front-end of conversational interface

master
jocavdh 6 years ago
commit dea6a52e3c

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assistant_id: proj_9X1nGB88Kx6
skills: []

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{
"id" : "proj_9X1nGB88Kx6",
"name" : "XPUB",
"intents" : [ {
"id" : "jocavdh:answer_yes",
"type" : "registry",
"version" : "0.6.0",
"language" : "en",
"slots" : [ {
"name" : "user_choice",
"id" : "slot_imported_oeLMta25N",
"entityId" : "entity_4noq4GNymOe",
"missingQuestion" : "",
"required" : false
} ],
"name" : "answer_yes",
"enabledByDefault" : false
}, {
"id" : "jocavdh:ask",
"type" : "registry",
"version" : "0.5.0",
"language" : "en",
"slots" : [ ],
"name" : "ask",
"enabledByDefault" : true
} ],
"createdAt" : "2019-03-29T19:35:28.294Z",
"language" : "en",
"version" : {
"nluModel" : "0.19.0"
},
"platform" : {
"type" : "raspberrypi"
},
"asr" : {
"type" : "snips"
},
"analyticsEnabled" : true,
"heartbeatEnabled" : true,
"hotword" : "hey_snips"
}

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{
"entityNameToFilename": {
"choice": "e_0.snips"
},
"intentNameToDisambigs": {
"jocavdh:answer_yes": {
"start": "`INTENT`4c40d5ef-686e-489f-8dcf-175ea8eb7b82`START",
"end": "`INTENT`4c40d5ef-686e-489f-8dcf-175ea8eb7b82`END"
},
"724cf256-45e2-4ab7-a741-5e7ca82a9756": {
"start": "`INTENT`724cf256-45e2-4ab7-a741-5e7ca82a9756`START",
"end": "`INTENT`724cf256-45e2-4ab7-a741-5e7ca82a9756`END"
},
"jocavdh:ask": {
"start": "`INTENT`ca101149-1495-494f-92f6-e9e042eb9266`START",
"end": "`INTENT`ca101149-1495-494f-92f6-e9e042eb9266`END"
}
},
"injectionsEngine": {
"version": 2
},
"language": "en",
"decoderConfig": {
"useFinalProbs": true,
"latticeBeamSize": 4.0,
"mbrConfig": {
"confidenceAdditionnalExponent": 1.0,
"unkProba": 0.5,
"unkThreshold": 0.3
},
"maxActive": 2500,
"minActive": 200,
"acousticScale": 0.83,
"beamSize": 12.0
},
"cacheSizeDynamic": 536870912,
"asrModel": {
"version": "0.1.0"
},
"asrLmAdaptation": {
"version": "0.19.7"
},
"endpointingRules": {
"rule3": {
"maxRelativeCost": 8.0,
"minTrailingSilence": 1.0,
"minUtteranceLength": 0.0,
"mustContainNonsilence": true
},
"rule2": {
"maxRelativeCost": 5.5,
"minTrailingSilence": 0.7,
"minUtteranceLength": 0.0,
"mustContainNonsilence": true
},
"rule1": {
"maxRelativeCost": 10000000000.0,
"minTrailingSilence": 4.0,
"minUtteranceLength": 0.0,
"mustContainNonsilence": false
},
"rule5": {
"maxRelativeCost": 10000000000.0,
"minTrailingSilence": 0.0,
"minUtteranceLength": 10.0,
"mustContainNonsilence": false
},
"rule4": {
"maxRelativeCost": 2.5,
"minTrailingSilence": 0.5,
"minUtteranceLength": 0.0,
"mustContainNonsilence": true
}
},
"postprocessors": [
{
"Regex": {
"pattern": "`INTENT`4c40d5ef-686e-489f-8dcf-175ea8eb7b82`START (.*?) `INTENT`4c40d5ef-686e-489f-8dcf-175ea8eb7b82`END",
"flags": "i",
"replacement": "$1"
}
},
{
"Regex": {
"pattern": "`INTENT`4c40d5ef-686e-489f-8dcf-175ea8eb7b82`START ",
"flags": "i",
"replacement": ""
}
},
{
"Regex": {
"pattern": "`INTENT`724cf256-45e2-4ab7-a741-5e7ca82a9756`START (.*?) `INTENT`724cf256-45e2-4ab7-a741-5e7ca82a9756`END",
"flags": "i",
"replacement": "$1"
}
},
{
"Regex": {
"pattern": "`INTENT`724cf256-45e2-4ab7-a741-5e7ca82a9756`START ",
"flags": "i",
"replacement": ""
}
},
{
"Regex": {
"pattern": "`INTENT`ca101149-1495-494f-92f6-e9e042eb9266`START (.*?) `INTENT`ca101149-1495-494f-92f6-e9e042eb9266`END",
"flags": "i",
"replacement": "$1"
}
},
{
"Regex": {
"pattern": "`INTENT`ca101149-1495-494f-92f6-e9e042eb9266`START ",
"flags": "i",
"replacement": ""
}
},
{
"Regex": {
"pattern": "`ENTITY`b048ef60-c29a-472d-bd67-8e5cc65a1e72`START (.*?) `ENTITY`b048ef60-c29a-472d-bd67-8e5cc65a1e72`END",
"flags": "i",
"replacement": "$1"
}
},
{
"Regex": {
"pattern": "`ENTITY`b048ef60-c29a-472d-bd67-8e5cc65a1e72`START ",
"flags": "i",
"replacement": ""
}
}
],
"asrEngine": {
"version": "0.1.0"
},
"entitiesFstMap": [
[
51,
"__root__"
],
[
52,
"e_0.snips"
]
],
"entityNameToDisambigs": {
"choice": {
"start": "`ENTITY`b048ef60-c29a-472d-bd67-8e5cc65a1e72`START",
"end": "`ENTITY`b048ef60-c29a-472d-bd67-8e5cc65a1e72`END",
"empty": [
"",
4
]
}
},
"inferenceEngine": {
"version": 2
}
}

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1 good
1 okay
1 by no means
1 amen
1 yea
1 nope
1 never
1 yeah
1 fine
1 affirmative
1 nix
1 no
1 not
1 not at all
1 yes
1 all right
1 true
1 no way
1 nay
1 no not at all

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{
"choice": {
"vanilla": "b048ef60-c29a-472d-bd67-8e5cc65a1e72.vanilla",
"ngram": true,
"added": "b048ef60-c29a-472d-bd67-8e5cc65a1e72.added",
"pruningThresholds": [],
"order": 3
}
}

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<eps> 0
's#1 1
a#1 2
affirmative#1 3
all#1 4
amen#1 5
ask#1 6
at#1 7
by#1 8
cancel#1 9
choose#1 10
fine#1 11
for#1 12
go#1 13
good#1 14
i#1 15
is#1 16
it#1 17
me#1 18
means#1 19
nay#1 20
never#1 21
nix#1 22
no#1 23
nope#1 24
not#1 25
okay#1 26
question#1 27
right#1 28
something#1 29
that#1 30
think#1 31
to#1 32
true#1 33
want#1 34
way#1 35
yea#1 36
yeah#1 37
yes#1 38
yo#1 39
#0 40
<s> 41
</s> 42
`ENTITY`b048ef60-c29a-472d-bd67-8e5cc65a1e72`END 43
`INTENT`724cf256-45e2-4ab7-a741-5e7ca82a9756`END 44
`INTENT`ca101149-1495-494f-92f6-e9e042eb9266`START 45
`INTENT`ca101149-1495-494f-92f6-e9e042eb9266`END 46
`ENTITY`b048ef60-c29a-472d-bd67-8e5cc65a1e72`START 47
`INTENT`724cf256-45e2-4ab7-a741-5e7ca82a9756`START 48
`INTENT`4c40d5ef-686e-489f-8dcf-175ea8eb7b82`END 49
`INTENT`4c40d5ef-686e-489f-8dcf-175ea8eb7b82`START 50
``ROOT 51
b048ef60-c29a-472d-bd67-8e5cc65a1e72 52
all 53
right 54
yea 55
is 56
it 57
affirmative 58
go 59
at 60
want 61
cancel 62
okay 63
yes 64
fine 65
for 66
no 67
not 68
means 69
question 70
to 71
that 72
choose 73
way 74
nay 75
nope 76
me 77
good 78
's 79
yo 80
amen 81
never 82
yeah 83
nix 84
ask 85
true 86
by 87
a 88
i 89
think 90
something 91

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{
"error": "error.wav",
"end_of_input": "end_of_input.wav",
"start_of_input": "start_of_input.wav"
}

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{
"language": "de",
"confirmation_cancel_pattern": "(?i)(?:\\W+|^)(?:abbrechen|brich ab|verwerfen|zur[üu]cknehmen|annullieren|stornieren|r[üu]ckbuchen|widerrufen|zur[üu]ckziehen)(?:\\W+|$)",
"confirmation_yes_pattern": "(?i)(?:\\W+|^)(?:ja|von mir aus|auf alle F[äa]lle|meinetwegen|auf jeden Fall|klar|unbedingt|absolut|einverstanden|passt|OK|sicher|bestimmt|joa)(?:\\W+|$)",
"confirmation_no_pattern": "(?i)(?:\\W+|^)(nein|auf keinen Fall|na|nee)(?:\\W+|$)",
"general_misunderstanding": "tut mir leid, aber ich verstehe nicht, was Sie wollen",
"answer_misunderstanding": "Ich konnte Ihre Antwort nicht verstehen"
}

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{
"language": "en",
"confirmation_cancel_pattern": "(?i)(?:\\W+|^)(?:cancel)(?:\\W+|$)",
"confirmation_yes_pattern": "(?i)(?:\\W+|^)(?:yes)(?:\\W+|$)",
"confirmation_no_pattern": "(?i)(?:\\W+|^)(?:no)(?:\\W+|$)",
"general_misunderstanding": "Sorry I didn't understand what you want",
"answer_misunderstanding": "I couldn't understand your answer"
}

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{
"language": "es",
"confirmation_cancel_pattern": "(?i)(?:\\W+|^)(?:cancelar)(?:\\W+|$)",
"confirmation_yes_pattern": "(?i)(?:\\W+|^)(?:s(?:í|i))(?:\\W+|$)",
"confirmation_no_pattern": "(?i)(?:\\W+|^)(?:no)(?:\\W+|$)",
"general_misunderstanding": "Lo siento no entendí lo que dijo",
"answer_misunderstanding": "No entendí su respuesta"
}

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{
"language": "fr",
"confirmation_cancel_pattern": "(?i)(?:\\W+|^)(?:annuler)(?:\\W+|$)",
"confirmation_yes_pattern": "(?i)(?:\\W+|^)(?:oui)(?:\\W+|$)",
"confirmation_no_pattern": "(?i)(?:\\W+|^)(?:non)(?:\\W+|$)",
"general_misunderstanding": "Désolé je n'ai pas compris ce que vous avez dit",
"answer_misunderstanding": "Je n'ai pas compris votre réponse"
}

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{
"language": "it",
"confirmation_cancel_pattern": "(?i)(?:\\W+|^)(?:annullare)(?:\\W+|$)",
"confirmation_yes_pattern": "(?i)(?:\\W+|^)(?:sì)(?:\\W+|$)",
"confirmation_no_pattern": "(?i)(?:\\W+|^)(?:no)(?:\\W+|$)",
"general_misunderstanding": "Scusami, non ho capito cosa hai detto",
"answer_misunderstanding": "Non ho capito la tua risposta"
}

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{
"language": "ko",
"confirmation_cancel_pattern": "(?i)(?:\\W+|^)(?:취소)(?:\\W+|$)",
"confirmation_yes_pattern": "(?i)(?:\\W+|^)(?:응)(?:\\W+|$)",
"confirmation_no_pattern": "(?i)(?:\\W+|^)(?:아니(에요|야)?)(?:\\W+|$)",
"general_misunderstanding": "죄송합니다, 뭣을 원하는지 이해 못했습니다",
"answer_misunderstanding": "당신의 대답을 이해 못했습니다"
}

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{
"dither": 1.0,
"featurizer": "mel",
"frame_length_ms": 25.0,
"frame_shift_ms": 10.0,
"high_freq": 0,
"hotword_key": "hey_snips",
"input_node_name": "input_node",
"mel_low_freq": 20,
"model_id": "workflow-hey_snips_subww_feedback_10seeds-2018_12_04T12_13_05_evaluated_model_0002",
"num_mel_bins": 20,
"output_node_name": "output_node",
"preemphasis_coefficient": 0.97,
"remove_dc_offset": false,
"sample_rate": 16000,
"streamable": true,
"use_log_fbank": true,
"w_max": 100,
"w_smooth": 30,
"window_type": "povey"
}

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@ -0,0 +1,4 @@
{
"language": "en",
"gazetteer_parser": null
}

@ -0,0 +1,5 @@
{
"language": "en",
"parser_directory": "parser",
"parser_usage": 2
}

@ -0,0 +1,8 @@
{
"parsers_metadata": [
{
"entity_identifier": "choice",
"entity_parser": "parser_1"
}
]
}

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{"version":"0.6.0","parser_filename":"parser","threshold":0.9,"stop_words":[],"edge_cases":[]}

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{
"config": {
"ignore_stop_words": true,
"max_pattern_length": 1000,
"max_queries": 500,
"unit_name": "deterministic_intent_parser"
},
"group_names_to_slot_names": {
"group0": "user_choice"
},
"language_code": "en",
"patterns": {
"jocavdh:answer_yes": [
"^\\s*(?P<group0>%CHOICE%)\\s*$",
"^\\s*(?P<group0>%CHOICE%)\\s*(?P<group0_2>%CHOICE%)\\s*$",
"^\\s*i\\s*go\\s*for\\s*(?P<group0>%CHOICE%)\\s*$",
"^\\s*i\\s*choose\\s*(?P<group0>%CHOICE%)\\s*$",
"^\\s*i\\s*think\\s*it\\s*is\\s*(?P<group0>%CHOICE%)\\s*$",
"^\\s*s\\s*(?P<group0>%CHOICE%)\\s*$"
],
"jocavdh:ask": [
"^\\s*i\\s*want\\s*ask\\s*question\\s*$",
"^\\s*ask\\s*$",
"^\\s*ask\\s*something\\s*$",
"^\\s*ask\\s*question\\s*$"
]
},
"slot_names_to_entities": {
"jocavdh:answer_yes": {
"user_choice": "choice"
},
"jocavdh:ask": {}
}
}

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{
"unit_name": "deterministic_intent_parser"
}

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{
"builtin_entity_parser": "builtin_entity_parser",
"config": {
"intent_parsers_configs": [
{
"ignore_stop_words": true,
"max_pattern_length": 1000,
"max_queries": 500,
"unit_name": "deterministic_intent_parser"
},
{
"intent_classifier_config": {
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"max_unknown_words": 5,
"min_utterances": 20,
"noise_factor": 5,
"unknown_word_prob": 0.5,
"unknown_words_replacement_string": "unknownword"
},
"featurizer_config": {
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"cooccurrence_vectorizer_config": {
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"keep_order": true,
"unit_name": "cooccurrence_vectorizer",
"unknown_words_replacement_string": "unknownword",
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},
"pvalue_threshold": 0.4,
"tfidf_vectorizer_config": {
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"word_clusters_name": null
},
"unit_name": "featurizer"
},
"random_seed": null,
"unit_name": "log_reg_intent_classifier"
},
"slot_filler_config": {
"crf_args": {
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"c1": 0.1,
"c2": 0.1
},
"data_augmentation_config": {
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"capitalization_ratio": 0,
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},
"feature_factory_configs": [
{
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"n": 1,
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},
"factory_name": "ngram",
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-2,
-1,
0,
1,
2
]
},
{
"args": {
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1
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{
"args": {},
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1
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},
{
"args": {},
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0
]
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{
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1,
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{
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-1,
0
]
},
{
"args": {
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},
"factory_name": "builtin_entity_match",
"offsets": [
-2,
-1,
0
]
},
{
"args": {
"cluster_name": "brown_clusters",
"use_stemming": false
},
"factory_name": "word_cluster",
"offsets": [
-2,
-1,
0,
1
]
}
],
"random_seed": null,
"tagging_scheme": 1,
"unit_name": "crf_slot_filler"
},
"unit_name": "probabilistic_intent_parser"
}
],
"unit_name": "nlu_engine"
},
"custom_entity_parser": "custom_entity_parser",
"dataset_metadata": {
"entities": {
"choice": {
"automatically_extensible": true
}
},
"language_code": "en",
"slot_name_mappings": {
"jocavdh:answer_yes": {
"user_choice": "choice"
},
"jocavdh:ask": {}
}
},
"intent_parsers": [
"deterministic_intent_parser",
"probabilistic_intent_parser"
],
"model_version": "0.19.0",
"training_package_version": "0.19.4",
"unit_name": "nlu_engine"
}

@ -0,0 +1,22 @@
{
"config": {
"added_cooccurrence_feature_ratio": 0,
"cooccurrence_vectorizer_config": {
"filter_stop_words": true,
"keep_order": true,
"unit_name": "cooccurrence_vectorizer",
"unknown_words_replacement_string": "unknownword",
"window_size": 5
},
"pvalue_threshold": 0.4,
"tfidf_vectorizer_config": {
"unit_name": "tfidf_vectorizer",
"use_stemming": false,
"word_clusters_name": null
},
"unit_name": "featurizer"
},
"cooccurrence_vectorizer": null,
"language_code": "en",
"tfidf_vectorizer": "tfidf_vectorizer"
}

@ -0,0 +1,77 @@
{
"builtin_entity_scope": [],
"config": {
"unit_name": "tfidf_vectorizer",
"use_stemming": false,
"word_clusters_name": null
},
"language_code": "en",
"vectorizer": {
"idf_diag": [
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@ -0,0 +1,46 @@
!
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@ -0,0 +1 @@
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[|t 596
e|] 597
i|] 598
o|] 599
.|e 600
! 601
' 602
® 603
æ 604
605
[ 606
] 607

@ -0,0 +1,232 @@
<eps> 0
| 1
_ 2
<unk> 3
s 4
k 5
a 6
r 7
m 8
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t 10
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o 12
e 13
ts 14
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n 17
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p 22
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v 27
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dz 37
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ɲ 46
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a|k 77
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o|n 79
r|d 80
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j|e 83
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s|e 85
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r|e 89
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l|e 91
ŋ 92
k|o 93
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r|a 99
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j|o 105
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k|t 108
e|r 109
dz|d 110
tʃ|i 111
i|ɔ 112
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d|i 114
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i|e 116
p|r 117
s|o 118
k|u 119
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ts|z 125
r|j 126
p|i 127
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z|o 130
m|e 131
i|i 132
i|ɛ 133
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k|e 135
p|p 136
ʃ|i 137
ɛ|z 138
m|m 139
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b|b 141
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o|a 144
i|v 145
f|f 146
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l|f 149
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d|ɔ 156
n|t 157
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j|j 159
e|m 160
s|ɔ 161
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r|b 163
i|n 164
i|p 165
a|d 166
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r|ʒ 175
t|i 176
i|o 177
l|i 178
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u|a 181
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v|e 183
v|u 184
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r|i 187
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b|r 189
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f|e 191
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e|w 193
i|a 194
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f|i 196
i|t 197
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o|l 199
o|v 200
s|f 201
ɔ|i 202
i|ʒ 203
l|a 204
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r|o 206
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n|tʃ 208
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k|ɛ 210
s|u 211
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n|ɔ 213
t|a 214
b|ɛ 215
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d|s 217
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a|ɔ 220
a|b 221
k|w 222
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w|e 225
i|s 226
dʒ|e 227
m|b 228
t|r 229
s|p 230
ɛ|k 231

@ -0,0 +1,17 @@
{
"sample_rate": 16000,
"frame_length_ms": 25.0,
"frame_shift_ms": 10.0,
"num_mfcc": 40,
"num_mel_bins": 40,
"mel_low_freq": 20,
"cepstral_lifter": 0.0,
"dither": 1.0,
"window_type": "povey",
"use_energy": false,
"energy_floor": 0.0,
"raw_energy": true,
"preemphasis_coefficient": 0.0,
"w_smooth": 30,
"w_max": 100
}

Binary file not shown.

@ -0,0 +1,68 @@
[
{
"threshold": 0.99997336,
"recall": 0.0013297872340425532,
"sensitivity": 0,
"far": 0.0
},
{
"threshold": 0.48656777,
"recall": 0.9295212765957447,
"sensitivity": 0.1,
"far": 0.0017921146953405018
},
{
"threshold": 0.42605102,
"recall": 0.9401595744680851,
"sensitivity": 0.2,
"far": 0.002986857825567503
},
{
"threshold": 0.42605102,
"recall": 0.9401595744680851,
"sensitivity": 0.3,
"far": 0.002986857825567503
},
{
"threshold": 0.3095676,
"recall": 0.9561170212765957,
"sensitivity": 0.4,
"far": 0.011350059737156512
},
{
"threshold": 0.2795505,
"recall": 0.961436170212766,
"sensitivity": 0.5,
"far": 0.016129032258064516
},
{
"threshold": 0.2795505,
"recall": 0.961436170212766,
"sensitivity": 0.6,
"far": 0.016129032258064516
},
{
"threshold": 0.24352217,
"recall": 0.964095744680851,
"sensitivity": 0.7,
"far": 0.022301871764237355
},
{
"threshold": 0.24352217,
"recall": 0.964095744680851,
"sensitivity": 0.8,
"far": 0.022301871764237355
},
{
"threshold": 0.09038606,
"recall": 0.9800531914893617,
"sensitivity": 0.9,
"far": 0.13102349661489446
},
{
"threshold": 0.0,
"recall": 1.0,
"sensitivity": 1.0,
"far": 1.0
}
]

@ -0,0 +1,49 @@
# Generated Snips skill
This is a generated python script using the `snips-template` tool. It is supposed to be used by Home Assistant.
It uses the `hass` object to call Home Assistant services, check https://www.home-assistant.io/components/python_script/
## Setup
Actions expected to happen when installing the final folder (on device)
- move python code in the Hass `python_scripts` folder. Scripts name MUST be lowercased.
- add `mqtt:` broker & port that Snips uses
- add `python_script:` component
- add `snips:` component to Hass configuration, see https://www.home-assistant.io/components/snips/
- edit Hass' `configuration.yaml` to point to the scripts that were added.
For instance, if you use Snips' Smart lights bundle, this is what your `configuration.yaml` would look like:
```
mqtt:
broker: 127.0.0.1
port: 1883
python_script:
snips:
intent_script:
lightsTurnOnSet:
action:
- service: python_script.action_lightsTurnOnSet_Smart_lights
data_template:
house_room: "{{ house_room }}"
number: "{{number}}"
unit: "{{unit}}"
lightsTurnOff:
action:
- service: python_script.action_lightsTurnOff_Smart_lights
data_template:
house_room: "{{house_room}}"
```
The `data_template` section is exposing Snips' intents `slot_value` to the python script. Therefore you'll be able to get slot values in the python script:
`house_room = data.get('house_room')`
## Executables
This dir contains a number of python executables named `action_*.py`.
One such file is generated per intent supported. These are standalone
executables and will be run by Hass when an intent is triggered.

@ -0,0 +1,2 @@
{{#each action_code as |a|}}{{a}}
{{/each}}

@ -0,0 +1,6 @@
{
"name":"homeassistant",
"language":"PYTHON",
"prelude": "\"\"\"\n<intent_slot_name> = data.get('<intent_slot_name>')\nservice_data = {'entity_id': '<intent_slot_name>' }\nhass.services.call('<hass_component>', '<hass_component_method>', service_data, False)\"\"\"",
"postlude": ""
}

@ -0,0 +1,17 @@
# Generated Snips skill
This is a generated javascript snips skill using the `snips-template` tool.
It is compatible with the format expected by the `snips-skill-server`
## Setup
This skill requires some javascript dependencies to work properly, these are
listed in the `package.json` file. You can use the `setup.sh` script to install
them.
## Executables
This dir contains a number of javascript executables named `action-*.js`.
One such file is generated per intent supported. These are standalone
executables and will perform a connection to MQTT and register on the
given intent using the `hermes-javascript` helper lib.

@ -0,0 +1,23 @@
#!/usr/bin/env node
const ini = require('ini')
const fs = require('fs')
const { withHermes } = require('hermes-javascript')
const configFile = fs.readFileSync('./config.ini', 'utf8')
const config = ini.parse(configFile)
withHermes((hermes, done) => {
try {
const dialog = hermes.dialog()
dialog.flow('intent/{{intent_id}}', async (message, flow) => {
{{#each action_code as |a|}}
{{a}}
{{/each}}
})
} catch (error) {
console.error(error.toString())
done()
}
})

@ -0,0 +1,9 @@
{
"name": "snips-action-template",
"description": "Auto-generated snips action template.",
"version": "0.0.1",
"dependencies": {
"hermes-javascript": "^0.1.0",
"ini": "^1.3.5"
}
}

@ -0,0 +1,3 @@
#/usr/bin/env bash -e
npm install

@ -0,0 +1,6 @@
{
"name":"javascript",
"language":"JAVASCRIPT",
"prelude": "/*\n Your action code will be wrapped inside a function with the following signature:\n\n function(message, flow, config) { /* action code */ }\n\n Where:\n - 'message' is the intent message object\n - 'flow' is the hermes-javascript flow object (see: https://github.com/snipsco/hermes-protocol/tree/develop/platforms/hermes-javascript#dialogflow for mode details)\n - 'config' is an object containing your App parameters\n `config.global.parameterName` or `config.secret.secretParameterName`\n*/\n",
"postlude": ""
}

@ -0,0 +1,18 @@
# Generated Snips skill
This is a generated Python 2 snips app using the `snips-template` tool.
It is compatible with the format expected by the `snips-skill-server`
## Setup
This app requires some python dependencies to work properly, these are
listed in the `requirements.txt`. You can use the `setup.sh` script to
create a python virtualenv that will be recognized by the skill server
and install them in it.
## Executables
This dir contains a number of python executables named `action-*.py`.
One such file is generated per intent supported. These are standalone
executables and will perform a connection to MQTT and register on the
given intent using the `hermes-python` helper lib.

@ -0,0 +1,41 @@
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import ConfigParser
from hermes_python.hermes import Hermes
from hermes_python.ffi.utils import MqttOptions
from hermes_python.ontology import *
import io
CONFIGURATION_ENCODING_FORMAT = "utf-8"
CONFIG_INI = "config.ini"
class SnipsConfigParser(ConfigParser.SafeConfigParser):
def to_dict(self):
return {section : {option_name : option for option_name, option in self.items(section)} for section in self.sections()}
def read_configuration_file(configuration_file):
try:
with io.open(configuration_file, encoding=CONFIGURATION_ENCODING_FORMAT) as f:
conf_parser = SnipsConfigParser()
conf_parser.readfp(f)
return conf_parser.to_dict()
except (IOError, ConfigParser.Error) as e:
return dict()
def subscribe_intent_callback(hermes, intentMessage):
conf = read_configuration_file(CONFIG_INI)
action_wrapper(hermes, intentMessage, conf)
def action_wrapper(hermes, intentMessage, conf):
{{#each action_code as |a|}}{{a}}
{{/each}}
if __name__ == "__main__":
mqtt_opts = MqttOptions()
with Hermes(mqtt_options=mqtt_opts) as h:
h.subscribe_intent("{{intent_id}}", subscribe_intent_callback) \
.start()

@ -0,0 +1,3 @@
# Bindings for the hermes protocol
hermes-python>=0.2

@ -0,0 +1,20 @@
#/usr/bin/env bash -e
VENV=venv
if [ ! -d "$VENV" ]
then
PYTHON=`which python2`
if [ ! -f $PYTHON ]
then
echo "could not find python"
fi
virtualenv -p $PYTHON $VENV
fi
. $VENV/bin/activate
pip install -r requirements.txt

@ -0,0 +1,6 @@
{
"name":"python2",
"language":"PYTHON_2",
"prelude": "\"\"\" Write the body of the function that will be executed once the intent is recognized. \nIn your scope, you have the following objects : \n- intentMessage : an object that represents the recognized intent\n- hermes : an object with methods to communicate with the MQTT bus following the hermes protocol. \n- conf : a dictionary that holds the skills parameters you defined. \n To access global parameters use conf['global']['parameterName']. For end-user parameters use conf['secret']['parameterName'] \n \nRefer to the documentation for further details. \n\"\"\" ",
"postlude": ""
}

@ -0,0 +1,18 @@
# Generated Snips skill
This is a generated Python 3 snips app, using the `snips-template` tool.
It is compatible with the format expected by the `snips-skill-server`
## Setup
This app requires some python dependencies to work properly, these are
listed in the `requirements.txt`. You can use the `setup.sh` script to
create a python virtualenv that will be recognized by the skill server
and install them in it.
## Executables
This dir contains a number of python executables named `action-*.py`.
One such file is generated per intent supported. These are standalone
executables and will perform a connection to MQTT and register on the
given intent using the `hermes-python` helper lib.

@ -0,0 +1,41 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import configparser
from hermes_python.hermes import Hermes
from hermes_python.ffi.utils import MqttOptions
from hermes_python.ontology import *
import io
CONFIGURATION_ENCODING_FORMAT = "utf-8"
CONFIG_INI = "config.ini"
class SnipsConfigParser(configparser.SafeConfigParser):
def to_dict(self):
return {section : {option_name : option for option_name, option in self.items(section)} for section in self.sections()}
def read_configuration_file(configuration_file):
try:
with io.open(configuration_file, encoding=CONFIGURATION_ENCODING_FORMAT) as f:
conf_parser = SnipsConfigParser()
conf_parser.readfp(f)
return conf_parser.to_dict()
except (IOError, configparser.Error) as e:
return dict()
def subscribe_intent_callback(hermes, intentMessage):
conf = read_configuration_file(CONFIG_INI)
action_wrapper(hermes, intentMessage, conf)
def action_wrapper(hermes, intentMessage, conf):
{{#each action_code as |a|}}{{a}}
{{/each}}
if __name__ == "__main__":
mqtt_opts = MqttOptions()
with Hermes(mqtt_options=mqtt_opts) as h:
h.subscribe_intent("{{intent_id}}", subscribe_intent_callback) \
.start()

@ -0,0 +1,3 @@
# Bindings for the hermes protocol
hermes-python>=0.2

@ -0,0 +1,20 @@
#/usr/bin/env bash -e
VENV=venv
if [ ! -d "$VENV" ]
then
PYTHON=`which python3`
if [ ! -f $PYTHON ]
then
echo "could not find python"
fi
virtualenv -p $PYTHON $VENV
fi
. $VENV/bin/activate
pip install -r requirements.txt

@ -0,0 +1,6 @@
{
"name":"python3",
"language":"PYTHON_3",
"prelude": "\"\"\" Write the body of the function that will be executed once the intent is recognized. \nIn your scope, you have the following objects : \n- intentMessage : an object that represents the recognized intent\n- hermes : an object with methods to communicate with the MQTT bus following the hermes protocol. \n- conf : a dictionary that holds the skills parameters you defined. \n To access global parameters use conf['global']['parameterName']. For end-user parameters use conf['secret']['parameterName'] \n \nRefer to the documentation for further details. \n\"\"\" ",
"postlude": ""
}
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