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vivos.py

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+# coding=utf-8
+# Copyright 2022 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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_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",
+    editor = "Murakami, Yohei  and
+      Lin, Donghui  and
+      Ide, Nancy  and
+      Pustejovsky, James",
+    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",
+    abstract = "In this paper we describe a non-expert setup for Vietnamese speech recognition
+    system using Kaldi toolkit. We collected a speech corpus over fifteen hours from about fifty
+    Vietnamese native speakers and using it to test the feasibility of our setup. The essential
+    linguistic components for the Automatic Speech Recognition (ASR) system was prepared basing
+    on the written form of the language instead of expertise knowledge on linguistic and phonology
+    as commonly seen in rich resource languages like English. The modeling of tones by integrating
+    them into the phoneme and using the phonetic decision tree is also discussed. Experimental
+    results showed this setup for ASR systems does yield competitive results while still have
+    potentials for further improvements.",
+}
+"""
+
+_DATASETNAME = "vivos"
+
+_DESCRIPTION = """\
+VIVOS is a Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
+Automatic Speech Recognition task. This speech corpus is collected by recording speech data
+from more than 50 native Vietnamese volunteers.
+"""
+
+_HOMEPAGE = "https://zenodo.org/records/7068130"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "audio": "https://huggingface.co/datasets/vivos/resolve/main/data/vivos.tar.gz",
+    "train_prompt": "https://huggingface.co/datasets/vivos/resolve/main/data/prompts-train.txt.gz",
+    "test_prompt": "https://huggingface.co/datasets/vivos/resolve/main/data/prompts-test.txt.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+logger = datasets.logging.get_logger(__name__)
+
+
+class VIVOSDataset(datasets.GeneratorBasedBuilder):
+    """
+    VIVOS is a Vietnamese speech corpus from https://zenodo.org/records/7068130.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_sptext",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_sptext",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "sentence": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_sptext":
+            features = schemas.speech_text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """
+        Returns SplitGenerators.
+        """
+
+        audio_path = dl_manager.download(_URLS["audio"])
+        train_prompt_path = dl_manager.download_and_extract(_URLS["train_prompt"])
+        test_prompt_path = dl_manager.download_and_extract(_URLS["test_prompt"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "prompts_path": train_prompt_path,
+                    "clips_path": "vivos/train/waves",
+                    "audio_files": dl_manager.iter_archive(audio_path),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "prompts_path": test_prompt_path,
+                    "clips_path": "vivos/test/waves",
+                    "audio_files": dl_manager.iter_archive(audio_path),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, prompts_path: Path, clips_path: Path, audio_files, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+        examples = {}
+        with open(prompts_path, encoding="utf-8") as f:
+            if self.config.schema == "source":
+                for row in f:
+                    data = row.strip().split(" ", 1)
+                    speaker_id = data[0].split("_")[0]
+                    audio_path = "/".join([clips_path, speaker_id, data[0] + ".wav"])
+                    examples[audio_path] = {
+                        "speaker_id": speaker_id,
+                        "path": audio_path,
+                        "sentence": data[1],
+                    }
+            elif self.config.schema == "seacrowd_sptext":
+                audio_id = 0
+                for row in f:
+                    data = row.strip().split(" ", 1)
+                    speaker_id = data[0].split("_")[0]
+                    audio_path = "/".join([clips_path, speaker_id, data[0] + ".wav"])
+                    examples[audio_path] = {
+                        "id": audio_id,
+                        "path": audio_path,
+                        "text": data[1],
+                        "speaker_id": speaker_id,
+                        "metadata": {
+                            "speaker_age": None,
+                            "speaker_gender": None,
+                        },
+                    }
+                    audio_id += 1
+
+        idx = 0
+        for path, f in audio_files:
+            if path.startswith(clips_path):
+                if path in examples:
+                    audio = {"path": path, "bytes": f.read()}
+                    yield idx, {**examples[path], "audio": audio}
+                    idx += 1
+            else:
+                continue