update Features, and data_dir

temp_africaNLP_keyword_spotting_for_african_languages.py

@@ -1,4 +1,4 @@
-# Testing how to load rarfile from Zenodo. 
+# 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
 
@@ -88,9 +88,9 @@ class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBui
             features = datasets.Features(
                 {
                     "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
+                    "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'
                 }
             )
         else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
@@ -128,7 +128,7 @@ 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 = dl_manager.download_and_extract(my_urls) / "data_17042021" # the rar file has a subfolder. 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -173,4 +173,5 @@ class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBui
                     yield id_, {
                         "sentence": data["sentence"],
                         "path": data["path"],
+                        "audio": data["path"], # set the audio feature, should be able to handle things automatically?
                     }