nena_speech_1_0_test.py

# coding=utf-8
# Copyright 2023 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.
""" NENA Speech Dataset"""


import csv
import os

import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm

from .dialects import DIALECTS
from .release_stats import STATS

_HOMEPAGE = "https://nena.ames.cam.ac.uk/"

_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"

_BASE_URL = "https://huggingface.co/datasets/mnazari/nena_speech_1_0_test/resolve/main/"

_AUDIO_URL = _BASE_URL + "audio/{dialect}/{split}.tar"

_TRANSCRIPT_URL = _BASE_URL + "transcript/{dialect}/{split}.tsv"

import datasets

class NENASpeechConfig(datasets.BuilderConfig):
    """BuilderConfig for NENASpeech."""
    def __init__(self, name, version, **kwargs):
        self.dialect = kwargs.pop("dialect", None)
        self.release_date = kwargs.pop("release_date", None)
        self.num_examples = kwargs.pop("num_examples", None)
        self.num_speakers = kwargs.pop("num_speakers", None)
        self.validated_hr = kwargs.pop("validated_hr", None)
        self.total_hr = kwargs.pop("total_hr", None)
        self.size_bytes = kwargs.pop("size_bytes", None)
        self.size_human = size_str(self.size_bytes)
        description = (
            f"NENA Speech dataset in the {self.dialect} dialect released on {self.release_date}. "
            f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
            f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
            f"The dataset contains {self.num_examples} examples and has a size of {self.size_human}."
        )
        super(NENASpeechConfig, self).__init__(
            name=name,
            version=datasets.Version(version),
            description=description,
            **kwargs,
        )

class NENASpeech(datasets.GeneratorBasedBuilder):
    DEFAULT_WRITER_BATCH_SIZE = 1000

    BUILDER_CONFIGS = sorted([
        NENASpeechConfig(
            name=dialect,
            version=STATS["version"],
            dialect=DIALECTS[dialect],
            release_date=STATS["date"],
            num_examples=dialect_stats["totalExamples"],
            num_speakers=dialect_stats["speakers"],
            
            # validated_hr=float(dialect_stats["validHrs"]) if dialect_stats["validHrs"] else None,
            # total_hr=float(dialect_stats["totalHrs"]) if dialect_stats["totalHrs"] else None,
            # size_bytes=int(dialect_stats["size"]) if dialect_stats["size"] else None,
        )
        for dialect, dialect_stats in STATS["dialects"].items()
    ], key=lambda config: config.num_examples)

    def _info(self):
        total_dialects = len(STATS["dialects"])
        total_duration = STATS["durationLabelled"] / 60
        description = (
            "NENA Speech is a multimodal dataset to help teach machines how real people speak "
            "the Northeastern Neo-Aramaic dialects. The dataset currently consists of "
            f"{total_duration} validated minutes of speech in {total_dialects} dialects, but "
            "more examples are actively being crowdsourced."
        )
        features = datasets.Features(
            {
                "transcription": datasets.Value("string"),
                "translation": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=48_000),
                "locale": datasets.Value("string"),
                "proficiency": datasets.Value("string"),
                "age": datasets.Value("string"),
                "crowdsourced": datasets.Value("bool"),
                "unlabelled": datasets.Value("bool"),
                "interrupted": datasets.Value("bool"),
                "client_id": datasets.Value("string"),
                "path": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=description,
            # citation=_CITATION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            features=features,
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        dialect = self.config.name

        audio_urls = {}
        splits = ("train", "dev", "test")
        for split in splits:
            audio_urls[split] = _AUDIO_URL.format(dialect=dialect, split=split)
        archive_paths = dl_manager.download(audio_urls)
        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}

        meta_urls = {split: _TRANSCRIPT_URL.format(dialect=dialect, split=split) for split in splits}
        meta_paths = dl_manager.download_and_extract(meta_urls)

        split_generators = []
        split_names = {
            "train": datasets.Split.TRAIN,
            "dev": datasets.Split.VALIDATION,
            "test": datasets.Split.TEST,
        }
        for split in splits:
            split_generators.append(
                datasets.SplitGenerator(
                    name=split_names.get(split, split),
                    gen_kwargs={
                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
                        "archive": dl_manager.iter_archive(archive_paths.get(split)),
                        "meta_path": meta_paths[split],
                    },
                ),
            )

        return split_generators

    def _generate_examples(self, local_extracted_archive_paths, archive, meta_path):
        data_fields = list(self._info().features.keys())
        metadata = {}
        with open(meta_path, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for row in tqdm(reader, desc="Reading metadata..."):
                for field in data_fields:
                    if field not in row:
                        row[field] = ""
                metadata[row["path"]] = row

        for path, file in archive:
            _, filename = os.path.split(path)
            if filename in metadata:
                result = dict(metadata[filename])
                path = os.path.join(local_extracted_archive_paths, path) if local_extracted_archive_paths else path
                result["audio"] = {"path": path, "bytes": file.read()}
                result["path"] = path
                yield path, result