add new criterion

app/criteria/comparison_whole_speech/criterion.py

@@ -0,0 +1,84 @@
+from bson import ObjectId
+
+from app.root_logger import get_root_logger
+from app.localisation import *
+from ..criterion_base import BaseCriterion
+from ..criterion_result import CriterionResult
+from app.audio import Audio
+from app.presentation import Presentation
+from app.utils import normalize_text
+from ..text_comparison import Doc2VecEvaluator
+
+logger = get_root_logger('web')
+
+
+class ComparisonWholeSpeechCriterion(BaseCriterion):
+    PARAMETERS = dict(
+        vector_size=int.__name__,
+        window=int.__name__,
+        min_count=int.__name__,
+        workers=int.__name__,
+        epochs=int.__name__,
+        dm=int.__name__,
+    )
+
+    def __init__(self, parameters, dependent_criteria, name=''):
+        super().__init__(
+            name=name,
+            parameters=parameters,
+            dependent_criteria=dependent_criteria,
+        )
+        vector_size = self.parameters['vector_size']
+        window = self.parameters['window']
+        min_count = self.parameters['min_count']
+        workers = self.parameters['workers']
+        epochs = self.parameters['epochs']
+        dm = self.parameters['dm']
+
+        self.model = Doc2VecEvaluator(vector_size, window, min_count, workers, epochs, dm)
+
+    @property
+    def description(self):
+        return {
+            "Критерий": t(self.name),
+            "Описание": t("Проверяет, что тема доклада студента совпадает с темой презентации"),
+            "Оценка": t(
+                "1, если тема доклада и презентации совпадают не менее, чем на 40%, иначе 2.5 * k, где k - степень соответствия темы доклада теме презентации")
+        }
+
+    def apply(self, audio: Audio, presentation: Presentation, training_id: ObjectId,
+              criteria_results: dict) -> CriterionResult:
+        normalized_speech = []
+        normalized_slides = []
+
+        for i in range(len(audio.audio_slides)):
+            # Список сказанных на слайде слов
+            current_slide_speech = audio.audio_slides[i].recognized_words
+            # Очистка списка от timestamp-ов и probability
+            current_slide_speech = list(map(lambda x: x.word.value, current_slide_speech))
+            #  Нормализация текста
+            current_slide_speech = " ".join(normalize_text(current_slide_speech))
+            if current_slide_speech != "":
+                normalized_speech.append(current_slide_speech)
+
+            # Текст из слайда презентации
+            current_slide_text = presentation.slides[i].words
+            # Нормализация текста слайда
+            current_slide_text = " ".join(normalize_text(current_slide_text.split()))
+            if current_slide_text != "":
+                normalized_slides.append(current_slide_text)
+
+        if len(normalized_speech) == 0:
+            return CriterionResult(0, "Тренажер не зафиксировал, что вы что-то говорили")
+        normalized_speech_text = " ".join(normalized_speech)
+
+        if len(normalized_slides) == 0:
+            return CriterionResult(0, "Загруженная вами презентация не содержит текста")
+        normalized_slides_text = " ".join(normalized_slides)
+
+        self.model.train_model([normalized_speech_text, normalized_slides_text])
+
+        score = 2.5 * self.model.evaluate_semantic_similarity(normalized_speech_text, normalized_slides_text)
+        logger.info(f"Score={score}")
+        return CriterionResult(1 if score >= 1 else score,
+                               "Ваша речь соответствует тексту презентации" if score >= 1 else "Ваша речь не полностью соответствует теме презентации")

app/criteria/criterions.py

@@ -9,3 +9,4 @@
 from .speech_pace.criterion import SpeechPaceCriterion
 from .strict_speech_duration.criterion import StrictSpeechDurationCriterion
 from .comparison_speech_slides.criterion import ComparisonSpeechSlidesCriterion
+from .comparison_whole_speech.criterion import ComparisonWholeSpeechCriterion

app/criteria/preconfigured_criterions.py

@@ -9,7 +9,8 @@
 
 from criteria import (FillersNumberCriterion, FillersRatioCriterion,
                       SpeechIsNotInDatabaseCriterion, SpeechPaceCriterion,
-                      StrictSpeechDurationCriterion, ComparisonSpeechSlidesCriterion)
+                      StrictSpeechDurationCriterion, ComparisonSpeechSlidesCriterion,
+                      ComparisonWholeSpeechCriterion)
 
 from .utils import DEFAULT_FILLERS
 from .utils import DEFAULT_SKIP_SLIDES
@@ -149,6 +150,19 @@
         name="ComparisonSpeechSlidesCriterion",
         parameters={"skip_slides": DEFAULT_SKIP_SLIDES},
         dependent_criteria=[],
+    ),
+
+    ComparisonWholeSpeechCriterion(
+        name="ComparisonWholeSpeechCriterion",
+        parameters={
+            "vector_size": 200,
+            "window": 5,
+            "min_count": 3,
+            "workers": 4,
+            "epochs": 40,
+            "dm": 0
+        },
+        dependent_criteria=[],
     )
 
 ]

app/criteria/text_comparison.py

@@ -1,5 +1,6 @@
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+from gensim.models.doc2vec import Doc2Vec, TaggedDocument
 
 
 class SlidesSimilarityEvaluator:
@@ -15,3 +16,22 @@ def evaluate_semantic_similarity(self, text1: str, text2: str) -> float:
         similarity = cosine_similarity(vector1, vector2)[0][0]
 
         return round(similarity, 3)
+
+
+class Doc2VecEvaluator:
+    def __init__(self, vector_size: int, window: int, min_count: int, workers: int, epochs: int, dm: int):
+        self.model = Doc2Vec(vector_size=vector_size, window=window, min_count=min_count, workers=workers,
+                             epochs=epochs, dm=dm)
+
+    def train_model(self, documents: list):
+        tagged_documents = [TaggedDocument(words=doc.split(), tags=[i]) for i, doc in enumerate(documents)]
+        self.model.build_vocab(tagged_documents)
+        self.model.train(tagged_documents, total_examples=self.model.corpus_count, epochs=self.model.epochs)
+
+    def evaluate_semantic_similarity(self, text1: str, text2: str) -> float:
+        text1 = text1.split()
+        text2 = text2.split()
+
+        similarity = self.model.wv.n_similarity(text1, text2)
+
+        return round(similarity, 3)

app/criteria_pack/preconfigured_pack.py

@@ -34,7 +34,8 @@
         [['SimpleNumberSlidesCriterion', 0.05],
          ['SlidesCheckerCriterion', 0.95]],
     'ComparisonPack':
-    [['ComparisonSpeechSlidesCriterion', 1]]
+    [['ComparisonSpeechSlidesCriterion', 0.5],
+     ['ComparisonWholeSpeechCriterion', 0.5]]
 }
 
 

app/feedback_evaluator.py

@@ -1,7 +1,7 @@
 import json
 
 from app.criteria import SpeechDurationCriterion, SpeechPaceCriterion, FillersRatioCriterion, FillersNumberCriterion, \
-    StrictSpeechDurationCriterion, ComparisonSpeechSlidesCriterion
+    StrictSpeechDurationCriterion, ComparisonSpeechSlidesCriterion, ComparisonWholeSpeechCriterion
 
 
 class Feedback: