specify paths to all archives

peoples_speech.py

@@ -62,8 +62,10 @@ _LICENSE = [
 ]
 
 # relative path to data inside dataset's repo
-# TODO: change according to the scheme of generating urls to the audio archives
-_DATA_URL = "train/{config}/{config}_00000{archive_id}.tar"
+_DATA_URL = "train/{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"
 
 # relative path to metadata inside dataset's repo
 _MANIFEST_URL = "train/{config}.json"
@@ -102,9 +104,12 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: for demo purposes I use just first 5 archives
-        # TODO: this should be changed to the actual number of archives further
-        urls = [_DATA_URL.format(config=self.config.name, archive_id=i) for i in range(5)]
+        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())
+
+        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]
 
         # In non-streaming mode, we extract the archives to have the data locally:
@@ -131,7 +136,7 @@ class PeoplesSpeech(datasets.GeneratorBasedBuilder):
                     "local_extracted_archive_paths": local_extracted_archive_paths,
                     # 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
+                    "manifest_path": manifest_path,
                 },
             ),
         ]