xiWQSrSSKrSSKrSSKrSSKJrJrJrJrJ r SSK J r SSK J r SSKrSSKrSSKJrJr SSKJr Sr"S S\5rS\\S\\\R444Sjr"SS\(a\O\5r"SS\5rS\\S\\\44Sjr"SS\(a\O\5r S\S\ \\\\4\\\444Sjr!g!\a! S r\ R."S 5 "S S 5rNf=f)z PyTorch Lightning DataModule for LoRA Training Handles data loading and preprocessing for training ACE-Step LoRA adapters. Supports both raw audio loading and preprocessed tensor loading. N)OptionalListDictAnyTuple)logger) safe_path)Dataset DataLoader)LightningDataModuleTFz?Lightning not installed. Training module will not be available.c\rSrSrSrg)r N)__name__ __module__ __qualname____firstlineno____static_attributes__rS/mnt/workspace/acestep.cpp/tests/../../ACE-Step-1.5/acestep/training/data_module.pyr r s rr c|\rSrSrSrS\4SjrS\S\\4SjrS\ 4Sjr S \ S\ \\ R44S jrS rg ) PreprocessedTensorDataset#aDataset that loads preprocessed tensor files. This is the recommended dataset for training as all tensors are pre-computed: - target_latents: VAE-encoded audio [T, 64] - encoder_hidden_states: Condition encoder output [L, D] - encoder_attention_mask: Condition mask [L] - context_latents: Source context [T, 65] - attention_mask: Audio latent mask [T] No VAE/text encoder needed during training - just load tensors directly! tensor_dirc[U5n[RRU5(d[ SU35eX l/Ul[SUR S9n[RRU5(ax[US5n[R"U5nSSS5 WRS/5nUH4nURU5nUcMUR RU5 M6 Os[R"UR 5HOnURS5(dMUS:wdM#UR R[X@R S95 MQ UR V s/sH+n [RRU 5(dM)U PM- sn Ul[#UR 5[#UR 5:waC[$R&"S[#UR 5[#UR 5- S 35 [$R("S [#UR 5S UR 35 g!,(df  GN=fs sn f) zInitialize from a directory of preprocessed .pt files. Args: tensor_dir: Directory containing preprocessed .pt files and manifest.json Raises: ValueError: If tensor_dir is not an existing directory or escapes safe root. zNot an existing directory: z manifest.jsonbaserNsamplesz.ptzSome tensor files not found: z missingzPreprocessedTensorDataset: z samples from )r ospathisdir ValueErrorr sample_pathsexistsopenjsonloadget_resolve_manifest_pathappendlistdirendswith valid_pathslenrwarninginfo) selfr validated_dir manifest_pathfmanifest raw_pathsrawresolvedps r__init__"PreprocessedTensorDataset.__init__0s"*- ww}}]++::,GH H'')"/H 77>>- ( (mS)Q99Q<* Y3I 66s;'%%,,X6! ZZ0::e$$o)=%%,,!!//:1(,'8'8N'8!BGGNN1.>*?)@AOO$ & 1*) OsI<(I1(I1 I.r8returnc[XRS9n[RR U5(aU$[U5n[RR U5(a[ R"SU35 U$[ R"SU35 g![ a Nrf=f![ a N6f=f)uResolve a single manifest sample path to a validated absolute path. Tries ``base=tensor_dir`` first (correct for new manifests that store paths relative to the tensor directory). If the resulting path does not exist on disk, falls back to resolving against the global safe root (backward compat for legacy manifests that stored CWD-relative paths like ``./datasets/…/foo.pt``). Returns: Validated absolute path, or ``None`` if the path cannot be resolved safely. rz-Resolved legacy manifest path via safe root: z%Skipping unresolvable manifest path: N) r rr r!r%r#rdebugr0)r2r8childs rr*0PreprocessedTensorDataset._resolve_manifest_path_s c8Eww~~e$$ %  cNEww~~e$$ CC5I % >seDE      s#8B"A B2" B/.B/2 B?>B?c,[UR5$N)r/r.r2s r__len__!PreprocessedTensorDataset.__len__s4##$$ridxc URUn[R"USSS9nUSUSUSUSUSURS 05S .$) zkLoad a preprocessed tensor file. Returns: Dictionary containing all pre-computed tensors for training cpuT) map_location weights_onlytarget_latentsattention_maskencoder_hidden_statesencoder_attention_maskcontext_latentsmetadatarLrMrNrOrPrQ)r.torchr(r))r2rG tensor_pathdatas r __getitem__%PreprocessedTensorDataset.__getitem__sm &&s+ zz+EM##34"#34%)*A%B&*+C&D#$56R0   r)r$rr.N)rrrr__doc__strr;rr*intrErrSTensorrVrrrrrr#sY - 3- ^!#!(3-!F%% s tC,='> rrbatchr=c [SU55n[SU55n/n/n/n/n/nUGH nUSn U RSU:aDU RXRS- U RS5n [R"X/SS9n UR U 5 USn U RSU:a6U RXRS- 5n [R"X/SS9n UR U 5 USn U RSU:aDU RXRS- U RS5n [R"X/SS9n UR U 5 US n U RSU:aDU RX-RS- U RS5n [R"X/SS9n UR U 5 US nURSU:a6URX.RS- 5n [R"X/SS9nUR U5 GM [R "U5[R "U5[R "U5[R "U5[R "U5UVs/sHoS PM snS .$s snf) aCollate function for preprocessed tensor batches. Handles variable-length tensors by padding to the longest in the batch. Args: batch: List of sample dictionaries with pre-computed tensors Returns: Batched dictionary with all tensors stacked c3D# UHoSRSv M g7f)rLrNshape.0ss r -collate_preprocessed_batch..sEu!+,2215u c3D# UHoSRSv M g7f)rNrNr_ras rrdresMu!34::1=urfrLr)dimrMrPrNrOrQrR)maxr` new_zerosrScatr+stack)r\max_latent_lenmax_encoder_lenrLattention_masksrNencoder_attention_masksrPsampletlpadamclehseamrcs rcollate_preprocessed_batchryszEuEENMuMMONO O $ % 88A; ',,~ ;RXXa[ICB9!,Bb!$ % 88A; ',,~ ; ! MC))SJA.C$$S)-. 99Q?C))SJA.C&&s+EJ ++n5++o6!&-B!C"'++.E"F ;;7,12Eqz]E2   3s,J?c^\rSrSrSrSS\S\S\S\S\S\S \S \4U4S jjjr SS \ \4S jjr S\ 4Sjr S\ \ 4SjrSrU=r$)PreprocessedDataModulezDataModule for preprocessed tensor files. This is the recommended DataModule for training. It loads pre-computed tensors directly without needing VAE, text encoder, or condition encoder at training time. r batch_size num_workers pin_memoryprefetch_factorpersistent_workerspin_memory_device val_splitc >[(a[T U] 5 XlX lX0lX@lXPlX`lXpl Xl SUl SUl g)a4Initialize the data module. Args: tensor_dir: Directory containing preprocessed .pt files batch_size: Training batch size num_workers: Number of data loading workers pin_memory: Whether to pin memory for faster GPU transfer val_split: Fraction of data for validation (0 = no validation) N) LIGHTNING_AVAILABLEsuperr;rr}r~rrrrr train_dataset val_dataset) r2rr}r~rrrrr __class__s rr;PreprocessedDataModule.__init__sR(   G  $$&$."4!2"!rstagec~US:XdUc[UR5nURS:a[U5S:ar[ S[ [U5UR-55n[U5U- n[ RRRX$U/5uUl Ul gX l SUl gg)zSetup datasets.fitNrrh) rrrr/rjrZrSutilsrU random_splitrr)r2r full_datasetn_valn_trains rsetupPreprocessedDataModule.setups E>U]4T__EL~~!c,&7!&;As3|#4t~~#EFGl+e37<{{7G7G7T7T E"284"D$4&2"#' +rr=c JURS:XaSO URnURS:XaSO URn[URUR SURUR [SUUS9 nUR(aURUS'[S0UD6$)zCreate training dataloader.rNFT) datasetr}shuffler~r collate_fn drop_lastrrrr) r~rrdictrr}rryrr r2rrkwargss rtrain_dataloader'PreprocessedDataModule.train_dataloaders"&"2"2a"7$T=Q=Q&*&6&6!&;UAXAX&&((1+1    ! !*.*@*@F& '#F##rc dURcgURS:XaSO URnURS:XaSO URn[ URUR SURUR [UUS9nUR(aURUS'[S0UD6$)zCreate validation dataloader.NrF)rr}rr~rrrrrr) rr~rrrr}rryrr rs rval_dataloader%PreprocessedDataModule.val_dataloader+s    #"&"2"2a"7$T=Q=Q&*&6&6!&;UAXAX$$((1+1    ! !*.*@*@F& '#F##r) r}r~rrrrrrrr)rhTTrC)rrrrrXrYrZboolfloatr;rrr rrr __classcell__rs@rr{r{s #'!#! ! !  !  !  ! !! ! ! ! F(8C=($$*$&$ 4$$rr{c\rSrSrSrSS\\\\4S\ S\ 4Sjjr S\\\\44Sjr S\ 4S jr S \ S\\\R44S jrS rg )AceStepTrainingDatasetiDajDataset for ACE-Step LoRA training from raw audio. DEPRECATED: Use PreprocessedTensorDataset instead for better performance. Audio Format Requirements (handled automatically): - Sample rate: 48kHz (resampled if different) - Channels: Stereo (2 channels, mono is duplicated) - Max duration: 240 seconds (4 minutes) - Min duration: 5 seconds (padded if shorter) r max_durationtarget_sample_ratecXlX lX0lX@lUR 5Ul[ R"S[UR 5S35 g)zInitialize the dataset.zDataset initialized with z valid samplesN) r dit_handlerrr_validate_samples valid_samplesrr1r/)r2rrrrs rr;AceStepTrainingDataset.__init__PsN &("4!335 /D4F4F0G/HWXrr=c>/n[UR5Hup#URSS5nU(d[R"SUS35 M:[ U5n[RRU5(d[R"SUSU35 MURS5(d[R"SUS35 M0UESU0EnURU5 M U$![ a! [R"SUSU35 GMf=f) zAValidate and filter samples, resolving audio paths to safe paths. audio_pathrzSample z: Missing audio_pathz: Rejected unsafe path: z: Audio file not found: captionz: Missing caption) enumeraterr)rr0r r#r r!isfiler+)r2validirrr validateds rr(AceStepTrainingDataset._validate_samples`s"4<<0IAL"5J+?@A %j1 77>>),,+CJ<PQ::i((+<=>98 i8F LL -10 ! +CJ<PQ s C11&DDc,[UR5$rC)r/rrDs rrEAceStepTrainingDataset.__len__}s4%%&&rrGcFURUnUSn[R"U5upEXPR:wa1[RR XPR5nU"U5nUR SS:XaURSS5nOUR SS:a USS2SS24n[URUR-5nUR SU:a USS2SU24n[SUR-5nUR SU:a=XR S- n [RRRUSU 45nUURSS5URS S 5URSS5URS S 5URS 5URS S5URS S5URSUR SUR- 5URSS5URSS5S.US.$)zGet a single training sample.rrrhrNg@rrlyricsz[Instrumental]bpmkeyscale timesignaturedurationlanguageunknownis_instrumentalT)rrrrrrrr)audiorrrQr)r torchaudior(r transformsResampler`repeatrZrrSnn functionalrtr)) r2rGrrrrsr resampler max_samples min_samplespaddings rrV"AceStepTrainingDataset.__getitem__s##C(L) OOJ/  (( ("--66r;R;RSIe$E ;;q>Q LLA&E [[^a "1"a%LE$++d.E.EEF ;;q>K '!\k\/*E# 7 778 ;;q>K '!KKN2GHH''++EAw<@Ezz)R0jj+;<!::i4 **X/?@zz%("JJz26!'OR!@"JJz5;;q>DD[D[3[\"JJz9=#)::.?#F %  r)rrrrrN)n@i)rrrrrXrrrYrrrZr;rrErSr[rVrrrrrrDs $"' Yd38n%Y Y  Y 4S#X#7:''+ s+ tC,='>+ rrc v[SU55n/n/nUHnUSnURSnXa:a0X- n[RRR USU45nUR U5 [R"U5nXa:aSXS&UR U5 M [R"U5[R"U5UV s/sHoSPM sn UV s/sHoSPM sn UV s/sHoSPM sn UV s/sHoS PM sn S .$s sn fs sn fs sn fs sn f) z0Collate function for raw audio batches (legacy).c3D# UHoSRSv M g7f)rrhNr_)rbrrs rrd)collate_training_batch..s?v/''*rfrrhrNrrrQr)rrMcaptionsrrQ audio_paths) rjr`rSrrrtr+onesrm) r\max_len padded_audiorprrr audio_lenrmaskrcs rcollate_training_batchrs&???GLOwKKN  )GHH''++EAw<@EE"zz'"   D t$ \*++o6+015ay\51(-.1X;.,12Eqz]E2167A,7  2.27sD',D, D1 D6c ^\rSrSrSrSS\\\\4S\ S\ S\ S\ S\ 4 U4S jjjr SS \ \4S jjrS \4S jrS \ \4SjrSrU=r$)AceStepDataModuleiztDataModule for raw audio loading (legacy). DEPRECATED: Use PreprocessedDataModule for better training performance. rr}r~rrrc>[(a[TU] 5 XlX lX0lX@lXPlX`lXpl SUl SUl grC) rrr;rrr}r~rrrrr) r2rrr}r~rrrrs rr;AceStepDataModule.__init__sL   G   &$&$("!rrcUS:XdUGceURS:Ga[UR5S:Ga[S[ [UR5UR-55n[ [ [UR555n[R"U5 USUnX2SnUVs/sHo`RUPM nnUVs/sHo`RUPM nn[XpRUR5Ul [XRUR5Ul g[URURUR5Ul SUl ggs snfs snf)Nrrrh)rr/rrjrZlistrangerandomrrrrrr) r2rrindices val_indices train_indicesr train_samples val_sampless rrAceStepDataModule.setups4 E>U]~~!c$,,&7!&;As3t||#4t~~#EFGuS%678w'%fuo ' :G H-Qa- H8CD 1||A D%;!#3#3T5F5F&"$:!1!143D3D$ &<LL$"2"2D4E4E&"$( /+!IDs 5E1E6r=c x[URURSURUR[ SS9$)NT)r}rr~rrr)r rr}r~rrrDs rr"AceStepDataModule.train_dataloaders8   ((-  rc URcg[URURSURUR[ S9$)NF)r}rr~rr)rr r}r~rrrDs rr AceStepDataModule.val_dataloadersD    #   ((-   r) r}rrr~rrrrr)rhrTrrrC)rrrrrXrrrYrrZrrr;rrr rrrrrs@rrrs# d38n%          0(8C=(4  *     4   rr json_pathc@[U5n[RRU5(d[ SU35e[ USSS9n[ R"U5nSSS5 WRS05nURS/5nXT4$!,(df  N5=f)zLoad a dataset from JSON file. Args: json_path: Path to the JSON dataset file. Returns: Tuple of (samples list, metadata dict). Raises: ValueError: If json_path does not point to an existing file or escapes safe root. zDataset JSON file not found: rzutf-8)encodingNrQr) r r r!rr#r&r'r(r))rrr5rUrQrs rload_dataset_from_jsonrs)$I 77>>) $ $8 DEE iw /1yy| 0xx B'Hhhy"%G   0 /s B B)"rXr r'rtypingrrrrrlogururacestep.training.path_safetyr rSrtorch.utils.datar r lightning.pytorchr r ImportErrorr0rrYr[ryobjectr{rrrrrrrrs+ 332 0 5r r jAd4jAT#u||:K5LAHa$4G0Va$Pg Wg T$t*c3h@N /B+N bceDc3h4H$sTWx.4X.YQ  NNTU   sC$C/.C/