Delete automatic_speech_recognition_dummy.py

automatic_speech_recognition_dummy.py

@@ -1,167 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-# Lint as: python3
-"""SUPERB: Speech processing Universal PERformance Benchmark."""
-import glob
-import os
-import textwrap
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-_CITATION = """\
-@article{DBLP:journals/corr/abs-2105-01051,
-  author    = {Shu{-}Wen Yang and
-               Po{-}Han Chi and
-               Yung{-}Sung Chuang and
-               Cheng{-}I Jeff Lai and
-               Kushal Lakhotia and
-               Yist Y. Lin and
-               Andy T. Liu and
-               Jiatong Shi and
-               Xuankai Chang and
-               Guan{-}Ting Lin and
-               Tzu{-}Hsien Huang and
-               Wei{-}Cheng Tseng and
-               Ko{-}tik Lee and
-               Da{-}Rong Liu and
-               Zili Huang and
-               Shuyan Dong and
-               Shang{-}Wen Li and
-               Shinji Watanabe and
-               Abdelrahman Mohamed and
-               Hung{-}yi Lee},
-  title     = {{SUPERB:} Speech processing Universal PERformance Benchmark},
-  journal   = {CoRR},
-  volume    = {abs/2105.01051},
-  year      = {2021},
-  url       = {https://arxiv.org/abs/2105.01051},
-  archivePrefix = {arXiv},
-  eprint    = {2105.01051},
-  timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-_DESCRIPTION = """\
-Self-supervised learning (SSL) has proven vital for advancing research in
-natural language processing (NLP) and computer vision (CV). The paradigm
-pretrains a shared model on large volumes of unlabeled data and achieves
-state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
-speech processing community lacks a similar setup to systematically explore the
-paradigm. To bridge this gap, we introduce Speech processing Universal
-PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
-performance of a shared model across a wide range of speech processing tasks
-with minimal architecture changes and labeled data. Among multiple usages of the
-shared model, we especially focus on extracting the representation learned from
-SSL due to its preferable re-usability. We present a simple framework to solve
-SUPERB tasks by learning task-specialized lightweight prediction heads on top of
-the frozen shared model. Our results demonstrate that the framework is promising
-as SSL representations show competitive generalizability and accessibility
-across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a
-benchmark toolkit to fuel the research in representation learning and general
-speech processing.
-Note that in order to limit the required storage for preparing this dataset, the
-audio is stored in the .flac format and is not converted to a float32 array. To
-convert, the audio file to a float32 array, please make use of the `.map()`
-function as follows:
-```python
-import soundfile as sf
-def map_to_array(batch):
-    speech_array, _ = sf.read(batch["file"])
-    batch["speech"] = speech_array
-    return batch
-dataset = dataset.map(map_to_array, remove_columns=["file"])
-```
-"""
-
-class AsrDummybConfig(datasets.BuilderConfig):
-    """BuilderConfig for Superb."""
-    def __init__(
-        self,
-        data_url,
-        url,
-        task_templates=None,
-        **kwargs,
-    ):
-        super(AsrDummybConfig, self).__init__(
-            version=datasets.Version("1.9.0", ""), **kwargs
-        )
-        self.data_url = data_url
-        self.url = url
-        self.task_templates = task_templates
-
-class AsrDummy(datasets.GeneratorBasedBuilder):
-    """Superb dataset."""
-    BUILDER_CONFIGS = [
-        AsrDummybConfig(
-            name="asr",
-            description=textwrap.dedent(
-                """\
-            ASR transcribes utterances into words. While PR analyzes the
-            improvement in modeling phonetics, ASR reflects the significance of
-            the improvement in a real-world scenario. LibriSpeech
-            train-clean-100/dev-clean/test-clean subsets are used for
-            training/validation/testing. The evaluation metric is word error
-            rate (WER)."""
-            ),
-            url="http://www.openslr.org/12",
-            data_url="http://www.openslr.org/resources/12/",
-            task_templates=[
-                AutomaticSpeechRecognition(
-                    audio_file_path_column="file", transcription_column="text"
-                )
-            ],
-        )
-    ]
-
-    DEFAULT_CONFIG_NAME = "asr"
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "file": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=("file",),
-            homepage=self.config.url,
-            citation=_CITATION,
-            task_templates=self.config.task_templates,
-        )
-
-    def _split_generators(self, dl_manager):
-        DL_URLS = [
-            f"https://huggingface.co/datasets/Narsil/automatic_speech_recognition_dummy/raw/main/{i}.flac"
-            for i in range(1, 4)
-        ]
-        archive_path = dl_manager.download_and_extract(DL_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"archive_path": archive_path},
-            ),
-        ]
-
-    def _generate_examples(self, archive_path):
-        """Generate examples."""
-        for i, filename in enumerate(archive_path):
-            key = str(i)
-            example = {
-                "id": key,
-                "file": filename,
-            }
-            yield key, example