add microset

peoples_speech.py

@@ -61,16 +61,16 @@ _LICENSE = [
     "cc-by-sa-3.0", "cc-by-sa-4.0"
 ]
 
-# _BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/"
+_BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/"
 
 # relative path to data inside dataset's repo
-_DATA_URL = "{split}/{config}/{config}_{archive_id:06d}.tar"
+_DATA_URL = _BASE_URL + "{split}/{config}/{config}_{archive_id:06d}.tar"
 
 # relative path to file containing number of audio archives inside dataset's repo
-_N_FILES_URL = "{split}/{config}/n_files.txt"
+_N_FILES_URL = _BASE_URL + "{split}/{config}/n_files.txt"
 
 # relative path to metadata inside dataset's repo
-_MANIFEST_URL = "{split}/{config}.json"
+_MANIFEST_URL = _BASE_URL + "{split}/{config}.json"
 
 
 class PeoplesSpeech(datasets.GeneratorBasedBuilder):
@@ -78,7 +78,7 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.1.0")
     BUILDER_CONFIGS = [
-        # TODO: add "subset" config
+        datasets.BuilderConfig(name="microset", version=VERSION, description="Small subset of clean data for example pusposes."),
         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."),
@@ -107,12 +107,33 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
 
     def _get_n_files(self, dl_manager, split, config):
         n_files_url = _N_FILES_URL.format(split=split, config=config)
-        n_files_paths = dl_manager.download_and_extract(n_files_url)
+        n_files_path = dl_manager.download_and_extract(n_files_url)
 
-        with open(n_files_paths, encoding="utf-8") as f:
+        with open(n_files_path, encoding="utf-8") as f:
             return int(f.read().strip())
 
     def _split_generators(self, dl_manager):
+
+        if self.config.name == "microset":
+            # take only first data archive for demo purposes
+            url = [_DATA_URL.format(split="train", config="clean", archive_id=0)]
+            archive_path = dl_manager.download(url)
+            local_extracted_archive_path = dl_manager.extract(archive_path) if not dl_manager.is_streaming else [None]
+            manifest_url = _MANIFEST_URL.format(split="train", config="clean_000000")  # train/clean_000000.json
+            manifest_path = dl_manager.download_and_extract(manifest_url)
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_path,
+                        # use iter_archive here to access the files in the TAR archives:
+                        "archives": [dl_manager.iter_archive(path) for path in archive_path],
+                        "manifest_path": manifest_path,
+                    },
+                ),
+            ]
+
         n_files_train = self._get_n_files(dl_manager, split="train", config=self.config.name)
         n_files_dev = self._get_n_files(dl_manager, split="dev", config="dev")
         n_files_test = self._get_n_files(dl_manager, split="test", config="test")