Update VBVLSP.py

VBVLSP.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
+# 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.
@@ -12,160 +12,79 @@
 # 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"""
+""" Common Voice Dataset"""
 
 
+import csv
+import os
+import json
+
 import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-import pandas as pd
-import re
-
-
-_DATA_URL = "https://husteduvn-my.sharepoint.com/:u:/g/personal/hoang_lv194767_sis_hust_edu_vn/EYhNns0j8GJEgZvb-G2aRS4Bt7AEdQMrGxYtyO2xjc6Img?e=gIpjeY&download=1"
-_PROMPTS_URLS = {
-    "train": "https://drive.google.com/uc?export=download&id=1qqvPo2tOco8_iDU2eAl-WuVivr2GJTS8",
-    "test": "https://drive.google.com/uc?export=download&id=1o7-ugckc1rKXfhLQHLKUeUaydOgJEEkO",
-    "validation": "https://drive.google.com/uc?export=download&id=1eTwNzCVWhQBf3MDLgS8coZ_mySbp67BM"
-}
-
-_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):
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
+
+
+# TODO: change "streaming" to "main" after merge!
+_BASE_URL = "https://huggingface.co/datasets/leviethoang/VBVLSP/resolve/main"
+
+_AUDIO_URL = _BASE_URL + "audio/vlsp2020_train_set_02.tar.gz"
 
+_TRANSCRIPT_URL = _BASE_URL + "transcript/{split}.tsv"
+
+
+class CommonVoice(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 = "./vlsp2020_train_set_02"
-        print(tsv_files)
-        da = pd.read_csv(tsv_files["train"], sep="\t")
-        print(len(da))
-        print(archive)
-        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."""
+        splits = ("train", , "test", "validation")
+        audio_url = _AUDIO_URL 
+        archive_path = dl_manager.download(audio_url)
+        local_extracted_archive_path = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+        meta_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits}
+        meta_paths = dl_manager.download_and_extract(meta_urls)
+
+        split_generators = []
+        split_names = {
+            "train": datasets.Split.TRAIN,
+            "validation": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+        for split in splits:
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=split_names.get(split, split),
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_path,
+                        "archives": dl_manager.iter_archive(archive_path),
+                        "meta_path": meta_paths[split],
+                    },
+                ),
+            )
+
+        return split_generators
+
+    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
         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}
+        metadata = {}
+        with open(meta_path, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for row in tqdm(reader, desc="Reading metadata..."):
+                # if data is incomplete, fill with empty values
+                for field in data_fields:
+                    if field not in row:
+                        row[field] = ""
+                metadata[row["file_path"]] = row
+
+        
+        for filename, file in audio_archive:
+            _, filename = os.path.split(filename)
+            if filename in metadata:
+                result = dict(metadata[filename])
+                # set the audio feature and the path to the extracted file
+                path = os.path.join(local_extracted_archive_path, filename) if local_extracted_archive_path else filename
+                result["audio"] = {"file_path": path, "bytes": file.read()}
+                # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
+                result["file_path"] = path if local_extracted_archive_paths else filename
+
+                yield path, result