Create automatic_speech_recognition_dummy.py

automatic_speech_recognition_dummy.py

@@ -0,0 +1,167 @@
+# 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