{
"cells": [
{
"cell_type": "markdown",
"id": "9febd313-2446-49bb-8508-997c0b2bc0ca",
"metadata": {},
"source": [
"# Imports and configs"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1929f9bb-5060-4530-811b-823d69a5b00f",
"metadata": {},
"outputs": [],
"source": [
"from experiment.luna.luna_record_provider import LunaRecordProvider\n",
"from sziszapangma.integration.experiment_manager import ExperimentManager\n",
"from sziszapangma.integration.repository.multi_files_experiment_repository import MultiFilesExperimentRepository\n",
"from sziszapangma.integration.path_filter import ExtensionPathFilter\n",
"from pymongo import MongoClient\n",
"from spacy.tokens.doc import Doc\n",
"import pandas as pd\n",
"from experiment.voicelab.voicelab_telco_record_provider import VoicelabTelcoRecordProvider\n",
"from sziszapangma.integration.repository.experiment_repository import ExperimentRepository\n",
"from dataclasses import dataclass\n",
"import itertools\n",
"from typing import Optional, Any, List\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "321a93d9-0c5d-4d42-ba8f-4b704a05d78c",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_rows', None)"
]
},
{
"cell_type": "markdown",
"id": "1b0a963f-ef3b-4f0f-bebc-68a0ea3e4f6c",
"metadata": {},
"source": [
"# Load datasets and other pipeline objects"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bbe2a7bf-bb6d-42ee-b5ce-48e6ec7fcd94",
"metadata": {},
"outputs": [],
"source": [
"VOICELAB_DATASET_DIRECTORY = 'experiment_data/dataset/voicelab_cbiz_testset_20220322'\n",
"LUNA_DATASET_DIRECTORY = 'experiment_data/dataset/LUNA.PL'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4bb1a1c0-8784-4e0d-9426-13495718e087",
"metadata": {},
"outputs": [],
"source": [
"## repository collections\n",
"GOLD_TRANSCRIPT = 'gold_transcript'\n",
"GOLD_TRANSCRIPT_SPACY = 'gold_transcript_spacy'\n",
"\n",
"TECHMO_POLISH_ASR = 'techmo_polish_asr'\n",
"WORD_TECHMO_MERTICS_WER = 'word_techmo_metrics_wer'\n",
"WORD_TECHMO_ALIGNMENT_WER = 'word_techmo_alignment_wer'\n",
"TECHMO_SPACY = 'techmo_spacy'\n",
"POS_TECHMO_ALIGNMENT_WER = 'pos_techmo_alignment_wer'\n",
"POS_TECHMO_METRICS_WER = 'pos_techmo_metrics_wer'\n",
"\n",
"AJN_POLISH_ASR = 'ajn_polish_asr'\n",
"WORD_AJN_MERTICS_WER = 'word_ajn_metrics_wer'\n",
"WORD_AJN_ALIGNMENT_WER = 'word_ajn_alignment_wer'\n",
"AJN_SPACY = 'ajn_spacy'\n",
"POS_AJN_ALIGNMENT_WER = 'pos_ajn_metrics_wer'\n",
"POS_AJN_METRICS_WER = 'pos_ajn_alignment_wer'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d4265253-755a-4160-97f7-72604fdf41d1",
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class CollectionsConfig:\n",
" config_name: str\n",
" gold_transcript: str\n",
" gold_transcript_spacy: str\n",
" asr: str\n",
" word_asr_metric_wer: str\n",
" word_asr_alignment_wer: str\n",
" asr_spacy: str\n",
" pos_asr_metric_wer: str\n",
" pos_asr_alignment_wer: str"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "950b0bb8-e5ae-46e0-97a2-a832b7c8a70f",
"metadata": {},
"outputs": [],
"source": [
"techmo_connections_config = CollectionsConfig(\n",
" config_name='TECHMO ASR',\n",
" gold_transcript=GOLD_TRANSCRIPT,\n",
" gold_transcript_spacy=GOLD_TRANSCRIPT_SPACY,\n",
" asr=TECHMO_POLISH_ASR,\n",
" word_asr_metric_wer=WORD_TECHMO_MERTICS_WER,\n",
" word_asr_alignment_wer=WORD_TECHMO_ALIGNMENT_WER,\n",
" asr_spacy=TECHMO_SPACY,\n",
" pos_asr_metric_wer=POS_TECHMO_METRICS_WER,\n",
" pos_asr_alignment_wer=POS_TECHMO_ALIGNMENT_WER\n",
")\n",
"ajn_connections_config = CollectionsConfig(\n",
" config_name='AJN ASR',\n",
" gold_transcript=GOLD_TRANSCRIPT,\n",
" gold_transcript_spacy=GOLD_TRANSCRIPT_SPACY,\n",
" asr=AJN_POLISH_ASR,\n",
" word_asr_metric_wer=WORD_AJN_MERTICS_WER,\n",
" word_asr_alignment_wer=WORD_AJN_ALIGNMENT_WER,\n",
" asr_spacy=AJN_SPACY,\n",
" pos_asr_metric_wer=POS_AJN_ALIGNMENT_WER,\n",
" pos_asr_alignment_wer=POS_AJN_METRICS_WER\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4dec626b-02e4-4c78-a238-04ef2f090ea5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"voicelab examples count 800\n",
"luna examples count 500\n"
]
}
],
"source": [
"voicelab_experiment_repository = MultiFilesExperimentRepository(\n",
" 'experiment_data/pipeline',\n",
" 'asr_benchmark_voicelab_cbiz_testset_20220322'\n",
")\n",
"luna_experiment_repository = MultiFilesExperimentRepository(\n",
" 'experiment_data/pipeline',\n",
" 'asr_benchmark_luna'\n",
")\n",
"print(f'voicelab examples count {len(voicelab_experiment_repository.get_all_record_ids())}')\n",
"print(f'luna examples count {len(luna_experiment_repository.get_all_record_ids())}')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "98c6ff1d-4fbd-4b68-9e23-ecea33852b12",
"metadata": {},
"outputs": [],
"source": [
"voicelab_record_provider = VoicelabTelcoRecordProvider(ExtensionPathFilter(\n",
" root_directory=VOICELAB_DATASET_DIRECTORY,\n",
" extension='wav',\n",
"), relation_manager_root_path='experiment_data/dataset_relation_manager_data/voicelab_cbiz_testset_20220322')\n",
"\n",
"luna_record_provider = LunaRecordProvider(ExtensionPathFilter(\n",
" root_directory=f'{LUNA_DATASET_DIRECTORY}/LUNA.PL',\n",
" extension='wav',\n",
"), relation_manager_root_path='experiment_data/dataset_relation_manager_data/luna')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "74bf17ba-8572-4758-8116-794708b6ea08",
"metadata": {},
"outputs": [],
"source": [
"def get_gold_transcript_for(record_id: str, experiment_repository: ExperimentRepository, \n",
" collections_config: CollectionsConfig) -> str:\n",
" gold_trnascript_property = experiment_repository.get_property_for_key(\n",
" record_id, collections_config.gold_transcript)\n",
" return ' '.join([it['word'] for it in gold_trnascript_property])\n",
"\n",
"def get_asr_transcript_for(record_id: str, experiment_repository: ExperimentRepository,\n",
" collections_config: CollectionsConfig) -> str:\n",
" gold_trnascript_property = experiment_repository.get_property_for_key(record_id, collections_config.asr)\n",
" return gold_trnascript_property['full_text']\n",
"\n",
"def get_word_alignment_df(record_id: str, experiment_repository: ExperimentRepository, \n",
" collections_config: CollectionsConfig) -> pd.DataFrame:\n",
" word_alignment_wer = experiment_repository.get_property_for_key(record_id, \n",
" collections_config.word_asr_alignment_wer)\n",
" arr = [\n",
" {\n",
" 'step_type': it['step_type'],\n",
" 'reference_word_text': it['step_words']['reference_word']['text'] \n",
" if 'reference_word' in it['step_words'] else '',\n",
" 'hypothesis_word_text': it['step_words']['hypothesis_word']['text']\n",
" if 'hypothesis_word' in it['step_words'] else '',\n",
" }\n",
" for it in word_alignment_wer\n",
" ]\n",
" return pd.DataFrame(arr)\n",
"\n",
"\n",
"def get_pos_alignment_df(record_id: str, experiment_repository: ExperimentRepository, \n",
" collections_config: CollectionsConfig) -> pd.DataFrame:\n",
" pos_alignment_wer = experiment_repository.get_property_for_key(record_id, \n",
" collections_config.pos_asr_alignment_wer)\n",
" gold_transcript_spacy = experiment_repository.get_property_for_key(record_id,\n",
" collections_config.gold_transcript_spacy)\n",
" gold_trnascript_spacy_word_dict = {it['id']: it['word'] for it in gold_transcript_spacy}\n",
" asr_spacy = experiment_repository.get_property_for_key(record_id, collections_config.asr_spacy)\n",
" asr_spacy_word_dict = {it['id']: it['word'] for it in asr_spacy}\n",
" arr = [\n",
" {\n",
" 'step_type': it['step_type'],\n",
" 'reference_word_pos': it['step_words']['reference_word']['text'] \n",
" if 'reference_word' in it['step_words'] else '',\n",
" 'reference_word_text': gold_trnascript_spacy_word_dict[it['step_words']['reference_word']['id']] \n",
" if 'reference_word' in it['step_words'] else '',\n",
" 'hypothesis_word_pos': it['step_words']['hypothesis_word']['text']\n",
" if 'hypothesis_word' in it['step_words'] else '',\n",
" 'hypothesis_word_text': asr_spacy_word_dict[it['step_words']['hypothesis_word']['id']] \n",
" if 'hypothesis_word' in it['step_words'] else ''\n",
" }\n",
" for it in pos_alignment_wer\n",
" ]\n",
" return pd.DataFrame(arr)\n",
" \n",
" \n",
"def show_report_for(record_id: str, experiment_repository: ExperimentRepository,\n",
" collections_config: CollectionsConfig):\n",
" print('record_id:', record_id)\n",
" print('properties_confiog:', collections_config.config_name)\n",
" print()\n",
" print(f'gold transcript: {get_gold_transcript_for(record_id, experiment_repository, collections_config)}') \n",
" print()\n",
" print(f'asr transcript: {get_asr_transcript_for(record_id, experiment_repository, collections_config)}')\n",
" print()\n",
" print(f\"word wer {experiment_repository.get_property_for_key(record_id, collections_config.word_asr_metric_wer)}\")\n",
" print()\n",
" print(f\"pos wer {experiment_repository.get_property_for_key(record_id, collections_config.pos_asr_metric_wer)}\")\n",
" print()\n",
" display(get_word_alignment_df(record_id, experiment_repository, collections_config))\n",
" print()\n",
" display(get_pos_alignment_df(record_id, experiment_repository, collections_config))\n",
" print('--------------------------------------------------------------')\n",
" print('--------------------------------------------------------------')\n",
" print('--------------------------------------------------------------')"
]
},
{
"cell_type": "markdown",
"id": "327bbc4b-7bcb-4a28-8d4a-22660ecaf6c2",
"metadata": {},
"source": [
"# Report for LUNA and TECHMO ASR"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3395e091-3488-4b49-aede-af15a0055a8b",
"metadata": {},
"outputs": [],
"source": [
"for it in list(luna_record_provider.get_all_records())[:1]:\n",
" show_report_for(it, luna_experiment_repository, techmo_connections_config)"
]
},
{
"cell_type": "markdown",
"id": "a4b4f1bc-f0b3-4d23-bf1d-97f75715f332",
"metadata": {},
"source": [
"# Report for LUNA and AJN ASR"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27797bad-c92a-45a6-87f2-071645c18150",
"metadata": {},
"outputs": [],
"source": [
"for it in list(luna_record_provider.get_all_records())[:1]:\n",
" show_report_for(it, luna_experiment_repository, ajn_connections_config)"
]
},
{
"cell_type": "markdown",
"id": "a698d611-d520-47bf-83ed-2f116a2327d7",
"metadata": {},
"source": [
"# Report for VOICELAB and TECHMO ASR"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b275d21a-7405-46d9-9764-8fc744fa0ea0",
"metadata": {},
"outputs": [],
"source": [
"for it in list(voicelab_record_provider.get_all_records())[:1]:\n",
" show_report_for(it, voicelab_experiment_repository, techmo_connections_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "413a547c-10fc-4873-9db3-8655678f6f9b",
"metadata": {},
"outputs": [],
"source": [
"for it in list(voicelab_record_provider.get_all_records())[:1]:\n",
" show_report_for(it, voicelab_experiment_repository, ajn_connections_config)"
]
},
{
"cell_type": "markdown",
"id": "32e0753f-debf-4946-9e14-1876e9e26e0c",
"metadata": {},
"source": [
"# Statistics of POS incorrect data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "aec7538f-60e7-4eee-9c3a-9875135bed1f",
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class AlignDetails:\n",
" value_reference: Optional[str]\n",
" value_hypothesis: Optional[str]\n",
" \n",
" @staticmethod\n",
" def _get_string_or_default(value: Optional[str]) -> str:\n",
" return value if value is not None else '___'\n",
" \n",
" def to_string(self) -> str:\n",
" return f'{self._get_string_or_default(self.value_reference)} -> {self._get_string_or_default(self.value_hypothesis)}'\n",
" \n",
" def is_correct(self) -> bool:\n",
" return self.value_reference == self.value_hypothesis\n",
" \n",
" \n",
"def get_align_details(repository_record: List[Any]) -> List[AlignDetails]:\n",
" return [\n",
" AlignDetails(\n",
" value_reference=it['step_words']['reference_word']['text'] if 'reference_word' in it['step_words'] else None,\n",
" value_hypothesis=it['step_words']['hypothesis_word']['text'] if 'hypothesis_word' in it['step_words'] else None\n",
" )\n",
" for it in repository_record\n",
" ]\n",
"\n",
"\n",
"def show_stats(experiment_repository: ExperimentRepository, collections_config: CollectionsConfig):\n",
" aggregated_aligned = [\n",
" get_align_details(experiment_repository.get_property_for_key(record_id, collections_config.pos_asr_alignment_wer))\n",
" for record_id in list(experiment_repository.get_all_record_ids())[:]\n",
" ]\n",
" list_of_aligns = list(itertools.chain(*aggregated_aligned))\n",
" list_of_changes = [it.to_string() for it in list_of_aligns if not it.is_correct()]\n",
" unique = np.unique(np.array(list_of_changes), return_counts=True)\n",
" return pd.DataFrame({'values': unique[0], 'counts': unique[1]}).sort_values(by=['counts'], ascending=False).head(16)"
]
},
{
"cell_type": "markdown",
"id": "264c3fd3-b890-4e24-ac8d-2756d171ccb8",
"metadata": {},
"source": [
"# Luna Techmo"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "1d456e71-301c-416f-b694-03ed7ccfdee0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>values</th>\n",
" <th>counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>NUM -> ___</td>\n",
" <td>2290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>PUNCT -> ___</td>\n",
" <td>1479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>NOUN -> ___</td>\n",
" <td>1204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td>PROPN -> NOUN</td>\n",
" <td>1177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>ADV -> ___</td>\n",
" <td>1073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>ADP -> ___</td>\n",
" <td>988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246</th>\n",
" <td>___ -> NOUN</td>\n",
" <td>918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>NUM -> X</td>\n",
" <td>900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>225</th>\n",
" <td>VERB -> ___</td>\n",
" <td>883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>PROPN -> ___</td>\n",
" <td>823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>PROPN -> ADJ</td>\n",
" <td>662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>252</th>\n",
" <td>___ -> VERB</td>\n",
" <td>632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>PRON -> ___</td>\n",
" <td>613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>PART -> ___</td>\n",
" <td>577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>ADJ -> ___</td>\n",
" <td>560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>CCONJ -> ___</td>\n",
" <td>495</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" values counts\n",
"131 NUM -> ___ 2290\n",
"192 PUNCT -> ___ 1479\n",
"114 NOUN -> ___ 1204\n",
"169 PROPN -> NOUN 1177\n",
"45 ADV -> ___ 1073\n",
"30 ADP -> ___ 988\n",
"246 ___ -> NOUN 918\n",
"130 NUM -> X 900\n",
"225 VERB -> ___ 883\n",
"176 PROPN -> ___ 823\n",
"162 PROPN -> ADJ 662\n",
"252 ___ -> VERB 632\n",
"161 PRON -> ___ 613\n",
"146 PART -> ___ 577\n",
"15 ADJ -> ___ 560\n",
"73 CCONJ -> ___ 495"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_stats(luna_experiment_repository, techmo_connections_config)"
]
},
{
"cell_type": "markdown",
"id": "7f018f54-75dc-47a5-8b04-839a008a3edc",
"metadata": {},
"source": [
"## Luna AJN"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f7dd73cf-1fff-416b-9fed-d368d2fc65d0",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th>values</th>\n",
" <th>counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>___ -> PUNCT</td>\n",
" <td>3344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>VERB -> ___</td>\n",
" <td>1645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>251</th>\n",
" <td>VERB -> PUNCT</td>\n",
" <td>1582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>279</th>\n",
" <td>___ -> NOUN</td>\n",
" <td>1548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>ADP -> PUNCT</td>\n",
" <td>1522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>ADV -> ___</td>\n",
" <td>1409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>ADP -> ___</td>\n",
" <td>1316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>NOUN -> PUNCT</td>\n",
" <td>1300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>ADV -> PUNCT</td>\n",
" <td>1197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246</th>\n",
" <td>VERB -> NOUN</td>\n",
" <td>1186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>ADV -> NOUN</td>\n",
" <td>1167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>NOUN -> ___</td>\n",
" <td>1137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>ADJ -> ___</td>\n",
" <td>879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>PART -> ___</td>\n",
" <td>876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>PRON -> ___</td>\n",
" <td>843</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ADJ -> PUNCT</td>\n",
" <td>830</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" values counts\n",
"284 ___ -> PUNCT 3344\n",
"256 VERB -> ___ 1645\n",
"251 VERB -> PUNCT 1582\n",
"279 ___ -> NOUN 1548\n",
"28 ADP -> PUNCT 1522\n",
"52 ADV -> ___ 1409\n",
"34 ADP -> ___ 1316\n",
"124 NOUN -> PUNCT 1300\n",
"46 ADV -> PUNCT 1197\n",
"246 VERB -> NOUN 1186\n",
"41 ADV -> NOUN 1167\n",
"129 NOUN -> ___ 1137\n",
"16 ADJ -> ___ 879\n",
"162 PART -> ___ 876\n",
"178 PRON -> ___ 843\n",
"11 ADJ -> PUNCT 830"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_stats(luna_experiment_repository, ajn_connections_config)"
]
},
{
"cell_type": "markdown",
"id": "e15befae-02ad-4517-9e97-58b3edf8c607",
"metadata": {},
"source": [
"## VoiceLab Techmo"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "9acb8d8e-7359-4e89-9902-ec5067d9a65d",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>values</th>\n",
" <th>counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>ADP -> ___</td>\n",
" <td>4527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>NOUN -> ___</td>\n",
" <td>4129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>PART -> ___</td>\n",
" <td>3585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>PRON -> ___</td>\n",
" <td>3424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>221</th>\n",
" <td>VERB -> ___</td>\n",
" <td>2935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>ADV -> ___</td>\n",
" <td>2727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>CCONJ -> ___</td>\n",
" <td>2360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>NUM -> X</td>\n",
" <td>1842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>NUM -> ___</td>\n",
" <td>1726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>DET -> ___</td>\n",
" <td>1715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>AUX -> ___</td>\n",
" <td>1634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>SCONJ -> ___</td>\n",
" <td>1587</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>ADJ -> ___</td>\n",
" <td>1461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>244</th>\n",
" <td>___ -> NOUN</td>\n",
" <td>1251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>250</th>\n",
" <td>___ -> VERB</td>\n",
" <td>1178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>NOUN -> VERB</td>\n",
" <td>677</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" values counts\n",
"30 ADP -> ___ 4527\n",
"121 NOUN -> ___ 4129\n",
"151 PART -> ___ 3585\n",
"167 PRON -> ___ 3424\n",
"221 VERB -> ___ 2935\n",
"46 ADV -> ___ 2727\n",
"77 CCONJ -> ___ 2360\n",
"135 NUM -> X 1842\n",
"136 NUM -> ___ 1726\n",
"92 DET -> ___ 1715\n",
"61 AUX -> ___ 1634\n",
"204 SCONJ -> ___ 1587\n",
"15 ADJ -> ___ 1461\n",
"244 ___ -> NOUN 1251\n",
"250 ___ -> VERB 1178\n",
"119 NOUN -> VERB 677"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_stats(voicelab_experiment_repository, techmo_connections_config)"
]
},
{
"cell_type": "markdown",
"id": "41e5bd80-87e0-4791-a87a-4c247ddb27cb",
"metadata": {},
"source": [
"## VoiceLab AJN"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "96b5b6d1-d7cd-4afb-adca-f6ed4bc1fedf",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>values</th>\n",
" <th>counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>259</th>\n",
" <td>VERB -> ___</td>\n",
" <td>11609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>NOUN -> ___</td>\n",
" <td>10416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>ADV -> ___</td>\n",
" <td>10127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>PART -> ___</td>\n",
" <td>9282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>ADP -> ___</td>\n",
" <td>8663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>PRON -> ___</td>\n",
" <td>8066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>287</th>\n",
" <td>___ -> PUNCT</td>\n",
" <td>6354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>DET -> ___</td>\n",
" <td>6147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>ADJ -> ___</td>\n",
" <td>5935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>231</th>\n",
" <td>SCONJ -> ___</td>\n",
" <td>5385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>254</th>\n",
" <td>VERB -> PUNCT</td>\n",
" <td>4842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>NOUN -> PUNCT</td>\n",
" <td>4632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>AUX -> ___</td>\n",
" <td>4016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>249</th>\n",
" <td>VERB -> NOUN</td>\n",
" <td>3772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>ADV -> PUNCT</td>\n",
" <td>3453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>CCONJ -> ___</td>\n",
" <td>3438</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" values counts\n",
"259 VERB -> ___ 11609\n",
"140 NOUN -> ___ 10416\n",
"53 ADV -> ___ 10127\n",
"175 PART -> ___ 9282\n",
"35 ADP -> ___ 8663\n",
"192 PRON -> ___ 8066\n",
"287 ___ -> PUNCT 6354\n",
"105 DET -> ___ 6147\n",
"17 ADJ -> ___ 5935\n",
"231 SCONJ -> ___ 5385\n",
"254 VERB -> PUNCT 4842\n",
"134 NOUN -> PUNCT 4632\n",
"70 AUX -> ___ 4016\n",
"249 VERB -> NOUN 3772\n",
"47 ADV -> PUNCT 3453\n",
"88 CCONJ -> ___ 3438"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_stats(voicelab_experiment_repository, ajn_connections_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17823c33-7065-43e6-9d2f-49a59fba26c1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"version": 3
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"file_extension": ".py",
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