add pylatex to cfg

data/ekorpus/exp.ini

@@ -1,6 +1,6 @@
 [EXP]
 root = ./
-name = results/exp_ekorpus_aud
+name = results/exp_ekorpus_wavlm
 save = True
 [DATA]
 databases = ['train', 'test']
@@ -21,7 +21,7 @@ labels = ['neutral', 'anger', 'joy', 'sadness']
 # which of the data splits to re-sample: train, test or all (both)
 sample_selection = all
 [FEATS]
-type = ['audmodel']
+type = ['wavlm-large']
 scale = standard
 [MODEL]
 type = svm

data/emofilm/exp.ini

@@ -1,6 +1,6 @@
 [EXP]
 root = ./
-name = results/exp_emofilm_wavlm
+name = results/exp_emofilm_wavlm_4
 save = True
 [DATA]
 databases = ['train', 'dev', 'test']
@@ -20,7 +20,7 @@ test.absolute_path = False
 test.split_strategy = test
 target = emotion
 no_reuse = True
-labels =['fea', 'con', 'hap', 'ang', 'sad']
+labels =['fea', 'hap', 'ang', 'sad']
 [FEATS]
 type = ['wavlm-large']
 scale = standard

data/meld/README.md

@@ -2,7 +2,7 @@
 
 Multimodal EmotionLines Dataset (MELD) has been created by enhancing and extending EmotionLines dataset. MELD contains the same dialogue instances available in EmotionLines, but it also encompasses audio and visual modality along with text. MELD has more than 1400 dialogues and 13000 utterances from Friends TV series. Multiple speakers participated in the dialogues. Each utterance in a dialogue has been labeled by any of these seven emotions -- Anger, Disgust, Sadness, Joy, Neutral, Surprise and Fear. MELD also has sentiment (positive, negative and neutral) annotation for each utterance [1].
 
-This worfklow assumes the dataset is download from [2].
+The dataset is available in [3]. This worfklow assumes the dataset is download from [2].
 
 ```bash
 $ python3 process_database.py
@@ -13,5 +13,6 @@ $ python3 -m nkululeko.nkululeko --config data/meld/exp.ini
 References:  
 [1] S. Poria, D. Hazarika, N. Majumder, G. Naik, R. Mihalcea,
 E. Cambria. MELD: A Multimodal Multi-Party Dataset
-for Emotion Recognition in Conversation. (2018)
-[2] https://github.com/bagustris/multilingual_speech_emotion_recognition_datasets
+for Emotion Recognition in Conversation. (2018)  
+[2] https://github.com/bagustris/multilingual_speech_emotion_recognition_datasets  
+[3] https://github.com/declare-lab/MELD

data/meld/exp.ini

@@ -1,6 +1,6 @@
 [EXP]
 root = ./
-name = results/exp_meld_wavlm
+name = results/exp_meld_audmodel
 save = True
 ; epochs = 10
 [DATA]
@@ -28,7 +28,7 @@ labels = ['anger', 'joy', 'neutral', 'sadness']
 [RESAMPLE]
 sample_selection = all
 [FEATS]
-type = ['wavlm-large']
+type = ['audmodel']
 scale = standard
 [MODEL]
 type = svm

setup.cfg

@@ -42,5 +42,6 @@ install_requires =
     transformers
     umap-learn
     xgboost
+    pylatex
 python_requires = >=3.6
 include_package_data = False