| |
| |
| """ |
| Created on Tue Apr 18 16:14:58 2023 |
| |
| @author: lin.kinwahedward |
| """ |
| |
| |
| import datasets |
| import numpy as np |
| import os |
| |
| """The Audio, Speech, and Vision Processing Lab - Emotional Sound Database (ASVP - ESD)""" |
|
|
| _CITATION = """\ |
| @article{gsj2020asvpesd, |
| title={ASVP-ESD:A dataset and its benchmark for emotion recognition using both speech and non-speech utterances}, |
| author={Dejoli Tientcheu Touko Landry and Qianhua He and Haikang Yan and Yanxiong Li}, |
| journal={Global Scientific Journals}, |
| volume={8}, |
| issue={6}, |
| pages={1793--1798}, |
| year={2020} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| ASVP-ESD |
| """ |
|
|
| _HOMEPAGE = "https://www.kaggle.com/datasets/dejolilandry/asvpesdspeech-nonspeech-emotional-utterances?resource=download-directory" |
|
|
| _LICENSE = "CC BY 4.0" |
|
|
| _DATA_URL = "https://drive.google.com/uc?export=download&id=1aKnr5kXgUjMB5MAhUTZmm3b8gjP8qA3O" |
|
|
|
|
| id2labels = { |
| 1: "boredom,sigh", |
| 2: "neutral,calm", |
| 3: "happy,laugh,gaggle", |
| 4: "sad,cry", |
| 5: "angry,grunt,frustration", |
| 6: "fearful,scream,panic", |
| 7: "disgust,dislike,contempt", |
| 8: "surprised,gasp,amazed", |
| 9: "excited", |
| 10: "pleasure", |
| 11: "pain,groan", |
| 12: "disappointment,disapproval", |
| 13: "breath" |
| } |
|
|
| |
| |
| class ASVP_ESD_Config(datasets.BuilderConfig): |
| |
| def __init__(self, name, description, homepage, data_url): |
| |
| super(ASVP_ESD_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 |
| |
| |
| class ASVP_ESD(datasets.GeneratorBasedBuilder): |
| |
| BUILDER_CONFIGS = [ASVP_ESD_Config( |
| name = "ASVP_ESD", |
| 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 = [ |
| "boredom,sigh", |
| "neutral,calm", |
| "happy,laugh,gaggle", |
| "sad,cry", |
| "angry,grunt,frustration", |
| "fearful,scream,panic", |
| "disgust,dislike,contempt", |
| "surprised,gasp,amazed", |
| "excited", |
| "pleasure", |
| "pain,groan", |
| "disappointment,disapproval", |
| "breath" |
| ]) |
| } |
| ) |
| |
| |
| 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 |
| actors = np.arange(129) |
| for dir_name in actors: |
| |
| dir_path = dataset_path + "/ASVP_ESD/Speech/actor_" + str(dir_name) |
| for filename in os.listdir(dir_path): |
| if filename.endswith(".wav"): |
| labels = filename[:-4].split("_") |
| yield key, { |
| "path": dir_path + "/" + filename, |
| |
| "audio": dir_path + "/" + filename, |
| "label": id2labels[int(labels[0])], |
| } |
| key += 1 |
| |
| dir_path = dataset_path + "/ASVP_ESD/NonSpeech/actor_" + str(dir_name) |
| for filename in os.listdir(dir_path): |
| if filename.endswith(".wav"): |
| labels = filename[:-4].split("_") |
| yield key, { |
| "path": dir_path + "/" + filename, |
| |
| "audio": dir_path + "/" + filename, |
| "label": id2labels[int(labels[0])], |
| } |
| key += 1 |
| |