Create MELD-Audio.py

MELD-Audio.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Apr 21 12:49:21 2023
+
+@author: lin.kinwahedward
+"""
+#------------------------------------------------------------------------------
+# Standard Libraries
+import datasets
+import csv
+#------------------------------------------------------------------------------
+"""The Audio, Speech, and Vision Processing Lab - Emotional Sound Database (ASVP - ESD)"""
+
+_CITATION = """\
+@article{poria2018meld,
+  title={Meld: A multimodal multi-party dataset for emotion recognition in conversations},
+  author={Poria, Soujanya and Hazarika, Devamanyu and Majumder, Navonil and Naik, Gautam and Cambria, Erik and Mihalcea, Rada},
+  journal={arXiv preprint arXiv:1810.02508},
+  year={2018}
+}
+@article{chen2018emotionlines,
+  title={Emotionlines: An emotion corpus of multi-party conversations},
+  author={Chen, Sheng-Yeh and Hsu, Chao-Chun and Kuo, Chuan-Chun and Ku, Lun-Wei and others},
+  journal={arXiv preprint arXiv:1802.08379},
+  year={2018}
+}
+"""
+
+_DESCRIPTION = """\
+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.
+
+This dataset is slightly modified, so that it concentrates on Emotion recognition in audio input only.
+"""
+
+_HOMEPAGE = "https://affective-meld.github.io/"
+
+_LICENSE = "CC BY 4.0"
+
+_DATA_URL = "https://drive.google.com/uc?export=download&id=1TPr9v5Vz1qQuxPWcr8RedfuQvLyuG1lm"
+
+#------------------------------------------------------------------------------
+# Define Dataset Configuration (e.g., subset of dataset, but it is not used here.)
+class DS_Config(datasets.BuilderConfig):
+    #--------------------------------------------------------------------------
+    def __init__(self, name, description, homepage, data_url):
+        
+        super(DS_Config, self).__init__(
+            name = self.name,
+            version = datasets.Version("1.0.0"),
+            description = self.description,
+        )
+        self.name = name
+        self.description = description
+        self.homepage = homepage
+        self.data_url = data_url
+#------------------------------------------------------------------------------
+# Define Dataset Class
+class MELD_Audio(datasets.GeneratorBasedBuilder):
+    #--------------------------------------------------------------------------
+    BUILDER_CONFIGS = [DS_Config(
+        name = "MELD_Audio",
+        description = _DESCRIPTION,
+        homepage = _HOMEPAGE,
+        data_url = _DATA_URL
+        )]
+    #--------------------------------------------------------------------------
+    '''
+        Define the "column header" (feature) of a datum.
+        3 Features:
+            1) path_to_file
+            2) audio samples
+            3) emotion label
+    '''
+    def _info(self):
+        
+        features = datasets.Features(
+                {
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate = 16000),
+                    "label": datasets.ClassLabel(
+                        names = [
+                            "neutral",
+                            "joy",
+                            "sadness",
+                            "anger",
+                            "surprise",
+                            "fear",
+                            "disgust"
+                        ])
+                }
+            )
+        
+        # return dataset info and data feature info
+        return datasets.DatasetInfo(
+            description = _DESCRIPTION,
+            features = features,
+            homepage = _HOMEPAGE,
+            citation = _CITATION,
+        )
+    #--------------------------------------------------------------------------
+    def _split_generators(self, dl_manager):
+        
+        dataset_path = dl_manager.download_and_extract(self.config.data_url)
+        
+        return [
+            datasets.SplitGenerator(
+                    name = datasets.Split.TRAIN,
+                    gen_kwargs = {"split_key": "train", "dataset_path": dataset_path},
+            ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"split_key": "dev", "dataset_path": dataset_path},
+            ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"split_key": "test", "dataset_path": dataset_path},
+            )
+        ]
+    #--------------------------------------------------------------------------
+    def _generate_examples(self, split_key, dataset_path):
+        '''
+            Get the audio file and set the corresponding labels
+        '''
+        key = 0
+        text_path = "./" + split_key + ".csv"
+        with open(text_path, encoding = "utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file, delimiter = ",", skipinitialspace=True)
+            next(csv_reader)
+            for row in csv_reader:
+                _, _, _, emotion, _, dialogue_id, utterance_id, _, _, _, _ = row
+                filename = "./" + split_key + "/dia" + dialogue_id + "_utt" + utterance_id + ".mp3"
+                yield key, {
+                    "path": dataset_path + "/" + filename,
+                    # huggingface dataset's will use soundfile to read the audio file
+                    "audio": dataset_path + "/" + filename,
+                    "label": emotion,
+                }
+                key += 1
+#------------------------------------------------------------------------------
+
+
+