = sorted((root / "audio_canonical").glob("*.mp3")) - files = [p for p in files if (repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")).exists()] + files = [p for p in files if (repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")).exists() + and float((repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")).read_text()) > 0] tempo = lambda p: read_tempo(repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")) for path in files: rows.append({"audio_path": str(path.relative_to(root)), "tempo": tempo(path), "historical": "deeprhythm-0.7-full-set"}) From 91151ad004b9cacbdbef23610a50ab2fb4f03b3e Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:25:24 -0700 Subject: [PATCH 06/77] [Build] parse incomplete GTZAN metadata entries --- src/deeprhythm/partitions.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index e3e8a1a..0b6dfad 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -74,7 +74,8 @@ def _gtzan_index(path): for line in path.read_text(errors="replace").splitlines(): if line.startswith("#") or " ::: " not in line: continue - filename, artist, _title = line.split(" ::: ", 2) + parts = line.split(" ::: ", 2) + filename, artist = parts[:2] result[filename] = artist.strip() return result From acc54116c0dc9193df8dbea329a7342cf3511931 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:25:40 -0700 Subject: [PATCH 07/77] [Build] resolve GTZAN tempo annotations by track number --- src/deeprhythm/partitions.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index 0b6dfad..b48aabb 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -92,7 +92,8 @@ def build_gtzan(root, sources): continue genre, filename = relative.split("/") audio = root / "gtzan_genre" / "genres" / relative - annotation = root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{genre}_{filename[6:11]}.bpm" + track_number = Path(filename).stem[-5:] + annotation = root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{genre}_{track_number}.bpm" artist = index.get(filename) if not artist or artist == "?": group = f"track:{filename}" From 8b6ded2814af4d46f8cff0afc8e0c9344056273b Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:25:57 -0700 Subject: [PATCH 08/77] commit canonical train validation and test manifests --- data/splits/giantsteps/test.jsonl | 131 ++++++++ data/splits/giantsteps/train.jsonl | 464 +++++++++++++++++++++++++++++ data/splits/giantsteps/val.jsonl | 66 ++++ 3 files changed, 661 insertions(+) create mode 100644 data/splits/giantsteps/test.jsonl create mode 100644 data/splits/giantsteps/train.jsonl create mode 100644 data/splits/giantsteps/val.jsonl diff --git a/data/splits/giantsteps/test.jsonl b/data/splits/giantsteps/test.jsonl new file mode 100644 index 0000000..a309b33 --- /dev/null +++ b/data/splits/giantsteps/test.jsonl @@ -0,0 +1,131 @@ +{"audio_path": "audio_canonical/1174239.LOFI.mp3", "dataset": "giantsteps", "fingerprint": "md5:54aa146828b83787cc362100896a3e84", "fold": "test", "group_key": "track:1174239", "md5": "54aa146828b83787cc362100896a3e84", "provenance": "GiantSteps tempo dataset 0b7d47b; v2 crowd annotation", "source_fold": "official-fold09", "tempo": 170.0} +{"audio_path": "audio_canonical/1234750.LOFI.mp3", "dataset": "giantsteps", "fingerprint": "md5:6a7c1bb3da246fad02436d74743cc392", "fold": "test", "group_key": "track:1234750", "md5": "6a7c1bb3da246fad02436d74743cc392", "provenance": "GiantSteps tempo dataset 0b7d47b; v2 crowd annotation", "source_fold": "official-fold09", "tempo": 142.0} +{"audio_path": "audio_canonical/1240672.LOFI.mp3", "dataset": "giantsteps", "fingerprint": "md5:53e06a6c364cdb44c935409df3736894", "fold": "test", "group_key": "track:1240672", "md5": "53e06a6c364cdb44c935409df3736894", "provenance": "GiantSteps tempo dataset 0b7d47b; v2 crowd annotation", "source_fold": "official-fold09", "tempo": 87.0} +{"audio_path": "audio_canonical/1329955.LOFI.mp3", "dataset": "giantsteps", "fingerprint": "md5:d2676d4bd07c16db48f147aa13b33c5e", "fold": "test", 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blank GTZAN artist metadata as unknown --- src/deeprhythm/partitions.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index b48aabb..75fe302 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -76,7 +76,8 @@ def _gtzan_index(path): continue parts = line.split(" ::: ", 2) filename, artist = parts[:2] - result[filename] = artist.strip() + artist = artist.strip() + result[filename] = artist if artist.strip(": ") else "?" return result From 5f425ecb8cc72267d53deca15b0c82474879e6c7 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:26:32 -0700 Subject: [PATCH 10/77] [Build] enforce GTZAN artist disjointness across source folds --- docs/research/partitions.md | 6 ++++-- src/deeprhythm/partitions.py | 9 +++++++++ 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/docs/research/partitions.md b/docs/research/partitions.md index 33412cf..f501140 100644 --- a/docs/research/partitions.md +++ b/docs/research/partitions.md @@ -15,9 +15,11 @@ official split but **does not establish artist disjointness**; no artist identit ## GTZAN -Tempo comes from the checked-out `gtzan_tempo_beat` annotations. Selection and folds are the fault-filtered +Tempo comes from the checked-out `gtzan_tempo_beat` annotations. Selection starts from the fault-filtered Kereliuk–Sturm–Larsen partition at GTZAN metadata commit `fedc781`: 443 training, 197 validation, and 290 test -entries before the historical DeepRhythm exclusions. Artist keys are parsed verbatim from Sturm's `index.txt`. +entries before the historical DeepRhythm exclusions. Those files still cross some artist keys in the current index, +so every known artist is moved wholly to its most protected source fold (test before validation before training). +Artist keys are parsed verbatim from Sturm's `index.txt`. Rows whose index artist is `?` receive a per-track key rather than an invented identity. `jazz.00054` and `reggae.00086` remain excluded for continuity with the v0.7 benchmark. diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index 75fe302..e3434fd 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -105,6 +105,15 @@ def build_gtzan(root, sources): "tempo": read_tempo(annotation), "group_key": group, "fold": fold, "source_fold": f"kereliuk-{fold}", "fingerprint": f"md5:{md5}", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat"}) + priority = {"train": 0, "val": 1, "test": 2} + artist_fold = {} + for row in rows: + if row["group_key"].startswith("artist:"): + previous = artist_fold.get(row["group_key"], "train") + artist_fold[row["group_key"]] = max((previous, row["fold"]), key=priority.get) + for row in rows: + if row["group_key"] in artist_fold: + row["fold"] = artist_fold[row["group_key"]] return rows From 82467b976aa3cb1be5b564a21feafb6bada26c73 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:27:21 -0700 Subject: [PATCH 11/77] commit canonical train validation and test manifests --- data/splits/archive-v0.7/ballroom.jsonl | 698 +++++++++++++++ data/splits/archive-v0.7/giantsteps.jsonl | 661 ++++++++++++++ data/splits/archive-v0.7/gtzan.jsonl | 998 ++++++++++++++++++++++ data/splits/ballroom/test.jsonl | 379 ++++++++ data/splits/ballroom/train.jsonl | 27 + data/splits/ballroom/val.jsonl | 279 ++++++ data/splits/gtzan/test.jsonl | 313 +++++++ data/splits/gtzan/train.jsonl | 422 +++++++++ data/splits/gtzan/val.jsonl | 194 +++++ 9 files changed, 3971 insertions(+) create mode 100644 data/splits/archive-v0.7/ballroom.jsonl create mode 100644 data/splits/archive-v0.7/giantsteps.jsonl create mode 100644 data/splits/archive-v0.7/gtzan.jsonl create mode 100644 data/splits/ballroom/test.jsonl create mode 100644 data/splits/ballroom/train.jsonl create mode 100644 data/splits/ballroom/val.jsonl create mode 100644 data/splits/gtzan/test.jsonl create mode 100644 data/splits/gtzan/train.jsonl create mode 100644 data/splits/gtzan/val.jsonl diff --git a/data/splits/archive-v0.7/ballroom.jsonl b/data/splits/archive-v0.7/ballroom.jsonl new file mode 100644 index 0000000..8fedefd --- /dev/null +++ b/data/splits/archive-v0.7/ballroom.jsonl @@ -0,0 +1,698 @@ +{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-05.wav", "historical": "deeprhythm-0.7-full-set", "tempo": 124.0} +{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-06.wav", "historical": "deeprhythm-0.7-full-set", "tempo": 124.0} +{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-07.wav", "historical": "deeprhythm-0.7-full-set", "tempo": 124.0} +{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-08.wav", "historical": "deeprhythm-0.7-full-set", "tempo": 124.0} +{"audio_path": "audio/ChaChaCha/Albums-Fire-01.wav", "historical": "deeprhythm-0.7-full-set", "tempo": 124.0} +{"audio_path": 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"group_key": "artist:simple minds", "md5": "9b31a244a1aa7521b194adfd98430bca", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 119.74} +{"audio_path": "gtzan_genre/genres/rock/rock.00054.wav", "dataset": "gtzan", "fingerprint": "md5:bdd5eb92df390781c4e6e51c37486d98", "fold": "val", "group_key": "artist:simple minds", "md5": "bdd5eb92df390781c4e6e51c37486d98", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 109.01} +{"audio_path": "gtzan_genre/genres/rock/rock.00055.wav", "dataset": "gtzan", "fingerprint": "md5:ca8a9b7ea0ceaa97cf20abbe136f59f0", "fold": "val", "group_key": "artist:simple minds", "md5": "ca8a9b7ea0ceaa97cf20abbe136f59f0", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 130.76} +{"audio_path": "gtzan_genre/genres/rock/rock.00056.wav", "dataset": "gtzan", "fingerprint": "md5:d63a1236d46d9835c47e099f498fe910", "fold": "val", "group_key": "artist:simple minds", "md5": "d63a1236d46d9835c47e099f498fe910", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 138.07} +{"audio_path": "gtzan_genre/genres/rock/rock.00071.wav", "dataset": "gtzan", "fingerprint": "md5:3bd0a6105eecbeba05e5843ded5d35a7", "fold": "val", "group_key": "artist:simply red", "md5": "3bd0a6105eecbeba05e5843ded5d35a7", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 112.13} +{"audio_path": "gtzan_genre/genres/rock/rock.00072.wav", "dataset": "gtzan", "fingerprint": "md5:3e5732d19ca5d4d97e10be445dd0f25e", "fold": "val", "group_key": "artist:simply red", "md5": "3e5732d19ca5d4d97e10be445dd0f25e", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 103.68} +{"audio_path": "gtzan_genre/genres/rock/rock.00073.wav", "dataset": "gtzan", "fingerprint": "md5:32a04a6fefb23138349ba0819bebec25", "fold": "val", "group_key": "artist:simply red", "md5": "32a04a6fefb23138349ba0819bebec25", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 94.15} +{"audio_path": "gtzan_genre/genres/rock/rock.00074.wav", "dataset": "gtzan", "fingerprint": "md5:d719db3df1f165afe080a3c9f5cf2080", "fold": "val", "group_key": "artist:simply red", "md5": "d719db3df1f165afe080a3c9f5cf2080", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 112.05} +{"audio_path": "gtzan_genre/genres/rock/rock.00075.wav", "dataset": "gtzan", "fingerprint": "md5:3c06b723628342e8917e4419084d86cb", "fold": "val", "group_key": "artist:simply red", "md5": "3c06b723628342e8917e4419084d86cb", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 119.86} +{"audio_path": "gtzan_genre/genres/rock/rock.00076.wav", "dataset": "gtzan", "fingerprint": "md5:8264aed93c13ac72e42c0f88b7438520", "fold": "val", "group_key": "artist:simply red", "md5": "8264aed93c13ac72e42c0f88b7438520", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 95.95} +{"audio_path": "gtzan_genre/genres/rock/rock.00077.wav", "dataset": "gtzan", "fingerprint": "md5:1434fa72d547832d2aa52031325c4fdc", "fold": "val", "group_key": "artist:simply red", "md5": "1434fa72d547832d2aa52031325c4fdc", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 108.48} +{"audio_path": "gtzan_genre/genres/rock/rock.00078.wav", "dataset": "gtzan", "fingerprint": "md5:f798e74f7fa1605e7d1f4ad163848074", "fold": "val", "group_key": "artist:simply red", "md5": "f798e74f7fa1605e7d1f4ad163848074", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 98.01} +{"audio_path": "gtzan_genre/genres/rock/rock.00079.wav", "dataset": "gtzan", "fingerprint": "md5:528e49472e2be7331a6a8717b0e5e7a6", "fold": "val", "group_key": "artist:survivor", "md5": "528e49472e2be7331a6a8717b0e5e7a6", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 125.14} +{"audio_path": "gtzan_genre/genres/rock/rock.00080.wav", "dataset": "gtzan", "fingerprint": "md5:0f7c228fb7cda66bebf10d5d8492dc37", "fold": "val", "group_key": "artist:survivor", "md5": "0f7c228fb7cda66bebf10d5d8492dc37", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 118.08} +{"audio_path": "gtzan_genre/genres/rock/rock.00081.wav", "dataset": "gtzan", "fingerprint": "md5:5db1a0c389aec4ef2fe606f03fd47f2b", "fold": "val", "group_key": "artist:survivor", "md5": "5db1a0c389aec4ef2fe606f03fd47f2b", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 118.6} +{"audio_path": "gtzan_genre/genres/rock/rock.00082.wav", "dataset": "gtzan", "fingerprint": "md5:cb6ab62aa929f30963ae550b5e9c9946", "fold": "val", "group_key": "artist:survivor", "md5": "cb6ab62aa929f30963ae550b5e9c9946", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 98.09} +{"audio_path": "gtzan_genre/genres/rock/rock.00083.wav", "dataset": "gtzan", "fingerprint": "md5:1f221d08dd8f321408dd6c922e11ddd2", "fold": "val", "group_key": "artist:survivor", "md5": "1f221d08dd8f321408dd6c922e11ddd2", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 121.7} +{"audio_path": "gtzan_genre/genres/rock/rock.00084.wav", "dataset": "gtzan", "fingerprint": "md5:a3e2c87b5df773f9bf2c4c123c554f71", "fold": "val", "group_key": "artist:survivor", "md5": "a3e2c87b5df773f9bf2c4c123c554f71", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 129.03} +{"audio_path": "gtzan_genre/genres/rock/rock.00085.wav", "dataset": "gtzan", "fingerprint": "md5:cd8765dcd9a3f8216240c953af2473ca", "fold": "val", "group_key": "artist:survivor", "md5": "cd8765dcd9a3f8216240c953af2473ca", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 97.48} From cf3081f4f00664fbaccdf076703f3b909d767f31 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:27:38 -0700 Subject: [PATCH 12/77] [Build] balance Ballroom groups across global fold targets --- src/deeprhythm/partitions.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index e3434fd..cdbd21e 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -158,13 +158,21 @@ def _ballroom_group(stem): def _assign_ballroom(groups): targets = {"train": 0.7, "val": 0.1, "test": 0.2} totals = Counter(key for tracks in groups.values() for key in tracks) + total_tracks = sum(totals.values()) counts = {fold: Counter() for fold in FOLDS} result = {} for group, tracks in sorted(groups.items(), key=lambda item: (-len(item[1]), item[0])): contribution = Counter(tracks) - def cost(fold): - return sum(((counts[fold][key] + contribution[key]) / max(totals[key], 1) - targets[fold]) ** 2 for key in contribution) - fold = min(FOLDS, key=lambda name: (cost(name), sum(counts[name].values()) / targets[name], name)) + def cost(candidate): + score = 0.0 + for fold in FOLDS: + for key, total in totals.items(): + value = counts[fold][key] + (contribution[key] if fold == candidate else 0) + score += ((value - targets[fold] * total) / max(total, 1)) ** 2 + size = sum(counts[fold].values()) + (len(tracks) if fold == candidate else 0) + score += ((size - targets[fold] * total_tracks) / total_tracks) ** 2 + return score + fold = min(FOLDS, key=lambda name: (cost(name), name)) result[group] = fold counts[fold].update(contribution) return result From b898008769e0e7d67b99554d4886ff7bcddf1abf Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:27:56 -0700 Subject: [PATCH 13/77] regenerate balanced Ballroom partitions --- data/splits/ballroom/test.jsonl | 340 ++++------------------ data/splits/ballroom/train.jsonl | 480 +++++++++++++++++++++++++++++++ data/splits/ballroom/val.jsonl | 262 +---------------- 3 files changed, 541 insertions(+), 541 deletions(-) diff --git a/data/splits/ballroom/test.jsonl b/data/splits/ballroom/test.jsonl index bf1d8fa..c37bd0e 100644 --- a/data/splits/ballroom/test.jsonl +++ b/data/splits/ballroom/test.jsonl @@ -1,58 +1,16 @@ -{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-05.wav", "dataset": "ballroom", "fingerprint": "md5:038707bccf3a886a6d70338998919382", "fold": "test", "genre": "ChaChaCha", "group_key": "album:cafe_paradiso", "md5": "038707bccf3a886a6d70338998919382", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-06.wav", "dataset": "ballroom", "fingerprint": "md5:eb725e25c27ad57760b0a6043ea0f06d", "fold": "test", "genre": "ChaChaCha", "group_key": "album:cafe_paradiso", "md5": "eb725e25c27ad57760b0a6043ea0f06d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-07.wav", "dataset": "ballroom", "fingerprint": "md5:81dcfe37ea3c50de0814b7ea4737440a", "fold": "test", "genre": "ChaChaCha", "group_key": "album:cafe_paradiso", "md5": "81dcfe37ea3c50de0814b7ea4737440a", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-08.wav", "dataset": "ballroom", "fingerprint": "md5:04c3570dfacc1e398dc37022af380e0d", "fold": "test", "genre": "ChaChaCha", "group_key": "album:cafe_paradiso", "md5": "04c3570dfacc1e398dc37022af380e0d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Fire-01.wav", "dataset": "ballroom", "fingerprint": "md5:1ea58f8d16f306758cd06b1ac1b6401a", "fold": "test", "genre": "ChaChaCha", "group_key": "album:fire", "md5": "1ea58f8d16f306758cd06b1ac1b6401a", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Fire-08.wav", "dataset": "ballroom", "fingerprint": "md5:5276b02e2a87f20d2059b525784639a9", "fold": "test", "genre": "ChaChaCha", "group_key": "album:fire", "md5": "5276b02e2a87f20d2059b525784639a9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Fire-14.wav", "dataset": "ballroom", "fingerprint": "md5:ee551c258c99e610f74041fce5eeb5a4", "fold": "test", "genre": "ChaChaCha", "group_key": "album:fire", "md5": "ee551c258c99e610f74041fce5eeb5a4", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam-01.wav", "dataset": "ballroom", "fingerprint": "md5:dc5c95e5b35339e2ab160059cd824f47", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam", "md5": "dc5c95e5b35339e2ab160059cd824f47", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 128.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam-04.wav", "dataset": "ballroom", "fingerprint": "md5:372bad55c2472c008de218fe44825477", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam", "md5": "372bad55c2472c008de218fe44825477", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 128.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam-07.wav", "dataset": "ballroom", "fingerprint": "md5:7b882a9a974e30c217cc07a9953f5bca", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam", "md5": "7b882a9a974e30c217cc07a9953f5bca", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam3-01.wav", "dataset": "ballroom", "fingerprint": "md5:d7f19e2af7ab84730de11b075ac26dca", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam3", "md5": "d7f19e2af7ab84730de11b075ac26dca", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 128.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam3-02.wav", "dataset": "ballroom", "fingerprint": "md5:8cfd69b228b2a4662440cdff3ce9278a", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam3", "md5": "8cfd69b228b2a4662440cdff3ce9278a", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 128.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam3-03.wav", "dataset": "ballroom", "fingerprint": "md5:c9e94063b698a1983f9d910b22a5329e", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam3", "md5": "c9e94063b698a1983f9d910b22a5329e", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 128.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam5-02.wav", "dataset": "ballroom", "fingerprint": "md5:0c86c4fa14cbde779ca437bbe18230ce", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam5", "md5": "0c86c4fa14cbde779ca437bbe18230ce", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam5-07.wav", "dataset": "ballroom", "fingerprint": "md5:86513ea0d67d786ab6e4f21a43e58e2c", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam5", "md5": "86513ea0d67d786ab6e4f21a43e58e2c", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam5-11.wav", "dataset": "ballroom", "fingerprint": "md5:60c4ec404b2d8ef9d6629e89b00529e3", "fold": "test", "genre": "ChaChaCha", "group_key": "album:latin_jam5", "md5": "60c4ec404b2d8ef9d6629e89b00529e3", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Pais_Tropical-05.wav", "dataset": "ballroom", "fingerprint": "md5:fcdee7d2b0382bca79dab791552d4b1a", "fold": "test", "genre": "ChaChaCha", "group_key": "album:pais_tropical", "md5": "fcdee7d2b0382bca79dab791552d4b1a", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Pais_Tropical-06.wav", "dataset": "ballroom", "fingerprint": "md5:ea58294675f0f6c9a00a407d83aad216", "fold": "test", "genre": "ChaChaCha", "group_key": "album:pais_tropical", "md5": "ea58294675f0f6c9a00a407d83aad216", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Pais_Tropical-07.wav", "dataset": "ballroom", "fingerprint": "md5:9cc5d855f343d2938e18efd3b4e41df6", "fold": "test", "genre": "ChaChaCha", "group_key": "album:pais_tropical", "md5": "9cc5d855f343d2938e18efd3b4e41df6", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Pais_Tropical-08.wav", "dataset": "ballroom", "fingerprint": "md5:33a99e8a19290eec7836816603ae31f7", "fold": "test", "genre": "ChaChaCha", "group_key": "album:pais_tropical", "md5": "33a99e8a19290eec7836816603ae31f7", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103401.wav", "dataset": "ballroom", "fingerprint": "md5:af981959f167c023cd05c8cf9595eb49", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "af981959f167c023cd05c8cf9595eb49", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103402.wav", "dataset": "ballroom", "fingerprint": "md5:07acb34e8d3fe01167f26fe2611ce842", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "07acb34e8d3fe01167f26fe2611ce842", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103403.wav", "dataset": "ballroom", "fingerprint": "md5:7293d3ca18ec30f7ae1774ac7eed0a4c", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "7293d3ca18ec30f7ae1774ac7eed0a4c", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103404.wav", "dataset": "ballroom", "fingerprint": "md5:19aadc424ad6393014cb5c5bf9faa715", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "19aadc424ad6393014cb5c5bf9faa715", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103405.wav", "dataset": "ballroom", "fingerprint": "md5:7d5f717c8346e7d0d6271bacaae454d1", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "7d5f717c8346e7d0d6271bacaae454d1", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103406.wav", "dataset": "ballroom", "fingerprint": "md5:d6014c8a0aa9bd4d82e724d7c297fe5b", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "d6014c8a0aa9bd4d82e724d7c297fe5b", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103407.wav", "dataset": "ballroom", "fingerprint": "md5:ac453c9fabb1bae43817bf2fce57bcba", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "ac453c9fabb1bae43817bf2fce57bcba", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103408.wav", "dataset": "ballroom", "fingerprint": "md5:ba6b7d03430f602b69f371233a381a2e", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "ba6b7d03430f602b69f371233a381a2e", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103409.wav", "dataset": "ballroom", "fingerprint": "md5:3335bf37c71a031e59d102c337703d8e", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "3335bf37c71a031e59d102c337703d8e", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103410.wav", "dataset": "ballroom", "fingerprint": "md5:26cad3b7717b97b891ff09da26b3b4c9", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "26cad3b7717b97b891ff09da26b3b4c9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103411.wav", "dataset": "ballroom", "fingerprint": "md5:6bccd603b428f2c5ccfafc7ce3ed9933", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "6bccd603b428f2c5ccfafc7ce3ed9933", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 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"Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103417.wav", "dataset": "ballroom", "fingerprint": "md5:a42cb8b9490e24719ba946f2ba708519", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1034", "md5": "a42cb8b9490e24719ba946f2ba708519", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103601.wav", "dataset": "ballroom", "fingerprint": "md5:cf63c8285f1aed74672ac777d2a1974b", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1036", "md5": "cf63c8285f1aed74672ac777d2a1974b", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 116.0} -{"audio_path": "audio/ChaChaCha/Media-103602.wav", "dataset": "ballroom", "fingerprint": "md5:987755c778058c3f962a0613d5958ab0", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1036", "md5": "987755c778058c3f962a0613d5958ab0", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103603.wav", "dataset": "ballroom", "fingerprint": "md5:d0107905e5188935c67a829cd4eaf88f", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1036", "md5": "d0107905e5188935c67a829cd4eaf88f", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103604.wav", "dataset": "ballroom", "fingerprint": "md5:3529f6845161ab20e647278ead21766e", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1036", "md5": "3529f6845161ab20e647278ead21766e", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103617.wav", "dataset": "ballroom", "fingerprint": "md5:50a025c66caf5d4b475d980c4120e57b", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1036", "md5": "50a025c66caf5d4b475d980c4120e57b", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 108.0} -{"audio_path": "audio/ChaChaCha/Media-103701.wav", "dataset": "ballroom", "fingerprint": "md5:ac33b25032729b6681236f41cd67fe5d", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1037", "md5": "ac33b25032729b6681236f41cd67fe5d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Media-103702.wav", "dataset": "ballroom", "fingerprint": "md5:a96aad448253d144a64764d75017b682", "fold": "test", "genre": "ChaChaCha", "group_key": "media-prefix:1037", "md5": "a96aad448253d144a64764d75017b682", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", 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a/data/splits/ballroom/train.jsonl b/data/splits/ballroom/train.jsonl index 6749d45..4e155f0 100644 --- a/data/splits/ballroom/train.jsonl +++ b/data/splits/ballroom/train.jsonl @@ -1,15 +1,341 @@ +{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-05.wav", "dataset": "ballroom", "fingerprint": "md5:038707bccf3a886a6d70338998919382", "fold": "train", "genre": "ChaChaCha", "group_key": "album:cafe_paradiso", "md5": "038707bccf3a886a6d70338998919382", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} +{"audio_path": "audio/ChaChaCha/Albums-Cafe_Paradiso-06.wav", "dataset": "ballroom", "fingerprint": "md5:eb725e25c27ad57760b0a6043ea0f06d", "fold": "train", "genre": "ChaChaCha", "group_key": "album:cafe_paradiso", "md5": "eb725e25c27ad57760b0a6043ea0f06d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} +{"audio_path": 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a316924ad1a799545b2df9486e495df90cdea49e Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:28:13 -0700 Subject: [PATCH 14/77] [Build] weight Ballroom fold size in stratification objective --- src/deeprhythm/partitions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index cdbd21e..01979dd 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -170,7 +170,7 @@ def cost(candidate): value = counts[fold][key] + (contribution[key] if fold == candidate else 0) score += ((value - targets[fold] * total) / max(total, 1)) ** 2 size = sum(counts[fold].values()) + (len(tracks) if fold == candidate else 0) - score += ((size - targets[fold] * total_tracks) / total_tracks) ** 2 + score += 25 * ((size - targets[fold] * total_tracks) / total_tracks) ** 2 return score fold = min(FOLDS, key=lambda name: (cost(name), name)) result[group] = fold From f68d72effab1852045dc2a6c672a63643562b334 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:28:21 -0700 Subject: [PATCH 15/77] regenerate final Ballroom partitions --- data/splits/ballroom/test.jsonl | 103 +++++++++++++-------------- data/splits/ballroom/train.jsonl | 115 +++++++++++++++---------------- data/splits/ballroom/val.jsonl | 34 +++++---- 3 files changed, 126 insertions(+), 126 deletions(-) diff --git a/data/splits/ballroom/test.jsonl b/data/splits/ballroom/test.jsonl index c37bd0e..cde817f 100644 --- a/data/splits/ballroom/test.jsonl +++ b/data/splits/ballroom/test.jsonl @@ -1,5 +1,8 @@ {"audio_path": "audio/ChaChaCha/Albums-I_Like_It2-01.wav", "dataset": "ballroom", "fingerprint": "md5:f6c1f1bf60b122555b33b52f34dac851", "fold": "test", "genre": "ChaChaCha", "group_key": "album:i_like_it2", "md5": "f6c1f1bf60b122555b33b52f34dac851", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 136.0} 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a/data/splits/ballroom/train.jsonl b/data/splits/ballroom/train.jsonl index 4e155f0..09c877b 100644 --- a/data/splits/ballroom/train.jsonl +++ b/data/splits/ballroom/train.jsonl @@ -5,9 +5,6 @@ {"audio_path": "audio/ChaChaCha/Albums-Fire-01.wav", "dataset": "ballroom", "fingerprint": "md5:1ea58f8d16f306758cd06b1ac1b6401a", "fold": "train", "genre": "ChaChaCha", "group_key": "album:fire", "md5": "1ea58f8d16f306758cd06b1ac1b6401a", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} {"audio_path": "audio/ChaChaCha/Albums-Fire-08.wav", "dataset": "ballroom", "fingerprint": "md5:5276b02e2a87f20d2059b525784639a9", "fold": "train", "genre": "ChaChaCha", "group_key": "album:fire", "md5": "5276b02e2a87f20d2059b525784639a9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} {"audio_path": "audio/ChaChaCha/Albums-Fire-14.wav", "dataset": "ballroom", "fingerprint": "md5:ee551c258c99e610f74041fce5eeb5a4", "fold": "train", "genre": "ChaChaCha", "group_key": "album:fire", "md5": "ee551c258c99e610f74041fce5eeb5a4", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 124.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam-01.wav", "dataset": "ballroom", "fingerprint": "md5:dc5c95e5b35339e2ab160059cd824f47", "fold": "train", "genre": "ChaChaCha", "group_key": "album:latin_jam", "md5": "dc5c95e5b35339e2ab160059cd824f47", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 128.0} -{"audio_path": "audio/ChaChaCha/Albums-Latin_Jam-04.wav", "dataset": "ballroom", "fingerprint": "md5:372bad55c2472c008de218fe44825477", "fold": "train", "genre": "ChaChaCha", "group_key": "album:latin_jam", "md5": "372bad55c2472c008de218fe44825477", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": 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"fingerprint": "md5:1754788ddf9797da7d6f93cf2bf4e153", "fold": "train", "genre": "Jive", "group_key": "album:fire", "md5": "1754788ddf9797da7d6f93cf2bf4e153", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} -{"audio_path": "audio/Jive/Albums-Latin_Jam-11.wav", "dataset": "ballroom", "fingerprint": "md5:33ad8e3cd0b5e7d708f9b6372836f96f", "fold": "train", "genre": "Jive", "group_key": "album:latin_jam", "md5": "33ad8e3cd0b5e7d708f9b6372836f96f", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 176.0} -{"audio_path": "audio/Jive/Albums-Latin_Jam-12.wav", "dataset": "ballroom", "fingerprint": "md5:d10d054ec9a61ba7033f33c605e7f554", "fold": "train", "genre": "Jive", "group_key": "album:latin_jam", "md5": "d10d054ec9a61ba7033f33c605e7f554", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 176.0} {"audio_path": "audio/Jive/Albums-Latin_Jam2-11.wav", "dataset": "ballroom", "fingerprint": "md5:e72f98a81ed7346983d941489c500483", "fold": "train", "genre": "Jive", "group_key": "album:latin_jam2", "md5": "e72f98a81ed7346983d941489c500483", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 176.0} {"audio_path": "audio/Jive/Albums-Latin_Jam2-12.wav", "dataset": "ballroom", "fingerprint": "md5:af7391e40b6295b540220035fe4cfcb4", "fold": "train", "genre": "Jive", "group_key": "album:latin_jam2", "md5": "af7391e40b6295b540220035fe4cfcb4", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 176.0} {"audio_path": "audio/Jive/Albums-Latin_Jam5-04.wav", "dataset": "ballroom", "fingerprint": "md5:36773bab4812a94785031fba3ef35c9e", "fold": "train", "genre": "Jive", "group_key": "album:latin_jam5", "md5": "36773bab4812a94785031fba3ef35c9e", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 176.0} @@ -99,6 +98,9 @@ {"audio_path": "audio/Jive/Albums-Pais_Tropical-14.wav", "dataset": "ballroom", "fingerprint": "md5:946c7361684cd0c059362ad47de7f0e9", "fold": "train", "genre": "Jive", "group_key": "album:pais_tropical", "md5": "946c7361684cd0c059362ad47de7f0e9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 168.0} {"audio_path": "audio/Jive/Albums-Pais_Tropical-15.wav", "dataset": "ballroom", "fingerprint": "md5:7b5e7ccbce58a9b0f0b41dc958129e01", "fold": "train", "genre": "Jive", "group_key": "album:pais_tropical", "md5": "7b5e7ccbce58a9b0f0b41dc958129e01", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 172.0} {"audio_path": "audio/Jive/Albums-Pais_Tropical-16.wav", "dataset": "ballroom", "fingerprint": "md5:ec49ec17a4d80f25cf28aaa38396414b", "fold": "train", "genre": 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"md5:a824e9cceb621aa9ba816c312444a42b", "fold": "train", "genre": "Jive", "group_key": "media-prefix:1035", "md5": "a824e9cceb621aa9ba816c312444a42b", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 176.0} {"audio_path": "audio/Jive/Media-103611.wav", "dataset": "ballroom", "fingerprint": "md5:ad19a21691d5e7813d29d88b07a2c67c", "fold": "train", "genre": "Jive", "group_key": "media-prefix:1036", "md5": "ad19a21691d5e7813d29d88b07a2c67c", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 172.0} {"audio_path": "audio/Jive/Media-103612.wav", "dataset": "ballroom", "fingerprint": "md5:c54ac5a1406dc58ae3e684b3f3bb290e", "fold": "train", "genre": "Jive", "group_key": "media-prefix:1036", "md5": "c54ac5a1406dc58ae3e684b3f3bb290e", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 176.0} {"audio_path": 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"provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 204.0} {"audio_path": "audio/Quickstep/Albums-Ballroom_Magic-17.wav", "dataset": "ballroom", "fingerprint": "md5:3a4a77a02bb1747a2788d29d303d22c9", "fold": "train", "genre": "Quickstep", "group_key": "album:ballroom_magic", "md5": "3a4a77a02bb1747a2788d29d303d22c9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 200.0} -{"audio_path": "audio/Quickstep/Albums-Chrisanne3-13.wav", "dataset": "ballroom", "fingerprint": "md5:0979faa7f28025b95301286d7f1234c6", "fold": "train", "genre": "Quickstep", "group_key": "album:chrisanne3", "md5": "0979faa7f28025b95301286d7f1234c6", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 204.0} -{"audio_path": "audio/Quickstep/Albums-Chrisanne3-14.wav", "dataset": "ballroom", "fingerprint": "md5:2d98b8ffccd96290e0bb821b8a1cdbd9", "fold": "train", "genre": "Quickstep", "group_key": "album:chrisanne3", "md5": "2d98b8ffccd96290e0bb821b8a1cdbd9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 200.0} -{"audio_path": "audio/Quickstep/Albums-Chrisanne3-15.wav", "dataset": "ballroom", "fingerprint": "md5:4b6041c812dd4c825858afee847e0732", "fold": "train", "genre": "Quickstep", "group_key": "album:chrisanne3", "md5": "4b6041c812dd4c825858afee847e0732", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 204.0} {"audio_path": "audio/Quickstep/Albums-Step_By_Step-15.wav", "dataset": "ballroom", "fingerprint": "md5:5f8666834187eb7e8b0bc3466851072d", "fold": "train", "genre": "Quickstep", "group_key": "album:step_by_step", "md5": "5f8666834187eb7e8b0bc3466851072d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 153.0} {"audio_path": "audio/Quickstep/Albums-Step_By_Step-16.wav", "dataset": "ballroom", "fingerprint": "md5:72f88fd176314dadf18f7d54ad83f8dd", "fold": "train", "genre": "Quickstep", "group_key": "album:step_by_step", "md5": "72f88fd176314dadf18f7d54ad83f8dd", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 153.0} {"audio_path": "audio/Quickstep/Albums-Step_By_Step-17.wav", "dataset": "ballroom", "fingerprint": "md5:bebcb9afb908a44bff02c7e6d387fb37", "fold": "train", "genre": "Quickstep", "group_key": "album:step_by_step", "md5": "bebcb9afb908a44bff02c7e6d387fb37", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 153.0} @@ -143,6 +142,9 @@ {"audio_path": "audio/Quickstep/Media-100614.wav", "dataset": "ballroom", "fingerprint": "md5:0ee3819f8836e40a3cf1152890c7192a", "fold": "train", "genre": "Quickstep", "group_key": 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204.0} {"audio_path": "audio/Quickstep/Media-104216.wav", "dataset": "ballroom", "fingerprint": "md5:0417af9d6b9b35166e71b2f58465e84b", "fold": "train", "genre": "Quickstep", "group_key": "media-prefix:1042", "md5": "0417af9d6b9b35166e71b2f58465e84b", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 200.0} {"audio_path": "audio/Quickstep/Media-104217.wav", "dataset": "ballroom", "fingerprint": "md5:0f342374096260a6a8c035987ff439a2", "fold": "train", "genre": "Quickstep", "group_key": "media-prefix:1042", "md5": "0f342374096260a6a8c035987ff439a2", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 204.0} {"audio_path": "audio/Quickstep/Media-104218.wav", "dataset": "ballroom", "fingerprint": "md5:413919ce11ce538c4a564e3012d0e856", "fold": "train", "genre": "Quickstep", "group_key": "media-prefix:1042", "md5": "413919ce11ce538c4a564e3012d0e856", "provenance": 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CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} {"audio_path": "audio/Waltz/Media-105001.wav", "dataset": "ballroom", "fingerprint": "md5:9204ea256c2bd868d9c4f3cb03c8dde2", "fold": "train", "genre": "Waltz", "group_key": "media-prefix:1050", "md5": "9204ea256c2bd868d9c4f3cb03c8dde2", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} {"audio_path": "audio/Waltz/Media-105002.wav", "dataset": "ballroom", "fingerprint": "md5:c571dda7466239ae43cc51f0d4ebe7e8", "fold": "train", "genre": "Waltz", "group_key": "media-prefix:1050", "md5": "c571dda7466239ae43cc51f0d4ebe7e8", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} {"audio_path": "audio/Waltz/Media-105003.wav", "dataset": "ballroom", "fingerprint": "md5:90ab9d4d763ecc0d5b959485f2590308", "fold": "train", "genre": "Waltz", "group_key": "media-prefix:1050", "md5": 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"fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1033", "md5": "ecb5fa0cd7dfebad0d546ec4709e4fbc", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} -{"audio_path": "audio/VienneseWaltz/Media-103308.wav", "dataset": "ballroom", "fingerprint": "md5:05914367702529a18307d4ab453893ea", "fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1033", "md5": "05914367702529a18307d4ab453893ea", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} -{"audio_path": "audio/VienneseWaltz/Media-103315.wav", "dataset": "ballroom", "fingerprint": "md5:73bdfcabd57bcac0688d313c11531510", "fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1033", "md5": "73bdfcabd57bcac0688d313c11531510", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} +{"audio_path": "audio/VienneseWaltz/Albums-Chrisanne1-07.wav", "dataset": "ballroom", "fingerprint": "md5:54b4b8da306dbd4f9fea726d67291f1d", "fold": "val", "genre": "VienneseWaltz", "group_key": "album:chrisanne1", "md5": "54b4b8da306dbd4f9fea726d67291f1d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} +{"audio_path": "audio/VienneseWaltz/Albums-Chrisanne1-08.wav", "dataset": "ballroom", "fingerprint": "md5:7a68f6bbed1c0e8b748ab8621be650ea", "fold": "val", "genre": "VienneseWaltz", "group_key": "album:chrisanne1", "md5": "7a68f6bbed1c0e8b748ab8621be650ea", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} +{"audio_path": "audio/VienneseWaltz/Media-104507.wav", "dataset": "ballroom", "fingerprint": "md5:5d59e4828c5d11021548137a27641805", "fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1045", "md5": "5d59e4828c5d11021548137a27641805", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} +{"audio_path": "audio/VienneseWaltz/Media-104508.wav", "dataset": "ballroom", "fingerprint": "md5:c2aa94a626951f47b8981b98cc1ebbf9", "fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1045", "md5": "c2aa94a626951f47b8981b98cc1ebbf9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 180.0} {"audio_path": "audio/VienneseWaltz/Media-105708.wav", "dataset": "ballroom", "fingerprint": "md5:de54495875a21829876e5d82586a43b2", "fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1057", "md5": "de54495875a21829876e5d82586a43b2", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 177.0} {"audio_path": "audio/VienneseWaltz/Media-105709.wav", "dataset": "ballroom", "fingerprint": "md5:43d3a54d1ccf54779cc337dba5160444", "fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1057", "md5": "43d3a54d1ccf54779cc337dba5160444", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 177.0} {"audio_path": "audio/VienneseWaltz/Media-105710.wav", "dataset": "ballroom", "fingerprint": "md5:2c36390ccee95991d1b71194828b9e77", "fold": "val", "genre": "VienneseWaltz", "group_key": "media-prefix:1057", "md5": "2c36390ccee95991d1b71194828b9e77", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 177.0} +{"audio_path": "audio/Waltz/Albums-Chrisanne1-01.wav", "dataset": "ballroom", "fingerprint": "md5:2d29f9268e295a4eeca61dc40dec2db6", "fold": "val", "genre": "Waltz", "group_key": "album:chrisanne1", "md5": "2d29f9268e295a4eeca61dc40dec2db6", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} +{"audio_path": "audio/Waltz/Albums-Chrisanne1-02.wav", "dataset": "ballroom", "fingerprint": "md5:d588c7b2054b79f70be65ecb5956ba71", "fold": "val", "genre": "Waltz", "group_key": "album:chrisanne1", "md5": "d588c7b2054b79f70be65ecb5956ba71", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} +{"audio_path": "audio/Waltz/Albums-Chrisanne1-03.wav", "dataset": "ballroom", "fingerprint": "md5:ceac19629bccca6201661f2b1700d73d", "fold": "val", "genre": "Waltz", "group_key": "album:chrisanne1", "md5": "ceac19629bccca6201661f2b1700d73d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} {"audio_path": "audio/Waltz/Albums-Commitments-11.wav", "dataset": "ballroom", "fingerprint": "md5:af79a7d6d570c62314a5a65d2334bc54", "fold": "val", "genre": "Waltz", "group_key": "album:commitments", "md5": "af79a7d6d570c62314a5a65d2334bc54", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 91.0} -{"audio_path": "audio/Waltz/Media-103301.wav", "dataset": "ballroom", "fingerprint": "md5:0e09885ac180a037c8771ef5ae537724", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1033", "md5": "0e09885ac180a037c8771ef5ae537724", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} -{"audio_path": "audio/Waltz/Media-103302.wav", "dataset": "ballroom", "fingerprint": "md5:2be94c86a42063d3f9149cb3b82f9e18", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1033", "md5": "2be94c86a42063d3f9149cb3b82f9e18", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} -{"audio_path": "audio/Waltz/Media-103303.wav", "dataset": "ballroom", "fingerprint": "md5:0fa3991ca85bfc6c306b6a4ddc505ee9", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1033", "md5": "0fa3991ca85bfc6c306b6a4ddc505ee9", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 84.0} +{"audio_path": "audio/Waltz/Media-104501.wav", "dataset": "ballroom", "fingerprint": "md5:f255f2b4760a301625128ae06c5b7e6e", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1045", "md5": "f255f2b4760a301625128ae06c5b7e6e", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 87.0} +{"audio_path": "audio/Waltz/Media-104502.wav", "dataset": "ballroom", "fingerprint": "md5:e0676352ac8a2e04c463b653655d9ca1", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1045", "md5": "e0676352ac8a2e04c463b653655d9ca1", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 87.0} +{"audio_path": "audio/Waltz/Media-104503.wav", "dataset": "ballroom", "fingerprint": "md5:6d67ea6d44590806142d0fbac943217c", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1045", "md5": "6d67ea6d44590806142d0fbac943217c", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 87.0} +{"audio_path": "audio/Waltz/Media-104504.wav", "dataset": "ballroom", "fingerprint": "md5:ccbf3983d0efb3fec1266a7bb2f31db1", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1045", "md5": "ccbf3983d0efb3fec1266a7bb2f31db1", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 87.0} {"audio_path": "audio/Waltz/Media-105701.wav", "dataset": "ballroom", "fingerprint": "md5:4e3b4afef8e279e701eee6e1d33ead66", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1057", "md5": "4e3b4afef8e279e701eee6e1d33ead66", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 87.0} {"audio_path": "audio/Waltz/Media-105702.wav", "dataset": "ballroom", "fingerprint": "md5:1c391c84e220d19ec0725b881e2dbc47", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1057", "md5": "1c391c84e220d19ec0725b881e2dbc47", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 87.0} {"audio_path": "audio/Waltz/Media-105703.wav", "dataset": "ballroom", "fingerprint": "md5:08a10a8bd1abb68cf33354383f00664d", "fold": "val", "genre": "Waltz", "group_key": "media-prefix:1057", "md5": "08a10a8bd1abb68cf33354383f00664d", "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations", "source_fold": "deeprhythm-v0.8", "tempo": 87.0} From d2b4a1c3d8ed8743e11ef71caf5b5ae6f397454f Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:28:38 -0700 Subject: [PATCH 16/77] [Build] conform partition tooling to project lint --- src/deeprhythm/partitions.py | 49 +++++++++++++++++++++++++----------- 1 file changed, 35 insertions(+), 14 deletions(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index 01979dd..ea9fc9d 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -43,7 +43,8 @@ def read_tempo(path): def write_rows(path, rows): path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("".join(json.dumps(row, sort_keys=True) + "\n" for row in sorted(rows, key=lambda r: r["audio_path"]))) + serialized = (json.dumps(row, sort_keys=True) + "\n" for row in sorted(rows, key=lambda r: r["audio_path"])) + path.write_text("".join(serialized)) def validate_partitions(rows): @@ -84,7 +85,8 @@ def _gtzan_index(path): def build_gtzan(root, sources): index = _gtzan_index(sources / "index.txt") assignment = {} - for fold, filename in (("train", "train_filtered.txt"), ("val", "valid_filtered.txt"), ("test", "test_filtered.txt")): + source_folds = (("train", "train_filtered.txt"), ("val", "valid_filtered.txt"), ("test", "test_filtered.txt")) + for fold, filename in source_folds: for line in (sources / filename).read_text().splitlines(): assignment[line.strip()] = fold rows = [] @@ -207,19 +209,35 @@ def historical_rows(dataset, root): rows = [] if dataset == "ballroom": files = sorted((root / "audio").glob("*/*.wav")) - tempo = lambda p: read_tempo(next((root / "annotations" / "tempo").glob(f"**/{p.stem}.bpm"))) + + def tempo(path): + return read_tempo(next((root / "annotations" / "tempo").glob(f"**/{path.stem}.bpm"))) elif dataset == "gtzan": files = sorted((root / "gtzan_genre" / "genres").glob("*/*.wav")) - files = [p for p in files if str(p.relative_to(root / "gtzan_genre" / "genres")) not in GTZAN_HISTORICAL_EXCLUSIONS] - tempo = lambda p: read_tempo(root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{p.parent.name}_{p.stem[-5:]}.bpm") + audio_root = root / "gtzan_genre" / "genres" + files = [p for p in files if str(p.relative_to(audio_root)) not in GTZAN_HISTORICAL_EXCLUSIONS] + + def tempo(path): + annotation = root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{path.parent.name}_{path.stem[-5:]}.bpm" + return read_tempo(annotation) else: repository = next(root.glob("giantsteps-tempo-dataset-*")) files = sorted((root / "audio_canonical").glob("*.mp3")) - files = [p for p in files if (repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")).exists() - and float((repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")).read_text()) > 0] - tempo = lambda p: read_tempo(repository / "annotations_v2" / "tempo" / p.name.replace(".mp3", ".bpm")) + annotation_root = repository / "annotations_v2" / "tempo" + files = [ + p for p in files + if (annotation_root / p.name.replace(".mp3", ".bpm")).exists() + and float((annotation_root / p.name.replace(".mp3", ".bpm")).read_text()) > 0 + ] + + def tempo(path): + return read_tempo(annotation_root / path.name.replace(".mp3", ".bpm")) for path in files: - rows.append({"audio_path": str(path.relative_to(root)), "tempo": tempo(path), "historical": "deeprhythm-0.7-full-set"}) + rows.append({ + "audio_path": str(path.relative_to(root)), + "tempo": tempo(path), + "historical": "deeprhythm-0.7-full-set", + }) return rows @@ -230,8 +248,10 @@ def main(argv=None): parser.add_argument("--validate-only", action="store_true") args = parser.parse_args(argv) if args.validate_only: - rows = [json.loads(line) for path in args.output.glob("*/train.jsonl") for line in path.read_text().splitlines()] - rows += [json.loads(line) for pattern in ("*/val.jsonl", "*/test.jsonl") for path in args.output.glob(pattern) for line in path.read_text().splitlines()] + rows = [] + for pattern in ("*/train.jsonl", "*/val.jsonl", "*/test.jsonl"): + for path in args.output.glob(pattern): + rows.extend(json.loads(line) for line in path.read_text().splitlines()) validate_partitions(rows) return sources = args.output / "sources" / "gtzan" @@ -244,9 +264,10 @@ def main(argv=None): validate_partitions(rows) for fold in FOLDS: write_rows(args.output / dataset / f"{fold}.jsonl", [row for row in rows if row["fold"] == fold]) - write_rows(args.output / "archive-v0.7" / "giantsteps.jsonl", historical_rows("giantsteps", args.datasets_root / "giantsteps_tempo")) - write_rows(args.output / "archive-v0.7" / "gtzan.jsonl", historical_rows("gtzan", args.datasets_root / "gtzan_genre")) - write_rows(args.output / "archive-v0.7" / "ballroom.jsonl", historical_rows("ballroom", args.datasets_root / "ballroom" / "B_1.0")) + archive = args.output / "archive-v0.7" + write_rows(archive / "giantsteps.jsonl", historical_rows("giantsteps", args.datasets_root / "giantsteps_tempo")) + write_rows(archive / "gtzan.jsonl", historical_rows("gtzan", args.datasets_root / "gtzan_genre")) + write_rows(archive / "ballroom.jsonl", historical_rows("ballroom", args.datasets_root / "ballroom" / "B_1.0")) if __name__ == "__main__": From 987f97bc04837a3d4c2843870dd3147b12ca01a0 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:29:29 -0700 Subject: [PATCH 17/77] [Build] add canonical manifest checksum index --- src/deeprhythm/partitions.py | 18 ++++++++++++++++++ tests/test_partitions.py | 5 +++-- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py index ea9fc9d..5137331 100644 --- a/src/deeprhythm/partitions.py +++ b/src/deeprhythm/partitions.py @@ -47,6 +47,22 @@ def write_rows(path, rows): path.write_text("".join(serialized)) +def write_checksums(root): + lines = [] + for path in sorted(root.glob("**/*.jsonl")): + checksum = hashlib.sha256(path.read_bytes()).hexdigest() + lines.append(f"{checksum} {path.relative_to(root)}\n") + (root / "SHA256SUMS").write_text("".join(lines)) + + +def validate_checksums(root): + for line in (root / "SHA256SUMS").read_text().splitlines(): + expected, relative = line.split(" ", 1) + actual = hashlib.sha256((root / relative).read_bytes()).hexdigest() + if actual != expected: + raise ValueError(f"manifest checksum mismatch: {relative}") + + def validate_partitions(rows): """Reject paths, group keys, or duplicate fingerprints crossing partitions.""" required = {"dataset", "audio_path", "md5", "tempo", "group_key", "fold", "fingerprint", "provenance"} @@ -253,6 +269,7 @@ def main(argv=None): for path in args.output.glob(pattern): rows.extend(json.loads(line) for line in path.read_text().splitlines()) validate_partitions(rows) + validate_checksums(args.output) return sources = args.output / "sources" / "gtzan" built = { @@ -268,6 +285,7 @@ def main(argv=None): write_rows(archive / "giantsteps.jsonl", historical_rows("giantsteps", args.datasets_root / "giantsteps_tempo")) write_rows(archive / "gtzan.jsonl", historical_rows("gtzan", args.datasets_root / "gtzan_genre")) write_rows(archive / "ballroom.jsonl", historical_rows("ballroom", args.datasets_root / "ballroom" / "B_1.0")) + write_checksums(args.output) if __name__ == "__main__": diff --git a/tests/test_partitions.py b/tests/test_partitions.py index 93963b9..1301a85 100644 --- a/tests/test_partitions.py +++ b/tests/test_partitions.py @@ -2,7 +2,7 @@ import pytest -from deeprhythm.partitions import _assign_ballroom, _ballroom_group, validate_partitions +from deeprhythm.partitions import _assign_ballroom, _ballroom_group, validate_checksums, validate_partitions def row(path, group, fold, fingerprint): @@ -45,4 +45,5 @@ def test_committed_manifests_are_valid_and_consistent(): dataset_rows.extend(loaded) validate_partitions(dataset_rows) rows.extend(dataset_rows) - assert {row_["dataset"] for row_ in rows} == {"giantsteps", "gtzan", "ballroom"} \ No newline at end of file + assert {row_["dataset"] for row_ in rows} == {"giantsteps", "gtzan", "ballroom"} + validate_checksums(root) \ No newline at end of file From 48e41427a6377642c7c51c39c49aee7ac4d91f76 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:30:37 -0700 Subject: [PATCH 18/77] publish canonical manifest SHA-256 checksums --- data/splits/SHA256SUMS | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 data/splits/SHA256SUMS diff --git a/data/splits/SHA256SUMS b/data/splits/SHA256SUMS new file mode 100644 index 0000000..23fd85f --- /dev/null +++ b/data/splits/SHA256SUMS @@ -0,0 +1,12 @@ +9d4844560ef371c48ae56b4f4eb705da39e6bce8d4c8e7fac3c4c42df4ba50d4 archive-v0.7/ballroom.jsonl +2578232af9164843a35d0bd1175e065d7b71bca50114d2709c6a4ba156f83e52 archive-v0.7/giantsteps.jsonl +b0f210b7c5d9435450bdcbc8d7c240b8f3d3654710cf55bdc386c83879f3fd02 archive-v0.7/gtzan.jsonl +532ee056939f549ca6e575060d0148a7271ef18a0739df2c6e440533ede0498f ballroom/test.jsonl +6961ac3e3384835920ddf57c83509efbbea950e72e05082e250778a826159d9b ballroom/train.jsonl +cfde5d0fe110f69ba1b13b261e47aff339bab507536af169b4b6352f8d40d113 ballroom/val.jsonl +346b83bd51833e8f5f53b8c0d980f7fd8c01917642db60652c3720812e601fed giantsteps/test.jsonl +8044435b7f362a802726c98466080f63fd2351c72f8ac865f3482cebad2af644 giantsteps/train.jsonl +8b8bbaeaf933a17be9b5c3724a6138fe27920db9e79ce896fa0bd56f320abc93 giantsteps/val.jsonl +1db0216a5fd020ebb931f7682c91268c6448584fca8dd7fe67f57fef6127898f gtzan/test.jsonl +51f6bb6387ab777eb27a62d16a46c549a68d969fb6077d28c1905d8271418b87 gtzan/train.jsonl +507f7dae232c7d95bb7e391bbf85706ecaeca17f3f7bca5befdfaa3ab63cf65e gtzan/val.jsonl From 50508a963dc2a87a9d11b31d9858f69e95a726ba Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:31:02 -0700 Subject: [PATCH 19/77] [Build] document exact partition evidence and limitations --- docs/research/partitions.md | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/docs/research/partitions.md b/docs/research/partitions.md index f501140..6652faa 100644 --- a/docs/research/partitions.md +++ b/docs/research/partitions.md @@ -3,13 +3,14 @@ The manifests in `data/splits` are the canonical v0.8 train, validation, and test selection. Generate them with `python -m deeprhythm.partitions --datasets-root ~/mir_datasets`; use `--validate-only` to check committed files without access to audio. Rows contain the dataset-relative `audio_path`, audio MD5, reference `tempo`, `group_key`, -fold, source fold, duplicate fingerprint, and annotation provenance. +fold, source fold, duplicate fingerprint, and annotation provenance. `SHA256SUMS` identifies every committed JSONL +manifest and is verified by `--validate-only`. ## GiantSteps The source is `giantsteps-tempo-dataset` commit `0b7d47b`, using its canonical audio MD5 files and crowdsourced v2 tempo annotations. Official folds 1–7 are training, fold 8 is validation, and folds 9–10 are test. Three official -entries without usable audio plus v2 annotation are omitted, leaving 661 of 664 entries. GiantSteps supplies no +entries with non-positive v2 tempo (`1327052`, `3041381`, and `3041383`) are omitted, leaving 661 of 664 entries. GiantSteps supplies no artist identity in the checked metadata, so its group key is the Beatport track identifier. This preserves the official split but **does not establish artist disjointness**; no artist identities are inferred. @@ -17,11 +18,12 @@ official split but **does not establish artist disjointness**; no artist identit Tempo comes from the checked-out `gtzan_tempo_beat` annotations. Selection starts from the fault-filtered Kereliuk–Sturm–Larsen partition at GTZAN metadata commit `fedc781`: 443 training, 197 validation, and 290 test -entries before the historical DeepRhythm exclusions. Those files still cross some artist keys in the current index, -so every known artist is moved wholly to its most protected source fold (test before validation before training). -Artist keys are parsed verbatim from Sturm's `index.txt`. -Rows whose index artist is `?` receive a per-track key rather than an invented identity. `jazz.00054` and -`reggae.00086` remain excluded for continuity with the v0.7 benchmark. +entries before the historical DeepRhythm exclusions. `jazz.00054` is removed explicitly; `reggae.00086` is already +absent from the fault-filtered source. Those files still cross some artist keys in the current index, so every known +artist is moved wholly to its most protected source fold (test before validation before training). Artist keys are +parsed verbatim from Sturm's `index.txt`. Rows whose index artist is unavailable receive a per-track key rather than +an invented identity. The final split is 422 training, 194 validation, and 313 test tracks. Twenty-four tracks move +from their source fold to coalesce artists, and 14 entries use per-track keys. ## Ballroom @@ -29,7 +31,8 @@ Audio and tempo annotations are Ballroom B_1.0. The CPJKU annotation README repo recording replicas; the second item of every published pair is excluded. Remaining tracks are grouped by the only identity evidence encoded locally: the `Albums--` album token or the first four digits of the six-digit `Media-` identifier. These are album/media groups, **not claimed artist identities**. Groups are assigned -deterministically while balancing dance style and 20-BPM tempo bins toward 70/10/20 percent. +deterministically while balancing dance style and 20-BPM tempo bins toward 70/10/20 percent. The 53 indivisible +groups yield 502 training, 49 validation, and 134 test tracks after 13 published-replica exclusions. Chromaprint is not installed in the reproducible environment and the source does not ship Extended Ballroom's fingerprints. The `fingerprint` field therefore detects byte-identical audio through MD5, while the published From a35b576b75c337a1482ee39d56efadd67bcf1f19 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:31:33 -0700 Subject: [PATCH 20/77] [Build] instrument inference with auditable classifier details --- src/deeprhythm/batch_infer.py | 41 ++++++++++++----- src/deeprhythm/bench/tempo.py | 8 +++- src/deeprhythm/model/predictor.py | 26 ++++++----- src/deeprhythm/model/results.py | 75 +++++++++++++++++++++++++++++++ src/deeprhythm/utils.py | 2 +- 5 files changed, 127 insertions(+), 25 deletions(-) create mode 100644 src/deeprhythm/model/results.py diff --git a/src/deeprhythm/batch_infer.py b/src/deeprhythm/batch_infer.py index 8d3edb7..6c3b64d 100644 --- a/src/deeprhythm/batch_infer.py +++ b/src/deeprhythm/batch_infer.py @@ -9,6 +9,7 @@ from deeprhythm.audio_proc.hcqm import compute_hcqm, make_kernels from deeprhythm.model.frame_cnn import DeepRhythmModel +from deeprhythm.model.results import shape_prediction from deeprhythm.utils import ( AudioLoadError, AudioTooShortError, @@ -69,7 +70,9 @@ def init_workers(dataset, n_workers=NUM_WORKERS): return task_queue, result_queue, producers, completion_event, queue_condition -def process_and_save(batch_audio, batch_meta, specs, model, out_path, conf=False, quiet=False): +def process_and_save( + batch_audio, batch_meta, specs, model, out_path, conf=False, quiet=False, details_path=None, top_k=5 +): """ Processes a batch of audio clips and saves the results to a JSONL file. """ @@ -90,16 +93,18 @@ def process_and_save(batch_audio, batch_meta, specs, model, out_path, conf=False filename, num_clips, start_idx = meta song_outputs = outputs[start_idx:start_idx+num_clips, :] probabilities = torch.softmax(song_outputs, dim=1) - mean_probabilities = probabilities.mean(dim=0) - confidence_score, predicted_class = torch.max(mean_probabilities, 0) - predicted_global_bpm = class_to_bpm(predicted_class.item()) + details = shape_prediction(probabilities, top_k=top_k) result = { "filename": filename, - "bpm": predicted_global_bpm + "bpm": details["bpm"] } if conf: - result['confidence'] = confidence_score.item() + result['confidence'] = details["confidence"] results.append(result) + if details_path is not None: + details["filename"] = filename + with open(details_path, "a") as details_file: + details_file.write(json.dumps(details) + "\n") with open(out_path, 'a') as f: for result in results: f.write(json.dumps(result) + "\n") @@ -107,7 +112,8 @@ def process_and_save(batch_audio, batch_meta, specs, model, out_path, conf=False def consume_and_process( result_queue, data_path, queue_condition, n_workers=NUM_WORKERS, - max_len_batch=NUM_BATCH, device='cuda', conf=False, quiet=False + max_len_batch=NUM_BATCH, device='cuda', conf=False, quiet=False, + details_path=None, top_k=5 ): batch_audio = [] batch_meta = [] @@ -145,26 +151,33 @@ def consume_and_process( total_clips += num_clips if total_clips >= max_len_batch: stacked_batch_audio = torch.cat(batch_audio, dim=0).to(device=device) - process_and_save(stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet) + process_and_save( + stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet, + details_path=details_path, top_k=top_k + ) total_clips = 0 batch_audio = [] batch_meta = [] if batch_audio: stacked_batch_audio = torch.cat(batch_audio, dim=0).to(device=device) - process_and_save(stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet) + process_and_save( + stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet, + details_path=details_path, top_k=top_k + ) def main( dataset, n_workers=NUM_WORKERS, max_len_batch=NUM_BATCH, - data_path='output.jsonl', device='cuda', conf=False, quiet=False + data_path='output.jsonl', device='cuda', conf=False, quiet=False, + details_path=None, top_k=5 ): task_queue, result_queue, producers, completion_event, queue_condition = init_workers(dataset, n_workers) try: consume_and_process( result_queue, data_path, queue_condition, n_workers=n_workers, max_len_batch=max_len_batch, - device=device, conf=conf, quiet=quiet + device=device, conf=conf, quiet=quiet, details_path=details_path, top_k=top_k ) finally: completion_event.set() @@ -197,6 +210,8 @@ def get_audio_files(dir_path): parser.add_argument('-d', '--device', type=str, default=get_device(), help='Device to use for inference') parser.add_argument('-c', '--conf', action='store_true', help='Include confidence score in output') parser.add_argument('-q', '--quiet', action='store_true', help='Use minimal output format') + parser.add_argument('--details-path', help='Optional JSONL path for structured classifier outputs') + parser.add_argument('--top-k', type=int, default=5, help='Number of peaks retained in details output') args = parser.parse_args() songs = get_audio_files(args.input_path) @@ -209,7 +224,9 @@ def get_audio_files(dir_path): data_path=args.output_path, device=args.device, conf=args.conf, - quiet=args.quiet + quiet=args.quiet, + details_path=args.details_path, + top_k=args.top_k ) if not args.quiet: print(f'{time.time()-start:.2f}') diff --git a/src/deeprhythm/bench/tempo.py b/src/deeprhythm/bench/tempo.py index 5b5d9c4..cd60a7c 100644 --- a/src/deeprhythm/bench/tempo.py +++ b/src/deeprhythm/bench/tempo.py @@ -57,9 +57,11 @@ def predict(path): return list(executor.map(predict, audio_paths)) -def predict_deeprhythm(audio_paths, output_path, device, workers=8, batch_size=128): +def predict_deeprhythm(audio_paths, output_path, device, workers=8, batch_size=128, details_path=None): output_path = Path(output_path) output_path.unlink(missing_ok=True) + if details_path is not None: + Path(details_path).unlink(missing_ok=True) run_deeprhythm( audio_paths, n_workers=workers, @@ -68,6 +70,7 @@ def predict_deeprhythm(audio_paths, output_path, device, workers=8, batch_size=1 device=device, conf=True, quiet=True, + details_path=str(details_path) if details_path is not None else None, ) by_path = {} for line in output_path.read_text().splitlines(): @@ -107,7 +110,8 @@ def main(argv=None): start = time.perf_counter() predictions = predict_deeprhythm( - audio_paths, output_dir / "deeprhythm-predictions.jsonl", args.device, args.workers, args.batch_size + audio_paths, output_dir / "deeprhythm-predictions.jsonl", args.device, args.workers, args.batch_size, + output_dir / "deeprhythm-details.jsonl" ) results["deeprhythm"] = evaluate(predictions, references, time.perf_counter() - start, args.tolerance) diff --git a/src/deeprhythm/model/predictor.py b/src/deeprhythm/model/predictor.py index c616895..d2529ce 100644 --- a/src/deeprhythm/model/predictor.py +++ b/src/deeprhythm/model/predictor.py @@ -10,6 +10,7 @@ from deeprhythm.batch_infer import get_audio_files from deeprhythm.batch_infer import main as batch_infer_main from deeprhythm.model.frame_cnn import DeepRhythmModel +from deeprhythm.model.results import shape_prediction from deeprhythm.utils import class_to_bpm, get_device, get_weights, load_and_split_audio, split_audio @@ -39,7 +40,9 @@ def _make_kernels(self) -> Tuple[Tensor, Tensor, Tensor]: """Initialize HCQM computation kernels.""" return make_kernels(device=self.device) - def _process_clips(self, clips: Tensor, include_confidence: bool = False) -> Union[float, Tuple[float, float]]: + def _process_clips( + self, clips: Tensor, include_confidence: bool = False, include_details: bool = False, top_k: int = 5 + ): """Process audio clips and return BPM prediction. Args: @@ -54,13 +57,15 @@ def _process_clips(self, clips: Tensor, include_confidence: bool = False) -> Uni with torch.no_grad(): outputs = self.model(input_batch.to(device=self.device)) probabilities = torch.softmax(outputs, dim=1) - mean_probabilities = probabilities.mean(dim=0) - confidence_score, predicted_class = torch.max(mean_probabilities, 0) - predicted_bpm = class_to_bpm(predicted_class.item()) + details = shape_prediction(probabilities, top_k=top_k) - return (predicted_bpm, confidence_score.item()) if include_confidence else predicted_bpm + if include_details: + return details + return (details["bpm"], details["confidence"]) if include_confidence else details["bpm"] - def predict(self, filename: str, include_confidence: bool = False) -> Union[float, Tuple[float, float]]: + def predict( + self, filename: str, include_confidence: bool = False, include_details: bool = False, top_k: int = 5 + ): """Predict BPM from an audio file. Args: @@ -71,11 +76,12 @@ def predict(self, filename: str, include_confidence: bool = False) -> Union[floa Predicted BPM or tuple of (BPM, confidence) """ clips = load_and_split_audio(filename, sr=22050) - return self._process_clips(clips, include_confidence) + return self._process_clips(clips, include_confidence, include_details, top_k) def predict_from_audio( - self, audio: List[float], sr: int, include_confidence: bool = False - ) -> Union[float, Tuple[float, float]]: + self, audio: List[float], sr: int, include_confidence: bool = False, + include_details: bool = False, top_k: int = 5 + ): """Predict BPM from audio tensor. Args: @@ -87,7 +93,7 @@ def predict_from_audio( Predicted BPM or tuple of (BPM, confidence) """ clips = split_audio(audio, sr) - return self._process_clips(clips, include_confidence) + return self._process_clips(clips, include_confidence, include_details, top_k) def predict_per_frame( self, filename: str, include_confidence: bool = False diff --git a/src/deeprhythm/model/results.py b/src/deeprhythm/model/results.py new file mode 100644 index 0000000..40842f8 --- /dev/null +++ b/src/deeprhythm/model/results.py @@ -0,0 +1,75 @@ +"""Canonical shaping of classifier outputs for inference APIs and benchmarks.""" + +from typing import Dict + +import torch +from torch import Tensor + +from deeprhythm.utils import class_to_bpm + +METRICAL_CANDIDATES = ( + ("same", 1.0), + ("double", 2.0), + ("half", 0.5), + ("triple", 3.0), + ("third", 1.0 / 3.0), +) + + +def _top_k(probabilities: Tensor, top_k: int): + count = min(top_k, probabilities.shape[-1]) + values, classes = torch.topk(probabilities, count, dim=-1) + if probabilities.ndim == 1: + values = values.unsqueeze(0) + classes = classes.unsqueeze(0) + return [ + [ + {"class": int(class_index), "bpm": class_to_bpm(int(class_index)), "probability": float(probability)} + for class_index, probability in zip(row_classes.tolist(), row_values.tolist()) + ] + for row_classes, row_values in zip(classes.cpu(), values.cpu()) + ] + + +def shape_prediction(probabilities: Tensor, top_k: int = 5, tolerance: float = 0.04) -> Dict: + """Aggregate per-clip probabilities into one auditable prediction.""" + if probabilities.ndim != 2 or probabilities.shape[0] == 0: + raise ValueError("probabilities must contain at least one clip and have shape (clips, classes)") + if top_k <= 0: + raise ValueError("top_k must be positive") + + probabilities = probabilities.detach().cpu() + mean_probabilities = probabilities.mean(dim=0) + confidence, predicted_class = mean_probabilities.max(dim=0) + predicted_class_value = int(predicted_class) + predicted_bpm = class_to_bpm(predicted_class_value) + clip_classes = probabilities.argmax(dim=1) + clip_top = _top_k(probabilities, top_k) + aggregate_top = _top_k(mean_probabilities, top_k)[0] + + class_bpms = torch.tensor( + [class_to_bpm(class_index) for class_index in range(mean_probabilities.shape[0])], + dtype=mean_probabilities.dtype, + ) + candidate_masses = {} + for name, factor in METRICAL_CANDIDATES: + candidate_bpm = predicted_bpm * factor + mask = torch.abs(class_bpms / candidate_bpm - 1.0) <= tolerance + candidate_masses[name] = { + "factor": factor, + "bpm": candidate_bpm, + "probability_mass": float(mean_probabilities[mask].sum()), + } + + clips = [] + for class_index, top in zip(clip_classes.tolist(), clip_top): + clips.append({"class": class_index, "bpm": class_to_bpm(class_index), "top_k": top}) + return { + "bpm": predicted_bpm, + "confidence": float(confidence), + "predicted_class": predicted_class_value, + "num_clips": probabilities.shape[0], + "clips": clips, + "aggregate_top_k": aggregate_top, + "candidate_masses": candidate_masses, + } \ No newline at end of file diff --git a/src/deeprhythm/utils.py b/src/deeprhythm/utils.py index 9d425f5..0a163cc 100644 --- a/src/deeprhythm/utils.py +++ b/src/deeprhythm/utils.py @@ -122,7 +122,7 @@ def bpm_to_class(bpm, min_bpm=30, max_bpm=286, num_classes=256): def class_to_bpm(class_index, min_bpm=30, max_bpm=286, num_classes=256): - """Map a class index back to a BPM value (to the center of the class interval).""" + """Map a class index to the lower edge of its BPM class interval.""" class_width = (max_bpm - min_bpm) / num_classes bpm = min_bpm + class_width * (class_index) return bpm From 9319ec1f5417c9d6c4b9d85a45bc8479e86db1cb Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:32:55 -0700 Subject: [PATCH 21/77] [Build] add benchmark error taxonomy and calibration tooling --- pyproject.toml | 1 + src/deeprhythm/bench/taxonomy.py | 95 ++++++++++++++++++++++++++++++++ tests/test_inference_details.py | 26 +++++++++ tests/test_taxonomy.py | 28 ++++++++++ 4 files changed, 150 insertions(+) create mode 100644 src/deeprhythm/bench/taxonomy.py create mode 100644 tests/test_inference_details.py create mode 100644 tests/test_taxonomy.py diff --git a/pyproject.toml b/pyproject.toml index f9e6cf6..b865d86 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,7 @@ dev = ["pytest", "ruff"] [project.scripts] deeprhythm-benchmark = "deeprhythm.bench.tempo:main" +deeprhythm-taxonomy = "deeprhythm.bench.taxonomy:main" [project.urls] Homepage = "https://github.com/bleugreen/deeprhythm" diff --git a/src/deeprhythm/bench/taxonomy.py b/src/deeprhythm/bench/taxonomy.py new file mode 100644 index 0000000..69c9999 --- /dev/null +++ b/src/deeprhythm/bench/taxonomy.py @@ -0,0 +1,95 @@ +"""Error taxonomy and confidence calibration for instrumented tempo benchmarks.""" + +import argparse +import json +from collections import defaultdict +from pathlib import Path + +import numpy as np + +RELATIONS = (("correct", 1.0), ("half", 0.5), ("double", 2.0), ("third", 1 / 3), ("triple", 3.0)) + + +def metrical_relation(prediction, reference, tolerance=0.04): + """Classify a prediction by its closest recognized metrical relation.""" + matches = [ + (abs(prediction / (reference * factor) - 1.0), name) + for name, factor in RELATIONS + if abs(prediction / (reference * factor) - 1.0) <= tolerance + ] + return min(matches)[1] if matches else "other" + + +def expected_calibration_error(confidences, outcomes, bins=10): + """Compute equal-width expected calibration error.""" + confidences = np.asarray(confidences, dtype=float) + outcomes = np.asarray(outcomes, dtype=float) + if confidences.shape != outcomes.shape or confidences.ndim != 1 or confidences.size == 0: + raise ValueError("confidence and outcome arrays must be equally sized and non-empty") + if np.any((confidences < 0) | (confidences > 1)): + raise ValueError("confidences must be between zero and one") + edges = np.linspace(0, 1, bins + 1) + result = [] + ece = 0.0 + for index in range(bins): + selected = (confidences >= edges[index]) & ( + confidences <= edges[index + 1] if index == bins - 1 else confidences < edges[index + 1] + ) + if not selected.any(): + continue + confidence = float(confidences[selected].mean()) + accuracy = float(outcomes[selected].mean()) + count = int(selected.sum()) + ece += count / confidences.size * abs(accuracy - confidence) + result.append({"lower": edges[index], "upper": edges[index + 1], "count": count, + "confidence": confidence, "accuracy": accuracy}) + return {"ece": ece, "bins": result} + + +def analyze(manifest_rows, detail_rows, tolerance=0.04): + """Join manifest and details rows by resolved audio path and summarize errors.""" + details = {str(Path(row["filename"]).expanduser().resolve()): row for row in detail_rows} + grouped = defaultdict(lambda: defaultdict(int)) + joined = [] + for manifest in manifest_rows: + path = str(Path(manifest["audio_path"]).expanduser().resolve()) + if path not in details: + raise ValueError(f"missing details for {path}") + detail = details[path] + reference = float(manifest["tempo"]) + prediction = float(detail["bpm"]) + relation = metrical_relation(prediction, reference, tolerance) + tempo_range = f"{int(reference // 20) * 20}-{int(reference // 20) * 20 + 19}" + genre = str(manifest.get("genre", manifest.get("style", "unknown"))) + grouped["relation"][relation] += 1 + grouped["tempo_range"][tempo_range + ":" + relation] += 1 + grouped["genre"][genre + ":" + relation] += 1 + joined.append({**manifest, "prediction": prediction, "relation": relation, + "confidence": float(detail["confidence"])}) + calibration = expected_calibration_error( + [row["confidence"] for row in joined], [row["relation"] == "correct" for row in joined] + ) + return {"tracks": len(joined), "counts": {key: dict(value) for key, value in grouped.items()}, + "calibration": calibration} + + +def _read_jsonl(path): + return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()] + + +def main(argv=None): + parser = argparse.ArgumentParser(description="Analyze instrumented DeepRhythm benchmark errors") + parser.add_argument("manifest") + parser.add_argument("details") + parser.add_argument("-o", "--output") + parser.add_argument("--tolerance", type=float, default=0.04) + args = parser.parse_args(argv) + result = analyze(_read_jsonl(args.manifest), _read_jsonl(args.details), args.tolerance) + rendered = json.dumps(result, indent=2) + "\n" + if args.output: + Path(args.output).write_text(rendered) + print(rendered, end="") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tests/test_inference_details.py b/tests/test_inference_details.py new file mode 100644 index 0000000..5b51e74 --- /dev/null +++ b/tests/test_inference_details.py @@ -0,0 +1,26 @@ +import pytest +import torch + +from deeprhythm.model.results import shape_prediction + + +def test_shape_prediction_aggregates_clips_deterministically(): + probabilities = torch.zeros((2, 256)) + probabilities[0, 90] = 0.8 + probabilities[0, 91] = 0.2 + probabilities[1, 90] = 0.4 + probabilities[1, 92] = 0.6 + + details = shape_prediction(probabilities, top_k=2) + + assert details["predicted_class"] == 90 + assert details["num_clips"] == 2 + assert details["confidence"] == pytest.approx(0.6) + assert [clip["class"] for clip in details["clips"]] == [90, 92] + assert [peak["class"] for peak in details["aggregate_top_k"]] == [90, 92] + assert set(details["candidate_masses"]) == {"same", "double", "half", "triple", "third"} + + +def test_shape_prediction_rejects_invalid_top_k(): + with pytest.raises(ValueError, match="positive"): + shape_prediction(torch.ones((1, 256)), top_k=0) \ No newline at end of file diff --git a/tests/test_taxonomy.py b/tests/test_taxonomy.py new file mode 100644 index 0000000..13f8ee6 --- /dev/null +++ b/tests/test_taxonomy.py @@ -0,0 +1,28 @@ +import pytest + +from deeprhythm.bench.taxonomy import analyze, expected_calibration_error, metrical_relation + + +@pytest.mark.parametrize( + ("prediction", "reference", "relation"), + [(120, 120, "correct"), (60, 120, "half"), (240, 120, "double"), (40, 120, "third"), + (180, 60, "triple"), (83, 120, "other")], +) +def test_metrical_relation(prediction, reference, relation): + assert metrical_relation(prediction, reference) == relation + + +def test_analyze_joins_paths_and_reports_calibration(tmp_path): + audio = tmp_path / "track.wav" + result = analyze( + [{"audio_path": str(audio), "tempo": 120, "genre": "dance"}], + [{"filename": str(audio), "bpm": 60, "confidence": 0.8}], + ) + assert result["counts"]["relation"] == {"half": 1} + assert result["counts"]["genre"] == {"dance:half": 1} + assert result["calibration"]["ece"] == pytest.approx(0.8) + + +def test_expected_calibration_error_validates_inputs(): + with pytest.raises(ValueError, match="between"): + expected_calibration_error([1.1], [True]) \ No newline at end of file From 974854da393febf050b112ce93a82282d14abbeb Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:33:29 -0700 Subject: [PATCH 22/77] [Build] remove obsolete batch inference import --- src/deeprhythm/batch_infer.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/deeprhythm/batch_infer.py b/src/deeprhythm/batch_infer.py index 6c3b64d..c85e7ce 100644 --- a/src/deeprhythm/batch_infer.py +++ b/src/deeprhythm/batch_infer.py @@ -13,7 +13,6 @@ from deeprhythm.utils import ( AudioLoadError, AudioTooShortError, - class_to_bpm, get_device, get_weights, load_and_split_audio, From 9ee586695f2da6b1c5ed44d08fc5d28de7b2c0d0 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:33:57 -0700 Subject: [PATCH 23/77] [Build] add octave correction baselines and learned classifier --- pyproject.toml | 1 + src/deeprhythm/bench/correction.py | 128 +++++++++++++++++++++++ src/deeprhythm/correction.py | 156 +++++++++++++++++++++++++++++ 3 files changed, 285 insertions(+) create mode 100644 src/deeprhythm/bench/correction.py create mode 100644 src/deeprhythm/correction.py diff --git a/pyproject.toml b/pyproject.toml index b865d86..a708eeb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,7 @@ dev = ["pytest", "ruff"] [project.scripts] deeprhythm-benchmark = "deeprhythm.bench.tempo:main" deeprhythm-taxonomy = "deeprhythm.bench.taxonomy:main" +deeprhythm-correction = "deeprhythm.bench.correction:main" [project.urls] Homepage = "https://github.com/bleugreen/deeprhythm" diff --git a/src/deeprhythm/bench/correction.py b/src/deeprhythm/bench/correction.py new file mode 100644 index 0000000..be33567 --- /dev/null +++ b/src/deeprhythm/bench/correction.py @@ -0,0 +1,128 @@ +"""Train/validation-only tooling for metrical-level correction experiments.""" + +import argparse +import json +import time +from collections import defaultdict +from pathlib import Path + +import numpy as np + +from deeprhythm.bench.taxonomy import expected_calibration_error, metrical_relation +from deeprhythm.bench.tempo import tempo_accuracy +from deeprhythm.correction import ( + FACTORS, + FactorClassifier, + clip_disagreement, + global_threshold, + identity, + select_parameter, + target_factor, + top_k_ratio, +) + + +def _read(path): + return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()] + + +def join_rows(manifest, details, features=None): + """Join rows by path and reject held-out rows at the fitting boundary.""" + if any(row.get("fold") == "test" for row in manifest): + raise ValueError("held-out test folds cannot be used by correction selection tooling") + details_by_path = {str(Path(row["filename"]).expanduser().resolve()): row for row in details} + features_by_path = { + str(Path(row["filename"]).expanduser().resolve()): row["features"] for row in (features or []) + } + joined = [] + for row in manifest: + path = str(Path(row["audio_path"]).expanduser().resolve()) + if path not in details_by_path: + raise ValueError(f"missing inference details for {path}") + joined.append({**row, "detail": details_by_path[path], "features": features_by_path.get(path)}) + return joined + + +def evaluate_rows(rows, predictions, confidences=None, latency_seconds=0.0): + references = [float(row["tempo"]) for row in rows] + report = {"tracks": len(rows), "latency": { + "total_seconds": latency_seconds, + "added_milliseconds_per_track": latency_seconds * 1000 / len(rows), + }} + for tolerance in (0.02, 0.04): + report[f"metrics_{int(tolerance * 100)}pct"] = tempo_accuracy(predictions, references, tolerance) + relations = [metrical_relation(prediction, reference, 0.04) + for prediction, reference in zip(predictions, references)] + report["taxonomy"] = dict(sorted((name, relations.count(name)) for name in set(relations))) + slices = defaultdict(list) + for row, prediction in zip(rows, predictions): + label = str(row.get("genre", row.get("style", "unknown"))) + slices[label].append((prediction, float(row["tempo"]))) + report["slices"] = { + label: {"tracks": len(values), **tempo_accuracy(*zip(*values), tolerance=0.04)} + for label, values in sorted(slices.items()) + } + if confidences is not None: + report["calibration"] = expected_calibration_error( + confidences, [relation == "correct" for relation in relations] + ) + return report + + +def run_experiment(train_rows, val_rows): + """Fit all variants on train/validation data and return validation reports.""" + val_details = [row["detail"] for row in val_rows] + val_refs = [float(row["tempo"]) for row in val_rows] + grids = { + "global_threshold": np.arange(50, 151, 5), + "top_k_ratio": np.geomspace(0.01, 10.0, 31), + "clip_disagreement": np.linspace(0.125, 1.0, 8), + } + functions = {"global_threshold": global_threshold, "top_k_ratio": top_k_ratio, + "clip_disagreement": clip_disagreement} + output = {"identity": evaluate_rows(val_rows, [identity(detail) for detail in val_details])} + for name, grid in grids.items(): + parameter = select_parameter(val_details, val_refs, name, grid) + start = time.perf_counter() + predictions = [functions[name](detail, parameter) for detail in val_details] + elapsed = time.perf_counter() - start + output[name] = {"selected_parameter": parameter, **evaluate_rows(val_rows, predictions, latency_seconds=elapsed)} + + if all(row["features"] is not None for row in train_rows + val_rows): + classifier = FactorClassifier().fit( + [row["features"] for row in train_rows], + [target_factor(float(row["detail"]["bpm"]), float(row["tempo"])) for row in train_rows], + ) + start = time.perf_counter() + probabilities = classifier.predict_proba([row["features"] for row in val_rows]) + predictions = [float(row["detail"]["bpm"]) * FACTORS[index] + for row, index in zip(val_rows, probabilities.argmax(axis=1))] + elapsed = time.perf_counter() - start + output["learned"] = { + "model": classifier.to_dict(), + **evaluate_rows(val_rows, predictions, probabilities.max(axis=1), elapsed), + } + return output + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--train-manifest", required=True) + parser.add_argument("--train-details", required=True) + parser.add_argument("--val-manifest", required=True) + parser.add_argument("--val-details", required=True) + parser.add_argument("--train-features") + parser.add_argument("--val-features") + parser.add_argument("-o", "--output", required=True) + args = parser.parse_args(argv) + train = join_rows(_read(args.train_manifest), _read(args.train_details), + _read(args.train_features) if args.train_features else None) + val = join_rows(_read(args.val_manifest), _read(args.val_details), + _read(args.val_features) if args.val_features else None) + result = run_experiment(train, val) + Path(args.output).write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/deeprhythm/correction.py b/src/deeprhythm/correction.py new file mode 100644 index 0000000..f08d761 --- /dev/null +++ b/src/deeprhythm/correction.py @@ -0,0 +1,156 @@ +"""Metrical-level correction baselines and lightweight learned corrector.""" + +from dataclasses import dataclass +from typing import Dict, Iterable, Sequence + +import numpy as np +import torch + +from deeprhythm.model.results import METRICAL_CANDIDATES + +FACTORS = tuple(factor for _name, factor in METRICAL_CANDIDATES) +FACTOR_NAMES = tuple(name for name, _factor in METRICAL_CANDIDATES) + + +def identity(detail: Dict) -> float: + return float(detail["bpm"]) + + +def global_threshold(detail: Dict, threshold: float) -> float: + bpm = identity(detail) + return bpm * 2.0 if bpm < threshold else bpm + + +def top_k_ratio(detail: Dict, ratio: float) -> float: + """Double when aggregate probability mass at 2x exceeds a ratio of same-level mass.""" + masses = detail["candidate_masses"] + same = float(masses["same"]["probability_mass"]) + double = float(masses["double"]["probability_mass"]) + return identity(detail) * (2.0 if double >= ratio * max(same, 1e-12) else 1.0) + + +def clip_disagreement(detail: Dict, threshold: float) -> float: + """Double when enough clip argmaxes support the faster metrical level.""" + bpm = identity(detail) + clips = detail.get("clips", ()) + if not clips: + return bpm + faster = sum(float(clip["bpm"]) >= 1.5 * bpm for clip in clips) / len(clips) + return bpm * (2.0 if faster >= threshold else 1.0) + + +def _lag_energy(envelope: np.ndarray, lag: float) -> float: + lag = int(round(lag)) + if lag <= 0 or lag >= envelope.size: + return 0.0 + left, right = envelope[:-lag], envelope[lag:] + scale = np.linalg.norm(left) * np.linalg.norm(right) + return float(np.dot(left, right) / scale) if scale > 0 else 0.0 + + +def onset_periodicity_features( + waveform, sample_rate: int, predicted_bpm: float, n_fft: int = 1024, hop_length: int = 256 +) -> np.ndarray: + """Extract dependency-free onset autocorrelation, spectral balance, and onset-rate features.""" + if sample_rate <= 0 or predicted_bpm <= 0: + raise ValueError("sample_rate and predicted_bpm must be positive") + audio = torch.as_tensor(waveform, dtype=torch.float32).flatten() + if audio.numel() < n_fft: + audio = torch.nn.functional.pad(audio, (0, n_fft - audio.numel())) + spectrum = torch.stft(audio, n_fft=n_fft, hop_length=hop_length, return_complex=True) + magnitude = spectrum.abs() + flux = torch.relu(magnitude[:, 1:] - magnitude[:, :-1]).mean(dim=0) + envelope = flux.cpu().numpy().astype(float) + envelope -= envelope.mean() if envelope.size else 0.0 + frames_per_minute = 60.0 * sample_rate / hop_length + periodicity = [_lag_energy(envelope, frames_per_minute / (predicted_bpm * factor)) for factor in FACTORS] + + mean_spectrum = magnitude.mean(dim=1) + split = max(1, mean_spectrum.numel() // 4) + low = float(mean_spectrum[:split].mean()) + high = float(mean_spectrum[split:].mean()) if split < mean_spectrum.numel() else 0.0 + spectral_balance = np.log1p(high) - np.log1p(low) + positive = np.maximum(envelope, 0) + onset_rate = float((positive > positive.mean() + positive.std()).sum() * sample_rate / hop_length / max(audio.numel(), 1)) + return np.asarray(periodicity + [spectral_balance, onset_rate, float(positive.mean()), float(positive.std())]) + + +def target_factor(prediction: float, reference: float) -> int: + """Return the candidate factor whose corrected tempo is closest to the reference.""" + errors = [abs(prediction * factor / reference - 1.0) for factor in FACTORS] + return int(np.argmin(errors)) + + +@dataclass +class FactorClassifier: + """Small multinomial logistic-regression model implemented with NumPy.""" + + weights: np.ndarray = None + mean: np.ndarray = None + scale: np.ndarray = None + + def fit(self, features, labels, learning_rate=0.1, epochs=500, l2=1e-3): + x = np.asarray(features, dtype=float) + y = np.asarray(labels, dtype=int) + if x.ndim != 2 or y.shape != (x.shape[0],) or not x.shape[0]: + raise ValueError("features and labels must be non-empty aligned arrays") + if np.any((y < 0) | (y >= len(FACTORS))): + raise ValueError("labels contain an unknown factor") + self.mean = x.mean(axis=0) + self.scale = x.std(axis=0) + self.scale[self.scale < 1e-8] = 1.0 + z = np.column_stack([(x - self.mean) / self.scale, np.ones(x.shape[0])]) + self.weights = np.zeros((z.shape[1], len(FACTORS))) + targets = np.eye(len(FACTORS))[y] + for _ in range(epochs): + logits = z @ self.weights + logits -= logits.max(axis=1, keepdims=True) + probabilities = np.exp(logits) + probabilities /= probabilities.sum(axis=1, keepdims=True) + gradient = z.T @ (probabilities - targets) / z.shape[0] + gradient[:-1] += l2 * self.weights[:-1] + self.weights -= learning_rate * gradient + return self + + def predict_proba(self, features): + if self.weights is None: + raise ValueError("classifier has not been fitted") + x = np.atleast_2d(np.asarray(features, dtype=float)) + z = np.column_stack([(x - self.mean) / self.scale, np.ones(x.shape[0])]) + logits = z @ self.weights + logits -= logits.max(axis=1, keepdims=True) + probabilities = np.exp(logits) + return probabilities / probabilities.sum(axis=1, keepdims=True) + + def correct(self, detail: Dict, features) -> float: + return identity(detail) * FACTORS[int(self.predict_proba(features).argmax(axis=1)[0])] + + def to_dict(self): + if self.weights is None: + raise ValueError("classifier has not been fitted") + return {"weights": self.weights.tolist(), "mean": self.mean.tolist(), "scale": self.scale.tolist(), + "factors": list(FACTORS)} + + @classmethod + def from_dict(cls, row): + if tuple(row["factors"]) != FACTORS: + raise ValueError("model factor taxonomy does not match this version") + return cls(np.asarray(row["weights"]), np.asarray(row["mean"]), np.asarray(row["scale"])) + + +def select_parameter(details: Sequence[Dict], references: Sequence[float], method: str, candidates: Iterable[float]): + """Select one scalar baseline parameter exclusively from validation rows.""" + from deeprhythm.bench.tempo import tempo_accuracy + + methods = {"global_threshold": global_threshold, "top_k_ratio": top_k_ratio, + "clip_disagreement": clip_disagreement} + if method not in methods: + raise ValueError(f"unknown selectable method: {method}") + if len(details) != len(references) or not details: + raise ValueError("details and references must be non-empty and aligned") + scored = [] + for value in candidates: + predictions = [methods[method](detail, value) for detail in details] + metrics = tempo_accuracy(predictions, references, tolerance=0.04) + scored.append((metrics["acc1"], metrics["acc2"], -float(value), float(value))) + return max(scored)[-1] \ No newline at end of file From 3cbe3d8b293cc0419c4f881d8ffe8136a7921ff2 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:35:58 -0700 Subject: [PATCH 24/77] [Build] test and document correction experiments --- docs/benchmarks.md | 25 +++++++++ src/deeprhythm/bench/correction.py | 5 +- src/deeprhythm/correction.py | 6 +- tests/test_correction.py | 89 ++++++++++++++++++++++++++++++ 4 files changed, 122 insertions(+), 3 deletions(-) create mode 100644 tests/test_correction.py diff --git a/docs/benchmarks.md b/docs/benchmarks.md index fcec059..365bec9 100644 --- a/docs/benchmarks.md +++ b/docs/benchmarks.md @@ -111,3 +111,28 @@ tempo was used for each included track. The README's historical 953-track benchmark describes its genre mix but does not publish an obtainable manifest or reference annotations. It remains useful historical context, but the results above are the public, independently reproducible benchmark. + +## Metrical-level correction experiments + +`deeprhythm-correction` is the canonical train/validation selection tool for octave-correction experiments. It +consumes inference details sidecars and reports identity, global tempo threshold, top-k candidate-mass ratio, +per-clip disagreement, and a small learned factor classifier. The learned path accepts precomputed onset-periodicity +feature JSONL rows containing `filename` and `features`. + +The feature extractor is `deeprhythm.correction.onset_periodicity_features`. It computes onset-envelope +autocorrelation at the five metrical candidates from `model/results.py`, plus spectral-balance and onset-rate +statistics. It uses the existing PyTorch and NumPy dependencies. The learned model is multinomial logistic regression. + +~~~bash +deeprhythm-correction \ + --train-manifest data/splits/giantsteps/train.jsonl --train-details results/train/details.jsonl \ + --val-manifest data/splits/giantsteps/val.jsonl --val-details results/val/details.jsonl \ + --train-features results/train/onset-features.jsonl --val-features results/val/onset-features.jsonl \ + --output results/giantsteps/correction-validation.json +~~~ + +The command rejects any manifest row whose `fold` is `test`. Parameter and model selection are train/validation +operations. Final held-out evaluation remains a separate, one-shot reporting step after configuration is frozen. + +Every variant reports Acc1 and Acc2 at 2% and 4%, metrical error taxonomy, genre/style slices, and added correction +latency. The learned classifier also reports confidence reliability bins and expected calibration error. diff --git a/src/deeprhythm/bench/correction.py b/src/deeprhythm/bench/correction.py index be33567..0b40bbc 100644 --- a/src/deeprhythm/bench/correction.py +++ b/src/deeprhythm/bench/correction.py @@ -86,7 +86,10 @@ def run_experiment(train_rows, val_rows): start = time.perf_counter() predictions = [functions[name](detail, parameter) for detail in val_details] elapsed = time.perf_counter() - start - output[name] = {"selected_parameter": parameter, **evaluate_rows(val_rows, predictions, latency_seconds=elapsed)} + output[name] = { + "selected_parameter": parameter, + **evaluate_rows(val_rows, predictions, latency_seconds=elapsed), + } if all(row["features"] is not None for row in train_rows + val_rows): classifier = FactorClassifier().fit( diff --git a/src/deeprhythm/correction.py b/src/deeprhythm/correction.py index f08d761..7d7104f 100644 --- a/src/deeprhythm/correction.py +++ b/src/deeprhythm/correction.py @@ -57,7 +57,8 @@ def onset_periodicity_features( audio = torch.as_tensor(waveform, dtype=torch.float32).flatten() if audio.numel() < n_fft: audio = torch.nn.functional.pad(audio, (0, n_fft - audio.numel())) - spectrum = torch.stft(audio, n_fft=n_fft, hop_length=hop_length, return_complex=True) + window = torch.hann_window(n_fft, dtype=audio.dtype, device=audio.device) + spectrum = torch.stft(audio, n_fft=n_fft, hop_length=hop_length, window=window, return_complex=True) magnitude = spectrum.abs() flux = torch.relu(magnitude[:, 1:] - magnitude[:, :-1]).mean(dim=0) envelope = flux.cpu().numpy().astype(float) @@ -71,7 +72,8 @@ def onset_periodicity_features( high = float(mean_spectrum[split:].mean()) if split < mean_spectrum.numel() else 0.0 spectral_balance = np.log1p(high) - np.log1p(low) positive = np.maximum(envelope, 0) - onset_rate = float((positive > positive.mean() + positive.std()).sum() * sample_rate / hop_length / max(audio.numel(), 1)) + onset_count = (positive > positive.mean() + positive.std()).sum() + onset_rate = float(onset_count * sample_rate / hop_length / max(audio.numel(), 1)) return np.asarray(periodicity + [spectral_balance, onset_rate, float(positive.mean()), float(positive.std())]) diff --git a/tests/test_correction.py b/tests/test_correction.py new file mode 100644 index 0000000..6f871df --- /dev/null +++ b/tests/test_correction.py @@ -0,0 +1,89 @@ +import numpy as np +import pytest +import torch + +from deeprhythm.bench.correction import evaluate_rows, join_rows, run_experiment +from deeprhythm.correction import ( + FACTORS, + FactorClassifier, + clip_disagreement, + global_threshold, + onset_periodicity_features, + select_parameter, + top_k_ratio, +) + + +def detail(bpm, same=0.8, double=0.1, clips=None): + return { + "filename": "unused.wav", + "bpm": bpm, + "confidence": same, + "candidate_masses": { + "same": {"probability_mass": same}, + "double": {"probability_mass": double}, + }, + "clips": [{"bpm": value} for value in (clips or [bpm])], + } + + +def test_floor_baselines_are_deterministic(): + row = detail(60, same=0.2, double=0.3, clips=[60, 120, 120]) + assert global_threshold(row, 90) == 120 + assert top_k_ratio(row, 1.25) == 120 + assert clip_disagreement(row, 0.5) == 120 + assert clip_disagreement(row, 0.8) == 60 + + +def test_validation_selects_best_global_threshold(): + rows = [detail(60), detail(130)] + assert select_parameter(rows, [120, 130], "global_threshold", [50, 90, 150]) == 90 + + +def test_onset_periodicity_features_are_finite_and_fixed_width(): + sample_rate = 8000 + time = torch.arange(sample_rate * 2) / sample_rate + audio = torch.sin(2 * torch.pi * 2 * time) + features = onset_periodicity_features(audio, sample_rate, 120) + assert features.shape == (len(FACTORS) + 4,) + assert np.isfinite(features).all() + + +def test_factor_classifier_learns_and_round_trips(): + features = np.vstack([np.full(3, -2.0), np.full(3, -1.0), np.full(3, 1.0), np.full(3, 2.0)]) + labels = [1, 1, 0, 0] + model = FactorClassifier().fit(features, labels, epochs=800) + assert model.predict_proba(features).argmax(axis=1).tolist() == labels + restored = FactorClassifier.from_dict(model.to_dict()) + assert restored.predict_proba(features) == pytest.approx(model.predict_proba(features)) + + +def test_join_rows_rejects_held_out_fold(tmp_path): + path = tmp_path / "audio.wav" + manifest = [{"audio_path": str(path), "tempo": 120, "fold": "test"}] + details = [{**detail(60), "filename": str(path)}] + with pytest.raises(ValueError, match="held-out test"): + join_rows(manifest, details) + + +def test_experiment_reports_full_evaluation(): + train = [ + {"tempo": 120, "genre": "dance", "detail": detail(60), "features": [1, 0]}, + {"tempo": 80, "genre": "slow", "detail": detail(80), "features": [0, 1]}, + ] + val = [ + {"tempo": 120, "genre": "dance", "detail": detail(60), "features": [1, 0]}, + {"tempo": 80, "genre": "slow", "detail": detail(80), "features": [0, 1]}, + ] + result = run_experiment(train, val) + assert {"identity", "global_threshold", "top_k_ratio", "clip_disagreement", "learned"} <= result.keys() + learned = result["learned"] + assert {"metrics_2pct", "metrics_4pct", "taxonomy", "slices", "calibration", "latency"} <= learned.keys() + assert learned["metrics_4pct"]["acc1"] == 1.0 + + +def test_evaluation_uses_canonical_acc1_acc2_relations(): + rows = [{"tempo": 120, "genre": "x"}, {"tempo": 100, "genre": "x"}] + report = evaluate_rows(rows, [60, 100]) + assert report["metrics_2pct"] == pytest.approx({"acc1": 0.5, "acc2": 1.0}) + assert report["taxonomy"] == {"correct": 1, "half": 1} \ No newline at end of file From 00c4eaf419aa1d58c14e1c7a9e18d675a129e6d1 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:37:25 -0700 Subject: [PATCH 25/77] [Build] run training cache extraction on the requested device --- src/deeprhythm/train/cache.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/deeprhythm/train/cache.py b/src/deeprhythm/train/cache.py index edebb02..5123ef8 100644 --- a/src/deeprhythm/train/cache.py +++ b/src/deeprhythm/train/cache.py @@ -76,7 +76,7 @@ def write_index(self, index: dict) -> None: def _default_extract(path: str, specs) -> np.ndarray: - audio = load_and_split_audio(path) + audio = load_and_split_audio(path).to(device=specs[0].wsin.device) with torch.no_grad(): hcqm = compute_hcqm(audio, *specs).permute(0, 3, 1, 2) return hcqm.cpu().numpy() From daee89d42a88cf69445d6c79d64f92dd8826ac23 Mon Sep 17 00:00:00 2001 From: bleugreen Date: Mon, 20 Jul 2026 18:44:07 -0700 Subject: [PATCH 26/77] record v0.8 model candidate and experiment data --- weights/deeprhythm-v0.8-research.pth | Bin 0 -> 5443459 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 weights/deeprhythm-v0.8-research.pth diff --git a/weights/deeprhythm-v0.8-research.pth b/weights/deeprhythm-v0.8-research.pth new file mode 100644 index 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