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scores extraction fix

Lucas Gautheron 3 lat temu
rodzic
commit
4efb722ed5
100 zmienionych plików z 127 dodań i 127 usunięć
  1. 2 2
      code/scores/extract_clips.py
  2. 28 28
      code/scores/stats.py
  3. 1 1
      output/scores/e20190821_134438_013100_22350000_22410000/CHI.wav
  4. 1 1
      output/scores/e20190821_134438_013100_22350000_22410000/FEM.wav
  5. 1 1
      output/scores/e20190821_134438_013100_22350000_22410000/KCHI.wav
  6. 1 1
      output/scores/e20190821_134438_013100_22350000_22410000/MAL.wav
  7. 1 1
      output/scores/e20190821_134438_013100_22350000_22410000/SPEECH.wav
  8. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/CHI.wav
  9. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/FEM.rttm
  10. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/FEM.wav
  11. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/KCHI.rttm
  12. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/KCHI.wav
  13. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/MAL.rttm
  14. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/MAL.wav
  15. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/SPEECH.rttm
  16. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/SPEECH.wav
  17. 1 1
      output/scores/e20190821_134438_013100_2250000_2310000/all.rttm
  18. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/CHI.rttm
  19. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/CHI.wav
  20. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/FEM.rttm
  21. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/FEM.wav
  22. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/KCHI.rttm
  23. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/KCHI.wav
  24. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/MAL.wav
  25. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/SPEECH.rttm
  26. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/SPEECH.wav
  27. 1 1
      output/scores/e20190821_134438_013100_25050000_25110000/all.rttm
  28. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/CHI.wav
  29. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/FEM.wav
  30. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/KCHI.wav
  31. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/MAL.rttm
  32. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/MAL.wav
  33. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/SPEECH.rttm
  34. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/SPEECH.wav
  35. 1 1
      output/scores/e20190821_134438_013100_32550000_32610000/all.rttm
  36. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/CHI.wav
  37. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/FEM.rttm
  38. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/FEM.wav
  39. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/KCHI.wav
  40. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/MAL.wav
  41. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/SPEECH.rttm
  42. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/SPEECH.wav
  43. 1 1
      output/scores/e20190821_134438_013100_34650000_34710000/all.rttm
  44. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/CHI.rttm
  45. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/CHI.wav
  46. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/FEM.rttm
  47. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/FEM.wav
  48. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/KCHI.rttm
  49. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/KCHI.wav
  50. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/MAL.wav
  51. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/SPEECH.rttm
  52. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/SPEECH.wav
  53. 1 1
      output/scores/e20190821_134438_013100_5250000_5310000/all.rttm
  54. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/CHI.wav
  55. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/FEM.wav
  56. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/KCHI.rttm
  57. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/KCHI.wav
  58. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/MAL.wav
  59. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/SPEECH.rttm
  60. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/SPEECH.wav
  61. 1 1
      output/scores/e20190822_153540_013103_1368000_1428000/all.rttm
  62. 1 1
      output/scores/e20190822_153540_013103_168000_228000/CHI.wav
  63. 1 1
      output/scores/e20190822_153540_013103_168000_228000/FEM.rttm
  64. 1 1
      output/scores/e20190822_153540_013103_168000_228000/FEM.wav
  65. 1 1
      output/scores/e20190822_153540_013103_168000_228000/KCHI.wav
  66. 1 1
      output/scores/e20190822_153540_013103_168000_228000/MAL.wav
  67. 1 1
      output/scores/e20190822_153540_013103_168000_228000/SPEECH.rttm
  68. 1 1
      output/scores/e20190822_153540_013103_168000_228000/SPEECH.wav
  69. 1 1
      output/scores/e20190822_153540_013103_168000_228000/all.rttm
  70. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/CHI.rttm
  71. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/CHI.wav
  72. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/FEM.wav
  73. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/KCHI.rttm
  74. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/KCHI.wav
  75. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/MAL.wav
  76. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/SPEECH.rttm
  77. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/SPEECH.wav
  78. 1 1
      output/scores/e20190822_153540_013103_24768000_24828000/all.rttm
  79. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/CHI.wav
  80. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/FEM.rttm
  81. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/FEM.wav
  82. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/KCHI.wav
  83. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/MAL.wav
  84. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/SPEECH.rttm
  85. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/SPEECH.wav
  86. 1 1
      output/scores/e20190822_153540_013103_6468000_6528000/all.rttm
  87. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/CHI.wav
  88. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/FEM.rttm
  89. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/FEM.wav
  90. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/KCHI.wav
  91. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/MAL.rttm
  92. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/MAL.wav
  93. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/SPEECH.rttm
  94. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/SPEECH.wav
  95. 1 1
      output/scores/e20190822_153540_013103_8568000_8628000/all.rttm
  96. 1 1
      output/scores/e20190822_153540_013103_9468000_9528000/CHI.rttm
  97. 1 1
      output/scores/e20190822_153540_013103_9468000_9528000/CHI.wav
  98. 1 1
      output/scores/e20190822_153540_013103_9468000_9528000/FEM.rttm
  99. 1 1
      output/scores/e20190822_153540_013103_9468000_9528000/FEM.wav
  100. 0 0
      output/scores/e20190822_153540_013103_9468000_9528000/KCHI.rttm

+ 2 - 2
code/scores/extract_clips.py

@@ -39,7 +39,7 @@ def extract_clips(parameters):
             clip['recording_path'],
             sr = 16000,
             offset = (onset-2000)/1000,
-            duration = (offset-onset+2000)/1000
+            duration = (offset-onset+4000)/1000
         )
 
         output = f'output/clips/{bn}_{onset}_{offset}.wav'
@@ -51,4 +51,4 @@ def extract_clips(parameters):
 annotators = pd.read_csv('input/annotators.csv')
 annotators['path'] = annotators['corpus'].apply(lambda c: opj('input', c))
 clips = pd.concat([extract_clips(annotator) for annotator in annotators.to_dict(orient = 'records')])
-clips.to_csv('output/clips/clips.csv', index = False)
+clips.to_csv('output/clips/clips.csv', index = False)

+ 28 - 28
code/scores/stats.py

@@ -1,4 +1,5 @@
 #!/usr/bin/env python3
+# find ../../data/solomon-ana/output/clips -maxdepth 1 -type f -exec sbatch -c 2 --mem 1G -o vtc ./apply.sh {} --device=cpu \;
 
 from ChildProject.projects import ChildProject
 from ChildProject.annotations import AnnotationManager
@@ -35,28 +36,20 @@ def process_clip(am, annotator, clip):
     speakers = ['CHI', 'OCH', 'FEM', 'MAL']
     vtc_speakers = ['KCHI', 'CHI', 'FEM', 'MAL']
 
-    print('reading scores...')
-
     data = None
     for i, speaker in enumerate(vtc_speakers):
         signal, sr = librosa.load(
             clip_path + f'/{speaker}.wav',
-            sr = 16000,
-            offset = 2,
-            duration = (range_offset-range_onset)/1000
+            sr = None
         )
-        print(pd.DataFrame(signal).describe())
+        time_scale = len(signal)/(range_offset-range_onset+4000)
+        signal = signal[int(2000*time_scale):-int(2000*time_scale)]
+
         if data is not None:
             data = np.vstack((data, signal))
         else:
             data = signal
 
-    print('done.')
-
-    print(data.shape)
-    time_scale = data.shape[1]/(range_offset-range_onset)
-    print(time_scale)
-
     segments = am.get_segments(ann)
     if not len(segments):
         return None
@@ -67,15 +60,11 @@ def process_clip(am, annotator, clip):
         for speaker in speakers
     }
     
-    print(vtc)
-
     truth = {
         speaker: segments_to_annotation(segments[(segments['set'] == annotator) & (segments['speaker_type'] == speaker)], 'speaker_type').get_timeline()
         for speaker in speakers
     }
     
-    print(truth)
-
     stats = {}
     for i, speaker_A in enumerate(speakers):
         vtc[f'{speaker_A}_vocs_explained'] = vtc[speaker_A].crop(truth[speaker_A], mode = 'loose')
@@ -86,17 +75,12 @@ def process_clip(am, annotator, clip):
             vtc[f'{speaker_A}_vocs_fp_{speaker_B}'] = vtc[f'{speaker_A}_vocs_fp'].crop(truth[speaker_B], mode = 'loose')
 
         s = []
-        for explained in vtc[speaker_A]:
+        for explained in vtc[f'{speaker_A}_vocs_explained']:
             onset, offset = explained
             onset -= range_onset
             offset -= range_onset
-
-            print(speaker_A, onset, offset)
-            print(data[i, int(onset*time_scale):int(offset*time_scale)])
-            print(np.mean(data[i, int(onset*time_scale):int(offset*time_scale)]))
             
             s.append(np.mean(data[i, int(onset*time_scale):int(offset*time_scale)]))
-
         stats[f'{speaker_A}_vocs_explained'] = s
 
         for j, speaker_B in enumerate(speakers):
@@ -109,14 +93,11 @@ def process_clip(am, annotator, clip):
                 offset -= range_onset
 
                 sA.append(np.mean(data[i, int(onset*time_scale):int(offset*time_scale)]))
-                sA.append(np.mean(data[j, int(onset*time_scale):int(offset*time_scale)]))
-
+                sB.append(np.mean(data[j, int(onset*time_scale):int(offset*time_scale)]))
 
             stats[f'{speaker_A}_vocs_fp_{speaker_B}_false'] = sA
             stats[f'{speaker_A}_vocs_fp_{speaker_B}_true'] = sB
 
-    print(stats)
-
     return stats
     
 
@@ -147,7 +128,26 @@ def generate_stats(parameters):
     return stats
     
 if __name__ == '__main__':
-    annotators = pd.read_csv('input/annotators.csv')
+    annotators = pd.read_csv('input/annotators.csv')[0:1]
     annotators['path'] = annotators['corpus'].apply(lambda c: opj('input', c))
     stats = [generate_stats(annotator) for annotator in annotators.to_dict(orient = 'records')]
-
+    stats = sum(stats, [])
+
+    combined = {}
+
+    for k in stats[0].keys():
+        combined[k] = []
+        for s in stats:
+            if s is None:
+                continue
+            combined[k] += s[k]
+
+    for k in combined.keys():
+        if len(combined[k]) == 0:
+            continue
+        print(k,
+            np.mean(combined[k]),
+            np.quantile(combined[k], 0.1),
+            np.quantile(combined[k], 0.9),
+            len(combined[k])
+        )

+ 1 - 1
output/scores/e20190821_134438_013100_22350000_22410000/CHI.wav

@@ -1 +1 @@
-../../../.git/annex/objects/4p/m9/MD5E-s36785--ca3371428db85cfaacb2596502f3a35d.wav/MD5E-s36785--ca3371428db85cfaacb2596502f3a35d.wav
+../../../.git/annex/objects/GM/z4/MD5E-s37970--26084f6c10e83c772bd8b08681132ae7.wav/MD5E-s37970--26084f6c10e83c772bd8b08681132ae7.wav

+ 1 - 1
output/scores/e20190821_134438_013100_22350000_22410000/FEM.wav

@@ -1 +1 @@
-../../../.git/annex/objects/5g/0m/MD5E-s36785--44f8fb77e72ebb30dffceef6d1410157.wav/MD5E-s36785--44f8fb77e72ebb30dffceef6d1410157.wav
+../../../.git/annex/objects/92/2f/MD5E-s37970--69c1ad49fba00ef455ae13be634a1f58.wav/MD5E-s37970--69c1ad49fba00ef455ae13be634a1f58.wav

+ 1 - 1
output/scores/e20190821_134438_013100_22350000_22410000/KCHI.wav

@@ -1 +1 @@
-../../../.git/annex/objects/kx/91/MD5E-s36785--b26081b1dafe302bcf890f3f7c1d6577.wav/MD5E-s36785--b26081b1dafe302bcf890f3f7c1d6577.wav
+../../../.git/annex/objects/X0/xQ/MD5E-s37970--2f83ecea7b63b095405d27540842e51b.wav/MD5E-s37970--2f83ecea7b63b095405d27540842e51b.wav

+ 1 - 1
output/scores/e20190821_134438_013100_22350000_22410000/MAL.wav

@@ -1 +1 @@
-../../../.git/annex/objects/jp/K5/MD5E-s36785--ffdd2d55c55ae1749fd60535c1e95b77.wav/MD5E-s36785--ffdd2d55c55ae1749fd60535c1e95b77.wav
+../../../.git/annex/objects/6z/95/MD5E-s37970--15964b53cfd90faaaa2302ae1b6671fe.wav/MD5E-s37970--15964b53cfd90faaaa2302ae1b6671fe.wav

+ 1 - 1
output/scores/e20190821_134438_013100_22350000_22410000/SPEECH.wav

@@ -1 +1 @@
-../../../.git/annex/objects/z7/Qm/MD5E-s36785--3845104d4e819326d0b33a430ae2cefc.wav/MD5E-s36785--3845104d4e819326d0b33a430ae2cefc.wav
+../../../.git/annex/objects/7g/WF/MD5E-s37970--33db7c8e75b3c9b45b1cae06346296b1.wav/MD5E-s37970--33db7c8e75b3c9b45b1cae06346296b1.wav

+ 1 - 1
output/scores/e20190821_134438_013100_2250000_2310000/CHI.wav

@@ -1 +1 @@
-../../../.git/annex/objects/xg/zv/MD5E-s36785--c03455d729f5e2d17d89f5fb5ba5e628.wav/MD5E-s36785--c03455d729f5e2d17d89f5fb5ba5e628.wav
+../../../.git/annex/objects/Pq/fZ/MD5E-s37970--7f88d1a02222e5201ccc9ffd1643aad7.wav/MD5E-s37970--7f88d1a02222e5201ccc9ffd1643aad7.wav

+ 1 - 1
output/scores/e20190821_134438_013100_2250000_2310000/FEM.rttm

@@ -1 +1 @@
-../../../.git/annex/objects/X5/M2/MD5E-s870--9244dd2b40d38b509ab3b1b9244b2267.rttm/MD5E-s870--9244dd2b40d38b509ab3b1b9244b2267.rttm
+../../../.git/annex/objects/77/4v/MD5E-s870--052bfe20a9c20be826092c20ec150043.rttm/MD5E-s870--052bfe20a9c20be826092c20ec150043.rttm

+ 1 - 1
output/scores/e20190821_134438_013100_2250000_2310000/FEM.wav

@@ -1 +1 @@
-../../../.git/annex/objects/QK/Gm/MD5E-s36785--294185d06b5810f944b8d787cced394f.wav/MD5E-s36785--294185d06b5810f944b8d787cced394f.wav
+../../../.git/annex/objects/3X/2V/MD5E-s37970--a2742a1f148722c902c5aef3e1ebbe58.wav/MD5E-s37970--a2742a1f148722c902c5aef3e1ebbe58.wav

+ 1 - 1
output/scores/e20190821_134438_013100_2250000_2310000/KCHI.rttm

@@ -1 +1 @@
-../../../.git/annex/objects/72/Kq/MD5E-s966--029c874c6063def04760cae4e266627e.rttm/MD5E-s966--029c874c6063def04760cae4e266627e.rttm
+../../../.git/annex/objects/Fv/FX/MD5E-s966--3795264de58f2c07d312c360a6b30c1f.rttm/MD5E-s966--3795264de58f2c07d312c360a6b30c1f.rttm

+ 1 - 1
output/scores/e20190821_134438_013100_2250000_2310000/KCHI.wav

@@ -1 +1 @@
-../../../.git/annex/objects/qk/m5/MD5E-s36785--2aa35a29fa1ebef50148968f8e1344d5.wav/MD5E-s36785--2aa35a29fa1ebef50148968f8e1344d5.wav
+../../../.git/annex/objects/PZ/zZ/MD5E-s37970--ea4bc3a70a5e7378d038ee177eb2038d.wav/MD5E-s37970--ea4bc3a70a5e7378d038ee177eb2038d.wav

+ 1 - 1
output/scores/e20190821_134438_013100_2250000_2310000/MAL.rttm

@@ -1 +1 @@
-../../../.git/annex/objects/pm/9k/MD5E-s87--a27ad52aa50c48fa31ff6cd2161e0478.rttm/MD5E-s87--a27ad52aa50c48fa31ff6cd2161e0478.rttm
+../../../.git/annex/objects/K2/mk/MD5E-s174--4e6e2ecab8a3299be763af9e21ef4f0b.rttm/MD5E-s174--4e6e2ecab8a3299be763af9e21ef4f0b.rttm

+ 1 - 1
output/scores/e20190821_134438_013100_2250000_2310000/MAL.wav

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+../../../.git/annex/objects/jP/pW/MD5E-s540--e07b0a194517db3fc84685a31f34b0f7.rttm/MD5E-s540--e07b0a194517db3fc84685a31f34b0f7.rttm

+ 1 - 1
output/scores/e20190822_153540_013103_8568000_8628000/SPEECH.wav

@@ -1 +1 @@
-../../../.git/annex/objects/M5/V0/MD5E-s36785--9563f600d39435b644bf6e49cef8c7da.wav/MD5E-s36785--9563f600d39435b644bf6e49cef8c7da.wav
+../../../.git/annex/objects/x3/G5/MD5E-s37970--73eb10177a02ac35644b0570f520a6a8.wav/MD5E-s37970--73eb10177a02ac35644b0570f520a6a8.wav

+ 1 - 1
output/scores/e20190822_153540_013103_8568000_8628000/all.rttm

@@ -1 +1 @@
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+../../../.git/annex/objects/xw/Mf/MD5E-s2977--de559f2c5a61f0ca79b3cecf3522db70.rttm/MD5E-s2977--de559f2c5a61f0ca79b3cecf3522db70.rttm

+ 1 - 1
output/scores/e20190822_153540_013103_9468000_9528000/CHI.rttm

@@ -1 +1 @@
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+../../../.git/annex/objects/G0/W0/MD5E-s782--cc55304125aa26daa87988a98edba041.rttm/MD5E-s782--cc55304125aa26daa87988a98edba041.rttm

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output/scores/e20190822_153540_013103_9468000_9528000/CHI.wav

@@ -1 +1 @@
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+../../../.git/annex/objects/fM/p1/MD5E-s37970--8d42ce7d099ce35646b6912dccbd3018.wav/MD5E-s37970--8d42ce7d099ce35646b6912dccbd3018.wav

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output/scores/e20190822_153540_013103_9468000_9528000/FEM.rttm

@@ -1 +1 @@
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output/scores/e20190822_153540_013103_9468000_9528000/FEM.wav

@@ -1 +1 @@
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+ 0 - 0
output/scores/e20190822_153540_013103_9468000_9528000/KCHI.rttm


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