Instructions to use trysem/stt_ml_fastconformer_ctc_med_punct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use trysem/stt_ml_fastconformer_ctc_med_punct with NeMo:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
Commit ·
8de7ec4
0
Parent(s):
Duplicate from kavyamanohar/stt_ml_fastconformer_ctc_med_punct
Browse filesCo-authored-by: Kavya Manohar <kavyamanohar@users.noreply.huggingface.co>
- .gitattributes +35 -0
- 299f3c3e10064a928e07b138ca8d25c8_vocab.txt +511 -0
- 49704422a930474bb04998e5f862af1e_tokenizer.model +3 -0
- 87be75af54e141a8a50481d3ebfa5088_tokenizer.vocab +512 -0
- README.md +196 -0
- model_config.yaml +743 -0
- model_weights.ckpt +3 -0
.gitattributes
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299f3c3e10064a928e07b138ca8d25c8_vocab.txt
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##ന്
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##ക്
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ക
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അ
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##തി
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വ
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ന
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##ങ്
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##യി
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സ
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##്പ
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##്യ
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##ച്
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എ
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##രു
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##ങ്ങ
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##്ര
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##റ്
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##്ല
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ഇ
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ആ
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ത
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ഒ
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പ്ര
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ര
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ഒരു
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ബ
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| 100 |
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|
| 101 |
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കു
|
| 102 |
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|
| 103 |
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|
| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 110 |
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| 122 |
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| 127 |
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|
| 128 |
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| 240 |
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| 241 |
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| 242 |
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| 243 |
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| 244 |
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| 245 |
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| 246 |
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| 247 |
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| 248 |
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| 249 |
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| 250 |
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|
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|
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##ಿ
|
49704422a930474bb04998e5f862af1e_tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
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|
|
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|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:ae65ec9e1cb9e5f400246142d19a9ac76cc1541c50c53d86c670575b556f2c11
|
| 3 |
+
size 248296
|
87be75af54e141a8a50481d3ebfa5088_tokenizer.vocab
ADDED
|
@@ -0,0 +1,512 @@
|
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| 1 |
+
<unk> 0
|
| 2 |
+
ന് -0
|
| 3 |
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ക് -1
|
| 4 |
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ത് -2
|
| 5 |
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ക്ക -3
|
| 6 |
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▁പ -4
|
| 7 |
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്ട -5
|
| 8 |
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ന്ന -6
|
| 9 |
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▁ക -7
|
| 10 |
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▁അ -8
|
| 11 |
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ും -9
|
| 12 |
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തി -10
|
| 13 |
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▁വ -11
|
| 14 |
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▁ന -12
|
| 15 |
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ങ് -13
|
| 16 |
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യി -14
|
| 17 |
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▁സ -15
|
| 18 |
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്പ -16
|
| 19 |
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ട്ട -17
|
| 20 |
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▁മ -18
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| 21 |
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്യ -19
|
| 22 |
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ച് -20
|
| 23 |
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▁എ -21
|
| 24 |
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രു -22
|
| 25 |
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ങ്ങ -23
|
| 26 |
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പ്പ -24
|
| 27 |
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്ര -25
|
| 28 |
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ാണ -26
|
| 29 |
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റ് -27
|
| 30 |
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ണ്ട -28
|
| 31 |
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രി -29
|
| 32 |
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്ല -30
|
| 33 |
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▁ഇ -31
|
| 34 |
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▁ആ -32
|
| 35 |
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ല്ല -33
|
| 36 |
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ത്ത -34
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| 37 |
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സ് -35
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| 38 |
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| 39 |
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|
| 40 |
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▁ത -38
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| 41 |
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▁ച -39
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| 42 |
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ച്ച -40
|
| 43 |
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ാണ് -41
|
| 44 |
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െയ -42
|
| 45 |
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ായി -43
|
| 46 |
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ക്ക് -44
|
| 47 |
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ുക -45
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| 48 |
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ിയ -46
|
| 49 |
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ിൽ -47
|
| 50 |
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ിക്ക -48
|
| 51 |
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▁ഒ -49
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| 52 |
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ില -50
|
| 53 |
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ടെ -51
|
| 54 |
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റ്റ -52
|
| 55 |
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ള് -53
|
| 56 |
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ള്ള -54
|
| 57 |
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ന്ന് -55
|
| 58 |
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ാന -56
|
| 59 |
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| 60 |
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ാര -58
|
| 61 |
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▁ഉ -59
|
| 62 |
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ാൻ -60
|
| 63 |
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ടു -61
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| 64 |
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▁നമ -62
|
| 65 |
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ഞ് -63
|
| 66 |
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ന്റ -64
|
| 67 |
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്മ -65
|
| 68 |
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ായ -66
|
| 69 |
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ങ്ങള -67
|
| 70 |
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ുള്ള -68
|
| 71 |
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ുമ -69
|
| 72 |
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ണ്ട് -70
|
| 73 |
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ങ്ക -71
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| 74 |
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▁ര -72
|
| 75 |
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▁വി -73
|
| 76 |
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വി -74
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| 77 |
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|
| 78 |
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|
| 79 |
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ന്റെ -77
|
| 80 |
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▁ബ -78
|
| 81 |
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ോൾ -79
|
| 82 |
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ങ്ങൾ -80
|
| 83 |
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|
| 84 |
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ട്ട് -82
|
| 85 |
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▁നി -83
|
| 86 |
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ഞ്ഞ -84
|
| 87 |
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|
| 88 |
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|
| 89 |
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ിക -87
|
| 90 |
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▁ജ -88
|
| 91 |
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ക്ഷ -89
|
| 92 |
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ുടെ -90
|
| 93 |
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രുന്ന -91
|
| 94 |
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▁പറ -92
|
| 95 |
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▁പി -93
|
| 96 |
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ദ് -94
|
| 97 |
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ാർ -95
|
| 98 |
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▁സ് -96
|
| 99 |
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ന്ത് -97
|
| 100 |
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▁നമ്മ -98
|
| 101 |
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യിൽ -99
|
| 102 |
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▁കു -100
|
| 103 |
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െങ്ക -101
|
| 104 |
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റി -102
|
| 105 |
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ാല -103
|
| 106 |
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▁അത -104
|
| 107 |
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പ്പോൾ -105
|
| 108 |
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▁ശ -106
|
| 109 |
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▁ഓ -107
|
| 110 |
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ന്ത -108
|
| 111 |
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▁സം -109
|
| 112 |
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ാവ -110
|
| 113 |
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ദേ -111
|
| 114 |
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ാക്ക -112
|
| 115 |
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ാം -113
|
| 116 |
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രിക്ക -114
|
| 117 |
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ത്ര -115
|
| 118 |
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▁അവ -116
|
| 119 |
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നി -117
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| 120 |
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നെ -118
|
| 121 |
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ൊക്കെ -119
|
| 122 |
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ുണ്ട് -120
|
| 123 |
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െന്ന് -121
|
| 124 |
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ലെ -122
|
| 125 |
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▁പോ -123
|
| 126 |
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ാൽ -124
|
| 127 |
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ണം -125
|
| 128 |
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▁ഞ -126
|
| 129 |
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|
| 130 |
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▁ല -128
|
| 131 |
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▁ചെയ്യ -129
|
| 132 |
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ട് -130
|
| 133 |
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പ്പെ -131
|
| 134 |
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|
| 135 |
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ടി -133
|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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▁ഗ -139
|
| 142 |
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▁ഭ -140
|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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റ്റ് -144
|
| 147 |
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▁ഫ -145
|
| 148 |
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|
| 149 |
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|
| 150 |
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രുന്നു -148
|
| 151 |
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|
| 152 |
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▁നട -150
|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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ാമ -154
|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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സി -159
|
| 162 |
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ഴി -160
|
| 163 |
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|
| 164 |
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|
| 165 |
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്യമ -163
|
| 166 |
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▁ഈ -164
|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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▁യ -176
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| 179 |
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ണ് -177
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| 180 |
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▁ഹ -178
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| 181 |
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ഡ് -179
|
| 182 |
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▁ഏ -180
|
| 183 |
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▁പു -181
|
| 184 |
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|
| 185 |
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| 186 |
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| 187 |
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രെ -185
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| 188 |
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| 189 |
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| 190 |
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|
| 191 |
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| 192 |
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|
| 193 |
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യ് -191
|
| 194 |
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ുപ -192
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| 195 |
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|
| 196 |
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|
| 197 |
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| 198 |
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▁റ -196
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| 199 |
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| 200 |
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|
| 201 |
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|
| 202 |
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| 203 |
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|
| 204 |
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േഷ -202
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| 205 |
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| 206 |
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|
| 207 |
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▁കാര്യ -205
|
| 208 |
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ാള -206
|
| 209 |
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ുട -207
|
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|
README.md
ADDED
|
@@ -0,0 +1,196 @@
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|
| 1 |
+
---
|
| 2 |
+
library_name: nemo
|
| 3 |
+
license: cc-by-4.0
|
| 4 |
+
tags:
|
| 5 |
+
- pytorch
|
| 6 |
+
- NeMo
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Stt Ml Fastconformer Ctc Med Punct
|
| 10 |
+
|
| 11 |
+
<style>
|
| 12 |
+
img {
|
| 13 |
+
display: inline;
|
| 14 |
+
}
|
| 15 |
+
</style>
|
| 16 |
+
|
| 17 |
+
[](#model-architecture)
|
| 18 |
+
| [](#model-architecture)
|
| 19 |
+
| [](#datasets)
|
| 20 |
+
|
| 21 |
+
**Put a short model description here.**
|
| 22 |
+
|
| 23 |
+
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/index.html) for complete architecture details.
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
## NVIDIA NeMo: Training
|
| 27 |
+
|
| 28 |
+
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
|
| 29 |
+
```
|
| 30 |
+
pip install nemo_toolkit['all']
|
| 31 |
+
```
|
| 32 |
+
|
| 33 |
+
## How to Use this Model
|
| 34 |
+
|
| 35 |
+
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
|
| 36 |
+
|
| 37 |
+
### Automatically instantiate the model
|
| 38 |
+
|
| 39 |
+
**NOTE**: Please update the model class below to match the class of the model being uploaded.
|
| 40 |
+
|
| 41 |
+
```python
|
| 42 |
+
import nemo.core import ModelPT
|
| 43 |
+
model = ModelPT.from_pretrained("kavyamanohar/stt_ml_fastconformer_ctc_med_punct")
|
| 44 |
+
```
|
| 45 |
+
|
| 46 |
+
### NOTE
|
| 47 |
+
|
| 48 |
+
Add some information about how to use the model here. An example is provided for ASR inference below.
|
| 49 |
+
|
| 50 |
+
### Transcribing using Python
|
| 51 |
+
First, let's get a sample
|
| 52 |
+
```
|
| 53 |
+
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
|
| 54 |
+
```
|
| 55 |
+
Then simply do:
|
| 56 |
+
```
|
| 57 |
+
asr_model.transcribe(['2086-149220-0033.wav'])
|
| 58 |
+
```
|
| 59 |
+
|
| 60 |
+
### Transcribing many audio files
|
| 61 |
+
|
| 62 |
+
```shell
|
| 63 |
+
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="kavyamanohar/stt_ml_fastconformer_ctc_med_punct" audio_dir=""
|
| 64 |
+
```
|
| 65 |
+
|
| 66 |
+
### Input
|
| 67 |
+
|
| 68 |
+
**Add some information about what are the inputs to this model**
|
| 69 |
+
|
| 70 |
+
### Output
|
| 71 |
+
|
| 72 |
+
**Add some information about what are the outputs of this model**
|
| 73 |
+
|
| 74 |
+
## Model Architecture
|
| 75 |
+
|
| 76 |
+
**Add information here discussing architectural details of the model or any comments to users about the model.**
|
| 77 |
+
|
| 78 |
+
## Training
|
| 79 |
+
|
| 80 |
+
**Add information here about how the model was trained. It should be as detailed as possible, potentially including the the link to the script used to train as well as the base config used to train the model. If extraneous scripts are used to prepare the components of the model, please include them here.**
|
| 81 |
+
|
| 82 |
+
### NOTE
|
| 83 |
+
|
| 84 |
+
An example is provided below for ASR
|
| 85 |
+
|
| 86 |
+
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml).
|
| 87 |
+
|
| 88 |
+
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
### Datasets
|
| 92 |
+
|
| 93 |
+
**Try to provide as detailed a list of datasets as possible. If possible, provide links to the datasets on HF by adding it to the manifest section at the top of the README (marked by ---).**
|
| 94 |
+
|
| 95 |
+
### NOTE
|
| 96 |
+
|
| 97 |
+
An example for the manifest section is provided below for ASR datasets
|
| 98 |
+
|
| 99 |
+
datasets:
|
| 100 |
+
- librispeech_asr
|
| 101 |
+
- fisher_corpus
|
| 102 |
+
- Switchboard-1
|
| 103 |
+
- WSJ-0
|
| 104 |
+
- WSJ-1
|
| 105 |
+
- National-Singapore-Corpus-Part-1
|
| 106 |
+
- National-Singapore-Corpus-Part-6
|
| 107 |
+
- vctk
|
| 108 |
+
- voxpopuli
|
| 109 |
+
- europarl
|
| 110 |
+
- multilingual_librispeech
|
| 111 |
+
- mozilla-foundation/common_voice_8_0
|
| 112 |
+
- MLCommons/peoples_speech
|
| 113 |
+
|
| 114 |
+
The corresponding text in this section for those datasets is stated below -
|
| 115 |
+
|
| 116 |
+
The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.
|
| 117 |
+
|
| 118 |
+
The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets:
|
| 119 |
+
|
| 120 |
+
- Librispeech 960 hours of English speech
|
| 121 |
+
- Fisher Corpus
|
| 122 |
+
- Switchboard-1 Dataset
|
| 123 |
+
- WSJ-0 and WSJ-1
|
| 124 |
+
- National Speech Corpus (Part 1, Part 6)
|
| 125 |
+
- VCTK
|
| 126 |
+
- VoxPopuli (EN)
|
| 127 |
+
- Europarl-ASR (EN)
|
| 128 |
+
- Multilingual Librispeech (MLS EN) - 2,000 hour subset
|
| 129 |
+
- Mozilla Common Voice (v7.0)
|
| 130 |
+
- People's Speech - 12,000 hour subset
|
| 131 |
+
|
| 132 |
+
|
| 133 |
+
## Performance
|
| 134 |
+
|
| 135 |
+
**Add information here about the performance of the model. Discuss what is the metric that is being used to evaluate the model and if there are external links explaning the custom metric, please link to it.
|
| 136 |
+
|
| 137 |
+
### NOTE
|
| 138 |
+
|
| 139 |
+
An example is provided below for ASR metrics list that can be added to the top of the README
|
| 140 |
+
|
| 141 |
+
model-index:
|
| 142 |
+
- name: PUT_MODEL_NAME
|
| 143 |
+
results:
|
| 144 |
+
- task:
|
| 145 |
+
name: Automatic Speech Recognition
|
| 146 |
+
type: automatic-speech-recognition
|
| 147 |
+
dataset:
|
| 148 |
+
name: AMI (Meetings test)
|
| 149 |
+
type: edinburghcstr/ami
|
| 150 |
+
config: ihm
|
| 151 |
+
split: test
|
| 152 |
+
args:
|
| 153 |
+
language: en
|
| 154 |
+
metrics:
|
| 155 |
+
- name: Test WER
|
| 156 |
+
type: wer
|
| 157 |
+
value: 17.10
|
| 158 |
+
- task:
|
| 159 |
+
name: Automatic Speech Recognition
|
| 160 |
+
type: automatic-speech-recognition
|
| 161 |
+
dataset:
|
| 162 |
+
name: Earnings-22
|
| 163 |
+
type: revdotcom/earnings22
|
| 164 |
+
split: test
|
| 165 |
+
args:
|
| 166 |
+
language: en
|
| 167 |
+
metrics:
|
| 168 |
+
- name: Test WER
|
| 169 |
+
type: wer
|
| 170 |
+
value: 14.11
|
| 171 |
+
|
| 172 |
+
Provide any caveats about the results presented in the top of the discussion so that nuance is not lost.
|
| 173 |
+
|
| 174 |
+
It should ideally be in a tabular format (you can use the following website to make your tables in markdown format - https://www.tablesgenerator.com/markdown_tables)**
|
| 175 |
+
|
| 176 |
+
## Limitations
|
| 177 |
+
|
| 178 |
+
**Discuss any practical limitations to the model when being used in real world cases. They can also be legal disclaimers, or discussion regarding the safety of the model (particularly in the case of LLMs).**
|
| 179 |
+
|
| 180 |
+
|
| 181 |
+
### Note
|
| 182 |
+
|
| 183 |
+
An example is provided below
|
| 184 |
+
|
| 185 |
+
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
|
| 186 |
+
|
| 187 |
+
|
| 188 |
+
## License
|
| 189 |
+
|
| 190 |
+
License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
|
| 191 |
+
|
| 192 |
+
## References
|
| 193 |
+
|
| 194 |
+
**Provide appropriate references in the markdown link format below. Please order them numerically.**
|
| 195 |
+
|
| 196 |
+
[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
model_config.yaml
ADDED
|
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|
| 1 |
+
sample_rate: 16000
|
| 2 |
+
log_prediction: true
|
| 3 |
+
ctc_reduction: mean_volume
|
| 4 |
+
skip_nan_grad: false
|
| 5 |
+
init_from_nemo_model: exp/fastconformer_512_1_2/2025-11-24_22-11-55/checkpoints/fastconformer_512_1_2.nemo
|
| 6 |
+
train_ds:
|
| 7 |
+
manifest_filepath: data/all_manifests/adalat-ai_common_voice_17_0_curated_train.jsonl,data/all_manifests/adalat-ai_festvox-iiith-ml_curated_train.jsonl,data/all_manifests/adalat-ai_fleurs_ml_curated_train.jsonl,data/all_manifests/adalat-ai_fleurs_ml_curated_validation.jsonl,data/all_manifests/adalat-ai_indic_tts_ml_curated_train.jsonl,data/all_manifests/adalat-ai_openslr63_ml_curated_train.jsonl,data/all_manifests/adalat-ai_Shrutilipi_ml_curated_train.jsonl,data/all_manifests/adalat-ai_IMaSC_ml_curated_train.jsonl,data/all_manifests/adalat-ai_IndicVoices-malayalam_curated_train.jsonl,data/all_manifests/adalat-ai_Kathbath_ml_curated_train.jsonl
|
| 8 |
+
sample_rate: 16000
|
| 9 |
+
batch_size: 128
|
| 10 |
+
shuffle: true
|
| 11 |
+
num_workers: 18
|
| 12 |
+
pin_memory: true
|
| 13 |
+
max_duration: 25
|
| 14 |
+
min_duration: 0.1
|
| 15 |
+
is_tarred: false
|
| 16 |
+
tarred_audio_filepaths: null
|
| 17 |
+
shuffle_n: 2048
|
| 18 |
+
bucketing_strategy: fully_randomized
|
| 19 |
+
bucketing_batch_size: null
|
| 20 |
+
validation_ds:
|
| 21 |
+
manifest_filepath: data/all_manifests/adalat-ai_curated-malayalam-test_test.jsonl
|
| 22 |
+
sample_rate: 16000
|
| 23 |
+
batch_size: 128
|
| 24 |
+
shuffle: false
|
| 25 |
+
use_start_end_token: false
|
| 26 |
+
num_workers: 8
|
| 27 |
+
pin_memory: true
|
| 28 |
+
test_ds:
|
| 29 |
+
manifest_filepath: data/all_manifests/adalat-ai_in22_ml_test.jsonl
|
| 30 |
+
sample_rate: 16000
|
| 31 |
+
batch_size: 64
|
| 32 |
+
shuffle: false
|
| 33 |
+
use_start_end_token: false
|
| 34 |
+
num_workers: 8
|
| 35 |
+
pin_memory: true
|
| 36 |
+
tokenizer:
|
| 37 |
+
dir: data/tokenizer-512-punct/tokenizer_spe_bpe_v512
|
| 38 |
+
type: bpe
|
| 39 |
+
model_path: nemo:49704422a930474bb04998e5f862af1e_tokenizer.model
|
| 40 |
+
vocab_path: nemo:299f3c3e10064a928e07b138ca8d25c8_vocab.txt
|
| 41 |
+
spe_tokenizer_vocab: nemo:87be75af54e141a8a50481d3ebfa5088_tokenizer.vocab
|
| 42 |
+
preprocessor:
|
| 43 |
+
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
| 44 |
+
sample_rate: 16000
|
| 45 |
+
normalize: per_feature
|
| 46 |
+
window_size: 0.025
|
| 47 |
+
window_stride: 0.01
|
| 48 |
+
window: hann
|
| 49 |
+
features: 80
|
| 50 |
+
n_fft: 512
|
| 51 |
+
log: true
|
| 52 |
+
frame_splicing: 1
|
| 53 |
+
dither: 1.0e-05
|
| 54 |
+
pad_to: 0
|
| 55 |
+
pad_value: 0.0
|
| 56 |
+
spec_augment:
|
| 57 |
+
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
| 58 |
+
freq_masks: 2
|
| 59 |
+
time_masks: 10
|
| 60 |
+
freq_width: 27
|
| 61 |
+
time_width: 0.05
|
| 62 |
+
encoder:
|
| 63 |
+
_target_: nemo.collections.asr.modules.ConformerEncoder
|
| 64 |
+
feat_in: 80
|
| 65 |
+
feat_out: -1
|
| 66 |
+
n_layers: 16
|
| 67 |
+
d_model: 256
|
| 68 |
+
subsampling: dw_striding
|
| 69 |
+
subsampling_factor: 8
|
| 70 |
+
subsampling_conv_channels: 256
|
| 71 |
+
causal_downsampling: false
|
| 72 |
+
ff_expansion_factor: 4
|
| 73 |
+
self_attention_model: rel_pos
|
| 74 |
+
n_heads: 4
|
| 75 |
+
att_context_size:
|
| 76 |
+
- 70
|
| 77 |
+
- 6
|
| 78 |
+
att_context_style: regular
|
| 79 |
+
xscaling: true
|
| 80 |
+
untie_biases: true
|
| 81 |
+
pos_emb_max_len: 5000
|
| 82 |
+
use_pytorch_sdpa: false
|
| 83 |
+
use_pytorch_sdpa_backends: []
|
| 84 |
+
conv_kernel_size: 9
|
| 85 |
+
conv_norm_type: batch_norm
|
| 86 |
+
conv_context_size: null
|
| 87 |
+
dropout: 0.1
|
| 88 |
+
dropout_pre_encoder: 0.1
|
| 89 |
+
dropout_emb: 0.0
|
| 90 |
+
dropout_att: 0.1
|
| 91 |
+
stochastic_depth_drop_prob: 0.0
|
| 92 |
+
stochastic_depth_mode: linear
|
| 93 |
+
stochastic_depth_start_layer: 1
|
| 94 |
+
decoder:
|
| 95 |
+
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
| 96 |
+
feat_in: 256
|
| 97 |
+
num_classes: 512
|
| 98 |
+
vocabulary:
|
| 99 |
+
- <unk>
|
| 100 |
+
- ന്
|
| 101 |
+
- ക്
|
| 102 |
+
- ത്
|
| 103 |
+
- ക്ക
|
| 104 |
+
- ▁പ
|
| 105 |
+
- ്ട
|
| 106 |
+
- ന്ന
|
| 107 |
+
- ▁ക
|
| 108 |
+
- ▁അ
|
| 109 |
+
- ും
|
| 110 |
+
- തി
|
| 111 |
+
- ▁വ
|
| 112 |
+
- ▁ന
|
| 113 |
+
- ങ്
|
| 114 |
+
- യി
|
| 115 |
+
- ▁സ
|
| 116 |
+
- ്പ
|
| 117 |
+
- ട്ട
|
| 118 |
+
- ▁മ
|
| 119 |
+
- ്യ
|
| 120 |
+
- ച്
|
| 121 |
+
- ▁എ
|
| 122 |
+
- രു
|
| 123 |
+
- ങ്ങ
|
| 124 |
+
- പ്പ
|
| 125 |
+
- ്ര
|
| 126 |
+
- ാണ
|
| 127 |
+
- റ്
|
| 128 |
+
- ണ്ട
|
| 129 |
+
- രി
|
| 130 |
+
- ്ല
|
| 131 |
+
- ▁ഇ
|
| 132 |
+
- ▁ആ
|
| 133 |
+
- ല്ല
|
| 134 |
+
- ത്ത
|
| 135 |
+
- സ്
|
| 136 |
+
- ത്തി
|
| 137 |
+
- ുന്ന
|
| 138 |
+
- ▁ത
|
| 139 |
+
- ▁ച
|
| 140 |
+
- ച്ച
|
| 141 |
+
- ാണ്
|
| 142 |
+
- െയ
|
| 143 |
+
- ായി
|
| 144 |
+
- ക്ക്
|
| 145 |
+
- ുക
|
| 146 |
+
- ിയ
|
| 147 |
+
- ിൽ
|
| 148 |
+
- ിക്ക
|
| 149 |
+
- ▁ഒ
|
| 150 |
+
- ില
|
| 151 |
+
- ടെ
|
| 152 |
+
- റ്റ
|
| 153 |
+
- ള്
|
| 154 |
+
- ള്ള
|
| 155 |
+
- ന്ന്
|
| 156 |
+
- ാന
|
| 157 |
+
- ▁പ്ര
|
| 158 |
+
- ാര
|
| 159 |
+
- ▁ഉ
|
| 160 |
+
- ാൻ
|
| 161 |
+
- ടു
|
| 162 |
+
- ▁നമ
|
| 163 |
+
- ഞ്
|
| 164 |
+
- ന്റ
|
| 165 |
+
- ്മ
|
| 166 |
+
- ായ
|
| 167 |
+
- ങ്ങള
|
| 168 |
+
- ുള്ള
|
| 169 |
+
- ുമ
|
| 170 |
+
- ണ്ട്
|
| 171 |
+
- ങ്ക
|
| 172 |
+
- ▁ര
|
| 173 |
+
- ▁വി
|
| 174 |
+
- വി
|
| 175 |
+
- ക്കെ
|
| 176 |
+
- ▁ഒരു
|
| 177 |
+
- ന്റെ
|
| 178 |
+
- ▁ബ
|
| 179 |
+
- ോൾ
|
| 180 |
+
- ങ്ങൾ
|
| 181 |
+
- ▁ചെയ
|
| 182 |
+
- ട്ട്
|
| 183 |
+
- ▁നി
|
| 184 |
+
- ഞ്ഞ
|
| 185 |
+
- ന്നെ
|
| 186 |
+
- ിച്ച
|
| 187 |
+
- ിക
|
| 188 |
+
- ▁ജ
|
| 189 |
+
- ക്ഷ
|
| 190 |
+
- ുടെ
|
| 191 |
+
- രുന്ന
|
| 192 |
+
- ▁പറ
|
| 193 |
+
- ▁പി
|
| 194 |
+
- ദ്
|
| 195 |
+
- ാർ
|
| 196 |
+
- ▁സ്
|
| 197 |
+
- ന്ത്
|
| 198 |
+
- ▁നമ്മ
|
| 199 |
+
- യിൽ
|
| 200 |
+
- ▁കു
|
| 201 |
+
- െങ്ക
|
| 202 |
+
- റി
|
| 203 |
+
- ാല
|
| 204 |
+
- ▁അത
|
| 205 |
+
- പ്പോൾ
|
| 206 |
+
- ▁ശ
|
| 207 |
+
- ▁ഓ
|
| 208 |
+
- ന്ത
|
| 209 |
+
- ▁സം
|
| 210 |
+
- ാവ
|
| 211 |
+
- ദേ
|
| 212 |
+
- ാക്ക
|
| 213 |
+
- ാം
|
| 214 |
+
- രിക്ക
|
| 215 |
+
- ത്ര
|
| 216 |
+
- ▁അവ
|
| 217 |
+
- നി
|
| 218 |
+
- നെ
|
| 219 |
+
- ൊക്കെ
|
| 220 |
+
- ുണ്ട്
|
| 221 |
+
- െന്ന്
|
| 222 |
+
- ലെ
|
| 223 |
+
- ▁പോ
|
| 224 |
+
- ാൽ
|
| 225 |
+
- ണം
|
| 226 |
+
- ▁ഞ
|
| 227 |
+
- ില്ല
|
| 228 |
+
- ▁ല
|
| 229 |
+
- ▁ചെയ്��
|
| 230 |
+
- ട്
|
| 231 |
+
- പ്പെ
|
| 232 |
+
- ച്ച്
|
| 233 |
+
- ടി
|
| 234 |
+
- ▁എന്ന
|
| 235 |
+
- ന്നും
|
| 236 |
+
- ുന്നത്
|
| 237 |
+
- ത്തിൽ
|
| 238 |
+
- ▁കാര
|
| 239 |
+
- ▁ഗ
|
| 240 |
+
- ▁ഭ
|
| 241 |
+
- ▁കൂ
|
| 242 |
+
- ▁കോ
|
| 243 |
+
- ▁കേ
|
| 244 |
+
- റ്റ്
|
| 245 |
+
- ▁ഫ
|
| 246 |
+
- ▁നമു
|
| 247 |
+
- ▁കൊ
|
| 248 |
+
- രുന്നു
|
| 249 |
+
- ോഗ
|
| 250 |
+
- ▁നട
|
| 251 |
+
- ത്ത്
|
| 252 |
+
- ▁അതി
|
| 253 |
+
- ▁മാ
|
| 254 |
+
- ാമ
|
| 255 |
+
- ▁വേ
|
| 256 |
+
- െങ്കിൽ
|
| 257 |
+
- ▁മു
|
| 258 |
+
- ത്തെ
|
| 259 |
+
- സി
|
| 260 |
+
- ഴി
|
| 261 |
+
- ▁സമ
|
| 262 |
+
- ിട്ട
|
| 263 |
+
- ്യമ
|
| 264 |
+
- ▁ഈ
|
| 265 |
+
- ▁അത്
|
| 266 |
+
- ന്ത്രി
|
| 267 |
+
- ▁പിന്നെ
|
| 268 |
+
- ▁പറഞ്ഞ
|
| 269 |
+
- ▁നമുക്ക്
|
| 270 |
+
- സ്ഥ
|
| 271 |
+
- ▁വെ
|
| 272 |
+
- ുകയ
|
| 273 |
+
- ുവ
|
| 274 |
+
- ന്ദ
|
| 275 |
+
- ▁ഡ
|
| 276 |
+
- ▁യ
|
| 277 |
+
- ണ്
|
| 278 |
+
- ▁ഹ
|
| 279 |
+
- ഡ്
|
| 280 |
+
- ▁ഏ
|
| 281 |
+
- ▁പു
|
| 282 |
+
- ിലും
|
| 283 |
+
- ▁നമ്മൾ
|
| 284 |
+
- ▁ദ
|
| 285 |
+
- രെ
|
| 286 |
+
- വർ
|
| 287 |
+
- ദേശ
|
| 288 |
+
- വും
|
| 289 |
+
- ാധ
|
| 290 |
+
- മായി
|
| 291 |
+
- യ്
|
| 292 |
+
- ുപ
|
| 293 |
+
- ുണ്ട
|
| 294 |
+
- ▁അങ്ങ
|
| 295 |
+
- ുകള
|
| 296 |
+
- ▁റ
|
| 297 |
+
- ▁ചെയ്
|
| 298 |
+
- ▁നല്ല
|
| 299 |
+
- ▁പറയ
|
| 300 |
+
- ▁എല്ല
|
| 301 |
+
- ടുക്ക
|
| 302 |
+
- േഷ
|
| 303 |
+
- ിക്കുന്ന
|
| 304 |
+
- ▁ഞാൻ
|
| 305 |
+
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|
| 306 |
+
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|
| 307 |
+
- ുട
|
| 308 |
+
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|
| 309 |
+
- ▁രാ
|
| 310 |
+
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|
| 311 |
+
- ▁ഉണ്ട
|
| 312 |
+
- ▁തന്നെ
|
| 313 |
+
- യില
|
| 314 |
+
- മ്മ
|
| 315 |
+
- ുകൾ
|
| 316 |
+
- ാക
|
| 317 |
+
- കള
|
| 318 |
+
- നം
|
| 319 |
+
- ▁കുറ
|
| 320 |
+
- ▁നേ
|
| 321 |
+
- ഷ്ട
|
| 322 |
+
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|
| 323 |
+
- ▁ട
|
| 324 |
+
- ദ്ധ
|
| 325 |
+
- ▁എന്ത
|
| 326 |
+
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|
| 327 |
+
- മെ
|
| 328 |
+
- ദ്യ
|
| 329 |
+
- രം
|
| 330 |
+
- ന്ദ്ര
|
| 331 |
+
- ▁ഇപ്പോൾ
|
| 332 |
+
- ▁സ്ഥ
|
| 333 |
+
- പ്പം
|
| 334 |
+
- ▁അപ്പോൾ
|
| 335 |
+
- ക്കും
|
| 336 |
+
- ിലെ
|
| 337 |
+
- ടുത്ത
|
| 338 |
+
- ▁പരി
|
| 339 |
+
- ▁വീ
|
| 340 |
+
- പാ
|
| 341 |
+
- െയാണ്
|
| 342 |
+
- യും
|
| 343 |
+
- ാസ
|
| 344 |
+
- ന്ധ
|
| 345 |
+
- നു
|
| 346 |
+
- മായ
|
| 347 |
+
- ികള
|
| 348 |
+
- ിച്ചു
|
| 349 |
+
- ▁ക്
|
| 350 |
+
- ശ്
|
| 351 |
+
- ാറ
|
| 352 |
+
- ▁തി
|
| 353 |
+
- ൽക
|
| 354 |
+
- റ്റി
|
| 355 |
+
- മാണ്
|
| 356 |
+
- പ്പ്
|
| 357 |
+
- റിയ
|
| 358 |
+
- ാഗ
|
| 359 |
+
- ച്ചു
|
| 360 |
+
- ▁കൂടു
|
| 361 |
+
- ഴ്
|
| 362 |
+
- ട്ടി
|
| 363 |
+
- ▁നൽക
|
| 364 |
+
- ▁പറഞ്ഞു
|
| 365 |
+
- ▁1
|
| 366 |
+
- ▁കൂടുത
|
| 367 |
+
- ണ്ടി
|
| 368 |
+
- ▁തുട
|
| 369 |
+
- ▁എന്ന്
|
| 370 |
+
- വിടെ
|
| 371 |
+
- കൾ
|
| 372 |
+
- ത്തില
|
| 373 |
+
- ാപ
|
| 374 |
+
- കാര
|
| 375 |
+
- രോ
|
| 376 |
+
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|
| 377 |
+
- തു
|
| 378 |
+
- ▁2
|
| 379 |
+
- ▁വ്യ
|
| 380 |
+
- ംഗ
|
| 381 |
+
- ികൾ
|
| 382 |
+
- സ്ഥാന
|
| 383 |
+
- മ്പ
|
| 384 |
+
- ഴിഞ്ഞ
|
| 385 |
+
- െങ്കിലും
|
| 386 |
+
- െന്നും
|
| 387 |
+
- തിയ
|
| 388 |
+
- നിക്ക്
|
| 389 |
+
- ധാന
|
| 390 |
+
- പ്പോ
|
| 391 |
+
- േക്ക്
|
| 392 |
+
- ത്യ
|
| 393 |
+
- ▁സി
|
| 394 |
+
- ൂർ
|
| 395 |
+
- കര
|
| 396 |
+
- െയും
|
| 397 |
+
- ദേഹ
|
| 398 |
+
- ▁നമ്മുടെ
|
| 399 |
+
- ▁ചെ
|
| 400 |
+
- ▁അവർ
|
| 401 |
+
- ▁ഉപ
|
| 402 |
+
- ▁സാധ
|
| 403 |
+
- ന്ത്യ
|
| 404 |
+
- ുകൊ
|
| 405 |
+
- ▁എനിക്ക്
|
| 406 |
+
- ത്തിന്റെ
|
| 407 |
+
- പ്പെട്ട
|
| 408 |
+
- ാത
|
| 409 |
+
- ദ്ദേഹ
|
| 410 |
+
- ്
|
| 411 |
+
- ▁
|
| 412 |
+
- ി
|
| 413 |
+
- ക
|
| 414 |
+
- ന
|
| 415 |
+
- ു
|
| 416 |
+
- ത
|
| 417 |
+
- ാ
|
| 418 |
+
- യ
|
| 419 |
+
- ട
|
| 420 |
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- പ
|
| 421 |
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- ര
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| 422 |
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| 423 |
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- മ
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- ല
|
| 425 |
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- വ
|
| 426 |
+
- ം
|
| 427 |
+
- റ
|
| 428 |
+
- സ
|
| 429 |
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|
| 430 |
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- ച
|
| 431 |
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- ങ
|
| 432 |
+
- ള
|
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| 434 |
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- .
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| 435 |
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- അ
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| 436 |
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|
| 437 |
+
- ൽ
|
| 438 |
+
- ർ
|
| 439 |
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| 440 |
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- ദ
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| 441 |
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- എ
|
| 442 |
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- ഞ
|
| 443 |
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|
| 444 |
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- ൻ
|
| 445 |
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- ഷ
|
| 446 |
+
- ഇ
|
| 447 |
+
- ൊ
|
| 448 |
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- ആ
|
| 449 |
+
- ശ
|
| 450 |
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- ൂ
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| 451 |
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- ','
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| 452 |
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- ഗ
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| 453 |
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- ധ
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| 454 |
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- ജ
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| 455 |
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- ബ
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| 456 |
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- ഒ
|
| 457 |
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- ഴ
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| 458 |
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- ഡ
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| 459 |
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- ഹ
|
| 460 |
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- ഉ
|
| 461 |
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- ഭ
|
| 462 |
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- ഥ
|
| 463 |
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- ഫ
|
| 464 |
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| 465 |
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- ഓ
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| 466 |
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| 467 |
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- '?'
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| 468 |
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- '0'
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| 469 |
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- ൺ
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| 470 |
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- ൃ
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| 471 |
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- ഈ
|
| 472 |
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- ഏ
|
| 473 |
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- '2'
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| 474 |
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- '1'
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| 475 |
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- ഘ
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| 476 |
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- ൗ
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| 477 |
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| 478 |
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|
| 479 |
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| 482 |
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| 483 |
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|
| 484 |
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- '7'
|
| 485 |
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- '8'
|
| 486 |
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- '9'
|
| 487 |
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- P
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- C
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| 489 |
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- I
|
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- ഛ
|
| 491 |
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- D
|
| 492 |
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- W
|
| 493 |
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- ഊ
|
| 494 |
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- S
|
| 495 |
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- A
|
| 496 |
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|
| 497 |
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- T
|
| 498 |
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- O
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| 499 |
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- R
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| 500 |
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- ':'
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| 501 |
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| 502 |
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- r
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| 505 |
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| 506 |
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- M
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- E
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| 508 |
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- L
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| 509 |
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| 510 |
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| 511 |
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| 515 |
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- ;
|
| 516 |
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- G
|
| 517 |
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- J
|
| 518 |
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- ഋ
|
| 519 |
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- t
|
| 520 |
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- ഝ
|
| 521 |
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- (
|
| 522 |
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- )
|
| 523 |
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- o
|
| 524 |
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- x
|
| 525 |
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- e
|
| 526 |
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- c
|
| 527 |
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- H
|
| 528 |
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- 'n'
|
| 529 |
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- i
|
| 530 |
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- a
|
| 531 |
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- u
|
| 532 |
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- K
|
| 533 |
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|
| 534 |
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- m
|
| 535 |
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- d
|
| 536 |
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- l
|
| 537 |
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- '%'
|
| 538 |
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- U
|
| 539 |
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- f
|
| 540 |
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- v
|
| 541 |
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- h
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| 542 |
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- p
|
| 543 |
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|
| 544 |
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|
| 545 |
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|
| 546 |
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- b
|
| 547 |
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- '൨'
|
| 548 |
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- —
|
| 549 |
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- X
|
| 550 |
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- '൩'
|
| 551 |
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|
| 552 |
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- '൪'
|
| 553 |
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- g
|
| 554 |
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- 'y'
|
| 555 |
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- ഌ
|
| 556 |
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- '൧'
|
| 557 |
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- '@'
|
| 558 |
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- ்
|
| 559 |
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- +
|
| 560 |
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- k
|
| 561 |
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|
| 562 |
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- ச
|
| 563 |
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- 。
|
| 564 |
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- ं
|
| 565 |
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- न
|
| 566 |
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- ्
|
| 567 |
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|
| 568 |
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|
| 569 |
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|
| 570 |
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|
| 571 |
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|
| 572 |
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| 573 |
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| 574 |
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| 575 |
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|
| 576 |
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|
| 577 |
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|
| 578 |
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| 579 |
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| 580 |
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| 581 |
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| 582 |
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| 583 |
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- र
|
| 588 |
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|
| 589 |
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- व
|
| 590 |
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- स
|
| 591 |
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- ह
|
| 592 |
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|
| 593 |
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- ु
|
| 594 |
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- े
|
| 595 |
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- ঠ
|
| 596 |
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|
| 597 |
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|
| 598 |
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|
| 599 |
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|
| 600 |
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|
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|
| 602 |
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|
| 603 |
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|
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- ల
|
| 605 |
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|
| 606 |
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|
| 607 |
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- ీ
|
| 608 |
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- ్
|
| 609 |
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- ತ
|
| 610 |
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- ಿ
|
| 611 |
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interctc:
|
| 612 |
+
loss_weights: []
|
| 613 |
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apply_at_layers: []
|
| 614 |
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optim:
|
| 615 |
+
name: adamw
|
| 616 |
+
lr: 0.001
|
| 617 |
+
betas:
|
| 618 |
+
- 0.9
|
| 619 |
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- 0.98
|
| 620 |
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weight_decay: 0.005
|
| 621 |
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sched:
|
| 622 |
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name: CosineAnnealing
|
| 623 |
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warmup_steps: 40000
|
| 624 |
+
warmup_ratio: null
|
| 625 |
+
min_lr: 1.0e-05
|
| 626 |
+
target: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE
|
| 627 |
+
nemo_version: 2.5.2
|
| 628 |
+
decoding:
|
| 629 |
+
strategy: greedy_batch
|
| 630 |
+
preserve_alignments: null
|
| 631 |
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compute_timestamps: null
|
| 632 |
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word_seperator: ' '
|
| 633 |
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segment_seperators:
|
| 634 |
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- .
|
| 635 |
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- '!'
|
| 636 |
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- '?'
|
| 637 |
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segment_gap_threshold: null
|
| 638 |
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ctc_timestamp_type: all
|
| 639 |
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batch_dim_index: 0
|
| 640 |
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greedy:
|
| 641 |
+
preserve_alignments: false
|
| 642 |
+
compute_timestamps: false
|
| 643 |
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preserve_frame_confidence: false
|
| 644 |
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confidence_method_cfg:
|
| 645 |
+
name: entropy
|
| 646 |
+
entropy_type: tsallis
|
| 647 |
+
alpha: 0.33
|
| 648 |
+
entropy_norm: exp
|
| 649 |
+
temperature: DEPRECATED
|
| 650 |
+
ngram_lm_model: null
|
| 651 |
+
ngram_lm_alpha: 0.0
|
| 652 |
+
boosting_tree:
|
| 653 |
+
model_path: null
|
| 654 |
+
key_phrases_file: null
|
| 655 |
+
key_phrases_list: null
|
| 656 |
+
context_score: 1.0
|
| 657 |
+
depth_scaling: 1.0
|
| 658 |
+
unk_score: 0.0
|
| 659 |
+
final_eos_score: 1.0
|
| 660 |
+
score_per_phrase: 0.0
|
| 661 |
+
source_lang: en
|
| 662 |
+
use_triton: true
|
| 663 |
+
uniform_weights: false
|
| 664 |
+
use_bpe_dropout: false
|
| 665 |
+
num_of_transcriptions: 5
|
| 666 |
+
bpe_alpha: 0.3
|
| 667 |
+
boosting_tree_alpha: 0.0
|
| 668 |
+
allow_cuda_graphs: true
|
| 669 |
+
beam:
|
| 670 |
+
beam_size: 4
|
| 671 |
+
search_type: default
|
| 672 |
+
preserve_alignments: false
|
| 673 |
+
compute_timestamps: false
|
| 674 |
+
return_best_hypothesis: true
|
| 675 |
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allow_cuda_graphs: true
|
| 676 |
+
beam_alpha: null
|
| 677 |
+
beam_beta: 1.0
|
| 678 |
+
beam_threshold: 20.0
|
| 679 |
+
kenlm_path: null
|
| 680 |
+
ngram_lm_alpha: 1.0
|
| 681 |
+
ngram_lm_model: null
|
| 682 |
+
boosting_tree:
|
| 683 |
+
model_path: null
|
| 684 |
+
key_phrases_file: null
|
| 685 |
+
key_phrases_list: null
|
| 686 |
+
context_score: 1.0
|
| 687 |
+
depth_scaling: 1.0
|
| 688 |
+
unk_score: 0.0
|
| 689 |
+
final_eos_score: 1.0
|
| 690 |
+
score_per_phrase: 0.0
|
| 691 |
+
source_lang: en
|
| 692 |
+
use_triton: true
|
| 693 |
+
uniform_weights: false
|
| 694 |
+
use_bpe_dropout: false
|
| 695 |
+
num_of_transcriptions: 5
|
| 696 |
+
bpe_alpha: 0.3
|
| 697 |
+
boosting_tree_alpha: 0.0
|
| 698 |
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flashlight_cfg:
|
| 699 |
+
lexicon_path: null
|
| 700 |
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boost_path: null
|
| 701 |
+
beam_size_token: 16
|
| 702 |
+
beam_threshold: 20.0
|
| 703 |
+
unk_weight: -.inf
|
| 704 |
+
sil_weight: 0.0
|
| 705 |
+
pyctcdecode_cfg:
|
| 706 |
+
beam_prune_logp: -10.0
|
| 707 |
+
token_min_logp: -5.0
|
| 708 |
+
prune_history: false
|
| 709 |
+
hotwords: null
|
| 710 |
+
hotword_weight: 10.0
|
| 711 |
+
wfst:
|
| 712 |
+
beam_size: 4
|
| 713 |
+
search_type: riva
|
| 714 |
+
return_best_hypothesis: true
|
| 715 |
+
preserve_alignments: false
|
| 716 |
+
compute_timestamps: false
|
| 717 |
+
decoding_mode: nbest
|
| 718 |
+
open_vocabulary_decoding: false
|
| 719 |
+
beam_width: 10.0
|
| 720 |
+
lm_weight: 1.0
|
| 721 |
+
device: cuda
|
| 722 |
+
arpa_lm_path: null
|
| 723 |
+
wfst_lm_path: null
|
| 724 |
+
riva_decoding_cfg: {}
|
| 725 |
+
k2_decoding_cfg:
|
| 726 |
+
search_beam: 20.0
|
| 727 |
+
output_beam: 10.0
|
| 728 |
+
min_active_states: 30
|
| 729 |
+
max_active_states: 10000
|
| 730 |
+
confidence_cfg:
|
| 731 |
+
preserve_frame_confidence: false
|
| 732 |
+
preserve_token_confidence: false
|
| 733 |
+
preserve_word_confidence: false
|
| 734 |
+
exclude_blank: true
|
| 735 |
+
aggregation: min
|
| 736 |
+
tdt_include_duration: false
|
| 737 |
+
method_cfg:
|
| 738 |
+
name: entropy
|
| 739 |
+
entropy_type: tsallis
|
| 740 |
+
alpha: 0.33
|
| 741 |
+
entropy_norm: exp
|
| 742 |
+
temperature: DEPRECATED
|
| 743 |
+
temperature: 1.0
|
model_weights.ckpt
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:f59ca749274296b867c83adb1832ba6e49a89170183f29a4caceba7b118d016d
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| 3 |
+
size 105409123
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