Add scriptified extract_emotion_labels.py

1_extract_emotion_labels.ipynb

@@ -257,7 +257,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,

src/extract_emotion_labels.py

@@ -0,0 +1,76 @@
+"""
+This script parses the dataset, extracts label and stores it at one place
+
+Run this script from root as python src/extract_emotion_labels.py
+"""
+
+import re
+import os
+import pandas as pd
+
+
+def extract_info():
+    """
+    returns info_dict containing important info from the IEMOCAP dataset
+    such as start time, end time, emotion labels etc.
+
+    extract_info: None -> Dict
+    """
+    info_dict = {'start_times': [], 'end_times': [], 'wav_file_names': [],
+                 'emotions': [], 'vals': [], 'acts': [], 'doms': []}
+
+    # regex used to identify useful info in the dataset files
+    info_line = re.compile(r'\[.+\]\n', re.IGNORECASE)
+    for sess in range(1, 6):
+        emo_evaluation_dir = 'data/IEMOCAP_full_release/Session{}/dialog/EmoEvaluation/'.format(sess)
+        # Only include the session files
+        evaluation_files = [l for l in os.listdir(emo_evaluation_dir)
+                            if 'Ses' in l]
+        for file in evaluation_files:
+            with open(emo_evaluation_dir + file) as f:
+                content = f.read()
+            # grab the important stuff
+            info_lines = re.findall(info_lines, content)
+            for line in info_line[1:]:  # skipping the first header line
+                # Refer to the dataset to see how `line` looks like
+                start_end_time, wav_file_name, emotion, val_act_dom = \
+                    line.strip().split('\t')
+                start_time, end_time = start_end_time[1:-1].split('-')
+                val, act, dom = val_act_dom[1:-1].split(',')
+                val, act, dom = float(val), float(act), float(dom)
+                start_time, end_time = float(start_time), float(end_time)
+                info_dict['start_times'].append(start_time)
+                info_dict['end_times'].append(end_time)
+                info_dict['wav_file_names'].append(wav_file_name)
+                info_dict['emotions'].append(emotion)
+                info_dict['vals'].append(val)
+                info_dict['acts'].append(act)
+                info_dict['doms'].append(dom)
+    return info_dict
+
+
+def compile_dataset(info_dict):
+    """
+    creates a csv file from info_dict which will serve as the dataset
+
+    compile_dataset: Dict -> None
+    """
+    df_iemocap = pd.DataFrame(columns=['start_time', 'end_time', 'wav_file', 'emotion', 'val', 'act', 'dom'])
+
+    df_iemocap['start_time'] = info_dict['start_times']
+    df_iemocap['end_time'] = info_dict['end_times']
+    df_iemocap['wav_file'] = info_dict['wav_file_names']
+    df_iemocap['emotion'] = info_dict['emotions']
+    df_iemocap['val'] = info_dict['vals']
+    df_iemocap['act'] = info_dict['acts']
+    df_iemocap['dom'] = info_dict['doms']
+    # Finally, save to a file
+    df_iemocap.to_csv('data/pre-processed/df_iemocap.csv', index=False)
+
+
+def main():
+    compile_dataset(extract_info())
+
+
+if __name__ == '__main__':
+    main()