add loading script

peoples_speech.py

@@ -0,0 +1,174 @@
+# 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.
+
+import json
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+from tqdm.auto import tqdm
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{DBLP:journals/corr/abs-2111-09344,
+  author    = {Daniel Galvez and
+               Greg Diamos and
+               Juan Ciro and
+               Juan Felipe Ceron and
+               Keith Achorn and
+               Anjali Gopi and
+               David Kanter and
+               Maximilian Lam and
+               Mark Mazumder and
+               Vijay Janapa Reddi},
+  title     = {The People's Speech: A Large-Scale Diverse English Speech Recognition
+               Dataset for Commercial Usage},
+  journal   = {CoRR},
+  volume    = {abs/2111.09344},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2111.09344},
+  eprinttype = {arXiv},
+  eprint    = {2111.09344},
+  timestamp = {Mon, 22 Nov 2021 16:44:07 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+The People's Speech is a free-to-download 30,000-hour and growing supervised 
+conversational English speech recognition dataset licensed for academic and 
+commercial usage under CC-BY-SA (with a CC-BY subset).
+"""
+
+_HOMEPAGE = "https://mlcommons.org/en/peoples-speech/"
+
+_LICENSE = [
+    "cc-by-2.0", "cc-by-2.5", "cc-by-3.0", "cc-by-4.0", "cc-by-sa-2.5",
+    "cc-by-sa-3.0", "cc-by-sa-4.0"
+]
+
+# 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 = {
+    "clean-cc-by": {
+        "audio_tar": "",
+        "manifest": "",
+    },
+    "dirty-cc-by": {
+        "audio_tar": "",
+        "manifest": "",
+    },
+    "clean-cc-by-sa": {
+        "audio_tar": "",
+        "manifest": "",
+    },
+    "dirty-cc-by-sa": {
+        "audio_tar": "",
+        "manifest": "",
+    },
+    "microset": {
+        "audio_tar": "",
+        "manifest": "",
+    },
+}
+
+# _BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/"
+
+# relative path to data inside dataset's repo
+_DATA_URL = "{config}/{config}_00000{archive_id}.tar"
+
+# relative path to metadata inside dataset's repo
+_MANIFEST_URL = "{config}.json"
+
+
+class PeoplesSpeech(datasets.GeneratorBasedBuilder):
+    """The People's Speech dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="clean", version=VERSION, description="Clean, CC-BY licensed subset."),
+        datasets.BuilderConfig(name="dirty", version=VERSION, description="Dirty, CC-BY licensed subset."),
+        datasets.BuilderConfig(name="clean_sa", version=VERSION, description="Clean, CC-BY-SA licensed subset."),
+        datasets.BuilderConfig(name="dirty_sa", version=VERSION, description="Dirty, CC-BY-SA licensed subset."),
+    ]
+    DEFAULT_CONFIG_NAME = "clean"
+    DEFAULT_WRITER_BATCH_SIZE = 1
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "duration_ms": datasets.Value("int32"),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            task_templates=[AutomaticSpeechRecognition()],
+            supervised_keys=("file", "text"),
+            homepage=_HOMEPAGE,
+            license="/".join(_LICENSE),  # license must be a string
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: for demo purposes I use just first 5 archives
+        # TODO: this should be changed to the actual number of archives further
+        urls = [_DATA_URL.format(config=self.config.name, archive_id=i) for i in range(5)]
+        archives = [dl_manager.iter_archive(dl_manager.download(url)) for url in urls]
+
+        manifest_url = _MANIFEST_URL.format(config=self.config.name)
+        manifest_path = dl_manager.download_and_extract(manifest_url)  # maybe just download?
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archives": archives,
+                    "manifest_path": manifest_path
+                },
+            ),
+        ]
+
+    def _generate_examples(self, archives, manifest_path):
+        meta = dict()
+        with open(manifest_path, "r", encoding="utf-8") as f:
+            for line in tqdm(f, desc="reading metadata file"):
+                sample_meta = json.loads(line)
+                _id = sample_meta["audio_document_id"]
+                texts = sample_meta["training_data"]["label"]
+                audio_filenames = sample_meta["training_data"]["name"]
+                durations = sample_meta["training_data"]["duration_ms"]
+                for audio_filename, text, duration in zip(audio_filenames, texts, durations):
+                    meta[audio_filename] = {
+                        "audio_document_id": _id,
+                        "text": text,
+                        "duration_ms": duration
+                    }
+
+        print("generating examples")
+        for archive in archives:
+            # note that you don't need to use `tarfile` library and open tar archives manually
+            # dl_manager.iter_archive() does it for you :)
+            for audio_filename, audio_file in archive:
+                yield audio_filename, {
+                    "id": audio_filename,
+                    "audio": {"path": audio_filename, "bytes": audio_file.read()},
+                    "text": meta[audio_filename]["text"],
+                    "duration_ms": meta[audio_filename]["duration_ms"]
+                }