Update VBVLSP.py

VBVLSP.py

@@ -0,0 +1,168 @@
+# coding=utf-8
+# Copyright 2021 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.
+""" VinDataVLSP Dataset"""
+
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+import pandas as pd
+import re
+
+
+_DATA_URL = "https://dutudn-my.sharepoint.com/:u:/g/personal/122180028_sv1_dut_udn_vn/ESeeV5dFDtVKmnvwJA3jUd4BLLJ7DhpOwsyb8QwpldKHwQ?download=1"
+_PROMPTS_URLS = {
+    "train": "https://drive.google.com/uc?export=download&id=1eOOvCDz0uOBBRzsHK7NALcGA70-XbQrd",
+    "test": "https://drive.google.com/uc?export=download&id=1r2wy5K0VL7wL_iMdtzMhGEy-_k3M2Gdv",
+    "validation": "https://drive.google.com/uc?export=download&id=1c0YsA4x1Up9qjDpsj1VKH_86m85cTi79"
+}
+
+_DESCRIPTION = """\
+"""
+
+_LANGUAGES = {
+    "vi": {
+        "Language": "Vietnamese",
+        "Date": "2021-12-11",
+        "Size": "11 GB",
+        "Version": "vi_100h_2021-12-11",
+    },
+}
+
+
+class VinDataVLSPConfig(datasets.BuilderConfig):
+    """BuilderConfig for CommonVoice."""
+
+    def __init__(self, name, sub_version, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files in the
+            downloaded .tar
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        self.sub_version = sub_version
+        self.language = kwargs.pop("language", None)
+        self.date_of_snapshot = kwargs.pop("date", None)
+        self.size = kwargs.pop("size", None)
+        self.validated_hr_total = kwargs.pop("val_hrs", None)
+        self.total_hr_total = kwargs.pop("total_hrs", None)
+        self.num_of_voice = kwargs.pop("num_of_voice", None)
+        description = ""
+        super(VinDataVLSPConfig, self).__init__(
+            name=name, version=datasets.Version("0.1.0", ""), description=description, **kwargs
+        )
+
+
+class VinDataVLSP(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+    BUILDER_CONFIGS = [
+        VinDataVLSPConfig(
+            name=lang_id,
+            language=_LANGUAGES[lang_id]["Language"],
+            sub_version=_LANGUAGES[lang_id]["Version"],
+            # date=_LANGUAGES[lang_id]["Date"],
+            # size=_LANGUAGES[lang_id]["Size"],
+            # val_hrs=_LANGUAGES[lang_id]["Validated_Hr_Total"],
+            # total_hrs=_LANGUAGES[lang_id]["Overall_Hr_Total"],
+            # num_of_voice=_LANGUAGES[lang_id]["Number_Of_Voice"],
+        )
+        for lang_id in _LANGUAGES.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "file_path": datasets.Value("string"),
+                "script": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            task_templates=[
+                AutomaticSpeechRecognition(audio_file_path_column="file_path", transcription_column="script")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        tsv_files = dl_manager.download(_PROMPTS_URLS)
+        archive = dl_manager.download(_DATA_URL)
+        path_to_clips = "./VinDataVLSP"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "tsv_files": tsv_files["train"],
+                    "audio_files": dl_manager.iter_archive(archive),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "tsv_files": tsv_files["test"],
+                    "audio_files": dl_manager.iter_archive(archive),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "tsv_files": tsv_files["validation"],
+                    "audio_files": dl_manager.iter_archive(archive),
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, tsv_files, audio_files, path_to_clips):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+
+        # audio is not a header of the csv files
+        data_fields.remove("audio")
+        examples = {}
+
+        df = pd.read_csv(tsv_files, sep="\t", header=0)
+        df = df.dropna()
+        chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]'
+
+        for file_path, script in zip(df["file_path"], df["script"]):
+            # set full path for mp3 audio file
+            audio_path = path_to_clips + "/" + file_path
+            # Preprocessing script
+            if ":" in script:
+                two_dot_index = script.index(":")
+                script = script[two_dot_index + 1:]
+            script = script.replace("\n", " ")
+            script = re.sub(chars_to_ignore_regex, '', script).lower()
+
+            examples[audio_path] = {
+                "file_path": audio_path,
+                "script": script,
+            }
+
+        for path, f in audio_files:
+            if path.startswith(path_to_clips):
+                if path in examples:
+                    audio = {"path": path, "bytes": f.read()}
+                    yield path, {**examples[path], "audio": audio}