Delete aesdd.py

aesdd.py

@@ -1,151 +0,0 @@
-# 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    
-#------------------------------------------------------------------------------