Update files from the datasets library (from 1.16.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.16.0

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for\nVietnamese Automatic Speech Recognition task.\nThe corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.\nWe publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.\n", "citation": "@InProceedings{vivos:2016,\nAddress = {Ho Chi Minh, Vietnam}\ntitle = {VIVOS: 15 hours of recording speech prepared for Vietnamese Automatic Speech Recognition},\nauthor={Prof. Vu Hai Quan},\nyear={2016}\n}\n", "homepage": "https://ailab.hcmus.edu.vn/vivos", "license": "cc-by-sa-4.0", "features": {"speaker_id": {"dtype": "string", "id": null, "_type": "Value"}, "path": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "vivos_dataset", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3186233, "num_examples": 11660, "dataset_name": "vivos_dataset"}, "test": {"name": "test", "num_bytes": 193258, "num_examples": 760, "dataset_name": "vivos_dataset"}}, "download_checksums": {"https://ailab.hcmus.edu.vn/assets/vivos.tar.gz": {"num_bytes": 1474408300, "checksum": "147477f7a7702cbafc2ee3808d1c142989d0dbc8d9fce8e07d5f329d5119e4ca"}}, "download_size": 1474408300, "post_processing_size": null, "dataset_size": 3379491, "size_in_bytes": 1477787791}}
+{"default": {"description": "VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for\nVietnamese Automatic Speech Recognition task.\nThe corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.\nWe publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.\n", "citation": "@InProceedings{vivos:2016,\nAddress = {Ho Chi Minh, Vietnam}\ntitle = {VIVOS: 15 hours of recording speech prepared for Vietnamese Automatic Speech Recognition},\nauthor={Prof. Vu Hai Quan},\nyear={2016}\n}\n", "homepage": "https://ailab.hcmus.edu.vn/vivos", "license": "cc-by-sa-4.0", "features": {"speaker_id": {"dtype": "string", "id": null, "_type": "Value"}, "path": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "_storage_dtype": "struct", "id": null, "_type": "Audio"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "vivos_dataset", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1722000675, "num_examples": 11660, "dataset_name": "vivos_dataset"}, "test": {"name": "test", "num_bytes": 86120132, "num_examples": 760, "dataset_name": "vivos_dataset"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/vivos/train/prompts.txt": {"num_bytes": 1075754, "checksum": "d6c6fcbe258d80d0f63e0f87d414b805f6ae11f41d40cdba5454152c3d6f14c0"}, "https://s3.amazonaws.com/datasets.huggingface.co/vivos/test/prompts.txt": {"num_bytes": 56446, "checksum": "ed27898d081eaa41b1e7e38451eb85f7ca06138896b471691510e7bab1187c2e"}, "https://ailab.hcmus.edu.vn/assets/vivos.tar.gz": {"num_bytes": 1474408300, "checksum": "147477f7a7702cbafc2ee3808d1c142989d0dbc8d9fce8e07d5f329d5119e4ca"}}, "download_size": 1475540500, "post_processing_size": null, "dataset_size": 1808120807, "size_in_bytes": 3283661307}}

dummy/1.1.0/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eb1e23106618cb63bd75edf5946355b066ad5cbf551937ebce16195a126a4990
-size 1884
+oid sha256:26b29828f134616652520ae1ebfadc70bc2e28c25f32ff60156a019a75dbd117
+size 14710

vivos.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import os
 
 import datasets
 
@@ -40,6 +39,11 @@ _LICENSE = "cc-by-sa-4.0"
 
 _DATA_URL = "https://ailab.hcmus.edu.vn/assets/vivos.tar.gz"
 
+_PROMPTS_URLS = {
+    "train": "https://s3.amazonaws.com/datasets.huggingface.co/vivos/train/prompts.txt",
+    "test": "https://s3.amazonaws.com/datasets.huggingface.co/vivos/test/prompts.txt",
+}
+
 
 class VivosDataset(datasets.GeneratorBasedBuilder):
     """VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
@@ -80,46 +84,55 @@ class VivosDataset(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        dl_path = dl_manager.download_and_extract(_DATA_URL)
-        data_dir = os.path.join(dl_path, "vivos")
-        train_dir = os.path.join(data_dir, "train")
-        test_dir = os.path.join(data_dir, "test")
+        prompts_paths = dl_manager.download(_PROMPTS_URLS)
+        archive = dl_manager.download(_DATA_URL)
+        train_dir = "vivos/train"
+        test_dir = "vivos/test"
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(train_dir, "prompts.txt"),
-                    "path_to_clips": os.path.join(train_dir, "waves"),
+                    "prompts_path": prompts_paths["train"],
+                    "path_to_clips": train_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(test_dir, "prompts.txt"),
-                    "path_to_clips": os.path.join(test_dir, "waves"),
+                    "prompts_path": prompts_paths["test"],
+                    "path_to_clips": test_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
                 },
             ),
         ]
 
-    def _generate_examples(
-        self,
-        filepath,
-        path_to_clips,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    ):
+    def _generate_examples(self, prompts_path, path_to_clips, audio_files):
         """Yields examples as (key, example) tuples."""
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
-
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
+        examples = {}
+        with open(prompts_path, encoding="utf-8") as f:
+            for row in f:
                 data = row.strip().split(" ", 1)
                 speaker_id = data[0].split("_")[0]
-                yield id_, {
+                audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"])
+                examples[audio_path] = {
                     "speaker_id": speaker_id,
-                    "path": os.path.join(path_to_clips, speaker_id, data[0] + ".wav"),
-                    "audio": os.path.join(path_to_clips, speaker_id, data[0] + ".wav"),
+                    "path": audio_path,
                     "sentence": data[1],
                 }
+        inside_clips_dir = False
+        id_ = 0
+        for path, f in audio_files:
+            if path.startswith(path_to_clips):
+                inside_clips_dir = True
+                if path in examples:
+                    audio = {"path": path, "bytes": f.read()}
+                    yield id_, {**examples[path], "audio": audio}
+                    id_ += 1
+            elif inside_clips_dir:
+                break