[Librispeech] Add 'all' config (#4184)

* [Librispeech] Add 'all' config

* Update datasets/librispeech_asr/librispeech_asr.py

* apply suggestions

* correct paths

* up

* up

* up

* up

* up

Co-authored-by: Patrick von Platen <[email protected]>

Commit from https://github.com/huggingface/datasets/commit/91d7171b81a962a6822b880f12ecd74e80a4e77a

README.md

@@ -20,7 +20,7 @@ task_categories:
 - automatic-speech-recognition
 - audio-classification
 task_ids:
-- audio-speaker-identification
+- speaker-identification
 ---
 
 # Dataset Card for librispeech_asr

dataset_infos.json

@@ -1 +1 @@
-{"clean": {"description": "LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,\nprepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read\naudiobooks from the LibriVox project, and has been carefully segmented and aligned.87\n\nNote that in order to limit the required storage for preparing this dataset, the audio\nis stored in the .flac format and is not converted to a float32 array. To convert, the audio\nfile to a float32 array, please make use of the `.map()` function as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n    speech_array, _ = sf.read(batch[\"file\"])\n    batch[\"speech\"] = speech_array\n    return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@inproceedings{panayotov2015librispeech,\n  title={Librispeech: an ASR corpus based on public domain audio books},\n  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n  pages={5206--5210},\n  year={2015},\n  organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "_storage_dtype": "struct", "id": null, "_type": "Audio"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "text"}, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "text"}], "builder_name": "librispeech_asr", "config_name": "clean", "version": {"version_str": "2.1.0", "description": "", "major": 2, "minor": 1, "patch": 0}, "splits": {"train.100": {"name": "train.100", "num_bytes": 6619683041, "num_examples": 28539, "dataset_name": "librispeech_asr"}, "train.360": {"name": "train.360", "num_bytes": 23898214592, "num_examples": 104014, "dataset_name": "librispeech_asr"}, "validation": {"name": "validation", "num_bytes": 359572231, "num_examples": 2703, "dataset_name": "librispeech_asr"}, "test": {"name": "test", "num_bytes": 367705423, "num_examples": 2620, "dataset_name": "librispeech_asr"}}, "download_checksums": {"http://www.openslr.org/resources/12/dev-clean.tar.gz": {"num_bytes": 337926286, "checksum": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3"}, "http://www.openslr.org/resources/12/test-clean.tar.gz": {"num_bytes": 346663984, "checksum": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23"}, "http://www.openslr.org/resources/12/train-clean-100.tar.gz": {"num_bytes": 6387309499, "checksum": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2"}, "http://www.openslr.org/resources/12/train-clean-360.tar.gz": {"num_bytes": 23049477885, "checksum": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf"}}, "download_size": 30121377654, "post_processing_size": null, "dataset_size": 31245175287, "size_in_bytes": 61366552941}, "other": {"description": "LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,\nprepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read\naudiobooks from the LibriVox project, and has been carefully segmented and aligned.87\n\nNote that in order to limit the required storage for preparing this dataset, the audio\nis stored in the .flac format and is not converted to a float32 array. To convert, the audio\nfile to a float32 array, please make use of the `.map()` function as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n    speech_array, _ = sf.read(batch[\"file\"])\n    batch[\"speech\"] = speech_array\n    return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@inproceedings{panayotov2015librispeech,\n  title={Librispeech: an ASR corpus based on public domain audio books},\n  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n  pages={5206--5210},\n  year={2015},\n  organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "_storage_dtype": "struct", "id": null, "_type": "Audio"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "text"}, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "text"}], "builder_name": "librispeech_asr", "config_name": "other", "version": {"version_str": "2.1.0", "description": "", "major": 2, "minor": 1, "patch": 0}, "splits": {"train.500": {"name": "train.500", "num_bytes": 31810256902, "num_examples": 148688, "dataset_name": "librispeech_asr"}, "validation": {"name": "validation", "num_bytes": 337283304, "num_examples": 2864, "dataset_name": "librispeech_asr"}, "test": {"name": "test", "num_bytes": 352396474, "num_examples": 2939, "dataset_name": "librispeech_asr"}}, "download_checksums": {"http://www.openslr.org/resources/12/test-other.tar.gz": {"num_bytes": 328757843, "checksum": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29"}, "http://www.openslr.org/resources/12/dev-other.tar.gz": {"num_bytes": 314305928, "checksum": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365"}, "http://www.openslr.org/resources/12/train-other-500.tar.gz": {"num_bytes": 30593501606, "checksum": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2"}}, "download_size": 31236565377, "post_processing_size": null, "dataset_size": 32499936680, "size_in_bytes": 63736502057}}
+{"clean": {"description": "LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,\nprepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read\naudiobooks from the LibriVox project, and has been carefully segmented and aligned.87\n\nNote that in order to limit the required storage for preparing this dataset, the audio\nis stored in the .flac format and is not converted to a float32 array. To convert, the audio\nfile to a float32 array, please make use of the `.map()` function as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n    speech_array, _ = sf.read(batch[\"file\"])\n    batch[\"speech\"] = speech_array\n    return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@inproceedings{panayotov2015librispeech,\n  title={Librispeech: an ASR corpus based on public domain audio books},\n  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n  pages={5206--5210},\n  year={2015},\n  organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "text"}, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "text"}], "builder_name": "librispeech_asr", "config_name": "clean", "version": {"version_str": "2.1.0", "description": "", "major": 2, "minor": 1, "patch": 0}, "splits": {"train.100": {"name": "train.100", "num_bytes": 6619683041, "num_examples": 28539, "dataset_name": "librispeech_asr"}, "train.360": {"name": "train.360", "num_bytes": 23898214592, "num_examples": 104014, "dataset_name": "librispeech_asr"}, "validation": {"name": "validation", "num_bytes": 359572231, "num_examples": 2703, "dataset_name": "librispeech_asr"}, "test": {"name": "test", "num_bytes": 367705423, "num_examples": 2620, "dataset_name": "librispeech_asr"}}, "download_checksums": {"http://www.openslr.org/resources/12/dev-clean.tar.gz": {"num_bytes": 337926286, "checksum": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3"}, "http://www.openslr.org/resources/12/test-clean.tar.gz": {"num_bytes": 346663984, "checksum": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23"}, "http://www.openslr.org/resources/12/train-clean-100.tar.gz": {"num_bytes": 6387309499, "checksum": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2"}, "http://www.openslr.org/resources/12/train-clean-360.tar.gz": {"num_bytes": 23049477885, "checksum": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf"}}, "download_size": 30121377654, "post_processing_size": null, "dataset_size": 31245175287, "size_in_bytes": 61366552941}, "other": {"description": "LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,\nprepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read\naudiobooks from the LibriVox project, and has been carefully segmented and aligned.87\n\nNote that in order to limit the required storage for preparing this dataset, the audio\nis stored in the .flac format and is not converted to a float32 array. To convert, the audio\nfile to a float32 array, please make use of the `.map()` function as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n    speech_array, _ = sf.read(batch[\"file\"])\n    batch[\"speech\"] = speech_array\n    return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@inproceedings{panayotov2015librispeech,\n  title={Librispeech: an ASR corpus based on public domain audio books},\n  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n  pages={5206--5210},\n  year={2015},\n  organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "text"}, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "text"}], "builder_name": "librispeech_asr", "config_name": "other", "version": {"version_str": "2.1.0", "description": "", "major": 2, "minor": 1, "patch": 0}, "splits": {"train.500": {"name": "train.500", "num_bytes": 31810256902, "num_examples": 148688, "dataset_name": "librispeech_asr"}, "validation": {"name": "validation", "num_bytes": 337283304, "num_examples": 2864, "dataset_name": "librispeech_asr"}, "test": {"name": "test", "num_bytes": 352396474, "num_examples": 2939, "dataset_name": "librispeech_asr"}}, "download_checksums": {"http://www.openslr.org/resources/12/test-other.tar.gz": {"num_bytes": 328757843, "checksum": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29"}, "http://www.openslr.org/resources/12/dev-other.tar.gz": {"num_bytes": 314305928, "checksum": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365"}, "http://www.openslr.org/resources/12/train-other-500.tar.gz": {"num_bytes": 30593501606, "checksum": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2"}}, "download_size": 31236565377, "post_processing_size": null, "dataset_size": 32499936680, "size_in_bytes": 63736502057}, "all": {"description": "LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,\nprepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read\naudiobooks from the LibriVox project, and has been carefully segmented and aligned.87\n", "citation": "@inproceedings{panayotov2015librispeech,\n  title={Librispeech: an ASR corpus based on public domain audio books},\n  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n  pages={5206--5210},\n  year={2015},\n  organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "text"}, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "text"}], "builder_name": "librispeech_asr", "config_name": "all", "version": {"version_str": "2.1.0", "description": "", "major": 2, "minor": 1, "patch": 0}, "splits": {"train.clean.100": {"name": "train.clean.100", "num_bytes": 6627791685, "num_examples": 28539, "dataset_name": "librispeech_asr"}, "train.clean.360": {"name": "train.clean.360", "num_bytes": 23927767570, "num_examples": 104014, "dataset_name": "librispeech_asr"}, "train.other.500": {"name": "train.other.500", "num_bytes": 31852502880, "num_examples": 148688, "dataset_name": "librispeech_asr"}, "validation.clean": {"name": "validation.clean", "num_bytes": 359505691, "num_examples": 2703, "dataset_name": "librispeech_asr"}, "validation.other": {"name": "validation.other", "num_bytes": 337213112, "num_examples": 2864, "dataset_name": "librispeech_asr"}, "test.clean": {"name": "test.clean", "num_bytes": 368449831, "num_examples": 2620, "dataset_name": "librispeech_asr"}, "test.other": {"name": "test.other", "num_bytes": 353231518, "num_examples": 2939, "dataset_name": "librispeech_asr"}}, "download_checksums": {"http://www.openslr.org/resources/12/dev-clean.tar.gz": {"num_bytes": 337926286, "checksum": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3"}, "http://www.openslr.org/resources/12/dev-other.tar.gz": {"num_bytes": 314305928, "checksum": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365"}, "http://www.openslr.org/resources/12/test-clean.tar.gz": {"num_bytes": 346663984, "checksum": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23"}, "http://www.openslr.org/resources/12/test-other.tar.gz": {"num_bytes": 328757843, "checksum": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29"}, "http://www.openslr.org/resources/12/train-clean-100.tar.gz": {"num_bytes": 6387309499, "checksum": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2"}, "http://www.openslr.org/resources/12/train-clean-360.tar.gz": {"num_bytes": 23049477885, "checksum": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf"}, "http://www.openslr.org/resources/12/train-other-500.tar.gz": {"num_bytes": 30593501606, "checksum": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2"}}, "download_size": 61357943031, "post_processing_size": null, "dataset_size": 63826462287, "size_in_bytes": 125184405318}}

librispeech_asr.py

@@ -17,6 +17,8 @@
 """Librispeech automatic speech recognition dataset."""
 
 
+import os
+
 import datasets
 from datasets.tasks import AutomaticSpeechRecognition
 
@@ -36,27 +38,12 @@ _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
-
-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:
-
-
-```python
-import soundfile as sf
-
-def map_to_array(batch):
-    speech_array, _ = sf.read(batch["file"])
-    batch["speech"] = speech_array
-    return batch
-
-dataset = dataset.map(map_to_array, remove_columns=["file"])
-```
 """
 
 _URL = "http://www.openslr.org/12"
 _DL_URL = "http://www.openslr.org/resources/12/"
 
+
 _DL_URLS = {
     "clean": {
         "dev": _DL_URL + "dev-clean.tar.gz",
@@ -69,6 +56,15 @@ _DL_URLS = {
         "dev": _DL_URL + "dev-other.tar.gz",
         "train.500": _DL_URL + "train-other-500.tar.gz",
     },
+    "all": {
+        "dev.clean": _DL_URL + "dev-clean.tar.gz",
+        "dev.other": _DL_URL + "dev-other.tar.gz",
+        "test.clean": _DL_URL + "test-clean.tar.gz",
+        "test.other": _DL_URL + "test-other.tar.gz",
+        "train.clean.100": _DL_URL + "train-clean-100.tar.gz",
+        "train.clean.360": _DL_URL + "train-clean-360.tar.gz",
+        "train.other.500": _DL_URL + "train-other-500.tar.gz",
+    },
 }
 
 
@@ -91,9 +87,11 @@ class LibrispeechASR(datasets.GeneratorBasedBuilder):
     """Librispeech dataset."""
 
     DEFAULT_WRITER_BATCH_SIZE = 256
+    DEFAULT_CONFIG_NAME = "all"
     BUILDER_CONFIGS = [
         LibrispeechASRConfig(name="clean", description="'Clean' speech."),
         LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
+        LibrispeechASRConfig(name="all", description="Combined clean and other dataset."),
     ]
 
     def _info(self):
@@ -117,33 +115,132 @@ class LibrispeechASR(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         archive_path = dl_manager.download(_DL_URLS[self.config.name])
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
 
         if self.config.name == "clean":
             train_splits = [
                 datasets.SplitGenerator(
-                    name="train.100", gen_kwargs={"files": dl_manager.iter_archive(archive_path["train.100"])}
+                    name="train.100",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.100"),
+                        "files": dl_manager.iter_archive(archive_path["train.100"]),
+                    },
                 ),
                 datasets.SplitGenerator(
-                    name="train.360", gen_kwargs={"files": dl_manager.iter_archive(archive_path["train.360"])}
+                    name="train.360",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.360"),
+                        "files": dl_manager.iter_archive(archive_path["train.360"]),
+                    },
                 ),
             ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev"),
+                        "files": dl_manager.iter_archive(archive_path["dev"]),
+                    },
+                )
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test"),
+                        "files": dl_manager.iter_archive(archive_path["test"]),
+                    },
+                )
+            ]
         elif self.config.name == "other":
             train_splits = [
                 datasets.SplitGenerator(
-                    name="train.500", gen_kwargs={"files": dl_manager.iter_archive(archive_path["train.500"])}
+                    name="train.500",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.500"),
+                        "files": dl_manager.iter_archive(archive_path["train.500"]),
+                    },
+                )
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev"),
+                        "files": dl_manager.iter_archive(archive_path["dev"]),
+                    },
+                )
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test"),
+                        "files": dl_manager.iter_archive(archive_path["test"]),
+                    },
+                )
+            ]
+        elif self.config.name == "all":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.clean.100",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.clean.100"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.100"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.clean.360",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.clean.360"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.360"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.other.500",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.other.500"),
+                        "files": dl_manager.iter_archive(archive_path["train.other.500"]),
+                    },
+                ),
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name="validation.clean",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("validation.clean"),
+                        "files": dl_manager.iter_archive(archive_path["dev.clean"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="validation.other",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("validation.other"),
+                        "files": dl_manager.iter_archive(archive_path["dev.other"]),
+                    },
+                ),
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name="test.clean",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test.clean"),
+                        "files": dl_manager.iter_archive(archive_path["test.clean"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="test.other",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test.other"),
+                        "files": dl_manager.iter_archive(archive_path["test.other"]),
+                    },
                 ),
             ]
 
-        return train_splits + [
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"files": dl_manager.iter_archive(archive_path["dev"])}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive_path["test"])}
-            ),
-        ]
+        return train_splits + dev_splits + test_splits
 
-    def _generate_examples(self, files):
+    def _generate_examples(self, files, local_extracted_archive):
         """Generate examples from a LibriSpeech archive_path."""
         key = 0
         audio_data = {}
@@ -159,6 +256,11 @@ class LibrispeechASR(datasets.GeneratorBasedBuilder):
                         id_, transcript = line.split(" ", 1)
                         audio_file = f"{id_}.flac"
                         speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
+                        audio_file = (
+                            os.path.join(local_extracted_archive, audio_file)
+                            if local_extracted_archive
+                            else audio_file
+                        )
                         transcripts.append(
                             {
                                 "id": id_,