Release: 1.18.1

Commit from https://github.com/huggingface/datasets/commit/218e496519ff14b4bc69ea559616af6f2ef89e57

vivos.py

@@ -1,138 +1,138 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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 datasets
-
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{vivos:2016,
-Address = {Ho Chi Minh, Vietnam}
-title = {VIVOS: 15 hours of recording speech prepared for Vietnamese Automatic Speech Recognition},
-author={Prof. Vu Hai Quan},
-year={2016}
-}
-"""
-
-_DESCRIPTION = """\
-VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
-Vietnamese Automatic Speech Recognition task.
-The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
-We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.
-"""
-
-_HOMEPAGE = "https://ailab.hcmus.edu.vn/vivos"
-
-_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
-    Vietnamese Automatic Speech Recognition task."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "speaker_id": datasets.Value("string"),
-                    "path": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=16_000),
-                    "sentence": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # 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
-        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={
-                    "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={
-                    "prompts_path": prompts_paths["test"],
-                    "path_to_clips": test_dir + "/waves",
-                    "audio_files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    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.
-        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]
-                audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"])
-                examples[audio_path] = {
-                    "speaker_id": speaker_id,
-                    "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
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{vivos:2016,
+Address = {Ho Chi Minh, Vietnam}
+title = {VIVOS: 15 hours of recording speech prepared for Vietnamese Automatic Speech Recognition},
+author={Prof. Vu Hai Quan},
+year={2016}
+}
+"""
+
+_DESCRIPTION = """\
+VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
+Vietnamese Automatic Speech Recognition task.
+The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
+We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.
+"""
+
+_HOMEPAGE = "https://ailab.hcmus.edu.vn/vivos"
+
+_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
+    Vietnamese Automatic Speech Recognition task."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "sentence": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # 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
+        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={
+                    "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={
+                    "prompts_path": prompts_paths["test"],
+                    "path_to_clips": test_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    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.
+        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]
+                audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"])
+                examples[audio_path] = {
+                    "speaker_id": speaker_id,
+                    "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