diff --git a/.gitignore b/.gitignore
index fa2beff..ac139a4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,8 @@
 __pycache__
-datasets/__pycache__
-src/__pycache__
-architectures/__pycache__
-.idea
\ No newline at end of file
+.idea
+venv
+models
+.vscode
+out
+.txt
+.csv
\ No newline at end of file
diff --git a/configs/mean_std.npz b/configs/mean_std.npz
new file mode 100644
index 0000000..cd50ff0
Binary files /dev/null and b/configs/mean_std.npz differ
diff --git a/configs/vocab.yaml b/configs/vocab.yaml
new file mode 100644
index 0000000..b83c499
--- /dev/null
+++ b/configs/vocab.yaml
@@ -0,0 +1,9419 @@
+idx_to_token:
+  0: <pad>
+  1: <sos>
+  2: <eos>
+  3: <unk>
+  4: ' '
+  5: '  '
+  6: '   '
+  7: '&'
+  8: ''''
+  9: '''s'
+  10: --
+  11: '...'
+  12: '1'
+  13: 1/4
+  14: '10'
+  15: '11'
+  16: '12'
+  17: '120'
+  18: '180'
+  19: 180deg
+  20: "180\u2070"
+  21: '2'
+  22: '270'
+  23: '3'
+  24: 3/4
+  25: 3/4th
+  26: '30'
+  27: '360'
+  28: '4'
+  29: '45'
+  30: '5'
+  31: '6'
+  32: '60'
+  33: '7'
+  34: '8'
+  35: '9'
+  36: '90'
+  37: '['
+  38: ']'
+  39: a
+  40: aback
+  41: abd
+  42: abdomen
+  43: abdominal
+  44: abit
+  45: able
+  46: about
+  47: above
+  48: abrupt
+  49: abruptly
+  50: accepting
+  51: accepts
+  52: accident
+  53: accordingly
+  54: acrobatic
+  55: across
+  56: act
+  57: acting
+  58: action
+  59: actions
+  60: active
+  61: activities
+  62: activity
+  63: acts
+  64: actually
+  65: adding
+  66: additional
+  67: adds
+  68: adjacent
+  69: adjust
+  70: adjusting
+  71: adjusts
+  72: admiring
+  73: adn
+  74: advance
+  75: advances
+  76: afraid
+  77: after
+  78: afterwards
+  79: again
+  80: against
+  81: agg
+  82: aggressive
+  83: aggressively
+  84: agin
+  85: ahead
+  86: aiming
+  87: aimlessly
+  88: aims
+  89: air
+  90: airplane
+  91: airs
+  92: aisle
+  93: akimbo
+  94: alive
+  95: all
+  96: allowing
+  97: allows
+  98: almost
+  99: alone
+  100: along
+  101: alot
+  102: also
+  103: alternate
+  104: alternately
+  105: alternates
+  106: alternating
+  107: alternatively
+  108: altogether
+  109: always
+  110: am
+  111: aman
+  112: ambles
+  113: amd
+  114: amount
+  115: ams
+  116: an
+  117: and
+  118: andlor
+  119: anf
+  120: ang
+  121: angel
+  122: anger
+  123: angle
+  124: angled
+  125: angling
+  126: angrily
+  127: angry
+  128: angular
+  129: animal
+  130: animated
+  131: animation
+  132: ankle
+  133: ankles
+  134: another
+  135: ans
+  136: answer
+  137: answering
+  138: answers
+  139: ant
+  140: anti
+  141: anticipation
+  142: anticlockwise
+  143: antilock
+  144: anxious
+  145: anxiously
+  146: any
+  147: anything
+  148: apart
+  149: ape
+  150: apparent
+  151: apparently
+  152: appear
+  153: appeared
+  154: appearing
+  155: appears
+  156: applauding
+  157: applauds
+  158: applause
+  159: applies
+  160: approaches
+  161: approaching
+  162: approximately
+  163: arabesque
+  164: arc
+  165: arch
+  166: arched
+  167: arches
+  168: arching
+  169: arcing
+  170: arcs
+  171: are
+  172: area
+  173: areas
+  174: arguing
+  175: argument
+  176: ark
+  177: arm
+  178: arma
+  179: armchair
+  180: armpit
+  181: armpits
+  182: armrests
+  183: arms
+  184: army
+  185: around
+  186: arranging
+  187: arrow
+  188: art
+  189: arts
+  190: as
+  191: ascends
+  192: asian
+  193: aside
+  194: asking
+  195: assembly
+  196: assist
+  197: assistance
+  198: assisted
+  199: assumes
+  200: assuming
+  201: at
+  202: athletic
+  203: atm
+  204: atop
+  205: attack
+  206: attacked
+  207: attacks
+  208: attempt
+  209: attempting
+  210: attempts
+  211: attention
+  212: audience
+  213: average
+  214: avoid
+  215: avoiding
+  216: avoids
+  217: aware
+  218: away
+  219: awhile
+  220: awkward
+  221: awkwardly
+  222: ax
+  223: axis
+  224: baby
+  225: back
+  226: backache
+  227: backed
+  228: backflip
+  229: backflips
+  230: background
+  231: backhand
+  232: backing
+  233: backpack
+  234: backs
+  235: backside
+  236: backstop
+  237: backstopping
+  238: backstroke
+  239: backtracks
+  240: backward
+  241: backwards
+  242: bad
+  243: badly
+  244: bag
+  245: baggage
+  246: bags
+  247: balance
+  248: balanced
+  249: balances
+  250: balancing
+  251: ball
+  252: ballerina
+  253: ballet
+  254: ballroom
+  255: balls
+  256: band
+  257: bands
+  258: banging
+  259: bangs
+  260: banister
+  261: banjo
+  262: bannister
+  263: bar
+  264: barbell
+  265: barely
+  266: barrel
+  267: bars
+  268: barstool
+  269: base
+  270: baseball
+  271: based
+  272: basket
+  273: basketball
+  274: bat
+  275: bats
+  276: batter
+  277: be
+  278: beam
+  279: bear
+  280: beat
+  281: beats
+  282: because
+  283: beckoning
+  284: beckons
+  285: become
+  286: becomes
+  287: bed
+  288: beds
+  289: been
+  290: beer
+  291: before
+  292: began
+  293: begging
+  294: begin
+  295: begining
+  296: beginning
+  297: begins
+  298: begun
+  299: behind
+  300: being
+  301: bell
+  302: bells
+  303: belly
+  304: below
+  305: belt
+  306: bench
+  307: bend
+  308: bended
+  309: bending
+  310: bends
+  311: beneath
+  312: bent
+  313: beside
+  314: better
+  315: between
+  316: beverage
+  317: bicep
+  318: biceps
+  319: big
+  320: bigger
+  321: bike
+  322: bilateral
+  323: bin
+  324: binoculars
+  325: bird
+  326: bit
+  327: bite
+  328: black
+  329: blanket
+  330: blind
+  331: block
+  332: blocked
+  333: blocking
+  334: blocks
+  335: blow
+  336: blowing
+  337: blows
+  338: blue
+  339: board
+  340: boat
+  341: bob
+  342: bobbing
+  343: bobs
+  344: bodies
+  345: body
+  346: bodybuilding
+  347: bodyweight
+  348: bolt
+  349: bomb
+  350: bongo
+  351: bongos
+  352: book
+  353: bopping
+  354: bot
+  355: both
+  356: bottle
+  357: bottom
+  358: bounce
+  359: bounces
+  360: bouncing
+  361: bouncy
+  362: bow
+  363: bowed
+  364: bowing
+  365: bowl
+  366: bowling
+  367: bows
+  368: box
+  369: boxer
+  370: boxes
+  371: boxing
+  372: boy
+  373: brace
+  374: braced
+  375: braces
+  376: bracing
+  377: branch
+  378: break
+  379: breakdance
+  380: breakdancing
+  381: breaking
+  382: breaks
+  383: breast
+  384: breaststroke
+  385: breath
+  386: bridge
+  387: brief
+  388: briefcase
+  389: briefly
+  390: bring
+  391: bringing
+  392: brings
+  393: brining
+  394: brisk
+  395: briskly
+  396: broad
+  397: broadly
+  398: broken
+  399: broom
+  400: brought
+  401: brow
+  402: brush
+  403: brushes
+  404: brushing
+  405: bucket
+  406: bucks
+  407: buddhism
+  408: bug
+  409: bugs
+  410: builder
+  411: building
+  412: bump
+  413: bumped
+  414: bumping
+  415: bumps
+  416: bunch
+  417: bunny
+  418: burger
+  419: burst
+  420: but
+  421: butt
+  422: butterfly
+  423: buttocks
+  424: button
+  425: buttons
+  426: by
+  427: bye
+  428: c
+  429: cab
+  430: cabinet
+  431: cable
+  432: calf
+  433: call
+  434: called
+  435: calling
+  436: calls
+  437: calm
+  438: calmly
+  439: came
+  440: camera
+  441: can
+  442: cane
+  443: cap
+  444: car
+  445: cards
+  446: care
+  447: careful
+  448: carefully
+  449: carelessly
+  450: caress
+  451: caring
+  452: carries
+  453: carring
+  454: carry
+  455: carrying
+  456: cart
+  457: cartwheel
+  458: cartwheels
+  459: case
+  460: casting
+  461: casts
+  462: casual
+  463: casually
+  464: cat
+  465: catch
+  466: catches
+  467: catching
+  468: catwalk
+  469: catwalks
+  470: caught
+  471: causes
+  472: causing
+  473: cautious
+  474: cautiously
+  475: ceiling
+  476: celebrate
+  477: celebrating
+  478: celebration
+  479: celebratory
+  480: cell
+  481: cellphone
+  482: center
+  483: centered
+  484: centers
+  485: centre
+  486: certain
+  487: cha
+  488: chacha
+  489: chair
+  490: chalkboard
+  491: change
+  492: changed
+  493: changes
+  494: changing
+  495: chaotically
+  496: character
+  497: charge
+  498: charges
+  499: charleston
+  500: check
+  501: checking
+  502: checks
+  503: cheer
+  504: cheerfully
+  505: cheering
+  506: cheers
+  507: chess
+  508: chest
+  509: chi
+  510: chicken
+  511: child
+  512: children
+  513: chin
+  514: chip
+  515: choking
+  516: choo
+  517: chop
+  518: chopping
+  519: choppy
+  520: chops
+  521: chucks
+  522: cigarette
+  523: circle
+  524: circles
+  525: circling
+  526: circuit
+  527: circular
+  528: clamp
+  529: clap
+  530: clapped
+  531: clapping
+  532: claps
+  533: clasp
+  534: clasped
+  535: clasping
+  536: clasps
+  537: class
+  538: clawing
+  539: claws
+  540: clean
+  541: cleaned
+  542: cleaner
+  543: cleaning
+  544: cleans
+  545: clear
+  546: clicks
+  547: cliff
+  548: climb
+  549: climbed
+  550: climbing
+  551: climbs
+  552: clip
+  553: clock
+  554: clockwise
+  555: close
+  556: closed
+  557: closer
+  558: closes
+  559: closing
+  560: cloth
+  561: clothes
+  562: clothing
+  563: club
+  564: clucking
+  565: clumsily
+  566: clumsy
+  567: clutch
+  568: clutches
+  569: clutching
+  570: coach
+  571: coat
+  572: cock
+  573: cocked
+  574: cocks
+  575: code
+  576: coffee
+  577: coiling
+  578: cold
+  579: collecting
+  580: colored
+  581: combination
+  582: combing
+  583: come
+  584: comes
+  585: comfort
+  586: comfortable
+  587: comfortably
+  588: coming
+  589: commences
+  590: communicating
+  591: compared
+  592: complete
+  593: completed
+  594: completely
+  595: completes
+  596: completing
+  597: composure
+  598: concerned
+  599: conducting
+  600: confidence
+  601: confident
+  602: confidently
+  603: confined
+  604: confused
+  605: confusion
+  606: connected
+  607: connecting
+  608: consecutive
+  609: considering
+  610: consistent
+  611: consistently
+  612: constant
+  613: constantly
+  614: consume
+  615: contact
+  616: container
+  617: containers
+  618: content
+  619: contents
+  620: continually
+  621: continue
+  622: continued
+  623: continues
+  624: continuing
+  625: continuous
+  626: continuously
+  627: contracting
+  628: contraction
+  629: control
+  630: controlling
+  631: controls
+  632: conversation
+  633: cooking
+  634: cool
+  635: cooler
+  636: coordinating
+  637: core
+  638: correct
+  639: corrects
+  640: corresponding
+  641: couch
+  642: cough
+  643: coughing
+  644: coughs
+  645: could
+  646: counter
+  647: counterclock
+  648: counterclockwise
+  649: countertop
+  650: counting
+  651: couple
+  652: course
+  653: cover
+  654: covering
+  655: covers
+  656: cower
+  657: cowering
+  658: cowers
+  659: crab
+  660: crack
+  661: cracks
+  662: cradle
+  663: cradled
+  664: cradles
+  665: cradling
+  666: crawl
+  667: crawled
+  668: crawling
+  669: crawls
+  670: crazily
+  671: crazy
+  672: create
+  673: creates
+  674: creating
+  675: creepily
+  676: creeping
+  677: creeps
+  678: creepy
+  679: crescent
+  680: cricket
+  681: cries
+  682: criss
+  683: crisscross
+  684: crisscrossed
+  685: crisscrossing
+  686: crooked
+  687: cross
+  688: crossed
+  689: crosses
+  690: crossing
+  691: crosslegged
+  692: crossover
+  693: crotch
+  694: crouch
+  695: crouched
+  696: crouches
+  697: crouching
+  698: crowd
+  699: crumples
+  700: crunch
+  701: crunches
+  702: cry
+  703: crying
+  704: cucumber
+  705: cup
+  706: cupped
+  707: cupping
+  708: cups
+  709: curious
+  710: curiously
+  711: curl
+  712: curled
+  713: curling
+  714: curls
+  715: curtsey
+  716: curtsied
+  717: curtsy
+  718: curve
+  719: curved
+  720: curves
+  721: curving
+  722: curvy
+  723: cut
+  724: cuts
+  725: cutting
+  726: cycles
+  727: dabbing
+  728: dabs
+  729: daintily
+  730: damage
+  731: dance
+  732: danced
+  733: dancer
+  734: dances
+  735: dancing
+  736: danger
+  737: dangerous
+  738: dangle
+  739: dangles
+  740: dangling
+  741: dark
+  742: dart
+  743: darts
+  744: dashes
+  745: dashing
+  746: day
+  747: dazed
+  748: dead
+  749: decent
+  750: decide
+  751: decides
+  752: deep
+  753: deeply
+  754: defeated
+  755: defend
+  756: defends
+  757: defense
+  758: defensive
+  759: defensively
+  760: defiant
+  761: degrees
+  762: dejected
+  763: deliberate
+  764: deliberately
+  765: delicately
+  766: delivering
+  767: delivers
+  768: delivery
+  769: demonstrate
+  770: demonstrating
+  771: depends
+  772: depressed
+  773: descending
+  774: descends
+  775: describe
+  776: describing
+  777: descriptions
+  778: descriptive
+  779: desk
+  780: destination
+  781: determined
+  782: device
+  783: diagnose
+  784: diagonal
+  785: diagonally
+  786: dial
+  787: dials
+  788: diamond
+  789: dice
+  790: did
+  791: different
+  792: difficult
+  793: difficulty
+  794: dig
+  795: digging
+  796: digs
+  797: dimensional
+  798: dinosaur
+  799: dip
+  800: dipping
+  801: dips
+  802: direct
+  803: directed
+  804: directing
+  805: direction
+  806: directions
+  807: directly
+  808: directs
+  809: dirt
+  810: disappointed
+  811: disc
+  812: disco
+  813: discus
+  814: dish
+  815: dishes
+  816: disoriented
+  817: displace
+  818: displaces
+  819: displaying
+  820: distance
+  821: distress
+  822: dive
+  823: diving
+  824: dizzy
+  825: do
+  826: dodge
+  827: dodgeball
+  828: dodges
+  829: dodging
+  830: doen
+  831: does
+  832: dog
+  833: doing
+  834: dominant
+  835: done
+  836: door
+  837: doorbell
+  838: doorknob
+  839: dose
+  840: double
+  841: doubled
+  842: doubles
+  843: dough
+  844: down
+  845: downhill
+  846: downs
+  847: downstairs
+  848: downward
+  849: downwards
+  850: drag
+  851: dragged
+  852: dragging
+  853: dragon
+  854: drags
+  855: dramatic
+  856: dramatically
+  857: drastically
+  858: draw
+  859: drawer
+  860: drawing
+  861: drawn
+  862: draws
+  863: dress
+  864: drew
+  865: dribble
+  866: dribbles
+  867: dribbling
+  868: dries
+  869: drift
+  870: drifting
+  871: drifts
+  872: drills
+  873: drink
+  874: drinking
+  875: drinks
+  876: dripping
+  877: drive
+  878: driver
+  879: drives
+  880: driving
+  881: drooping
+  882: drop
+  883: dropped
+  884: dropping
+  885: drops
+  886: drum
+  887: drumming
+  888: drums
+  889: drunk
+  890: drunken
+  891: drunkenly
+  892: dry
+  893: drying
+  894: drys
+  895: duck
+  896: ducked
+  897: ducking
+  898: ducks
+  899: duckwalking
+  900: due
+  901: dui
+  902: dumb
+  903: dumbbell
+  904: dumbbells
+  905: dump
+  906: dunks
+  907: during
+  908: dust
+  909: dusting
+  910: dusts
+  911: each
+  912: eachother
+  913: eagerly
+  914: eagle
+  915: ear
+  916: earlier
+  917: ears
+  918: ease
+  919: easily
+  920: east
+  921: easy
+  922: eat
+  923: eating
+  924: eats
+  925: edge
+  926: edges
+  927: effective
+  928: effectively
+  929: effort
+  930: effortlessly
+  931: egg
+  932: egyptian
+  933: eighty
+  934: eith
+  935: either
+  936: elaborate
+  937: elbow
+  938: elbows
+  939: elderly
+  940: elegantly
+  941: elephant
+  942: elevated
+  943: eleven
+  944: ellipse
+  945: elongated
+  946: else
+  947: emotion
+  948: emphasize
+  949: empty
+  950: emptying
+  951: emulates
+  952: end
+  953: ended
+  954: ending
+  955: ends
+  956: energetic
+  957: energetically
+  958: energy
+  959: engaged
+  960: engages
+  961: enjoy
+  962: enjoying
+  963: ensure
+  964: enter
+  965: entering
+  966: enters
+  967: enthusiasm
+  968: enthusiastic
+  969: enthusiastically
+  970: entire
+  971: equal
+  972: erect
+  973: erratic
+  974: erratically
+  975: evade
+  976: even
+  977: evenly
+  978: event
+  979: eventually
+  980: ever
+  981: every
+  982: everyone
+  983: everything
+  984: exact
+  985: exaggerated
+  986: examine
+  987: examines
+  988: examining
+  989: exasperation
+  990: except
+  991: excercise
+  992: excercises
+  993: excercising
+  994: excited
+  995: excitedly
+  996: excitement
+  997: executes
+  998: exercise
+  999: exercises
+  1000: exercising
+  1001: exhausted
+  1002: exits
+  1003: exotic
+  1004: expanded
+  1005: expecting
+  1006: experiences
+  1007: experiment
+  1008: explain
+  1009: explaining
+  1010: explosive
+  1011: express
+  1012: expressing
+  1013: expressive
+  1014: extend
+  1015: extended
+  1016: extending
+  1017: extends
+  1018: extension
+  1019: extra
+  1020: extremely
+  1021: extremities
+  1022: eye
+  1023: eyes
+  1024: eyesight
+  1025: face
+  1026: faces
+  1027: facing
+  1028: fail
+  1029: failing
+  1030: fails
+  1031: fairly
+  1032: fake
+  1033: fakes
+  1034: faking
+  1035: fall
+  1036: fallen
+  1037: falling
+  1038: falls
+  1039: family
+  1040: fan
+  1041: fancy
+  1042: fans
+  1043: far
+  1044: farther
+  1045: fashion
+  1046: fast
+  1047: fastens
+  1048: faster
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+  1982: opens
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+  2000: ot
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+  2002: others
+  2003: otherwise
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+  2005: out
+  2006: outline
+  2007: outreach
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+  2009: outside
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+  2011: outstretching
+  2012: outward
+  2013: outwardly
+  2014: outwards
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+  2016: ove
+  2017: over
+  2018: overall
+  2019: overhand
+  2020: overhead
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+  2022: own
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+  2042: pant
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+  2086: peer
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+  2108: person
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+  2419: return
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+  2498: safely
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+  2518: saxophone
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+  2529: scary
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+  2607: shampoo
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+  2693: similar
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+  2703: sip
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+  2718: skidding
+  2719: skiing
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+  2723: sky
+  2724: skyward
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+  2734: slashing
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+  2745: slightly
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+  2750: sloppily
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+  2753: slouches
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+  2765: small
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+  2773: smoking
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+  2775: smoothing
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+  2778: snakey
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+  2782: sneakily
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+  2786: sneezes
+  2787: sneezing
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+  2799: soldier
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+  2808: somethign
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+  2814: somewhat
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+  2817: son
+  2818: song
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+  2822: soup
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+  2842: spin
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+  2849: spiritless
+  2850: splashes
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+  2857: sporting
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+  2880: squirrel
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+  2907: standup
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+  2917: startled
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+  2922: stationary
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+  2931: stealthily
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+  2943: sticking
+  2944: stickman
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+  2980: strangely
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+  3000: string
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+  3002: stroke
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+  3005: stroll
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+  3009: strongly
+  3010: struck
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+  3013: struggling
+  3014: strum
+  3015: strumming
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+  3017: strut
+  3018: struts
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+  trajectory: 3251
+  trance: 3
+  transfer: 3252
+  transferring: 3253
+  transfers: 3254
+  transforms: 3
+  transit: 3
+  transitioning: 3
+  transitions: 3255
+  translator: 3
+  translatory: 3
+  transversing: 3
+  trash: 3256
+  traumatic: 3
+  travel: 3257
+  traveling: 3258
+  travelling: 3
+  tray: 3259
+  tread: 3
+  treading: 3
+  treadmill: 3260
+  tred: 3261
+  tree: 3262
+  trembles: 3263
+  trembling: 3264
+  tremendously: 3
+  tremors: 3
+  trial: 3265
+  trialling: 3
+  triangle: 3266
+  triangular: 3267
+  triceps: 3
+  trick: 3268
+  tricks: 3
+  tried: 3
+  tries: 3269
+  trikonasana: 3
+  trip: 3270
+  tripped: 3271
+  tripping: 3272
+  trips: 3273
+  triumph: 3
+  triumphantly: 3
+  trombone: 3
+  trophy: 3
+  trots: 3274
+  trotting: 3
+  trouble: 3275
+  troubling: 3
+  trough: 3
+  trousers: 3
+  truck: 3276
+  trudges: 3277
+  trudging: 3
+  'true': 3
+  trumpet: 3
+  trunk: 3278
+  try: 3279
+  trying: 3280
+  trys: 3281
+  ttwo: 3
+  tube: 3282
+  tuck: 3283
+  tucked: 3284
+  tucks: 3285
+  tug: 3
+  tugging: 3
+  tugs: 3286
+  tumble: 3
+  tumbles: 3287
+  tummy: 3
+  tune: 3288
+  tup: 3
+  turing: 3
+  turn: 3289
+  turnaround: 3290
+  turned: 3291
+  turning: 3292
+  turns: 3293
+  turnt: 3
+  twelve: 3294
+  twenties: 3
+  twenty: 3
+  twice: 3295
+  twirl: 3296
+  twirling: 3297
+  twirls: 3298
+  twist: 3299
+  twistdance: 3
+  twisted: 3300
+  twisters: 3
+  twisting: 3301
+  twists: 3302
+  twitch: 3303
+  twitches: 3304
+  twitching: 3305
+  twitchy: 3
+  tye: 3
+  tying: 3306
+  type: 3307
+  types: 3308
+  typical: 3309
+  typically: 3
+  typing: 3310
+  u: 3311
+  ultimately: 3312
+  umbrella: 3313
+  umpire: 3314
+  un: 3315
+  unable: 3316
+  unaffected: 3
+  unbalance: 3
+  unbalanced: 3317
+  unbending: 3
+  uncas: 3318
+  uncertain: 3
+  unclasp: 3319
+  unclogs: 3
+  uncomfortable: 3320
+  uncomfortably: 3
+  uncontrollably: 3321
+  uncork: 3
+  uncross: 3322
+  uncrossed: 3323
+  uncrossing: 3324
+  under: 3325
+  underarm: 3326
+  underarms: 3
+  underhand: 3327
+  underhanded: 3328
+  underneath: 3329
+  underside: 3330
+  understands: 3
+  underwater: 3331
+  undoes: 3
+  undulating: 3
+  uneven: 3332
+  unevenly: 3333
+  unfocused: 3
+  unfolding: 3
+  unfolds: 3334
+  unhindered: 3
+  unidentified: 3
+  unique: 3
+  unison: 3335
+  unit: 3
+  universal: 3
+  unknown: 3336
+  unlocking: 3
+  unlocks: 3
+  unmoving: 3337
+  unorganized: 3
+  unravelling: 3
+  unsatisfactory: 3
+  unscrew: 3338
+  unscrewing: 3339
+  unscrews: 3340
+  unseen: 3341
+  unstable: 3342
+  unsteadily: 3343
+  unsteady: 3344
+  unstuck: 3
+  unsuccessfully: 3
+  unsure: 3345
+  untangling: 3
+  until: 3346
+  unturned: 3
+  untwist: 3347
+  unwell: 3
+  unwraps: 3
+  unzip: 3348
+  unzipping: 3349
+  unzips: 3350
+  uo: 3351
+  up: 3352
+  uphill: 3353
+  upleft: 3
+  uplift: 3354
+  uplifted: 3355
+  uplifting: 3356
+  upon: 3357
+  upper: 3358
+  uppercase: 3
+  uppercut: 3359
+  uppercuts: 3360
+  upping: 3
+  upraised: 3
+  upright: 3361
+  ups: 3362
+  upset: 3363
+  upstage: 3
+  upstair: 3
+  upstairs: 3364
+  upturned: 3
+  upward: 3365
+  upwards: 3366
+  urgency: 3
+  urgently: 3367
+  urinating: 3
+  us: 3368
+  usain: 3
+  use: 3369
+  used: 3370
+  user: 3371
+  uses: 3372
+  usesboth: 3
+  using: 3373
+  usually: 3
+  utilizes: 3374
+  v: 3375
+  vacation: 3376
+  vacuum: 3377
+  vacuuming: 3378
+  vaguely: 3379
+  valet: 3
+  valley: 3380
+  valuable: 3
+  valves: 3
+  van: 3
+  variant: 3
+  variation: 3
+  variety: 3381
+  various: 3382
+  variously: 3
+  varying: 3383
+  vault: 3
+  vaulting: 3
+  vaults: 3384
+  ve: 3385
+  veer: 3386
+  veering: 3387
+  veers: 3388
+  vegeta: 3
+  vegetable: 3
+  vegetables: 3389
+  vehicle: 3390
+  velocity: 3391
+  vendor: 3
+  verge: 3
+  versa: 3392
+  verse: 3
+  verses: 3
+  vertical: 3393
+  vertically: 3394
+  very: 3395
+  vessel: 3396
+  vessels: 3
+  via: 3
+  vibrate: 3
+  vibrates: 3
+  vice: 3397
+  viciously: 3398
+  victorious: 3399
+  victory: 3400
+  video: 3401
+  view: 3402
+  viewer: 3403
+  viewing: 3
+  vigorous: 3404
+  vigorously: 3405
+  vigour: 3
+  viola: 3
+  violation: 3406
+  violent: 3407
+  violently: 3408
+  violin: 3409
+  vision: 3410
+  visual: 3411
+  vivid: 3
+  vocalize: 3
+  volley: 3412
+  volleyball: 3413
+  volleys: 3
+  volts: 3414
+  vomiting: 3415
+  vrikshasana: 3
+  w: 3416
+  waddle: 3
+  waddles: 3417
+  waddling: 3
+  wading: 3
+  wafting: 3
+  wags: 3418
+  waist: 3419
+  waists: 3
+  wait: 3420
+  waiting: 3421
+  waits: 3422
+  waivers: 3423
+  waives: 3424
+  waiving: 3425
+  wake: 3426
+  waked: 3
+  wakes: 3
+  waking: 3427
+  waling: 3428
+  walk: 3429
+  walked: 3430
+  walkin: 3431
+  walking: 3432
+  walks: 3433
+  walksclockwose: 3
+  walkthrough: 3
+  walkway: 3434
+  wall: 3435
+  wallet: 3436
+  walls: 3437
+  walter: 3
+  waltz: 3438
+  waltzes: 3439
+  waltzing: 3440
+  wandered: 3
+  wandering: 3441
+  wanders: 3442
+  want: 3443
+  wanted: 3
+  wanting: 3444
+  wants: 3445
+  war: 3446
+  ward: 3447
+  wards: 3448
+  warm: 3449
+  warmed: 3
+  warming: 3450
+  warms: 3451
+  warmth: 3
+  warmup: 3452
+  warning: 3
+  was: 3453
+  wash: 3454
+  washed: 3
+  washes: 3455
+  washing: 3456
+  waste: 3457
+  wat: 3
+  watch: 3458
+  watchers: 3
+  watches: 3459
+  watching: 3460
+  water: 3461
+  wave: 3462
+  waved: 3463
+  wavelike: 3
+  waver: 3
+  wavering: 3464
+  waves: 3465
+  waving: 3466
+  wavy: 3467
+  wax: 3
+  way: 3468
+  ways: 3469
+  we: 3470
+  weaker: 3
+  weapon: 3471
+  wearing: 3
+  weather: 3
+  weave: 3
+  weaves: 3472
+  weaving: 3
+  weeding: 3
+  weighs: 3
+  weight: 3473
+  weightlifter: 3474
+  weightlifting: 3475
+  weights: 3476
+  weird: 3477
+  weirdly: 3
+  welcome: 3
+  well: 3478
+  went: 3479
+  were: 3480
+  west: 3
+  western: 3
+  westside: 3
+  westward: 3481
+  wet: 3482
+  whaddaya: 3
+  whale: 3483
+  whales: 3
+  whang: 3
+  what: 3484
+  whatever: 3485
+  wheel: 3486
+  wheels: 3487
+  when: 3488
+  whence: 3
+  where: 3489
+  which: 3490
+  while: 3491
+  whiles: 3
+  whilst: 3492
+  whip: 3
+  whipping: 3
+  whirl: 3
+  whis: 3
+  whisk: 3
+  whisking: 3
+  whisks: 3
+  whispered: 3
+  whistling: 3
+  whit: 3493
+  white: 3494
+  whites: 3495
+  whith: 3496
+  who: 3497
+  whoa: 3
+  whole: 3498
+  whoops: 3
+  whose: 3
+  wide: 3499
+  widely: 3500
+  widening: 3501
+  widens: 3502
+  wider: 3503
+  widewalking: 3
+  width: 3504
+  wife: 3
+  wiggle: 3505
+  wiggles: 3506
+  wiggling: 3507
+  wiggly: 3508
+  wiht: 3
+  wild: 3509
+  wildly: 3510
+  will: 3511
+  wince: 3
+  winch: 3
+  wincing: 3
+  wind: 3512
+  winding: 3513
+  windmill: 3514
+  windmilling: 3515
+  windmills: 3516
+  window: 3517
+  windows: 3518
+  winds: 3519
+  windup: 3
+  wine: 3
+  wing: 3520
+  winged: 3
+  wings: 3521
+  wingspan: 3522
+  wipe: 3523
+  wiped: 3524
+  wipes: 3525
+  wiping: 3526
+  wirh: 3527
+  wise: 3528
+  wit: 3529
+  with: 3530
+  withdraw: 3
+  withdraws: 3
+  withers: 3
+  within: 3531
+  without: 3532
+  witht: 3
+  withthe: 3
+  wobble: 3533
+  wobbles: 3534
+  wobbling: 3535
+  wobbly: 3536
+  woke: 3537
+  woman: 3538
+  womanly: 3539
+  women: 3540
+  won: 3541
+  wonderful: 3
+  wonders: 3
+  wood: 3542
+  woods: 3543
+  wooly: 3
+  word: 3544
+  words: 3545
+  work: 3546
+  worked: 3547
+  worker: 3548
+  workin: 3
+  working: 3549
+  workout: 3550
+  works: 3551
+  worktop: 3
+  world: 3
+  worried: 3
+  worriedly: 3
+  worryingly: 3
+  woth: 3552
+  would: 3553
+  wounded: 3554
+  wrap: 3555
+  wrapped: 3556
+  wrapper: 3
+  wrapping: 3557
+  wraps: 3558
+  wrench: 3559
+  wrestle: 3560
+  wrestler: 3561
+  wrestling: 3562
+  wriggles: 3563
+  wriggling: 3564
+  wringing: 3565
+  wrings: 3566
+  wrinkling: 3
+  wrist: 3567
+  wristankle: 3568
+  wrists: 3569
+  wristwatch: 3570
+  writ: 3
+  write: 3571
+  writers: 3572
+  writes: 3573
+  writhing: 3
+  writing: 3574
+  writs: 3
+  wrote: 3
+  x: 3575
+  xu: 3576
+  y: 3577
+  yard: 3578
+  yawn: 3579
+  yawning: 3580
+  yawns: 3581
+  yea: 3
+  years: 3
+  yell: 3582
+  yelling: 3583
+  yells: 3584
+  'yes': 3
+  yet: 3585
+  yo: 3586
+  yoga: 3587
+  yokoyama: 3
+  you: 3588
+  young: 3589
+  your: 3590
+  yourself: 3
+  z: 3591
+  zag: 3592
+  zagged: 3593
+  zagging: 3594
+  zig: 3595
+  zigzag: 3596
+  zigzagging: 3
+  zigzags: 3597
+  zing: 3598
+  zipper: 3
+  zippers: 3
+  zips: 3599
+  zombie: 3600
+  zucchini: 3
+  zumba: 3
+  "\u2013": 3601
+  "\u2019": 3602
+  "\u201C": 3603
+  "\u201D": 3604
diff --git a/datasets/build_data.py b/datasets/build_data.py
new file mode 100644
index 0000000..09bd81c
--- /dev/null
+++ b/datasets/build_data.py
@@ -0,0 +1,90 @@
+import os
+
+import numpy as np
+
+from tqdm import tqdm
+import pickle
+
+pj = os.path.join
+
+# TODO SELECT THE DATASET
+# ------------------------ FOR KIT 2022 (21 JOINTS) -----------------------------
+dataset_name = "kit"
+
+# ---------------------- FOR HumanML3D (22 JOINTS) -------------------------------
+# dataset_name =  "h3D"
+
+if dataset_name=="kit":
+    folder_name="KIT-ML"
+    save_to_path = r"/media/hdd/usr/tao/momask-codes/dataset/KIT-ML/kit_with_splits_2023.npz"
+else:
+    folder_name="HumanML3D"
+    save_to_path = r"/media/hdd/usr/tao/momask-codes/dataset/HumanML3D/all_humanML3D.npz"
+
+
+abs_path = r"/media/hdd/usr/tao/momask-codes/dataset"
+splits = abs_path + f"/{folder_name}/"
+poses_dir = splits+ "new_joints"
+texts = splits + "texts"
+
+
+ids = []
+list_desc = []
+kit_poses = []
+nameid = []
+with open(splits+"train.txt") as f:
+    train_ids = f.read().split("\n")
+with open(splits+"test.txt") as f:
+    test_ids = f.read().split("\n")
+with open(splits+"val.txt") as f:
+    val_ids = f.read().split("\n")
+
+split_ids = []
+for ndesc in tqdm(os.listdir(texts)):
+    npose = ndesc.replace("txt","npy")
+    try:
+        kit_poses.append( np.load(pj(poses_dir,npose)))
+    except FileNotFoundError:
+        print("Pose without description Id: ",npose)
+        continue
+    with open(pj(texts,ndesc),encoding='utf-8') as f:
+        list_desc +=[[ph.split("#")[0] for ph in f.readlines()]]
+        ids += [npose.split(".")[0]]
+        nameid.append(npose.split(".")[0])
+        #print(nameid[-1])
+        if nameid[-1] in train_ids:
+            split_ids.append("train")
+        elif nameid[-1] in test_ids:
+            split_ids.append("test")
+        elif nameid[-1]  in val_ids:
+            split_ids.append("val")
+        else:
+            print("Unclassified sample ID: ",nameid)
+            break
+        
+
+    assert npose.split(".")[0]== ndesc.split(".")[0]
+
+if dataset_name=='kit':
+    N_samples = 6016
+    assert len(kit_poses)==N_samples
+    assert len(list_desc)==len(kit_poses)
+    
+with open(save_to_path, 'wb') as f:
+    pickle.dump({
+        'kitmld_array': kit_poses,
+        'old_desc': list_desc,
+        'sample_ids': nameid,
+        'splits_ids': split_ids
+    }, f)
+
+
+# ids_train = np.where(np.asarray(split_ids)=="train")[0]
+# ids_train_val = np.where(np.asarray(split_ids)=="val")[0]
+# ids_test= np.where(np.asarray(split_ids)=="test")[0]
+
+
+
+
+
+
diff --git a/datasets/h3d_m2t_dataset_.py b/datasets/h3d_m2t_dataset_.py
index 248eab0..779d56f 100644
--- a/datasets/h3d_m2t_dataset_.py
+++ b/datasets/h3d_m2t_dataset_.py
@@ -5,7 +5,6 @@
 from torch.utils.data import Dataset, DataLoader
 import torch
 from datasets.vocabulary import vocabulary
-from datasets.classes import categorize_tokens_new
 
 
 class dataset_class(Dataset):
@@ -23,8 +22,9 @@ def __init__(self, path, train=False, test=False,val=False, path_txt=None,min_fr
 
         self.poses = data['all_motions'] if joint_angles else np.asarray([xyz.reshape(-1,22*3) for xyz in data['kitmld_array']],dtype=object)
         self.poses = self.shift_poses()
+        print(len(self.poses))
         self.poses = np.asarray([ps.reshape(-1, 22 * 3) for ps in self.poses], dtype=object)
-
+        # TODO find correspondence in os.getcwd()+"/datasets/humanML3dtext_2022.npz"
         data_cls = data["old_desc"]
         assert len(data_cls)==len(self.poses)
         self.sentences = [d for ds in data_cls for d in ds] #flat all descriptions in order
@@ -39,13 +39,16 @@ def __init__(self, path, train=False, test=False,val=False, path_txt=None,min_fr
                 for desc in double_desc:
                     ls = desc.split('\t')
                     self.sentences.append(ls[1][1:].replace("\n","")) #append the corrections and remove first space and newline symbol
-        else:  # Cartesian coordinates
-            self.poses, self.sentences = self.read_data(path_txt=path_txt)
+
+        # Cartesian coordinates
+        else:
+            pass
+            #self.poses, self.sentences = self.read_data(path_txt=path_txt)
         if filter_data:
             logging.info("FILTER POSE WITH NO DESCRIPTION ")
-            _ , id_pose_annotated = self.filter_text(self.sentences)
-            self.poses = self.poses[id_pose_annotated]
-            self.sentences = self.sentences[id_pose_annotated]
+            # _ , id_pose_annotated = self.filter_text(self.sentences)
+            # self.poses = self.poses[id_pose_annotated]
+            # self.sentences = self.sentences[id_pose_annotated]
 
         correct_tokens = False if path_txt or not filter_data and not joint_angles else True
         logging.info(f"CORRECT TOKENS {correct_tokens}")
@@ -53,10 +56,12 @@ def __init__(self, path, train=False, test=False,val=False, path_txt=None,min_fr
         logging.info(f"Building vocabulary with minimum frequency : {min_freq}")
         self.lang.build_vocabulary(min_freq=min_freq)
         self.corrected_sentences = self.lang.corrected_sentences
+        
+        logging.info(f"Vocabulary size {self.lang.vocab_size} --> {self.lang.vocab_size_unk}")
 
         if filter_data and not joint_angles:
             logging.info("Normalize and save poses")
-            self.poses = self.shift_poses()
+            #self.poses = self.shift_poses()
             self.poses = np.asarray([ps.reshape(-1,22*3) for ps in self.poses],dtype=object)
 
         logging.info("Convert token to numerical values")
@@ -88,7 +93,7 @@ def sort_wlen(sequence1, sequence2):
             logging.info(f"Random state {random_state} fixed to generate the same split")
             self.indxs = list(range(len(self.poses)))
             self.indx_train, self.indx_test = train_test_split(self.indxs, test_size=0.1, random_state=random_state,
-                                                               shuffle=True)
+                                                               shuffle=True)  # IMPORTANT SET SHUFFLE TO FALSE TO PRESERVE POSE LENGTH ORDER SAME IN DATALOADER
             self.indx_train, self.indx_val = train_test_split(self.indx_train, test_size=0.1, random_state=random_state,
                                                               shuffle=True)
         else:
@@ -96,11 +101,12 @@ def sort_wlen(sequence1, sequence2):
             logging.info('Using official split (No random state)')
             self.indx_train = np.where(np.asarray(self.splits_ids) == "train")[0]
             self.indx_val = np.where(np.asarray(self.splits_ids) == "val")[0]
-            self.indx_test = np.where(np.asarray(self.splits_ids) == "test")[0]
+            self.indx_test = np.where(np.asarray(self.splits_ids) == "test")[0]        #Sanity check
 
         len_annot = list((self.map_ids_len).values())  # take lengths of list of descriptions per motion
         cum_len = np.cumsum(len_annot)
         use_multiple_samples = False
+       # TODO WAS CHANGED DENSE DOWN SAMPLING
         dw_factor = 2
 
         filter_per_src = lambda data, min_len, max_len: [j for j, y in enumerate(data) if len(y) >= min_len and len(y)<=max_len]
@@ -109,9 +115,13 @@ def sort_wlen(sequence1, sequence2):
 
         max_src_len = 200 # Maximum motion duration - 10s
         min_src_len = 40 # Minimum motion duration - 2s
-        max_trg_len = 20 # Maximum number of words to generate
         min_refs = 3 # Minimum number of text descriptions for same motion
 
+        # THIS CONSTRAINT FOUND TO BE VERY IMPORTANT TO AVOID PURE LANGUAGE MODELING
+        # UNCONDITIONED ON MOTION WHICH LEDS TO OVER-FITTING
+
+        max_trg_len = 20 # Maximum number of words from refs samples
+
         if not multiple_references:
             # Number of annotations per motion (idmot) <=> self.map_ids_len[id_mot]
             self.X_train, self.X_val, self.X_test  = [np.asarray([ self.poses[id_mot] for id_mot in ids for _ in range(self.map_ids_len[id_mot])],dtype=object) #ids is the index of each pose
@@ -124,13 +134,13 @@ def sort_wlen(sequence1, sequence2):
                                                                                  for id_mot in ids]),dtype=object) for ids in [self.indx_train, self.indx_val, self.indx_test]
                                                       ]
 
-            assert len(self.X_train)==len(self.y_train)
+            assert len(self.X_train)==len(self.y_train), f"X_train {len(self.X_train)} != y_train {len(self.y_train)}"
 
             # FILTER PER MOTION LENGTH
             self.filter(lambda x: filter_per_src(x,min_len=min_src_len,max_len=max_src_len),True,multiple_references)
             # FILTER PER REFS NUMBER
             self.filter(lambda x: filter_per_refs(x,min_refs=min_refs),False,multiple_references)
-            # # FILTER PER TRG LENGTH
+            # FILTER PER TRG LENGTH
             self.filter(lambda x:filter_per_trg(x,max_trg_len=max_trg_len),False,multiple_references)
 
             logging.info("Sort with respect to pose seq_len to reduce padding percentage --> optimize time")
@@ -147,7 +157,7 @@ def sort_wlen(sequence1, sequence2):
             logging.info("Number of flattened samples VAL: %d TRAIN: %d, TEST: %d " % (len(self.X_val), len(self.X_train), len(self.X_test)))
 
         else:
-            self.X_ref_train, self.X_ref_val, self.X_ref_test = [np.asarray([ self.poses[id_mot] for id_mot in ids],dtype=object)          #ids is the index of each pose
+            self.X_ref_train, self.X_ref_val, self.X_ref_test = [np.asarray([ self.poses[id_mot] for id_mot in ids],dtype=object)                        #ids is the index of each pose
                                                                             for ids in [self.indx_train, self.indx_val, self.indx_test] ]
             self.y_ref_train, self.y_ref_val, self.y_ref_test  = [
                                                       np.asarray([self.corrected_sentences_numeric[0 if id_mot==0 else cum_len[id_mot-1] : cum_len[id_mot]]
@@ -160,6 +170,9 @@ def sort_wlen(sequence1, sequence2):
             # FILTER PER REFS NUMBER
             self.filter(lambda x: filter_per_refs(x,min_refs=min_refs),False,multiple_references)
 
+            # NOT TO BE APPLIED DURING INFERENCE
+            # # FILTER PER TRG LENGTH
+            # self.filter(lambda x:filter_per_trg(x,max_trg_len=max_trg_len),False,multiple_references)
 
             logging.info("Sort with respect to pose seq_len to reduce padding percentage --> optimize time")
             self.X_ref_train, self.y_ref_train = sort_wlen(self.X_ref_train, self.y_ref_train)
@@ -175,22 +188,6 @@ def sort_wlen(sequence1, sequence2):
         self.Sets = [self.X_train,self.y_train,self.X_val,self.y_val,self.X_test,self.y_test] \
                if not self.multiple_references else [self.X_ref_train,self.y_ref_train,self.X_ref_val,self.y_ref_val,self.X_ref_test,self.y_ref_test]
 
-        # ------------- Guided attention for supervision--------------
-
-        self.gt_dict,self.gt_alpha = categorize_tokens_new(self.lang.idx_to_token.values())
-
-        self.gth_gates = []
-        self.gth_alphas = []
-        for seti in  np.asarray(self.Sets,dtype=object)[[1,3,5]] :
-            gates_per_phrase = []
-            alphas_per_phrase = []
-            for setii in seti:
-                gates_per_phrase += [[self.gt_dict[self.lang.idx_to_token[word]] for word in (setii if not self.multiple_references else setii[0]) ]]
-                alphas_per_phrase += [[self.gt_alpha[self.lang.idx_to_token[word]] for word in (setii if not self.multiple_references else setii[0])]]
-
-            self.gth_gates.append(gates_per_phrase)
-            self.gth_alphas.append(alphas_per_phrase)
-
     def filter(self,filter_function,src=False,multiple_references=False):
         if multiple_references:
             keep_ids = filter_function(self.X_ref_train if src else self.y_ref_train)
@@ -217,14 +214,13 @@ def filter(self,filter_function,src=False,multiple_references=False):
             keep_ids = filter_function(self.X_val if src else self.y_val)
             self.y_val = self.y_val[keep_ids]
             self.X_val = self.X_val[keep_ids]
-
     def __getitem__(self, item):
         if self.train_:
-            return self.Sets[0][item], self.Sets[1][item], self.gth_gates[0][item] , self.gth_alphas[0][item]
+            return self.Sets[0][item], self.Sets[1][item]  # (variable_sequence_length,n_joint,joint_dim)
         elif self.val_:
-            return self.Sets[2][item], self.Sets[3][item], self.gth_gates[1][item] , self.gth_alphas[1][item]
+            return self.Sets[2][item], self.Sets[3][item]  # (variable_sequence_length,n_joint,joint_dim)
         elif self.test_:
-            return self.Sets[4][item], self.Sets[5][item] , self.gth_gates[2][item] , self.gth_alphas[2][item]
+            return self.Sets[4][item], self.Sets[5][item]  # (variable_sequence_length,n_joint,joint_dim)
 
     def __len__(self):
         if self.train_:
@@ -248,6 +244,12 @@ def shift_poses(self):
         normalized_poses = []
         meandata = np.expand_dims(np.array([mx, my, mz]), axis=(0, 1))
         stddata =  np.expand_dims(np.array([sx, sy, sz]), axis=(0, 1))
+        self.meandata = meandata
+        self.stddata = stddata
+        
+        np.savez("configs/mean_std.npz",mean=meandata,std=stddata)
+        
+        logging.info(f"Mean [{mx}, {my}, {mz}], Std [{sx}, {sy}, {sz}]")
         for k in range(len(poseswtx)):
             normalized_poses.append((poseswtx[k].reshape(-1,22,3) - meandata) /stddata )
 
@@ -256,11 +258,22 @@ def shift_poses(self):
              shift_poses.append(normalized_poses[k]-np.expand_dims(normalized_poses[k].reshape(-1,22,3)[0,0,:],axis=(0,1)))
 
         return  np.asarray(normalized_poses,dtype=object)
+    
+    def normalize_poses(self, poses):
+        logging.info("Mean/Std Normalization")
+        poses = [ps.reshape(-1,22,3) for ps in poses]
+        
+        for k in range(len(poses)):
+            poses[k] = (poses[k] - self.meandata) / self.stddata
+
+        return poses
+
+        
 
     def read_data(self,path_txt=None):
         kitmld = np.load(self.original_path, allow_pickle=True)
         poses = kitmld['kitmld_array']
-        descriptions = kitmld['old_desc'] #kitmld['descriptions']
+        descriptions = kitmld['old_desc']
         if  path_txt:
             return poses, np.asarray(descriptions, dtype=str)
         else:
@@ -276,4 +289,10 @@ def filter_text(self,descriptions):
             else:
                 pose_annotation.append(idsample)
 
-        return missed_annotation, pose_annotation
\ No newline at end of file
+        return missed_annotation, pose_annotation
+
+
+if __name__ == "__main__":
+    # RUN FIRST TO CREATE DATASET CORRECTION
+    path = '/media/hdd/usr/tao/momask-codes/dataset/HumanML3D/' + r"all_humanML3D.npz"
+    data =dataset_class(path,filter_data=True,min_freq=3)
\ No newline at end of file
diff --git a/datasets/vocabulary.py b/datasets/vocabulary.py
index 1e615a5..db428aa 100644
--- a/datasets/vocabulary.py
+++ b/datasets/vocabulary.py
@@ -1,8 +1,11 @@
 import spacy
 #import contextualSpellCheck
-#from spellchecker import SpellChecker
+from spellchecker import SpellChecker
+# from datasets.kit_dataset import kitDataset   # datasets is required for external import of this file
 import logging
 
+import yaml
+
 logging.basicConfig(
     level=logging.INFO,
     format="\t\033[1;32m[%(levelname)s] %(message)s",#%(asctime)s
@@ -11,15 +14,74 @@
         logging.StreamHandler()
     ]
 )
+# TODO READ FROM FILE CORRECTIONS
+replace = {".":"",
+"rigth":"right",
+"bakcward":"backward",
+"ciricle":"circle",
+"cirl":"circle",
+"quater":"quarter",
+"perfoms":"performs",
+"continuos":"continuously",
+"trurns":"turns",
+"trun":"turn",
+"hight":"height",
+"wolks":"walks",
+"walkes":"walks",
+"degress":"degrees",
+"denn":"then",
+"180degree":"180 degrees",
+"angles":"angle",
+"wawing":"waving",
+"sqatted":"squatted",
+"quartercircle":"quarter circle",
+"danse":"dance",
+"beeing":"being",
+'someonewho': 'someone',
+"somone":"someone",
+"befor":"before",
+"bhind":"behind",
+"ontop":"on top",
+"quter":"quarter",
+"inital":"initial",
+"startspot":"start spot",
+"stepstones":"steps",
+"persone":"person",
+u'°': ' degrees',
+'°':' degrees',
+"\n":"",
+"90degrees":"90 degrees",
+'cha cha': 'cha-cha-cha',
+'cha cha cha': 'cha-cha-cha',
+'/ which': ' which',
+'fastly moves': 'moves fast',
+'the person kneeled down is standing up': 'a kneeling person is standing up',
+'tu rns': 'turns',
+"degree":"degrees",
+# '1,5': "medium",
+# "3,5":"big",
+" cm ":" centimeters ",
+"cm ":" centimeters ",
+" centi ":" centimeters ",
+" meter ":" m ",
+" meters ": " m ",
+"andddd":"and",
+' "chicken" ':' chicken ',
+" loki " : " looking "
+}
 
+text2int = {'ninety': '90', 'one': '1','two': '2', 'three': '3', 'four': '4', 'five': '5', 'six': '6', 'seven': '7',
+            'eight': '8', 'nine': '9', 'ten': '10','zero': '0'}
 
 class vocabulary():
     def __init__(self, sentences, context_correction=False, correct_tokens=False,ask_user=False):
 
         self.sentences = [ref.lower() for ref in sentences] # list of sentences
+
         self.points = ["-", "=", "<", ">", "\n", "/", '.', ',', '(', ')',
                        ':', ';',":","..",'!',"\r", '?', '"']
 
+
         self.correct_tokens = correct_tokens
         self.context_correction = context_correction
         self.corrected_sentences = []
@@ -27,10 +89,11 @@ def __init__(self, sentences, context_correction=False, correct_tokens=False,ask
         self.spacy_eng = spacy.load("en_core_web_sm")
         if self.context_correction:
             # Warning time expansive
-            #contextualSpellCheck.add_to_pipe(self.spacy_eng)
+            contextualSpellCheck.add_to_pipe(self.spacy_eng)
             self.context_sentence_correction()
         elif self.correct_tokens:
-            pass
+            # Correction don't take account the context but more fast
+            self.token_correction(ask_user)
         else:
             self.corrected_sentences = self.sentences[:] # independent copy
 
@@ -41,12 +104,70 @@ def context_sentence_correction(self):
             if len(corrected_desc) >= 1:
                 self.corrected_sentences.append(desc)
                 # for user examination
-                with open("sentence_correction.txt", mode="a") as fa:
+                with open("sentence_correction.txt", mode="a",encoding='utf-8') as fa:
                     fa.write("%s \t %s\n" % (desc, corrected_desc))
             # no correction made conserve the original description
             else:
                 self.corrected_sentences.append(desc)
 
+    def token_correction(self,ask_user=False):
+
+        logging.info("INITIAL CORRECTIONS AND LOWER CASING ...")
+        self.old_sentences = self.sentences[:]
+        for token in replace.keys():
+            for i in range(len(self.sentences)):
+                print("\t%d/%d\r"%(i+1,len(self.sentences)),end="")
+                self.sentences[i] = [ref.lower().replace(token,replace[token]) for ref in self.sentences[i]]
+
+        spell = SpellChecker()
+        shift = 0
+        # CREATE FILE TO SAVE CORRECTED DESCRIPTIONS
+        file_name_corrections = "sentences_corrections"
+        with open(file_name_corrections+".csv", mode="w", encoding='utf-8') as fa: pass
+        for idx, desc in enumerate(self.old_sentences):
+            desc = str(desc)
+            # SOME SPECIAL PRE-PROCESSING
+            tokens = self.language_tokenizer(str(desc))
+            for it,token in enumerate(tokens.copy()):
+                if token in text2int:
+                    logging.info(f'Text to int, {tokens[it]} --> {text2int[token]} ')
+                    tokens[it] = text2int[token]
+                if token in replace:
+                    logging.info("Replacing ...")
+                    tokens[it] = replace[token]
+            # logging.info(tokens,type(tokens[0]))
+            if "" in tokens : tokens.remove("")
+            misppelled = spell.unknown(tokens)
+            if len(list(misppelled)) != 0 :
+                for token in list(misppelled):
+                    if ' ' in token :
+                        logging.info("EMPTY TOKEN [[%s]]" % token)
+                        tokens.remove(token)
+                    elif ask_user:
+                        candidates = spell.candidates(token)
+                        # Ask User to fix
+                        user_correction = input(
+                            '"{}" in sentence "{}" seems to be misspelled. Here are some suggestions: {}\n'.format(
+                                token, desc, candidates))
+                        tokens[tokens.index(token)] = user_correction
+                    if not ask_user or not user_correction: # ask_user is first evaluated
+                        try:
+                            tokens[tokens.index(token)] = spell.correction(token) if spell.correction(token)!='i' else token
+                            logging.info("Token index %d corrected %s ----> %s " % (idx, token, spell.correction(token)))
+                        except ValueError:
+                            logging.info("token %s not found" % token)
+
+            tokens = [token for token in tokens if token not in self.points]
+            desc = " ".join(tokens)
+            self.corrected_sentences.append(desc)
+            # FOR USER EXAMINATION:
+            try:
+                with open(file_name_corrections+".csv", mode="a",encoding='utf-8') as fa:
+                    fa.write("%s \t %s\n" % (self.old_sentences[idx].replace("\n",""),self.corrected_sentences[idx-shift]))
+            except IndexError:
+                    logging.info("description %s NOT USED! ",self.sentences[idx])
+        logging.info("Corrected sentences saved to csv file ")
+
     def build_vocabulary(self, min_freq=1):
         self.token_to_idx = {"<pad>": 0, "<sos>": 1, "<eos>": 2, "<unk>": 3}
         self.idx_to_token = {}
@@ -133,4 +254,15 @@ def decode_numeric_sentence(self,list_int,remove_sos_eos=False,ignore_pad = Fals
 
     def language_tokenizer(self, sentence):
         sentence = str(sentence) # numpy str --> str
-        return [str(token) for token in self.spacy_eng.tokenizer(sentence)]
\ No newline at end of file
+        return [str(token) for token in self.spacy_eng.tokenizer(sentence)]
+    
+    @staticmethod
+    def from_yaml(file):
+        with open(file, 'r') as file:
+            data = yaml.load(file, Loader=yaml.FullLoader)
+            
+        vocab = vocabulary([])
+        vocab.idx_to_token = data['idx_to_token']
+        vocab.vocab_size = len(vocab.idx_to_token)
+        vocab.token_to_idx = data['token_to_idx']
+        return vocab
\ No newline at end of file
diff --git a/environement.yaml b/environement.yaml
index 887cc1b..f63f44d 100644
--- a/environement.yaml
+++ b/environement.yaml
@@ -5,164 +5,164 @@ channels:
   - defaults
   - conda-forge
 dependencies:
-  - abseil-cpp=20211102.0=h0e60522_0
-  - absl-py=1.4.0=pyhd8ed1ab_0
-  - aiofiles=22.1.0=py310haa95532_0
-  - aiohttp=3.8.1=py310he2412df_1
-  - aiosignal=1.3.1=pyhd8ed1ab_0
-  - aiosqlite=0.18.0=py310haa95532_0
-  - anyio=3.5.0=py310haa95532_0
-  - argon2-cffi=21.3.0=pyhd3eb1b0_0
-  - argon2-cffi-bindings=21.2.0=py310h2bbff1b_0
-  - async-timeout=4.0.2=pyhd8ed1ab_0
-  - attrs=23.1.0=pyh71513ae_1
-  - babel=2.11.0=py310haa95532_0
-  - backcall=0.2.0=pyhd3eb1b0_0
-  - beautifulsoup4=4.12.2=py310haa95532_0
-  - bleach=4.1.0=pyhd3eb1b0_0
-  - blinker=1.6.2=pyhd8ed1ab_0
-  - brotlipy=0.7.0=py310he2412df_1004
-  - bzip2=1.0.8=he774522_0
-  - c-ares=1.19.0=h2bbff1b_0
-  - ca-certificates=2023.08.22=haa95532_0
-  - cachetools=5.3.0=pyhd8ed1ab_0
-  - cffi=1.15.1=py310hcbf9ad4_0
-  - click=8.1.3=py310h5588dad_1
-  - colorama=0.4.6=pyhd8ed1ab_0
-  - comm=0.1.2=py310haa95532_0
-  - cryptography=3.4.8=py310ha857299_1
-  - debugpy=1.6.7=py310hd77b12b_0
-  - decorator=5.1.1=pyhd3eb1b0_0
-  - defusedxml=0.7.1=pyhd3eb1b0_0
-  - entrypoints=0.4=py310haa95532_0
-  - exceptiongroup=1.0.4=py310haa95532_0
-  - frozenlist=1.3.3=py310h2bbff1b_0
-  - google-auth=2.19.0=pyh1a96a4e_0
-  - google-auth-oauthlib=1.0.0=pyhd8ed1ab_0
-  - grpc-cpp=1.48.2=hf108199_0
-  - icu=73.1=h6c2663c_0
-  - idna=3.4=pyhd8ed1ab_0
-  - importlib-metadata=6.6.0=pyha770c72_0
-  - intel-openmp=2023.1.0=h57928b3_46319
-  - ipykernel=6.25.0=py310h9909e9c_0
-  - ipython_genutils=0.2.0=pyhd3eb1b0_1
-  - ipywidgets=8.0.4=py310haa95532_0
-  - jinja2=3.1.2=py310haa95532_0
-  - jpeg=9e=h2bbff1b_1
-  - json5=0.9.6=pyhd3eb1b0_0
-  - jsonschema-specifications=2023.7.1=py310haa95532_0
-  - jupyter=1.0.0=py310haa95532_8
-  - jupyter_client=7.4.9=py310haa95532_0
-  - jupyter_console=6.6.3=py310haa95532_0
-  - jupyter_core=5.5.0=py310haa95532_0
-  - jupyter_events=0.6.3=py310haa95532_0
-  - jupyter_server=1.23.4=py310haa95532_0
-  - jupyter_server_fileid=0.9.0=py310haa95532_0
-  - jupyter_server_ydoc=0.8.0=py310haa95532_1
-  - jupyter_ydoc=0.2.4=py310haa95532_0
-  - jupyterlab=3.6.3=py310haa95532_0
-  - jupyterlab_pygments=0.1.2=py_0
-  - jupyterlab_server=2.22.0=py310haa95532_0
-  - jupyterlab_widgets=3.0.5=py310haa95532_0
-  - krb5=1.20.1=h5b6d351_1
-  - libblas=3.9.0=8_mkl
-  - libcblas=3.9.0=8_mkl
-  - libclang=14.0.6=default_hb5a9fac_1
-  - libclang13=14.0.6=default_h8e68704_1
-  - libffi=3.4.4=hd77b12b_0
-  - liblapack=3.9.0=8_mkl
-  - libpng=1.6.39=h8cc25b3_0
-  - libpq=12.15=hb652d5d_1
-  - libprotobuf=3.20.3=h23ce68f_0
-  - libsodium=1.0.18=h62dcd97_0
-  - lz4-c=1.9.4=h2bbff1b_0
-  - markdown=3.4.3=pyhd8ed1ab_0
-  - matplotlib-inline=0.1.6=py310haa95532_0
-  - mistune=2.0.4=py310haa95532_0
-  - mkl=2020.4=hb70f87d_311
-  - multidict=6.0.2=py310h2bbff1b_0
-  - nbclassic=0.5.5=py310haa95532_0
-  - nbclient=0.8.0=py310haa95532_0
-  - nbconvert=7.10.0=py310haa95532_0
-  - nbformat=5.9.2=py310haa95532_0
-  - nest-asyncio=1.5.6=py310haa95532_0
-  - notebook=6.5.4=py310haa95532_1
-  - notebook-shim=0.2.2=py310haa95532_0
-  - oauthlib=3.2.2=pyhd8ed1ab_0
-  - openssl=1.1.1w=h2bbff1b_0
-  - packaging=23.1=py310haa95532_0
-  - pandocfilters=1.5.0=pyhd3eb1b0_0
-  - parso=0.8.3=pyhd3eb1b0_0
-  - pickleshare=0.7.5=pyhd3eb1b0_1003
-  - platformdirs=3.10.0=py310haa95532_0
-  - ply=3.11=py310haa95532_0
-  - prometheus_client=0.14.1=py310haa95532_0
-  - prompt_toolkit=3.0.36=hd3eb1b0_0
-  - protobuf=3.20.3=py310h5588dad_1
-  - pure_eval=0.2.2=pyhd3eb1b0_0
-  - pyasn1=0.4.8=py_0
-  - pyasn1-modules=0.2.7=py_0
-  - pycparser=2.21=pyhd8ed1ab_0
-  - pygments=2.15.1=py310haa95532_1
-  - pyjwt=2.7.0=pyhd8ed1ab_0
-  - pyopenssl=20.0.1=pyhd8ed1ab_0
-  - pyqt=5.15.10=py310hd77b12b_0
-  - pyqt5-sip=12.13.0=py310h2bbff1b_0
-  - pysocks=1.7.1=py310h5588dad_5
-  - python=3.10.11=h966fe2a_2
-  - python-dateutil=2.8.2=pyhd3eb1b0_0
-  - python-fastjsonschema=2.16.2=py310haa95532_0
-  - python-json-logger=2.0.7=py310haa95532_0
-  - python_abi=3.10=2_cp310
-  - pyu2f=0.1.5=pyhd8ed1ab_0
-  - pywin32=305=py310h2bbff1b_0
-  - pywinpty=2.0.10=py310h5da7b33_0
-  - pyzmq=23.2.0=py310hd77b12b_0
-  - qt-main=5.15.2=he2df7e6_10
-  - qtconsole=5.4.2=py310haa95532_0
-  - qtpy=2.2.0=py310haa95532_0
-  - re2=2022.04.01=h0e60522_0
-  - referencing=0.30.2=py310haa95532_0
-  - requests=2.31.0=pyhd8ed1ab_0
-  - requests-oauthlib=1.3.1=pyhd8ed1ab_0
-  - rfc3339-validator=0.1.4=py310haa95532_0
-  - rfc3986-validator=0.1.1=py310haa95532_0
-  - rpds-py=0.10.6=py310h062c2fa_0
-  - rsa=4.9=pyhd8ed1ab_0
-  - send2trash=1.8.0=pyhd3eb1b0_1
-  - setuptools=66.0.0=py310haa95532_0
-  - sip=6.7.12=py310hd77b12b_0
-  - six=1.16.0=pyh6c4a22f_0
-  - sniffio=1.2.0=py310haa95532_1
-  - soupsieve=2.5=py310haa95532_0
-  - sqlite=3.41.2=h2bbff1b_0
-  - stack_data=0.2.0=pyhd3eb1b0_0
-  - tensorboard=2.13.0=pyhd8ed1ab_0
-  - tensorboard-data-server=0.7.0=py310h5588dad_0
-  - terminado=0.17.1=py310haa95532_0
-  - tinycss2=1.2.1=py310haa95532_0
-  - tk=8.6.12=h2bbff1b_0
-  - tomli=2.0.1=py310haa95532_0
-  - tornado=6.3.3=py310h2bbff1b_0
-  - typing_extensions=4.6.2=pyha770c72_0
-  - vc=14.2=h21ff451_1
-  - vs2015_runtime=14.27.29016=h5e58377_2
-  - webencodings=0.5.1=py310haa95532_1
-  - websocket-client=0.58.0=py310haa95532_4
-  - werkzeug=2.1.2=pyhd8ed1ab_1
-  - wheel=0.38.4=py310haa95532_0
-  - widgetsnbextension=4.0.5=py310haa95532_0
-  - win_inet_pton=1.1.0=py310h5588dad_5
-  - winpty=0.4.3=4
-  - xz=5.4.2=h8cc25b3_0
-  - y-py=0.5.9=py310hb6bf4ef_0
-  - yaml=0.2.5=he774522_0
-  - yarl=1.7.2=py310he2412df_2
-  - ypy-websocket=0.8.2=py310haa95532_0
-  - zeromq=4.3.4=hd77b12b_0
-  - zipp=3.15.0=pyhd8ed1ab_0
-  - zlib=1.2.13=h8cc25b3_0
-  - zstd=1.5.5=hd43e919_0
+  - abseil-cpp=20211102.0
+  - absl-py=1.4.0
+  - aiofiles=22.1.0
+  - aiohttp=3.8.1
+  - aiosignal=1.3.1
+  - aiosqlite=0.18.0
+  - anyio=3.5.0
+  - argon2-cffi=21.3.0
+  - argon2-cffi-bindings=21.2.0
+  - async-timeout=4.0.2
+  - attrs=23.1.0
+  - babel=2.11.0
+  - backcall=0.2.0
+  - beautifulsoup4=4.12.2
+  - bleach=4.1.0
+  - blinker=1.6.2
+  - brotlipy=0.7.0
+  - bzip2=1.0.8
+  - c-ares=1.19.0
+  - ca-certificates=2023.08.22
+  - cachetools=5.3.0
+  - cffi=1.15.1
+  - click=8.1.3
+  - colorama=0.4.6
+  - comm=0.1.2
+  - cryptography=3.4.8
+  - debugpy=1.6.7
+  - decorator=5.1.1
+  - defusedxml=0.7.1
+  - entrypoints=0.4
+  - exceptiongroup=1.0.4
+  - frozenlist=1.3.3
+  - google-auth=2.19.0
+  - google-auth-oauthlib=1.0.0
+  - grpc-cpp=1.48.2
+  - icu=73.1
+  - idna=3.4
+  - importlib-metadata=6.6.0
+  - intel-openmp=2023.1.0
+  - ipykernel=6.25.0
+  - ipython_genutils=0.2.0
+  - ipywidgets=8.0.4
+  - jinja2=3.1.2
+  - jpeg=9e
+  - json5=0.9.6
+  - jsonschema-specifications=2023.7.1
+  - jupyter=1.0.0
+  - jupyter_client=7.4.9
+  - jupyter_console=6.6.3
+  - jupyter_core=5.5.0
+  - jupyter_events=0.6.3
+  - jupyter_server=1.23.4
+  - jupyter_server_fileid=0.9.0
+  - jupyter_server_ydoc=0.8.0
+  - jupyter_ydoc=0.2.4
+  - jupyterlab=3.6.3
+  - jupyterlab_pygments=0.1.2
+  - jupyterlab_server=2.22.0
+  - jupyterlab_widgets=3.0.5
+  - krb5=1.20.1
+  - libblas=3.9.0
+  - libcblas=3.9.0
+  - libclang=14.0.6
+  - libclang13=14.0.6
+  - libffi=3.4.4
+  - liblapack=3.9.0
+  - libpng=1.6.39
+  - libpq=12.15
+  - libprotobuf=3.20.3
+  - libsodium=1.0.18
+  - lz4-c=1.9.4
+  - markdown=3.4.3
+  - matplotlib-inline=0.1.6
+  - mistune=2.0.4
+  - mkl=2020.4
+  - multidict=6.0.2
+  - nbclassic=0.5.5
+  - nbclient=0.8.0
+  - nbconvert=7.10.0
+  - nbformat=5.9.2
+  - nest-asyncio=1.5.6
+  - notebook=6.5.4
+  - notebook-shim=0.2.2
+  - oauthlib=3.2.2
+  - openssl=1.1.1w
+  - packaging=23.1
+  - pandocfilters=1.5.0
+  - parso=0.8.3
+  - pickleshare=0.7.5
+  - platformdirs=3.10.0
+  - ply=3.11
+  - prometheus_client=0.14.1
+  - prompt_toolkit=3.0.36
+  - protobuf=3.20.3
+  - pure_eval=0.2.2
+  - pyasn1=0.4.8
+  - pyasn1-modules=0.2.7
+  - pycparser=2.21
+  - pygments=2.15.1
+  - pyjwt=2.7.0
+  - pyopenssl=20.0.1
+  - pyqt=5.15.10
+  - pyqt5-sip=12.13.0
+  - pysocks=1.7.1
+  - python=3.10.11
+  - python-dateutil=2.8.2
+  - python-fastjsonschema=2.16.2
+  - python-json-logger=2.0.7
+  - python_abi=3.10
+  - pyu2f=0.1.5
+  # - pywin32=305
+  # - pywinpty=2.0.10
+  - pyzmq=23.2.0
+  - qt-main=5.15.2
+  - qtconsole=5.4.2
+  - qtpy=2.2.0
+  - re2=2022.04.01
+  - referencing=0.30.2
+  - requests=2.31.0
+  - requests-oauthlib=1.3.1
+  - rfc3339-validator=0.1.4
+  - rfc3986-validator=0.1.1
+  - rpds-py=0.10.6
+  - rsa=4.9
+  - send2trash=1.8.0
+  - setuptools=66.0.0
+  - sip=6.7.12
+  - six=1.16.0
+  - sniffio=1.2.0
+  - soupsieve=2.5
+  - sqlite=3.41.2
+  - stack_data=0.2.0
+  - tensorboard=2.13.0
+  - tensorboard-data-server=0.7.0
+  - terminado=0.17.1
+  - tinycss2=1.2.1
+  - tk=8.6.12
+  - tomli=2.0.1
+  - tornado=6.3.3
+  - typing_extensions=4.6.2
+  # - vc=14.2
+  # - vs2015_runtime=14.27.29016
+  - webencodings=0.5.1
+  - websocket-client=0.58.0
+  - werkzeug=2.1.2
+  - wheel=0.38.4
+  - widgetsnbextension=4.0.5
+  # - win_inet_pton=1.1./0
+  # - winpty=0.4.3
+  - xz=5.4.2
+  - y-py=0.5.9
+  - yaml=0.2.5
+  - yarl=1.7.2
+  - ypy-websocket=0.8.2
+  - zeromq=4.3.4
+  - zipp=3.15.0
+  - zlib=1.2.13
+  - zstd=1.5.5
   - pip:
     - appdirs==1.4.4
     - asttokens==2.2.1
@@ -250,11 +250,11 @@ dependencies:
     - thinc==8.1.10
     - threadpoolctl==3.1.0
     - tokenizers==0.13.3
-    - torch==2.0.1+cu117
-    - torchaudio==2.0.2+cu117
+    - torch==2.0.1
+    - torchaudio==2.0.2
     - torchdata==0.6.1
     - torchtext==0.15.2
-    - torchvision==0.15.2+cu117
+    - torchvision==0.15.2
     - tqdm==4.65.0
     - traitlets==5.9.0
     - transformers==4.31.0
diff --git a/inference.py b/inference.py
new file mode 100644
index 0000000..cc8064b
--- /dev/null
+++ b/inference.py
@@ -0,0 +1,145 @@
+import argparse
+import logging
+import os
+import torch
+import numpy as np
+import yaml
+from torch.nn.utils.rnn import pad_sequence
+from datasets.loader import build_data
+from datasets.vocabulary import vocabulary
+
+BASE_PATH = "/media/hdd/usr/tao/momask-codes/"
+
+def load_model_config(args, device):
+    # Load model configuration and weights
+    if "kit" in args.dataset_name:
+        from architectures.LSTM_kit import seq2seq
+    elif args.dataset_name == "h3D":
+        from architectures.LSTM_h3D import seq2seq
+
+    loaded_model = seq2seq(
+        args.vocab_size, args.hidden_size, args.embedding_dim, num_layers=1, device=device,
+        attention=args.attention_type, beam_size=args.beam_size, hidden_dim=args.hidden_dim, K=args.K
+    ).to(device)
+
+    model_dict = torch.load(args.path, map_location=device)
+    loaded_model.load_state_dict(model_dict["model"])
+    return loaded_model
+
+def load_data(args):
+    project_path = BASE_PATH + "dataset/HumanML3D"
+    aug_path = BASE_PATH + "dataset/HumanML3D"
+
+    if "kit" in args.dataset_name:
+        from datasets.kit_h3mld import dataset_class
+        path_txt = os.path.join(project_path, "datasets/sentences_corrections.csv")
+        path_motion = os.path.join(aug_path, "kit_with_splits_2023.npz")
+    elif args.dataset_name == "h3D":
+        from datasets.h3d_m2t_dataset_ import dataset_class
+        path_txt = os.path.join(aug_path, "sentences_corrections_h3d.csv")
+        path_motion = os.path.join(aug_path, "all_humanML3D.npz")
+
+    train_data_loader, val_data_loader, test_data_loader = build_data(
+        dataset_class=dataset_class, min_freq=args.min_freq, path=path_motion,
+        train_batch_size=args.batch_size, test_batch_size=args.batch_size,
+        return_lengths=True, path_txt=path_txt, return_trg_len=True,
+        joint_angles=False, multiple_references=args.multiple_references
+    )
+
+    return train_data_loader, val_data_loader, test_data_loader
+
+def get_vocab_size(args):
+    train_data_loader, _, _ = load_data(args)
+    return train_data_loader.dataset.lang.vocab_size_unk
+
+def get_vocab(args):
+    data = pickle.load(open(args.dataset_path, 'rb'))
+    
+    sentences = data['old_desc']
+    sentences = [d for ds in sentences for d in ds] #flat all descriptions in order
+    
+    correct_tokens = False
+    vocab = vocabulary(
+        sentences=sentences, correct_tokens=correct_tokens
+    )
+    vocab.build_vocabulary(min_freq=args.min_freq)
+    assert vocab.vocab_size == args.vocab_size, f"Vocab size mismatch: {vocab.vocab_size} != {args.vocab_size}"
+    return vocab
+
+def preprocess_motions(input_folder, mean, std):
+    motions = []
+    for file in os.listdir(input_folder):
+        if file.endswith(".npy"):
+            motion = np.load(os.path.join(input_folder, file))
+            motion = (motion - mean) / std
+            motions.append(torch.tensor(motion, dtype=torch.float32))
+    return pad_sequence(motions, batch_first=True)
+
+@torch.no_grad()
+def perform_inference(model, motions, device):
+    model.eval()
+    
+    B, T, J, _ = motions.shape
+    src = motions.view(B,T,J*3).permute(1, 0, 2).to(device) # T x B x (3J)
+    src_lens = [len(motion) for motion in motions]
+    trg = torch.zeros((src.size(0), src.size(1)), dtype=torch.long).to(device)
+    output = model(src, trg, teacher_force_ratio=0, src_lens=src_lens)
+    return output
+
+def save_captions(captions, output_folder, vocab: vocabulary):
+    # postprocess captions
+    if isinstance(captions, torch.Tensor):
+        captions = [captions.argmax(dim=-1).T.tolist()]
+        
+    os.makedirs(output_folder, exist_ok=True)
+    for sample in captions:
+        for i, caption in enumerate(sample):
+            caption = vocab.decode_numeric_sentence(caption, remove_sos_eos=True, ignore_pad=True)
+            logging.info(f"Caption {i}: {caption}")
+            with open(os.path.join(output_folder, f"caption_{i}.txt"), "w") as f:
+                f.write(caption)
+    
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_folder", type=str, required=True, help="Folder containing input motion npy files")
+    parser.add_argument("--output_folder", type=str, required=True, help="Folder to save the generated captions")
+    parser.add_argument("--path", type=str, required=True, help="Path of model weights")
+    parser.add_argument("--dataset_name", type=str, default="h3D", choices=["h3D", "kit"])
+    parser.add_argument("--device", type=str, default="cuda:1")
+    parser.add_argument("--attention_type", type=str, default="relative_bahdanau")
+    parser.add_argument("--hidden_dim", type=int, default=256)
+    parser.add_argument("--hidden_size", type=int, default=256)
+    parser.add_argument("--embedding_dim", type=int, default=128)
+    parser.add_argument("--min_freq", type=int, default=3)
+    parser.add_argument("--beam_size", type=int, default=1)
+    parser.add_argument("--K", type=int, default=6)
+    parser.add_argument("--batch_size", type=int, default=1024)
+    parser.add_argument("--vocab_size", type=int, default=3605)
+    parser.add_argument("--multiple_references", type=bool, default=False)
+    parser.add_argument("--vocab_path", type=str, default="configs/vocab.yaml")
+    parser.add_argument("--dataset_path", type=str, default="all_humanML3D.npz")
+    args = parser.parse_args()
+
+    device = torch.device(args.device)
+    # train, val, test = load_data(args)
+    # # # vocab = get_vocab(args)
+    # vocab = train.dataset.lang
+    # assert train.dataset.lang.vocab_size_unk == args.vocab_size, f"Vocab size mismatch: {train.dataset.lang.vocab_size_unk} != {args.vocab_size}"
+    
+    vocab = vocabulary.from_yaml(args.vocab_path)
+    
+    data = np.load('configs/mean_std.npz')
+    mean = data['mean']
+    std = data['std']
+    
+    # save token_to_id
+    with open(args.vocab_path, 'w') as file:
+        yaml.dump({
+                'token_to_idx': vocab.token_to_idx,
+                'idx_to_token': vocab.idx_to_token,                
+            }, file)
+        
+    model = load_model_config(args, device)
+    motions = preprocess_motions(args.input_folder, mean, std)
+    captions = perform_inference(model, motions, device)
+    save_captions(captions, args.output_folder, vocab)