Create AESDD.py

AESDD.py

@@ -0,0 +1,151 @@
+# coding=utf-8
+# Copyright 2022 The PolyAI and HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#------------------------------------------------------------------------------
+# Standard Libraries
+import datasets
+import os
+#------------------------------------------------------------------------------
+"""Acted Emotional Speech Dynamic Database v1.0"""
+
+_CITATION = """\
+@article{vryzas2018speech,
+  title={Speech emotion recognition for performance interaction},
+  author={Vryzas, Nikolaos and Kotsakis, Rigas and Liatsou, Aikaterini and Dimoulas, Charalampos A and Kalliris, George},
+  journal={Journal of the Audio Engineering Society},
+  volume={66},
+  number={6},
+  pages={457--467},
+  year={2018},
+  publisher={Audio Engineering Society}
+}
+"""
+
+_DESCRIPTION = """\
+AESDD v1.0 was created on October 2017 in the Laboratory of Electronic Media, 
+School of Journalism and Mass Communications, Aristotle University of Thessaloniki, 
+for the needs of Speech Emotion Recognition research of the Multidisciplinary Media & 
+Mediated Communication Research Group (M3C, http://m3c.web.auth.gr/).
+
+For the creation of v.1 of the database, 5 (3 female and 2 male) professional actors were recorded. 
+19 utterances of ambiguous out of context emotional content were chosen. 
+The actors acted these 19 utterances in every one of the 5 chosen emotions. 
+One extra improvised utterance was added for every actor and emotion. 
+The guidance of the actors and the choice of the final recordings were supervised by 
+a scientific expert in dramatology. For some of the utterances, more that one takes were qualified. 
+Consequently, around 500 utterances occured in the final database.
+"""
+
+_HOMEPAGE = "http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/"
+
+_LICENSE = "CC BY 4.0"
+
+_DATA_URL = "https://drive.google.com/uc?export=download&id=1-pelMaCrfwoUCmwxUtlacRUBwbFnXlXA"
+
+#------------------------------------------------------------------------------
+# Define Dataset Configuration (e.g., subset of dataset, but it is not used here.)
+class AESDDConfig(datasets.BuilderConfig):
+    #--------------------------------------------------------------------------
+    def __init__(self, name, description, homepage, data_url):
+        
+        super(AESDDConfig, 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 AESDD(datasets.GeneratorBasedBuilder):
+    #--------------------------------------------------------------------------
+    BUILDER_CONFIGS = [AESDDConfig(
+        name = "AESDD",
+        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
+            4) utterance: 1,2,...,20
+            5) speaker id
+    '''
+    def _info(self):
+        
+        features = datasets.Features(
+                {
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate = 441000),
+                    "label": datasets.ClassLabel(
+                        names = [
+                            "anger",
+                            "disgust",
+                            "fear",
+                            "happiness",
+                            "sadness",
+                        ]),
+                    "utterance": datasets.Value("float"),
+                    "speaker": datasets.Value("float")
+                }
+            )
+        
+        # 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(
+                # set the whole dataset as "training set". No worry, can split later! 
+                name = datasets.Split.TRAIN,
+                # _generate_examples()'s parameters, thus name must match!
+                gen_kwargs = {
+                    "dataset_path": dataset_path
+                },
+            )
+        ]
+    #--------------------------------------------------------------------------   
+    def _generate_examples(self, dataset_path):
+        '''
+            Get the audio file and set the corresponding labels
+        '''
+        key = 0
+        for dir_name in ["anger", "disgust", "fear", "happiness", "sadness"]:
+            dir_path = dataset_path + "/AESDD/" + dir_name
+            for file_name in os.listdir(dir_path):
+                if file_name.endswith(".wav"):
+                    yield key, {
+                        "path": dir_path + "/" + file_name,
+                        # huggingface dataset's will use soundfile to read the audio file
+                        "audio": dir_path + "/" + file_name,
+                        "label": dir_name,
+                        "utterance": float(file_name[1:3]),
+                        "speaker": float(file_name[file_name.find("(")+1:file_name.find(")")]),
+                    }
+                    key += 1    
+#------------------------------------------------------------------------------