Create MELD_Audio_3Labels.py

MELD_Audio_3Labels.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr 25 13:21:54 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"
+
+# The actual place where the data is stored!
+_DATA_URL = "https://drive.google.com/uc?export=download&id=1J8wBcuXD-E98k3Ls3oE59xT7Qd6m1qjY"
+
+#------------------------------------------------------------------------------
+# 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_3Labels(datasets.GeneratorBasedBuilder):
+    #--------------------------------------------------------------------------
+    BUILDER_CONFIGS = [DS_Config(
+        name = "MELD_Audio_3Labels",
+        description = _DESCRIPTION,
+        homepage = _HOMEPAGE,
+        data_url = _DATA_URL
+        )]
+    #--------------------------------------------------------------------------
+    '''
+        Define the "column header" (feature) of a datum.
+        2 Features:
+            1) audio samples
+            2) emotion label
+    '''
+    def _info(self):
+        
+        features = datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate = 16000),
+                    "label": datasets.ClassLabel(
+                        names = [
+                            "neutral",
+                            "joy",
+                            "anger"
+                        ])
+                }
+            )
+        
+        # return dataset info and data feature info
+        return datasets.DatasetInfo(
+            description = _DESCRIPTION,
+            features = features,
+            homepage = _HOMEPAGE,
+            citation = _CITATION,
+        )
+    #--------------------------------------------------------------------------
+    def _split_generators(self, dl_manager):
+        '''
+            Split the dataset into datasets.Split.{"TRAIN", "VALIDATION", "TEST", "ALL"}
+            
+            The dataset can be further modified, please see below link for details.            
+            https://huggingface.co/docs/datasets/process
+        '''
+        
+        # Get the dataset and store at the machine where this script is executed!
+        dataset_path = dl_manager.download_and_extract(self.config.data_url)
+        
+        # "audio_path" and "csv_path" would be the parameters passed to def _generate_examples()
+        return [
+            datasets.SplitGenerator(
+                    name = datasets.Split.TRAIN,
+                    gen_kwargs = {"audio_path": dataset_path + "/MELD_Audio_3Labels/train/",
+                                  "csv_path": dataset_path + "/MELD_Audio_3Labels/train.csv"
+                                  },
+            ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs = {"audio_path": dataset_path + "/MELD_Audio_3Labels/dev/",
+                                  "csv_path": dataset_path + "/MELD_Audio_3Labels/dev.csv"
+                                  },
+            ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs = {"audio_path": dataset_path + "/MELD_Audio_3Labels/test/",
+                                  "csv_path": dataset_path + "/MELD_Audio_3Labels/test.csv"
+                                  },
+            ),
+        ]
+    #--------------------------------------------------------------------------
+    def _generate_examples(self, audio_path, csv_path):
+        '''
+            Get the audio file and set the corresponding labels
+            
+            Must execute till yield, otherwise, error will occur!
+        '''
+        key = 0
+        with open(csv_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 = "dia" + dialogue_id + "_utt" + utterance_id + ".mp3"
+                yield key, {
+                    # huggingface dataset's will use soundfile to read the audio file
+                    "audio": audio_path + filename,
+                    "label": emotion,
+                }
+                key += 1
+#------------------------------------------------------------------------------