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# Copyright 2020 The 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.
"""DAPS Dataset"""
import glob
import os
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{mysore2014can,
title={Can we automatically transform speech recorded on common consumer devices in real-world environments into professional production quality speech?—a dataset, insights, and challenges},
author={Mysore, Gautham J},
journal={IEEE Signal Processing Letters},
volume={22},
number={8},
pages={1006--1010},
year={2014},
publisher={IEEE}
}
"""
# You can copy an official description
_DESCRIPTION = """\
The DAPS (Device and Produced Speech) dataset is a collection of aligned versions of professionally produced studio speech recordings and recordings of the same speech on common consumer devices (tablet and smartphone) in real-world environments. It has 15 versions of audio (3 professional versions and 12 consumer device/real-world environment combinations). Each version consists of about 4 1/2 hours of data (about 14 minutes from each of 20 speakers).
"""
_HOMEPAGE = "https://ccrma.stanford.edu/~gautham/Site/daps.html"
_LICENSE = "Creative Commons Attribution Non Commercial 4.0 International"
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = "https://zenodo.org/record/4660670/files/daps.tar.gz"
class DapsDataset(datasets.GeneratorBasedBuilder):
"""The DAPS (Device and Produced Speech) dataset is a collection of aligned versions of professionally produced studio speech recordings and recordings of the same speech on common consumer devices (tablet and smartphone) in real-world environments."""
VERSION = datasets.Version("2.12.0")
DEFAULT_CONFIG_NAME = "aligned_examples" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
features = datasets.Features(
{
"recording_environment": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"script_id": datasets.Value("string"),
"clean_path": datasets.Value("string"),
"produced_path": datasets.Value("string"),
"device_path": datasets.Value("string"),
"clean_audio": datasets.Audio(sampling_rate=44_100),
"produced_audio": datasets.Audio(sampling_rate=44_100),
"device_audio": datasets.Audio(sampling_rate=44_100),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
urls = _URLS
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
gt = ["clean", "produced"]
environments = [
"ipad_balcony1",
"ipad_livingroom1",
"ipadflat_office1",
"ipad_bedroom1",
"ipad_office1",
"iphone_balcony1",
"ipad_confroom1",
"ipad_office2",
"iphone_bedroom1",
"ipad_confroom2",
"ipadflat_confroom1",
"iphone_livingroom1",
]
# example path: daps/iphone_bedroom1/m8_script5_iphone_bedroom1.wav
for env in environments:
for device_path in glob.glob(os.path.join(filepath, env) + "/*.wav"):
speaker_id = os.path.basename(device_path).split("_")[-4]
script_id = os.path.basename(device_path).split("_")[-3]
clean_path = device_path.replace(env, "clean")
produced_path = device_path.replace(env, "produced")
with open(clean_path, "rb") as f:
clean_audio = {"path": clean_path, "bytes": f.read()}
with open(produced_path, "rb") as f:
produced_audio = {"path": produced_path, "bytes": f.read()}
with open(device_path, "rb") as f:
device_audio = {"path": device_path, "bytes": f.read()}
yield f"{speaker_id}_{script_id}_{env}", {
"recording_environment": env,
"speaker_id": speaker_id,
"script_id": script_id,
"clean_path": clean_path,
"produced_path": produced_path,
"device_path": device_path,
"clean_audio": clean_audio,
"produced_audio": produced_audio,
"device_audio": device_audio,
}
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