feat: add loading script

waxal-wolof.py

@@ -0,0 +1,224 @@
+# 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.
+"""TODO: Add a description here."""
+
+
+import csv
+import itertools
+import logging
+import os
+
+import datasets
+
+logger = logging.getLogger()
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
+
+_MODALITIES_COMBINATION = [
+  ["audio", "image", "text"],
+  ["audio", "text"],
+  ["audio", "image"],
+  ["image", "text"],
+  ["audio"], 
+  ["image"], 
+  ["text"],
+]
+
+_URLs = {
+    "train-transcriptions": "train_transcriptions.csv",
+    "test-transcriptions": "test_transcriptions.csv",
+    "image-files": "images.tar.gz",
+    "captioned-images": "captioned_images.tar.gz",
+    "audio-files": "audios.tar.gz",
+    "transcribed-audio": "transcribed_audio.tar.gz"
+}
+
+
+class WaxalConfig(datasets.BuilderConfig):
+  """BuilderConfig for Waxal dataset."""
+
+  def __init__(self, name, version, modalities, **kwargs):
+    self.modalities = modalities
+    self.language = kwargs.pop("language", None)
+
+    modalities_str = " to ".join(self.modalities)
+    description = f"Waxal {modalities_str} in {self.language}"
+
+    super(WaxalConfig, self).__init__(
+        name=name,
+        version=version,
+        description=description,
+        **kwargs,
+    )
+
+
+class WaxalWolof(datasets.GeneratorBasedBuilder):
+  """TODO: Short description of my dataset."""
+
+  BUILDER_CONFIGS = [
+      WaxalConfig(
+          name="-".join(modalities),
+          version=datasets.Version("1.1.0"),
+          modalities=modalities,
+          language="wolof",
+      )
+      for modalities in _MODALITIES_COMBINATION
+  ]
+
+  DEFAULT_CONFIG_NAME = "audio-text"
+
+  def _info(self):
+    features = {}
+
+    if "audio" in self.config.modalities:
+      features["audio"] = datasets.features.Audio()
+      features["audio_duration"] = datasets.Value("float")
+      features["participant"] = datasets.Value("int32")
+
+    if "image" in self.config.modalities:
+      features["image"] = datasets.features.Image()
+
+    if "text" in self.config.modalities:
+      features["text_annotation"] = datasets.Value("string")
+
+    return datasets.DatasetInfo(
+        description=_DESCRIPTION,
+        features=datasets.Features(features),
+        homepage=_HOMEPAGE,
+        license=_LICENSE,
+        citation=_CITATION,
+    )
+
+  @property
+  def with_audio(self):
+    return "audio" in self.config.modalities
+
+  @property
+  def with_image(self):
+    return "image" in self.config.modalities
+
+  @property
+  def with_text(self):
+    return "text" in self.config.modalities
+
+  def _split_generators(self, dl_manager):
+    logger.debug("splitting")
+    audio_url_key = "transcribed-audio" if self.with_text else "audio-files"
+    image_url_key = "captioned-images" if self.with_text else "image-files"
+
+    audio_files = (
+        dl_manager.download_and_extract(_URLs[audio_url_key])
+        if self.with_audio
+        else None
+    )
+
+    image_files = (
+        dl_manager.download_and_extract(_URLs[image_url_key])
+        if self.with_image
+        else None
+    )
+
+    return [
+        datasets.SplitGenerator(
+            name=datasets.Split.TRAIN,
+            gen_kwargs={
+                "metadata_path": dl_manager.download(
+                    _URLs["train-transcriptions"]
+                ),
+                "audio_files": audio_files,
+                "image_files": image_files,
+            },
+        ),
+        datasets.SplitGenerator(
+            name=datasets.Split.TEST,
+            gen_kwargs={
+                "metadata_path": dl_manager.download(
+                    _URLs["test-transcriptions"]
+                ),
+                "audio_files": audio_files,
+                "image_files": image_files,
+            },
+        ),
+    ]
+
+  def _generate_examples(
+      self,
+      metadata_path,
+      audio_files=None,
+      path_to_audio="transcribed_audio",
+      image_files=None,
+      path_to_images="captioned_images",
+  ):
+    metadata = {}
+
+    with open(metadata_path) as buf:
+      reader = csv.DictReader(buf)
+      for row in reader:
+        del row["prompt"]  # TODO(shpotes): remove it in future versions!
+
+        if self.with_text:
+          if not row["transcription"]:
+            continue
+
+        if self.with_image:
+          row["image_file_path"] = os.path.join(
+              path_to_images, self.config.language, row["image_file_name"]
+          )
+        if self.with_audio:
+          row["audio_file_path"] = os.path.join(
+              path_to_audio, row["audio_file_name"]
+          )  # TODO(shpotes): add lang name to the csv path.
+
+        metadata[row["idx"]] = row
+
+    for idx, sample in metadata.items():
+      result = {}
+
+      if self.with_audio:
+        result["participant"] = sample["participant"]
+        result["audio_duration"] = sample["duration"]
+        audio_path = os.path.join(audio_files, sample["audio_file_path"])
+        with open(audio_path, "rb") as f:
+          result["audio"] = {"path": audio_path, "bytes": f.read()}
+
+      if self.with_image:
+        image_path = os.path.join(image_files, sample["image_file_path"])
+        with open(image_path, "rb") as f:
+          result["image"] = {"path": image_path, "bytes": f.read()}
+
+      if self.with_text:
+        result["text_annotation"] = sample["transcription"]
+
+      yield idx, result