refactor script, add test and val configs

peoples_speech.py

@@ -67,25 +67,33 @@ _BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/ma
 _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 = _BASE_URL + "{split}/{config}/n_files.txt"
+_N_SHARDS_URL = _BASE_URL + "n_shards.json"
 
 # relative path to metadata inside dataset's repo
 _MANIFEST_URL = _BASE_URL + "{split}/{config}.json"
 
 
+class PeoplesSpeechConfig(datasets.BuilderConfig):
+
+    def __init__(self, *args, **kwargs):
+        super.__init__(*args, **kwargs)
+
+
 class PeoplesSpeech(datasets.GeneratorBasedBuilder):
     """The People's Speech dataset."""
 
     VERSION = datasets.Version("1.1.0")
     BUILDER_CONFIGS = [
-        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."),
-        datasets.BuilderConfig(name="dirty_sa", version=VERSION, description="Dirty, CC-BY-SA licensed subset."),
+        PeoplesSpeechConfig(name="microset", version=VERSION, description="Small subset of clean data for example pusposes."),
+        PeoplesSpeechConfig(name="clean", version=VERSION, description="Clean, CC-BY licensed subset."),
+        PeoplesSpeechConfig(name="dirty", version=VERSION, description="Dirty, CC-BY licensed subset."),
+        PeoplesSpeechConfig(name="clean_sa", version=VERSION, description="Clean, CC-BY-SA licensed subset."),
+        PeoplesSpeechConfig(name="dirty_sa", version=VERSION, description="Dirty, CC-BY-SA licensed subset."),
+        PeoplesSpeechConfig(name="test", version=VERSION, description="Only test data."),
+        PeoplesSpeechConfig(name="validation", version=VERSION, description="Only validation data."),
     ]
     DEFAULT_CONFIG_NAME = "clean"
-    DEFAULT_WRITER_BATCH_SIZE = 1
+    DEFAULT_WRITER_BATCH_SIZE = 512
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -99,19 +107,11 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
                 }
             ),
             task_templates=[AutomaticSpeechRecognition()],
-            supervised_keys=("file", "text"),
             homepage=_HOMEPAGE,
             license="/".join(_LICENSE),  # license must be a string
             citation=_CITATION,
         )
 
-    def _get_n_files(self, dl_manager, split, config):
-        n_files_url = _N_FILES_URL.format(split=split, config=config)
-        n_files_path = dl_manager.download_and_extract(n_files_url)
-
-        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":
@@ -134,29 +134,33 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
                 ),
             ]
 
-        n_files_train = self._get_n_files(dl_manager, split="train", config=self.config.name)
-        n_files_validation = self._get_n_files(dl_manager, split="validation", config="validation")
-        n_files_test = self._get_n_files(dl_manager, split="test", config="test")
+        n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
+        with open(n_shards_path, encoding="utf-8") as f:
+            n_shards = json.load(f)
+
+        if self.config.name in ["validation", "test"]:
+            splits_to_configs = {self.config.name: self.config.name}
+        else:
+            splits_to_configs = {
+                "train": self.config.name,
+                "validation": "validation",
+                "test": "test"
+            }
 
-        urls = {
-            "train": [_DATA_URL.format(split="train", config=self.config.name, archive_id=i) for i in range(n_files_train)],
-            "validation": [_DATA_URL.format(split="validation", config="validation", archive_id=i) for i in range(n_files_validation)],
-            "test": [_DATA_URL.format(split="test", config="test", archive_id=i) for i in range(n_files_test)],
+        audio_urls = {
+            split: [
+                _DATA_URL.format(split=split, config=config, archive_id=i) for i in range(n_shards[split])
+            ] for split, config in splits_to_configs.items()
         }
-        archive_paths = dl_manager.download(urls)
+        audio_archive_paths = dl_manager.download(audio_urls)
 
         # In non-streaming mode, we extract the archives to have the data locally:
-        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else \
-            {
-                "train": [None] * len(archive_paths),
-                "validation": [None] * len(archive_paths),
-                "test": [None] * len(archive_paths),
-            }
+        local_extracted_archive_paths = dl_manager.extract(audio_archive_paths) \
+            if not dl_manager.is_streaming else \
+            {split: [None] * len(audio_archive_paths) for split in splits_to_configs}
 
         manifest_urls = {
-            "train": _MANIFEST_URL.format(split="train", config=self.config.name),
-            "validation": _MANIFEST_URL.format(split="validation", config="validation"),
-            "test": _MANIFEST_URL.format(split="test", config="test"),
+            split: _MANIFEST_URL.format(split=split, config=config) for split, config in splits_to_configs.items()
         }
         manifest_paths = dl_manager.download_and_extract(manifest_urls)
 
@@ -169,36 +173,24 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
         #
         # The iter_archive method returns an iterable of (path_within_archive, file_obj) for every
         # file in a TAR archive.
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["train"],
-                    # use iter_archive here to access the files in the TAR archives:
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["train"]],
-                    "manifest_path": manifest_paths["train"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["validation"],
-                    # use iter_archive here to access the files in the TAR archives:
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["validation"]],
-                    "manifest_path": manifest_paths["validation"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["validation"],
-                    # use iter_archive here to access the files in the TAR archives:
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["test"]],
-                    "manifest_path": manifest_paths["test"],
-                },
-            ),
-        ]
+        splits_to_names = {
+            "train": datasets.Split.TRAIN,
+            "validation": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+        split_generators = []
+        for split in splits_to_configs:
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=splits_to_names[split],
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths[split],
+                        # use iter_archive here to access the files in the TAR archives:
+                        "archives": [dl_manager.iter_archive(path) for path in audio_archive_paths[split]],
+                        "manifest_path": manifest_paths[split],
+                    }
+                )
+            )
 
     def _generate_examples(self, local_extracted_archive_paths, archives, manifest_path):
         meta = dict()