black/isort

temp_africaNLP_keyword_spotting_for_african_languages.py

@@ -1,4 +1,4 @@
-# Testing how to load rarfile from Zenodo, specifically https://zenodo.org/record/4661645. 
+# Testing how to load rarfile from Zenodo, specifically https://zenodo.org/record/4661645.
 # https://github.com/huggingface/datasets/blob/dfdd2f949c1840926c02ae47f0f0c43083ef0b1f/datasets/common_voice/common_voice.py#L661 provided some inspiration
 # also https://huggingface.co/docs/datasets/master/dataset_script.html
 
@@ -22,11 +22,10 @@
 import csv
 import json
 import os
-import rarfile
-import pandas as pd
 
 import datasets
-
+import pandas as pd
+import rarfile
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -55,7 +54,7 @@ _LICENSE = ""
 # The HuggingFace dataset library don't host the datasets but only point to the original files
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLs = {
-    'first_domain': "https://zenodo.org/record/4661645/files/Keyword_spotting_dataset_v0.01_17042021.rar",
+    "first_domain": "https://zenodo.org/record/4661645/files/Keyword_spotting_dataset_v0.01_17042021.rar",
 }
 
 
@@ -77,20 +76,26 @@ class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBui
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(
+            name="first_domain",
+            version=VERSION,
+            description="This part of my dataset covers a first domain",
+        ),
     ]
 
     DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        if (
+            self.config.name == "first_domain"
+        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
             features = datasets.Features(
                 {
                     "sentence": datasets.Value("string"),
                     "path": datasets.Value("string"),
-                    "audio": datasets.features.Audio() # TODO: sampling rate? https://huggingface.co/docs/datasets/master/package_reference/main_classes.html#datasets.Audio
-                    # 'id', 'client_id', 'path', 'sentence', 'original_sentence_id', 'created_at', 'bucket', 'locale_id'
+                    "audio": datasets.features.Audio()  # TODO: sampling rate? https://huggingface.co/docs/datasets/master/package_reference/main_classes.html#datasets.Audio
+                    # TODO: 'id', 'client_id', 'path', 'sentence', 'original_sentence_id', 'created_at', 'bucket', 'locale_id'
                 }
             )
         else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
@@ -128,8 +133,10 @@ class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBui
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         my_urls = _URLs[self.config.name]
-        data_dir = dl_manager.download_and_extract(my_urls) 
-        data_dir = os.path.join(data_dir, "data_17042021") # the rar file has a subfolder. 
+        data_dir = dl_manager.download_and_extract(my_urls)
+        data_dir = os.path.join(
+            data_dir, "data_17042021"
+        )  # the rar file has a subfolder.
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -137,36 +144,39 @@ class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBui
                 gen_kwargs={
                     "filepath": os.path.join(data_dir, "clips.xlsx"),
                     "split": "train",
-                    "data_dir":data_dir,
+                    "data_dir": data_dir,
                 },
             ),
-#            
-#            datasets.SplitGenerator(
-#                name=datasets.Split.TEST,
-#                # These kwargs will be passed to _generate_examples
-#                gen_kwargs={
-#                    "filepath": os.path.join(data_dir, "test.jsonl"),
-#                    "split": "test"
-#                },
-#            ),
-#            datasets.SplitGenerator(
-#                name=datasets.Split.VALIDATION,
-#                # These kwargs will be passed to _generate_examples
-#                gen_kwargs={
-#                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-#                    "split": "dev",
-#                },
-#            ),
-#            
+            #
+            #            datasets.SplitGenerator(
+            #                name=datasets.Split.TEST,
+            #                # These kwargs will be passed to _generate_examples
+            #                gen_kwargs={
+            #                    "filepath": os.path.join(data_dir, "test.jsonl"),
+            #                    "split": "test"
+            #                },
+            #            ),
+            #            datasets.SplitGenerator(
+            #                name=datasets.Split.VALIDATION,
+            #                # These kwargs will be passed to _generate_examples
+            #                gen_kwargs={
+            #                    "filepath": os.path.join(data_dir, "dev.jsonl"),
+            #                    "split": "dev",
+            #                },
+            #            ),
+            #
         ]
 
     def _generate_examples(
-        self, filepath, split, data_dir # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+        self,
+        filepath,
+        split,
+        data_dir,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     ):
-        """ Yields examples as (key, example) tuples. """
+        """Yields examples as (key, example) tuples."""
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
-        clips_df= pd.read_excel(filepath)
+        clips_df = pd.read_excel(filepath)
         with open(filepath, encoding="utf-8") as f:
             for id_, row in clips_df.iterrows():
                 data = row
@@ -175,5 +185,7 @@ class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBui
                     yield id_, {
                         "sentence": data["sentence"],
                         "path": data["path"],
-                        "audio": os.path.join(data_dir, data["path"]), # set the audio feature, should be able to handle things automatically?
-                    }
+                        "audio": os.path.join(
+                            data_dir, data["path"]
+                        ),  # set the audio feature, should be able to handle things automatically?
+                    }