Update librispeech_asr_test.py

librispeech_asr_test.py

@@ -24,7 +24,6 @@ import os
 import datasets
 
 
-
 _CITATION = """\
 @inproceedings{panayotov2015librispeech,
   title={Librispeech: an ASR corpus based on public domain audio books},
@@ -39,7 +38,7 @@ _CITATION = """\
 _DESCRIPTION = """\
 LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
 prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
-audiobooks from the LibriVox project, and has been carefully segmented and aligned.87
+audiobooks from the LibriVox project, and has been carefully segmented and aligned.
 Note that in order to limit the required storage for preparing this dataset, the audio
 is stored in the .flac format and is not converted to a float32 array. To convert, the audio
 file to a float32 array, please make use of the `.map()` function as follows:
@@ -54,15 +53,13 @@ dataset = dataset.map(map_to_array, remove_columns=["file"])
 """
 
 _URL = "http://www.openslr.org/12"
-_DL_URL = "http://www.openslr.org/resources/12/"
+_DL_URL = "https://s3.amazonaws.com/datasets.huggingface.co/librispeech_asr/2.1.0/"
+_DL_URL = "https://s3.amazonaws.com/datasets.huggingface.co/librispeech_asr/2.1.0/"
 
 _DL_URLS = {
     "clean": {
-        "test": _DL_URL + "test-clean.tar.gz",
-    },
-    "other": {
-        "test": _DL_URL + "test-other.tar.gz",
-    },
+        "test": _DL_URL + "test_clean.tar.gz",
+    }
 }
 
 
@@ -95,7 +92,7 @@ class LibrispeechASR(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
                     "text": datasets.Value("string"),
                     "speaker_id": datasets.Value("int64"),
                     "chapter_id": datasets.Value("int64"),
@@ -110,7 +107,7 @@ class LibrispeechASR(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         archive_path = dl_manager.download_and_extract(_DL_URLS[self.config.name])
         return [
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path["test"], "split_name": f"test_{self.config.name}"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path["test"], "split_name": f"test_{self.config.name}"}),
         ]
 
     def _generate_examples(self, archive_path, split_name):