Delete loading script

speech_commands.py

@@ -1,229 +0,0 @@
-# coding=utf-8
-# Copyright 2021 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.
-
-"""Speech Commands, an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. """
-
-
-import textwrap
-
-import datasets
-
-
-_CITATION = """
-@article{speechcommandsv2,
-   author = { {Warden}, P.},
-    title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
-  journal = {ArXiv e-prints},
-  archivePrefix = "arXiv",
-  eprint = {1804.03209},
-  primaryClass = "cs.CL",
-  keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
-    year = 2018,
-    month = apr,
-    url = {https://arxiv.org/abs/1804.03209},
-}
-"""
-
-_DESCRIPTION = """
-This is a set of one-second .wav audio files, each containing a single spoken
-English word or background noise. These words are from a small set of commands, and are spoken by a
-variety of different speakers. This data set is designed to help train simple
-machine learning models. This dataset is covered in more detail at
-[https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
-
-Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
-64,727 audio files.
-
-In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
-"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
-"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".
-
-
-In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
-
-In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
-"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
-it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
-from unrecognized ones.
-
-The `_silence_` class contains a set of longer audio clips that are either recordings or
-a mathematical simulation of noise.
-
-"""
-
-_LICENSE = "Creative Commons BY 4.0 License"
-
-_URL = "https://www.tensorflow.org/datasets/catalog/speech_commands"
-
-_DL_URL = "https://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/{name}/{name}_{split}.tar.gz"
-
-WORDS = [
-    "yes",
-    "no",
-    "up",
-    "down",
-    "left",
-    "right",
-    "on",
-    "off",
-    "stop",
-    "go",
-]
-
-UNKNOWN_WORDS_V1 = [
-    "zero",
-    "one",
-    "two",
-    "three",
-    "four",
-    "five",
-    "six",
-    "seven",
-    "eight",
-    "nine",
-    "bed",
-    "bird",
-    "cat",
-    "dog",
-    "happy",
-    "house",
-    "marvin",
-    "sheila",
-    "tree",
-    "wow",
-]
-
-UNKNOWN_WORDS_V2 = UNKNOWN_WORDS_V1 + [
-    "backward",
-    "forward",
-    "follow",
-    "learn",
-    "visual",
-]
-
-SILENCE = "_silence_"  # background noise
-LABELS_V1 = WORDS + UNKNOWN_WORDS_V1 + [SILENCE]
-LABELS_V2 = WORDS + UNKNOWN_WORDS_V2 + [SILENCE]
-
-
-class SpeechCommandsConfig(datasets.BuilderConfig):
-    """BuilderConfig for SpeechCommands."""
-
-    def __init__(self, labels, **kwargs):
-        super(SpeechCommandsConfig, self).__init__(**kwargs)
-        self.labels = labels
-
-
-class SpeechCommands(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        SpeechCommandsConfig(
-            name="v0.01",
-            description=textwrap.dedent(
-                """\
-                Version 0.01 of the SpeechCommands dataset. Contains 30 words
-                (20 of them are auxiliary) and background noise.
-                """
-            ),
-            labels=LABELS_V1,
-            version=datasets.Version("0.1.0"),
-        ),
-        SpeechCommandsConfig(
-            name="v0.02",
-            description=textwrap.dedent(
-                """\
-                Version 0.02 of the SpeechCommands dataset.
-                Contains 35 words (25 of them are auxiliary) and background noise.
-                """
-            ),
-            labels=LABELS_V2,
-            version=datasets.Version("0.2.0"),
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "file": datasets.Value("string"),
-                    "audio": datasets.features.Audio(sampling_rate=16_000),
-                    "label": datasets.ClassLabel(names=self.config.labels),
-                    "is_unknown": datasets.Value("bool"),
-                    "speaker_id": datasets.Value("string"),
-                    "utterance_id": datasets.Value("int8"),
-                }
-            ),
-            homepage=_URL,
-            citation=_CITATION,
-            license=_LICENSE,
-            version=self.config.version,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        archive_paths = dl_manager.download(
-            {
-                "train": _DL_URL.format(name=self.config.name, split="train"),
-                "validation": _DL_URL.format(name=self.config.name, split="validation"),
-                "test": _DL_URL.format(name=self.config.name, split="test"),
-            }
-        )
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "archive": dl_manager.iter_archive(archive_paths["train"]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "archive": dl_manager.iter_archive(archive_paths["validation"]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "archive": dl_manager.iter_archive(archive_paths["test"]),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, archive):
-        for path, file in archive:
-            if not path.endswith(".wav"):
-                continue
-
-            word, audio_filename = path.split("/")
-            is_unknown = False
-
-            if word == SILENCE:
-                speaker_id, utterance_id = None, 0
-
-            else:  # word is either in WORDS or unknown
-                if word not in WORDS:
-                    is_unknown = True
-                # an audio filename looks like `0bac8a71_nohash_0.wav`
-                speaker_id, _, utterance_id = audio_filename.split(".wav")[0].split("_")
-
-            yield path, {
-                "file": path,
-                "audio": {"path": path, "bytes": file.read()},
-                "label": word,
-                "is_unknown": is_unknown,
-                "speaker_id": speaker_id,
-                "utterance_id": utterance_id,
-            }