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| 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" |
|
|
| |
| |
| 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 |
| |
| |
| 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 = 44100), |
| "label": datasets.ClassLabel( |
| names = [ |
| "anger", |
| "disgust", |
| "fear", |
| "happiness", |
| "sadness", |
| ]), |
| "utterance": datasets.Value("float"), |
| "speaker": datasets.Value("float") |
| } |
| ) |
| |
| |
| 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 = { |
| "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, |
| |
| "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 |
| |
|
|
