bvcc-voicemos2022.py

# 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,
                    }