Upload vivos-preprocessed.py

vivos-preprocessed.py

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+# 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
+
+
+_CITATION = """\
+@inproceedings{luong-vu-2016-non,
+    title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
+    author = "Luong, Hieu-Thi  and
+      Vu, Hai-Quan",
+    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
+    month = dec,
+    year = "2016",
+    address = "Osaka, Japan",
+    publisher = "The COLING 2016 Organizing Committee",
+    url = "https://aclanthology.org/W16-5207",
+    pages = "51--55",
+}
+"""
+
+_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://doi.org/10.5281/zenodo.7068130"
+
+_LICENSE = "CC BY-NC-SA 4.0"
+
+# Source data: "https://zenodo.org/record/7068130/files/vivos.tar.gz"
+_DATA_URL = "data/vivos.tar.gz"
+
+_PROMPTS_URLS = {
+    "train": "data/prompts-train.txt.gz",
+    "preprocessed_train": "data/preprocessed-prompts-train.txt.gz",
+    "test": "data/prompts-test.txt.gz",
+    "preprocessed_test": "data/preprocessed-prompts-test.txt.gz",
+
+}
+
+
+class VivosPreprocessedDataset(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("1.0.0")
+
+    # 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(
+                {
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "original_sentence": datasets.Value("string"),
+                    "preprocessed_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_and_extract(_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"],
+                    "preprocessed_prompts_path": prompts_paths["preprocessed_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"],
+                    "preprocessed_prompts_path": prompts_paths["preprocessed_test"],
+                    "path_to_clips": test_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, prompts_path, preprocessed_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 = {}
+
+        prep_prompts = {}
+        with open(preprocessed_prompts_path, encoding="utf-8") as f:
+            for row in f:
+                if row.strip() == "":
+                    continue
+                data = row.strip().split(" ", 1)
+                prep_prompts[data[0]] = data[1].strip()
+
+        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] = {
+                    "path": audio_path,
+                    "original_sentence": data[1],
+                    "preprocessed_sentence": prep_prompts[data[0]]
+                }
+        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