voxceleb/voxceleb.py

# 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 sys

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# 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 = {
    "voxceleb": "https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxceleb_v0.2_blind.zip"
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class SLUEVoxceleb(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="voxceleb", version=VERSION, description="This part of my dataset covers a first domain"),
    ]

    #DEFAULT_CONFIG_NAME = "first_domain"  # 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 == "voxceleb": # This is the name of the configuration selected in BUILDER_CONFIGS above
            # get the current split

            features = datasets.Features(
        {
            "id": datasets.Value("string"),
            "normalized_text": datasets.Value("string"),
            "speaker_id": datasets.Value("int32"),
            "split": datasets.Value("string"),
            "sentiment": datasets.Value("string"),
            "start_second": datasets.Value("float32"),
            "end_second": datasets.Value("float32"),
            "audio_path": datasets.Value("string"),
        }
    )              

        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):
        # 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
        urls = _URLS[self.config.name]
        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": os.path.join(data_dir, "slue-voxceleb", "slue-voxceleb_fine-tune.tsv"),
                    "split": "fine-tune",
                    "audio_dir": os.path.join(data_dir, "slue-voxceleb", "fine-tune_raw"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "slue-voxceleb", "slue-voxceleb_dev.tsv"),
                    "split": "dev",
                    "audio_dir": os.path.join(data_dir, "slue-voxceleb", "dev_raw"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "slue-voxceleb", "slue-voxceleb_test_blind.tsv"),
                    "split": "test-blind",
                    "audio_dir": os.path.join(data_dir, "slue-voxceleb", "test_raw"),
                },
            ),
        ]

            
    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split, audio_dir):
        # read tsv file by rows
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for row in reader:
                speaker_id = row["speaker_id"]
                if not speaker_id.isdigit():
                    speaker_id = -1
                else:
                    speaker_id = int(speaker_id)
                
                audio_file = f"{row['id']}.flac"
                if split == "test-blind":
                    yield row["id"], {
                        "id": row["id"],
                        "normalized_text": None,
                        "speaker_id": speaker_id,
                        "split": split,
                        "sentiment": None,
                        "start_second": float(row["start_second"]),
                        "end_second": float(row["end_second"]),
                        "audio_path": os.path.join(audio_dir, audio_file),
                    }
                elif split == "fine-tune" or split == "dev":
                    yield row["id"], {
                        "id": row["id"],
                        "normalized_text": row["normalized_text"],
                        "speaker_id": speaker_id,
                        "split": split,
                        "sentiment": row["sentiment"],
                        "start_second": float(row["start_second"]),
                        "end_second": float(row["end_second"]),
                        "audio_path": os.path.join(audio_dir, audio_file), 
                    }