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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 18 16:14:58 2023
@author: lin.kinwahedward
"""
#------------------------------------------------------------------------------
# Standard Libraries
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"
}
#------------------------------------------------------------------------------
# Define Dataset Configuration (e.g., subset of dataset, but it is not used here.)
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
#------------------------------------------------------------------------------
# Define Dataset Class
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 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
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,
# huggingface dataset's will use soundfile to read the audio file
"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,
# huggingface dataset's will use soundfile to read the audio file
"audio": dir_path + "/" + filename,
"label": id2labels[int(labels[0])],
}
key += 1
#------------------------------------------------------------------------------ |