add splits support

peoples_speech.py

@@ -61,14 +61,16 @@ _LICENSE = [
     "cc-by-sa-3.0", "cc-by-sa-4.0"
 ]
 
+# _BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/"
+
 # relative path to data inside dataset's repo
-_DATA_URL = "train/{config}/{config}_{archive_id:06d}.tar"
+_DATA_URL = "{split}/{config}/{config}_{archive_id:06d}.tar"
 
 # relative path to file containing number of audio archives inside dataset's repo
-_N_FILES_URL = "train/{config}/n_files.txt"
+_N_FILES_URL = "{split}/{config}/n_files.txt"
 
 # relative path to metadata inside dataset's repo
-_MANIFEST_URL = "train/{config}.json"
+_MANIFEST_URL = "{split}/{config}.json"
 
 
 class PeoplesSpeech(datasets.GeneratorBasedBuilder):
@@ -103,21 +105,39 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
             citation=_CITATION,
         )
 
+    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)
+
+        with open(n_files_paths, encoding="utf-8") as f:
+            return int(f.read().strip())
+
     def _split_generators(self, dl_manager):
-        n_files_url = _N_FILES_URL.format(config=self.config.name)
-        n_files_path = dl_manager.download_and_extract(n_files_url)
-        with open(n_files_path, encoding="utf-8") as f:
-            n_files = int(f.read().strip())
+        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")
 
-        urls = [_DATA_URL.format(config=self.config.name, archive_id=i) for i in range(n_files)]
-        archive_paths = [dl_manager.download(url) for url in urls]
+        urls = {
+            "train": [_DATA_URL.format(split="train", config=self.config.name, archive_id=i) for i in range(n_files_train)],
+            "dev": [_DATA_URL.format(split="dev", config="dev", archive_id=i) for i in range(n_files_dev)],
+            "test": [_DATA_URL.format(split="test", config="test", archive_id=i) for i in range(n_files_test)],
+        }
+        archive_paths = dl_manager.download(urls)
 
         # In non-streaming mode, we extract the archives to have the data locally:
-        local_extracted_archive_paths = [dl_manager.extract(path) for path in archive_paths] \
-            if not dl_manager.is_streaming else [None] * len(archive_paths)
-
-        manifest_url = _MANIFEST_URL.format(config=self.config.name)
-        manifest_path = dl_manager.download_and_extract(manifest_url)
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else \
+            {
+                "train": [None] * len(archive_paths),
+                "dev": [None] * len(archive_paths),
+                "test": [None] * len(archive_paths),
+            }
+
+        manifest_urls = {
+            "train": _MANIFEST_URL.format(split="train", config=self.config.name),
+            "dev": _MANIFEST_URL.format(split="dev", config="dev"),
+            "test": _MANIFEST_URL.format(split="test", config="test"),
+        }
+        manifest_paths = dl_manager.download_and_extract(manifest_urls)
 
         # To access the audio data from the TAR archives using the download manager,
         # we have to use the dl_manager.iter_archive method
@@ -133,10 +153,28 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths,
+                    "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["dev"],
+                    # use iter_archive here to access the files in the TAR archives:
+                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["dev"]],
+                    "manifest_path": manifest_paths["dev"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_extracted_archive_paths": local_extracted_archive_paths["dev"],
                     # use iter_archive here to access the files in the TAR archives:
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths],
-                    "manifest_path": manifest_path,
+                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["test"]],
+                    "manifest_path": manifest_paths["test"],
                 },
             ),
         ]
@@ -151,6 +189,7 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
                 audio_filenames = sample_meta["training_data"]["name"]
                 durations = sample_meta["training_data"]["duration_ms"]
                 for audio_filename, text, duration in zip(audio_filenames, texts, durations):
+                    audio_filename = audio_filename.lstrip("./")
                     meta[audio_filename] = {
                         "audio_document_id": _id,
                         "text": text,
@@ -160,6 +199,7 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
         for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives):
             # Here we iterate over all the files within the TAR archive:
             for audio_filename, audio_file in archive:
+                audio_filename = audio_filename.lstrip("./")
                 # if an audio file exists locally (i.e. in default, non-streaming mode) set the full path to it
                 # joining path to directory that the archive was extracted to and audio filename.
                 path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path \