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bvcc-voicemos2022 / bvcc-voicemos2022.py
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# coding=utf-8
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import datasets
_CITATION = """\
@misc{cooper2021generalization,
title={Generalization Ability of MOS Prediction Networks},
author={Erica Cooper and Wen-Chin Huang and Tomoki Toda and Junichi Yamagishi},
year={2021},
eprint={2110.02635},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset is for internal use only. For voicemos challenge
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://codalab.lisn.upsaclay.fr/competitions/695"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "INTERNAL"
class BvccDataset(datasets.GeneratorBasedBuilder):
"""BVCC dataset for voicemos challenge 2022"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="main_track", version=VERSION, description="main track dataset by wavfiles"),
datasets.BuilderConfig(name="main_track_listeners", version=VERSION, description="main track dataset by listener rating"),
datasets.BuilderConfig(name="ood_track", version=VERSION, description="Out of domain dataset"),
datasets.BuilderConfig(name="ood_track_unlabeled", version=VERSION, description="Out of domain dataset unlabeled"),
datasets.BuilderConfig(name="ood_track_listeners", version=VERSION, description="ood track dataset by listener rating"),
]
DEFAULT_CONFIG_NAME = "main_track" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if self.config.name == "main_track": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"path": datasets.Value("string"),
"audio" : datasets.Audio(sampling_rate=16_000),
"sysID": datasets.Value("string"),
"uttID": datasets.Value("string"),
"averaged rating": datasets.Value("float32"),
# These are the features of your dataset like images, labels ...
}
)
elif self.config.name == "main_track_listeners":
# sysID,uttID,rating,ignore,listenerinfo
# {}_AGERANGE_LISTENERID_GENDER_[ignore]_[ignore]_HEARINGIMPAIRMENT
features = datasets.Features(
{
"path": datasets.Value("string"),
"audio" : datasets.Audio(sampling_rate=16_000),
"sysID": datasets.Value("string"),
"uttID": datasets.Value("string"),
"rating": datasets.Value("int8"),
"age range": datasets.Value("string"),
"listener id": datasets.Value("string"),
"gender": datasets.Value("string"),
"hearing impairment": datasets.Value("string"),
}
)
elif self.config.name == "ood_track": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"path": datasets.Value("string"),
"audio" : datasets.Audio(sampling_rate=16_000),
"sysID": datasets.Value("string"),
"uttID": datasets.Value("string"),
"averaged rating": datasets.Value("float32"),
# These are the features of your dataset like images, labels ...
}
)
elif self.config.name == "ood_track_listeners":
# sysID,uttID,rating,ignore,listenerinfo
# {}_AGERANGE_LISTENERID_GENDER_[ignore]_[ignore]_HEARINGIMPAIRMENT
features = datasets.Features(
{
"path": datasets.Value("string"),
"audio" : datasets.Audio(sampling_rate=16_000),
"sysID": datasets.Value("string"),
"uttID": datasets.Value("string"),
"rating": datasets.Value("int8"),
"age range": datasets.Value("string"),
"listener id": datasets.Value("string"),
"gender": datasets.Value("string"),
"hearing impairment": datasets.Value("string"),
}
)
elif self.config.name == "ood_track_unlabeled":
# sysID,uttID,rating,ignore,listenerinfo
# {}_AGERANGE_LISTENERID_GENDER_[ignore]_[ignore]_HEARINGIMPAIRMENT
features = datasets.Features(
{
"path": datasets.Value("string"),
"audio" : datasets.Audio(sampling_rate=16_000),
"sysID": datasets.Value("string"),
"uttID": datasets.Value("string"),
}
)
else:
raise ValueError(f"invalid config name {self.config.name}")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# 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
data_dir = self.config.data_dir
if "listeners" in self.config.name:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir,"DATA/sets/TRAINSET"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir,"DATA/sets/DEVSET"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir,"DATA/sets/test.scp"),
"split": "test",
},
),
]
elif "unlabeled" in self.config.name:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir,"DATA/sets/unlabeled_mos_list.txt"),
"split": "train",
},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir,"DATA/sets/train_mos_list.txt"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir,"DATA/sets/val_mos_list.txt"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir,"DATA/sets/test.scp"),
"split": "test",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f.readlines()):
data = row.strip().split(',')
if self.config.name == "main_track":
sysID, uttID= data[0].split('-')
uttID= uttID.replace('.wav', '')
if len(data) > 1:
score = data[1]
else:
score = 999
# Yields examples as (key, example) tuples
path = os.path.join(self.config.data_dir, "DATA/wav/", data[0])
yield key, {
"path": path,
"audio": path,
"sysID": sysID,
"uttID": uttID,
"averaged rating": score,
}
elif self.config.name == "main_track_listeners":
if len(data) > 1:
rating = data[1]
sysID, path, rating, _, listenerinfo = data
_, age, listenrID, gender, _ , _, hearingImpairement= listenerinfo.split("_")
else:
sysID, uttID= data[0].split('-')
uttID= uttID.replace('.wav', '')
rating = 999
age= 999
listenrID = 999
gender = 999
path = data[0]
uttID = path.split('-')[-1]
uttID = uttID.replace(".wav", "")
path = os.path.join(self.config.data_dir, "DATA/wav/", path)
yield key, {
"path": path,
"audio": path,
"sysID": sysID,
"uttID": uttID,
"rating": rating,
"age range": age,
"listener id": listenrID,
"gender": gender,
"hearing impairment": hearingImpairement,
}
if self.config.name == "ood_track":
sysID, uttID= data[0].split('-')
uttID= uttID.replace('.wav', '')
if len(data) > 1:
score = data[1]
else:
score = 999
# Yields examples as (key, example) tuples
path = os.path.join(self.config.data_dir, "DATA/wav/", data[0])
yield key, {
"path": path,
"audio": path,
"sysID": sysID,
"uttID": uttID,
"averaged rating": score,
}
elif self.config.name == "ood_track_listeners":
if len(data) > 1:
rating = data[1]
sysID, path, rating, _, listenerinfo = data
_, age, listenrID, gender, _ , _, hearingImpairement= listenerinfo.split("_")
else:
sysID, uttID= data[0].split('-')
uttID= uttID.replace('.wav', '')
path = data[0]
rating = 999
age= 999
listenrID = 999
gender = 999
uttID = path.split('-')[-1]
uttID = uttID.replace(".wav", "")
path = os.path.join(self.config.data_dir, "DATA/wav/", path)
yield key, {
"path": path,
"audio": path,
"sysID": sysID,
"uttID": uttID,
"rating": rating,
"age range": age,
"listener id": listenrID,
"gender": gender,
"hearing impairment": hearingImpairement,
}
if self.config.name == "ood_track_unlabeled":
sysID, uttID= data[0].strip().split('-')
uttID= uttID.replace('.wav', '')
# Yields examples as (key, example) tuples
path = os.path.join(self.config.data_dir, "DATA/wav/", data[0].strip())
yield key, {
"path": path,
"audio": path,
"sysID": sysID,
"uttID": uttID,
}