Convert dataset to Parquet
#1
by
albertvillanova
HF staff
- opened
- README.md +1421 -0
- data/speech_commands_test_set_v0.01.zip → asr/test-00000-of-00001.parquet +2 -2
- data/IEMOCAP_full_release.zip → er/session1-00000-of-00001.parquet +2 -2
- data/LibriSpeech-test-clean.zip → ic/test-00000-of-00001.parquet +2 -2
- data/fluent_speech_commands_dataset.zip → ks/test-00000-of-00001.parquet +2 -2
- data/VoxCeleb1.zip → si/test-00000-of-00001.parquet +2 -2
- superb_demo.py +0 -435
README.md
CHANGED
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@@ -1,3 +1,1424 @@
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|
| 1 |
# Disclaimer
|
| 2 |
This is a tiny subset of the SUPERB dataset, which is intended only for demo purposes!
|
| 3 |
|
|
|
|
| 1 |
+
---
|
| 2 |
+
dataset_info:
|
| 3 |
+
- config_name: asr
|
| 4 |
+
features:
|
| 5 |
+
- name: file
|
| 6 |
+
dtype: string
|
| 7 |
+
- name: audio
|
| 8 |
+
dtype:
|
| 9 |
+
audio:
|
| 10 |
+
sampling_rate: 16000
|
| 11 |
+
- name: text
|
| 12 |
+
dtype: string
|
| 13 |
+
- name: speaker_id
|
| 14 |
+
dtype: int64
|
| 15 |
+
- name: chapter_id
|
| 16 |
+
dtype: int64
|
| 17 |
+
- name: id
|
| 18 |
+
dtype: string
|
| 19 |
+
splits:
|
| 20 |
+
- name: test
|
| 21 |
+
num_bytes: 671089.0
|
| 22 |
+
num_examples: 6
|
| 23 |
+
download_size: 632857
|
| 24 |
+
dataset_size: 671089.0
|
| 25 |
+
- config_name: er
|
| 26 |
+
features:
|
| 27 |
+
- name: file
|
| 28 |
+
dtype: string
|
| 29 |
+
- name: audio
|
| 30 |
+
dtype:
|
| 31 |
+
audio:
|
| 32 |
+
sampling_rate: 16000
|
| 33 |
+
- name: label
|
| 34 |
+
dtype:
|
| 35 |
+
class_label:
|
| 36 |
+
names:
|
| 37 |
+
'0': neu
|
| 38 |
+
'1': hap
|
| 39 |
+
'2': ang
|
| 40 |
+
'3': sad
|
| 41 |
+
splits:
|
| 42 |
+
- name: session1
|
| 43 |
+
num_bytes: 1386599.0
|
| 44 |
+
num_examples: 6
|
| 45 |
+
download_size: 1389189
|
| 46 |
+
dataset_size: 1386599.0
|
| 47 |
+
- config_name: ic
|
| 48 |
+
features:
|
| 49 |
+
- name: file
|
| 50 |
+
dtype: string
|
| 51 |
+
- name: audio
|
| 52 |
+
dtype:
|
| 53 |
+
audio:
|
| 54 |
+
sampling_rate: 16000
|
| 55 |
+
- name: speaker_id
|
| 56 |
+
dtype: string
|
| 57 |
+
- name: text
|
| 58 |
+
dtype: string
|
| 59 |
+
- name: action
|
| 60 |
+
dtype:
|
| 61 |
+
class_label:
|
| 62 |
+
names:
|
| 63 |
+
'0': activate
|
| 64 |
+
'1': bring
|
| 65 |
+
'2': change language
|
| 66 |
+
'3': deactivate
|
| 67 |
+
'4': decrease
|
| 68 |
+
'5': increase
|
| 69 |
+
- name: object
|
| 70 |
+
dtype:
|
| 71 |
+
class_label:
|
| 72 |
+
names:
|
| 73 |
+
'0': Chinese
|
| 74 |
+
'1': English
|
| 75 |
+
'2': German
|
| 76 |
+
'3': Korean
|
| 77 |
+
'4': heat
|
| 78 |
+
'5': juice
|
| 79 |
+
'6': lamp
|
| 80 |
+
'7': lights
|
| 81 |
+
'8': music
|
| 82 |
+
'9': newspaper
|
| 83 |
+
'10': none
|
| 84 |
+
'11': shoes
|
| 85 |
+
'12': socks
|
| 86 |
+
'13': volume
|
| 87 |
+
- name: location
|
| 88 |
+
dtype:
|
| 89 |
+
class_label:
|
| 90 |
+
names:
|
| 91 |
+
'0': bedroom
|
| 92 |
+
'1': kitchen
|
| 93 |
+
'2': none
|
| 94 |
+
'3': washroom
|
| 95 |
+
splits:
|
| 96 |
+
- name: test
|
| 97 |
+
num_bytes: 526542.0
|
| 98 |
+
num_examples: 6
|
| 99 |
+
download_size: 466270
|
| 100 |
+
dataset_size: 526542.0
|
| 101 |
+
- config_name: ks
|
| 102 |
+
features:
|
| 103 |
+
- name: file
|
| 104 |
+
dtype: string
|
| 105 |
+
- name: audio
|
| 106 |
+
dtype:
|
| 107 |
+
audio:
|
| 108 |
+
sampling_rate: 16000
|
| 109 |
+
- name: label
|
| 110 |
+
dtype:
|
| 111 |
+
class_label:
|
| 112 |
+
names:
|
| 113 |
+
'0': 'yes'
|
| 114 |
+
'1': 'no'
|
| 115 |
+
'2': up
|
| 116 |
+
'3': down
|
| 117 |
+
'4': left
|
| 118 |
+
'5': right
|
| 119 |
+
'6': 'on'
|
| 120 |
+
'7': 'off'
|
| 121 |
+
'8': stop
|
| 122 |
+
'9': go
|
| 123 |
+
'10': _silence_
|
| 124 |
+
'11': _unknown_
|
| 125 |
+
splits:
|
| 126 |
+
- name: test
|
| 127 |
+
num_bytes: 257722.0
|
| 128 |
+
num_examples: 8
|
| 129 |
+
download_size: 253966
|
| 130 |
+
dataset_size: 257722.0
|
| 131 |
+
- config_name: si
|
| 132 |
+
features:
|
| 133 |
+
- name: file
|
| 134 |
+
dtype: string
|
| 135 |
+
- name: audio
|
| 136 |
+
dtype:
|
| 137 |
+
audio:
|
| 138 |
+
sampling_rate: 16000
|
| 139 |
+
- name: label
|
| 140 |
+
dtype:
|
| 141 |
+
class_label:
|
| 142 |
+
names:
|
| 143 |
+
'0': id10001
|
| 144 |
+
'1': id10002
|
| 145 |
+
'2': id10003
|
| 146 |
+
'3': id10004
|
| 147 |
+
'4': id10005
|
| 148 |
+
'5': id10006
|
| 149 |
+
'6': id10007
|
| 150 |
+
'7': id10008
|
| 151 |
+
'8': id10009
|
| 152 |
+
'9': id10010
|
| 153 |
+
'10': id10011
|
| 154 |
+
'11': id10012
|
| 155 |
+
'12': id10013
|
| 156 |
+
'13': id10014
|
| 157 |
+
'14': id10015
|
| 158 |
+
'15': id10016
|
| 159 |
+
'16': id10017
|
| 160 |
+
'17': id10018
|
| 161 |
+
'18': id10019
|
| 162 |
+
'19': id10020
|
| 163 |
+
'20': id10021
|
| 164 |
+
'21': id10022
|
| 165 |
+
'22': id10023
|
| 166 |
+
'23': id10024
|
| 167 |
+
'24': id10025
|
| 168 |
+
'25': id10026
|
| 169 |
+
'26': id10027
|
| 170 |
+
'27': id10028
|
| 171 |
+
'28': id10029
|
| 172 |
+
'29': id10030
|
| 173 |
+
'30': id10031
|
| 174 |
+
'31': id10032
|
| 175 |
+
'32': id10033
|
| 176 |
+
'33': id10034
|
| 177 |
+
'34': id10035
|
| 178 |
+
'35': id10036
|
| 179 |
+
'36': id10037
|
| 180 |
+
'37': id10038
|
| 181 |
+
'38': id10039
|
| 182 |
+
'39': id10040
|
| 183 |
+
'40': id10041
|
| 184 |
+
'41': id10042
|
| 185 |
+
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|
| 1023 |
+
'880': id10881
|
| 1024 |
+
'881': id10882
|
| 1025 |
+
'882': id10883
|
| 1026 |
+
'883': id10884
|
| 1027 |
+
'884': id10885
|
| 1028 |
+
'885': id10886
|
| 1029 |
+
'886': id10887
|
| 1030 |
+
'887': id10888
|
| 1031 |
+
'888': id10889
|
| 1032 |
+
'889': id10890
|
| 1033 |
+
'890': id10891
|
| 1034 |
+
'891': id10892
|
| 1035 |
+
'892': id10893
|
| 1036 |
+
'893': id10894
|
| 1037 |
+
'894': id10895
|
| 1038 |
+
'895': id10896
|
| 1039 |
+
'896': id10897
|
| 1040 |
+
'897': id10898
|
| 1041 |
+
'898': id10899
|
| 1042 |
+
'899': id10900
|
| 1043 |
+
'900': id10901
|
| 1044 |
+
'901': id10902
|
| 1045 |
+
'902': id10903
|
| 1046 |
+
'903': id10904
|
| 1047 |
+
'904': id10905
|
| 1048 |
+
'905': id10906
|
| 1049 |
+
'906': id10907
|
| 1050 |
+
'907': id10908
|
| 1051 |
+
'908': id10909
|
| 1052 |
+
'909': id10910
|
| 1053 |
+
'910': id10911
|
| 1054 |
+
'911': id10912
|
| 1055 |
+
'912': id10913
|
| 1056 |
+
'913': id10914
|
| 1057 |
+
'914': id10915
|
| 1058 |
+
'915': id10916
|
| 1059 |
+
'916': id10917
|
| 1060 |
+
'917': id10918
|
| 1061 |
+
'918': id10919
|
| 1062 |
+
'919': id10920
|
| 1063 |
+
'920': id10921
|
| 1064 |
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'921': id10922
|
| 1065 |
+
'922': id10923
|
| 1066 |
+
'923': id10924
|
| 1067 |
+
'924': id10925
|
| 1068 |
+
'925': id10926
|
| 1069 |
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'926': id10927
|
| 1070 |
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'927': id10928
|
| 1071 |
+
'928': id10929
|
| 1072 |
+
'929': id10930
|
| 1073 |
+
'930': id10931
|
| 1074 |
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'931': id10932
|
| 1075 |
+
'932': id10933
|
| 1076 |
+
'933': id10934
|
| 1077 |
+
'934': id10935
|
| 1078 |
+
'935': id10936
|
| 1079 |
+
'936': id10937
|
| 1080 |
+
'937': id10938
|
| 1081 |
+
'938': id10939
|
| 1082 |
+
'939': id10940
|
| 1083 |
+
'940': id10941
|
| 1084 |
+
'941': id10942
|
| 1085 |
+
'942': id10943
|
| 1086 |
+
'943': id10944
|
| 1087 |
+
'944': id10945
|
| 1088 |
+
'945': id10946
|
| 1089 |
+
'946': id10947
|
| 1090 |
+
'947': id10948
|
| 1091 |
+
'948': id10949
|
| 1092 |
+
'949': id10950
|
| 1093 |
+
'950': id10951
|
| 1094 |
+
'951': id10952
|
| 1095 |
+
'952': id10953
|
| 1096 |
+
'953': id10954
|
| 1097 |
+
'954': id10955
|
| 1098 |
+
'955': id10956
|
| 1099 |
+
'956': id10957
|
| 1100 |
+
'957': id10958
|
| 1101 |
+
'958': id10959
|
| 1102 |
+
'959': id10960
|
| 1103 |
+
'960': id10961
|
| 1104 |
+
'961': id10962
|
| 1105 |
+
'962': id10963
|
| 1106 |
+
'963': id10964
|
| 1107 |
+
'964': id10965
|
| 1108 |
+
'965': id10966
|
| 1109 |
+
'966': id10967
|
| 1110 |
+
'967': id10968
|
| 1111 |
+
'968': id10969
|
| 1112 |
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'969': id10970
|
| 1113 |
+
'970': id10971
|
| 1114 |
+
'971': id10972
|
| 1115 |
+
'972': id10973
|
| 1116 |
+
'973': id10974
|
| 1117 |
+
'974': id10975
|
| 1118 |
+
'975': id10976
|
| 1119 |
+
'976': id10977
|
| 1120 |
+
'977': id10978
|
| 1121 |
+
'978': id10979
|
| 1122 |
+
'979': id10980
|
| 1123 |
+
'980': id10981
|
| 1124 |
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'981': id10982
|
| 1125 |
+
'982': id10983
|
| 1126 |
+
'983': id10984
|
| 1127 |
+
'984': id10985
|
| 1128 |
+
'985': id10986
|
| 1129 |
+
'986': id10987
|
| 1130 |
+
'987': id10988
|
| 1131 |
+
'988': id10989
|
| 1132 |
+
'989': id10990
|
| 1133 |
+
'990': id10991
|
| 1134 |
+
'991': id10992
|
| 1135 |
+
'992': id10993
|
| 1136 |
+
'993': id10994
|
| 1137 |
+
'994': id10995
|
| 1138 |
+
'995': id10996
|
| 1139 |
+
'996': id10997
|
| 1140 |
+
'997': id10998
|
| 1141 |
+
'998': id10999
|
| 1142 |
+
'999': id11000
|
| 1143 |
+
'1000': id11001
|
| 1144 |
+
'1001': id11002
|
| 1145 |
+
'1002': id11003
|
| 1146 |
+
'1003': id11004
|
| 1147 |
+
'1004': id11005
|
| 1148 |
+
'1005': id11006
|
| 1149 |
+
'1006': id11007
|
| 1150 |
+
'1007': id11008
|
| 1151 |
+
'1008': id11009
|
| 1152 |
+
'1009': id11010
|
| 1153 |
+
'1010': id11011
|
| 1154 |
+
'1011': id11012
|
| 1155 |
+
'1012': id11013
|
| 1156 |
+
'1013': id11014
|
| 1157 |
+
'1014': id11015
|
| 1158 |
+
'1015': id11016
|
| 1159 |
+
'1016': id11017
|
| 1160 |
+
'1017': id11018
|
| 1161 |
+
'1018': id11019
|
| 1162 |
+
'1019': id11020
|
| 1163 |
+
'1020': id11021
|
| 1164 |
+
'1021': id11022
|
| 1165 |
+
'1022': id11023
|
| 1166 |
+
'1023': id11024
|
| 1167 |
+
'1024': id11025
|
| 1168 |
+
'1025': id11026
|
| 1169 |
+
'1026': id11027
|
| 1170 |
+
'1027': id11028
|
| 1171 |
+
'1028': id11029
|
| 1172 |
+
'1029': id11030
|
| 1173 |
+
'1030': id11031
|
| 1174 |
+
'1031': id11032
|
| 1175 |
+
'1032': id11033
|
| 1176 |
+
'1033': id11034
|
| 1177 |
+
'1034': id11035
|
| 1178 |
+
'1035': id11036
|
| 1179 |
+
'1036': id11037
|
| 1180 |
+
'1037': id11038
|
| 1181 |
+
'1038': id11039
|
| 1182 |
+
'1039': id11040
|
| 1183 |
+
'1040': id11041
|
| 1184 |
+
'1041': id11042
|
| 1185 |
+
'1042': id11043
|
| 1186 |
+
'1043': id11044
|
| 1187 |
+
'1044': id11045
|
| 1188 |
+
'1045': id11046
|
| 1189 |
+
'1046': id11047
|
| 1190 |
+
'1047': id11048
|
| 1191 |
+
'1048': id11049
|
| 1192 |
+
'1049': id11050
|
| 1193 |
+
'1050': id11051
|
| 1194 |
+
'1051': id11052
|
| 1195 |
+
'1052': id11053
|
| 1196 |
+
'1053': id11054
|
| 1197 |
+
'1054': id11055
|
| 1198 |
+
'1055': id11056
|
| 1199 |
+
'1056': id11057
|
| 1200 |
+
'1057': id11058
|
| 1201 |
+
'1058': id11059
|
| 1202 |
+
'1059': id11060
|
| 1203 |
+
'1060': id11061
|
| 1204 |
+
'1061': id11062
|
| 1205 |
+
'1062': id11063
|
| 1206 |
+
'1063': id11064
|
| 1207 |
+
'1064': id11065
|
| 1208 |
+
'1065': id11066
|
| 1209 |
+
'1066': id11067
|
| 1210 |
+
'1067': id11068
|
| 1211 |
+
'1068': id11069
|
| 1212 |
+
'1069': id11070
|
| 1213 |
+
'1070': id11071
|
| 1214 |
+
'1071': id11072
|
| 1215 |
+
'1072': id11073
|
| 1216 |
+
'1073': id11074
|
| 1217 |
+
'1074': id11075
|
| 1218 |
+
'1075': id11076
|
| 1219 |
+
'1076': id11077
|
| 1220 |
+
'1077': id11078
|
| 1221 |
+
'1078': id11079
|
| 1222 |
+
'1079': id11080
|
| 1223 |
+
'1080': id11081
|
| 1224 |
+
'1081': id11082
|
| 1225 |
+
'1082': id11083
|
| 1226 |
+
'1083': id11084
|
| 1227 |
+
'1084': id11085
|
| 1228 |
+
'1085': id11086
|
| 1229 |
+
'1086': id11087
|
| 1230 |
+
'1087': id11088
|
| 1231 |
+
'1088': id11089
|
| 1232 |
+
'1089': id11090
|
| 1233 |
+
'1090': id11091
|
| 1234 |
+
'1091': id11092
|
| 1235 |
+
'1092': id11093
|
| 1236 |
+
'1093': id11094
|
| 1237 |
+
'1094': id11095
|
| 1238 |
+
'1095': id11096
|
| 1239 |
+
'1096': id11097
|
| 1240 |
+
'1097': id11098
|
| 1241 |
+
'1098': id11099
|
| 1242 |
+
'1099': id11100
|
| 1243 |
+
'1100': id11101
|
| 1244 |
+
'1101': id11102
|
| 1245 |
+
'1102': id11103
|
| 1246 |
+
'1103': id11104
|
| 1247 |
+
'1104': id11105
|
| 1248 |
+
'1105': id11106
|
| 1249 |
+
'1106': id11107
|
| 1250 |
+
'1107': id11108
|
| 1251 |
+
'1108': id11109
|
| 1252 |
+
'1109': id11110
|
| 1253 |
+
'1110': id11111
|
| 1254 |
+
'1111': id11112
|
| 1255 |
+
'1112': id11113
|
| 1256 |
+
'1113': id11114
|
| 1257 |
+
'1114': id11115
|
| 1258 |
+
'1115': id11116
|
| 1259 |
+
'1116': id11117
|
| 1260 |
+
'1117': id11118
|
| 1261 |
+
'1118': id11119
|
| 1262 |
+
'1119': id11120
|
| 1263 |
+
'1120': id11121
|
| 1264 |
+
'1121': id11122
|
| 1265 |
+
'1122': id11123
|
| 1266 |
+
'1123': id11124
|
| 1267 |
+
'1124': id11125
|
| 1268 |
+
'1125': id11126
|
| 1269 |
+
'1126': id11127
|
| 1270 |
+
'1127': id11128
|
| 1271 |
+
'1128': id11129
|
| 1272 |
+
'1129': id11130
|
| 1273 |
+
'1130': id11131
|
| 1274 |
+
'1131': id11132
|
| 1275 |
+
'1132': id11133
|
| 1276 |
+
'1133': id11134
|
| 1277 |
+
'1134': id11135
|
| 1278 |
+
'1135': id11136
|
| 1279 |
+
'1136': id11137
|
| 1280 |
+
'1137': id11138
|
| 1281 |
+
'1138': id11139
|
| 1282 |
+
'1139': id11140
|
| 1283 |
+
'1140': id11141
|
| 1284 |
+
'1141': id11142
|
| 1285 |
+
'1142': id11143
|
| 1286 |
+
'1143': id11144
|
| 1287 |
+
'1144': id11145
|
| 1288 |
+
'1145': id11146
|
| 1289 |
+
'1146': id11147
|
| 1290 |
+
'1147': id11148
|
| 1291 |
+
'1148': id11149
|
| 1292 |
+
'1149': id11150
|
| 1293 |
+
'1150': id11151
|
| 1294 |
+
'1151': id11152
|
| 1295 |
+
'1152': id11153
|
| 1296 |
+
'1153': id11154
|
| 1297 |
+
'1154': id11155
|
| 1298 |
+
'1155': id11156
|
| 1299 |
+
'1156': id11157
|
| 1300 |
+
'1157': id11158
|
| 1301 |
+
'1158': id11159
|
| 1302 |
+
'1159': id11160
|
| 1303 |
+
'1160': id11161
|
| 1304 |
+
'1161': id11162
|
| 1305 |
+
'1162': id11163
|
| 1306 |
+
'1163': id11164
|
| 1307 |
+
'1164': id11165
|
| 1308 |
+
'1165': id11166
|
| 1309 |
+
'1166': id11167
|
| 1310 |
+
'1167': id11168
|
| 1311 |
+
'1168': id11169
|
| 1312 |
+
'1169': id11170
|
| 1313 |
+
'1170': id11171
|
| 1314 |
+
'1171': id11172
|
| 1315 |
+
'1172': id11173
|
| 1316 |
+
'1173': id11174
|
| 1317 |
+
'1174': id11175
|
| 1318 |
+
'1175': id11176
|
| 1319 |
+
'1176': id11177
|
| 1320 |
+
'1177': id11178
|
| 1321 |
+
'1178': id11179
|
| 1322 |
+
'1179': id11180
|
| 1323 |
+
'1180': id11181
|
| 1324 |
+
'1181': id11182
|
| 1325 |
+
'1182': id11183
|
| 1326 |
+
'1183': id11184
|
| 1327 |
+
'1184': id11185
|
| 1328 |
+
'1185': id11186
|
| 1329 |
+
'1186': id11187
|
| 1330 |
+
'1187': id11188
|
| 1331 |
+
'1188': id11189
|
| 1332 |
+
'1189': id11190
|
| 1333 |
+
'1190': id11191
|
| 1334 |
+
'1191': id11192
|
| 1335 |
+
'1192': id11193
|
| 1336 |
+
'1193': id11194
|
| 1337 |
+
'1194': id11195
|
| 1338 |
+
'1195': id11196
|
| 1339 |
+
'1196': id11197
|
| 1340 |
+
'1197': id11198
|
| 1341 |
+
'1198': id11199
|
| 1342 |
+
'1199': id11200
|
| 1343 |
+
'1200': id11201
|
| 1344 |
+
'1201': id11202
|
| 1345 |
+
'1202': id11203
|
| 1346 |
+
'1203': id11204
|
| 1347 |
+
'1204': id11205
|
| 1348 |
+
'1205': id11206
|
| 1349 |
+
'1206': id11207
|
| 1350 |
+
'1207': id11208
|
| 1351 |
+
'1208': id11209
|
| 1352 |
+
'1209': id11210
|
| 1353 |
+
'1210': id11211
|
| 1354 |
+
'1211': id11212
|
| 1355 |
+
'1212': id11213
|
| 1356 |
+
'1213': id11214
|
| 1357 |
+
'1214': id11215
|
| 1358 |
+
'1215': id11216
|
| 1359 |
+
'1216': id11217
|
| 1360 |
+
'1217': id11218
|
| 1361 |
+
'1218': id11219
|
| 1362 |
+
'1219': id11220
|
| 1363 |
+
'1220': id11221
|
| 1364 |
+
'1221': id11222
|
| 1365 |
+
'1222': id11223
|
| 1366 |
+
'1223': id11224
|
| 1367 |
+
'1224': id11225
|
| 1368 |
+
'1225': id11226
|
| 1369 |
+
'1226': id11227
|
| 1370 |
+
'1227': id11228
|
| 1371 |
+
'1228': id11229
|
| 1372 |
+
'1229': id11230
|
| 1373 |
+
'1230': id11231
|
| 1374 |
+
'1231': id11232
|
| 1375 |
+
'1232': id11233
|
| 1376 |
+
'1233': id11234
|
| 1377 |
+
'1234': id11235
|
| 1378 |
+
'1235': id11236
|
| 1379 |
+
'1236': id11237
|
| 1380 |
+
'1237': id11238
|
| 1381 |
+
'1238': id11239
|
| 1382 |
+
'1239': id11240
|
| 1383 |
+
'1240': id11241
|
| 1384 |
+
'1241': id11242
|
| 1385 |
+
'1242': id11243
|
| 1386 |
+
'1243': id11244
|
| 1387 |
+
'1244': id11245
|
| 1388 |
+
'1245': id11246
|
| 1389 |
+
'1246': id11247
|
| 1390 |
+
'1247': id11248
|
| 1391 |
+
'1248': id11249
|
| 1392 |
+
'1249': id11250
|
| 1393 |
+
'1250': id11251
|
| 1394 |
+
splits:
|
| 1395 |
+
- name: test
|
| 1396 |
+
num_bytes: 1798447.0
|
| 1397 |
+
num_examples: 6
|
| 1398 |
+
download_size: 1832975
|
| 1399 |
+
dataset_size: 1798447.0
|
| 1400 |
+
configs:
|
| 1401 |
+
- config_name: asr
|
| 1402 |
+
data_files:
|
| 1403 |
+
- split: test
|
| 1404 |
+
path: asr/test-*
|
| 1405 |
+
- config_name: er
|
| 1406 |
+
data_files:
|
| 1407 |
+
- split: session1
|
| 1408 |
+
path: er/session1-*
|
| 1409 |
+
- config_name: ic
|
| 1410 |
+
data_files:
|
| 1411 |
+
- split: test
|
| 1412 |
+
path: ic/test-*
|
| 1413 |
+
- config_name: ks
|
| 1414 |
+
data_files:
|
| 1415 |
+
- split: test
|
| 1416 |
+
path: ks/test-*
|
| 1417 |
+
- config_name: si
|
| 1418 |
+
data_files:
|
| 1419 |
+
- split: test
|
| 1420 |
+
path: si/test-*
|
| 1421 |
+
---
|
| 1422 |
# Disclaimer
|
| 1423 |
This is a tiny subset of the SUPERB dataset, which is intended only for demo purposes!
|
| 1424 |
|
data/speech_commands_test_set_v0.01.zip → asr/test-00000-of-00001.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c62f287a74c9e2d8b8eafecc5b289be62ea63ff5fb0e274f6ced9b10d08244db
|
| 3 |
+
size 632857
|
data/IEMOCAP_full_release.zip → er/session1-00000-of-00001.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7da39fd893144fae1d40d55cc0cba4441ccfac64db7b3990d5e7dbbc63787cc
|
| 3 |
+
size 1389189
|
data/LibriSpeech-test-clean.zip → ic/test-00000-of-00001.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a026dd37e154c3965d1feab04ff612c094991ce23f0dd330fb756dbf58713567
|
| 3 |
+
size 466270
|
data/fluent_speech_commands_dataset.zip → ks/test-00000-of-00001.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
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|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:57daf5ffe13c52b6c42962a8d24f83926d96ca7bfe950291a85abe383eed41e5
|
| 3 |
+
size 253966
|
data/VoxCeleb1.zip → si/test-00000-of-00001.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3f7ee6b215237e9f59be863d36fbeb2690dfbfda3f33c994fd38e1f4a45086b3
|
| 3 |
+
size 1832975
|
superb_demo.py
DELETED
|
@@ -1,435 +0,0 @@
|
|
| 1 |
-
# coding=utf-8
|
| 2 |
-
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
|
| 3 |
-
#
|
| 4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
# you may not use this file except in compliance with the License.
|
| 6 |
-
# You may obtain a copy of the License at
|
| 7 |
-
#
|
| 8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
#
|
| 10 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
# See the License for the specific language governing permissions and
|
| 14 |
-
# limitations under the License.
|
| 15 |
-
|
| 16 |
-
# Lint as: python3
|
| 17 |
-
"""SUPERB: Speech processing Universal PERformance Benchmark."""
|
| 18 |
-
|
| 19 |
-
import csv
|
| 20 |
-
import glob
|
| 21 |
-
import os
|
| 22 |
-
import textwrap
|
| 23 |
-
|
| 24 |
-
import datasets
|
| 25 |
-
from datasets.tasks import AutomaticSpeechRecognition
|
| 26 |
-
|
| 27 |
-
_CITATION = """\
|
| 28 |
-
@article{DBLP:journals/corr/abs-2105-01051,
|
| 29 |
-
author = {Shu{-}Wen Yang and
|
| 30 |
-
Po{-}Han Chi and
|
| 31 |
-
Yung{-}Sung Chuang and
|
| 32 |
-
Cheng{-}I Jeff Lai and
|
| 33 |
-
Kushal Lakhotia and
|
| 34 |
-
Yist Y. Lin and
|
| 35 |
-
Andy T. Liu and
|
| 36 |
-
Jiatong Shi and
|
| 37 |
-
Xuankai Chang and
|
| 38 |
-
Guan{-}Ting Lin and
|
| 39 |
-
Tzu{-}Hsien Huang and
|
| 40 |
-
Wei{-}Cheng Tseng and
|
| 41 |
-
Ko{-}tik Lee and
|
| 42 |
-
Da{-}Rong Liu and
|
| 43 |
-
Zili Huang and
|
| 44 |
-
Shuyan Dong and
|
| 45 |
-
Shang{-}Wen Li and
|
| 46 |
-
Shinji Watanabe and
|
| 47 |
-
Abdelrahman Mohamed and
|
| 48 |
-
Hung{-}yi Lee},
|
| 49 |
-
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
|
| 50 |
-
journal = {CoRR},
|
| 51 |
-
volume = {abs/2105.01051},
|
| 52 |
-
year = {2021},
|
| 53 |
-
url = {https://arxiv.org/abs/2105.01051},
|
| 54 |
-
archivePrefix = {arXiv},
|
| 55 |
-
eprint = {2105.01051},
|
| 56 |
-
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
|
| 57 |
-
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
|
| 58 |
-
bibsource = {dblp computer science bibliography, https://dblp.org}
|
| 59 |
-
}
|
| 60 |
-
"""
|
| 61 |
-
|
| 62 |
-
_DESCRIPTION = """\
|
| 63 |
-
Self-supervised learning (SSL) has proven vital for advancing research in
|
| 64 |
-
natural language processing (NLP) and computer vision (CV). The paradigm
|
| 65 |
-
pretrains a shared model on large volumes of unlabeled data and achieves
|
| 66 |
-
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
|
| 67 |
-
speech processing community lacks a similar setup to systematically explore the
|
| 68 |
-
paradigm. To bridge this gap, we introduce Speech processing Universal
|
| 69 |
-
PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
|
| 70 |
-
performance of a shared model across a wide range of speech processing tasks
|
| 71 |
-
with minimal architecture changes and labeled data. Among multiple usages of the
|
| 72 |
-
shared model, we especially focus on extracting the representation learned from
|
| 73 |
-
SSL due to its preferable re-usability. We present a simple framework to solve
|
| 74 |
-
SUPERB tasks by learning task-specialized lightweight prediction heads on top of
|
| 75 |
-
the frozen shared model. Our results demonstrate that the framework is promising
|
| 76 |
-
as SSL representations show competitive generalizability and accessibility
|
| 77 |
-
across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a
|
| 78 |
-
benchmark toolkit to fuel the research in representation learning and general
|
| 79 |
-
speech processing.
|
| 80 |
-
|
| 81 |
-
Note that in order to limit the required storage for preparing this dataset, the
|
| 82 |
-
audio is stored in the .flac format and is not converted to a float32 array. To
|
| 83 |
-
convert, the audio file to a float32 array, please make use of the `.map()`
|
| 84 |
-
function as follows:
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
```python
|
| 88 |
-
import soundfile as sf
|
| 89 |
-
|
| 90 |
-
def map_to_array(batch):
|
| 91 |
-
speech_array, _ = sf.read(batch["file"])
|
| 92 |
-
batch["speech"] = speech_array
|
| 93 |
-
return batch
|
| 94 |
-
|
| 95 |
-
dataset = dataset.map(map_to_array, remove_columns=["file"])
|
| 96 |
-
```
|
| 97 |
-
"""
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
class SuperbConfig(datasets.BuilderConfig):
|
| 101 |
-
"""BuilderConfig for Superb."""
|
| 102 |
-
|
| 103 |
-
def __init__(
|
| 104 |
-
self,
|
| 105 |
-
features,
|
| 106 |
-
url,
|
| 107 |
-
data_url=None,
|
| 108 |
-
supervised_keys=None,
|
| 109 |
-
task_templates=None,
|
| 110 |
-
**kwargs,
|
| 111 |
-
):
|
| 112 |
-
super().__init__(version=datasets.Version("1.9.0", ""), **kwargs)
|
| 113 |
-
self.features = features
|
| 114 |
-
self.data_url = data_url
|
| 115 |
-
self.url = url
|
| 116 |
-
self.supervised_keys = supervised_keys
|
| 117 |
-
self.task_templates = task_templates
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
class Superb(datasets.GeneratorBasedBuilder):
|
| 121 |
-
"""Superb dataset."""
|
| 122 |
-
|
| 123 |
-
BUILDER_CONFIGS = [
|
| 124 |
-
SuperbConfig(
|
| 125 |
-
name="asr",
|
| 126 |
-
description=textwrap.dedent(
|
| 127 |
-
"""\
|
| 128 |
-
ASR transcribes utterances into words. While PR analyzes the
|
| 129 |
-
improvement in modeling phonetics, ASR reflects the significance of
|
| 130 |
-
the improvement in a real-world scenario. LibriSpeech
|
| 131 |
-
train-clean-100/dev-clean/test-clean subsets are used for
|
| 132 |
-
training/validation/testing. The evaluation metric is word error
|
| 133 |
-
rate (WER)."""
|
| 134 |
-
),
|
| 135 |
-
features=datasets.Features(
|
| 136 |
-
{
|
| 137 |
-
"file": datasets.Value("string"),
|
| 138 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
| 139 |
-
"text": datasets.Value("string"),
|
| 140 |
-
"speaker_id": datasets.Value("int64"),
|
| 141 |
-
"chapter_id": datasets.Value("int64"),
|
| 142 |
-
"id": datasets.Value("string"),
|
| 143 |
-
}
|
| 144 |
-
),
|
| 145 |
-
supervised_keys=("file", "text"),
|
| 146 |
-
url="http://www.openslr.org/12",
|
| 147 |
-
data_url="data/LibriSpeech-test-clean.zip",
|
| 148 |
-
),
|
| 149 |
-
SuperbConfig(
|
| 150 |
-
name="ks",
|
| 151 |
-
description=textwrap.dedent(
|
| 152 |
-
"""\
|
| 153 |
-
Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of
|
| 154 |
-
words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and
|
| 155 |
-
inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task.
|
| 156 |
-
The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the
|
| 157 |
-
false positive. The evaluation metric is accuracy (ACC)"""
|
| 158 |
-
),
|
| 159 |
-
features=datasets.Features(
|
| 160 |
-
{
|
| 161 |
-
"file": datasets.Value("string"),
|
| 162 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
| 163 |
-
"label": datasets.ClassLabel(
|
| 164 |
-
names=[
|
| 165 |
-
"yes",
|
| 166 |
-
"no",
|
| 167 |
-
"up",
|
| 168 |
-
"down",
|
| 169 |
-
"left",
|
| 170 |
-
"right",
|
| 171 |
-
"on",
|
| 172 |
-
"off",
|
| 173 |
-
"stop",
|
| 174 |
-
"go",
|
| 175 |
-
"_silence_",
|
| 176 |
-
"_unknown_",
|
| 177 |
-
]
|
| 178 |
-
),
|
| 179 |
-
}
|
| 180 |
-
),
|
| 181 |
-
supervised_keys=("file", "label"),
|
| 182 |
-
url="https://www.tensorflow.org/datasets/catalog/speech_commands",
|
| 183 |
-
data_url="data/speech_commands_test_set_v0.01.zip",
|
| 184 |
-
),
|
| 185 |
-
SuperbConfig(
|
| 186 |
-
name="ic",
|
| 187 |
-
description=textwrap.dedent(
|
| 188 |
-
"""\
|
| 189 |
-
Intent Classification (IC) classifies utterances into predefined classes to determine the intent of
|
| 190 |
-
speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent
|
| 191 |
-
labels: action, object, and location. The evaluation metric is accuracy (ACC)."""
|
| 192 |
-
),
|
| 193 |
-
features=datasets.Features(
|
| 194 |
-
{
|
| 195 |
-
"file": datasets.Value("string"),
|
| 196 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
| 197 |
-
"speaker_id": datasets.Value("string"),
|
| 198 |
-
"text": datasets.Value("string"),
|
| 199 |
-
"action": datasets.ClassLabel(
|
| 200 |
-
names=["activate", "bring", "change language", "deactivate", "decrease", "increase"]
|
| 201 |
-
),
|
| 202 |
-
"object": datasets.ClassLabel(
|
| 203 |
-
names=[
|
| 204 |
-
"Chinese",
|
| 205 |
-
"English",
|
| 206 |
-
"German",
|
| 207 |
-
"Korean",
|
| 208 |
-
"heat",
|
| 209 |
-
"juice",
|
| 210 |
-
"lamp",
|
| 211 |
-
"lights",
|
| 212 |
-
"music",
|
| 213 |
-
"newspaper",
|
| 214 |
-
"none",
|
| 215 |
-
"shoes",
|
| 216 |
-
"socks",
|
| 217 |
-
"volume",
|
| 218 |
-
]
|
| 219 |
-
),
|
| 220 |
-
"location": datasets.ClassLabel(names=["bedroom", "kitchen", "none", "washroom"]),
|
| 221 |
-
}
|
| 222 |
-
),
|
| 223 |
-
# no default supervised keys, since there are 3 labels
|
| 224 |
-
supervised_keys=None,
|
| 225 |
-
url="https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/",
|
| 226 |
-
data_url="data/fluent_speech_commands_dataset.zip",
|
| 227 |
-
),
|
| 228 |
-
SuperbConfig(
|
| 229 |
-
name="si",
|
| 230 |
-
description=textwrap.dedent(
|
| 231 |
-
"""\
|
| 232 |
-
Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class
|
| 233 |
-
classification, where speakers are in the same predefined set for both training and testing. The widely
|
| 234 |
-
used VoxCeleb1 dataset is adopted, and the evaluation metric is accuracy (ACC)."""
|
| 235 |
-
),
|
| 236 |
-
features=datasets.Features(
|
| 237 |
-
{
|
| 238 |
-
"file": datasets.Value("string"),
|
| 239 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
| 240 |
-
"label": datasets.ClassLabel(names=[f"id{i + 10001}" for i in range(1251)]),
|
| 241 |
-
}
|
| 242 |
-
),
|
| 243 |
-
supervised_keys=("file", "label"),
|
| 244 |
-
url="https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html",
|
| 245 |
-
data_url="data/VoxCeleb1.zip"
|
| 246 |
-
),
|
| 247 |
-
SuperbConfig(
|
| 248 |
-
name="er",
|
| 249 |
-
description=textwrap.dedent(
|
| 250 |
-
"""\
|
| 251 |
-
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset
|
| 252 |
-
IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion
|
| 253 |
-
classes to leave the final four classes with a similar amount of data points and cross-validates on five
|
| 254 |
-
folds of the standard splits. The evaluation metric is accuracy (ACC)."""
|
| 255 |
-
),
|
| 256 |
-
features=datasets.Features(
|
| 257 |
-
{
|
| 258 |
-
"file": datasets.Value("string"),
|
| 259 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
| 260 |
-
"label": datasets.ClassLabel(names=['neu', 'hap', 'ang', 'sad']),
|
| 261 |
-
}
|
| 262 |
-
),
|
| 263 |
-
supervised_keys=("file", "label"),
|
| 264 |
-
url="https://sail.usc.edu/iemocap/",
|
| 265 |
-
data_url="data/IEMOCAP_full_release.zip"
|
| 266 |
-
),
|
| 267 |
-
]
|
| 268 |
-
|
| 269 |
-
def _info(self):
|
| 270 |
-
return datasets.DatasetInfo(
|
| 271 |
-
description=_DESCRIPTION,
|
| 272 |
-
features=self.config.features,
|
| 273 |
-
supervised_keys=self.config.supervised_keys,
|
| 274 |
-
homepage=self.config.url,
|
| 275 |
-
citation=_CITATION,
|
| 276 |
-
task_templates=self.config.task_templates,
|
| 277 |
-
)
|
| 278 |
-
|
| 279 |
-
def _split_generators(self, dl_manager):
|
| 280 |
-
if self.config.name == "asr":
|
| 281 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
| 282 |
-
return [
|
| 283 |
-
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path}),
|
| 284 |
-
]
|
| 285 |
-
elif self.config.name == "ks":
|
| 286 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
| 287 |
-
return [
|
| 288 |
-
datasets.SplitGenerator(
|
| 289 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
| 290 |
-
),
|
| 291 |
-
]
|
| 292 |
-
elif self.config.name == "ic":
|
| 293 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
| 294 |
-
return [
|
| 295 |
-
datasets.SplitGenerator(
|
| 296 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
| 297 |
-
),
|
| 298 |
-
]
|
| 299 |
-
elif self.config.name == "si":
|
| 300 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
| 301 |
-
return [
|
| 302 |
-
datasets.SplitGenerator(
|
| 303 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": 3}
|
| 304 |
-
),
|
| 305 |
-
]
|
| 306 |
-
elif self.config.name == "sd":
|
| 307 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
| 308 |
-
return [
|
| 309 |
-
datasets.SplitGenerator(
|
| 310 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
| 311 |
-
)
|
| 312 |
-
]
|
| 313 |
-
elif self.config.name == "er":
|
| 314 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
| 315 |
-
return [
|
| 316 |
-
datasets.SplitGenerator(
|
| 317 |
-
name="session1", gen_kwargs={"archive_path": archive_path, "split": 1},
|
| 318 |
-
)
|
| 319 |
-
]
|
| 320 |
-
|
| 321 |
-
def _generate_examples(self, archive_path, split=None):
|
| 322 |
-
"""Generate examples."""
|
| 323 |
-
if self.config.name == "asr":
|
| 324 |
-
transcripts_glob = os.path.join(archive_path, "LibriSpeech", "*/*/*/*.txt")
|
| 325 |
-
key = 0
|
| 326 |
-
for transcript_path in sorted(glob.glob(transcripts_glob)):
|
| 327 |
-
transcript_dir_path = os.path.dirname(transcript_path)
|
| 328 |
-
with open(transcript_path, "r", encoding="utf-8") as f:
|
| 329 |
-
for line in f:
|
| 330 |
-
line = line.strip()
|
| 331 |
-
id_, transcript = line.split(" ", 1)
|
| 332 |
-
audio_file = f"{id_}.flac"
|
| 333 |
-
speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
|
| 334 |
-
audio_path = os.path.join(transcript_dir_path, audio_file)
|
| 335 |
-
yield key, {
|
| 336 |
-
"id": id_,
|
| 337 |
-
"speaker_id": speaker_id,
|
| 338 |
-
"chapter_id": chapter_id,
|
| 339 |
-
"file": audio_path,
|
| 340 |
-
"audio": audio_path,
|
| 341 |
-
"text": transcript,
|
| 342 |
-
}
|
| 343 |
-
key += 1
|
| 344 |
-
elif self.config.name == "ks":
|
| 345 |
-
words = ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go"]
|
| 346 |
-
splits = _split_ks_files(archive_path, split)
|
| 347 |
-
for key, audio_file in enumerate(sorted(splits[split])):
|
| 348 |
-
base_dir, file_name = os.path.split(audio_file)
|
| 349 |
-
_, word = os.path.split(base_dir)
|
| 350 |
-
if word in words:
|
| 351 |
-
label = word
|
| 352 |
-
elif word == "_silence_" or word == "_background_noise_":
|
| 353 |
-
label = "_silence_"
|
| 354 |
-
else:
|
| 355 |
-
label = "_unknown_"
|
| 356 |
-
yield key, {"file": audio_file, "audio": audio_file, "label": label}
|
| 357 |
-
elif self.config.name == "ic":
|
| 358 |
-
root_path = os.path.join(archive_path, "fluent_speech_commands_dataset/")
|
| 359 |
-
csv_path = os.path.join(root_path, f"data/{split}_data.csv")
|
| 360 |
-
with open(csv_path, encoding="utf-8") as csv_file:
|
| 361 |
-
csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
|
| 362 |
-
next(csv_reader)
|
| 363 |
-
for row in csv_reader:
|
| 364 |
-
key, file_path, speaker_id, text, action, object_, location = row
|
| 365 |
-
audio_path = os.path.join(root_path, file_path)
|
| 366 |
-
yield key, {
|
| 367 |
-
"file": audio_path,
|
| 368 |
-
"audio": audio_path,
|
| 369 |
-
"speaker_id": speaker_id,
|
| 370 |
-
"text": text,
|
| 371 |
-
"action": action,
|
| 372 |
-
"object": object_,
|
| 373 |
-
"location": location,
|
| 374 |
-
}
|
| 375 |
-
elif self.config.name == "si":
|
| 376 |
-
wav_path = os.path.join(archive_path, "wav/")
|
| 377 |
-
splits_path = os.path.join(archive_path, "veri_test_class.txt")
|
| 378 |
-
with open(splits_path, "r", encoding="utf-8") as f:
|
| 379 |
-
for key, line in enumerate(f):
|
| 380 |
-
split_id, file_path = line.strip().split(" ")
|
| 381 |
-
if int(split_id) != split:
|
| 382 |
-
continue
|
| 383 |
-
speaker_id = file_path.split("/")[0]
|
| 384 |
-
audio_path = os.path.join(wav_path, file_path)
|
| 385 |
-
yield key, {
|
| 386 |
-
"file": audio_path,
|
| 387 |
-
"audio": audio_path,
|
| 388 |
-
"label": speaker_id,
|
| 389 |
-
}
|
| 390 |
-
elif self.config.name == "er":
|
| 391 |
-
root_path = os.path.join(archive_path, f"Session{split}/")
|
| 392 |
-
wav_path = os.path.join(root_path, "sentences/wav/")
|
| 393 |
-
labels_path = os.path.join(root_path, "dialog/EmoEvaluation/*.txt")
|
| 394 |
-
emotions = ['neu', 'hap', 'ang', 'sad', 'exc']
|
| 395 |
-
key = 0
|
| 396 |
-
for labels_file in sorted(glob.glob(labels_path)):
|
| 397 |
-
with open(labels_file, "r", encoding="utf-8") as f:
|
| 398 |
-
for line in f:
|
| 399 |
-
if line[0] != "[":
|
| 400 |
-
continue
|
| 401 |
-
_, filename, emo, _ = line.split("\t")
|
| 402 |
-
if emo not in emotions:
|
| 403 |
-
continue
|
| 404 |
-
wav_subdir = filename.rsplit("_", 1)[0]
|
| 405 |
-
filename = f"{filename}.wav"
|
| 406 |
-
audio_path = os.path.join(wav_path, wav_subdir, filename)
|
| 407 |
-
yield key, {
|
| 408 |
-
"file": audio_path,
|
| 409 |
-
"audio": audio_path,
|
| 410 |
-
"label": emo.replace("exc", "hap"),
|
| 411 |
-
}
|
| 412 |
-
key += 1
|
| 413 |
-
|
| 414 |
-
|
| 415 |
-
def _split_ks_files(archive_path, split):
|
| 416 |
-
audio_path = os.path.join(archive_path, "**/*.wav")
|
| 417 |
-
audio_paths = glob.glob(audio_path)
|
| 418 |
-
if split == "test":
|
| 419 |
-
# use all available files for the test archive
|
| 420 |
-
return {"test": audio_paths}
|
| 421 |
-
|
| 422 |
-
val_list_file = os.path.join(archive_path, "validation_list.txt")
|
| 423 |
-
test_list_file = os.path.join(archive_path, "testing_list.txt")
|
| 424 |
-
with open(val_list_file, encoding="utf-8") as f:
|
| 425 |
-
val_paths = f.read().strip().splitlines()
|
| 426 |
-
val_paths = [os.path.join(archive_path, p) for p in val_paths]
|
| 427 |
-
with open(test_list_file, encoding="utf-8") as f:
|
| 428 |
-
test_paths = f.read().strip().splitlines()
|
| 429 |
-
test_paths = [os.path.join(archive_path, p) for p in test_paths]
|
| 430 |
-
|
| 431 |
-
# the paths for the train set is just whichever paths that do not exist in
|
| 432 |
-
# either the test or validation splits
|
| 433 |
-
train_paths = list(set(audio_paths) - set(val_paths) - set(test_paths))
|
| 434 |
-
|
| 435 |
-
return {"train": train_paths, "val": val_paths}
|
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