thanks to H-Liu1997 ❤

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+        intervals [172]:
+            xmin = 46.65 
+            xmax = 46.86 
+            text = "i" 
+        intervals [173]:
+            xmin = 46.86 
+            xmax = 47.16 
+            text = "like" 
+        intervals [174]:
+            xmin = 47.16 
+            xmax = 47.39 
+            text = "to" 
+        intervals [175]:
+            xmin = 47.39 
+            xmax = 47.86 
+            text = "listen" 
+        intervals [176]:
+            xmin = 47.86 
+            xmax = 48.03 
+            text = "to" 
+        intervals [177]:
+            xmin = 48.03 
+            xmax = 48.41 
+            text = "music" 
+        intervals [178]:
+            xmin = 48.41 
+            xmax = 48.73 
+            text = "and" 
+        intervals [179]:
+            xmin = 48.73 
+            xmax = 48.76 
+            text = "" 
+        intervals [180]:
+            xmin = 48.76 
+            xmax = 49.01 
+            text = "we're" 
+        intervals [181]:
+            xmin = 49.01 
+            xmax = 49.3 
+            text = "watch" 
+        intervals [182]:
+            xmin = 49.3 
+            xmax = 49.38 
+            text = "a" 
+        intervals [183]:
+            xmin = 49.38 
+            xmax = 50.05 
+            text = "documentary" 
+        intervals [184]:
+            xmin = 50.05 
+            xmax = 50.51 
+            text = "movies" 
+        intervals [185]:
+            xmin = 50.51 
+            xmax = 50.82 
+            text = "on" 
+        intervals [186]:
+            xmin = 50.82 
+            xmax = 51.11 
+            text = "my" 
+        intervals [187]:
+            xmin = 51.11 
+            xmax = 51.81 
+            text = "laptop" 
+        intervals [188]:
+            xmin = 51.81 
+            xmax = 52.14 
+            text = "" 
+        intervals [189]:
+            xmin = 52.14 
+            xmax = 52.44 
+            text = "but" 
+        intervals [190]:
+            xmin = 52.44 
+            xmax = 52.86 
+            text = "sometimes" 
+        intervals [191]:
+            xmin = 52.86 
+            xmax = 52.93 
+            text = "it" 
+        intervals [192]:
+            xmin = 52.93 
+            xmax = 53.13 
+            text = "just" 
+        intervals [193]:
+            xmin = 53.13 
+            xmax = 53.61 
+            text = "sleep" 
+        intervals [194]:
+            xmin = 53.61 
+            xmax = 53.65 
+            text = "" 
+        intervals [195]:
+            xmin = 53.65 
+            xmax = 53.83 
+            text = "i" 
+        intervals [196]:
+            xmin = 53.83 
+            xmax = 54.27 
+            text = "especially" 
+        intervals [197]:
+            xmin = 54.27 
+            xmax = 54.61 
+            text = "liked" 
+        intervals [198]:
+            xmin = 54.61 
+            xmax = 55.01 
+            text = "watching" 
+        intervals [199]:
+            xmin = 55.01 
+            xmax = 55.62 
+            text = "japanese" 
+        intervals [200]:
+            xmin = 55.62 
+            xmax = 55.91 
+            text = "anime" 
+        intervals [201]:
+            xmin = 55.91 
+            xmax = 56.33 
+            text = "i" 
+        intervals [202]:
+            xmin = 56.33 
+            xmax = 56.85 
+            text = "" 
+        intervals [203]:
+            xmin = 56.85 
+            xmax = 57.12 
+            text = "think" 
+        intervals [204]:
+            xmin = 57.12 
+            xmax = 57.43 
+            text = "watching" 
+        intervals [205]:
+            xmin = 57.43 
+            xmax = 57.62 
+            text = "a" 
+        intervals [206]:
+            xmin = 57.62 
+            xmax = 57.79 
+            text = "me" 
+        intervals [207]:
+            xmin = 57.79 
+            xmax = 58.09 
+            text = "is" 
+        intervals [208]:
+            xmin = 58.09 
+            xmax = 58.39 
+            text = "anime" 
+        intervals [209]:
+            xmin = 58.39 
+            xmax = 59.06 
+            text = "is" 
+        intervals [210]:
+            xmin = 59.06 
+            xmax = 59.31 
+            text = "very" 
+        intervals [211]:
+            xmin = 59.31 
+            xmax = 59.67 
+            text = "helpful" 
+        intervals [212]:
+            xmin = 59.67 
+            xmax = 59.81 
+            text = "for" 
+        intervals [213]:
+            xmin = 59.81 
+            xmax = 59.98 
+            text = "me" 
+        intervals [214]:
+            xmin = 59.98 
+            xmax = 60.28 
+            text = "to" 
+        intervals [215]:
+            xmin = 60.28 
+            xmax = 60.69 
+            text = "learn" 
+        intervals [216]:
+            xmin = 60.69 
+            xmax = 60.78 
+            text = "and" 
+        intervals [217]:
+            xmin = 60.78 
+            xmax = 61.21 
+            text = "express" 
+        intervals [218]:
+            xmin = 61.21 
+            xmax = 61.89 
+            text = "japanese" 
+        intervals [219]:
+            xmin = 61.89 
+            xmax = 62.42 
+            text = "better" 
+        intervals [220]:
+            xmin = 62.42 
+            xmax = 64.097375 
+            text = "" 
+    item [2]:
+        class = "IntervalTier" 
+        name = "phones" 
+        xmin = 0 
+        xmax = 64.097375 
+        intervals: size = 684 
+        intervals [1]:
+            xmin = 0 
+            xmax = 1.42 
+            text = "" 
+        intervals [2]:
+            xmin = 1.42 
+            xmax = 1.48 
+            text = "DH" 
+        intervals [3]:
+            xmin = 1.48 
+            xmax = 1.52 
+            text = "AH0" 
+        intervals [4]:
+            xmin = 1.52 
+            xmax = 1.62 
+            text = "F" 
+        intervals [5]:
+            xmin = 1.62 
+            xmax = 1.72 
+            text = "ER1" 
+        intervals [6]:
+            xmin = 1.72 
+            xmax = 1.75 
+            text = "S" 
+        intervals [7]:
+            xmin = 1.75 
+            xmax = 1.78 
+            text = "T" 
+        intervals [8]:
+            xmin = 1.78 
+            xmax = 1.81 
+            text = "TH" 
+        intervals [9]:
+            xmin = 1.81 
+            xmax = 1.88 
+            text = "IH1" 
+        intervals [10]:
+            xmin = 1.88 
+            xmax = 1.97 
+            text = "NG" 
+        intervals [11]:
+            xmin = 1.97 
+            xmax = 2.04 
+            text = "AY1" 
+        intervals [12]:
+            xmin = 2.04 
+            xmax = 2.08 
+            text = "L" 
+        intervals [13]:
+            xmin = 2.08 
+            xmax = 2.17 
+            text = "AY1" 
+        intervals [14]:
+            xmin = 2.17 
+            xmax = 2.21 
+            text = "K" 
+        intervals [15]:
+            xmin = 2.21 
+            xmax = 2.24 
+            text = "T" 
+        intervals [16]:
+            xmin = 2.24 
+            xmax = 2.28 
+            text = "IH0" 
+        intervals [17]:
+            xmin = 2.28 
+            xmax = 2.34 
+            text = "D" 
+        intervals [18]:
+            xmin = 2.34 
+            xmax = 2.47 
+            text = "UW1" 
+        intervals [19]:
+            xmin = 2.47 
+            xmax = 2.58 
+            text = "AA1" 
+        intervals [20]:
+            xmin = 2.58 
+            xmax = 2.63 
+            text = "N" 
+        intervals [21]:
+            xmin = 2.63 
+            xmax = 2.68 
+            text = "W" 
+        intervals [22]:
+            xmin = 2.68 
+            xmax = 2.78 
+            text = "IY1" 
+        intervals [23]:
+            xmin = 2.78 
+            xmax = 2.88 
+            text = "K" 
+        intervals [24]:
+            xmin = 2.88 
+            xmax = 3.01 
+            text = "EH2" 
+        intervals [25]:
+            xmin = 3.01 
+            xmax = 3.14 
+            text = "N" 
+        intervals [26]:
+            xmin = 3.14 
+            xmax = 3.2 
+            text = "D" 
+        intervals [27]:
+            xmin = 3.2 
+            xmax = 3.32 
+            text = "Z" 
+        intervals [28]:
+            xmin = 3.32 
+            xmax = 3.47 
+            text = "IH1" 
+        intervals [29]:
+            xmin = 3.47 
+            xmax = 3.58 
+            text = "Z" 
+        intervals [30]:
+            xmin = 3.58 
+            xmax = 3.64 
+            text = "R" 
+        intervals [31]:
+            xmin = 3.64 
+            xmax = 3.7 
+            text = "IY0" 
+        intervals [32]:
+            xmin = 3.7 
+            xmax = 3.8 
+            text = "L" 
+        intervals [33]:
+            xmin = 3.8 
+            xmax = 3.96 
+            text = "AE1" 
+        intervals [34]:
+            xmin = 3.96 
+            xmax = 4.02 
+            text = "K" 
+        intervals [35]:
+            xmin = 4.02 
+            xmax = 4.11 
+            text = "S" 
+        intervals [36]:
+            xmin = 4.11 
+            xmax = 4.2 
+            text = "IH0" 
+        intervals [37]:
+            xmin = 4.2 
+            xmax = 4.41 
+            text = "NG" 
+        intervals [38]:
+            xmin = 4.41 
+            xmax = 4.52 
+            text = "" 
+        intervals [39]:
+            xmin = 4.52 
+            xmax = 4.97 
+            text = "AH0" 
+        intervals [40]:
+            xmin = 4.97 
+            xmax = 5.01 
+            text = "N" 
+        intervals [41]:
+            xmin = 5.01 
+            xmax = 5.05 
+            text = "D" 
+        intervals [42]:
+            xmin = 5.05 
+            xmax = 5.14 
+            text = "AY1" 
+        intervals [43]:
+            xmin = 5.14 
+            xmax = 5.19 
+            text = "TH" 
+        intervals [44]:
+            xmin = 5.19 
+            xmax = 5.25 
+            text = "IH1" 
+        intervals [45]:
+            xmin = 5.25 
+            xmax = 5.29 
+            text = "NG" 
+        intervals [46]:
+            xmin = 5.29 
+            xmax = 5.33 
+            text = "K" 
+        intervals [47]:
+            xmin = 5.33 
+            xmax = 5.36 
+            text = "AY1" 
+        intervals [48]:
+            xmin = 5.36 
+            xmax = 5.41 
+            text = "L" 
+        intervals [49]:
+            xmin = 5.41 
+            xmax = 5.44 
+            text = "G" 
+        intervals [50]:
+            xmin = 5.44 
+            xmax = 5.5 
+            text = "OW1" 
+        intervals [51]:
+            xmin = 5.5 
+            xmax = 5.68 
+            text = "SH" 
+        intervals [52]:
+            xmin = 5.68 
+            xmax = 5.87 
+            text = "AA1" 
+        intervals [53]:
+            xmin = 5.87 
+            xmax = 5.92 
+            text = "P" 
+        intervals [54]:
+            xmin = 5.92 
+            xmax = 5.96 
+            text = "IH0" 
+        intervals [55]:
+            xmin = 5.96 
+            xmax = 6 
+            text = "NG" 
+        intervals [56]:
+            xmin = 6 
+            xmax = 6.06 
+            text = "IH0" 
+        intervals [57]:
+            xmin = 6.06 
+            xmax = 6.11 
+            text = "F" 
+        intervals [58]:
+            xmin = 6.11 
+            xmax = 6.16 
+            text = "AY1" 
+        intervals [59]:
+            xmin = 6.16 
+            xmax = 6.29 
+            text = "M" 
+        intervals [60]:
+            xmin = 6.29 
+            xmax = 6.35 
+            text = "N" 
+        intervals [61]:
+            xmin = 6.35 
+            xmax = 6.48 
+            text = "AA1" 
+        intervals [62]:
+            xmin = 6.48 
+            xmax = 6.54 
+            text = "T" 
+        intervals [63]:
+            xmin = 6.54 
+            xmax = 6.58 
+            text = "DH" 
+        intervals [64]:
+            xmin = 6.58 
+            xmax = 6.64 
+            text = "AE1" 
+        intervals [65]:
+            xmin = 6.64 
+            xmax = 6.7 
+            text = "T" 
+        intervals [66]:
+            xmin = 6.7 
+            xmax = 6.78 
+            text = "T" 
+        intervals [67]:
+            xmin = 6.78 
+            xmax = 6.93 
+            text = "AY1" 
+        intervals [68]:
+            xmin = 6.93 
+            xmax = 7.08 
+            text = "ER0" 
+        intervals [69]:
+            xmin = 7.08 
+            xmax = 7.19 
+            text = "D" 
+        intervals [70]:
+            xmin = 7.19 
+            xmax = 7.45 
+            text = "" 
+        intervals [71]:
+            xmin = 7.45 
+            xmax = 7.59 
+            text = "S" 
+        intervals [72]:
+            xmin = 7.59 
+            xmax = 7.62 
+            text = "OW1" 
+        intervals [73]:
+            xmin = 7.62 
+            xmax = 7.66 
+            text = "DH" 
+        intervals [74]:
+            xmin = 7.66 
+            xmax = 7.71 
+            text = "AE1" 
+        intervals [75]:
+            xmin = 7.71 
+            xmax = 7.74 
+            text = "T" 
+        intervals [76]:
+            xmin = 7.74 
+            xmax = 7.77 
+            text = "Y" 
+        intervals [77]:
+            xmin = 7.77 
+            xmax = 7.85 
+            text = "UW1" 
+        intervals [78]:
+            xmin = 7.85 
+            xmax = 7.92 
+            text = "S" 
+        intervals [79]:
+            xmin = 7.92 
+            xmax = 7.97 
+            text = "T" 
+        intervals [80]:
+            xmin = 7.97 
+            xmax = 8.02 
+            text = "AA1" 
+        intervals [81]:
+            xmin = 8.02 
+            xmax = 8.05 
+            text = "R" 
+        intervals [82]:
+            xmin = 8.05 
+            xmax = 8.08 
+            text = "T" 
+        intervals [83]:
+            xmin = 8.08 
+            xmax = 8.11 
+            text = "AH0" 
+        intervals [84]:
+            xmin = 8.11 
+            xmax = 8.14 
+            text = "D" 
+        intervals [85]:
+            xmin = 8.14 
+            xmax = 8.17 
+            text = "B" 
+        intervals [86]:
+            xmin = 8.17 
+            xmax = 8.24 
+            text = "AY1" 
+        intervals [87]:
+            xmin = 8.24 
+            xmax = 8.35 
+            text = "JH" 
+        intervals [88]:
+            xmin = 8.35 
+            xmax = 8.48 
+            text = "AA1" 
+        intervals [89]:
+            xmin = 8.48 
+            xmax = 8.52 
+            text = "B" 
+        intervals [90]:
+            xmin = 8.52 
+            xmax = 8.59 
+            text = "AY1" 
+        intervals [91]:
+            xmin = 8.59 
+            xmax = 8.64 
+            text = "TH" 
+        intervals [92]:
+            xmin = 8.64 
+            xmax = 8.69 
+            text = "IH1" 
+        intervals [93]:
+            xmin = 8.69 
+            xmax = 8.72 
+            text = "NG" 
+        intervals [94]:
+            xmin = 8.72 
+            xmax = 8.75 
+            text = "K" 
+        intervals [95]:
+            xmin = 8.75 
+            xmax = 8.79 
+            text = "IH1" 
+        intervals [96]:
+            xmin = 8.79 
+            xmax = 8.84 
+            text = "T" 
+        intervals [97]:
+            xmin = 8.84 
+            xmax = 8.88 
+            text = "S" 
+        intervals [98]:
+            xmin = 8.88 
+            xmax = 9.08 
+            text = "V" 
+        intervals [99]:
+            xmin = 9.08 
+            xmax = 9.2 
+            text = "EH1" 
+        intervals [100]:
+            xmin = 9.2 
+            xmax = 9.28 
+            text = "R" 
+        intervals [101]:
+            xmin = 9.28 
+            xmax = 9.35 
+            text = "IY0" 
+        intervals [102]:
+            xmin = 9.35 
+            xmax = 9.4 
+            text = "IH0" 
+        intervals [103]:
+            xmin = 9.4 
+            xmax = 9.46 
+            text = "M" 
+        intervals [104]:
+            xmin = 9.46 
+            xmax = 9.55 
+            text = "P" 
+        intervals [105]:
+            xmin = 9.55 
+            xmax = 9.63 
+            text = "AO1" 
+        intervals [106]:
+            xmin = 9.63 
+            xmax = 9.68 
+            text = "R" 
+        intervals [107]:
+            xmin = 9.68 
+            xmax = 9.71 
+            text = "T" 
+        intervals [108]:
+            xmin = 9.71 
+            xmax = 9.74 
+            text = "AH0" 
+        intervals [109]:
+            xmin = 9.74 
+            xmax = 9.77 
+            text = "N" 
+        intervals [110]:
+            xmin = 9.77 
+            xmax = 9.8 
+            text = "T" 
+        intervals [111]:
+            xmin = 9.8 
+            xmax = 9.83 
+            text = "T" 
+        intervals [112]:
+            xmin = 9.83 
+            xmax = 9.87 
+            text = "IH0" 
+        intervals [113]:
+            xmin = 9.87 
+            xmax = 9.93 
+            text = "G" 
+        intervals [114]:
+            xmin = 9.93 
+            xmax = 9.96 
+            text = "EH1" 
+        intervals [115]:
+            xmin = 9.96 
+            xmax = 9.99 
+            text = "T" 
+        intervals [116]:
+            xmin = 9.99 
+            xmax = 10.03 
+            text = "AH0" 
+        intervals [117]:
+            xmin = 10.03 
+            xmax = 10.07 
+            text = "G" 
+        intervals [118]:
+            xmin = 10.07 
+            xmax = 10.1 
+            text = "IH0" 
+        intervals [119]:
+            xmin = 10.1 
+            xmax = 10.17 
+            text = "D" 
+        intervals [120]:
+            xmin = 10.17 
+            xmax = 10.35 
+            text = "S" 
+        intervals [121]:
+            xmin = 10.35 
+            xmax = 10.43 
+            text = "L" 
+        intervals [122]:
+            xmin = 10.43 
+            xmax = 10.53 
+            text = "IY1" 
+        intervals [123]:
+            xmin = 10.53 
+            xmax = 10.56 
+            text = "P" 
+        intervals [124]:
+            xmin = 10.56 
+            xmax = 10.8 
+            text = "D" 
+        intervals [125]:
+            xmin = 10.8 
+            xmax = 10.92 
+            text = "ER1" 
+        intervals [126]:
+            xmin = 10.92 
+            xmax = 10.99 
+            text = "IH0" 
+        intervals [127]:
+            xmin = 10.99 
+            xmax = 11.14 
+            text = "NG" 
+        intervals [128]:
+            xmin = 11.14 
+            xmax = 11.2 
+            text = "Y" 
+        intervals [129]:
+            xmin = 11.2 
+            xmax = 11.23 
+            text = "UH1" 
+        intervals [130]:
+            xmin = 11.23 
+            xmax = 11.32 
+            text = "R" 
+        intervals [131]:
+            xmin = 11.32 
+            xmax = 11.4 
+            text = "W" 
+        intervals [132]:
+            xmin = 11.4 
+            xmax = 11.51 
+            text = "IY1" 
+        intervals [133]:
+            xmin = 11.51 
+            xmax = 11.6 
+            text = "K" 
+        intervals [134]:
+            xmin = 11.6 
+            xmax = 11.68 
+            text = "EH2" 
+        intervals [135]:
+            xmin = 11.68 
+            xmax = 11.74 
+            text = "N" 
+        intervals [136]:
+            xmin = 11.74 
+            xmax = 11.77 
+            text = "D" 
+        intervals [137]:
+            xmin = 11.77 
+            xmax = 11.8 
+            text = "B" 
+        intervals [138]:
+            xmin = 11.8 
+            xmax = 11.88 
+            text = "IH0" 
+        intervals [139]:
+            xmin = 11.88 
+            xmax = 12 
+            text = "K" 
+        intervals [140]:
+            xmin = 12 
+            xmax = 12.26 
+            text = "AH1" 
+        intervals [141]:
+            xmin = 12.26 
+            xmax = 12.4 
+            text = "Z" 
+        intervals [142]:
+            xmin = 12.4 
+            xmax = 12.6 
+            text = "W" 
+        intervals [143]:
+            xmin = 12.6 
+            xmax = 12.88 
+            text = "EH1" 
+        intervals [144]:
+            xmin = 12.88 
+            xmax = 12.95 
+            text = "N" 
+        intervals [145]:
+            xmin = 12.95 
+            xmax = 12.99 
+            text = "Y" 
+        intervals [146]:
+            xmin = 12.99 
+            xmax = 13.04 
+            text = "UW1" 
+        intervals [147]:
+            xmin = 13.04 
+            xmax = 13.07 
+            text = "HH" 
+        intervals [148]:
+            xmin = 13.07 
+            xmax = 13.16 
+            text = "AE1" 
+        intervals [149]:
+            xmin = 13.16 
+            xmax = 13.19 
+            text = "V" 
+        intervals [150]:
+            xmin = 13.19 
+            xmax = 13.22 
+            text = "T" 
+        intervals [151]:
+            xmin = 13.22 
+            xmax = 13.27 
+            text = "UW1" 
+        intervals [152]:
+            xmin = 13.27 
+            xmax = 13.32 
+            text = "W" 
+        intervals [153]:
+            xmin = 13.32 
+            xmax = 13.4 
+            text = "ER1" 
+        intervals [154]:
+            xmin = 13.4 
+            xmax = 13.44 
+            text = "K" 
+        intervals [155]:
+            xmin = 13.44 
+            xmax = 13.51 
+            text = "AA1" 
+        intervals [156]:
+            xmin = 13.51 
+            xmax = 13.58 
+            text = "N" 
+        intervals [157]:
+            xmin = 13.58 
+            xmax = 13.66 
+            text = "M" 
+        intervals [158]:
+            xmin = 13.66 
+            xmax = 13.76 
+            text = "AH1" 
+        intervals [159]:
+            xmin = 13.76 
+            xmax = 13.81 
+            text = "N" 
+        intervals [160]:
+            xmin = 13.81 
+            xmax = 13.85 
+            text = "D" 
+        intervals [161]:
+            xmin = 13.85 
+            xmax = 13.96 
+            text = "EY2" 
+        intervals [162]:
+            xmin = 13.96 
+            xmax = 14.01 
+            text = "TH" 
+        intervals [163]:
+            xmin = 14.01 
+            xmax = 14.04 
+            text = "R" 
+        intervals [164]:
+            xmin = 14.04 
+            xmax = 14.1 
+            text = "UW1" 
+        intervals [165]:
+            xmin = 14.1 
+            xmax = 14.17 
+            text = "F" 
+        intervals [166]:
+            xmin = 14.17 
+            xmax = 14.26 
+            text = "R" 
+        intervals [167]:
+            xmin = 14.26 
+            xmax = 14.4 
+            text = "AY1" 
+        intervals [168]:
+            xmin = 14.4 
+            xmax = 14.45 
+            text = "D" 
+        intervals [169]:
+            xmin = 14.45 
+            xmax = 14.75 
+            text = "EY2" 
+        intervals [170]:
+            xmin = 14.75 
+            xmax = 15.41 
+            text = "" 
+        intervals [171]:
+            xmin = 15.41 
+            xmax = 15.49 
+            text = "DH" 
+        intervals [172]:
+            xmin = 15.49 
+            xmax = 15.53 
+            text = "AH1" 
+        intervals [173]:
+            xmin = 15.53 
+            xmax = 15.62 
+            text = "HH" 
+        intervals [174]:
+            xmin = 15.62 
+            xmax = 15.67 
+            text = "OW1" 
+        intervals [175]:
+            xmin = 15.67 
+            xmax = 15.75 
+            text = "L" 
+        intervals [176]:
+            xmin = 15.75 
+            xmax = 15.8 
+            text = "W" 
+        intervals [177]:
+            xmin = 15.8 
+            xmax = 15.94 
+            text = "IY1" 
+        intervals [178]:
+            xmin = 15.94 
+            xmax = 16.09 
+            text = "K" 
+        intervals [179]:
+            xmin = 16.09 
+            xmax = 16.28 
+            text = "" 
+        intervals [180]:
+            xmin = 16.28 
+            xmax = 16.38 
+            text = "Y" 
+        intervals [181]:
+            xmin = 16.38 
+            xmax = 16.42 
+            text = "UW1" 
+        intervals [182]:
+            xmin = 16.42 
+            xmax = 16.49 
+            text = "ER0" 
+        intervals [183]:
+            xmin = 16.49 
+            xmax = 16.55 
+            text = "V" 
+        intervals [184]:
+            xmin = 16.55 
+            xmax = 16.58 
+            text = "EH1" 
+        intervals [185]:
+            xmin = 16.58 
+            xmax = 16.65 
+            text = "R" 
+        intervals [186]:
+            xmin = 16.65 
+            xmax = 16.73 
+            text = "IY0" 
+        intervals [187]:
+            xmin = 16.73 
+            xmax = 16.92 
+            text = "T" 
+        intervals [188]:
+            xmin = 16.92 
+            xmax = 17.08 
+            text = "AY1" 
+        intervals [189]:
+            xmin = 17.08 
+            xmax = 17.22 
+            text = "ER0" 
+        intervals [190]:
+            xmin = 17.22 
+            xmax = 17.59 
+            text = "D" 
+        intervals [191]:
+            xmin = 17.59 
+            xmax = 17.83 
+            text = "" 
+        intervals [192]:
+            xmin = 17.83 
+            xmax = 18.02 
+            text = "S" 
+        intervals [193]:
+            xmin = 18.02 
+            xmax = 18.29 
+            text = "OW1" 
+        intervals [194]:
+            xmin = 18.29 
+            xmax = 18.37 
+            text = "G" 
+        intervals [195]:
+            xmin = 18.37 
+            xmax = 18.42 
+            text = "IH1" 
+        intervals [196]:
+            xmin = 18.42 
+            xmax = 18.46 
+            text = "T" 
+        intervals [197]:
+            xmin = 18.46 
+            xmax = 18.5 
+            text = "IH0" 
+        intervals [198]:
+            xmin = 18.5 
+            xmax = 18.55 
+            text = "NG" 
+        intervals [199]:
+            xmin = 18.55 
+            xmax = 18.61 
+            text = "EY1" 
+        intervals [200]:
+            xmin = 18.61 
+            xmax = 18.67 
+            text = "G" 
+        intervals [201]:
+            xmin = 18.67 
+            xmax = 18.73 
+            text = "UH1" 
+        intervals [202]:
+            xmin = 18.73 
+            xmax = 18.78 
+            text = "D" 
+        intervals [203]:
+            xmin = 18.78 
+            xmax = 18.86 
+            text = "R" 
+        intervals [204]:
+            xmin = 18.86 
+            xmax = 18.97 
+            text = "EH1" 
+        intervals [205]:
+            xmin = 18.97 
+            xmax = 19.05 
+            text = "S" 
+        intervals [206]:
+            xmin = 19.05 
+            xmax = 19.08 
+            text = "T" 
+        intervals [207]:
+            xmin = 19.08 
+            xmax = 19.13 
+            text = "IH0" 
+        intervals [208]:
+            xmin = 19.13 
+            xmax = 19.21 
+            text = "Z" 
+        intervals [209]:
+            xmin = 19.21 
+            xmax = 19.24 
+            text = "EH1" 
+        intervals [210]:
+            xmin = 19.24 
+            xmax = 19.3 
+            text = "Z" 
+        intervals [211]:
+            xmin = 19.3 
+            xmax = 19.34 
+            text = "IH0" 
+        intervals [212]:
+            xmin = 19.34 
+            xmax = 19.38 
+            text = "M" 
+        intervals [213]:
+            xmin = 19.38 
+            xmax = 19.48 
+            text = "P" 
+        intervals [214]:
+            xmin = 19.48 
+            xmax = 19.55 
+            text = "AO1" 
+        intervals [215]:
+            xmin = 19.55 
+            xmax = 19.59 
+            text = "R" 
+        intervals [216]:
+            xmin = 19.59 
+            xmax = 19.62 
+            text = "T" 
+        intervals [217]:
+            xmin = 19.62 
+            xmax = 19.65 
+            text = "AH0" 
+        intervals [218]:
+            xmin = 19.65 
+            xmax = 19.68 
+            text = "N" 
+        intervals [219]:
+            xmin = 19.68 
+            xmax = 19.77 
+            text = "T" 
+        intervals [220]:
+            xmin = 19.77 
+            xmax = 19.94 
+            text = "AE1" 
+        intervals [221]:
+            xmin = 19.94 
+            xmax = 20.16 
+            text = "Z" 
+        intervals [222]:
+            xmin = 20.16 
+            xmax = 20.3 
+            text = "" 
+        intervals [223]:
+            xmin = 20.3 
+            xmax = 20.39 
+            text = "K" 
+        intervals [224]:
+            xmin = 20.39 
+            xmax = 20.43 
+            text = "AH0" 
+        intervals [225]:
+            xmin = 20.43 
+            xmax = 20.46 
+            text = "M" 
+        intervals [226]:
+            xmin = 20.46 
+            xmax = 20.53 
+            text = "P" 
+        intervals [227]:
+            xmin = 20.53 
+            xmax = 20.59 
+            text = "L" 
+        intervals [228]:
+            xmin = 20.59 
+            xmax = 20.63 
+            text = "EY1" 
+        intervals [229]:
+            xmin = 20.63 
+            xmax = 20.66 
+            text = "N" 
+        intervals [230]:
+            xmin = 20.66 
+            xmax = 20.69 
+            text = "T" 
+        intervals [231]:
+            xmin = 20.69 
+            xmax = 20.75 
+            text = "AH0" 
+        intervals [232]:
+            xmin = 20.75 
+            xmax = 20.87 
+            text = "JH" 
+        intervals [233]:
+            xmin = 20.87 
+            xmax = 21.09 
+            text = "AO1" 
+        intervals [234]:
+            xmin = 21.09 
+            xmax = 21.3 
+            text = "ER0" 
+        intervals [235]:
+            xmin = 21.3 
+            xmax = 21.44 
+            text = "K" 
+        intervals [236]:
+            xmin = 21.44 
+            xmax = 21.47 
+            text = "AH0" 
+        intervals [237]:
+            xmin = 21.47 
+            xmax = 21.5 
+            text = "M" 
+        intervals [238]:
+            xmin = 21.5 
+            xmax = 21.53 
+            text = "P" 
+        intervals [239]:
+            xmin = 21.53 
+            xmax = 21.6 
+            text = "L" 
+        intervals [240]:
+            xmin = 21.6 
+            xmax = 21.63 
+            text = "IY1" 
+        intervals [241]:
+            xmin = 21.63 
+            xmax = 21.66 
+            text = "T" 
+        intervals [242]:
+            xmin = 21.66 
+            xmax = 21.72 
+            text = "IH0" 
+        intervals [243]:
+            xmin = 21.72 
+            xmax = 21.79 
+            text = "NG" 
+        intervals [244]:
+            xmin = 21.79 
+            xmax = 21.83 
+            text = "AH0" 
+        intervals [245]:
+            xmin = 21.83 
+            xmax = 21.9 
+            text = "N" 
+        intervals [246]:
+            xmin = 21.9 
+            xmax = 21.98 
+            text = "EH1" 
+        intervals [247]:
+            xmin = 21.98 
+            xmax = 22.03 
+            text = "K" 
+        intervals [248]:
+            xmin = 22.03 
+            xmax = 22.07 
+            text = "S" 
+        intervals [249]:
+            xmin = 22.07 
+            xmax = 22.11 
+            text = "AH0" 
+        intervals [250]:
+            xmin = 22.11 
+            xmax = 22.14 
+            text = "L" 
+        intervals [251]:
+            xmin = 22.14 
+            xmax = 22.17 
+            text = "AH0" 
+        intervals [252]:
+            xmin = 22.17 
+            xmax = 22.2 
+            text = "N" 
+        intervals [253]:
+            xmin = 22.2 
+            xmax = 22.23 
+            text = "T" 
+        intervals [254]:
+            xmin = 22.23 
+            xmax = 22.34 
+            text = "JH" 
+        intervals [255]:
+            xmin = 22.34 
+            xmax = 22.5 
+            text = "AA1" 
+        intervals [256]:
+            xmin = 22.5 
+            xmax = 22.64 
+            text = "B" 
+        intervals [257]:
+            xmin = 22.64 
+            xmax = 23.04 
+            text = "" 
+        intervals [258]:
+            xmin = 23.04 
+            xmax = 23.14 
+            text = "IH0" 
+        intervals [259]:
+            xmin = 23.14 
+            xmax = 23.17 
+            text = "N" 
+        intervals [260]:
+            xmin = 23.17 
+            xmax = 23.2 
+            text = "M" 
+        intervals [261]:
+            xmin = 23.2 
+            xmax = 23.29 
+            text = "AY1" 
+        intervals [262]:
+            xmin = 23.29 
+            xmax = 23.36 
+            text = "S" 
+        intervals [263]:
+            xmin = 23.36 
+            xmax = 23.41 
+            text = "P" 
+        intervals [264]:
+            xmin = 23.41 
+            xmax = 23.52 
+            text = "EH1" 
+        intervals [265]:
+            xmin = 23.52 
+            xmax = 23.56 
+            text = "R" 
+        intervals [266]:
+            xmin = 23.56 
+            xmax = 23.65 
+            text = "T" 
+        intervals [267]:
+            xmin = 23.65 
+            xmax = 23.76 
+            text = "AY1" 
+        intervals [268]:
+            xmin = 23.76 
+            xmax = 23.8 
+            text = "M" 
+        intervals [269]:
+            xmin = 23.8 
+            xmax = 23.85 
+            text = "IH0" 
+        intervals [270]:
+            xmin = 23.85 
+            xmax = 23.88 
+            text = "F" 
+        intervals [271]:
+            xmin = 23.88 
+            xmax = 23.98 
+            text = "AY1" 
+        intervals [272]:
+            xmin = 23.98 
+            xmax = 24.04 
+            text = "F" 
+        intervals [273]:
+            xmin = 24.04 
+            xmax = 24.13 
+            text = "IY1" 
+        intervals [274]:
+            xmin = 24.13 
+            xmax = 24.18 
+            text = "L" 
+        intervals [275]:
+            xmin = 24.18 
+            xmax = 24.26 
+            text = "OW2" 
+        intervals [276]:
+            xmin = 24.26 
+            xmax = 24.39 
+            text = "K" 
+        intervals [277]:
+            xmin = 24.39 
+            xmax = 24.84 
+            text = "EY1" 
+        intervals [278]:
+            xmin = 24.84 
+            xmax = 25.07 
+            text = "AY1" 
+        intervals [279]:
+            xmin = 25.07 
+            xmax = 25.1 
+            text = "" 
+        intervals [280]:
+            xmin = 25.1 
+            xmax = 25.29 
+            text = "L" 
+        intervals [281]:
+            xmin = 25.29 
+            xmax = 25.35 
+            text = "AY1" 
+        intervals [282]:
+            xmin = 25.35 
+            xmax = 25.38 
+            text = "K" 
+        intervals [283]:
+            xmin = 25.38 
+            xmax = 25.41 
+            text = "T" 
+        intervals [284]:
+            xmin = 25.41 
+            xmax = 25.44 
+            text = "IH0" 
+        intervals [285]:
+            xmin = 25.44 
+            xmax = 25.5 
+            text = "G" 
+        intervals [286]:
+            xmin = 25.5 
+            xmax = 25.55 
+            text = "OW1" 
+        intervals [287]:
+            xmin = 25.55 
+            xmax = 25.59 
+            text = "F" 
+        intervals [288]:
+            xmin = 25.59 
+            xmax = 25.79 
+            text = "ER0" 
+        intervals [289]:
+            xmin = 25.79 
+            xmax = 25.83 
+            text = "AH0" 
+        intervals [290]:
+            xmin = 25.83 
+            xmax = 25.94 
+            text = "HH" 
+        intervals [291]:
+            xmin = 25.94 
+            xmax = 26.06 
+            text = "AY1" 
+        intervals [292]:
+            xmin = 26.06 
+            xmax = 26.12 
+            text = "K" 
+        intervals [293]:
+            xmin = 26.12 
+            xmax = 26.17 
+            text = "IH1" 
+        intervals [294]:
+            xmin = 26.17 
+            xmax = 26.21 
+            text = "N" 
+        intervals [295]:
+            xmin = 26.21 
+            xmax = 26.27 
+            text = "N" 
+        intervals [296]:
+            xmin = 26.27 
+            xmax = 26.4 
+            text = "EY1" 
+        intervals [297]:
+            xmin = 26.4 
+            xmax = 26.53 
+            text = "CH" 
+        intervals [298]:
+            xmin = 26.53 
+            xmax = 26.81 
+            text = "ER0" 
+        intervals [299]:
+            xmin = 26.81 
+            xmax = 27.11 
+            text = "" 
+        intervals [300]:
+            xmin = 27.11 
+            xmax = 27.21 
+            text = "S" 
+        intervals [301]:
+            xmin = 27.21 
+            xmax = 27.25 
+            text = "AH1" 
+        intervals [302]:
+            xmin = 27.25 
+            xmax = 27.28 
+            text = "M" 
+        intervals [303]:
+            xmin = 27.28 
+            xmax = 27.31 
+            text = "T" 
+        intervals [304]:
+            xmin = 27.31 
+            xmax = 27.38 
+            text = "AY2" 
+        intervals [305]:
+            xmin = 27.38 
+            xmax = 27.41 
+            text = "M" 
+        intervals [306]:
+            xmin = 27.41 
+            xmax = 27.45 
+            text = "Z" 
+        intervals [307]:
+            xmin = 27.45 
+            xmax = 27.51 
+            text = "AY1" 
+        intervals [308]:
+            xmin = 27.51 
+            xmax = 27.6 
+            text = "T" 
+        intervals [309]:
+            xmin = 27.6 
+            xmax = 27.67 
+            text = "R" 
+        intervals [310]:
+            xmin = 27.67 
+            xmax = 27.74 
+            text = "AY1" 
+        intervals [311]:
+            xmin = 27.74 
+            xmax = 27.77 
+            text = "T" 
+        intervals [312]:
+            xmin = 27.77 
+            xmax = 27.88 
+            text = "AH0" 
+        intervals [313]:
+            xmin = 27.88 
+            xmax = 28.02 
+            text = "AO1" 
+        intervals [314]:
+            xmin = 28.02 
+            xmax = 28.07 
+            text = "R" 
+        intervals [315]:
+            xmin = 28.07 
+            xmax = 28.12 
+            text = "G" 
+        intervals [316]:
+            xmin = 28.12 
+            xmax = 28.15 
+            text = "AH0" 
+        intervals [317]:
+            xmin = 28.15 
+            xmax = 28.18 
+            text = "N" 
+        intervals [318]:
+            xmin = 28.18 
+            xmax = 28.3 
+            text = "AY2" 
+        intervals [319]:
+            xmin = 28.3 
+            xmax = 28.37 
+            text = "Z" 
+        intervals [320]:
+            xmin = 28.37 
+            xmax = 28.42 
+            text = "S" 
+        intervals [321]:
+            xmin = 28.42 
+            xmax = 28.47 
+            text = "AH1" 
+        intervals [322]:
+            xmin = 28.47 
+            xmax = 28.5 
+            text = "M" 
+        intervals [323]:
+            xmin = 28.5 
+            xmax = 28.53 
+            text = "TH" 
+        intervals [324]:
+            xmin = 28.53 
+            xmax = 28.61 
+            text = "IH0" 
+        intervals [325]:
+            xmin = 28.61 
+            xmax = 28.94 
+            text = "NG" 
+        intervals [326]:
+            xmin = 28.94 
+            xmax = 28.98 
+            text = "" 
+        intervals [327]:
+            xmin = 28.98 
+            xmax = 29.08 
+            text = "F" 
+        intervals [328]:
+            xmin = 29.08 
+            xmax = 29.13 
+            text = "AO1" 
+        intervals [329]:
+            xmin = 29.13 
+            xmax = 29.19 
+            text = "R" 
+        intervals [330]:
+            xmin = 29.19 
+            xmax = 29.23 
+            text = "M" 
+        intervals [331]:
+            xmin = 29.23 
+            xmax = 29.32 
+            text = "AY1" 
+        intervals [332]:
+            xmin = 29.32 
+            xmax = 29.41 
+            text = "F" 
+        intervals [333]:
+            xmin = 29.41 
+            xmax = 29.49 
+            text = "R" 
+        intervals [334]:
+            xmin = 29.49 
+            xmax = 29.6 
+            text = "EH1" 
+        intervals [335]:
+            xmin = 29.6 
+            xmax = 29.65 
+            text = "N" 
+        intervals [336]:
+            xmin = 29.65 
+            xmax = 29.7 
+            text = "D" 
+        intervals [337]:
+            xmin = 29.7 
+            xmax = 29.89 
+            text = "Z" 
+        intervals [338]:
+            xmin = 29.89 
+            xmax = 29.92 
+            text = "" 
+        intervals [339]:
+            xmin = 29.92 
+            xmax = 29.95 
+            text = "AY1" 
+        intervals [340]:
+            xmin = 29.95 
+            xmax = 30.2 
+            text = "" 
+        intervals [341]:
+            xmin = 30.2 
+            xmax = 30.26 
+            text = "V" 
+        intervals [342]:
+            xmin = 30.26 
+            xmax = 30.39 
+            text = "AA2" 
+        intervals [343]:
+            xmin = 30.39 
+            xmax = 30.45 
+            text = "L" 
+        intervals [344]:
+            xmin = 30.45 
+            xmax = 30.48 
+            text = "AH0" 
+        intervals [345]:
+            xmin = 30.48 
+            xmax = 30.51 
+            text = "N" 
+        intervals [346]:
+            xmin = 30.51 
+            xmax = 30.6 
+            text = "T" 
+        intervals [347]:
+            xmin = 30.6 
+            xmax = 30.67 
+            text = "IH1" 
+        intervals [348]:
+            xmin = 30.67 
+            xmax = 30.73 
+            text = "R" 
+        intervals [349]:
+            xmin = 30.73 
+            xmax = 30.77 
+            text = "AE1" 
+        intervals [350]:
+            xmin = 30.77 
+            xmax = 30.86 
+            text = "T" 
+        intervals [351]:
+            xmin = 30.86 
+            xmax = 30.91 
+            text = "DH" 
+        intervals [352]:
+            xmin = 30.91 
+            xmax = 30.97 
+            text = "AH1" 
+        intervals [353]:
+            xmin = 30.97 
+            xmax = 31.13 
+            text = "B" 
+        intervals [354]:
+            xmin = 31.13 
+            xmax = 31.19 
+            text = "UW1" 
+        intervals [355]:
+            xmin = 31.19 
+            xmax = 31.24 
+            text = "D" 
+        intervals [356]:
+            xmin = 31.24 
+            xmax = 31.3 
+            text = "AH0" 
+        intervals [357]:
+            xmin = 31.3 
+            xmax = 31.35 
+            text = "S" 
+        intervals [358]:
+            xmin = 31.35 
+            xmax = 31.38 
+            text = "T" 
+        intervals [359]:
+            xmin = 31.38 
+            xmax = 31.41 
+            text = "T" 
+        intervals [360]:
+            xmin = 31.41 
+            xmax = 31.47 
+            text = "EH1" 
+        intervals [361]:
+            xmin = 31.47 
+            xmax = 31.52 
+            text = "M" 
+        intervals [362]:
+            xmin = 31.52 
+            xmax = 31.56 
+            text = "P" 
+        intervals [363]:
+            xmin = 31.56 
+            xmax = 31.61 
+            text = "AH0" 
+        intervals [364]:
+            xmin = 31.61 
+            xmax = 31.83 
+            text = "L" 
+        intervals [365]:
+            xmin = 31.83 
+            xmax = 31.9 
+            text = "AO1" 
+        intervals [366]:
+            xmin = 31.9 
+            xmax = 31.94 
+            text = "N" 
+        intervals [367]:
+            xmin = 31.94 
+            xmax = 31.97 
+            text = "DH" 
+        intervals [368]:
+            xmin = 31.97 
+            xmax = 32.01 
+            text = "AH1" 
+        intervals [369]:
+            xmin = 32.01 
+            xmax = 32.08 
+            text = "W" 
+        intervals [370]:
+            xmin = 32.08 
+            xmax = 32.17 
+            text = "IY1" 
+        intervals [371]:
+            xmin = 32.17 
+            xmax = 32.26 
+            text = "K" 
+        intervals [372]:
+            xmin = 32.26 
+            xmax = 32.45 
+            text = "EH2" 
+        intervals [373]:
+            xmin = 32.45 
+            xmax = 32.51 
+            text = "N" 
+        intervals [374]:
+            xmin = 32.51 
+            xmax = 32.6 
+            text = "D" 
+        intervals [375]:
+            xmin = 32.6 
+            xmax = 32.88 
+            text = "AO1" 
+        intervals [376]:
+            xmin = 32.88 
+            xmax = 33.01 
+            text = "R" 
+        intervals [377]:
+            xmin = 33.01 
+            xmax = 33.24 
+            text = "AY1" 
+        intervals [378]:
+            xmin = 33.24 
+            xmax = 33.36 
+            text = "K" 
+        intervals [379]:
+            xmin = 33.36 
+            xmax = 33.51 
+            text = "AE1" 
+        intervals [380]:
+            xmin = 33.51 
+            xmax = 33.62 
+            text = "N" 
+        intervals [381]:
+            xmin = 33.62 
+            xmax = 33.7 
+            text = "JH" 
+        intervals [382]:
+            xmin = 33.7 
+            xmax = 33.77 
+            text = "IH0" 
+        intervals [383]:
+            xmin = 33.77 
+            xmax = 33.8 
+            text = "S" 
+        intervals [384]:
+            xmin = 33.8 
+            xmax = 33.91 
+            text = "T" 
+        intervals [385]:
+            xmin = 33.91 
+            xmax = 33.96 
+            text = "W" 
+        intervals [386]:
+            xmin = 33.96 
+            xmax = 34.2 
+            text = "AO1" 
+        intervals [387]:
+            xmin = 34.2 
+            xmax = 34.3 
+            text = "K" 
+        intervals [388]:
+            xmin = 34.3 
+            xmax = 34.42 
+            text = "ER0" 
+        intervals [389]:
+            xmin = 34.42 
+            xmax = 34.63 
+            text = "AW1" 
+        intervals [390]:
+            xmin = 34.63 
+            xmax = 34.69 
+            text = "N" 
+        intervals [391]:
+            xmin = 34.69 
+            xmax = 34.76 
+            text = "IH0" 
+        intervals [392]:
+            xmin = 34.76 
+            xmax = 34.8 
+            text = "N" 
+        intervals [393]:
+            xmin = 34.8 
+            xmax = 34.9 
+            text = "JH" 
+        intervals [394]:
+            xmin = 34.9 
+            xmax = 34.99 
+            text = "OY1" 
+        intervals [395]:
+            xmin = 34.99 
+            xmax = 35.03 
+            text = "IH0" 
+        intervals [396]:
+            xmin = 35.03 
+            xmax = 35.08 
+            text = "NG" 
+        intervals [397]:
+            xmin = 35.08 
+            xmax = 35.12 
+            text = "DH" 
+        intervals [398]:
+            xmin = 35.12 
+            xmax = 35.17 
+            text = "AH0" 
+        intervals [399]:
+            xmin = 35.17 
+            xmax = 35.26 
+            text = "S" 
+        intervals [400]:
+            xmin = 35.26 
+            xmax = 35.33 
+            text = "AH1" 
+        intervals [401]:
+            xmin = 35.33 
+            xmax = 35.4 
+            text = "N" 
+        intervals [402]:
+            xmin = 35.4 
+            xmax = 35.53 
+            text = "SH" 
+        intervals [403]:
+            xmin = 35.53 
+            xmax = 35.69 
+            text = "AY2" 
+        intervals [404]:
+            xmin = 35.69 
+            xmax = 35.87 
+            text = "N" 
+        intervals [405]:
+            xmin = 35.87 
+            xmax = 36.15 
+            text = "" 
+        intervals [406]:
+            xmin = 36.15 
+            xmax = 36.3 
+            text = "AY1" 
+        intervals [407]:
+            xmin = 36.3 
+            xmax = 36.34 
+            text = "D" 
+        intervals [408]:
+            xmin = 36.34 
+            xmax = 36.38 
+            text = "L" 
+        intervals [409]:
+            xmin = 36.38 
+            xmax = 36.49 
+            text = "AY1" 
+        intervals [410]:
+            xmin = 36.49 
+            xmax = 36.52 
+            text = "K" 
+        intervals [411]:
+            xmin = 36.52 
+            xmax = 36.56 
+            text = "T" 
+        intervals [412]:
+            xmin = 36.56 
+            xmax = 36.59 
+            text = "AH0" 
+        intervals [413]:
+            xmin = 36.59 
+            xmax = 36.62 
+            text = "HH" 
+        intervals [414]:
+            xmin = 36.62 
+            xmax = 36.7 
+            text = "AE1" 
+        intervals [415]:
+            xmin = 36.7 
+            xmax = 36.74 
+            text = "V" 
+        intervals [416]:
+            xmin = 36.74 
+            xmax = 36.79 
+            text = "AH0" 
+        intervals [417]:
+            xmin = 36.79 
+            xmax = 36.83 
+            text = "HH" 
+        intervals [418]:
+            xmin = 36.83 
+            xmax = 36.88 
+            text = "EH1" 
+        intervals [419]:
+            xmin = 36.88 
+            xmax = 36.93 
+            text = "L" 
+        intervals [420]:
+            xmin = 36.93 
+            xmax = 37.01 
+            text = "TH" 
+        intervals [421]:
+            xmin = 37.01 
+            xmax = 37.06 
+            text = "IY0" 
+        intervals [422]:
+            xmin = 37.06 
+            xmax = 37.12 
+            text = "L" 
+        intervals [423]:
+            xmin = 37.12 
+            xmax = 37.23 
+            text = "AY1" 
+        intervals [424]:
+            xmin = 37.23 
+            xmax = 37.27 
+            text = "F" 
+        intervals [425]:
+            xmin = 37.27 
+            xmax = 37.34 
+            text = "S" 
+        intervals [426]:
+            xmin = 37.34 
+            xmax = 37.39 
+            text = "T" 
+        intervals [427]:
+            xmin = 37.39 
+            xmax = 37.56 
+            text = "AY2" 
+        intervals [428]:
+            xmin = 37.56 
+            xmax = 37.66 
+            text = "L" 
+        intervals [429]:
+            xmin = 37.66 
+            xmax = 37.73 
+            text = "K" 
+        intervals [430]:
+            xmin = 37.73 
+            xmax = 37.77 
+            text = "AH0" 
+        intervals [431]:
+            xmin = 37.77 
+            xmax = 37.82 
+            text = "N" 
+        intervals [432]:
+            xmin = 37.82 
+            xmax = 37.87 
+            text = "S" 
+        intervals [433]:
+            xmin = 37.87 
+            xmax = 37.91 
+            text = "IH1" 
+        intervals [434]:
+            xmin = 37.91 
+            xmax = 37.94 
+            text = "D" 
+        intervals [435]:
+            xmin = 37.94 
+            xmax = 37.98 
+            text = "ER0" 
+        intervals [436]:
+            xmin = 37.98 
+            xmax = 38.02 
+            text = "IH0" 
+        intervals [437]:
+            xmin = 38.02 
+            xmax = 38.06 
+            text = "NG" 
+        intervals [438]:
+            xmin = 38.06 
+            xmax = 38.13 
+            text = "HH" 
+        intervals [439]:
+            xmin = 38.13 
+            xmax = 38.17 
+            text = "AW1" 
+        intervals [440]:
+            xmin = 38.17 
+            xmax = 38.23 
+            text = "M" 
+        intervals [441]:
+            xmin = 38.23 
+            xmax = 38.27 
+            text = "AH1" 
+        intervals [442]:
+            xmin = 38.27 
+            xmax = 38.38 
+            text = "CH" 
+        intervals [443]:
+            xmin = 38.38 
+            xmax = 38.5 
+            text = "T" 
+        intervals [444]:
+            xmin = 38.5 
+            xmax = 38.67 
+            text = "AY1" 
+        intervals [445]:
+            xmin = 38.67 
+            xmax = 38.74 
+            text = "M" 
+        intervals [446]:
+            xmin = 38.74 
+            xmax = 38.81 
+            text = "AY1" 
+        intervals [447]:
+            xmin = 38.81 
+            xmax = 38.95 
+            text = "S" 
+        intervals [448]:
+            xmin = 38.95 
+            xmax = 39.02 
+            text = "P" 
+        intervals [449]:
+            xmin = 39.02 
+            xmax = 39.09 
+            text = "EH1" 
+        intervals [450]:
+            xmin = 39.09 
+            xmax = 39.12 
+            text = "N" 
+        intervals [451]:
+            xmin = 39.12 
+            xmax = 39.18 
+            text = "D" 
+        intervals [452]:
+            xmin = 39.18 
+            xmax = 39.21 
+            text = "AE1" 
+        intervals [453]:
+            xmin = 39.21 
+            xmax = 39.29 
+            text = "T" 
+        intervals [454]:
+            xmin = 39.29 
+            xmax = 39.47 
+            text = "W" 
+        intervals [455]:
+            xmin = 39.47 
+            xmax = 39.69 
+            text = "ER1" 
+        intervals [456]:
+            xmin = 39.69 
+            xmax = 39.84 
+            text = "K" 
+        intervals [457]:
+            xmin = 39.84 
+            xmax = 40.29 
+            text = "" 
+        intervals [458]:
+            xmin = 40.29 
+            xmax = 40.52 
+            text = "AY1" 
+        intervals [459]:
+            xmin = 40.52 
+            xmax = 40.56 
+            text = "AO1" 
+        intervals [460]:
+            xmin = 40.56 
+            xmax = 40.59 
+            text = "L" 
+        intervals [461]:
+            xmin = 40.59 
+            xmax = 40.66 
+            text = "W" 
+        intervals [462]:
+            xmin = 40.66 
+            xmax = 40.7 
+            text = "IY0" 
+        intervals [463]:
+            xmin = 40.7 
+            xmax = 40.79 
+            text = "Z" 
+        intervals [464]:
+            xmin = 40.79 
+            xmax = 40.94 
+            text = "T" 
+        intervals [465]:
+            xmin = 40.94 
+            xmax = 41.05 
+            text = "R" 
+        intervals [466]:
+            xmin = 41.05 
+            xmax = 41.28 
+            text = "AY1" 
+        intervals [467]:
+            xmin = 41.28 
+            xmax = 41.38 
+            text = "T" 
+        intervals [468]:
+            xmin = 41.38 
+            xmax = 41.47 
+            text = "IH0" 
+        intervals [469]:
+            xmin = 41.47 
+            xmax = 41.7 
+            text = "M" 
+        intervals [470]:
+            xmin = 41.7 
+            xmax = 41.77 
+            text = "UW1" 
+        intervals [471]:
+            xmin = 41.77 
+            xmax = 41.85 
+            text = "V" 
+        intervals [472]:
+            xmin = 41.85 
+            xmax = 41.9 
+            text = "EH1" 
+        intervals [473]:
+            xmin = 41.9 
+            xmax = 42 
+            text = "Z" 
+        intervals [474]:
+            xmin = 42 
+            xmax = 42.08 
+            text = "M" 
+        intervals [475]:
+            xmin = 42.08 
+            xmax = 42.13 
+            text = "AH1" 
+        intervals [476]:
+            xmin = 42.13 
+            xmax = 42.22 
+            text = "CH" 
+        intervals [477]:
+            xmin = 42.22 
+            xmax = 42.26 
+            text = "EH1" 
+        intervals [478]:
+            xmin = 42.26 
+            xmax = 42.31 
+            text = "Z" 
+        intervals [479]:
+            xmin = 42.31 
+            xmax = 42.4 
+            text = "AY1" 
+        intervals [480]:
+            xmin = 42.4 
+            xmax = 42.51 
+            text = "K" 
+        intervals [481]:
+            xmin = 42.51 
+            xmax = 42.64 
+            text = "AE1" 
+        intervals [482]:
+            xmin = 42.64 
+            xmax = 42.76 
+            text = "N" 
+        intervals [483]:
+            xmin = 42.76 
+            xmax = 42.81 
+            text = "W" 
+        intervals [484]:
+            xmin = 42.81 
+            xmax = 42.84 
+            text = "EH1" 
+        intervals [485]:
+            xmin = 42.84 
+            xmax = 42.89 
+            text = "N" 
+        intervals [486]:
+            xmin = 42.89 
+            xmax = 42.95 
+            text = "AH0" 
+        intervals [487]:
+            xmin = 42.95 
+            xmax = 42.98 
+            text = "M" 
+        intervals [488]:
+            xmin = 42.98 
+            xmax = 43.03 
+            text = "N" 
+        intervals [489]:
+            xmin = 43.03 
+            xmax = 43.12 
+            text = "AA1" 
+        intervals [490]:
+            xmin = 43.12 
+            xmax = 43.18 
+            text = "T" 
+        intervals [491]:
+            xmin = 43.18 
+            xmax = 43.28 
+            text = "W" 
+        intervals [492]:
+            xmin = 43.28 
+            xmax = 43.42 
+            text = "ER1" 
+        intervals [493]:
+            xmin = 43.42 
+            xmax = 43.49 
+            text = "K" 
+        intervals [494]:
+            xmin = 43.49 
+            xmax = 43.53 
+            text = "IH0" 
+        intervals [495]:
+            xmin = 43.53 
+            xmax = 43.76 
+            text = "NG" 
+        intervals [496]:
+            xmin = 43.76 
+            xmax = 44.5 
+            text = "" 
+        intervals [497]:
+            xmin = 44.5 
+            xmax = 44.86 
+            text = "AH0" 
+        intervals [498]:
+            xmin = 44.86 
+            xmax = 45.15 
+            text = "N" 
+        intervals [499]:
+            xmin = 45.15 
+            xmax = 45.19 
+            text = "D" 
+        intervals [500]:
+            xmin = 45.19 
+            xmax = 45.27 
+            text = "AA1" 
+        intervals [501]:
+            xmin = 45.27 
+            xmax = 45.32 
+            text = "N" 
+        intervals [502]:
+            xmin = 45.32 
+            xmax = 45.4 
+            text = "AH1" 
+        intervals [503]:
+            xmin = 45.4 
+            xmax = 45.46 
+            text = "DH" 
+        intervals [504]:
+            xmin = 45.46 
+            xmax = 45.49 
+            text = "ER0" 
+        intervals [505]:
+            xmin = 45.49 
+            xmax = 45.55 
+            text = "D" 
+        intervals [506]:
+            xmin = 45.55 
+            xmax = 45.74 
+            text = "EY1" 
+        intervals [507]:
+            xmin = 45.74 
+            xmax = 45.82 
+            text = "Z" 
+        intervals [508]:
+            xmin = 45.82 
+            xmax = 45.89 
+            text = "W" 
+        intervals [509]:
+            xmin = 45.89 
+            xmax = 45.92 
+            text = "EH1" 
+        intervals [510]:
+            xmin = 45.92 
+            xmax = 45.96 
+            text = "N" 
+        intervals [511]:
+            xmin = 45.96 
+            xmax = 46.09 
+            text = "AY1" 
+        intervals [512]:
+            xmin = 46.09 
+            xmax = 46.16 
+            text = "M" 
+        intervals [513]:
+            xmin = 46.16 
+            xmax = 46.29 
+            text = "F" 
+        intervals [514]:
+            xmin = 46.29 
+            xmax = 46.39 
+            text = "R" 
+        intervals [515]:
+            xmin = 46.39 
+            xmax = 46.65 
+            text = "IY1" 
+        intervals [516]:
+            xmin = 46.65 
+            xmax = 46.86 
+            text = "AY1" 
+        intervals [517]:
+            xmin = 46.86 
+            xmax = 46.94 
+            text = "L" 
+        intervals [518]:
+            xmin = 46.94 
+            xmax = 47.08 
+            text = "AY1" 
+        intervals [519]:
+            xmin = 47.08 
+            xmax = 47.16 
+            text = "K" 
+        intervals [520]:
+            xmin = 47.16 
+            xmax = 47.25 
+            text = "T" 
+        intervals [521]:
+            xmin = 47.25 
+            xmax = 47.39 
+            text = "UW1" 
+        intervals [522]:
+            xmin = 47.39 
+            xmax = 47.48 
+            text = "L" 
+        intervals [523]:
+            xmin = 47.48 
+            xmax = 47.53 
+            text = "IH1" 
+        intervals [524]:
+            xmin = 47.53 
+            xmax = 47.6 
+            text = "S" 
+        intervals [525]:
+            xmin = 47.6 
+            xmax = 47.64 
+            text = "AH0" 
+        intervals [526]:
+            xmin = 47.64 
+            xmax = 47.86 
+            text = "N" 
+        intervals [527]:
+            xmin = 47.86 
+            xmax = 47.93 
+            text = "T" 
+        intervals [528]:
+            xmin = 47.93 
+            xmax = 48.03 
+            text = "IH0" 
+        intervals [529]:
+            xmin = 48.03 
+            xmax = 48.07 
+            text = "M" 
+        intervals [530]:
+            xmin = 48.07 
+            xmax = 48.15 
+            text = "Y" 
+        intervals [531]:
+            xmin = 48.15 
+            xmax = 48.2 
+            text = "UW1" 
+        intervals [532]:
+            xmin = 48.2 
+            xmax = 48.27 
+            text = "Z" 
+        intervals [533]:
+            xmin = 48.27 
+            xmax = 48.35 
+            text = "IH0" 
+        intervals [534]:
+            xmin = 48.35 
+            xmax = 48.41 
+            text = "K" 
+        intervals [535]:
+            xmin = 48.41 
+            xmax = 48.48 
+            text = "AH0" 
+        intervals [536]:
+            xmin = 48.48 
+            xmax = 48.56 
+            text = "N" 
+        intervals [537]:
+            xmin = 48.56 
+            xmax = 48.73 
+            text = "D" 
+        intervals [538]:
+            xmin = 48.73 
+            xmax = 48.76 
+            text = "" 
+        intervals [539]:
+            xmin = 48.76 
+            xmax = 48.91 
+            text = "W" 
+        intervals [540]:
+            xmin = 48.91 
+            xmax = 49.01 
+            text = "ER1" 
+        intervals [541]:
+            xmin = 49.01 
+            xmax = 49.13 
+            text = "W" 
+        intervals [542]:
+            xmin = 49.13 
+            xmax = 49.23 
+            text = "AA1" 
+        intervals [543]:
+            xmin = 49.23 
+            xmax = 49.3 
+            text = "CH" 
+        intervals [544]:
+            xmin = 49.3 
+            xmax = 49.38 
+            text = "AH0" 
+        intervals [545]:
+            xmin = 49.38 
+            xmax = 49.46 
+            text = "D" 
+        intervals [546]:
+            xmin = 49.46 
+            xmax = 49.56 
+            text = "AA2" 
+        intervals [547]:
+            xmin = 49.56 
+            xmax = 49.62 
+            text = "K" 
+        intervals [548]:
+            xmin = 49.62 
+            xmax = 49.66 
+            text = "Y" 
+        intervals [549]:
+            xmin = 49.66 
+            xmax = 49.7 
+            text = "AH0" 
+        intervals [550]:
+            xmin = 49.7 
+            xmax = 49.76 
+            text = "M" 
+        intervals [551]:
+            xmin = 49.76 
+            xmax = 49.81 
+            text = "EH1" 
+        intervals [552]:
+            xmin = 49.81 
+            xmax = 49.85 
+            text = "N" 
+        intervals [553]:
+            xmin = 49.85 
+            xmax = 49.98 
+            text = "ER0" 
+        intervals [554]:
+            xmin = 49.98 
+            xmax = 50.05 
+            text = "IY0" 
+        intervals [555]:
+            xmin = 50.05 
+            xmax = 50.17 
+            text = "M" 
+        intervals [556]:
+            xmin = 50.17 
+            xmax = 50.2 
+            text = "UW1" 
+        intervals [557]:
+            xmin = 50.2 
+            xmax = 50.28 
+            text = "V" 
+        intervals [558]:
+            xmin = 50.28 
+            xmax = 50.38 
+            text = "IY0" 
+        intervals [559]:
+            xmin = 50.38 
+            xmax = 50.51 
+            text = "Z" 
+        intervals [560]:
+            xmin = 50.51 
+            xmax = 50.75 
+            text = "AA1" 
+        intervals [561]:
+            xmin = 50.75 
+            xmax = 50.82 
+            text = "N" 
+        intervals [562]:
+            xmin = 50.82 
+            xmax = 50.9 
+            text = "M" 
+        intervals [563]:
+            xmin = 50.9 
+            xmax = 51.11 
+            text = "AY1" 
+        intervals [564]:
+            xmin = 51.11 
+            xmax = 51.22 
+            text = "L" 
+        intervals [565]:
+            xmin = 51.22 
+            xmax = 51.39 
+            text = "AE1" 
+        intervals [566]:
+            xmin = 51.39 
+            xmax = 51.44 
+            text = "P" 
+        intervals [567]:
+            xmin = 51.44 
+            xmax = 51.49 
+            text = "T" 
+        intervals [568]:
+            xmin = 51.49 
+            xmax = 51.66 
+            text = "AA2" 
+        intervals [569]:
+            xmin = 51.66 
+            xmax = 51.81 
+            text = "P" 
+        intervals [570]:
+            xmin = 51.81 
+            xmax = 52.14 
+            text = "" 
+        intervals [571]:
+            xmin = 52.14 
+            xmax = 52.2 
+            text = "B" 
+        intervals [572]:
+            xmin = 52.2 
+            xmax = 52.33 
+            text = "AH1" 
+        intervals [573]:
+            xmin = 52.33 
+            xmax = 52.44 
+            text = "T" 
+        intervals [574]:
+            xmin = 52.44 
+            xmax = 52.51 
+            text = "S" 
+        intervals [575]:
+            xmin = 52.51 
+            xmax = 52.59 
+            text = "AH1" 
+        intervals [576]:
+            xmin = 52.59 
+            xmax = 52.64 
+            text = "M" 
+        intervals [577]:
+            xmin = 52.64 
+            xmax = 52.67 
+            text = "T" 
+        intervals [578]:
+            xmin = 52.67 
+            xmax = 52.77 
+            text = "AY2" 
+        intervals [579]:
+            xmin = 52.77 
+            xmax = 52.82 
+            text = "M" 
+        intervals [580]:
+            xmin = 52.82 
+            xmax = 52.86 
+            text = "Z" 
+        intervals [581]:
+            xmin = 52.86 
+            xmax = 52.9 
+            text = "IH1" 
+        intervals [582]:
+            xmin = 52.9 
+            xmax = 52.93 
+            text = "T" 
+        intervals [583]:
+            xmin = 52.93 
+            xmax = 52.98 
+            text = "JH" 
+        intervals [584]:
+            xmin = 52.98 
+            xmax = 53.07 
+            text = "IH0" 
+        intervals [585]:
+            xmin = 53.07 
+            xmax = 53.1 
+            text = "S" 
+        intervals [586]:
+            xmin = 53.1 
+            xmax = 53.13 
+            text = "T" 
+        intervals [587]:
+            xmin = 53.13 
+            xmax = 53.18 
+            text = "S" 
+        intervals [588]:
+            xmin = 53.18 
+            xmax = 53.26 
+            text = "L" 
+        intervals [589]:
+            xmin = 53.26 
+            xmax = 53.35 
+            text = "IY1" 
+        intervals [590]:
+            xmin = 53.35 
+            xmax = 53.61 
+            text = "P" 
+        intervals [591]:
+            xmin = 53.61 
+            xmax = 53.65 
+            text = "" 
+        intervals [592]:
+            xmin = 53.65 
+            xmax = 53.83 
+            text = "AY1" 
+        intervals [593]:
+            xmin = 53.83 
+            xmax = 53.88 
+            text = "AH0" 
+        intervals [594]:
+            xmin = 53.88 
+            xmax = 53.95 
+            text = "S" 
+        intervals [595]:
+            xmin = 53.95 
+            xmax = 54 
+            text = "P" 
+        intervals [596]:
+            xmin = 54 
+            xmax = 54.09 
+            text = "EH1" 
+        intervals [597]:
+            xmin = 54.09 
+            xmax = 54.19 
+            text = "SH" 
+        intervals [598]:
+            xmin = 54.19 
+            xmax = 54.22 
+            text = "L" 
+        intervals [599]:
+            xmin = 54.22 
+            xmax = 54.27 
+            text = "IY0" 
+        intervals [600]:
+            xmin = 54.27 
+            xmax = 54.33 
+            text = "L" 
+        intervals [601]:
+            xmin = 54.33 
+            xmax = 54.43 
+            text = "AY1" 
+        intervals [602]:
+            xmin = 54.43 
+            xmax = 54.57 
+            text = "K" 
+        intervals [603]:
+            xmin = 54.57 
+            xmax = 54.61 
+            text = "T" 
+        intervals [604]:
+            xmin = 54.61 
+            xmax = 54.69 
+            text = "W" 
+        intervals [605]:
+            xmin = 54.69 
+            xmax = 54.79 
+            text = "AA1" 
+        intervals [606]:
+            xmin = 54.79 
+            xmax = 54.85 
+            text = "CH" 
+        intervals [607]:
+            xmin = 54.85 
+            xmax = 54.89 
+            text = "IH0" 
+        intervals [608]:
+            xmin = 54.89 
+            xmax = 55.01 
+            text = "NG" 
+        intervals [609]:
+            xmin = 55.01 
+            xmax = 55.12 
+            text = "JH" 
+        intervals [610]:
+            xmin = 55.12 
+            xmax = 55.25 
+            text = "AE2" 
+        intervals [611]:
+            xmin = 55.25 
+            xmax = 55.3 
+            text = "P" 
+        intervals [612]:
+            xmin = 55.3 
+            xmax = 55.35 
+            text = "AH0" 
+        intervals [613]:
+            xmin = 55.35 
+            xmax = 55.4 
+            text = "N" 
+        intervals [614]:
+            xmin = 55.4 
+            xmax = 55.59 
+            text = "IY1" 
+        intervals [615]:
+            xmin = 55.59 
+            xmax = 55.62 
+            text = "Z" 
+        intervals [616]:
+            xmin = 55.62 
+            xmax = 55.77 
+            text = "AE1" 
+        intervals [617]:
+            xmin = 55.77 
+            xmax = 55.83 
+            text = "N" 
+        intervals [618]:
+            xmin = 55.83 
+            xmax = 55.87 
+            text = "AH0" 
+        intervals [619]:
+            xmin = 55.87 
+            xmax = 55.91 
+            text = "M" 
+        intervals [620]:
+            xmin = 55.91 
+            xmax = 56.33 
+            text = "AY1" 
+        intervals [621]:
+            xmin = 56.33 
+            xmax = 56.85 
+            text = "" 
+        intervals [622]:
+            xmin = 56.85 
+            xmax = 56.99 
+            text = "TH" 
+        intervals [623]:
+            xmin = 56.99 
+            xmax = 57.05 
+            text = "IH1" 
+        intervals [624]:
+            xmin = 57.05 
+            xmax = 57.09 
+            text = "NG" 
+        intervals [625]:
+            xmin = 57.09 
+            xmax = 57.12 
+            text = "K" 
+        intervals [626]:
+            xmin = 57.12 
+            xmax = 57.2 
+            text = "W" 
+        intervals [627]:
+            xmin = 57.2 
+            xmax = 57.27 
+            text = "AA1" 
+        intervals [628]:
+            xmin = 57.27 
+            xmax = 57.35 
+            text = "CH" 
+        intervals [629]:
+            xmin = 57.35 
+            xmax = 57.4 
+            text = "IH0" 
+        intervals [630]:
+            xmin = 57.4 
+            xmax = 57.43 
+            text = "NG" 
+        intervals [631]:
+            xmin = 57.43 
+            xmax = 57.62 
+            text = "EY1" 
+        intervals [632]:
+            xmin = 57.62 
+            xmax = 57.69 
+            text = "M" 
+        intervals [633]:
+            xmin = 57.69 
+            xmax = 57.79 
+            text = "IY1" 
+        intervals [634]:
+            xmin = 57.79 
+            xmax = 57.92 
+            text = "IH0" 
+        intervals [635]:
+            xmin = 57.92 
+            xmax = 58.09 
+            text = "Z" 
+        intervals [636]:
+            xmin = 58.09 
+            xmax = 58.12 
+            text = "AE1" 
+        intervals [637]:
+            xmin = 58.12 
+            xmax = 58.19 
+            text = "N" 
+        intervals [638]:
+            xmin = 58.19 
+            xmax = 58.23 
+            text = "AH0" 
+        intervals [639]:
+            xmin = 58.23 
+            xmax = 58.39 
+            text = "M" 
+        intervals [640]:
+            xmin = 58.39 
+            xmax = 58.97 
+            text = "IH1" 
+        intervals [641]:
+            xmin = 58.97 
+            xmax = 59.06 
+            text = "Z" 
+        intervals [642]:
+            xmin = 59.06 
+            xmax = 59.11 
+            text = "V" 
+        intervals [643]:
+            xmin = 59.11 
+            xmax = 59.15 
+            text = "EH1" 
+        intervals [644]:
+            xmin = 59.15 
+            xmax = 59.24 
+            text = "R" 
+        intervals [645]:
+            xmin = 59.24 
+            xmax = 59.31 
+            text = "IY0" 
+        intervals [646]:
+            xmin = 59.31 
+            xmax = 59.38 
+            text = "HH" 
+        intervals [647]:
+            xmin = 59.38 
+            xmax = 59.43 
+            text = "EH1" 
+        intervals [648]:
+            xmin = 59.43 
+            xmax = 59.52 
+            text = "L" 
+        intervals [649]:
+            xmin = 59.52 
+            xmax = 59.55 
+            text = "P" 
+        intervals [650]:
+            xmin = 59.55 
+            xmax = 59.58 
+            text = "F" 
+        intervals [651]:
+            xmin = 59.58 
+            xmax = 59.61 
+            text = "AH0" 
+        intervals [652]:
+            xmin = 59.61 
+            xmax = 59.67 
+            text = "L" 
+        intervals [653]:
+            xmin = 59.67 
+            xmax = 59.72 
+            text = "F" 
+        intervals [654]:
+            xmin = 59.72 
+            xmax = 59.75 
+            text = "R" 
+        intervals [655]:
+            xmin = 59.75 
+            xmax = 59.81 
+            text = "ER0" 
+        intervals [656]:
+            xmin = 59.81 
+            xmax = 59.88 
+            text = "M" 
+        intervals [657]:
+            xmin = 59.88 
+            xmax = 59.98 
+            text = "IY1" 
+        intervals [658]:
+            xmin = 59.98 
+            xmax = 60.08 
+            text = "T" 
+        intervals [659]:
+            xmin = 60.08 
+            xmax = 60.28 
+            text = "UW1" 
+        intervals [660]:
+            xmin = 60.28 
+            xmax = 60.42 
+            text = "L" 
+        intervals [661]:
+            xmin = 60.42 
+            xmax = 60.63 
+            text = "ER1" 
+        intervals [662]:
+            xmin = 60.63 
+            xmax = 60.69 
+            text = "N" 
+        intervals [663]:
+            xmin = 60.69 
+            xmax = 60.72 
+            text = "AE1" 
+        intervals [664]:
+            xmin = 60.72 
+            xmax = 60.75 
+            text = "N" 
+        intervals [665]:
+            xmin = 60.75 
+            xmax = 60.78 
+            text = "D" 
+        intervals [666]:
+            xmin = 60.78 
+            xmax = 60.84 
+            text = "IH0" 
+        intervals [667]:
+            xmin = 60.84 
+            xmax = 60.88 
+            text = "K" 
+        intervals [668]:
+            xmin = 60.88 
+            xmax = 60.95 
+            text = "S" 
+        intervals [669]:
+            xmin = 60.95 
+            xmax = 61.01 
+            text = "P" 
+        intervals [670]:
+            xmin = 61.01 
+            xmax = 61.09 
+            text = "R" 
+        intervals [671]:
+            xmin = 61.09 
+            xmax = 61.14 
+            text = "EH1" 
+        intervals [672]:
+            xmin = 61.14 
+            xmax = 61.21 
+            text = "S" 
+        intervals [673]:
+            xmin = 61.21 
+            xmax = 61.33 
+            text = "JH" 
+        intervals [674]:
+            xmin = 61.33 
+            xmax = 61.45 
+            text = "AE2" 
+        intervals [675]:
+            xmin = 61.45 
+            xmax = 61.51 
+            text = "P" 
+        intervals [676]:
+            xmin = 61.51 
+            xmax = 61.55 
+            text = "AH0" 
+        intervals [677]:
+            xmin = 61.55 
+            xmax = 61.59 
+            text = "N" 
+        intervals [678]:
+            xmin = 61.59 
+            xmax = 61.75 
+            text = "IY1" 
+        intervals [679]:
+            xmin = 61.75 
+            xmax = 61.89 
+            text = "Z" 
+        intervals [680]:
+            xmin = 61.89 
+            xmax = 62.02 
+            text = "B" 
+        intervals [681]:
+            xmin = 62.02 
+            xmax = 62.11 
+            text = "EH1" 
+        intervals [682]:
+            xmin = 62.11 
+            xmax = 62.19 
+            text = "T" 
+        intervals [683]:
+            xmin = 62.19 
+            xmax = 62.42 
+            text = "ER0" 
+        intervals [684]:
+            xmin = 62.42 
+            xmax = 64.097375 
+            text = "" 

EMAGE/test_sequences/textgrid/2_scott_0_2_2.TextGrid

@@ -0,0 +1,3716 @@
+File type = "ooTextFile"
+Object class = "TextGrid"
+
+xmin = 0.0
+xmax = 62
+tiers? <exists>
+size = 2
+item []:
+	item [1]:
+		class = "IntervalTier"
+		name = "words"
+		xmin = 0.0
+		xmax = 62
+		intervals: size = 223
+			intervals [1]:
+				xmin = 0.0
+				xmax = 1.45
+				text = ""
+			intervals [2]:
+				xmin = 1.45
+				xmax = 1.87
+				text = "so"
+			intervals [3]:
+				xmin = 1.87
+				xmax = 2.02
+				text = "when"
+			intervals [4]:
+				xmin = 2.02
+				xmax = 2.13
+				text = "i"
+			intervals [5]:
+				xmin = 2.13
+				xmax = 2.35
+				text = "have"
+			intervals [6]:
+				xmin = 2.35
+				xmax = 2.57
+				text = "time"
+			intervals [7]:
+				xmin = 2.57
+				xmax = 2.65
+				text = "to"
+			intervals [8]:
+				xmin = 2.65
+				xmax = 3.18
+				text = "kill"
+			intervals [9]:
+				xmin = 3.18
+				xmax = 3.22
+				text = ""
+			intervals [10]:
+				xmin = 3.22
+				xmax = 3.41
+				text = "i"
+			intervals [11]:
+				xmin = 3.41
+				xmax = 3.6
+				text = "like"
+			intervals [12]:
+				xmin = 3.6
+				xmax = 3.68
+				text = "to"
+			intervals [13]:
+				xmin = 3.68
+				xmax = 3.88
+				text = "play"
+			intervals [14]:
+				xmin = 3.88
+				xmax = 3.96
+				text = "on"
+			intervals [15]:
+				xmin = 3.96
+				xmax = 4.08
+				text = "the"
+			intervals [16]:
+				xmin = 4.08
+				xmax = 4.5
+				text = "internet"
+			intervals [17]:
+				xmin = 4.5
+				xmax = 4.66
+				text = "and"
+			intervals [18]:
+				xmin = 4.66
+				xmax = 4.87
+				text = "play"
+			intervals [19]:
+				xmin = 4.87
+				xmax = 5.19
+				text = "close"
+			intervals [20]:
+				xmin = 5.19
+				xmax = 5.67
+				text = "attention"
+			intervals [21]:
+				xmin = 5.67
+				xmax = 6.0
+				text = "to"
+			intervals [22]:
+				xmin = 6.0
+				xmax = 6.26
+				text = "new"
+			intervals [23]:
+				xmin = 6.26
+				xmax = 6.71
+				text = "fashion"
+			intervals [24]:
+				xmin = 6.71
+				xmax = 7.17
+				text = "events"
+			intervals [25]:
+				xmin = 7.17
+				xmax = 7.43
+				text = ""
+			intervals [26]:
+				xmin = 7.43
+				xmax = 7.76
+				text = "such"
+			intervals [27]:
+				xmin = 7.76
+				xmax = 8.14
+				text = "as"
+			intervals [28]:
+				xmin = 8.14
+				xmax = 8.19
+				text = ""
+			intervals [29]:
+				xmin = 8.19
+				xmax = 8.34
+				text = "the"
+			intervals [30]:
+				xmin = 8.34
+				xmax = 8.47
+				text = "new"
+			intervals [31]:
+				xmin = 8.47
+				xmax = 8.68
+				text = "york"
+			intervals [32]:
+				xmin = 8.68
+				xmax = 9.12
+				text = "fashion"
+			intervals [33]:
+				xmin = 9.12
+				xmax = 9.42
+				text = "week"
+			intervals [34]:
+				xmin = 9.42
+				xmax = 9.49
+				text = "the"
+			intervals [35]:
+				xmin = 9.49
+				xmax = 9.87
+				text = "paris"
+			intervals [36]:
+				xmin = 9.87
+				xmax = 10.25
+				text = "fashion"
+			intervals [37]:
+				xmin = 10.25
+				xmax = 10.56
+				text = "week"
+			intervals [38]:
+				xmin = 10.56
+				xmax = 10.66
+				text = "the"
+			intervals [39]:
+				xmin = 10.66
+				xmax = 11.07
+				text = "london"
+			intervals [40]:
+				xmin = 11.07
+				xmax = 11.51
+				text = "fashion"
+			intervals [41]:
+				xmin = 11.51
+				xmax = 11.78
+				text = "week"
+			intervals [42]:
+				xmin = 11.78
+				xmax = 12.17
+				text = "and"
+			intervals [43]:
+				xmin = 12.17
+				xmax = 12.21
+				text = ""
+			intervals [44]:
+				xmin = 12.21
+				xmax = 12.83
+				text = "milan"
+			intervals [45]:
+				xmin = 12.83
+				xmax = 13.24
+				text = "fashion"
+			intervals [46]:
+				xmin = 13.24
+				xmax = 13.62
+				text = "week"
+			intervals [47]:
+				xmin = 13.62
+				xmax = 14.03
+				text = ""
+			intervals [48]:
+				xmin = 14.03
+				xmax = 14.15
+				text = "the"
+			intervals [49]:
+				xmin = 14.15
+				xmax = 14.35
+				text = "rest"
+			intervals [50]:
+				xmin = 14.35
+				xmax = 14.43
+				text = "of"
+			intervals [51]:
+				xmin = 14.43
+				xmax = 14.49
+				text = "the"
+			intervals [52]:
+				xmin = 14.49
+				xmax = 14.8
+				text = "time"
+			intervals [53]:
+				xmin = 14.8
+				xmax = 14.87
+				text = "i"
+			intervals [54]:
+				xmin = 14.87
+				xmax = 15.2
+				text = "usually"
+			intervals [55]:
+				xmin = 15.2
+				xmax = 15.3
+				text = "go"
+			intervals [56]:
+				xmin = 15.3
+				xmax = 15.36
+				text = "to"
+			intervals [57]:
+				xmin = 15.36
+				xmax = 15.44
+				text = "the"
+			intervals [58]:
+				xmin = 15.44
+				xmax = 15.93
+				text = "library"
+			intervals [59]:
+				xmin = 15.93
+				xmax = 16.04
+				text = "and"
+			intervals [60]:
+				xmin = 16.04
+				xmax = 16.25
+				text = "find"
+			intervals [61]:
+				xmin = 16.25
+				xmax = 16.35
+				text = "some"
+			intervals [62]:
+				xmin = 16.35
+				xmax = 16.71
+				text = "interesting"
+			intervals [63]:
+				xmin = 16.71
+				xmax = 17.19
+				text = "books"
+			intervals [64]:
+				xmin = 17.19
+				xmax = 17.31
+				text = "and"
+			intervals [65]:
+				xmin = 17.31
+				xmax = 17.51
+				text = "then"
+			intervals [66]:
+				xmin = 17.51
+				xmax = 17.63
+				text = "go"
+			intervals [67]:
+				xmin = 17.63
+				xmax = 17.7
+				text = "to"
+			intervals [68]:
+				xmin = 17.7
+				xmax = 17.78
+				text = "a"
+			intervals [69]:
+				xmin = 17.78
+				xmax = 18.08
+				text = "park"
+			intervals [70]:
+				xmin = 18.08
+				xmax = 18.17
+				text = "and"
+			intervals [71]:
+				xmin = 18.17
+				xmax = 18.75
+				text = "relax"
+			intervals [72]:
+				xmin = 18.75
+				xmax = 19.04
+				text = ""
+			intervals [73]:
+				xmin = 19.04
+				xmax = 19.22
+				text = "there"
+			intervals [74]:
+				xmin = 19.22
+				xmax = 19.27
+				text = "are"
+			intervals [75]:
+				xmin = 19.27
+				xmax = 19.5
+				text = "many"
+			intervals [76]:
+				xmin = 19.5
+				xmax = 19.78
+				text = "books"
+			intervals [77]:
+				xmin = 19.78
+				xmax = 19.93
+				text = "that"
+			intervals [78]:
+				xmin = 19.93
+				xmax = 20.11
+				text = "i"
+			intervals [79]:
+				xmin = 20.11
+				xmax = 20.4
+				text = "find"
+			intervals [80]:
+				xmin = 20.4
+				xmax = 20.92
+				text = "interesting"
+			intervals [81]:
+				xmin = 20.92
+				xmax = 21.15
+				text = "such"
+			intervals [82]:
+				xmin = 21.15
+				xmax = 21.3
+				text = "as"
+			intervals [83]:
+				xmin = 21.3
+				xmax = 21.62
+				text = "fashion"
+			intervals [84]:
+				xmin = 21.62
+				xmax = 22.19
+				text = "magazines"
+			intervals [85]:
+				xmin = 22.19
+				xmax = 22.8
+				text = "inspirational"
+			intervals [86]:
+				xmin = 22.8
+				xmax = 23.15
+				text = "books"
+			intervals [87]:
+				xmin = 23.15
+				xmax = 23.44
+				text = "and"
+			intervals [88]:
+				xmin = 23.44
+				xmax = 24.04
+				text = "professional"
+			intervals [89]:
+				xmin = 24.04
+				xmax = 24.46
+				text = "books"
+			intervals [90]:
+				xmin = 24.46
+				xmax = 24.83
+				text = ""
+			intervals [91]:
+				xmin = 24.83
+				xmax = 25.06
+				text = "these"
+			intervals [92]:
+				xmin = 25.06
+				xmax = 25.37
+				text = "books"
+			intervals [93]:
+				xmin = 25.37
+				xmax = 25.54
+				text = "can"
+			intervals [94]:
+				xmin = 25.54
+				xmax = 25.66
+				text = "give"
+			intervals [95]:
+				xmin = 25.66
+				xmax = 25.76
+				text = "me"
+			intervals [96]:
+				xmin = 25.76
+				xmax = 25.86
+				text = "the"
+			intervals [97]:
+				xmin = 25.86
+				xmax = 26.85
+				text = "motivation"
+			intervals [98]:
+				xmin = 26.85
+				xmax = 26.88
+				text = ""
+			intervals [99]:
+				xmin = 26.88
+				xmax = 27.07
+				text = "to"
+			intervals [100]:
+				xmin = 27.07
+				xmax = 27.37
+				text = "be"
+			intervals [101]:
+				xmin = 27.37
+				xmax = 28.01
+				text = "healthier"
+			intervals [102]:
+				xmin = 28.01
+				xmax = 28.18
+				text = "and"
+			intervals [103]:
+				xmin = 28.18
+				xmax = 28.9
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+				text = ""
+			intervals [74]:
+				xmin = 7.43
+				xmax = 7.55
+				text = "S"
+			intervals [75]:
+				xmin = 7.55
+				xmax = 7.63
+				text = "AH1"
+			intervals [76]:
+				xmin = 7.63
+				xmax = 7.76
+				text = "CH"
+			intervals [77]:
+				xmin = 7.76
+				xmax = 7.94
+				text = "EH1"
+			intervals [78]:
+				xmin = 7.94
+				xmax = 8.14
+				text = "Z"
+			intervals [79]:
+				xmin = 8.14
+				xmax = 8.19
+				text = ""
+			intervals [80]:
+				xmin = 8.19
+				xmax = 8.28
+				text = "DH"
+			intervals [81]:
+				xmin = 8.28
+				xmax = 8.34
+				text = "AH0"
+			intervals [82]:
+				xmin = 8.34
+				xmax = 8.44
+				text = "N"
+			intervals [83]:
+				xmin = 8.44
+				xmax = 8.47
+				text = "UW1"
+			intervals [84]:
+				xmin = 8.47
+				xmax = 8.52
+				text = "Y"
+			intervals [85]:
+				xmin = 8.52
+				xmax = 8.56
+				text = "AO1"
+			intervals [86]:
+				xmin = 8.56
+				xmax = 8.62
+				text = "R"
+			intervals [87]:
+				xmin = 8.62
+				xmax = 8.68
+				text = "K"
+			intervals [88]:
+				xmin = 8.68
+				xmax = 8.79
+				text = "F"
+			intervals [89]:
+				xmin = 8.79
+				xmax = 8.93
+				text = "AE1"
+			intervals [90]:
+				xmin = 8.93
+				xmax = 9.03
+				text = "SH"
+			intervals [91]:
+				xmin = 9.03
+				xmax = 9.07
+				text = "AH0"
+			intervals [92]:
+				xmin = 9.07
+				xmax = 9.12
+				text = "N"
+			intervals [93]:
+				xmin = 9.12
+				xmax = 9.19
+				text = "W"
+			intervals [94]:
+				xmin = 9.19
+				xmax = 9.33
+				text = "IY1"
+			intervals [95]:
+				xmin = 9.33
+				xmax = 9.42
+				text = "K"
+			intervals [96]:
+				xmin = 9.42
+				xmax = 9.46
+				text = "DH"
+			intervals [97]:
+				xmin = 9.46
+				xmax = 9.49
+				text = "AH0"
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+				xmin = 9.49
+				xmax = 9.57
+				text = "P"
+			intervals [99]:
+				xmin = 9.57
+				xmax = 9.64
+				text = "EH1"
+			intervals [100]:
+				xmin = 9.64
+				xmax = 9.75
+				text = "R"
+			intervals [101]:
+				xmin = 9.75
+				xmax = 9.8
+				text = "IH0"
+			intervals [102]:
+				xmin = 9.8
+				xmax = 9.87
+				text = "S"
+			intervals [103]:
+				xmin = 9.87
+				xmax = 9.93
+				text = "F"
+			intervals [104]:
+				xmin = 9.93
+				xmax = 10.09
+				text = "AE1"
+			intervals [105]:
+				xmin = 10.09
+				xmax = 10.19
+				text = "SH"
+			intervals [106]:
+				xmin = 10.19
+				xmax = 10.22
+				text = "AH0"
+			intervals [107]:
+				xmin = 10.22
+				xmax = 10.25
+				text = "N"
+			intervals [108]:
+				xmin = 10.25
+				xmax = 10.32
+				text = "W"
+			intervals [109]:
+				xmin = 10.32
+				xmax = 10.49
+				text = "IY1"
+			intervals [110]:
+				xmin = 10.49
+				xmax = 10.56
+				text = "K"
+			intervals [111]:
+				xmin = 10.56
+				xmax = 10.6
+				text = "DH"
+			intervals [112]:
+				xmin = 10.6
+				xmax = 10.66
+				text = "AH0"
+			intervals [113]:
+				xmin = 10.66
+				xmax = 10.76
+				text = "L"
+			intervals [114]:
+				xmin = 10.76
+				xmax = 10.81
+				text = "AH1"
+			intervals [115]:
+				xmin = 10.81
+				xmax = 10.87
+				text = "N"
+			intervals [116]:
+				xmin = 10.87
+				xmax = 10.92
+				text = "D"
+			intervals [117]:
+				xmin = 10.92
+				xmax = 10.97
+				text = "AH0"
+			intervals [118]:
+				xmin = 10.97
+				xmax = 11.07
+				text = "N"
+			intervals [119]:
+				xmin = 11.07
+				xmax = 11.18
+				text = "F"
+			intervals [120]:
+				xmin = 11.18
+				xmax = 11.31
+				text = "AE1"
+			intervals [121]:
+				xmin = 11.31
+				xmax = 11.42
+				text = "SH"
+			intervals [122]:
+				xmin = 11.42
+				xmax = 11.46
+				text = "AH0"
+			intervals [123]:
+				xmin = 11.46
+				xmax = 11.51
+				text = "N"
+			intervals [124]:
+				xmin = 11.51
+				xmax = 11.56
+				text = "W"
+			intervals [125]:
+				xmin = 11.56
+				xmax = 11.69
+				text = "IY1"
+			intervals [126]:
+				xmin = 11.69
+				xmax = 11.78
+				text = "K"
+			intervals [127]:
+				xmin = 11.78
+				xmax = 11.84
+				text = "AE1"
+			intervals [128]:
+				xmin = 11.84
+				xmax = 11.99
+				text = "N"
+			intervals [129]:
+				xmin = 11.99
+				xmax = 12.17
+				text = "D"
+			intervals [130]:
+				xmin = 12.17
+				xmax = 12.21
+				text = ""
+			intervals [131]:
+				xmin = 12.21
+				xmax = 12.36
+				text = "M"
+			intervals [132]:
+				xmin = 12.36
+				xmax = 12.48
+				text = "IH0"
+			intervals [133]:
+				xmin = 12.48
+				xmax = 12.55
+				text = "L"
+			intervals [134]:
+				xmin = 12.55
+				xmax = 12.73
+				text = "AA1"
+			intervals [135]:
+				xmin = 12.73
+				xmax = 12.83
+				text = "N"
+			intervals [136]:
+				xmin = 12.83
+				xmax = 12.92
+				text = "F"
+			intervals [137]:
+				xmin = 12.92
+				xmax = 13.06
+				text = "AE1"
+			intervals [138]:
+				xmin = 13.06
+				xmax = 13.16
+				text = "SH"
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+				xmin = 13.16
+				xmax = 13.21
+				text = "AH0"
+			intervals [140]:
+				xmin = 13.21
+				xmax = 13.24
+				text = "N"
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+				xmin = 13.24
+				xmax = 13.29
+				text = "W"
+			intervals [142]:
+				xmin = 13.29
+				xmax = 13.41
+				text = "IY1"
+			intervals [143]:
+				xmin = 13.41
+				xmax = 13.62
+				text = "K"
+			intervals [144]:
+				xmin = 13.62
+				xmax = 14.03
+				text = ""
+			intervals [145]:
+				xmin = 14.03
+				xmax = 14.11
+				text = "DH"
+			intervals [146]:
+				xmin = 14.11
+				xmax = 14.15
+				text = "AH1"
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+				xmin = 14.15
+				xmax = 14.22
+				text = "R"
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+				xmin = 14.22
+				xmax = 14.26
+				text = "EH1"
+			intervals [149]:
+				xmin = 14.26
+				xmax = 14.31
+				text = "S"
+			intervals [150]:
+				xmin = 14.31
+				xmax = 14.35
+				text = "T"
+			intervals [151]:
+				xmin = 14.35
+				xmax = 14.4
+				text = "AH0"
+			intervals [152]:
+				xmin = 14.4
+				xmax = 14.43
+				text = "V"
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+				xmin = 14.43
+				xmax = 14.46
+				text = "DH"
+			intervals [154]:
+				xmin = 14.46
+				xmax = 14.49
+				text = "AH0"
+			intervals [155]:
+				xmin = 14.49
+				xmax = 14.55
+				text = "T"
+			intervals [156]:
+				xmin = 14.55
+				xmax = 14.73
+				text = "AY1"
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+				xmin = 14.73
+				xmax = 14.8
+				text = "M"
+			intervals [158]:
+				xmin = 14.8
+				xmax = 14.87
+				text = "AY1"
+			intervals [159]:
+				xmin = 14.87
+				xmax = 14.96
+				text = "Y"
+			intervals [160]:
+				xmin = 14.96
+				xmax = 14.99
+				text = "UW1"
+			intervals [161]:
+				xmin = 14.99
+				xmax = 15.08
+				text = "ZH"
+			intervals [162]:
+				xmin = 15.08
+				xmax = 15.11
+				text = "AH0"
+			intervals [163]:
+				xmin = 15.11
+				xmax = 15.14
+				text = "L"
+			intervals [164]:
+				xmin = 15.14
+				xmax = 15.2
+				text = "IY0"
+			intervals [165]:
+				xmin = 15.2
+				xmax = 15.23
+				text = "G"
+			intervals [166]:
+				xmin = 15.23
+				xmax = 15.3
+				text = "OW1"
+			intervals [167]:
+				xmin = 15.3
+				xmax = 15.33
+				text = "T"
+			intervals [168]:
+				xmin = 15.33
+				xmax = 15.36
+				text = "AH0"
+			intervals [169]:
+				xmin = 15.36
+				xmax = 15.39
+				text = "DH"
+			intervals [170]:
+				xmin = 15.39
+				xmax = 15.44
+				text = "AH1"
+			intervals [171]:
+				xmin = 15.44
+				xmax = 15.51
+				text = "L"
+			intervals [172]:
+				xmin = 15.51
+				xmax = 15.67
+				text = "AY1"
+			intervals [173]:
+				xmin = 15.67
+				xmax = 15.71
+				text = "B"
+			intervals [174]:
+				xmin = 15.71
+				xmax = 15.74
+				text = "R"
+			intervals [175]:
+				xmin = 15.74
+				xmax = 15.83
+				text = "EH2"
+			intervals [176]:
+				xmin = 15.83
+				xmax = 15.9
+				text = "R"
+			intervals [177]:
+				xmin = 15.9
+				xmax = 15.93
+				text = "IY0"
+			intervals [178]:
+				xmin = 15.93
+				xmax = 15.96
+				text = "AH0"
+			intervals [179]:
+				xmin = 15.96
+				xmax = 15.99
+				text = "N"
+			intervals [180]:
+				xmin = 15.99
+				xmax = 16.04
+				text = "D"
+			intervals [181]:
+				xmin = 16.04
+				xmax = 16.11
+				text = "F"
+			intervals [182]:
+				xmin = 16.11
+				xmax = 16.18
+				text = "AY1"
+			intervals [183]:
+				xmin = 16.18
+				xmax = 16.21
+				text = "N"
+			intervals [184]:
+				xmin = 16.21
+				xmax = 16.25
+				text = "D"
+			intervals [185]:
+				xmin = 16.25
+				xmax = 16.29
+				text = "S"
+			intervals [186]:
+				xmin = 16.29
+				xmax = 16.32
+				text = "AH1"
+			intervals [187]:
+				xmin = 16.32
+				xmax = 16.35
+				text = "M"
+			intervals [188]:
+				xmin = 16.35
+				xmax = 16.38
+				text = "IH1"
+			intervals [189]:
+				xmin = 16.38
+				xmax = 16.41
+				text = "N"
+			intervals [190]:
+				xmin = 16.41
+				xmax = 16.46
+				text = "T"
+			intervals [191]:
+				xmin = 16.46
+				xmax = 16.49
+				text = "R"
+			intervals [192]:
+				xmin = 16.49
+				xmax = 16.53
+				text = "IH0"
+			intervals [193]:
+				xmin = 16.53
+				xmax = 16.57
+				text = "S"
+			intervals [194]:
+				xmin = 16.57
+				xmax = 16.6
+				text = "T"
+			intervals [195]:
+				xmin = 16.6
+				xmax = 16.64
+				text = "IH0"
+			intervals [196]:
+				xmin = 16.64
+				xmax = 16.71
+				text = "NG"
+			intervals [197]:
+				xmin = 16.71
+				xmax = 16.78
+				text = "B"
+			intervals [198]:
+				xmin = 16.78
+				xmax = 17.0
+				text = "UH1"
+			intervals [199]:
+				xmin = 17.0
+				xmax = 17.09
+				text = "K"
+			intervals [200]:
+				xmin = 17.09
+				xmax = 17.19
+				text = "S"
+			intervals [201]:
+				xmin = 17.19
+				xmax = 17.25
+				text = "AH0"
+			intervals [202]:
+				xmin = 17.25
+				xmax = 17.28
+				text = "N"
+			intervals [203]:
+				xmin = 17.28
+				xmax = 17.31
+				text = "D"
+			intervals [204]:
+				xmin = 17.31
+				xmax = 17.34
+				text = "DH"
+			intervals [205]:
+				xmin = 17.34
+				xmax = 17.42
+				text = "EH1"
+			intervals [206]:
+				xmin = 17.42
+				xmax = 17.51
+				text = "N"
+			intervals [207]:
+				xmin = 17.51
+				xmax = 17.58
+				text = "G"
+			intervals [208]:
+				xmin = 17.58
+				xmax = 17.63
+				text = "OW1"
+			intervals [209]:
+				xmin = 17.63
+				xmax = 17.67
+				text = "T"
+			intervals [210]:
+				xmin = 17.67
+				xmax = 17.7
+				text = "AH0"
+			intervals [211]:
+				xmin = 17.7
+				xmax = 17.78
+				text = "AH0"
+			intervals [212]:
+				xmin = 17.78
+				xmax = 17.89
+				text = "P"
+			intervals [213]:
+				xmin = 17.89
+				xmax = 17.95
+				text = "AA1"
+			intervals [214]:
+				xmin = 17.95
+				xmax = 18.04
+				text = "R"
+			intervals [215]:
+				xmin = 18.04
+				xmax = 18.08
+				text = "K"
+			intervals [216]:
+				xmin = 18.08
+				xmax = 18.11
+				text = "AH0"
+			intervals [217]:
+				xmin = 18.11
+				xmax = 18.14
+				text = "N"
+			intervals [218]:
+				xmin = 18.14
+				xmax = 18.17
+				text = "D"
+			intervals [219]:
+				xmin = 18.17
+				xmax = 18.2
+				text = "R"
+			intervals [220]:
+				xmin = 18.2
+				xmax = 18.25
+				text = "IH0"
+			intervals [221]:
+				xmin = 18.25
+				xmax = 18.33
+				text = "L"
+			intervals [222]:
+				xmin = 18.33
+				xmax = 18.53
+				text = "AE1"
+			intervals [223]:
+				xmin = 18.53
+				xmax = 18.58
+				text = "K"
+			intervals [224]:
+				xmin = 18.58
+				xmax = 18.75
+				text = "S"
+			intervals [225]:
+				xmin = 18.75
+				xmax = 19.04
+				text = ""
+			intervals [226]:
+				xmin = 19.04
+				xmax = 19.14
+				text = "DH"
+			intervals [227]:
+				xmin = 19.14
+				xmax = 19.18
+				text = "EH1"
+			intervals [228]:
+				xmin = 19.18
+				xmax = 19.22
+				text = "R"
+			intervals [229]:
+				xmin = 19.22
+				xmax = 19.27
+				text = "ER0"
+			intervals [230]:
+				xmin = 19.27
+				xmax = 19.34
+				text = "M"
+			intervals [231]:
+				xmin = 19.34
+				xmax = 19.39
+				text = "EH1"
+			intervals [232]:
+				xmin = 19.39
+				xmax = 19.43
+				text = "N"
+			intervals [233]:
+				xmin = 19.43
+				xmax = 19.5
+				text = "IY0"
+			intervals [234]:
+				xmin = 19.5
+				xmax = 19.56
+				text = "B"
+			intervals [235]:
+				xmin = 19.56
+				xmax = 19.66
+				text = "UH1"
+			intervals [236]:
+				xmin = 19.66
+				xmax = 19.72
+				text = "K"
+			intervals [237]:
+				xmin = 19.72
+				xmax = 19.78
+				text = "S"
+			intervals [238]:
+				xmin = 19.78
+				xmax = 19.81
+				text = "DH"
+			intervals [239]:
+				xmin = 19.81
+				xmax = 19.84
+				text = "AH0"
+			intervals [240]:
+				xmin = 19.84
+				xmax = 19.93
+				text = "T"
+			intervals [241]:
+				xmin = 19.93
+				xmax = 20.11
+				text = "AY1"
+			intervals [242]:
+				xmin = 20.11
+				xmax = 20.22
+				text = "F"
+			intervals [243]:
+				xmin = 20.22
+				xmax = 20.3
+				text = "AY1"
+			intervals [244]:
+				xmin = 20.3
+				xmax = 20.37
+				text = "N"
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+				xmin = 20.37
+				xmax = 20.4
+				text = "D"
+			intervals [246]:
+				xmin = 20.4
+				xmax = 20.52
+				text = "IH1"
+			intervals [247]:
+				xmin = 20.52
+				xmax = 20.55
+				text = "N"
+			intervals [248]:
+				xmin = 20.55
+				xmax = 20.59
+				text = "T"
+			intervals [249]:
+				xmin = 20.59
+				xmax = 20.62
+				text = "R"
+			intervals [250]:
+				xmin = 20.62
+				xmax = 20.67
+				text = "AH0"
+			intervals [251]:
+				xmin = 20.67
+				xmax = 20.74
+				text = "S"
+			intervals [252]:
+				xmin = 20.74
+				xmax = 20.78
+				text = "T"
+			intervals [253]:
+				xmin = 20.78
+				xmax = 20.85
+				text = "IH0"
+			intervals [254]:
+				xmin = 20.85
+				xmax = 20.92
+				text = "NG"
+			intervals [255]:
+				xmin = 20.92
+				xmax = 21.02
+				text = "S"
+			intervals [256]:
+				xmin = 21.02
+				xmax = 21.06
+				text = "AH1"
+			intervals [257]:
+				xmin = 21.06
+				xmax = 21.15
+				text = "CH"
+			intervals [258]:
+				xmin = 21.15
+				xmax = 21.2
+				text = "EH1"
+			intervals [259]:
+				xmin = 21.2
+				xmax = 21.3
+				text = "Z"
+			intervals [260]:
+				xmin = 21.3
+				xmax = 21.36
+				text = "F"
+			intervals [261]:
+				xmin = 21.36
+				xmax = 21.47
+				text = "AE1"
+			intervals [262]:
+				xmin = 21.47
+				xmax = 21.56
+				text = "SH"
+			intervals [263]:
+				xmin = 21.56
+				xmax = 21.59
+				text = "AH0"
+			intervals [264]:
+				xmin = 21.59
+				xmax = 21.62
+				text = "N"
+			intervals [265]:
+				xmin = 21.62
+				xmax = 21.68
+				text = "M"
+			intervals [266]:
+				xmin = 21.68
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+				text = "AE1"
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+				xmin = 21.76
+				xmax = 21.81
+				text = "G"
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+				xmin = 21.81
+				xmax = 21.85
+				text = "AH0"
+			intervals [269]:
+				xmin = 21.85
+				xmax = 21.9
+				text = "Z"
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+				xmin = 21.9
+				xmax = 22.0
+				text = "IY2"
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+				xmin = 22.0
+				xmax = 22.1
+				text = "N"
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+				xmax = 22.19
+				text = "Z"
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+				xmin = 22.19
+				xmax = 22.22
+				text = "IH2"
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+				xmax = 22.29
+				text = "N"
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+				xmin = 22.29
+				xmax = 22.34
+				text = "S"
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+				xmin = 22.48
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+				xmin = 22.55
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+				xmin = 22.64
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+				text = "AH0"
+			intervals [281]:
+				xmin = 22.67
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+				xmin = 22.7
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+				xmin = 22.88
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+				xmin = 23.15
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+				xmin = 23.24
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+				xmin = 23.76
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+			intervals [298]:
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+				text = "N"
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+				xmin = 23.94
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+				text = "AH0"
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+				xmin = 24.46
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+				text = "IY1"
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+				xmin = 24.98
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+				text = "UH1"
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+				xmin = 25.51
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+				xmin = 26.85
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+				xmin = 27.55
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+				text = "UW1"
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+				text = "EH1"
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+				xmin = 32.97
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+				text = "UW1"
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+				text = "UH1"
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+				xmax = 34.91
+				text = "AY1"
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+				xmax = 35.02
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+				text = "IH0"
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+				xmin = 35.26
+				xmax = 35.36
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+				xmax = 35.47
+				text = "AE1"
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+				text = "AY1"
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+				xmax = 35.9
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+				text = "R"
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+				xmin = 35.98
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+				text = "AY1"
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+				text = "AH1"
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+				text = "EH1"
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+				xmax = 37.55
+				text = "AA2"
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+				xmin = 38.5
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+				xmin = 56.32
+				xmax = 56.37
+				text = "S"
+			intervals [652]:
+				xmin = 56.37
+				xmax = 56.44
+				text = "T"
+			intervals [653]:
+				xmin = 56.44
+				xmax = 56.64
+				text = "AA1"
+			intervals [654]:
+				xmin = 56.64
+				xmax = 56.68
+				text = "N"
+			intervals [655]:
+				xmin = 56.68
+				xmax = 56.72
+				text = "DH"
+			intervals [656]:
+				xmin = 56.72
+				xmax = 56.9
+				text = "IH0"
+			intervals [657]:
+				xmin = 56.9
+				xmax = 56.99
+				text = "S"
+			intervals [658]:
+				xmin = 56.99
+				xmax = 57.05
+				text = "R"
+			intervals [659]:
+				xmin = 57.05
+				xmax = 57.11
+				text = "AE1"
+			intervals [660]:
+				xmin = 57.11
+				xmax = 57.16
+				text = "NG"
+			intervals [661]:
+				xmin = 57.16
+				xmax = 57.22
+				text = "K"
+			intervals [662]:
+				xmin = 57.22
+				xmax = 57.29
+				text = "IH0"
+			intervals [663]:
+				xmin = 57.29
+				xmax = 57.35
+				text = "NG"
+			intervals [664]:
+				xmin = 57.35
+				xmax = 57.4
+				text = "AH0"
+			intervals [665]:
+				xmin = 57.4
+				xmax = 57.43
+				text = "N"
+			intervals [666]:
+				xmin = 57.43
+				xmax = 57.53
+				text = "D"
+			intervals [667]:
+				xmin = 57.53
+				xmax = 57.7
+				text = "EH1"
+			intervals [668]:
+				xmin = 57.7
+				xmax = 57.76
+				text = "V"
+			intervals [669]:
+				xmin = 57.76
+				xmax = 57.82
+				text = "R"
+			intervals [670]:
+				xmin = 57.82
+				xmax = 57.86
+				text = "IY0"
+			intervals [671]:
+				xmin = 57.86
+				xmax = 57.95
+				text = "T"
+			intervals [672]:
+				xmin = 57.95
+				xmax = 58.19
+				text = "AY1"
+			intervals [673]:
+				xmin = 58.19
+				xmax = 58.44
+				text = "M"
+			intervals [674]:
+				xmin = 58.44
+				xmax = 59.02
+				text = ""
+			intervals [675]:
+				xmin = 59.02
+				xmax = 59.12
+				text = "Y"
+			intervals [676]:
+				xmin = 59.12
+				xmax = 59.15
+				text = "UH1"
+			intervals [677]:
+				xmin = 59.15
+				xmax = 59.2
+				text = "R"
+			intervals [678]:
+				xmin = 59.2
+				xmax = 59.32
+				text = "S"
+			intervals [679]:
+				xmin = 59.32
+				xmax = 59.41
+				text = "AE1"
+			intervals [680]:
+				xmin = 59.41
+				xmax = 59.44
+				text = "T"
+			intervals [681]:
+				xmin = 59.44
+				xmax = 59.49
+				text = "AH0"
+			intervals [682]:
+				xmin = 59.49
+				xmax = 59.55
+				text = "S"
+			intervals [683]:
+				xmin = 59.55
+				xmax = 59.62
+				text = "F"
+			intervals [684]:
+				xmin = 59.62
+				xmax = 59.69
+				text = "AY2"
+			intervals [685]:
+				xmin = 59.69
+				xmax = 59.72
+				text = "D"
+			intervals [686]:
+				xmin = 59.72
+				xmax = 59.77
+				text = "W"
+			intervals [687]:
+				xmin = 59.77
+				xmax = 59.82
+				text = "IH0"
+			intervals [688]:
+				xmin = 59.82
+				xmax = 59.85
+				text = "DH"
+			intervals [689]:
+				xmin = 59.85
+				xmax = 59.88
+				text = "DH"
+			intervals [690]:
+				xmin = 59.88
+				xmax = 60.0
+				text = "IY1"
+			intervals [691]:
+				xmin = 60.0
+				xmax = 60.1
+				text = "Z"
+			intervals [692]:
+				xmin = 60.1
+				xmax = 60.15
+				text = "R"
+			intervals [693]:
+				xmin = 60.15
+				xmax = 60.23
+				text = "EH1"
+			intervals [694]:
+				xmin = 60.23
+				xmax = 60.28
+				text = "S"
+			intervals [695]:
+				xmin = 60.28
+				xmax = 60.34
+				text = "T"
+			intervals [696]:
+				xmin = 60.34
+				xmax = 60.4
+				text = "R"
+			intervals [697]:
+				xmin = 60.4
+				xmax = 60.52
+				text = "AA2"
+			intervals [698]:
+				xmin = 60.52
+				xmax = 60.57
+				text = "N"
+			intervals [699]:
+				xmin = 60.57
+				xmax = 60.62
+				text = "T"
+			intervals [700]:
+				xmin = 60.62
+				xmax = 60.81
+				text = "S"
+			intervals [701]:
+				xmin = 60.81
+				xmax = 62
+				text = ""

EMAGE/test_sequences/textgrid/2_scott_0_3_3.TextGrid

@@ -0,0 +1,3676 @@
+File type = "ooTextFile"
+Object class = "TextGrid"
+
+xmin = 0.0
+xmax = 68
+tiers? <exists>
+size = 2
+item []:
+	item [1]:
+		class = "IntervalTier"
+		name = "words"
+		xmin = 0.0
+		xmax = 68
+		intervals: size = 213
+			intervals [1]:
+				xmin = 0.0
+				xmax = 1.47
+				text = ""
+			intervals [2]:
+				xmin = 1.47
+				xmax = 2.36
+				text = "well"
+			intervals [3]:
+				xmin = 2.36
+				xmax = 2.56
+				text = ""
+			intervals [4]:
+				xmin = 2.56
+				xmax = 3.05
+				text = "in"
+			intervals [5]:
+				xmin = 3.05
+				xmax = 3.43
+				text = "my"
+			intervals [6]:
+				xmin = 3.43
+				xmax = 4.29
+				text = "opinion"
+			intervals [7]:
+				xmin = 4.29
+				xmax = 4.43
+				text = "i"
+			intervals [8]:
+				xmin = 4.43
+				xmax = 4.7
+				text = "think"
+			intervals [9]:
+				xmin = 4.7
+				xmax = 4.77
+				text = "the"
+			intervals [10]:
+				xmin = 4.77
+				xmax = 5.06
+				text = "best"
+			intervals [11]:
+				xmin = 5.06
+				xmax = 5.31
+				text = "job"
+			intervals [12]:
+				xmin = 5.31
+				xmax = 5.41
+				text = "for"
+			intervals [13]:
+				xmin = 5.41
+				xmax = 5.62
+				text = "me"
+			intervals [14]:
+				xmin = 5.62
+				xmax = 5.76
+				text = "is"
+			intervals [15]:
+				xmin = 5.76
+				xmax = 5.84
+				text = "to"
+			intervals [16]:
+				xmin = 5.84
+				xmax = 6.11
+				text = "become"
+			intervals [17]:
+				xmin = 6.11
+				xmax = 6.2
+				text = "a"
+			intervals [18]:
+				xmin = 6.2
+				xmax = 6.93
+				text = "journalist"
+			intervals [19]:
+				xmin = 6.93
+				xmax = 7.3
+				text = "cuz"
+			intervals [20]:
+				xmin = 7.3
+				xmax = 7.53
+				text = "this"
+			intervals [21]:
+				xmin = 7.53
+				xmax = 7.63
+				text = "is"
+			intervals [22]:
+				xmin = 7.63
+				xmax = 7.79
+				text = "my"
+			intervals [23]:
+				xmin = 7.79
+				xmax = 8.14
+				text = "dream"
+			intervals [24]:
+				xmin = 8.14
+				xmax = 8.53
+				text = "job"
+			intervals [25]:
+				xmin = 8.53
+				xmax = 8.71
+				text = "i've"
+			intervals [26]:
+				xmin = 8.71
+				xmax = 9.13
+				text = "always"
+			intervals [27]:
+				xmin = 9.13
+				xmax = 9.53
+				text = "wanted"
+			intervals [28]:
+				xmin = 9.53
+				xmax = 9.6
+				text = "to"
+			intervals [29]:
+				xmin = 9.6
+				xmax = 9.77
+				text = "be"
+			intervals [30]:
+				xmin = 9.77
+				xmax = 9.84
+				text = "a"
+			intervals [31]:
+				xmin = 9.84
+				xmax = 10.29
+				text = "journalist"
+			intervals [32]:
+				xmin = 10.29
+				xmax = 10.48
+				text = "since"
+			intervals [33]:
+				xmin = 10.48
+				xmax = 10.54
+				text = "i"
+			intervals [34]:
+				xmin = 10.54
+				xmax = 10.71
+				text = "was"
+			intervals [35]:
+				xmin = 10.71
+				xmax = 10.78
+				text = "in"
+			intervals [36]:
+				xmin = 10.78
+				xmax = 11.01
+				text = "middle"
+			intervals [37]:
+				xmin = 11.01
+				xmax = 11.4
+				text = "school"
+			intervals [38]:
+				xmin = 11.4
+				xmax = 11.96
+				text = ""
+			intervals [39]:
+				xmin = 11.96
+				xmax = 12.92
+				text = "journalists"
+			intervals [40]:
+				xmin = 12.92
+				xmax = 13.73
+				text = "never"
+			intervals [41]:
+				xmin = 13.73
+				xmax = 13.81
+				text = ""
+			intervals [42]:
+				xmin = 13.81
+				xmax = 14.38
+				text = "tell"
+			intervals [43]:
+				xmin = 14.38
+				xmax = 15.08
+				text = "lies"
+			intervals [44]:
+				xmin = 15.08
+				xmax = 15.19
+				text = "and"
+			intervals [45]:
+				xmin = 15.19
+				xmax = 15.38
+				text = "are"
+			intervals [46]:
+				xmin = 15.38
+				xmax = 15.75
+				text = "always"
+			intervals [47]:
+				xmin = 15.75
+				xmax = 16.07
+				text = "seeking"
+			intervals [48]:
+				xmin = 16.07
+				xmax = 16.17
+				text = "the"
+			intervals [49]:
+				xmin = 16.17
+				xmax = 16.69
+				text = "truth"
+			intervals [50]:
+				xmin = 16.69
+				xmax = 16.85
+				text = ""
+			intervals [51]:
+				xmin = 16.85
+				xmax = 17.09
+				text = "i"
+			intervals [52]:
+				xmin = 17.09
+				xmax = 17.27
+				text = "want"
+			intervals [53]:
+				xmin = 17.27
+				xmax = 17.33
+				text = "to"
+			intervals [54]:
+				xmin = 17.33
+				xmax = 17.45
+				text = "be"
+			intervals [55]:
+				xmin = 17.45
+				xmax = 17.73
+				text = "just"
+			intervals [56]:
+				xmin = 17.73
+				xmax = 18.0
+				text = "like"
+			intervals [57]:
+				xmin = 18.0
+				xmax = 18.36
+				text = "that"
+			intervals [58]:
+				xmin = 18.36
+				xmax = 18.73
+				text = ""
+			intervals [59]:
+				xmin = 18.73
+				xmax = 19.54
+				text = "i"
+			intervals [60]:
+				xmin = 19.54
+				xmax = 19.71
+				text = ""
+			intervals [61]:
+				xmin = 19.71
+				xmax = 20.27
+				text = "usually"
+			intervals [62]:
+				xmin = 20.27
+				xmax = 20.52
+				text = "feel"
+			intervals [63]:
+				xmin = 20.52
+				xmax = 20.96
+				text = "shy"
+			intervals [64]:
+				xmin = 20.96
+				xmax = 21.14
+				text = "when"
+			intervals [65]:
+				xmin = 21.14
+				xmax = 21.26
+				text = "i"
+			intervals [66]:
+				xmin = 21.26
+				xmax = 21.38
+				text = "am"
+			intervals [67]:
+				xmin = 21.38
+				xmax = 21.75
+				text = "talking"
+			intervals [68]:
+				xmin = 21.75
+				xmax = 21.86
+				text = "to"
+			intervals [69]:
+				xmin = 21.86
+				xmax = 22.22
+				text = "others"
+			intervals [70]:
+				xmin = 22.22
+				xmax = 22.37
+				text = "and"
+			intervals [71]:
+				xmin = 22.37
+				xmax = 22.46
+				text = "i"
+			intervals [72]:
+				xmin = 22.46
+				xmax = 22.8
+				text = "know"
+			intervals [73]:
+				xmin = 22.8
+				xmax = 23.1
+				text = "that"
+			intervals [74]:
+				xmin = 23.1
+				xmax = 23.79
+				text = "journalists"
+			intervals [75]:
+				xmin = 23.79
+				xmax = 23.99
+				text = "are"
+			intervals [76]:
+				xmin = 23.99
+				xmax = 24.73
+				text = "very"
+			intervals [77]:
+				xmin = 24.73
+				xmax = 25.29
+				text = "good"
+			intervals [78]:
+				xmin = 25.29
+				xmax = 25.44
+				text = ""
+			intervals [79]:
+				xmin = 25.44
+				xmax = 25.7
+				text = "at"
+			intervals [80]:
+				xmin = 25.7
+				xmax = 26.41
+				text = "communicating"
+			intervals [81]:
+				xmin = 26.41
+				xmax = 26.94
+				text = "because"
+			intervals [82]:
+				xmin = 26.94
+				xmax = 26.98
+				text = ""
+			intervals [83]:
+				xmin = 26.98
+				xmax = 27.2
+				text = "good"
+			intervals [84]:
+				xmin = 27.2
+				xmax = 27.95
+				text = "communication"
+			intervals [85]:
+				xmin = 27.95
+				xmax = 28.36
+				text = "skills"
+			intervals [86]:
+				xmin = 28.36
+				xmax = 28.51
+				text = "are"
+			intervals [87]:
+				xmin = 28.51
+				xmax = 28.75
+				text = "very"
+			intervals [88]:
+				xmin = 28.75
+				xmax = 29.22
+				text = "important"
+			intervals [89]:
+				xmin = 29.22
+				xmax = 29.39
+				text = "when"
+			intervals [90]:
+				xmin = 29.39
+				xmax = 29.51
+				text = "you're"
+			intervals [91]:
+				xmin = 29.51
+				xmax = 29.85
+				text = "doing"
+			intervals [92]:
+				xmin = 29.85
+				xmax = 30.43
+				text = "interviews"
+			intervals [93]:
+				xmin = 30.43
+				xmax = 30.71
+				text = ""
+			intervals [94]:
+				xmin = 30.71
+				xmax = 30.99
+				text = "i"
+			intervals [95]:
+				xmin = 30.99
+				xmax = 31.21
+				text = "want"
+			intervals [96]:
+				xmin = 31.21
+				xmax = 31.3
+				text = "to"
+			intervals [97]:
+				xmin = 31.3
+				xmax = 31.71
+				text = "possess"
+			intervals [98]:
+				xmin = 31.71
+				xmax = 31.82
+				text = "the"
+			intervals [99]:
+				xmin = 31.82
+				xmax = 32.16
+				text = "skill"
+			intervals [100]:
+				xmin = 32.16
+				xmax = 32.93
+				text = "myself"
+			intervals [101]:
+				xmin = 32.93
+				xmax = 33.0
+				text = ""
+			intervals [102]:
+				xmin = 33.0
+				xmax = 33.53
+				text = "so"
+			intervals [103]:
+				xmin = 33.53
+				xmax = 33.56
+				text = ""
+			intervals [104]:
+				xmin = 33.56
+				xmax = 33.95
+				text = "that's"
+			intervals [105]:
+				xmin = 33.95
+				xmax = 34.31
+				text = "why"
+			intervals [106]:
+				xmin = 34.31
+				xmax = 34.53
+				text = "i"
+			intervals [107]:
+				xmin = 34.53
+				xmax = 34.83
+				text = "want"
+			intervals [108]:
+				xmin = 34.83
+				xmax = 34.89
+				text = "to"
+			intervals [109]:
+				xmin = 34.89
+				xmax = 35.42
+				text = "become"
+			intervals [110]:
+				xmin = 35.42
+				xmax = 35.46
+				text = ""
+			intervals [111]:
+				xmin = 35.46
+				xmax = 35.59
+				text = "a"
+			intervals [112]:
+				xmin = 35.59
+				xmax = 36.37
+				text = "journalist"
+			intervals [113]:
+				xmin = 36.37
+				xmax = 36.74
+				text = ""
+			intervals [114]:
+				xmin = 36.74
+				xmax = 37.02
+				text = "other"
+			intervals [115]:
+				xmin = 37.02
+				xmax = 37.18
+				text = "than"
+			intervals [116]:
+				xmin = 37.18
+				xmax = 37.73
+				text = "that"
+			intervals [117]:
+				xmin = 37.73
+				xmax = 37.76
+				text = ""
+			intervals [118]:
+				xmin = 37.76
+				xmax = 38.72
+				text = "photography"
+			intervals [119]:
+				xmin = 38.72
+				xmax = 38.96
+				text = ""
+			intervals [120]:
+				xmin = 38.96
+				xmax = 39.4
+				text = "often"
+			intervals [121]:
+				xmin = 39.4
+				xmax = 39.66
+				text = "makes"
+			intervals [122]:
+				xmin = 39.66
+				xmax = 39.8
+				text = "me"
+			intervals [123]:
+				xmin = 39.8
+				xmax = 40.39
+				text = "feel"
+			intervals [124]:
+				xmin = 40.39
+				xmax = 40.72
+				text = "like"
+			intervals [125]:
+				xmin = 40.72
+				xmax = 41.42
+				text = "i'm"
+			intervals [126]:
+				xmin = 41.42
+				xmax = 41.77
+				text = ""
+			intervals [127]:
+				xmin = 41.77
+				xmax = 42.06
+				text = "doing"
+			intervals [128]:
+				xmin = 42.06
+				xmax = 42.13
+				text = "a"
+			intervals [129]:
+				xmin = 42.13
+				xmax = 42.41
+				text = "job"
+			intervals [130]:
+				xmin = 42.41
+				xmax = 42.58
+				text = "full"
+			intervals [131]:
+				xmin = 42.58
+				xmax = 42.64
+				text = "of"
+			intervals [132]:
+				xmin = 42.64
+				xmax = 42.99
+				text = "design"
+			intervals [133]:
+				xmin = 42.99
+				xmax = 43.12
+				text = "and"
+			intervals [134]:
+				xmin = 43.12
+				xmax = 43.25
+				text = "in"
+			intervals [135]:
+				xmin = 43.25
+				xmax = 43.52
+				text = "for"
+			intervals [136]:
+				xmin = 43.52
+				xmax = 44.31
+				text = "innovation"
+			intervals [137]:
+				xmin = 44.31
+				xmax = 45.18
+				text = ""
+			intervals [138]:
+				xmin = 45.18
+				xmax = 45.73
+				text = "because"
+			intervals [139]:
+				xmin = 45.73
+				xmax = 45.89
+				text = "for"
+			intervals [140]:
+				xmin = 45.89
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+				text = "AH0"
+			intervals [121]:
+				xmin = 12.64
+				xmax = 12.83
+				text = "S"
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+				xmin = 12.83
+				xmax = 12.86
+				text = "T"
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+				xmin = 12.86
+				xmax = 12.92
+				text = "S"
+			intervals [124]:
+				xmin = 12.92
+				xmax = 13.14
+				text = "N"
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+				xmin = 13.14
+				xmax = 13.34
+				text = "EH1"
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+				xmin = 13.34
+				xmax = 13.46
+				text = "V"
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+				xmin = 13.46
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+				text = "ER0"
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+				xmin = 13.73
+				xmax = 13.81
+				text = ""
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+				xmin = 13.81
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+				xmin = 14.17
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+				text = "L"
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+				xmin = 14.38
+				xmax = 14.43
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+				xmin = 14.43
+				xmax = 14.95
+				text = "AY1"
+			intervals [134]:
+				xmin = 14.95
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+				xmin = 15.08
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+				text = "AE1"
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+				xmin = 15.13
+				xmax = 15.16
+				text = "N"
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+				xmin = 15.16
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+				xmin = 15.19
+				xmax = 15.38
+				text = "ER0"
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+				xmin = 15.38
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+				text = "AO1"
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+				xmin = 15.43
+				xmax = 15.48
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+				xmin = 15.48
+				xmax = 15.57
+				text = "W"
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+				xmin = 15.57
+				xmax = 15.61
+				text = "IY0"
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+				xmin = 15.61
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+				text = "Z"
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+				xmin = 15.75
+				xmax = 15.8
+				text = "S"
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+				xmin = 15.8
+				xmax = 15.89
+				text = "IY1"
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+				xmin = 15.89
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+				xmin = 15.96
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+				text = "IH0"
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+				text = "NG"
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+				xmin = 16.07
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+				xmin = 16.12
+				xmax = 16.17
+				text = "AH0"
+			intervals [151]:
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+				text = "T"
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+				xmin = 16.3
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+				text = "R"
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+				xmin = 16.36
+				xmax = 16.48
+				text = "UW1"
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+				xmin = 16.48
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+				xmin = 16.69
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+				text = ""
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+				xmin = 16.85
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+				text = "AA1"
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+				xmin = 17.21
+				xmax = 17.24
+				text = "N"
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+				xmin = 17.24
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+				text = "T"
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+				xmin = 17.27
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+				text = "T"
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+				xmin = 17.3
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+				text = "AH0"
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+				xmin = 17.33
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+				text = "B"
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+				xmin = 17.37
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+				text = "IY0"
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+				xmin = 17.45
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+				text = "JH"
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+				xmin = 17.57
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+				text = "IH0"
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+				xmin = 17.64
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+				text = "S"
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+				xmin = 17.7
+				xmax = 17.73
+				text = "T"
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+				xmin = 17.73
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+				text = "L"
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+				xmin = 17.81
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+				text = "AY1"
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+				xmin = 17.91
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+				xmin = 18.36
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+				text = ""
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+				text = "AY1"
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+				xmin = 19.54
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+				text = ""
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+				text = "AY1"
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+				xmin = 21.57
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+				text = "IH0"
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+				text = "T"
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+				text = "AH0"
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+				text = "AH1"
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+				text = "DH"
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+				text = "ER0"
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+				text = "AE1"
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+				text = "N"
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+				text = "D"
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+				xmin = 22.37
+				xmax = 22.46
+				text = "AY1"
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+				xmin = 22.46
+				xmax = 22.6
+				text = "N"
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+				text = "OW1"
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+				xmax = 22.85
+				text = "DH"
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+				xmin = 22.85
+				xmax = 22.98
+				text = "AH0"
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+				xmax = 23.1
+				text = "T"
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+				xmax = 23.27
+				text = "JH"
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+				xmin = 23.27
+				xmax = 23.35
+				text = "ER1"
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+				xmin = 23.35
+				xmax = 23.39
+				text = "N"
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+				xmin = 23.39
+				xmax = 23.46
+				text = "AH0"
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+				xmin = 23.46
+				xmax = 23.54
+				text = "L"
+			intervals [220]:
+				xmin = 23.54
+				xmax = 23.61
+				text = "AH0"
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+				xmax = 23.69
+				text = "S"
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+				xmin = 23.69
+				xmax = 23.72
+				text = "T"
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+				xmin = 23.72
+				xmax = 23.79
+				text = "S"
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+				xmin = 23.79
+				xmax = 23.89
+				text = "AA1"
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+				text = "EH1"
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+				xmin = 24.49
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+				text = "R"
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+				xmin = 24.64
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+				text = "IY0"
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+				text = "G"
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+				text = "UH1"
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+				text = "D"
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+				text = ""
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+				xmax = 25.61
+				text = "AE1"
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+				xmin = 25.61
+				xmax = 25.7
+				text = "T"
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+				xmin = 25.7
+				xmax = 25.73
+				text = "K"
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+				text = "AH0"
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+				text = "M"
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+				xmin = 25.83
+				xmax = 25.89
+				text = "Y"
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+				xmin = 25.89
+				xmax = 25.93
+				text = "UW1"
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+				xmin = 25.93
+				xmax = 25.98
+				text = "N"
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+				xmin = 25.98
+				xmax = 26.05
+				text = "AH0"
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+				xmin = 26.05
+				xmax = 26.19
+				text = "K"
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+				xmin = 26.19
+				xmax = 26.25
+				text = "EY2"
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+				xmin = 26.25
+				xmax = 26.28
+				text = "T"
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+				text = "IH0"
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+				xmin = 26.35
+				xmax = 26.41
+				text = "NG"
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+				xmin = 26.41
+				xmax = 26.46
+				text = "B"
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+				xmin = 26.46
+				xmax = 26.54
+				text = "IH0"
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+				xmin = 26.54
+				xmax = 26.64
+				text = "K"
+			intervals [251]:
+				xmin = 26.64
+				xmax = 26.84
+				text = "AH0"
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+				xmin = 26.84
+				xmax = 26.94
+				text = "Z"
+			intervals [253]:
+				xmin = 26.94
+				xmax = 26.98
+				text = ""
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+				xmin = 26.98
+				xmax = 27.07
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+				xmin = 27.07
+				xmax = 27.15
+				text = "UH1"
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+				xmax = 27.2
+				text = "D"
+			intervals [257]:
+				xmin = 27.2
+				xmax = 27.27
+				text = "K"
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+				text = "AH0"
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+				text = "M"
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+				xmin = 27.38
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+				text = "Y"
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+				xmax = 27.46
+				text = "UW2"
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+				xmin = 27.46
+				xmax = 27.49
+				text = "N"
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+				xmin = 27.49
+				xmax = 27.54
+				text = "AH0"
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+				xmax = 27.65
+				text = "K"
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+				xmin = 27.65
+				xmax = 27.75
+				text = "EY1"
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+				xmin = 27.75
+				xmax = 27.84
+				text = "SH"
+			intervals [267]:
+				xmin = 27.84
+				xmax = 27.89
+				text = "AH0"
+			intervals [268]:
+				xmin = 27.89
+				xmax = 27.95
+				text = "N"
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+				xmin = 27.95
+				xmax = 28.03
+				text = "S"
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+				xmin = 28.03
+				xmax = 28.08
+				text = "K"
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+				xmin = 28.08
+				xmax = 28.15
+				text = "IH1"
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+				xmin = 28.15
+				xmax = 28.27
+				text = "L"
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+				xmax = 28.36
+				text = "Z"
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+				xmax = 28.42
+				text = "AA1"
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+				xmax = 28.51
+				text = "R"
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+				xmax = 28.61
+				text = "EH1"
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+				xmin = 28.61
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+				xmin = 28.72
+				xmax = 28.75
+				text = "IY0"
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+				xmin = 28.75
+				xmax = 28.8
+				text = "IH0"
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+				xmin = 28.8
+				xmax = 28.86
+				text = "M"
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+				xmax = 29.01
+				text = "AO1"
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+				text = "R"
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+				text = "AH0"
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+				xmax = 29.18
+				text = "N"
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+				xmin = 29.18
+				xmax = 29.22
+				text = "T"
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+				text = "W"
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+				xmax = 29.32
+				text = "EH1"
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+				xmax = 29.39
+				text = "N"
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+				xmin = 29.39
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+				text = "Y"
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+				xmax = 29.46
+				text = "UH1"
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+				xmin = 29.46
+				xmax = 29.51
+				text = "R"
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+				xmin = 29.51
+				xmax = 29.6
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+				xmax = 29.7
+				text = "UW1"
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+				xmax = 29.76
+				text = "IH0"
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+				xmax = 29.85
+				text = "NG"
+			intervals [299]:
+				xmin = 29.85
+				xmax = 29.88
+				text = "IH1"
+			intervals [300]:
+				xmin = 29.88
+				xmax = 29.99
+				text = "N"
+			intervals [301]:
+				xmin = 29.99
+				xmax = 30.06
+				text = "ER0"
+			intervals [302]:
+				xmin = 30.06
+				xmax = 30.1
+				text = "V"
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+				xmin = 30.1
+				xmax = 30.21
+				text = "Y"
+			intervals [304]:
+				xmin = 30.21
+				xmax = 30.28
+				text = "UW2"
+			intervals [305]:
+				xmin = 30.28
+				xmax = 30.43
+				text = "Z"
+			intervals [306]:
+				xmin = 30.43
+				xmax = 30.71
+				text = ""
+			intervals [307]:
+				xmin = 30.71
+				xmax = 30.99
+				text = "AY1"
+			intervals [308]:
+				xmin = 30.99
+				xmax = 31.11
+				text = "W"
+			intervals [309]:
+				xmin = 31.11
+				xmax = 31.15
+				text = "AA1"
+			intervals [310]:
+				xmin = 31.15
+				xmax = 31.18
+				text = "N"
+			intervals [311]:
+				xmin = 31.18
+				xmax = 31.21
+				text = "T"
+			intervals [312]:
+				xmin = 31.21
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+			intervals [697]:
+				xmin = 66.7
+				xmax = 66.76
+				text = "AH1"
+			intervals [698]:
+				xmin = 66.76
+				xmax = 66.82
+				text = "DH"
+			intervals [699]:
+				xmin = 66.82
+				xmax = 66.95
+				text = "ER0"
+			intervals [700]:
+				xmin = 66.95
+				xmax = 67.19
+				text = "Z"
+			intervals [701]:
+				xmin = 67.19
+				xmax = 68
+				text = ""

EMAGE/test_sequences/textgrid/2_scott_0_4_4.TextGrid

@@ -0,0 +1,3844 @@
+File type = "ooTextFile"
+Object class = "TextGrid"
+
+xmin = 0.0
+xmax = 67
+tiers? <exists>
+size = 2
+item []:
+	item [1]:
+		class = "IntervalTier"
+		name = "words"
+		xmin = 0.0
+		xmax = 67
+		intervals: size = 235
+			intervals [1]:
+				xmin = 0.0
+				xmax = 0.53
+				text = ""
+			intervals [2]:
+				xmin = 0.53
+				xmax = 0.93
+				text = "my"
+			intervals [3]:
+				xmin = 0.93
+				xmax = 1.34
+				text = "favorite"
+			intervals [4]:
+				xmin = 1.34
+				xmax = 1.57
+				text = "kind"
+			intervals [5]:
+				xmin = 1.57
+				xmax = 1.65
+				text = "of"
+			intervals [6]:
+				xmin = 1.65
+				xmax = 2.2
+				text = "movies"
+			intervals [7]:
+				xmin = 2.2
+				xmax = 2.45
+				text = "are"
+			intervals [8]:
+				xmin = 2.45
+				xmax = 3.2
+				text = "romantic"
+			intervals [9]:
+				xmin = 3.2
+				xmax = 3.72
+				text = "movies"
+			intervals [10]:
+				xmin = 3.72
+				xmax = 3.75
+				text = ""
+			intervals [11]:
+				xmin = 3.75
+				xmax = 4.1
+				text = "such"
+			intervals [12]:
+				xmin = 4.1
+				xmax = 4.33
+				text = "as"
+			intervals [13]:
+				xmin = 4.33
+				xmax = 5.23
+				text = "titanic"
+			intervals [14]:
+				xmin = 5.23
+				xmax = 5.78
+				text = ""
+			intervals [15]:
+				xmin = 5.78
+				xmax = 6.19
+				text = "it's"
+			intervals [16]:
+				xmin = 6.19
+				xmax = 6.46
+				text = "a"
+			intervals [17]:
+				xmin = 6.46
+				xmax = 6.49
+				text = ""
+			intervals [18]:
+				xmin = 6.49
+				xmax = 7.13
+				text = "fantastic"
+			intervals [19]:
+				xmin = 7.13
+				xmax = 7.56
+				text = "film"
+			intervals [20]:
+				xmin = 7.56
+				xmax = 7.77
+				text = "it"
+			intervals [21]:
+				xmin = 7.77
+				xmax = 8.28
+				text = "captured"
+			intervals [22]:
+				xmin = 8.28
+				xmax = 8.73
+				text = "many"
+			intervals [23]:
+				xmin = 8.73
+				xmax = 9.1
+				text = "young"
+			intervals [24]:
+				xmin = 9.1
+				xmax = 9.44
+				text = "people's"
+			intervals [25]:
+				xmin = 9.44
+				xmax = 9.79
+				text = "hearts"
+			intervals [26]:
+				xmin = 9.79
+				xmax = 10.02
+				text = "with"
+			intervals [27]:
+				xmin = 10.02
+				xmax = 10.17
+				text = "it's"
+			intervals [28]:
+				xmin = 10.17
+				xmax = 10.65
+				text = "amazing"
+			intervals [29]:
+				xmin = 10.65
+				xmax = 11.12
+				text = "music"
+			intervals [30]:
+				xmin = 11.12
+				xmax = 11.36
+				text = "and"
+			intervals [31]:
+				xmin = 11.36
+				xmax = 11.92
+				text = "sentimental"
+			intervals [32]:
+				xmin = 11.92
+				xmax = 12.47
+				text = "plots"
+			intervals [33]:
+				xmin = 12.47
+				xmax = 12.84
+				text = ""
+			intervals [34]:
+				xmin = 12.84
+				xmax = 12.98
+				text = "when"
+			intervals [35]:
+				xmin = 12.98
+				xmax = 13.12
+				text = "i"
+			intervals [36]:
+				xmin = 13.12
+				xmax = 13.28
+				text = "think"
+			intervals [37]:
+				xmin = 13.28
+				xmax = 13.35
+				text = "of"
+			intervals [38]:
+				xmin = 13.35
+				xmax = 13.42
+				text = "the"
+			intervals [39]:
+				xmin = 13.42
+				xmax = 13.62
+				text = "movie"
+			intervals [40]:
+				xmin = 13.62
+				xmax = 14.2
+				text = "titanic"
+			intervals [41]:
+				xmin = 14.2
+				xmax = 14.23
+				text = ""
+			intervals [42]:
+				xmin = 14.23
+				xmax = 14.42
+				text = "the"
+			intervals [43]:
+				xmin = 14.42
+				xmax = 14.92
+				text = "word"
+			intervals [44]:
+				xmin = 14.92
+				xmax = 15.06
+				text = "that"
+			intervals [45]:
+				xmin = 15.06
+				xmax = 15.3
+				text = "comes"
+			intervals [46]:
+				xmin = 15.3
+				xmax = 15.39
+				text = "to"
+			intervals [47]:
+				xmin = 15.39
+				xmax = 15.5
+				text = "my"
+			intervals [48]:
+				xmin = 15.5
+				xmax = 15.91
+				text = "mind"
+			intervals [49]:
+				xmin = 15.91
+				xmax = 16.06
+				text = "mind"
+			intervals [50]:
+				xmin = 16.06
+				xmax = 16.41
+				text = ""
+			intervals [51]:
+				xmin = 16.41
+				xmax = 16.6
+				text = "to"
+			intervals [52]:
+				xmin = 16.6
+				xmax = 17.07
+				text = "mises"
+			intervals [53]:
+				xmin = 17.07
+				xmax = 17.15
+				text = "the"
+			intervals [54]:
+				xmin = 17.15
+				xmax = 17.39
+				text = "whole"
+			intervals [55]:
+				xmin = 17.39
+				xmax = 17.94
+				text = "film"
+			intervals [56]:
+				xmin = 17.94
+				xmax = 17.97
+				text = ""
+			intervals [57]:
+				xmin = 17.97
+				xmax = 18.18
+				text = "would"
+			intervals [58]:
+				xmin = 18.18
+				xmax = 18.62
+				text = "be"
+			intervals [59]:
+				xmin = 18.62
+				xmax = 19.09
+				text = ""
+			intervals [60]:
+				xmin = 19.09
+				xmax = 19.94
+				text = "love"
+			intervals [61]:
+				xmin = 19.94
+				xmax = 20.07
+				text = ""
+			intervals [62]:
+				xmin = 20.07
+				xmax = 20.27
+				text = "it's"
+			intervals [63]:
+				xmin = 20.27
+				xmax = 20.36
+				text = "a"
+			intervals [64]:
+				xmin = 20.36
+				xmax = 20.83
+				text = "kind"
+			intervals [65]:
+				xmin = 20.83
+				xmax = 20.98
+				text = "of"
+			intervals [66]:
+				xmin = 20.98
+				xmax = 21.25
+				text = "thing"
+			intervals [67]:
+				xmin = 21.25
+				xmax = 21.43
+				text = "that"
+			intervals [68]:
+				xmin = 21.43
+				xmax = 21.8
+				text = "makes"
+			intervals [69]:
+				xmin = 21.8
+				xmax = 22.28
+				text = "you"
+			intervals [70]:
+				xmin = 22.28
+				xmax = 22.31
+				text = ""
+			intervals [71]:
+				xmin = 22.31
+				xmax = 22.8
+				text = "makes"
+			intervals [72]:
+				xmin = 22.8
+				xmax = 22.91
+				text = "the"
+			intervals [73]:
+				xmin = 22.91
+				xmax = 23.21
+				text = "world"
+			intervals [74]:
+				xmin = 23.21
+				xmax = 23.38
+				text = "go"
+			intervals [75]:
+				xmin = 23.38
+				xmax = 23.87
+				text = "round"
+			intervals [76]:
+				xmin = 23.87
+				xmax = 24.08
+				text = ""
+			intervals [77]:
+				xmin = 24.08
+				xmax = 24.6
+				text = "watching"
+			intervals [78]:
+				xmin = 24.6
+				xmax = 24.8
+				text = "these"
+			intervals [79]:
+				xmin = 24.8
+				xmax = 25.18
+				text = "kinds"
+			intervals [80]:
+				xmin = 25.18
+				xmax = 25.29
+				text = "of"
+			intervals [81]:
+				xmin = 25.29
+				xmax = 25.83
+				text = "romantic"
+			intervals [82]:
+				xmin = 25.83
+				xmax = 26.23
+				text = "movies"
+			intervals [83]:
+				xmin = 26.23
+				xmax = 26.43
+				text = "is"
+			intervals [84]:
+				xmin = 26.43
+				xmax = 26.86
+				text = "just"
+			intervals [85]:
+				xmin = 26.86
+				xmax = 27.07
+				text = "like"
+			intervals [86]:
+				xmin = 27.07
+				xmax = 27.49
+				text = "reading"
+			intervals [87]:
+				xmin = 27.49
+				xmax = 27.56
+				text = "a"
+			intervals [88]:
+				xmin = 27.56
+				xmax = 27.98
+				text = "book"
+			intervals [89]:
+				xmin = 27.98
+				xmax = 28.11
+				text = ""
+			intervals [90]:
+				xmin = 28.11
+				xmax = 28.29
+				text = "that"
+			intervals [91]:
+				xmin = 28.29
+				xmax = 28.65
+				text = "teaches"
+			intervals [92]:
+				xmin = 28.65
+				xmax = 28.78
+				text = "me"
+			intervals [93]:
+				xmin = 28.78
+				xmax = 28.99
+				text = "how"
+			intervals [94]:
+				xmin = 28.99
+				xmax = 29.19
+				text = "to"
+			intervals [95]:
+				xmin = 29.19
+				xmax = 29.61
+				text = "love"
+			intervals [96]:
+				xmin = 29.61
+				xmax = 29.93
+				text = "and"
+			intervals [97]:
+				xmin = 29.93
+				xmax = 30.09
+				text = "be"
+			intervals [98]:
+				xmin = 30.09
+				xmax = 30.53
+				text = "loved"
+			intervals [99]:
+				xmin = 30.53
+				xmax = 30.96
+				text = ""
+			intervals [100]:
+				xmin = 30.96
+				xmax = 31.68
+				text = "moreover"
+			intervals [101]:
+				xmin = 31.68
+				xmax = 31.81
+				text = "we"
+			intervals [102]:
+				xmin = 31.81
+				xmax = 32.01
+				text = ""
+			intervals [103]:
+				xmin = 32.01
+				xmax = 32.51
+				text = "can"
+			intervals [104]:
+				xmin = 32.51
+				xmax = 32.56
+				text = ""
+			intervals [105]:
+				xmin = 32.56
+				xmax = 32.72
+				text = "learn"
+			intervals [106]:
+				xmin = 32.72
+				xmax = 33.09
+				text = "we"
+			intervals [107]:
+				xmin = 33.09
+				xmax = 33.25
+				text = "can"
+			intervals [108]:
+				xmin = 33.25
+				xmax = 34.05
+				text = "learn"
+			intervals [109]:
+				xmin = 34.05
+				xmax = 34.2
+				text = ""
+			intervals [110]:
+				xmin = 34.2
+				xmax = 35.12
+				text = "more"
+			intervals [111]:
+				xmin = 35.12
+				xmax = 35.44
+				text = "from"
+			intervals [112]:
+				xmin = 35.44
+				xmax = 35.66
+				text = "it"
+			intervals [113]:
+				xmin = 35.66
+				xmax = 35.98
+				text = "such"
+			intervals [114]:
+				xmin = 35.98
+				xmax = 36.35
+				text = "things"
+			intervals [115]:
+				xmin = 36.35
+				xmax = 36.69
+				text = "as"
+			intervals [116]:
+				xmin = 36.69
+				xmax = 36.89
+				text = ""
+			intervals [117]:
+				xmin = 36.89
+				xmax = 37.59
+				text = "loyalty"
+			intervals [118]:
+				xmin = 37.59
+				xmax = 37.76
+				text = "and"
+			intervals [119]:
+				xmin = 37.76
+				xmax = 37.88
+				text = "what"
+			intervals [120]:
+				xmin = 37.88
+				xmax = 37.99
+				text = "we"
+			intervals [121]:
+				xmin = 37.99
+				xmax = 38.47
+				text = "treasure"
+			intervals [122]:
+				xmin = 38.47
+				xmax = 38.58
+				text = "in"
+			intervals [123]:
+				xmin = 38.58
+				xmax = 38.71
+				text = "our"
+			intervals [124]:
+				xmin = 38.71
+				xmax = 39.11
+				text = "lives"
+			intervals [125]:
+				xmin = 39.11
+				xmax = 39.4
+				text = ""
+			intervals [126]:
+				xmin = 39.4
+				xmax = 39.8
+				text = "another"
+			intervals [127]:
+				xmin = 39.8
+				xmax = 40.13
+				text = "movie"
+			intervals [128]:
+				xmin = 40.13
+				xmax = 40.51
+				text = "about"
+			intervals [129]:
+				xmin = 40.51
+				xmax = 40.83
+				text = "love"
+			intervals [130]:
+				xmin = 40.83
+				xmax = 41.08
+				text = "is"
+			intervals [131]:
+				xmin = 41.08
+				xmax = 41.24
+				text = "the"
+			intervals [132]:
+				xmin = 41.24
+				xmax = 41.3
+				text = ""
+			intervals [133]:
+				xmin = 41.3
+				xmax = 41.9
+				text = "secret"
+			intervals [134]:
+				xmin = 41.9
+				xmax = 42.13
+				text = ""
+			intervals [135]:
+				xmin = 42.13
+				xmax = 42.47
+				text = "the"
+			intervals [136]:
+				xmin = 42.47
+				xmax = 43.01
+				text = "movie"
+			intervals [137]:
+				xmin = 43.01
+				xmax = 43.58
+				text = "secret"
+			intervals [138]:
+				xmin = 43.58
+				xmax = 43.71
+				text = "is"
+			intervals [139]:
+				xmin = 43.71
+				xmax = 44.1
+				text = "about"
+			intervals [140]:
+				xmin = 44.1
+				xmax = 44.15
+				text = "a"
+			intervals [141]:
+				xmin = 44.15
+				xmax = 44.88
+				text = "story"
+			intervals [142]:
+				xmin = 44.88
+				xmax = 44.95
+				text = ""
+			intervals [143]:
+				xmin = 44.95
+				xmax = 45.14
+				text = "of"
+			intervals [144]:
+				xmin = 45.14
+				xmax = 45.21
+				text = "a"
+			intervals [145]:
+				xmin = 45.21
+				xmax = 45.56
+				text = "musical"
+			intervals [146]:
+				xmin = 45.56
+				xmax = 45.96
+				text = "prodigy"
+			intervals [147]:
+				xmin = 45.96
+				xmax = 46.04
+				text = "do"
+			intervals [148]:
+				xmin = 46.04
+				xmax = 46.17
+				text = "that"
+			intervals [149]:
+				xmin = 46.17
+				xmax = 46.42
+				text = "falls"
+			intervals [150]:
+				xmin = 46.42
+				xmax = 46.49
+				text = "in"
+			intervals [151]:
+				xmin = 46.49
+				xmax = 46.62
+				text = "love"
+			intervals [152]:
+				xmin = 46.62
+				xmax = 46.72
+				text = "with"
+			intervals [153]:
+				xmin = 46.72
+				xmax = 46.85
+				text = "a"
+			intervals [154]:
+				xmin = 46.85
+				xmax = 46.91
+				text = ""
+			intervals [155]:
+				xmin = 46.91
+				xmax = 47.21
+				text = "girl"
+			intervals [156]:
+				xmin = 47.21
+				xmax = 47.46
+				text = "who's"
+			intervals [157]:
+				xmin = 47.46
+				xmax = 48.01
+				text = "dying"
+			intervals [158]:
+				xmin = 48.01
+				xmax = 49.08
+				text = ""
+			intervals [159]:
+				xmin = 49.08
+				xmax = 49.33
+				text = "there"
+			intervals [160]:
+				xmin = 49.33
+				xmax = 49.39
+				text = "are"
+			intervals [161]:
+				xmin = 49.39
+				xmax = 49.46
+				text = "a"
+			intervals [162]:
+				xmin = 49.46
+				xmax = 49.82
+				text = "lot"
+			intervals [163]:
+				xmin = 49.82
+				xmax = 50.2
+				text = "of"
+			intervals [164]:
+				xmin = 50.2
+				xmax = 50.29
+				text = ""
+			intervals [165]:
+				xmin = 50.29
+				xmax = 50.88
+				text = "enviable"
+			intervals [166]:
+				xmin = 50.88
+				xmax = 51.3
+				text = "moments"
+			intervals [167]:
+				xmin = 51.3
+				xmax = 51.37
+				text = "in"
+			intervals [168]:
+				xmin = 51.37
+				xmax = 51.53
+				text = "this"
+			intervals [169]:
+				xmin = 51.53
+				xmax = 51.77
+				text = "film"
+			intervals [170]:
+				xmin = 51.77
+				xmax = 52.01
+				text = "such"
+			intervals [171]:
+				xmin = 52.01
+				xmax = 52.2
+				text = "as"
+			intervals [172]:
+				xmin = 52.2
+				xmax = 52.3
+				text = "the"
+			intervals [173]:
+				xmin = 52.3
+				xmax = 52.57
+				text = "simple"
+			intervals [174]:
+				xmin = 52.57
+				xmax = 52.74
+				text = "love"
+			intervals [175]:
+				xmin = 52.74
+				xmax = 53.06
+				text = "between"
+			intervals [176]:
+				xmin = 53.06
+				xmax = 53.26
+				text = "high"
+			intervals [177]:
+				xmin = 53.26
+				xmax = 53.52
+				text = "school"
+			intervals [178]:
+				xmin = 53.52
+				xmax = 54.09
+				text = "students"
+			intervals [179]:
+				xmin = 54.09
+				xmax = 54.32
+				text = ""
+			intervals [180]:
+				xmin = 54.32
+				xmax = 54.56
+				text = "every"
+			intervals [181]:
+				xmin = 54.56
+				xmax = 54.93
+				text = "time"
+			intervals [182]:
+				xmin = 54.93
+				xmax = 54.96
+				text = ""
+			intervals [183]:
+				xmin = 54.96
+				xmax = 55.08
+				text = "i"
+			intervals [184]:
+				xmin = 55.08
+				xmax = 55.45
+				text = "watch"
+			intervals [185]:
+				xmin = 55.45
+				xmax = 55.68
+				text = "this"
+			intervals [186]:
+				xmin = 55.68
+				xmax = 55.89
+				text = "movie"
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+				xmin = 55.89
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+				xmax = 28.65
+				text = "Z"
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+				xmax = 28.69
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+				xmin = 28.69
+				xmax = 28.78
+				text = "IY1"
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+				xmin = 28.78
+				xmax = 28.91
+				text = "HH"
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+				xmax = 28.99
+				text = "AW1"
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+				xmin = 28.99
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+				text = "AH0"
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+				text = "AH1"
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+				xmax = 29.86
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+				xmax = 29.97
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+				xmax = 32.68
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+				xmax = 32.72
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EMAGE/test_sequences/weights/AESKConv_240_100.bin

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EMAGE/test_sequences/weights/mean_vel_smplxflame_30.npy

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EMAGE/test_sequences/weights/vocab.pkl

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+size 13821361

README.md

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+---
+title: EMAGE
+emoji: ⚡
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 4.24.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+This is a demo is a audio-only version of the approach described in the paper, ["EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling"](https://arxiv.org/abs/2401.00374)
+
+```
+@misc{liu2023emage,
+      title={EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling}, 
+      author={Liu, Haiyang and Zhu, Zihao and Becherini, Giorgio and Peng, Yichen and Su, Mingyang and Zhou, You and Zhe, Xuefei and Iwamoto, Naoya and Zheng, Bo and Black, Michael J},
+      year={2023},
+      eprint={2401.00374},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

ae_trainer.py

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+import train
+import os
+import time
+import csv
+import sys
+import warnings
+import random
+import numpy as np
+import time
+import pprint
+import pickle
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+from torch.nn.parallel import DistributedDataParallel as DDP
+from loguru import logger
+import smplx
+
+from utils import config, logger_tools, other_tools, metric
+from utils import rotation_conversions as rc
+from dataloaders import data_tools
+from optimizers.optim_factory import create_optimizer
+from optimizers.scheduler_factory import create_scheduler
+from optimizers.loss_factory import get_loss_func
+from scipy.spatial.transform import Rotation
+
+
+class CustomTrainer(train.BaseTrainer):
+    """
+    motion representation learning
+    """
+    def __init__(self, args):
+        super().__init__(args)
+        self.joints = self.train_data.joints
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        self.tracker = other_tools.EpochTracker(["rec", "vel", "ver", "com", "kl", "acc"], [False, False, False, False, False, False])
+        if not self.args.rot6d: #"rot6d" not in args.pose_rep:
+            logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}")
+        self.rec_loss = get_loss_func("GeodesicLoss")
+        self.vel_loss = torch.nn.L1Loss(reduction='mean')
+        self.vectices_loss = torch.nn.MSELoss(reduction='mean')
+    
+    def inverse_selection(self, filtered_t, selection_array, n):
+        # 创建一个全为零的数组，形状为 n*165
+        original_shape_t = np.zeros((n, selection_array.size))
+        
+        # 找到选择数组中为1的索引位置
+        selected_indices = np.where(selection_array == 1)[0]
+        
+        # 将 filtered_t 的值填充到 original_shape_t 中相应的位置
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+            
+        return original_shape_t
+    
+    def inverse_selection_tensor(self, filtered_t, selection_array, n):
+    # 创建一个全为零的数组，形状为 n*165
+        selection_array = torch.from_numpy(selection_array).cuda()
+        original_shape_t = torch.zeros((n, 165)).cuda()
+        
+        # 找到选择数组中为1的索引位置
+        selected_indices = torch.where(selection_array == 1)[0]
+        
+        # 将 filtered_t 的值填充到 original_shape_t 中相应的位置
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+            
+        return original_shape_t
+
+    def train(self, epoch):
+        self.model.train()
+        t_start = time.time()
+        self.tracker.reset()
+        for its, dict_data in enumerate(self.train_loader):
+            tar_pose = dict_data["pose"]
+            tar_beta = dict_data["beta"].cuda()
+            tar_trans = dict_data["trans"].cuda()
+            tar_pose = tar_pose.cuda()  
+            bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+            tar_exps = torch.zeros((bs, n, 100)).cuda()
+            tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+            tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+            t_data = time.time() - t_start 
+            
+            self.opt.zero_grad()
+            g_loss_final = 0
+            net_out = self.model(tar_pose)
+            rec_pose = net_out["rec_pose"]
+            rec_pose = rec_pose.reshape(bs, n, j, 6)
+            rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+            tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+            loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+            self.tracker.update_meter("rec", "train", loss_rec.item())
+            g_loss_final += loss_rec
+
+            velocity_loss =  self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight
+            acceleration_loss =  self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight
+            self.tracker.update_meter("vel", "train", velocity_loss.item())
+            self.tracker.update_meter("acc", "train", acceleration_loss.item())
+            g_loss_final += velocity_loss 
+            g_loss_final += acceleration_loss 
+             # vertices loss
+            if self.args.rec_ver_weight > 0:
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
+                tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
+                vertices_rec = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=tar_trans.reshape(bs*n, 3), 
+                    expression=tar_exps.reshape(bs*n, 100), 
+                    jaw_pose=rec_pose[:, 66:69], 
+                    global_orient=rec_pose[:,:3], 
+                    body_pose=rec_pose[:,3:21*3+3], 
+                    left_hand_pose=rec_pose[:,25*3:40*3], 
+                    right_hand_pose=rec_pose[:,40*3:55*3], 
+                    return_verts=True,
+                    return_joints=True,
+                    leye_pose=tar_pose[:, 69:72], 
+                    reye_pose=tar_pose[:, 72:75],
+                )
+                vertices_tar = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=tar_trans.reshape(bs*n, 3), 
+                    expression=tar_exps.reshape(bs*n, 100), 
+                    jaw_pose=tar_pose[:, 66:69], 
+                    global_orient=tar_pose[:,:3], 
+                    body_pose=tar_pose[:,3:21*3+3], 
+                    left_hand_pose=tar_pose[:,25*3:40*3], 
+                    right_hand_pose=tar_pose[:,40*3:55*3], 
+                    return_verts=True,
+                    return_joints=True,
+                    leye_pose=tar_pose[:, 69:72], 
+                    reye_pose=tar_pose[:, 72:75],
+                )  
+                vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices'])
+                self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight
+
+                vertices_vel_loss = self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight
+                vertices_acc_loss = self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight
+                g_loss_final += vertices_vel_loss * self.args.rec_weight * self.args.rec_ver_weight
+                g_loss_final += vertices_acc_loss * self.args.rec_weight * self.args.rec_ver_weight 
+            
+            # if self.args.vel_weight > 0:  
+            #     pos_rec_vel = other_tools.estimate_linear_velocity(vertices_rec['joints'], 1/self.pose_fps)
+            #     pos_tar_vel = other_tools.estimate_linear_velocity(vertices_tar['joints'], 1/self.pose_fps)
+            #     vel_rec_loss = self.vel_loss(pos_rec_vel, pos_tar_vel)
+            #     tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+            #     rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3))
+            #     rot_rec_vel = other_tools.estimate_angular_velocity(rec_pose, 1/self.pose_fps)
+            #     rot_tar_vel = other_tools.estimate_angular_velocity(tar_pose, 1/self.pose_fps)
+            #     vel_rec_loss += self.vel_loss(pos_rec_vel, pos_tar_vel)
+            #     self.tracker.update_meter("vel", "train", vel_rec_loss.item()*self.args.vel_weight)
+            #     loss += (vel_rec_loss*self.args.vel_weight)
+
+            # ---------------------- vae -------------------------- #
+            if "VQVAE" in self.args.g_name:
+                loss_embedding = net_out["embedding_loss"]
+                g_loss_final += loss_embedding
+                self.tracker.update_meter("com", "train", loss_embedding.item())
+            # elif "VAE" in self.args.g_name:
+            #     pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] 
+            #     KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp())
+            #     if epoch < 0:
+            #         KLD_weight = 0
+            #     else:
+            #         KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01
+            #     loss += KLD_weight * KLD
+            #     self.tracker.update_meter("kl", "train", KLD_weight * KLD.item())    
+            g_loss_final.backward()
+            if self.args.grad_norm != 0: 
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm)
+            self.opt.step()
+            t_train = time.time() - t_start - t_data
+            t_start = time.time()
+            mem_cost = torch.cuda.memory_cached() / 1E9
+            lr_g = self.opt.param_groups[0]['lr']
+            if its % self.args.log_period == 0:
+                self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g)   
+            if self.args.debug:
+                if its == 1: break
+        self.opt_s.step(epoch)
+                    
+    def val(self, epoch):
+        self.model.eval()
+        t_start = time.time()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.val_loader):
+                tar_pose = dict_data["pose"]
+                tar_beta = dict_data["beta"].cuda()
+                tar_trans = dict_data["trans"].cuda()
+                tar_pose = tar_pose.cuda()  
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_exps = torch.zeros((bs, n, 100)).cuda()
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                t_data = time.time() - t_start 
+
+                #self.opt.zero_grad()
+                #g_loss_final = 0
+                net_out = self.model(tar_pose)
+                rec_pose = net_out["rec_pose"]
+                rec_pose = rec_pose.reshape(bs, n, j, 6)
+                rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+                self.tracker.update_meter("rec", "val", loss_rec.item())
+                #g_loss_final += loss_rec
+
+                 # vertices loss
+                if self.args.rec_ver_weight > 0:
+                    tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
+                    tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
+                    vertices_rec = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=tar_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100), 
+                        jaw_pose=rec_pose[:, 66:69], 
+                        global_orient=rec_pose[:,:3], 
+                        body_pose=rec_pose[:,3:21*3+3], 
+                        left_hand_pose=rec_pose[:,25*3:40*3], 
+                        right_hand_pose=rec_pose[:,40*3:55*3], 
+                        return_verts=True, 
+                        leye_pose=tar_pose[:, 69:72], 
+                        reye_pose=tar_pose[:, 72:75],
+                    )
+                    vertices_tar = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=tar_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100), 
+                        jaw_pose=tar_pose[:, 66:69], 
+                        global_orient=tar_pose[:,:3], 
+                        body_pose=tar_pose[:,3:21*3+3], 
+                        left_hand_pose=tar_pose[:,25*3:40*3], 
+                        right_hand_pose=tar_pose[:,40*3:55*3], 
+                        return_verts=True, 
+                        leye_pose=tar_pose[:, 69:72], 
+                        reye_pose=tar_pose[:, 72:75],
+                    )  
+                    vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices'])
+                    self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                if "VQVAE" in self.args.g_name:
+                    loss_embedding = net_out["embedding_loss"]
+                    self.tracker.update_meter("com", "val", loss_embedding.item())
+                    #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight
+            self.val_recording(epoch)
+            
+    def test(self, epoch):
+        results_save_path = self.checkpoint_path + f"/{epoch}/"
+        if os.path.exists(results_save_path): 
+            return 0
+        os.makedirs(results_save_path)
+        start_time = time.time()
+        total_length = 0
+        test_seq_list = self.test_data.selected_file
+        self.model.eval()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.test_loader):
+                tar_pose = dict_data["pose"]
+                tar_pose = tar_pose.cuda()
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                remain = n%self.args.pose_length
+                tar_pose = tar_pose[:, :n-remain, :]
+                #print(tar_pose.shape)
+                if True:
+                    net_out = self.model(tar_pose)
+                    rec_pose = net_out["rec_pose"]
+                    n = rec_pose.shape[1]
+                    tar_pose = tar_pose[:, :n, :]
+                    rec_pose = rec_pose.reshape(bs, n, j, 6) 
+                    rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    rec_pose = rec_pose.cpu().numpy()
+                else:
+                    pass
+#                     for i in range(tar_pose.shape[1]//(self.args.vae_test_len)):
+#                         tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:]
+#                         net_out = self.model(**dict(inputs=tar_pose_new))
+#                         rec_pose = net_out["rec_pose"]
+#                         rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                         if "rot6d" in self.args.pose_rep:
+#                             rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6))
+#                             rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                             if "smplx" not in self.args.pose_rep:
+#                                 rec_pose = torch.rad2deg(rec_pose)
+#                             rec_pose = rec_pose * self.joint_mask_cuda
+                            
+#                         out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2])
+#                         if i != 0:
+#                             out_final = np.concatenate((out_final,out_sub), 0)
+#                         else:
+#                             out_final = out_sub
+                            
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                tar_pose = tar_pose.cpu().numpy()
+                
+                total_length += n 
+                # --- save --- #
+                if 'smplx' in self.args.pose_rep:
+                    gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True)
+                    stride = int(30 / self.args.pose_fps)
+                    tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0])
+                    np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=tar_pose[:n],
+                        expressions=gt_npz["expressions"]-gt_npz["expressions"],
+                        trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )
+                    rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0])
+                    np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=rec_pose,
+                        expressions=gt_npz["expressions"]-gt_npz["expressions"],
+                        trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )       
+                else:
+                    rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3)))
+                    rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy()                
+                    tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3)))
+                    tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() 
+                    #trans="0.000000 0.000000 0.000000"
+                    
+                    with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo:
+                        with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt:
+                            with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real:
+                                for i, line_data in enumerate(f_demo.readlines()):
+                                    if i < 431:
+                                        f_real.write(line_data)
+                                        f_gt.write(line_data)
+                                    else: break
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_real.write(line_data[1:-2]+'\n')
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_gt.write(line_data[1:-2]+'\n')
+                # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw:
+                #     pickle.dump(new_dict, fw)
+                # #new_dict2["fullpose"] = out_final
+                # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1:
+                #     pickle.dump(new_dict2, fw1)
+
+                # other_tools.render_one_sequence(
+                #     results_save_path+"res_"+test_seq_list[its]+'.pkl',
+                #     results_save_path+"gt_"+test_seq_list[its]+'.pkl',
+                #     results_save_path,
+                #     self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy',
+                # )
+                                                                                                
+                #if its == 1:break
+        end_time = time.time() - start_time
+        logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")

aeface_trainer.py

@@ -0,0 +1,388 @@
+import train
+import os
+import time
+import csv
+import sys
+import warnings
+import random
+import numpy as np
+import time
+import pprint
+import pickle
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+from torch.nn.parallel import DistributedDataParallel as DDP
+from loguru import logger
+import smplx
+
+from utils import config, logger_tools, other_tools, metric
+from utils import rotation_conversions as rc
+from dataloaders import data_tools
+from optimizers.optim_factory import create_optimizer
+from optimizers.scheduler_factory import create_scheduler
+from optimizers.loss_factory import get_loss_func
+from scipy.spatial.transform import Rotation
+
+
+class CustomTrainer(train.BaseTrainer):
+    """
+    motion representation learning
+    """
+    def __init__(self, args):
+        super().__init__(args)
+        self.joints = self.train_data.joints
+        self.tracker = other_tools.EpochTracker(["rec", "vel", "acc", "com", "face", "face_vel", "face_acc", "ver", "ver_vel", "ver_acc"], [False, False, False, False, False, False, False, False, False, False])
+        self.rec_loss = get_loss_func("GeodesicLoss")
+        self.mse_loss = torch.nn.MSELoss(reduction='mean')
+        self.vel_loss = torch.nn.MSELoss(reduction='mean') #torch.nn.L1Loss(reduction='mean')
+        self.vectices_loss = torch.nn.MSELoss(reduction='mean')
+    
+    def inverse_selection(self, filtered_t, selection_array, n):
+        # 创建一个全为零的数组，形状为 n*165
+        original_shape_t = np.zeros((n, selection_array.size))
+        
+        # 找到选择数组中为1的索引位置
+        selected_indices = np.where(selection_array == 1)[0]
+        
+        # 将 filtered_t 的值填充到 original_shape_t 中相应的位置
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+            
+        return original_shape_t
+
+    def train(self, epoch):
+        self.model.train()
+        t_start = time.time()
+        self.tracker.reset()
+        for its, dict_data in enumerate(self.train_loader):
+            tar_pose = dict_data["pose"]
+            tar_beta = dict_data["beta"].cuda()
+            tar_trans = dict_data["trans"].cuda()
+            tar_pose = tar_pose.cuda()  
+            bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+            tar_exps = dict_data["facial"].to(self.rank)
+            tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+            tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+            in_tar_pose = torch.cat([tar_pose, tar_exps], -1) # 103
+            t_data = time.time() - t_start 
+            
+            self.opt.zero_grad()
+            g_loss_final = 0
+            net_out = self.model(in_tar_pose)
+            # jaw open 6d loss
+            rec_pose = net_out["rec_pose"][:, :, :j*6]
+            rec_pose = rec_pose.reshape(bs, n, j, 6)
+            rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+            tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+            loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+            self.tracker.update_meter("rec", "train", loss_rec.item())
+            g_loss_final += loss_rec
+            # jaw open 6d vel and acc loss
+            velocity_loss =  self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight
+            acceleration_loss =  self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight
+            self.tracker.update_meter("vel", "train", velocity_loss.item())
+            self.tracker.update_meter("acc", "train", acceleration_loss.item())
+            g_loss_final += velocity_loss 
+            g_loss_final += acceleration_loss 
+            # face parameter l1 loss
+            rec_exps = net_out["rec_pose"][:, :, j*6:]
+            loss_face = self.mse_loss(rec_exps, tar_exps) * self.args.rec_weight
+            self.tracker.update_meter("face", "train", loss_face.item())
+            g_loss_final += loss_face
+            # face parameter l1 vel and acc loss
+            face_velocity_loss =  self.vel_loss(rec_exps[:, 1:] - rec_exps[:, :-1], tar_exps[:, 1:] - tar_exps[:, :-1]) * self.args.rec_weight
+            face_acceleration_loss =  self.vel_loss(rec_exps[:, 2:] + rec_exps[:, :-2] - 2 * rec_exps[:, 1:-1], tar_exps[:, 2:] + tar_exps[:, :-2] - 2 * tar_exps[:, 1:-1]) * self.args.rec_weight
+            self.tracker.update_meter("face_vel", "train", face_velocity_loss.item())
+            self.tracker.update_meter("face_acc", "train", face_acceleration_loss.item())
+            g_loss_final += face_velocity_loss
+            g_loss_final += face_acceleration_loss
+
+             # vertices loss
+            if self.args.rec_ver_weight > 0:
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                vertices_rec = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                    expression=tar_exps.reshape(bs*n, 100), 
+                    jaw_pose=rec_pose, 
+                    global_orient=torch.zeros(bs*n, 3).cuda(), 
+                    body_pose=torch.zeros(bs*n, 21*3).cuda(), 
+                    left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                    right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                    return_verts=True,
+                    # return_joints=True,
+                    leye_pose=torch.zeros(bs*n, 3).cuda(), 
+                    reye_pose=torch.zeros(bs*n, 3).cuda(),
+                )
+                vertices_tar = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                    expression=rec_exps.reshape(bs*n, 100), 
+                    jaw_pose=tar_pose, 
+                    global_orient=torch.zeros(bs*n, 3).cuda(), 
+                    body_pose=torch.zeros(bs*n, 21*3).cuda(), 
+                    left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                    right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                    return_verts=True,
+                    # return_joints=True,
+                    leye_pose=torch.zeros(bs*n, 3).cuda(), 
+                    reye_pose=torch.zeros(bs*n, 3).cuda(),
+                )  
+                vectices_loss = self.mse_loss(vertices_rec['vertices'], vertices_tar['vertices'])
+                self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight
+                # vertices vel and acc loss
+                vert_velocity_loss =  self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight * self.args.rec_ver_weight
+                vert_acceleration_loss =  self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight * self.args.rec_ver_weight
+                self.tracker.update_meter("ver_vel", "train", vert_velocity_loss.item())
+                self.tracker.update_meter("ver_acc", "train", vert_acceleration_loss.item())
+                g_loss_final += vert_velocity_loss
+                g_loss_final += vert_acceleration_loss
+            
+            # ---------------------- vae -------------------------- #
+            if "VQVAE" in self.args.g_name:
+                loss_embedding = net_out["embedding_loss"]
+                g_loss_final += loss_embedding
+                self.tracker.update_meter("com", "train", loss_embedding.item())
+            # elif "VAE" in self.args.g_name:
+            #     pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] 
+            #     KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp())
+            #     if epoch < 0:
+            #         KLD_weight = 0
+            #     else:
+            #         KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01
+            #     loss += KLD_weight * KLD
+            #     self.tracker.update_meter("kl", "train", KLD_weight * KLD.item())    
+            g_loss_final.backward()
+            if self.args.grad_norm != 0: 
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm)
+            self.opt.step()
+            t_train = time.time() - t_start - t_data
+            t_start = time.time()
+            mem_cost = torch.cuda.memory_cached() / 1E9
+            lr_g = self.opt.param_groups[0]['lr']
+            if its % self.args.log_period == 0:
+                self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g)   
+            if self.args.debug:
+                if its == 1: break
+        self.opt_s.step(epoch)
+                    
+    def val(self, epoch):
+        self.model.eval()
+        t_start = time.time()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.val_loader):
+                tar_pose = dict_data["pose"]
+                tar_beta = dict_data["beta"].cuda()
+                tar_trans = dict_data["trans"].cuda()
+                tar_pose = tar_pose.cuda()  
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_exps = dict_data["facial"].to(self.rank)
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                in_tar_pose = torch.cat([tar_pose, tar_exps], -1) # 103
+                # print(tar_pose.shape, in_tar_pose.shape, tar_exps.shape)
+                t_data = time.time() - t_start 
+
+                #self.opt.zero_grad()
+                #g_loss_final = 0
+                net_out = self.model(in_tar_pose)
+                            # jaw open 6d loss
+                rec_pose = net_out["rec_pose"][:, :, :j*6]
+                rec_pose = rec_pose.reshape(bs, n, j, 6)
+                rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+                self.tracker.update_meter("rec", "val", loss_rec.item())
+                # g_loss_final += loss_rec
+                # jaw open 6d vel and acc loss
+                velocity_loss =  self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight
+                acceleration_loss =  self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight
+                self.tracker.update_meter("vel", "val", velocity_loss.item())
+                self.tracker.update_meter("acc", "val", acceleration_loss.item())
+                # g_loss_final += velocity_loss 
+                # g_loss_final += acceleration_loss 
+                # face parameter l1 loss
+                rec_exps = net_out["rec_pose"][:, :, j*6:]
+                loss_face = self.vel_loss(rec_exps, tar_exps) * self.args.rec_weight
+                self.tracker.update_meter("face", "val", loss_face.item())
+                # g_loss_final += loss_face
+                # face parameter l1 vel and acc loss
+                face_velocity_loss =  self.vel_loss(rec_exps[:, 1:] - rec_exps[:, :-1], tar_exps[:, 1:] - tar_exps[:, :-1]) * self.args.rec_weight
+                face_acceleration_loss =  self.vel_loss(rec_exps[:, 2:] + rec_exps[:, :-2] - 2 * rec_exps[:, 1:-1], tar_exps[:, 2:] + tar_exps[:, :-2] - 2 * tar_exps[:, 1:-1]) * self.args.rec_weight
+                self.tracker.update_meter("face_vel", "val", face_velocity_loss.item())
+                self.tracker.update_meter("face_acc", "val", face_acceleration_loss.item())
+                # g_loss_final += face_velocity_loss
+                # g_loss_final += face_acceleration_loss
+
+                # vertices loss
+                if self.args.rec_ver_weight > 0:
+                    tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    vertices_rec = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100), 
+                        jaw_pose=rec_pose, 
+                        global_orient=torch.zeros(bs*n, 3).cuda(), 
+                        body_pose=torch.zeros(bs*n, 21*3).cuda(), 
+                        left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                        right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                        return_verts=True,
+                        # return_joints=True,
+                        leye_pose=torch.zeros(bs*n, 3).cuda(), 
+                        reye_pose=torch.zeros(bs*n, 3).cuda(),
+                    )
+                    vertices_tar = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                        expression=rec_exps.reshape(bs*n, 100), 
+                        jaw_pose=tar_pose, 
+                        global_orient=torch.zeros(bs*n, 3).cuda(), 
+                        body_pose=torch.zeros(bs*n, 21*3).cuda(), 
+                        left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                        right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
+                        return_verts=True,
+                        # return_joints=True,
+                        leye_pose=torch.zeros(bs*n, 3).cuda(), 
+                        reye_pose=torch.zeros(bs*n, 3).cuda(),
+                    )  
+                    vectices_loss = self.mse_loss(vertices_rec['vertices'], vertices_tar['vertices'])
+                    self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                    # g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight
+                    # vertices vel and acc loss
+                    vert_velocity_loss =  self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight * self.args.rec_ver_weight
+                    vert_acceleration_loss =  self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight * self.args.rec_ver_weight
+                    self.tracker.update_meter("ver_vel", "val", vert_velocity_loss.item())
+                    self.tracker.update_meter("ver_acc", "val", vert_acceleration_loss.item())
+                    # g_loss_final += vert_velocity_loss
+                    # g_loss_final += vert_acceleration_loss
+                    if "VQVAE" in self.args.g_name:
+                        loss_embedding = net_out["embedding_loss"]
+                        self.tracker.update_meter("com", "val", loss_embedding.item())
+                    #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight
+        self.val_recording(epoch)
+            
+    def test(self, epoch):
+        results_save_path = self.checkpoint_path + f"/{epoch}/"
+        if os.path.exists(results_save_path): 
+            return 0
+        os.makedirs(results_save_path)
+        start_time = time.time()
+        total_length = 0
+        test_seq_list = self.test_data.selected_file
+        self.model.eval()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.test_loader):
+                tar_pose = dict_data["pose"]
+                tar_pose = tar_pose.cuda()
+                tar_exps = dict_data["facial"].to(self.rank)
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                remain = n%self.args.pose_length
+                tar_pose = tar_pose[:, :n-remain, :]
+                # print(tar_exps.shape)
+                in_tar_pose = torch.cat([tar_pose, tar_exps[:, :n-remain, :]], -1) # 103
+                #print(tar_pose.shape)
+                if True:
+                    net_out = self.model(in_tar_pose)
+                    rec_pose = net_out["rec_pose"][:, :, :j*6]
+                    n = rec_pose.shape[1]
+                    tar_pose = tar_pose[:, :n, :]
+                    rec_pose = rec_pose.reshape(bs, n, j, 6) 
+                    rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    rec_pose = rec_pose.cpu().numpy()
+                    rec_exps = net_out["rec_pose"][:, :, j*6:]
+                    rec_exps = rec_exps.cpu().numpy().reshape(bs*n, 100)
+                else:
+                    pass
+#                     for i in range(tar_pose.shape[1]//(self.args.vae_test_len)):
+#                         tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:]
+#                         net_out = self.model(**dict(inputs=tar_pose_new))
+#                         rec_pose = net_out["rec_pose"]
+#                         rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                         if "rot6d" in self.args.pose_rep:
+#                             rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6))
+#                             rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                             if "smplx" not in self.args.pose_rep:
+#                                 rec_pose = torch.rad2deg(rec_pose)
+#                             rec_pose = rec_pose * self.joint_mask_cuda
+                            
+#                         out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2])
+#                         if i != 0:
+#                             out_final = np.concatenate((out_final,out_sub), 0)
+#                         else:
+#                             out_final = out_sub
+                            
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                tar_pose = tar_pose.cpu().numpy()
+                
+                total_length += n 
+                # --- save --- #
+                if 'smplx' in self.args.pose_rep:
+                    gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True)
+                    stride = int(30 / self.args.pose_fps)
+                    tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0])
+                    np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=tar_pose[:n],
+                        expressions=gt_npz["expressions"],
+                        trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )
+                    rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0])
+                    np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=rec_pose,
+                        expressions=rec_exps,
+                        trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )       
+                else:
+                    rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3)))
+                    rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy()                
+                    tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3)))
+                    tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() 
+                    #trans="0.000000 0.000000 0.000000"
+                    
+                    with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo:
+                        with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt:
+                            with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real:
+                                for i, line_data in enumerate(f_demo.readlines()):
+                                    if i < 431:
+                                        f_real.write(line_data)
+                                        f_gt.write(line_data)
+                                    else: break
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_real.write(line_data[1:-2]+'\n')
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_gt.write(line_data[1:-2]+'\n')
+                # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw:
+                #     pickle.dump(new_dict, fw)
+                # #new_dict2["fullpose"] = out_final
+                # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1:
+                #     pickle.dump(new_dict2, fw1)
+
+                # other_tools.render_one_sequence(
+                #     results_save_path+"res_"+test_seq_list[its]+'.pkl',
+                #     results_save_path+"gt_"+test_seq_list[its]+'.pkl',
+                #     results_save_path,
+                #     self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy',
+                # )
+                                                                                                
+                #if its == 1:break
+        end_time = time.time() - start_time
+        logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")

aelower_trainer.py

@@ -0,0 +1,494 @@
+import train
+import os
+import time
+import csv
+import sys
+import warnings
+import random
+import numpy as np
+import time
+import pprint
+import pickle
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+from torch.nn.parallel import DistributedDataParallel as DDP
+from loguru import logger
+import smplx
+
+from utils import config, logger_tools, other_tools, metric
+from utils import rotation_conversions as rc
+from dataloaders import data_tools
+from optimizers.optim_factory import create_optimizer
+from optimizers.scheduler_factory import create_scheduler
+from optimizers.loss_factory import get_loss_func
+from scipy.spatial.transform import Rotation
+
+
+class CustomTrainer(train.BaseTrainer):
+    """
+    motion representation learning
+    """
+    def __init__(self, args):
+        super().__init__(args)
+        self.joints = self.train_data.joints
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        self.tracker = other_tools.EpochTracker(["rec", "contact", "vel", "foot", "ver", "com", "kl", "acc", "trans", "transv"], [False,False, False, False, False, False, False, False, False, False])
+        if not self.args.rot6d: #"rot6d" not in args.pose_rep:
+            logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}")
+        self.rec_loss = get_loss_func("GeodesicLoss")
+        self.vel_loss = torch.nn.L1Loss(reduction='mean')
+        self.vectices_loss = torch.nn.MSELoss(reduction='mean')
+    
+    def inverse_selection(self, filtered_t, selection_array, n):
+        # 创建一个全为零的数组，形状为 n*165
+        original_shape_t = np.zeros((n, selection_array.size))
+        
+        # 找到选择数组中为1的索引位置
+        selected_indices = np.where(selection_array == 1)[0]
+        
+        # 将 filtered_t 的值填充到 original_shape_t 中相应的位置
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+            
+        return original_shape_t
+
+    def inverse_selection_tensor(self, filtered_t, selection_array, n):
+    # 创建一个全为零的数组，形状为 n*165
+        selection_array = torch.from_numpy(selection_array).cuda()
+        original_shape_t = torch.zeros((n, 165)).cuda()
+        
+        # 找到选择数组中为1的索引位置
+        selected_indices = torch.where(selection_array == 1)[0]
+        
+        # 将 filtered_t 的值填充到 original_shape_t 中相应的位置
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+            
+        return original_shape_t
+
+
+    def train(self, epoch):
+        self.model.train()
+        t_start = time.time()
+        self.tracker.reset()
+        for its, dict_data in enumerate(self.train_loader):
+            tar_pose_raw = dict_data["pose"]
+            tar_beta = dict_data["beta"].cuda()
+            tar_trans = dict_data["trans"].cuda()
+            tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps)
+            tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps)
+            tar_pose = tar_pose_raw[:, :, :27].cuda() 
+            tar_contact = tar_pose_raw[:, :, 27:31].cuda() 
+            bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+            tar_exps = torch.zeros((bs, n, 100)).cuda()
+            tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+            tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+            tar_trans_copy = tar_trans-tar_trans
+            tar_contact_copy = tar_contact-tar_contact
+            in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1)
+            
+            t_data = time.time() - t_start 
+            
+            self.opt.zero_grad()
+            g_loss_final = 0
+            net_out = self.model(in_tar_pose)
+            rec_pose = tar_pose#net_out["rec_pose"][:, :, :j*6]
+            rec_pose = rec_pose.reshape(bs, n, j, 6)
+            rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+            tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+            # loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+            # self.tracker.update_meter("rec", "train", loss_rec.item())
+            # g_loss_final += loss_rec
+
+            rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+            loss_contact = self.vectices_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight
+            self.tracker.update_meter("contact", "train", loss_contact.item())
+            g_loss_final += loss_contact 
+
+            # velocity_loss =  self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight
+            # acceleration_loss =  self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight
+            # self.tracker.update_meter("vel", "train", velocity_loss.item())
+            # self.tracker.update_meter("acc", "train", acceleration_loss.item())
+            # g_loss_final += velocity_loss 
+            # g_loss_final += acceleration_loss 
+
+            rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+            rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+            rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+            rec_y_trans = rec_trans[:,:,1:2]
+            rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+            loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \
+            + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight 
+            v3 =  self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \
+            + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight
+            a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \
+            + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight
+            g_loss_final += 5*v3 
+            g_loss_final += 5*a3
+            v2 =  self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight
+            a2 =  self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight
+            g_loss_final += 5*v2 
+            g_loss_final += 5*a2 
+            self.tracker.update_meter("transv", "train", loss_trans_vel.item())
+            g_loss_final += loss_trans_vel
+            loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight 
+            self.tracker.update_meter("trans", "train", loss_trans.item())
+            g_loss_final += loss_trans
+
+             # vertices loss
+            if self.args.rec_ver_weight > 0:
+                # print(tar_pose.shape, j)
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
+                tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
+                vertices_rec = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=rec_xyz_trans.reshape(bs*n, 3), 
+                    expression=tar_exps.reshape(bs*n, 100), 
+                    jaw_pose=rec_pose[:, 66:69], 
+                    global_orient=rec_pose[:,:3], 
+                    body_pose=rec_pose[:,3:21*3+3], 
+                    left_hand_pose=rec_pose[:,25*3:40*3], 
+                    right_hand_pose=rec_pose[:,40*3:55*3], 
+                    return_verts=True,
+                    return_joints=True,
+                    leye_pose=tar_pose[:, 69:72], 
+                    reye_pose=tar_pose[:, 72:75],
+                )
+                vertices_tar = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=tar_trans.reshape(bs*n, 3), 
+                    expression=tar_exps.reshape(bs*n, 100), 
+                    jaw_pose=tar_pose[:, 66:69], 
+                    global_orient=tar_pose[:,:3], 
+                    body_pose=tar_pose[:,3:21*3+3], 
+                    left_hand_pose=tar_pose[:,25*3:40*3], 
+                    right_hand_pose=tar_pose[:,40*3:55*3], 
+                    return_verts=True,
+                    return_joints=True,
+                    leye_pose=tar_pose[:, 69:72], 
+                    reye_pose=tar_pose[:, 72:75],
+                )  
+                joints_rec = vertices_rec['joints']
+                # print(joints_rec.shape)
+                joints_rec = joints_rec.reshape(bs, n, -1, 3)
+                vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices'])
+                vertices_vel_loss = self.vectices_loss(
+                    vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1],
+                    vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1])
+                vertices_acc_loss = self.vectices_loss(
+                    vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1],
+                    vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1])
+                foot_idx = [7, 8, 10, 11]
+                model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+                # find static indices consistent with model's own predictions
+                static_idx = model_contact > 0.95  # N x S x 4
+                # print(model_contact,static_idx)
+                model_feet = joints_rec[:, :, foot_idx]  # foot positions (N, S, 4, 3)
+                model_foot_v = torch.zeros_like(model_feet)
+                model_foot_v[:, :-1] = (
+                    model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :]
+                )  # (N, S-1, 4, 3)
+                model_foot_v[~static_idx] = 0
+                foot_loss = self.vel_loss(
+                    model_foot_v, torch.zeros_like(model_foot_v)
+                )
+                self.tracker.update_meter("foot", "train", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight*1000)
+                self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                g_loss_final += (vectices_loss+5*vertices_vel_loss+5*vertices_acc_loss)*self.args.rec_weight*self.args.rec_ver_weight 
+                g_loss_final += foot_loss*self.args.rec_weight*self.args.rec_ver_weight*20 
+            
+            # ---------------------- vae -------------------------- #
+            if "VQVAE" in self.args.g_name:
+                loss_embedding = net_out["embedding_loss"]
+                g_loss_final += loss_embedding
+                self.tracker.update_meter("com", "train", loss_embedding.item())
+            # elif "VAE" in self.args.g_name:
+            #     pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] 
+            #     KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp())
+            #     if epoch < 0:
+            #         KLD_weight = 0
+            #     else:
+            #         KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01
+            #     loss += KLD_weight * KLD
+            #     self.tracker.update_meter("kl", "train", KLD_weight * KLD.item())    
+            g_loss_final.backward()
+            if self.args.grad_norm != 0: 
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm)
+            self.opt.step()
+            t_train = time.time() - t_start - t_data
+            t_start = time.time()
+            mem_cost = torch.cuda.memory_cached() / 1E9
+            lr_g = self.opt.param_groups[0]['lr']
+            if its % self.args.log_period == 0:
+                self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g)   
+            if self.args.debug:
+                if its == 1: break
+        self.opt_s.step(epoch)
+                    
+    def val(self, epoch):
+        self.model.eval()
+        t_start = time.time()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.val_loader):
+                tar_pose_raw = dict_data["pose"]
+                tar_beta = dict_data["beta"].cuda()
+                tar_trans = dict_data["trans"].cuda()
+                tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps)
+                tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps)
+                #print(tar_pose.shape)
+                tar_pose = tar_pose_raw[:, :, :27].cuda() 
+
+                tar_contact = tar_pose_raw[:, :, 27:31].cuda()  
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_exps = torch.zeros((bs, n, 100)).cuda()
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                tar_trans_copy = tar_trans-tar_trans
+                tar_contact_copy = tar_contact-tar_contact
+                in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1)
+                t_data = time.time() - t_start 
+
+                #self.opt.zero_grad()
+                #g_loss_final = 0
+                net_out = self.model(in_tar_pose)
+                rec_pose = tar_pose
+                rec_pose = rec_pose.reshape(bs, n, j, 6)
+                rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                # loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+                # self.tracker.update_meter("rec", "val", loss_rec.item())
+                rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+                # print(rec_contact.shape, tar_contact.shape)
+                loss_contact = self.vel_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight
+                self.tracker.update_meter("contact", "val", loss_contact.item())
+                #g_loss_final += loss_rec
+                # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+                # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+                # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+                # rec_y_trans = rec_trans[:,:,1:2]
+                # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+
+                rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+                rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+                rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+                rec_y_trans = rec_trans[:,:,1:2]
+                rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+                loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \
+                + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight 
+                # v3 =  self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \
+                # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight
+                # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \
+                # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight
+                # #g_loss_final += 5*v3 
+                # #g_loss_final += 5*a3
+                # v2 =  self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight
+                # a2 =  self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight
+                #g_loss_final += 5*v2 
+                #g_loss_final += 5*a2 
+                self.tracker.update_meter("transv", "val", loss_trans_vel.item())
+                #g_loss_final += loss_trans_vel
+                loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight 
+                self.tracker.update_meter("trans", "val", loss_trans.item())
+                #g_loss_final += loss_trans
+
+                 # vertices loss
+                if self.args.rec_ver_weight > 0:
+                    tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
+                    tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
+                    vertices_rec = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=rec_xyz_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100), 
+                        jaw_pose=rec_pose[:, 66:69], 
+                        global_orient=rec_pose[:,:3], 
+                        body_pose=rec_pose[:,3:21*3+3], 
+                        left_hand_pose=rec_pose[:,25*3:40*3], 
+                        right_hand_pose=rec_pose[:,40*3:55*3], 
+                        return_verts=False, 
+                        return_joints=True,
+                        leye_pose=tar_pose[:, 69:72], 
+                        reye_pose=tar_pose[:, 72:75],
+                    )
+                    vertices_tar = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=tar_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100), 
+                        jaw_pose=tar_pose[:, 66:69], 
+                        global_orient=tar_pose[:,:3], 
+                        body_pose=tar_pose[:,3:21*3+3], 
+                        left_hand_pose=tar_pose[:,25*3:40*3], 
+                        right_hand_pose=tar_pose[:,40*3:55*3], 
+                        return_verts=False, 
+                        return_joints=True,
+                        leye_pose=tar_pose[:, 69:72], 
+                        reye_pose=tar_pose[:, 72:75],
+                    )  
+                    joints_rec = vertices_rec['joints']
+                    joints_rec = joints_rec.reshape(bs, n, -1, 3)
+                    vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints'])
+                    foot_idx = [7, 8, 10, 11]
+                    model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+                    # find static indices consistent with model's own predictions
+                    static_idx = model_contact > 0.95  # N x S x 4
+                    # print(model_contact)
+                    model_feet = joints_rec[:, :, foot_idx]  # foot positions (N, S, 4, 3)
+                    model_foot_v = torch.zeros_like(model_feet)
+                    model_foot_v[:, :-1] = (
+                        model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :]
+                    )  # (N, S-1, 4, 3)
+                    model_foot_v[~static_idx] = 0
+                    foot_loss = self.vectices_loss(
+                        model_foot_v, torch.zeros_like(model_foot_v)
+                    )
+                    self.tracker.update_meter("foot", "val", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                    self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                if "VQVAE" in self.args.g_name:
+                    loss_embedding = net_out["embedding_loss"]
+                    self.tracker.update_meter("com", "val", loss_embedding.item())
+                    #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight
+            self.val_recording(epoch)
+            
+    def test(self, epoch):
+        results_save_path = self.checkpoint_path + f"/{epoch}/"
+        if os.path.exists(results_save_path): 
+            return 0
+        os.makedirs(results_save_path)
+        start_time = time.time()
+        total_length = 0
+        test_seq_list = self.test_data.selected_file
+        self.model.eval()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.test_loader):
+                tar_pose_raw = dict_data["pose"]
+                tar_trans = dict_data["trans"].to(self.rank)
+                tar_pose = tar_pose_raw[:, :, :27].cuda() 
+                tar_contact = tar_pose_raw[:, :, 27:31].cuda() 
+                # tar_pose = tar_pose.cuda()
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                remain = n%self.args.pose_length
+                tar_pose = tar_pose[:, :n-remain, :]
+                tar_contact = tar_contact[:, :n-remain, :]
+                tar_trans_copy = tar_trans[:, :n-remain, :]-tar_trans[:, :n-remain, :]
+                tar_contact_copy = tar_contact-tar_contact
+                in_tar_pose = torch.cat([tar_pose, tar_trans_copy, tar_contact_copy], dim=-1)
+                #print(tar_pose.shape)
+                if True:
+                    net_out = self.model(in_tar_pose)
+                    rec_pose = tar_pose #net_out["rec_pose"][:, :, :j*6]
+                    rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+                    # print(rec_trans.shape)
+                    rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+                    rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+                    rec_y_trans = rec_trans[:,:,1:2]
+                    rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+                    n = rec_pose.shape[1]
+                    rec_trans = rec_trans.cpu().numpy().reshape(bs*n, 3)
+                    tar_pose = tar_pose[:, :n, :]
+                    rec_pose = rec_pose.reshape(bs, n, j, 6) 
+                    rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    rec_pose = rec_pose.cpu().numpy()
+                else:
+                    pass
+#                     for i in range(tar_pose.shape[1]//(self.args.vae_test_len)):
+#                         tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:]
+#                         net_out = self.model(**dict(inputs=tar_pose_new))
+#                         rec_pose = net_out["rec_pose"]
+#                         rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                         if "rot6d" in self.args.pose_rep:
+#                             rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6))
+#                             rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                             if "smplx" not in self.args.pose_rep:
+#                                 rec_pose = torch.rad2deg(rec_pose)
+#                             rec_pose = rec_pose * self.joint_mask_cuda
+                            
+#                         out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2])
+#                         if i != 0:
+#                             out_final = np.concatenate((out_final,out_sub), 0)
+#                         else:
+#                             out_final = out_sub
+                            
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                tar_pose = tar_pose.cpu().numpy()
+                
+                total_length += n 
+                # --- save --- #
+                if 'smplx' in self.args.pose_rep:
+                    gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True)
+                    stride = int(30 / self.args.pose_fps)
+                    tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0])
+                    np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=tar_pose[:n],
+                        expressions=gt_npz["expressions"]-gt_npz["expressions"],
+                        trans=gt_npz["trans"][::stride][:n],
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )
+                    rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0])
+                    np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=rec_pose,
+                        expressions=gt_npz["expressions"]-gt_npz["expressions"],
+                        trans=rec_trans,
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )       
+                else:
+                    rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3)))
+                    rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy()                
+                    tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3)))
+                    tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() 
+                    #trans="0.000000 0.000000 0.000000"
+                    
+                    with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo:
+                        with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt:
+                            with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real:
+                                for i, line_data in enumerate(f_demo.readlines()):
+                                    if i < 431:
+                                        f_real.write(line_data)
+                                        f_gt.write(line_data)
+                                    else: break
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_real.write(line_data[1:-2]+'\n')
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_gt.write(line_data[1:-2]+'\n')
+                # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw:
+                #     pickle.dump(new_dict, fw)
+                # #new_dict2["fullpose"] = out_final
+                # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1:
+                #     pickle.dump(new_dict2, fw1)
+
+                # other_tools.render_one_sequence(
+                #     results_save_path+"res_"+test_seq_list[its]+'.pkl',
+                #     results_save_path+"gt_"+test_seq_list[its]+'.pkl',
+                #     results_save_path,
+                #     self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy',
+                # )
+                                                                                                
+                #if its == 1:break
+        end_time = time.time() - start_time
+        logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")

aelowerfoot_trainer.py

@@ -0,0 +1,491 @@
+import train
+import os
+import time
+import csv
+import sys
+import warnings
+import random
+import numpy as np
+import time
+import pprint
+import pickle
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+from torch.nn.parallel import DistributedDataParallel as DDP
+from loguru import logger
+import smplx
+
+from utils import config, logger_tools, other_tools, metric
+from utils import rotation_conversions as rc
+from dataloaders import data_tools
+from optimizers.optim_factory import create_optimizer
+from optimizers.scheduler_factory import create_scheduler
+from optimizers.loss_factory import get_loss_func
+from scipy.spatial.transform import Rotation
+
+
+class CustomTrainer(train.BaseTrainer):
+    """
+    motion representation learning
+    """
+    def __init__(self, args):
+        super().__init__(args)
+        self.joints = self.train_data.joints
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).cuda().eval()
+        self.tracker = other_tools.EpochTracker(["rec", "contact", "vel", "foot", "ver", "com", "kl", "acc", "trans", "transv"], [False,False, False, False, False, False, False, False, False, False])
+        if not self.args.rot6d: #"rot6d" not in args.pose_rep:
+            logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}")
+        self.rec_loss = get_loss_func("GeodesicLoss")
+        self.vel_loss = torch.nn.L1Loss(reduction='mean')
+        self.vectices_loss = torch.nn.MSELoss(reduction='mean')
+    
+    def inverse_selection(self, filtered_t, selection_array, n):
+        # 创建一个全为零的数组，形状为 n*165
+        original_shape_t = np.zeros((n, selection_array.size))
+        
+        # 找到选择数组中为1的索引位置
+        selected_indices = np.where(selection_array == 1)[0]
+        
+        # 将 filtered_t 的值填充到 original_shape_t 中相应的位置
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+            
+        return original_shape_t
+
+    def inverse_selection_tensor(self, filtered_t, selection_array, n):
+    # 创建一个全为零的数组，形状为 n*165
+        selection_array = torch.from_numpy(selection_array).cuda()
+        original_shape_t = torch.zeros((n, 165)).cuda()
+        
+        # 找到选择数组中为1的索引位置
+        selected_indices = torch.where(selection_array == 1)[0]
+        
+        # 将 filtered_t 的值填充到 original_shape_t 中相应的位置
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+            
+        return original_shape_t
+
+
+    def train(self, epoch):
+        self.model.train()
+        t_start = time.time()
+        self.tracker.reset()
+        for its, dict_data in enumerate(self.train_loader):
+            tar_pose_raw = dict_data["pose"]
+            tar_beta = dict_data["beta"].cuda()
+            tar_trans = dict_data["trans"].cuda()
+            # tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps)
+            # tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps)
+            tar_pose = tar_pose_raw[:, :, :27].cuda() 
+            tar_contact = tar_pose_raw[:, :, 27:31].cuda() 
+            bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+            tar_exps = torch.zeros((bs, n, 100)).cuda()
+            tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+            tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+            tar_trans_copy = tar_trans
+            tar_contact_copy = tar_contact
+            in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1)
+            
+            t_data = time.time() - t_start 
+            
+            self.opt.zero_grad()
+            g_loss_final = 0
+            net_out = self.model(in_tar_pose)
+            rec_pose = net_out["rec_pose"][:, :, :j*6]
+            rec_pose = rec_pose.reshape(bs, n, j, 6)
+            rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+            tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+            loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+            self.tracker.update_meter("rec", "train", loss_rec.item())
+            g_loss_final += loss_rec
+
+            rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+            loss_contact = self.vectices_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight
+            self.tracker.update_meter("contact", "train", loss_contact.item())
+            g_loss_final += loss_contact 
+
+            velocity_loss =  self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight
+            acceleration_loss =  self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight
+            self.tracker.update_meter("vel", "train", velocity_loss.item())
+            self.tracker.update_meter("acc", "train", acceleration_loss.item())
+            g_loss_final += velocity_loss 
+            g_loss_final += acceleration_loss 
+
+            # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+            # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+            # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+            # rec_y_trans = rec_trans[:,:,1:2]
+            # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+            # loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \
+            # + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight 
+            # v3 =  self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \
+            # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight
+            # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \
+            # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight
+            # g_loss_final += 5*v3 
+            # g_loss_final += 5*a3
+            # v2 =  self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight
+            # a2 =  self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight
+            # g_loss_final += 5*v2 
+            # g_loss_final += 5*a2 
+            # self.tracker.update_meter("transv", "train", loss_trans_vel.item())
+            # g_loss_final += loss_trans_vel
+            # loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight 
+            # self.tracker.update_meter("trans", "train", loss_trans.item())
+            # g_loss_final += loss_trans
+
+             # vertices loss
+            if self.args.rec_ver_weight > 0:
+                # print(tar_pose.shape, bs, n, j)
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
+                tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
+                vertices_rec = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                    expression=tar_exps.reshape(bs*n, 100), 
+                    jaw_pose=rec_pose[:, 66:69], 
+                    global_orient=rec_pose[:,:3], 
+                    body_pose=rec_pose[:,3:21*3+3], 
+                    left_hand_pose=rec_pose[:,25*3:40*3], 
+                    right_hand_pose=rec_pose[:,40*3:55*3], 
+                    return_verts=False,
+                    return_joints=True,
+                    leye_pose=tar_pose[:, 69:72], 
+                    reye_pose=tar_pose[:, 72:75],
+                )
+                vertices_tar = self.smplx(
+                    betas=tar_beta.reshape(bs*n, 300), 
+                    transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                    expression=tar_exps.reshape(bs*n, 100), 
+                    jaw_pose=tar_pose[:, 66:69], 
+                    global_orient=tar_pose[:,:3], 
+                    body_pose=tar_pose[:,3:21*3+3], 
+                    left_hand_pose=tar_pose[:,25*3:40*3], 
+                    right_hand_pose=tar_pose[:,40*3:55*3], 
+                    return_verts=False,
+                    return_joints=True,
+                    leye_pose=tar_pose[:, 69:72], 
+                    reye_pose=tar_pose[:, 72:75],
+                )  
+                joints_rec = vertices_rec['joints']
+                # print(joints_rec.shape)
+                joints_rec = joints_rec.reshape(bs, n, -1, 3)
+                vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints'])
+                foot_idx = [7, 8, 10, 11]
+                model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+                # find static indices consistent with model's own predictions
+                static_idx = model_contact > 0.95  # N x S x 4
+                # print(model_contact,static_idx)
+                model_feet = joints_rec[:, :, foot_idx]  # foot positions (N, S, 4, 3)
+                model_foot_v = torch.zeros_like(model_feet)
+                model_foot_v[:, :-1] = (
+                    model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :]
+                )  # (N, S-1, 4, 3)
+                model_foot_v[~static_idx] = 0
+                foot_loss = self.vel_loss(
+                    model_foot_v, torch.zeros_like(model_foot_v)
+                )
+                self.tracker.update_meter("foot", "train", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight*20)
+                self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                g_loss_final += (vectices_loss)*self.args.rec_weight*self.args.rec_ver_weight 
+                g_loss_final += foot_loss*self.args.rec_weight*self.args.rec_ver_weight*20 
+            
+            # ---------------------- vae -------------------------- #
+            if "VQVAE" in self.args.g_name:
+                loss_embedding = net_out["embedding_loss"]
+                g_loss_final += loss_embedding
+                self.tracker.update_meter("com", "train", loss_embedding.item())
+            # elif "VAE" in self.args.g_name:
+            #     pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] 
+            #     KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp())
+            #     if epoch < 0:
+            #         KLD_weight = 0
+            #     else:
+            #         KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01
+            #     loss += KLD_weight * KLD
+            #     self.tracker.update_meter("kl", "train", KLD_weight * KLD.item())    
+            g_loss_final.backward()
+            if self.args.grad_norm != 0: 
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm)
+            self.opt.step()
+            t_train = time.time() - t_start - t_data
+            t_start = time.time()
+            mem_cost = torch.cuda.memory_cached() / 1E9
+            lr_g = self.opt.param_groups[0]['lr']
+            if its % self.args.log_period == 0:
+                self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g)   
+            if self.args.debug:
+                if its == 1: break
+        self.opt_s.step(epoch)
+                    
+    def val(self, epoch):
+        self.model.eval()
+        t_start = time.time()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.val_loader):
+                tar_pose_raw = dict_data["pose"]
+                tar_beta = dict_data["beta"].cuda()
+                tar_trans = dict_data["trans"].cuda()
+                tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps)
+                tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps)
+                #print(tar_pose.shape)
+                tar_pose = tar_pose_raw[:, :, :27].cuda() 
+
+                tar_contact = tar_pose_raw[:, :, 27:31].cuda()  
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_exps = torch.zeros((bs, n, 100)).cuda()
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                tar_trans_copy = tar_trans
+                tar_contact_copy = tar_contact
+                in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1)
+                t_data = time.time() - t_start 
+
+                #self.opt.zero_grad()
+                #g_loss_final = 0
+                net_out = self.model(in_tar_pose)
+                rec_pose = net_out["rec_pose"][:, :, :j*6]
+                rec_pose = rec_pose.reshape(bs, n, j, 6)
+                rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight
+                self.tracker.update_meter("rec", "val", loss_rec.item())
+                rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+                # print(rec_contact.shape, tar_contact.shape)
+                loss_contact = self.vel_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight
+                self.tracker.update_meter("contact", "val", loss_contact.item())
+                #g_loss_final += loss_rec
+                rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+                rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+                rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+                rec_y_trans = rec_trans[:,:,1:2]
+                rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+
+                # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+                # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+                # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+                # rec_y_trans = rec_trans[:,:,1:2]
+                # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+                # loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \
+                # + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight 
+                # v3 =  self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \
+                # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight
+                # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \
+                # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight
+                # #g_loss_final += 5*v3 
+                # #g_loss_final += 5*a3
+                # v2 =  self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight
+                # a2 =  self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight
+                #g_loss_final += 5*v2 
+                #g_loss_final += 5*a2 
+                # self.tracker.update_meter("transv", "val", loss_trans_vel.item())
+                # #g_loss_final += loss_trans_vel
+                # loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight 
+                # self.tracker.update_meter("trans", "val", loss_trans.item())
+                #g_loss_final += loss_trans
+
+                 # vertices loss
+                if self.args.rec_ver_weight > 0:
+                    # print(tar_pose.shape, bs, n, j)
+                    tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
+                    tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
+                    vertices_rec = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100), 
+                        jaw_pose=rec_pose[:, 66:69], 
+                        global_orient=rec_pose[:,:3], 
+                        body_pose=rec_pose[:,3:21*3+3], 
+                        left_hand_pose=rec_pose[:,25*3:40*3], 
+                        right_hand_pose=rec_pose[:,40*3:55*3], 
+                        return_verts=False, 
+                        return_joints=True,
+                        leye_pose=tar_pose[:, 69:72], 
+                        reye_pose=tar_pose[:, 72:75],
+                    )
+                    vertices_tar = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100), 
+                        jaw_pose=tar_pose[:, 66:69], 
+                        global_orient=tar_pose[:,:3], 
+                        body_pose=tar_pose[:,3:21*3+3], 
+                        left_hand_pose=tar_pose[:,25*3:40*3], 
+                        right_hand_pose=tar_pose[:,40*3:55*3], 
+                        return_verts=False, 
+                        return_joints=True,
+                        leye_pose=tar_pose[:, 69:72], 
+                        reye_pose=tar_pose[:, 72:75],
+                    )  
+                    joints_rec = vertices_rec['joints']
+                    joints_rec = joints_rec.reshape(bs, n, -1, 3)
+                    vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints'])
+                    foot_idx = [7, 8, 10, 11]
+                    model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7]
+                    # find static indices consistent with model's own predictions
+                    static_idx = model_contact > 0.95  # N x S x 4
+                    # print(model_contact)
+                    model_feet = joints_rec[:, :, foot_idx]  # foot positions (N, S, 4, 3)
+                    model_foot_v = torch.zeros_like(model_feet)
+                    model_foot_v[:, :-1] = (
+                        model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :]
+                    )  # (N, S-1, 4, 3)
+                    model_foot_v[~static_idx] = 0
+                    foot_loss = self.vectices_loss(
+                        model_foot_v, torch.zeros_like(model_foot_v)
+                    )
+                    self.tracker.update_meter("foot", "val", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                    self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
+                if "VQVAE" in self.args.g_name:
+                    loss_embedding = net_out["embedding_loss"]
+                    self.tracker.update_meter("com", "val", loss_embedding.item())
+                    #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight
+                if self.args.debug:
+                    if its == 1: break
+            self.val_recording(epoch)
+            
+    def test(self, epoch):
+        results_save_path = self.checkpoint_path + f"/{epoch}/"
+        if os.path.exists(results_save_path): 
+            return 0
+        os.makedirs(results_save_path)
+        start_time = time.time()
+        total_length = 0
+        test_seq_list = self.test_data.selected_file
+        self.model.eval()
+        with torch.no_grad():
+            for its, dict_data in enumerate(self.test_loader):
+                tar_pose_raw = dict_data["pose"]
+                tar_trans = dict_data["trans"].to(self.rank)
+                tar_pose = tar_pose_raw[:, :, :27].cuda() 
+                tar_contact = tar_pose_raw[:, :, 27:31].cuda() 
+                # tar_pose = tar_pose.cuda()
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+                tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+                tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                remain = n%self.args.pose_length
+                tar_pose = tar_pose[:, :n-remain, :]
+                tar_contact = tar_contact[:, :n-remain, :]
+                tar_trans_copy = tar_trans[:, :n-remain, :]
+                tar_contact_copy = tar_contact
+                in_tar_pose = torch.cat([tar_pose, tar_trans_copy, tar_contact_copy], dim=-1)
+                #print(tar_pose.shape)
+                if True:
+                    net_out = self.model(in_tar_pose)
+                    rec_pose = net_out["rec_pose"][:, :, :j*6]
+                    rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - net_out["rec_pose"][:, :, j*6:j*6+3]
+                    # print(rec_trans.shape)
+                    rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1])
+                    rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3])
+                    rec_y_trans = rec_trans[:,:,1:2]
+                    rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+                    n = rec_pose.shape[1]
+                    rec_trans = rec_trans.cpu().numpy().reshape(bs*n, 3)
+                    tar_pose = tar_pose[:, :n, :]
+                    rec_pose = rec_pose.reshape(bs, n, j, 6) 
+                    rec_pose = rc.rotation_6d_to_matrix(rec_pose)#
+                    rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                    rec_pose = rec_pose.cpu().numpy()
+                else:
+                    pass
+#                     for i in range(tar_pose.shape[1]//(self.args.vae_test_len)):
+#                         tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:]
+#                         net_out = self.model(**dict(inputs=tar_pose_new))
+#                         rec_pose = net_out["rec_pose"]
+#                         rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                         if "rot6d" in self.args.pose_rep:
+#                             rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6))
+#                             rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1)
+#                             if "smplx" not in self.args.pose_rep:
+#                                 rec_pose = torch.rad2deg(rec_pose)
+#                             rec_pose = rec_pose * self.joint_mask_cuda
+                            
+#                         out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2])
+#                         if i != 0:
+#                             out_final = np.concatenate((out_final,out_sub), 0)
+#                         else:
+#                             out_final = out_sub
+                            
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                tar_pose = tar_pose.cpu().numpy()
+                
+                total_length += n 
+                # --- save --- #
+                if 'smplx' in self.args.pose_rep:
+                    gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True)
+                    stride = int(30 / self.args.pose_fps)
+                    tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0])
+                    np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=tar_pose[:n],
+                        expressions=gt_npz["expressions"]-gt_npz["expressions"],
+                        trans=rec_trans-rec_trans,
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )
+                    rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0])
+                    np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
+                        betas=gt_npz["betas"],
+                        poses=rec_pose,
+                        expressions=gt_npz["expressions"]-gt_npz["expressions"],
+                        trans=rec_trans-rec_trans,
+                        model='smplx2020',
+                        gender='neutral',
+                        mocap_frame_rate = 30 ,
+                    )       
+                else:
+                    rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3)))
+                    rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy()                
+                    tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3)))
+                    tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() 
+                    #trans="0.000000 0.000000 0.000000"
+                    
+                    with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo:
+                        with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt:
+                            with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real:
+                                for i, line_data in enumerate(f_demo.readlines()):
+                                    if i < 431:
+                                        f_real.write(line_data)
+                                        f_gt.write(line_data)
+                                    else: break
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_real.write(line_data[1:-2]+'\n')
+                                for line_id in range(n): #,args.pre_frames, args.pose_length
+                                    line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ')
+                                    f_gt.write(line_data[1:-2]+'\n')
+                # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw:
+                #     pickle.dump(new_dict, fw)
+                # #new_dict2["fullpose"] = out_final
+                # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1:
+                #     pickle.dump(new_dict2, fw1)
+
+                # other_tools.render_one_sequence(
+                #     results_save_path+"res_"+test_seq_list[its]+'.pkl',
+                #     results_save_path+"gt_"+test_seq_list[its]+'.pkl',
+                #     results_save_path,
+                #     self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy',
+                # )
+                                                                                                
+                #if its == 1:break
+        end_time = time.time() - start_time
+        logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")

app.py

@@ -0,0 +1,671 @@
+import spaces
+import os
+# os.system("Xvfb :99 -ac &")
+# os.environ["DISPLAY"] = ":99"
+import OpenGL.GL as gl
+os.environ["PYOPENGL_PLATFORM"] = "egl"
+os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"
+import signal
+import time
+import csv
+import sys
+import warnings
+import random
+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+import torch.multiprocessing as mp
+import numpy as np
+import time
+import pprint
+from loguru import logger
+import smplx
+from torch.utils.tensorboard import SummaryWriter
+import wandb
+import matplotlib.pyplot as plt
+from utils import config, logger_tools, other_tools_hf, metric, data_transfer
+from dataloaders import data_tools
+from dataloaders.build_vocab import Vocab
+from optimizers.optim_factory import create_optimizer
+from optimizers.scheduler_factory import create_scheduler
+from optimizers.loss_factory import get_loss_func
+from dataloaders.data_tools import joints_list
+from utils import rotation_conversions as rc
+import soundfile as sf
+import librosa 
+
+def inverse_selection_tensor(filtered_t, selection_array, n):
+    selection_array = torch.from_numpy(selection_array).cuda()
+    original_shape_t = torch.zeros((n, 165)).cuda()
+    selected_indices = torch.where(selection_array == 1)[0]
+    for i in range(n):
+        original_shape_t[i, selected_indices] = filtered_t[i]
+    return original_shape_t
+
+@spaces.GPU(duration=120) 
+def test_demo_gpu(
+    model, vq_model_face, vq_model_upper, vq_model_hands, vq_model_lower, global_motion, smplx_model,
+    dict_data,
+    args,
+    joints, joint_mask_upper, joint_mask_lower, joint_mask_hands,
+    log_softmax,
+):
+    rank = 0
+    other_tools_hf.load_checkpoints(vq_model_face, args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name)
+    other_tools_hf.load_checkpoints(vq_model_upper, args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name)
+    other_tools_hf.load_checkpoints(vq_model_hands, args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name)
+    other_tools_hf.load_checkpoints(vq_model_lower, args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name)
+    other_tools_hf.load_checkpoints(global_motion, args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name)
+    other_tools_hf.load_checkpoints(model, args.test_ckpt, args.g_name)
+    model.to(rank).eval()
+    smplx_model.to(rank).eval()
+    vq_model_face.to(rank).eval()
+    vq_model_upper.to(rank).eval()
+    vq_model_hands.to(rank).eval()
+    vq_model_lower.to(rank).eval()
+    global_motion.to(rank).eval()
+        
+    with torch.no_grad():
+        tar_pose_raw = dict_data["pose"]
+        tar_pose = tar_pose_raw[:, :, :165].to(rank)
+        tar_contact = tar_pose_raw[:, :, 165:169].to(rank)
+        tar_trans = dict_data["trans"].to(rank)
+        tar_exps = dict_data["facial"].to(rank)
+        in_audio = dict_data["audio"].to(rank) 
+        in_word = None# dict_data["word"].to(rank)
+        tar_beta = dict_data["beta"].to(rank)
+        tar_id = dict_data["id"].to(rank).long()
+        bs, n, j = tar_pose.shape[0], tar_pose.shape[1], joints
+
+        tar_pose_jaw = tar_pose[:, :, 66:69]
+        tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3))
+        tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6)
+        tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2)
+
+        tar_pose_hands = tar_pose[:, :, 25*3:55*3]
+        tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3))
+        tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6)
+
+        tar_pose_upper = tar_pose[:, :, joint_mask_upper.astype(bool)]
+        tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3))
+        tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6)
+
+        tar_pose_leg = tar_pose[:, :, joint_mask_lower.astype(bool)]
+        tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3))
+        tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6)
+        tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2)
+
+        # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+        # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+        tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2)
+
+        tar_index_value_face_top = vq_model_face.map2index(tar_pose_face) # bs*n/4
+        tar_index_value_upper_top = vq_model_upper.map2index(tar_pose_upper) # bs*n/4
+        tar_index_value_hands_top = vq_model_hands.map2index(tar_pose_hands) # bs*n/4
+        tar_index_value_lower_top = vq_model_lower.map2index(tar_pose_lower) # bs*n/4
+      
+        latent_face_top = vq_model_face.map2latent(tar_pose_face) # bs*n/4
+        latent_upper_top = vq_model_upper.map2latent(tar_pose_upper) # bs*n/4
+        latent_hands_top = vq_model_hands.map2latent(tar_pose_hands) # bs*n/4
+        latent_lower_top = vq_model_lower.map2latent(tar_pose_lower) # bs*n/4
+        
+        latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2)
+        
+        index_in = torch.stack([tar_index_value_upper_top, tar_index_value_hands_top, tar_index_value_lower_top], dim=-1).long()
+        
+        tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3))
+        tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6)
+        latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1)
+
+        loaded_data = {
+            "tar_pose_jaw": tar_pose_jaw,
+            "tar_pose_face": tar_pose_face,
+            "tar_pose_upper": tar_pose_upper,
+            "tar_pose_lower": tar_pose_lower,
+            "tar_pose_hands": tar_pose_hands,
+            'tar_pose_leg': tar_pose_leg,
+            "in_audio": in_audio,
+            "in_word": in_word,
+            "tar_trans": tar_trans,
+            "tar_exps": tar_exps,
+            "tar_beta": tar_beta,
+            "tar_pose": tar_pose,
+            "tar4dis": tar4dis,
+            "tar_index_value_face_top": tar_index_value_face_top,
+            "tar_index_value_upper_top": tar_index_value_upper_top,
+            "tar_index_value_hands_top": tar_index_value_hands_top,
+            "tar_index_value_lower_top": tar_index_value_lower_top,
+            "latent_face_top": latent_face_top,
+            "latent_upper_top": latent_upper_top,
+            "latent_hands_top": latent_hands_top,
+            "latent_lower_top": latent_lower_top,
+            "latent_in":  latent_in,
+            "index_in": index_in,
+            "tar_id": tar_id,
+            "latent_all": latent_all,
+            "tar_pose_6d": tar_pose_6d,
+            "tar_contact": tar_contact,
+        }
+
+        mode = 'test'
+        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], joints 
+        tar_pose = loaded_data["tar_pose"]
+        tar_beta = loaded_data["tar_beta"]
+        in_word =None# loaded_data["in_word"]
+        tar_exps = loaded_data["tar_exps"]
+        tar_contact = loaded_data["tar_contact"]
+        in_audio = loaded_data["in_audio"]
+        tar_trans = loaded_data["tar_trans"]
+      
+        remain = n%8
+        if remain != 0:
+            tar_pose = tar_pose[:, :-remain, :]
+            tar_beta = tar_beta[:, :-remain, :]
+            tar_trans = tar_trans[:, :-remain, :]
+            # in_word = in_word[:, :-remain]
+            tar_exps = tar_exps[:, :-remain, :]
+            tar_contact = tar_contact[:, :-remain, :]
+            n = n - remain
+
+        tar_pose_jaw = tar_pose[:, :, 66:69]
+        tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3))
+        tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6)
+        tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2)
+
+        tar_pose_hands = tar_pose[:, :, 25*3:55*3]
+        tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3))
+        tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6)
+
+        tar_pose_upper = tar_pose[:, :, joint_mask_upper.astype(bool)]
+        tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3))
+        tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6)
+
+        tar_pose_leg = tar_pose[:, :, joint_mask_lower.astype(bool)]
+        tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3))
+        tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6)
+        tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2)
+        
+        tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3))
+        tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6)
+        latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1)
+        
+        rec_index_all_face = []
+        rec_index_all_upper = []
+        rec_index_all_lower = []
+        rec_index_all_hands = []
+        
+        roundt = (n - args.pre_frames) // (args.pose_length - args.pre_frames)
+        remain = (n - args.pre_frames) % (args.pose_length - args.pre_frames)
+        round_l = args.pose_length - args.pre_frames
+
+        for i in range(0, roundt):
+            # in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+args.pre_frames]
+            # audio fps is 16000 and pose fps is 30
+            in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*args.pre_frames]
+            in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+args.pre_frames]
+            mask_val = torch.ones(bs, args.pose_length, args.pose_dims+3+4).float().cuda()
+            mask_val[:, :args.pre_frames, :] = 0.0
+            if i == 0:
+                latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :]
+            else:
+                latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :]
+                # print(latent_all_tmp.shape, latent_last.shape)
+                latent_all_tmp[:, :args.pre_frames, :] = latent_last[:, -args.pre_frames:, :]
+            
+            net_out_val = model(
+                in_audio = in_audio_tmp,
+                in_word=None, #in_word_tmp,
+                mask=mask_val,
+                in_motion = latent_all_tmp,
+                in_id = in_id_tmp,
+                use_attentions=True,)
+            
+            if args.cu != 0:
+                rec_index_upper = log_softmax(net_out_val["cls_upper"]).reshape(-1, args.vae_codebook_size)
+                _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
+                #rec_upper = vq_model_upper.decode(rec_index_upper)
+            else:
+                _, rec_index_upper, _, _ = vq_model_upper.quantizer(net_out_val["rec_upper"])
+                #rec_upper = vq_model_upper.decoder(rec_index_upper)
+            if args.cl != 0:
+                rec_index_lower = log_softmax(net_out_val["cls_lower"]).reshape(-1, args.vae_codebook_size)
+                _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
+                #rec_lower = vq_model_lower.decode(rec_index_lower)
+            else:
+                _, rec_index_lower, _, _ = vq_model_lower.quantizer(net_out_val["rec_lower"])
+                #rec_lower = vq_model_lower.decoder(rec_index_lower)
+            if args.ch != 0:
+                rec_index_hands = log_softmax(net_out_val["cls_hands"]).reshape(-1, args.vae_codebook_size)
+                _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
+                #rec_hands = vq_model_hands.decode(rec_index_hands)
+            else:
+                _, rec_index_hands, _, _ = vq_model_hands.quantizer(net_out_val["rec_hands"])
+                #rec_hands = vq_model_hands.decoder(rec_index_hands)
+            if args.cf != 0:
+                rec_index_face = log_softmax(net_out_val["cls_face"]).reshape(-1, args.vae_codebook_size)
+                _, rec_index_face = torch.max(rec_index_face.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
+                #rec_face = vq_model_face.decoder(rec_index_face)
+            else:
+                _, rec_index_face, _, _ = vq_model_face.quantizer(net_out_val["rec_face"])
+                #rec_face = vq_model_face.decoder(rec_index_face)
+
+            if i == 0:
+                rec_index_all_face.append(rec_index_face)
+                rec_index_all_upper.append(rec_index_upper)
+                rec_index_all_lower.append(rec_index_lower)
+                rec_index_all_hands.append(rec_index_hands)
+            else:
+                rec_index_all_face.append(rec_index_face[:, args.pre_frames:])
+                rec_index_all_upper.append(rec_index_upper[:, args.pre_frames:])
+                rec_index_all_lower.append(rec_index_lower[:, args.pre_frames:])
+                rec_index_all_hands.append(rec_index_hands[:, args.pre_frames:])
+
+            if args.cu != 0:
+                rec_upper_last = vq_model_upper.decode(rec_index_upper)
+            else:
+                rec_upper_last = vq_model_upper.decoder(rec_index_upper)
+            if args.cl != 0:
+                rec_lower_last = vq_model_lower.decode(rec_index_lower)
+            else:
+                rec_lower_last = vq_model_lower.decoder(rec_index_lower)
+            if args.ch != 0:
+                rec_hands_last = vq_model_hands.decode(rec_index_hands)
+            else:
+                rec_hands_last = vq_model_hands.decoder(rec_index_hands)
+            # if args.cf != 0:
+            #     rec_face_last = vq_model_face.decode(rec_index_face)
+            # else:
+            #     rec_face_last = vq_model_face.decoder(rec_index_face)
+
+            rec_pose_legs = rec_lower_last[:, :, :54]
+            bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1]
+            rec_pose_upper = rec_upper_last.reshape(bs, n, 13, 6)
+            rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)#
+            rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3)
+            rec_pose_upper_recover = inverse_selection_tensor(rec_pose_upper, joint_mask_upper, bs*n)
+            rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6)
+            rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower)
+            rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3)
+            rec_pose_lower_recover = inverse_selection_tensor(rec_pose_lower, joint_mask_lower, bs*n)
+            rec_pose_hands = rec_hands_last.reshape(bs, n, 30, 6)
+            rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands)
+            rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3)
+            rec_pose_hands_recover = inverse_selection_tensor(rec_pose_hands, joint_mask_hands, bs*n)
+            rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover 
+            rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3))
+            rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
+            rec_trans_v_s = rec_lower_last[:, :, 54:57]
+            rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1])
+            rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/args.pose_fps, tar_trans[:, 0, 2:3])
+            rec_y_trans = rec_trans_v_s[:,:,1:2]
+            rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+            latent_last = torch.cat([rec_pose, rec_trans, rec_lower_last[:, :, 57:61]], dim=-1)
+
+        rec_index_face = torch.cat(rec_index_all_face, dim=1)
+        rec_index_upper = torch.cat(rec_index_all_upper, dim=1)
+        rec_index_lower = torch.cat(rec_index_all_lower, dim=1)
+        rec_index_hands = torch.cat(rec_index_all_hands, dim=1)
+        if args.cu != 0:
+            rec_upper = vq_model_upper.decode(rec_index_upper)
+        else:
+            rec_upper = vq_model_upper.decoder(rec_index_upper)
+        if args.cl != 0:
+            rec_lower = vq_model_lower.decode(rec_index_lower)
+        else:
+            rec_lower = vq_model_lower.decoder(rec_index_lower)
+        if args.ch != 0:
+            rec_hands = vq_model_hands.decode(rec_index_hands)
+        else:
+            rec_hands = vq_model_hands.decoder(rec_index_hands)
+        if args.cf != 0:
+            rec_face = vq_model_face.decode(rec_index_face)
+        else:
+            rec_face = vq_model_face.decoder(rec_index_face)
+
+        rec_exps = rec_face[:, :, 6:]
+        rec_pose_jaw = rec_face[:, :, :6]
+        rec_pose_legs = rec_lower[:, :, :54]
+        bs, n = rec_pose_jaw.shape[0], rec_pose_jaw.shape[1]
+        rec_pose_upper = rec_upper.reshape(bs, n, 13, 6)
+        rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)#
+        rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3)
+        rec_pose_upper_recover = inverse_selection_tensor(rec_pose_upper, joint_mask_upper, bs*n)
+        rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6)
+        rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower)
+        rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6)
+        rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3)
+        rec_pose_lower_recover = inverse_selection_tensor(rec_pose_lower, joint_mask_lower, bs*n)
+        rec_pose_hands = rec_hands.reshape(bs, n, 30, 6)
+        rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands)
+        rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3)
+        rec_pose_hands_recover = inverse_selection_tensor(rec_pose_hands, joint_mask_hands, bs*n)
+        rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6)
+        rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw)
+        rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3)
+        rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover 
+        rec_pose[:, 66:69] = rec_pose_jaw
+
+        to_global = rec_lower
+        to_global[:, :, 54:57] = 0.0
+        to_global[:, :, :54] = rec_lower2global
+        rec_global = global_motion(to_global)
+
+        rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57]
+        rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1])
+        rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/args.pose_fps, tar_trans[:, 0, 2:3])
+        rec_y_trans = rec_trans_v_s[:,:,1:2]
+        rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1)
+        tar_pose = tar_pose[:, :n, :]
+        tar_exps = tar_exps[:, :n, :]
+        tar_trans = tar_trans[:, :n, :]
+        tar_beta = tar_beta[:, :n, :]
+
+        rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3))
+        rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
+        tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3))
+        tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+        
+        net_out =  {
+            'rec_pose': rec_pose,
+            'rec_trans': rec_trans,
+            'tar_pose': tar_pose,
+            'tar_exps': tar_exps,
+            'tar_beta': tar_beta,
+            'tar_trans': tar_trans,
+            'rec_exps': rec_exps,
+        }
+        
+
+        tar_pose = net_out['tar_pose']
+        rec_pose = net_out['rec_pose']
+        tar_exps = net_out['tar_exps']
+        tar_beta = net_out['tar_beta']
+        rec_trans = net_out['rec_trans']
+        tar_trans = net_out['tar_trans']
+        rec_exps = net_out['rec_exps']
+                # print(rec_pose.shape, tar_pose.shape)
+        bs, n, j = tar_pose.shape[0], tar_pose.shape[1], joints
+                # interpolate to 30fps  
+        if (30/args.pose_fps) != 1:
+            assert 30%args.pose_fps == 0
+            n *= int(30/args.pose_fps)
+            tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1)
+            rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1)
+                
+                # print(rec_pose.shape, tar_pose.shape)
+        rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6))
+        rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+
+        tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6))
+        tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+        
+    return tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j
+
+    
+class BaseTrainer(object):
+    def __init__(self, args, sp, ap, tp):
+        hf_dir = "hf"
+        if not os.path.exists(args.out_path + "custom/" + hf_dir + "/"):
+            os.makedirs(args.out_path + "custom/" + hf_dir + "/")
+        sf.write(args.out_path + "custom/" + hf_dir + "/tmp.wav", ap[1], ap[0])
+        self.audio_path = args.out_path + "custom/" + hf_dir + "/tmp.wav"
+        audio, ssr = librosa.load(self.audio_path)
+        ap = (ssr, audio)
+        self.args = args
+        self.rank = 0 # dist.get_rank()
+       
+        #self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name
+        self.checkpoint_path = args.out_path + "custom/" + hf_dir + "/" 
+        if self.rank == 0:
+            self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test", smplx_path=sp, audio_path=ap, text_path=tp)
+            self.test_loader = torch.utils.data.DataLoader(
+                self.test_data, 
+                batch_size=1,  
+                shuffle=False,  
+                num_workers=args.loader_workers,
+                drop_last=False,
+            )
+        logger.info(f"Init test dataloader success")
+        model_module = __import__(f"models.{args.model}", fromlist=["something"])
+        
+        if args.ddp:
+            self.model = getattr(model_module, args.g_name)(args).to(self.rank)
+            process_group = torch.distributed.new_group()
+            self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group)   
+            self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank,
+                             broadcast_buffers=False, find_unused_parameters=False)
+        else: 
+            self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cpu()
+        
+        if self.rank == 0:
+            logger.info(self.model)
+            logger.info(f"init {args.g_name} success")
+
+        self.smplx = smplx.create(
+        self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        )
+                
+        self.args = args
+        self.joints = self.test_data.joints
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list_face = joints_list["beat_smplx_face"]
+        self.tar_joint_list_upper = joints_list["beat_smplx_upper"]
+        self.tar_joint_list_hands = joints_list["beat_smplx_hands"]
+        self.tar_joint_list_lower = joints_list["beat_smplx_lower"]
+
+        self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        self.joints = 55
+        for joint_name in self.tar_joint_list_face:
+            self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        for joint_name in self.tar_joint_list_upper:
+            self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        for joint_name in self.tar_joint_list_hands:
+            self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+        for joint_name in self.tar_joint_list_lower:
+            self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+
+        self.tracker = other_tools_hf.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False,False,False])
+
+        vq_model_module = __import__(f"models.motion_representation", fromlist=["something"])
+        self.args.vae_layer = 2
+        self.args.vae_length = 256
+        self.args.vae_test_dim = 106
+        self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu()
+        # print(self.vq_model_face)
+        # other_tools_hf.load_checkpoints(self.vq_model_face, self.args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name)
+        self.args.vae_test_dim = 78
+        self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu()
+        # other_tools_hf.load_checkpoints(self.vq_model_upper, self.args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name)
+        self.args.vae_test_dim = 180
+        self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu()
+        # other_tools_hf.load_checkpoints(self.vq_model_hands, self.args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name)
+        self.args.vae_test_dim = 61
+        self.args.vae_layer = 4
+        self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu()
+        # other_tools_hf.load_checkpoints(self.vq_model_lower, self.args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name)
+        self.args.vae_test_dim = 61
+        self.args.vae_layer = 4
+        self.global_motion = getattr(vq_model_module, "VAEConvZero")(self.args).cpu()
+        # other_tools_hf.load_checkpoints(self.global_motion, self.args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name)
+        self.args.vae_test_dim = 330
+        self.args.vae_layer = 4
+        self.args.vae_length = 240
+        
+        # self.cls_loss = nn.NLLLoss().to(self.rank)
+        # self.reclatent_loss = nn.MSELoss().to(self.rank)
+        # self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank)
+        # self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) 
+        self.log_softmax = nn.LogSoftmax(dim=2)
+      
+    
+    def inverse_selection(self, filtered_t, selection_array, n):
+        original_shape_t = np.zeros((n, selection_array.size))
+        selected_indices = np.where(selection_array == 1)[0]
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+        return original_shape_t
+    
+    def inverse_selection_tensor(self, filtered_t, selection_array, n):
+        selection_array = torch.from_numpy(selection_array).cuda()
+        original_shape_t = torch.zeros((n, 165)).cuda()
+        selected_indices = torch.where(selection_array == 1)[0]
+        for i in range(n):
+            original_shape_t[i, selected_indices] = filtered_t[i]
+        return original_shape_t
+
+    
+    def test_demo(self, epoch):
+        '''
+        input audio and text, output motion
+        do not calculate loss and metric
+        save video
+        '''
+        results_save_path = self.checkpoint_path + f"/{epoch}/"
+        if os.path.exists(results_save_path): 
+            import shutil
+            shutil.rmtree(results_save_path)
+        os.makedirs(results_save_path)
+        start_time = time.time()
+        total_length = 0
+        test_seq_list = self.test_data.selected_file
+        align = 0 
+        latent_out = []
+        latent_ori = []
+        l2_all = 0 
+        lvel = 0
+        for its, batch_data in enumerate(self.test_loader):
+            tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j = test_demo_gpu(
+                self.model, self.vq_model_face, self.vq_model_upper, self.vq_model_hands, self.vq_model_lower, self.global_motion, self.smplx,
+                batch_data,
+                self.args,
+                self.joints, self.joint_mask_upper, self.joint_mask_lower, self.joint_mask_hands,
+                self.log_softmax,
+            )
+                           
+            tar_pose_np = tar_pose.detach().cpu().numpy()
+            rec_pose_np = rec_pose.detach().cpu().numpy()
+            rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3)
+            rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100)
+            tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100)
+            tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3)
+                #'''
+        # its = 0
+            gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True)
+            np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
+                    betas=gt_npz["betas"],
+                    poses=tar_pose_np,
+                    expressions=tar_exp_np,
+                    trans=tar_trans_np,
+                    model='smplx2020',
+                    gender='neutral',
+                    mocap_frame_rate = 30,
+                )
+            np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
+                    betas=gt_npz["betas"],
+                    poses=rec_pose_np,
+                    expressions=rec_exp_np,
+                    trans=rec_trans_np,
+                    model='smplx2020',
+                    gender='neutral',
+                    mocap_frame_rate = 30,
+                )
+        
+            total_length += n
+            # render_vid_path = other_tools_hf.render_one_sequence_no_gt(
+            # results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', 
+            #         # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', 
+            #         results_save_path,
+            #         self.audio_path,
+            #         self.args.data_path_1+"smplx_models/",
+            #         use_matplotlib = False,
+            #         args = self.args,
+            #         )
+            render_vid_path = other_tools_hf.render_one_sequence_with_face(
+                    results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
+                    results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
+                    results_save_path,
+                    self.audio_path,
+                    self.args.data_path_1+"smplx_models/",
+                    use_matplotlib = False,
+                    args = self.args,
+                    )
+        result = gr.Video(value=render_vid_path, visible=True)
+        end_time = time.time() - start_time
+        logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")
+        return result
+
+       
+@logger.catch
+def emage(audio_path):
+    smplx_path = None
+    text_path = None
+    rank = 0
+    world_size = 1
+    args = config.parse_args()
+    #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/"
+    if not sys.warnoptions:
+        warnings.simplefilter("ignore")
+    # dist.init_process_group(backend="gloo", rank=rank, world_size=world_size)
+
+    #logger_tools.set_args_and_logger(args, rank)
+    other_tools_hf.set_random_seed(args)
+    other_tools_hf.print_exp_info(args)
+
+    # return one intance of trainer
+    trainer = BaseTrainer(args, sp = smplx_path, ap = audio_path, tp = text_path)
+    result = trainer.test_demo(999)
+    return result
+
+examples = [
+    ["./EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav"],
+    ["./EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav"],
+    ["./EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav"],
+]
+
+demo = gr.Interface(
+    emage,  # function
+    inputs=[
+        # gr.File(label="Please upload SMPL-X file with npz format here.", file_types=["npz", "NPZ"]),
+        gr.Audio(),
+        # gr.File(label="Please upload textgrid format file here.", file_types=["TextGrid", "Textgrid", "textgrid"])
+    ],  # input type
+    outputs=gr.Video(format="mp4", visible=True),
+    title='\
+    <div align="center">\
+    EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling <br/>\
+    CVPR 2024  <br/>\
+    </div>',
+    description='\
+    <div align="center">\
+    Haiyang Liu1*,  Zihao Zhu2*,  Giorgio Becherini3,  Yichen Peng4,  Mingyang Su5,<br/>\
+    You Zhou,  Xuefei Zhe,  Naoya Iwamoto,  Bo Zheng,  Michael J. Black3 <br/>\
+    (*Equal Contribution) <br/>\
+    1The University of Tokyo, 2Keio University, 4Japan Advanced Institute of Science and Technology, <br/>\
+    3Max Planck Institute for Intelligent Systems, 5Tsinghua University <br/>\
+    </div>\
+    ',
+    article="\
+    Due to the limited resources in this space, we process the first 60s of your uploaded audio，try to develop this space locally for longer motion generation, [Project Page](https://pantomatrix.github.io/EMAGE/)\
+    ",
+    examples=examples,
+)
+
+            
+if __name__ == "__main__":
+    os.environ["MASTER_ADDR"]='127.0.0.1'
+    os.environ["MASTER_PORT"]='8675'
+    #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
+    demo.launch(share=True)

camn_trainer.py

@@ -0,0 +1,361 @@
+import train
+import os
+import time
+import csv
+import sys
+import warnings
+import random
+import numpy as np
+import time
+import pprint
+import pickle
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+from torch.nn.parallel import DistributedDataParallel as DDP
+from loguru import logger
+import smplx
+import librosa
+
+from utils import config, logger_tools, other_tools, metric
+from utils import rotation_conversions as rc
+from dataloaders import data_tools
+from optimizers.optim_factory import create_optimizer
+from optimizers.scheduler_factory import create_scheduler
+from optimizers.loss_factory import get_loss_func
+from scipy.spatial.transform import Rotation
+
+
+class CustomTrainer(train.BaseTrainer):
+    def __init__(self, args):
+        super().__init__(args)
+        self.joints = self.train_data.joints
+        self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'div_reg', "kl"], [False,True,True, False, False, False, False, False, False, False, False, False, False])
+        if not self.args.rot6d: #"rot6d" not in args.pose_rep:
+            logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}")
+        self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank)
+        self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank)
+
+    def _load_data(self, dict_data):
+        tar_pose = dict_data["pose"].to(self.rank)
+        tar_trans = dict_data["trans"].to(self.rank)
+        tar_exps = dict_data["facial"].to(self.rank)
+        tar_beta = dict_data["beta"].to(self.rank)
+        tar_id = dict_data["id"].to(self.rank).long()
+        tar_word = dict_data["word"].to(self.rank)
+        in_audio = dict_data["audio"].to(self.rank)
+        in_emo = dict_data["emo"].to(self.rank) 
+        #in_sem = dict_data["sem"].to(self.rank) 
+        bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
+        tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
+        tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+        in_pre_pose_cat = torch.cat([tar_pose[:, 0:self.args.pre_frames], tar_trans[:, :self.args.pre_frames]], dim=2).to(self.rank)
+
+        in_pre_pose = tar_pose.new_zeros((bs, n, j*6+1+3)).to(self.rank)
+        in_pre_pose[:, 0:self.args.pre_frames, :-1] = in_pre_pose_cat[:, 0:self.args.pre_frames]
+        in_pre_pose[:, 0:self.args.pre_frames, -1] = 1
+        return {
+            "tar_pose": tar_pose,
+            "in_audio": in_audio,
+            "in_motion": in_pre_pose,
+            "tar_trans": tar_trans,
+            "tar_exps": tar_exps,
+            "tar_beta": tar_beta,
+            "tar_word": tar_word,
+            'tar_id': tar_id,
+            'in_emo': in_emo,
+            #'in_sem': in_sem,
+        }
+    
+    def _d_training(self, loaded_data):
+        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints 
+        net_out  = self.model(in_audio = loaded_data['in_audio'], pre_seq = loaded_data["in_motion"], in_text=loaded_data["tar_word"], in_id=loaded_data["tar_id"], in_emo=loaded_data["in_emo"], in_facial = loaded_data["tar_exps"])
+        rec_pose = net_out["rec_pose"][:, :, :j*6]
+        # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+        
+        rec_pose = rec_pose.reshape(bs, n, j, 6)
+        rec_pose = rc.rotation_6d_to_matrix(rec_pose)
+        rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
+        tar_pose = rc.rotation_6d_to_matrix(loaded_data["tar_pose"].reshape(bs, n, j, 6))
+        tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+        out_d_fake = self.d_model(rec_pose)
+        out_d_real = self.d_model(tar_pose)
+        
+        d_loss_adv = torch.sum(-torch.mean(torch.log(out_d_real + 1e-8) + torch.log(1 - out_d_fake + 1e-8)))
+        self.tracker.update_meter("dis", "train", d_loss_adv.item())
+        return d_loss_adv
+    
+    def _g_training(self, loaded_data, use_adv, mode="train"):
+        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints 
+        net_out  = self.model(in_audio = loaded_data['in_audio'], pre_seq = loaded_data["in_motion"], in_text=loaded_data["tar_word"], in_id=loaded_data["tar_id"], in_emo=loaded_data["in_emo"], in_facial = loaded_data["tar_exps"])
+        rec_pose = net_out["rec_pose"][:, :, :j*6]
+        rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
+        # print(rec_pose.shape, bs, n, j, loaded_data['in_audio'].shape, loaded_data["in_motion"].shape)
+        rec_pose = rec_pose.reshape(bs, n, j, 6)
+        rec_pose = rc.rotation_6d_to_matrix(rec_pose)
+        tar_pose = rc.rotation_6d_to_matrix(loaded_data["tar_pose"].reshape(bs, n, j, 6))
+
+        rec_loss = self.rec_loss(tar_pose, rec_pose)
+        rec_loss *= self.args.rec_weight
+        self.tracker.update_meter("rec", mode, rec_loss.item())
+        # rec_loss_vel = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1])
+        # self.tracker.update_meter("vel", mode, rec_loss_vel.item())
+        # rec_loss_acc = self.vel_loss(rec_pose[:, 2:] - 2*rec_pose[:, 1:-1] + rec_pose[:, :-2], tar_pose[:, 2:] - 2*tar_pose[:, 1:-1] + tar_pose[:, :-2])
+        # self.tracker.update_meter("acc", mode, rec_loss_acc.item())
+        
+        rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
+        tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+        if self.args.pose_dims < 330 and mode != "train":
+            rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs, n, j, 6))
+            rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs, n, j*3)
+            rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
+            rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, 55, 3))
+            rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, 55*6)
+
+            tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6))
+            tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs, n, j*3)
+            tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
+            tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3))
+            tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, 55*6)
+        if use_adv and mode == 'train':
+            out_d_fake = self.d_model(rec_pose)
+            d_loss_adv = -torch.mean(torch.log(out_d_fake + 1e-8))
+            self.tracker.update_meter("gen", mode, d_loss_adv.item())
+        else:
+            d_loss_adv = 0
+
+        if self.args.train_trans:
+            trans_loss = self.vel_loss(rec_trans, loaded_data["tar_trans"])
+            trans_loss *= self.args.rec_weight
+            self.tracker.update_meter("trans", mode, trans_loss.item())
+        else:
+            trans_loss = 0
+        # trans_loss_vel = self.vel_loss(rec_trans[:, 1:] - rec_trans[:, :-1], loaded_data["tar_trans"][:, 1:] - loaded_data["tar_trans"][:, :-1])
+        # self.tracker.update_meter("transv", mode, trans_loss_vel.item())
+        # trans_loss_acc = self.vel_loss(rec_trans[:, 2:] - 2*rec_trans[:, 1:-1] + rec_trans[:, :-2], loaded_data["tar_trans"][:, 2:] - 2*loaded_data["tar_trans"][:, 1:-1] + loaded_data["tar_trans"][:, :-2])
+        # self.tracker.update_meter("transa", mode, trans_loss_acc.item())
+
+        if mode == 'train':
+            return d_loss_adv + rec_loss + trans_loss # + rec_loss_vel + rec_loss_acc + trans_loss_vel + trans_loss_acc
+        elif mode == 'val':
+            return {
+                'rec_pose': rec_pose,
+                'rec_trans': rec_trans,
+                'tar_pose': tar_pose,
+            }
+        else:
+            return {
+                'rec_pose': rec_pose,
+                'rec_trans': rec_trans,
+                'tar_pose': tar_pose,
+                'tar_exps': loaded_data["tar_exps"],
+                'tar_beta': loaded_data["tar_beta"],
+                'tar_trans': loaded_data["tar_trans"],
+            }
+        
+    def train(self, epoch):
+        use_adv = bool(epoch>=self.args.no_adv_epoch)
+        self.model.train()
+        self.d_model.train()
+        self.tracker.reset()
+        t_start = time.time()
+        for its, batch_data in enumerate(self.train_loader):
+            loaded_data = self._load_data(batch_data)
+            t_data = time.time() - t_start
+    
+            if use_adv:
+                d_loss_final = 0
+                self.opt_d.zero_grad()
+                d_loss_adv = self._d_training(loaded_data)
+                d_loss_final += d_loss_adv
+                d_loss_final.backward()
+                self.opt_d.step() 
+    
+            self.opt.zero_grad()
+            g_loss_final = 0
+            g_loss_final += self._g_training(loaded_data, use_adv, 'train')
+            g_loss_final.backward()
+            self.opt.step()
+
+            mem_cost = torch.cuda.memory_cached() / 1E9
+            lr_g = self.opt.param_groups[0]['lr']
+            lr_d = self.opt_d.param_groups[0]['lr']
+            t_train = time.time() - t_start - t_data
+            t_start = time.time()
+            if its % self.args.log_period == 0:
+                self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g, lr_d=lr_d)   
+            if self.args.debug:
+                if its == 1: break
+        self.opt_s.step(epoch)
+        self.opt_d_s.step(epoch)        
+    
+    
+    def val(self, epoch):
+        self.model.eval()
+        self.d_model.eval()
+        with torch.no_grad():
+            for its, batch_data in enumerate(self.train_loader):
+                loaded_data = self._load_data(batch_data)    
+                net_out = self._g_training(loaded_data, False, 'val')
+                tar_pose = net_out['tar_pose']
+                rec_pose = net_out['rec_pose']
+                n = tar_pose.shape[1]
+                if (30/self.args.pose_fps) != 1:
+                    assert 30%self.args.pose_fps == 0
+                    n *= int(30/self.args.pose_fps)
+                    tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                    rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                n = tar_pose.shape[1]
+                remain = n%self.args.vae_test_len
+                tar_pose = tar_pose[:, :n-remain, :]
+                rec_pose = rec_pose[:, :n-remain, :]
+                latent_out = self.eval_copy.map2latent(rec_pose).reshape(-1, self.args.vae_length).cpu().numpy()
+                latent_ori = self.eval_copy.map2latent(tar_pose).reshape(-1, self.args.vae_length).cpu().numpy()
+                if its == 0:
+                    latent_out_motion_all = latent_out
+                    latent_ori_all = latent_ori
+                else:
+                    latent_out_motion_all = np.concatenate([latent_out_motion_all, latent_out], axis=0)                 
+                    latent_ori_all = np.concatenate([latent_ori_all, latent_ori], axis=0)
+                if self.args.debug:
+                    if its == 1: break
+        fid_motion = data_tools.FIDCalculator.frechet_distance(latent_out_motion_all, latent_ori_all)
+        self.tracker.update_meter("fid", "val", fid_motion)
+        self.val_recording(epoch)  
+        
+    def test(self, epoch):
+        results_save_path = self.checkpoint_path + f"/{epoch}/"
+        if os.path.exists(results_save_path): 
+            return 0
+        os.makedirs(results_save_path)
+        start_time = time.time()
+        total_length = 0
+        test_seq_list = self.test_data.selected_file
+        align = 0 
+        latent_out = []
+        latent_ori = []
+        self.model.eval()
+        self.smplx.eval()
+        self.eval_copy.eval()
+        with torch.no_grad():
+            for its, batch_data in enumerate(self.test_loader):
+                loaded_data = self._load_data(batch_data)    
+                net_out = self._g_training(loaded_data, False, 'test')
+                tar_pose = net_out['tar_pose']
+                rec_pose = net_out['rec_pose']
+                tar_exps = net_out['tar_exps']
+                tar_beta = net_out['tar_beta']
+                rec_trans = net_out['rec_trans']
+                tar_trans = net_out['tar_trans']
+                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], 55
+                if (30/self.args.pose_fps) != 1:
+                    assert 30%self.args.pose_fps == 0
+                    n *= int(30/self.args.pose_fps)
+                    tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                    rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                    tar_beta = torch.nn.functional.interpolate(tar_beta.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                    tar_exps = torch.nn.functional.interpolate(tar_exps.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) 
+                    tar_trans = torch.nn.functional.interpolate(tar_trans.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                    rec_trans = torch.nn.functional.interpolate(rec_trans.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
+                
+                # print(rec_pose.shape, tar_pose.shape)
+                # rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6))
+                # rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
+                # tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6))
+                # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
+                remain = n%self.args.vae_test_len
+                latent_out.append(self.eval_copy.map2latent(rec_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy()) # bs * n/8 * 240
+                latent_ori.append(self.eval_copy.map2latent(tar_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy())
+                
+                rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6))
+                rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3)
+                tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6))
+                tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3)
+                
+                vertices_rec = self.smplx(
+                        betas=tar_beta.reshape(bs*n, 300), 
+                        transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), 
+                        expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100),
+                        jaw_pose=rec_pose[:, 66:69], 
+                        global_orient=rec_pose[:,:3], 
+                        body_pose=rec_pose[:,3:21*3+3], 
+                        left_hand_pose=rec_pose[:,25*3:40*3], 
+                        right_hand_pose=rec_pose[:,40*3:55*3], 
+                        return_joints=True, 
+                        leye_pose=rec_pose[:, 69:72], 
+                        reye_pose=rec_pose[:, 72:75],
+                    )
+                # vertices_tar = self.smplx(
+                #         betas=tar_beta.reshape(bs*n, 300), 
+                #         transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), 
+                #         expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100),
+                #         jaw_pose=tar_pose[:, 66:69], 
+                #         global_orient=tar_pose[:,:3], 
+                #         body_pose=tar_pose[:,3:21*3+3], 
+                #         left_hand_pose=tar_pose[:,25*3:40*3], 
+                #         right_hand_pose=tar_pose[:,40*3:55*3], 
+                #         return_joints=True, 
+                #         leye_pose=tar_pose[:, 69:72], 
+                #         reye_pose=tar_pose[:, 72:75],
+                #     )
+                joints_rec = vertices_rec["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3]
+                # joints_tar = vertices_tar["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3]
+                _ = self.l1_calculator.run(joints_rec)
+                if self.alignmenter is not None:
+                    in_audio_eval, sr = librosa.load(self.args.data_path+"wave16k/"+test_seq_list.iloc[its]['id']+".wav")
+                    in_audio_eval = librosa.resample(in_audio_eval, orig_sr=sr, target_sr=self.args.audio_sr)
+                    a_offset = int(self.align_mask * (self.args.audio_sr / self.args.pose_fps))
+                    onset_bt = self.alignmenter.load_audio(in_audio_eval[:int(self.args.audio_sr / self.args.pose_fps*n)], a_offset, len(in_audio_eval)-a_offset, True)
+                    beat_vel = self.alignmenter.load_pose(joints_rec, self.align_mask, n-self.align_mask, 30, True)
+                    # print(beat_vel)
+                    align += (self.alignmenter.calculate_align(onset_bt, beat_vel, 30) * (n-2*self.align_mask))
+               
+                tar_pose_axis_np = tar_pose.detach().cpu().numpy()
+                rec_pose_axis_np = rec_pose.detach().cpu().numpy()
+                rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3)
+                rec_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exps.detach().cpu().numpy().reshape(bs*n, 100)
+                tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exps.detach().cpu().numpy().reshape(bs*n, 100)
+                tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3)
+                gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True)
+                if not self.args.train_trans:
+                    tar_trans_np = tar_trans_np - tar_trans_np
+                    rec_trans_np = rec_trans_np - rec_trans_np
+                np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
+                    betas=gt_npz["betas"],
+                    poses=tar_pose_axis_np,
+                    expressions=tar_exp_np,
+                    trans=tar_trans_np,
+                    model='smplx2020',
+                    gender='neutral',
+                    mocap_frame_rate = 30 ,
+                )
+                np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
+                    betas=gt_npz["betas"],
+                    poses=rec_pose_axis_np,
+                    expressions=rec_exp_np,
+                    trans=rec_trans_np,
+                    model='smplx2020',
+                    gender='neutral',
+                    mocap_frame_rate = 30,
+                )
+                total_length += n
+
+        latent_out_all = np.concatenate(latent_out, axis=0)
+        latent_ori_all = np.concatenate(latent_ori, axis=0)
+        fid = data_tools.FIDCalculator.frechet_distance(latent_out_all, latent_ori_all)
+        logger.info(f"fid score: {fid}")
+        self.test_recording("fid", fid, epoch) 
+        
+        align_avg = align/(total_length-2*len(self.test_loader)*self.align_mask)
+        logger.info(f"align score: {align_avg}")
+        self.test_recording("bc", align_avg, epoch)
+
+        l1div = self.l1_calculator.avg()
+        logger.info(f"l1div score: {l1div}")
+        self.test_recording("l1div", l1div, epoch)
+
+        # data_tools.result2target_vis(self.args.pose_version, results_save_path, results_save_path, self.test_demo, False)
+        end_time = time.time() - start_time
+        logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")

configs/.ipynb_checkpoints/emage_test_hf-checkpoint.yaml

@@ -0,0 +1,101 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./EMAGE/test_sequences/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./EMAGE/emage_audio_175.bin
+data_path_1: ./EMAGE/
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/
+dataset: beat_testonly_hf
+new_cache: True
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 64
+stride: 20
+test_length: 64
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+# audio config
+audio_rep: wave16k
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: none
+# a_fix_pre: False
+
+# text config
+# word_rep: textgrid
+# word_index_num: 11195
+# word_dims: 300
+# freeze_wordembed: False
+# word_f: 256
+# t_pre_encoder: fasttext
+# t_encoder: null
+# t_fix_pre: False
+
+# facial config
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+# speaker config
+id_rep: onehot
+speaker_f: 0
+
+# model config
+batch_size: 64
+# warmup_epochs: 1
+# warmup_lr: 1e-6
+lr_base: 5e-4
+model: emage_audio
+g_name: MAGE_Transformer
+trainer: emage
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 400
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1

configs/camn.yaml

@@ -0,0 +1,101 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./BEAT2/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./EMAGE/camn.bin
+data_path_1: ./EMAGE/
+vae_test_len: 64
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: datasets/beat_cache/beat_smplx_en_camn/
+dataset: beat_sep
+new_cache: False
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 15
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 32
+stride: 10
+test_length: 32
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+# audio config
+audio_rep: wave16k
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 128
+# a_pre_encoder: tcn_camn
+# a_encoder: none
+# a_fix_pre: False
+
+# text config
+word_rep: textgrid
+word_index_num: 11195
+word_dims: 300
+freeze_wordembed: False
+word_f: 128
+t_pre_encoder: fasttext
+t_encoder: null
+t_fix_pre: False
+
+# facial config
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 64
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+# speaker config
+id_rep: onehot
+speaker_f: 16
+emo_rep: emo
+emotion_f: 8
+# sem_rep: sem
+
+
+# model config
+batch_size: 128
+# warmup_epochs: 1
+# warmup_lr: 1e-6
+lr_base: 3e-4
+model: camn
+g_name: CaMN
+d_name: ConvDiscriminator
+trainer: camn
+hidden_size: 512
+n_layer: 4
+rec_weight: 500
+no_adv_epoch: 999
+# rec_pos_weight: 1
+# rec_ver_weight: 0
+# rec_fac_weight: 1
+# grad_norm: 1
+epochs: 100
+test_period: 20

configs/cnn_vqvae_face_30.yaml

@@ -0,0 +1,82 @@
+is_train: True
+ddp: False
+stat: ts
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+root_path: ./
+out_path: ./outputs/audio2pose/
+cache_path: datasets/beat_cache/beat_smplx_en_face/
+project: mage_smplx
+data_path: ./BEAT2/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+test_ckpt: weights/multi.bin
+data_path_1: ./EMAGE/
+#torch_hub_path: datasets/hub/
+additional_data: False
+dataset: beat_sep
+new_cache: False
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_face
+pose_rep: smplxflame_30
+facial_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+
+
+vae_test_len: 64
+vae_test_dim: 106
+vae_test_stride: 20
+vae_length: 256
+vae_codebook_size: 256
+vae_layer: 2
+vae_grow: [1,1,2,1]
+variational: False
+
+pose_dims: 106
+pose_length: 64
+stride: 20
+facial_dims: 100
+word_index_num: 11195
+word_dims: 300
+batch_size: 64
+lr_base: 3e-4
+model: motion_representation
+g_name: VQVAEConvZero
+#eval_model: motion_autoencoder
+#e_name: HalfEmbeddingNet
+trainer: aeface
+decay_epochs: 780
+# audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: lp
+# a_fix_pre: False
+
+# freeze_wordembed: False
+# word_f: 128
+# t_pre_encoder: fasttext
+# t_encoder: lp
+# t_fix_pre: False
+
+# motion_f: 256
+# m_pre_encoder: lp
+# m_encoder: lp
+# m_fix_pre: False
+
+# facial_f: 128
+# f_pre_encoder: lp
+# f_encoder: lp
+# f_fix_pre: False
+
+#m_decoder: lstm
+#decode_fusion: cat
+#n_layer: 2
+#hidden_size: 512
+rec_weight: 1
+rec_pos_weight: 1
+rec_ver_weight: 1
+# rec_fac_weight: 1
+#ita_weight: 0
+#iwa_weight: 0
+#fusion_mode: sum
+# grad_norm: 1
+epochs: 800
+test_period: 100

configs/cnn_vqvae_hands_30.yaml

@@ -0,0 +1,81 @@
+is_train: True
+ddp: False
+stat: ts
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+root_path: ./
+out_path: ./outputs/audio2pose/
+cache_path: datasets/beat_cache/beat_smplx_en_hands/
+project: mage_smplx
+data_path: ./BEAT2/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+test_ckpt: weights/multi.bin
+data_path_1: ./EMAGE/
+#torch_hub_path: datasets/hub/
+additional_data: False
+dataset: beat_sep
+new_cache: False
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_hands
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+
+
+vae_test_len: 64
+vae_test_dim: 180
+vae_test_stride: 20
+vae_length: 256
+vae_codebook_size: 256
+vae_layer: 2
+vae_grow: [1,1,2,1]
+variational: False
+
+pose_dims: 180
+pose_length: 64
+stride: 20
+facial_dims: 100
+word_index_num: 11195
+word_dims: 300
+batch_size: 64
+lr_base: 3e-4
+model: motion_representation
+g_name: VQVAEConvZero
+#eval_model: motion_autoencoder
+#e_name: HalfEmbeddingNet
+trainer: ae
+decay_epochs: 780
+# audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: lp
+# a_fix_pre: False
+
+# freeze_wordembed: False
+# word_f: 128
+# t_pre_encoder: fasttext
+# t_encoder: lp
+# t_fix_pre: False
+
+# motion_f: 256
+# m_pre_encoder: lp
+# m_encoder: lp
+# m_fix_pre: False
+
+# facial_f: 128
+# f_pre_encoder: lp
+# f_encoder: lp
+# f_fix_pre: False
+
+#m_decoder: lstm
+#decode_fusion: cat
+#n_layer: 2
+#hidden_size: 512
+rec_weight: 1
+rec_pos_weight: 1
+rec_ver_weight: 1
+# rec_fac_weight: 1
+#ita_weight: 0
+#iwa_weight: 0
+#fusion_mode: sum
+# grad_norm: 1
+epochs: 800
+test_period: 100

configs/cnn_vqvae_lower_30.yaml

@@ -0,0 +1,81 @@
+is_train: True
+ddp: False
+stat: ts
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+root_path: ./
+out_path: ./outputs/audio2pose/
+cache_path: datasets/beat_cache/beat_smplx_en_lower/
+project: mage_smplx
+data_path: ./BEAT2/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+test_ckpt: weights/multi.bin
+data_path_1: ./EMAGE/
+#torch_hub_path: datasets/hub/
+additional_data: False
+dataset: beat_sep_lower
+new_cache: False
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_lower
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+
+
+vae_test_len: 64
+vae_test_dim: 61
+vae_test_stride: 20
+vae_length: 256
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+pose_dims: 61
+pose_length: 64
+stride: 20
+facial_dims: 100
+word_index_num: 11195
+word_dims: 300
+batch_size: 64
+lr_base: 3e-4
+model: motion_representation
+g_name: VAEConvZero
+#eval_model: motion_autoencoder
+#e_name: HalfEmbeddingNet
+trainer: aelower
+decay_epochs: 780
+# audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: lp
+# a_fix_pre: False
+
+# freeze_wordembed: False
+# word_f: 128
+# t_pre_encoder: fasttext
+# t_encoder: lp
+# t_fix_pre: False
+
+# motion_f: 256
+# m_pre_encoder: lp
+# m_encoder: lp
+# m_fix_pre: False
+
+# facial_f: 128
+# f_pre_encoder: lp
+# f_encoder: lp
+# f_fix_pre: False
+
+#m_decoder: lstm
+#decode_fusion: cat
+#n_layer: 2
+#hidden_size: 512
+rec_weight: 1
+rec_pos_weight: 1
+rec_ver_weight: 1
+# rec_fac_weight: 1
+#ita_weight: 0
+#iwa_weight: 0
+#fusion_mode: sum
+# grad_norm: 1
+epochs: 800
+test_period: 100

configs/cnn_vqvae_lower_foot_30.yaml

@@ -0,0 +1,81 @@
+is_train: True
+ddp: False
+stat: ts
+training_speakers: [2]
+root_path: ./
+out_path: ./outputs/audio2pose/
+cache_path: datasets/beat_cache/beat_smplx_en_lower/
+project: mage_smplx
+data_path: ./BEAT2/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+test_ckpt: weights/multi.bin
+data_path_1: ./EMAGE/
+#torch_hub_path: datasets/hub/
+additional_data: False
+dataset: beat_sep_lower
+new_cache: False
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_lower
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+
+
+vae_test_len: 64
+vae_test_dim: 61
+vae_test_stride: 20
+vae_length: 256
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+pose_dims: 61
+pose_length: 64
+stride: 20
+facial_dims: 100
+word_index_num: 11195
+word_dims: 300
+batch_size: 64
+lr_base: 3e-4
+model: motion_representation
+g_name: VQVAEConvZero
+#eval_model: motion_autoencoder
+#e_name: HalfEmbeddingNet
+trainer: aelowerfoot
+decay_epochs: 780
+# audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: lp
+# a_fix_pre: False
+
+# freeze_wordembed: False
+# word_f: 128
+# t_pre_encoder: fasttext
+# t_encoder: lp
+# t_fix_pre: False
+
+# motion_f: 256
+# m_pre_encoder: lp
+# m_encoder: lp
+# m_fix_pre: False
+
+# facial_f: 128
+# f_pre_encoder: lp
+# f_encoder: lp
+# f_fix_pre: False
+
+#m_decoder: lstm
+#decode_fusion: cat
+#n_layer: 2
+#hidden_size: 512
+rec_weight: 1
+rec_pos_weight: 1
+rec_ver_weight: 1
+# rec_fac_weight: 1
+#ita_weight: 0
+#iwa_weight: 0
+#fusion_mode: sum
+# grad_norm: 1
+epochs: 800
+test_period: 100

configs/cnn_vqvae_upper_30.yaml

@@ -0,0 +1,82 @@
+is_train: True
+ddp: False
+stat: ts
+training_speakers: [2]
+root_path: ./
+out_path: ./outputs/audio2pose/
+cache_path: datasets/beat_cache/beat_smplx_en_upper/
+project: mage_smplx
+data_path: ./BEAT2/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+test_ckpt: weights/multi.bin
+data_path_1: ./EMAGE/
+#torch_hub_path: datasets/hub/
+additional_data: False
+dataset: beat_sep
+new_cache: False
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_upper
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+
+
+vae_test_len: 64
+vae_test_dim: 78
+vae_test_stride: 20
+vae_length: 256
+vae_codebook_size: 256
+vae_layer: 2
+vae_grow: [1,1,2,1]
+variational: False
+
+pose_dims: 78
+pose_length: 64
+stride: 20
+facial_dims: 100
+word_index_num: 11195
+word_dims: 300
+batch_size: 64
+lr_base: 3e-4
+decay_epochs: 9999
+model: motion_representation
+g_name: VQVAEConvZero
+#eval_model: motion_autoencoder
+#e_name: HalfEmbeddingNet
+trainer: ae
+
+# audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: lp
+# a_fix_pre: False
+
+# freeze_wordembed: False
+# word_f: 128
+# t_pre_encoder: fasttext
+# t_encoder: lp
+# t_fix_pre: False
+
+# motion_f: 256
+# m_pre_encoder: lp
+# m_encoder: lp
+# m_fix_pre: False
+
+# facial_f: 128
+# f_pre_encoder: lp
+# f_encoder: lp
+# f_fix_pre: False
+
+#m_decoder: lstm
+#decode_fusion: cat
+#n_layer: 2
+#hidden_size: 512
+rec_weight: 1
+rec_pos_weight: 1
+rec_ver_weight: 1
+# rec_fac_weight: 1
+#ita_weight: 0
+#iwa_weight: 0
+#fusion_mode: sum
+# grad_norm: 1
+epochs: 500
+test_period: 100

configs/emage.yaml

@@ -0,0 +1,101 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./BEAT2/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./EMAGE/emage_240.bin
+data_path_1: ./EMAGE/
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: datasets/beat_cache/beat_smplx_en_emage/
+dataset: beat_sep_lower
+new_cache: False
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 64
+stride: 20
+test_length: 64
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+# audio config
+audio_rep: onset+amplitude
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: none
+# a_fix_pre: False
+
+# text config
+word_rep: textgrid
+word_index_num: 11195
+word_dims: 300
+freeze_wordembed: False
+word_f: 256
+t_pre_encoder: fasttext
+t_encoder: null
+t_fix_pre: False
+
+# facial config
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+# speaker config
+id_rep: onehot
+speaker_f: 0
+
+# model config
+batch_size: 64
+# warmup_epochs: 1
+# warmup_lr: 1e-6
+lr_base: 5e-4
+model: emage
+g_name: MAGE_Transformer
+trainer: emage
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 400
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1

configs/emage_test.yaml

@@ -0,0 +1,101 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./EMAGE/test_sequences/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./EMAGE/emage_240.bin
+data_path_1: ./EMAGE/
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/
+dataset: beat_testonly
+new_cache: True
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 64
+stride: 20
+test_length: 64
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+# audio config
+audio_rep: onset+amplitude
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: none
+# a_fix_pre: False
+
+# text config
+word_rep: textgrid
+word_index_num: 11195
+word_dims: 300
+freeze_wordembed: False
+word_f: 256
+t_pre_encoder: fasttext
+t_encoder: null
+t_fix_pre: False
+
+# facial config
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+# speaker config
+id_rep: onehot
+speaker_f: 0
+
+# model config
+batch_size: 64
+# warmup_epochs: 1
+# warmup_lr: 1e-6
+lr_base: 5e-4
+model: emage
+g_name: MAGE_Transformer
+trainer: emage
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 400
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1

configs/emage_test_colab.yaml

@@ -0,0 +1,101 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./EMAGE/test_sequences/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./EMAGE/emage_240.bin
+data_path_1: ./EMAGE/
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/
+dataset: beat_testonly_colab
+new_cache: True
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 64
+stride: 20
+test_length: 64
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+# audio config
+audio_rep: onset+amplitude
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: none
+# a_fix_pre: False
+
+# text config
+word_rep: textgrid
+word_index_num: 11195
+word_dims: 300
+freeze_wordembed: False
+word_f: 256
+t_pre_encoder: fasttext
+t_encoder: null
+t_fix_pre: False
+
+# facial config
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+# speaker config
+id_rep: onehot
+speaker_f: 0
+
+# model config
+batch_size: 64
+# warmup_epochs: 1
+# warmup_lr: 1e-6
+lr_base: 5e-4
+model: emage
+g_name: MAGE_Transformer
+trainer: emage
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 400
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1

configs/emage_test_hf.yaml

@@ -0,0 +1,101 @@
+is_train: True
+ddp: False
+stat: ts
+root_path: ./
+out_path: ./outputs/audio2pose/
+project: s2g
+data_path: ./EMAGE/test_sequences/
+e_path:  weights/AESKConv_240_100.bin
+eval_model: motion_representation
+e_name: VAESKConv
+test_ckpt: ./EMAGE/emage_audio_175.bin
+data_path_1: ./EMAGE/
+vae_test_len: 32
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_codebook_size: 256
+vae_layer: 4
+vae_grow: [1,1,2,1]
+variational: False
+
+# data config
+training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+additional_data: False
+cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/
+dataset: beat_testonly_hf
+new_cache: True
+
+# motion config
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+rot6d: True
+pre_frames: 4
+pose_dims: 330
+pose_length: 64
+stride: 20
+test_length: 64
+motion_f: 256
+m_pre_encoder: null
+m_encoder: null
+m_fix_pre: False
+
+# audio config
+audio_rep: wave16k
+audio_sr: 16000
+audio_fps: 16000
+audio_norm: False
+audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: none
+# a_fix_pre: False
+
+# text config
+# word_rep: textgrid
+# word_index_num: 11195
+# word_dims: 300
+# freeze_wordembed: False
+# word_f: 256
+# t_pre_encoder: fasttext
+# t_encoder: null
+# t_fix_pre: False
+
+# facial config
+facial_rep: smplxflame_30
+facial_dims: 100
+facial_norm: False
+facial_f: 0
+f_pre_encoder: null
+f_encoder: null
+f_fix_pre: False
+
+# speaker config
+id_rep: onehot
+speaker_f: 0
+
+# model config
+batch_size: 64
+# warmup_epochs: 1
+# warmup_lr: 1e-6
+lr_base: 5e-4
+model: emage_audio
+g_name: MAGE_Transformer
+trainer: emage
+hidden_size: 768
+n_layer: 1
+ 
+rec_weight: 1
+grad_norm: 0.99
+epochs: 400
+test_period: 20
+ll: 3
+lf: 3
+lu: 3
+lh: 3
+cl: 1
+cf: 0
+cu: 1
+ch: 1

configs/skcnn_ae.yaml

@@ -0,0 +1,80 @@
+is_train: True
+ddp: False
+stat: ts
+training_speakers: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
+root_path: /home/s24273/
+out_path: /home/s24273/outputs/audio2pose/
+cache_path: datasets/beat_cache/beat_smplx_en/
+project: mage_smplx
+data_path: /home/s24273/datasets/beat_v2.0.0/beat_english_v2.0.0/
+e_path:  weights/AESKConv_240_100.bin
+test_ckpt: weights/multi.bin
+data_path_1: /home/s24273/datasets/hub/
+#torch_hub_path: datasets/hub/
+additional_data: False
+dataset: beat_smplx2020
+new_cache: False
+ori_joints: beat_smplx_joints
+tar_joints: beat_smplx_full
+pose_rep: smplxflame_30
+pose_norm: False
+pose_fps: 30
+
+
+vae_test_len: 64
+vae_test_dim: 330
+vae_test_stride: 20
+vae_length: 240
+vae_layer: 2
+vae_grow: [1,2]
+variational: False
+
+pose_dims: 330
+pose_length: 64
+stride: 20
+facial_dims: 100
+word_index_num: 11195
+word_dims: 300
+batch_size: 32
+lr_base: 1e-4
+model: motion_representation
+g_name: VAESKConv
+#eval_model: motion_autoencoder
+#e_name: HalfEmbeddingNet
+trainer: ae
+decay_epochs: 950
+# audio_f: 256
+# a_pre_encoder: tcn_camn
+# a_encoder: lp
+# a_fix_pre: False
+
+# freeze_wordembed: False
+# word_f: 128
+# t_pre_encoder: fasttext
+# t_encoder: lp
+# t_fix_pre: False
+
+# motion_f: 256
+# m_pre_encoder: lp
+# m_encoder: lp
+# m_fix_pre: False
+
+# facial_f: 128
+# f_pre_encoder: lp
+# f_encoder: lp
+# f_fix_pre: False
+
+#m_decoder: lstm
+#decode_fusion: cat
+#n_layer: 2
+#hidden_size: 512
+rec_weight: 1
+rec_pos_weight: 10
+rec_ver_weight: 0
+# rec_fac_weight: 1
+#ita_weight: 0
+#iwa_weight: 0
+#fusion_mode: sum
+# grad_norm: 1
+epochs: 1000
+test_period: 100

dataloaders/.ipynb_checkpoints/beat_testonly_hf-checkpoint.py

@@ -0,0 +1,740 @@
+import os
+import pickle
+import math
+import shutil
+import numpy as np
+import lmdb as lmdb
+import textgrid as tg
+import pandas as pd
+import torch
+import glob
+import json
+from termcolor import colored
+from loguru import logger
+from collections import defaultdict
+from torch.utils.data import Dataset
+import torch.distributed as dist
+import pyarrow
+import librosa
+import smplx
+
+from .build_vocab import Vocab
+from .utils.audio_features import Wav2Vec2Model
+from .data_tools import joints_list
+from .utils import rotation_conversions as rc
+from .utils import other_tools_hf
+
+class CustomDataset(Dataset):
+    def __init__(self, args, loader_type, smplx_path=None, audio_path=None, text_path=None, augmentation=None, kwargs=None, build_cache=True):
+        self.args = args
+        self.loader_type = loader_type
+        self.smplx_path = "./EMAGE/test_sequences/smplxflame_30/2_scott_0_1_1.npz"
+        self.audio_path = audio_path
+        self.text_path = "./EMAGE/test_sequences/textgrid/2_scott_0_1_1.TextGrid"
+        self.rank = 0 # dist.get_rank()
+        self.ori_stride = self.args.stride
+        self.ori_length = self.args.pose_length
+        self.alignment = [0,0] # for trinity
+        
+        self.ori_joint_list = joints_list[self.args.ori_joints]
+        self.tar_joint_list = joints_list[self.args.tar_joints]
+        if 'smplx' in self.args.pose_rep:
+            self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3)
+            self.joints = len(list(self.tar_joint_list.keys()))  
+            for joint_name in self.tar_joint_list:
+                self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        else:
+            self.joints = len(list(self.ori_joint_list.keys()))+1
+            self.joint_mask = np.zeros(self.joints*3)
+            for joint_name in self.tar_joint_list:
+                if joint_name == "Hips":
+                    self.joint_mask[3:6] = 1
+                else:
+                    self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1
+        # select trainable joints
+        self.smplx = smplx.create(
+            self.args.data_path_1+"smplx_models/", 
+            model_type='smplx',
+            gender='NEUTRAL_2020', 
+            use_face_contour=False,
+            num_betas=300,
+            num_expression_coeffs=100, 
+            ext='npz',
+            use_pca=False,
+        ).eval()
+
+        split_rule = pd.read_csv(args.data_path+"test.csv")
+        self.selected_file = split_rule
+        self.data_dir = args.data_path 
+        
+        if loader_type == "test": 
+            self.args.multi_length_training = [1.0]
+        self.max_length = int(args.pose_length * self.args.multi_length_training[-1])
+        self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr)
+        if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: 
+            self.max_audio_pre_len = self.args.test_length*self.args.audio_sr
+        
+        if args.word_rep is not None:
+            with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f:
+                self.lang_model = pickle.load(f)
+                
+        preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache"        
+        if build_cache and self.rank == 0:
+            self.build_cache(preloaded_dir)
+        self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False)
+        with self.lmdb_env.begin() as txn:
+            self.n_samples = txn.stat()["entries"] 
+        
+
+    def build_cache(self, preloaded_dir):
+        logger.info(f"Audio bit rate: {self.args.audio_fps}")
+        logger.info("Reading data '{}'...".format(self.data_dir))
+        logger.info("Creating the dataset cache...")
+        if self.args.new_cache:
+            if os.path.exists(preloaded_dir):
+                shutil.rmtree(preloaded_dir)
+        if os.path.exists(preloaded_dir):
+            logger.info("Found the cache {}".format(preloaded_dir))
+        elif self.loader_type == "test":
+            self.cache_generation(
+                preloaded_dir, True, 
+                0, 0,
+                is_test=True)
+        else:
+            self.cache_generation(
+                preloaded_dir, self.args.disable_filtering, 
+                self.args.clean_first_seconds, self.args.clean_final_seconds,
+                is_test=False)
+        
+
+    def __len__(self):
+        return self.n_samples
+    
+
+    def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds,  clean_final_seconds, is_test=False):      
+        self.n_out_samples = 0
+        # create db for samples
+        if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir)
+        if len(self.args.training_speakers) == 1:
+            #dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G
+            dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 0.5))# 500M
+        else:
+            dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 200))# 200G
+        n_filtered_out = defaultdict(int)
+    
+        #for index, file_name in self.selected_file.iterrows():
+        #f_name = file_name["id"]
+        ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh"
+        pose_file = self.smplx_path#self.data_dir + self.args.pose_rep + "/" + f_name + ext
+        pose_each_file = []
+        trans_each_file = []
+        shape_each_file = []
+        audio_each_file = []
+        facial_each_file = []
+        word_each_file = []
+        emo_each_file = []
+        sem_each_file = []
+        vid_each_file = []
+        id_pose = "dummy 2nd"#f_name
+        
+        logger.info(colored(f"# ---- Building cache for Pose   {id_pose} ---- #", "blue"))
+        if "smplx" in self.args.pose_rep:
+            pose_data = np.load(pose_file, allow_pickle=True)
+            assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30'
+            stride = int(30/self.args.pose_fps)
+            pose_each_file = pose_data["poses"][::stride] 
+            trans_each_file = pose_data["trans"][::stride]
+            shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0)
+            
+            assert self.args.pose_fps == 30, "should 30"
+            m_data = np.load(pose_file, allow_pickle=True)
+            betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"]
+            n, c = poses.shape[0], poses.shape[1]
+            betas = betas.reshape(1, 300)
+            betas = np.tile(betas, (n, 1))
+            betas = torch.from_numpy(betas).float()
+            poses = torch.from_numpy(poses.reshape(n, c)).float()
+            exps = torch.from_numpy(exps.reshape(n, 100)).float()
+            trans = torch.from_numpy(trans.reshape(n, 3)).float()
+            max_length = 128
+            s, r = n//max_length, n%max_length
+            #print(n, s, r)
+            all_tensor = []
+            for i in range(s):
+                with torch.no_grad():
+                    joints = self.smplx(
+                        betas=betas[i*max_length:(i+1)*max_length], 
+                        transl=trans[i*max_length:(i+1)*max_length], 
+                        expression=exps[i*max_length:(i+1)*max_length], 
+                        jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], 
+                        global_orient=poses[i*max_length:(i+1)*max_length,:3], 
+                        body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], 
+                        left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], 
+                        right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], 
+                        return_verts=True,
+                        return_joints=True,
+                        leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], 
+                        reye_pose=poses[i*max_length:(i+1)*max_length, 72:75],
+                    )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu()
+                all_tensor.append(joints)
+            if r != 0:
+                with torch.no_grad():
+                    joints = self.smplx(
+                        betas=betas[s*max_length:s*max_length+r], 
+                        transl=trans[s*max_length:s*max_length+r], 
+                        expression=exps[s*max_length:s*max_length+r], 
+                        jaw_pose=poses[s*max_length:s*max_length+r, 66:69], 
+                        global_orient=poses[s*max_length:s*max_length+r,:3], 
+                        body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], 
+                        left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], 
+                        right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], 
+                        return_verts=True,
+                        return_joints=True,
+                        leye_pose=poses[s*max_length:s*max_length+r, 69:72], 
+                        reye_pose=poses[s*max_length:s*max_length+r, 72:75],
+                    )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu()
+                all_tensor.append(joints)
+            joints = torch.cat(all_tensor, axis=0) # all, 4, 3
+            # print(joints.shape)
+            feetv = torch.zeros(joints.shape[1], joints.shape[0])
+            joints = joints.permute(1, 0, 2)
+            #print(joints.shape, feetv.shape)
+            feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1)
+            #print(feetv.shape)
+            contacts = (feetv < 0.01).numpy().astype(float)
+            # print(contacts.shape, contacts)
+            contacts = contacts.transpose(1, 0)
+            pose_each_file = pose_each_file * self.joint_mask
+            pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)]
+            pose_each_file = np.concatenate([pose_each_file, contacts], axis=1)
+            # print(pose_each_file.shape)
+            
+            
+            if self.args.facial_rep is not None:
+                logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                facial_each_file = pose_data["expressions"][::stride]
+                if self.args.facial_norm: 
+                    facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                
+        else:
+            assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+            stride = int(120/self.args.pose_fps)
+            with open(pose_file, "r") as pose_data:
+                for j, line in enumerate(pose_data.readlines()):
+                    if j < 431: continue     
+                    if j%stride != 0:continue
+                    data = np.fromstring(line, dtype=float, sep=" ")
+                    rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ")
+                    rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) 
+                    rot_data = rot_data.numpy() * self.joint_mask
+                    
+                    pose_each_file.append(rot_data)
+                    trans_each_file.append(data[:3])
+                    
+            pose_each_file = np.array(pose_each_file)
+            trans_each_file = np.array(trans_each_file)
+            shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0)
+            if self.args.facial_rep is not None:
+                logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #")
+                facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json")
+                assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120'
+                stride = int(60/self.args.pose_fps)
+                if not os.path.exists(facial_file):
+                    logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #")
+                    #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                    #continue
+                with open(facial_file, 'r') as facial_data_file:
+                    facial_data = json.load(facial_data_file)
+                    for j, frame_data in enumerate(facial_data['frames']):
+                        if j%stride != 0:continue
+                        facial_each_file.append(frame_data['weights'])
+                facial_each_file = np.array(facial_each_file)
+                if self.args.facial_norm: 
+                    facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial
+                    
+        if self.args.id_rep is not None:
+            int_value = 1
+            vid_each_file = np.repeat(np.array(int_value).reshape(1, 1), pose_each_file.shape[0], axis=0)
+    
+        if self.args.audio_rep is not None:
+            logger.info(f"# ---- Building cache for Audio  {id_pose} and Pose {id_pose} ---- #")
+            audio_file = self.audio_path[1]#pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav")
+            sr = self.audio_path[0]
+            print(sr)
+            #if not os.path.exists(audio_file):
+            #    logger.warning(f"# ---- file not found for Audio  {id_pose}, skip all files with the same id ---- #")
+                #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                #continue
+            #audio_each_file, sr = librosa.load(audio_file)
+            audio_each_file = audio_file.astype(np.float32)
+            print(audio_each_file.shape)
+            audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr) 
+            print(audio_each_file.shape)
+            if self.args.audio_rep == "onset+amplitude":
+                from numpy.lib import stride_tricks
+                frame_length = 1024
+                # hop_length = 512
+                shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length)
+                strides = (audio_each_file.strides[-1], audio_each_file.strides[-1])
+                rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides)
+                amplitude_envelope = np.max(np.abs(rolling_view), axis=1)
+                # pad the last frame_length-1 samples
+                amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1])
+                audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames')
+                onset_array = np.zeros(len(audio_each_file), dtype=float)
+                onset_array[audio_onset_f] = 1.0
+                # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape)
+                audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1)
+            elif self.args.audio_rep == "mfcc":
+                audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps))
+                audio_each_file = audio_each_file.transpose(1, 0)
+                # print(audio_each_file.shape, pose_each_file.shape)
+            if self.args.audio_norm and self.args.audio_rep == "wave16k": 
+                audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio
+                
+        time_offset = 0
+        if self.args.word_rep is not None:
+            logger.info(f"# ---- Building cache for Word   {id_pose} and Pose {id_pose} ---- #")
+            word_file = self.text_path#f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid"
+            if not os.path.exists(word_file):
+                logger.warning(f"# ---- file not found for Word   {id_pose}, skip all files with the same id ---- #")
+                #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index)
+                #continue
+            tgrid = tg.TextGrid.fromFile(word_file)
+            if self.args.t_pre_encoder == "bert":
+                from transformers import AutoTokenizer, BertModel
+                tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True)
+                model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval()
+                list_word = []
+                all_hidden = []
+                max_len = 400
+                last = 0
+                word_token_mapping = []
+                first = True
+                for i, word in enumerate(tgrid[0]):
+                    last = i
+                    if (i%max_len != 0) or (i==0):
+                        if word.mark == "":
+                            list_word.append(".")
+                        else:
+                            list_word.append(word.mark)
+                    else:
+                        max_counter = max_len
+                        str_word = ' '.join(map(str, list_word))
+                        if first:
+                            global_len = 0
+                        end = -1
+                        offset_word = []
+                        for k, wordvalue in enumerate(list_word):
+                            start = end+1 
+                            end = start+len(wordvalue)
+                            offset_word.append((start, end))
+                        #print(offset_word)
+                        token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                        #print(token_scan)
+                        for start, end in offset_word:
+                            sub_mapping = []
+                            for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                                if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                    #print(i+global_len)
+                                    sub_mapping.append(i+global_len)
+                            word_token_mapping.append(sub_mapping)
+                        #print(len(word_token_mapping))
+                        global_len = word_token_mapping[-1][-1] + 1    
+                        list_word = []
+                        if word.mark == "":
+                            list_word.append(".")
+                        else:
+                            list_word.append(word.mark)
+                        
+                        with torch.no_grad():
+                            inputs = tokenizer(str_word, return_tensors="pt")
+                            outputs = model(**inputs)
+                            last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                        all_hidden.append(last_hidden_states)
+                    
+                #list_word = list_word[:10]
+                if list_word == []:
+                    pass
+                else:
+                    if first: 
+                        global_len = 0
+                    str_word = ' '.join(map(str, list_word))
+                    end = -1
+                    offset_word = []
+                    for k, wordvalue in enumerate(list_word):
+                        start = end+1 
+                        end = start+len(wordvalue)
+                        offset_word.append((start, end))
+                    #print(offset_word)
+                    token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping']
+                    #print(token_scan)
+                    for start, end in offset_word:
+                        sub_mapping = []
+                        for i, (start_t, end_t) in enumerate(token_scan[1:-1]):
+                            if int(start) <= int(start_t) and int(end_t) <= int(end):
+                                sub_mapping.append(i+global_len)
+                                #print(sub_mapping)
+                        word_token_mapping.append(sub_mapping)
+                    #print(len(word_token_mapping))
+                    with torch.no_grad():
+                        inputs = tokenizer(str_word, return_tensors="pt")
+                        outputs = model(**inputs)
+                        last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :]
+                    all_hidden.append(last_hidden_states)
+                last_hidden_states = np.concatenate(all_hidden, axis=0)
+        
+            for i in range(pose_each_file.shape[0]):
+                found_flag = False
+                current_time = i/self.args.pose_fps + time_offset
+                j_last = 0
+                for j, word in enumerate(tgrid[0]): 
+                    word_n, word_s, word_e = word.mark, word.minTime, word.maxTime
+                    if word_s<=current_time and current_time<=word_e:
+                        if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                            mapping_index = word_token_mapping[j]
+                            #print(mapping_index, word_s, word_e)
+                            s_t = np.linspace(word_s, word_e, len(mapping_index)+1)
+                            #print(s_t)
+                            for tt, t_sep in enumerate(s_t[1:]):
+                                if current_time <= t_sep:
+                                    #if len(mapping_index) > 1: print(mapping_index[tt])
+                                    word_each_file.append(last_hidden_states[mapping_index[tt]])
+                                    break
+                        else:
+                            if word_n == " ":
+                                word_each_file.append(self.lang_model.PAD_token)
+                            else:
+                                word_each_file.append(self.lang_model.get_word_index(word_n))
+                        found_flag = True
+                        j_last = j
+                        break
+                    else: continue   
+                if not found_flag: 
+                    if self.args.word_cache and self.args.t_pre_encoder == 'bert':
+                        word_each_file.append(last_hidden_states[j_last])
+                    else:
+                        word_each_file.append(self.lang_model.UNK_token)
+            word_each_file = np.array(word_each_file)
+            #print(word_each_file.shape)
+            
+        if self.args.emo_rep is not None:
+            logger.info(f"# ---- Building cache for Emo    {id_pose} and Pose {id_pose} ---- #")
+            rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3])
+            if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6:
+                if start >= 1 and start <= 64:
+                    score = 0
+                elif start >= 65 and start <= 72:
+                    score = 1
+                elif start >= 73 and start <= 80:
+                    score = 2
+                elif start >= 81 and start <= 86:
+                    score = 3
+                elif start >= 87 and start <= 94:
+                    score = 4
+                elif start >= 95 and start <= 102:
+                    score = 5
+                elif start >= 103 and start <= 110:
+                    score = 6
+                elif start >= 111 and start <= 118:
+                    score = 7
+                else: pass
+            else:
+                # you may denote as unknown in the future
+                score = 0
+            emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0)    
+            #print(emo_each_file)
+            
+        if self.args.sem_rep is not None:
+            logger.info(f"# ---- Building cache for Sem    {id_pose} and Pose {id_pose} ---- #")
+            sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" 
+            sem_all = pd.read_csv(sem_file, 
+                sep='\t', 
+                names=["name", "start_time", "end_time", "duration", "score", "keywords"])
+            # we adopt motion-level semantic score here. 
+            for i in range(pose_each_file.shape[0]):
+                found_flag = False
+                for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])):
+                    current_time = i/self.args.pose_fps + time_offset
+                    if start<=current_time and current_time<=end: 
+                        sem_each_file.append(score)
+                        found_flag=True
+                        break
+                    else: continue 
+                if not found_flag: sem_each_file.append(0.)
+            sem_each_file = np.array(sem_each_file)
+            #print(sem_each_file)
+        
+        filtered_result = self._sample_from_clip(
+            dst_lmdb_env,
+            audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file,
+            vid_each_file, emo_each_file, sem_each_file,
+            disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+            ) 
+        for type in filtered_result.keys():
+            n_filtered_out[type] += filtered_result[type]
+                                
+        with dst_lmdb_env.begin() as txn:
+            logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan"))
+            n_total_filtered = 0
+            for type, n_filtered in n_filtered_out.items():
+                logger.info("{}: {}".format(type, n_filtered))
+                n_total_filtered += n_filtered
+            logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format(
+                n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan"))
+        dst_lmdb_env.sync()
+        dst_lmdb_env.close()
+    
+    def _sample_from_clip(
+        self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file,
+        vid_each_file, emo_each_file, sem_each_file,
+        disable_filtering, clean_first_seconds, clean_final_seconds, is_test,
+        ):
+        """
+        for data cleaning, we ignore the data for first and final n s
+        for test, we return all data 
+        """
+        # audio_start = int(self.alignment[0] * self.args.audio_fps)
+        # pose_start = int(self.alignment[1] * self.args.pose_fps)
+        #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}")
+        # audio_each_file = audio_each_file[audio_start:]
+        # pose_each_file = pose_each_file[pose_start:]
+        # trans_each_file = 
+        #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}")
+        #print(pose_each_file.shape)
+        round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps  # assume 1500 frames / 15 fps = 100 s
+        print(pose_each_file.shape[0])
+        #print(round_seconds_skeleton)
+        #if audio_each_file != []:
+        if self.args.audio_rep != "wave16k":
+            round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s
+        elif self.args.audio_rep == "mfcc":
+            round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps
+        else:
+            round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr
+        # if facial_each_file != []:
+        round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps
+        logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s")
+        round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+        max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial)
+        if round_seconds_skeleton != max_round: 
+            logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")  
+        # else:
+        #     logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s")
+        #     round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton)
+        #     max_round = max(round_seconds_audio, round_seconds_skeleton)
+        #     if round_seconds_skeleton != max_round: 
+        #         logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s")
+        
+        clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s
+        clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000]
+        clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15]
+        
+        
+        for ratio in self.args.multi_length_training:
+            if is_test:# stride = length for test
+                cut_length = clip_e_f_pose - clip_s_f_pose
+                self.args.stride = cut_length
+                self.max_length = cut_length
+            else:
+                self.args.stride = int(ratio*self.ori_stride)
+                cut_length = int(self.ori_length*ratio)
+                
+            num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1
+            logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}")
+            logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}")
+            
+            # if audio_each_file != []:
+            audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps)
+            logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}")
+             
+            n_filtered_out = defaultdict(int)
+            sample_pose_list = []
+            sample_audio_list = []
+            sample_facial_list = []
+            sample_shape_list = []
+            sample_word_list = []
+            sample_emo_list = []
+            sample_sem_list = []
+            sample_vid_list = []
+            sample_trans_list = []
+           
+            for i in range(num_subdivision): # cut into around 2s chip, (self npose)
+                start_idx = clip_s_f_pose + i * self.args.stride
+                fin_idx = start_idx + cut_length 
+                sample_pose = pose_each_file[start_idx:fin_idx]
+
+                sample_trans = trans_each_file[start_idx:fin_idx]
+                sample_shape = shape_each_file[start_idx:fin_idx]
+                # print(sample_pose.shape)
+                if self.args.audio_rep is not None:
+                    audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps)
+                    audio_end = audio_start + audio_short_length
+                    sample_audio = audio_each_file[audio_start:audio_end]
+                else:
+                    sample_audio = np.array([-1])
+                sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1])
+                sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1])
+                sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1])
+                sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1])
+                sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1])
+                
+                if sample_pose.any() != None:
+                    # filtering motion skeleton data
+                    sample_pose, filtering_message = MotionPreprocessor(sample_pose).get()
+                    is_correct_motion = True #(sample_pose != [])
+                    if is_correct_motion or disable_filtering:
+                        sample_pose_list.append(sample_pose)
+                        sample_audio_list.append(sample_audio)
+                        sample_facial_list.append(sample_facial)
+                        sample_shape_list.append(sample_shape)
+                        sample_word_list.append(sample_word)
+                        sample_vid_list.append(sample_vid)
+                        sample_emo_list.append(sample_emo)
+                        sample_sem_list.append(sample_sem)
+                        sample_trans_list.append(sample_trans)
+                    else:
+                        n_filtered_out[filtering_message] += 1
+
+            if len(sample_pose_list) > 0:
+                with dst_lmdb_env.begin(write=True) as txn:
+                    for pose, audio, facial, shape, word, vid, emo, sem, trans in zip(
+                        sample_pose_list,
+                        sample_audio_list,
+                        sample_facial_list,
+                        sample_shape_list,
+                        sample_word_list,
+                        sample_vid_list,
+                        sample_emo_list,
+                        sample_sem_list,
+                        sample_trans_list,):
+                        k = "{:005}".format(self.n_out_samples).encode("ascii")
+                        v = [pose, audio, facial, shape, word, emo, sem, vid, trans]
+                        # v = pyarrow.serialize(v).to_buffer()
+                        # txn.put(k, v)
+                        # self.n_out_samples += 1
+                        v = pickle.dumps(v)  
+                        txn.put(k, v)
+                        self.n_out_samples += 1
+        return n_filtered_out
+
+    def __getitem__(self, idx):
+        with self.lmdb_env.begin(write=False) as txn:
+            key = "{:005}".format(idx).encode("ascii")
+            sample = txn.get(key)
+            # sample = pyarrow.deserialize(sample)
+            if sample is not None:
+                sample = pickle.loads(sample)  
+            tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample
+            #print(in_shape)
+            #vid = torch.from_numpy(vid).int()
+            emo = torch.from_numpy(emo).int()
+            sem = torch.from_numpy(sem).float() 
+            in_audio = torch.from_numpy(in_audio).float() 
+            in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() 
+            if self.loader_type == "test":
+                tar_pose = torch.from_numpy(tar_pose).float()
+                trans = torch.from_numpy(trans).float()
+                in_facial = torch.from_numpy(in_facial).float()
+                vid = torch.from_numpy(vid).float()
+                in_shape = torch.from_numpy(in_shape).float()
+            else:
+                in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float()
+                trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float()
+                vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float()
+                tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float()
+                in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float()
+            return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans}
+
+         
+class MotionPreprocessor:
+    def __init__(self, skeletons):
+        self.skeletons = skeletons
+        #self.mean_pose = mean_pose
+        self.filtering_message = "PASS"
+
+    def get(self):
+        assert (self.skeletons is not None)
+
+        # filtering
+        # if self.skeletons != []:
+        #     if self.check_pose_diff():
+        #         self.skeletons = []
+        #         self.filtering_message = "pose"
+            # elif self.check_spine_angle():
+            #     self.skeletons = []
+            #     self.filtering_message = "spine angle"
+            # elif self.check_static_motion():
+            #     self.skeletons = []
+            #     self.filtering_message = "motion"
+
+        # if self.skeletons != []:
+        #     self.skeletons = self.skeletons.tolist()
+        #     for i, frame in enumerate(self.skeletons):
+        #         assert not np.isnan(self.skeletons[i]).any()  # missing joints
+
+        return self.skeletons, self.filtering_message
+
+    def check_static_motion(self, verbose=True):
+        def get_variance(skeleton, joint_idx):
+            wrist_pos = skeleton[:, joint_idx]
+            variance = np.sum(np.var(wrist_pos, axis=0))
+            return variance
+
+        left_arm_var = get_variance(self.skeletons, 6)
+        right_arm_var = get_variance(self.skeletons, 9)
+
+        th = 0.0014  # exclude 13110
+        # th = 0.002  # exclude 16905
+        if left_arm_var < th and right_arm_var < th:
+            if verbose:
+                print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return True
+        else:
+            if verbose:
+                print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var))
+            return False
+
+
+    def check_pose_diff(self, verbose=False):
+#         diff = np.abs(self.skeletons - self.mean_pose) # 186*1
+#         diff = np.mean(diff)
+
+#         # th = 0.017
+#         th = 0.02 #0.02  # exclude 3594
+#         if diff < th:
+#             if verbose:
+#                 print("skip - check_pose_diff {:.5f}".format(diff))
+#             return True
+# #         th = 3.5 #0.02  # exclude 3594
+# #         if 3.5 < diff < 5:
+# #             if verbose:
+# #                 print("skip - check_pose_diff {:.5f}".format(diff))
+# #             return True
+#         else:
+#             if verbose:
+#                 print("pass - check_pose_diff {:.5f}".format(diff))
+        return False
+
+
+    def check_spine_angle(self, verbose=True):
+        def angle_between(v1, v2):
+            v1_u = v1 / np.linalg.norm(v1)
+            v2_u = v2 / np.linalg.norm(v2)
+            return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
+
+        angles = []
+        for i in range(self.skeletons.shape[0]):
+            spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0]
+            angle = angle_between(spine_vec, [0, -1, 0])
+            angles.append(angle)
+
+        if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20:  # exclude 4495
+        # if np.rad2deg(max(angles)) > 20:  # exclude 8270
+            if verbose:
+                print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles)))
+            return True
+        else:
+            if verbose:
+                print("pass - check_spine_angle {:.5f}".format(max(angles)))
+            return False