add loading script, dataset info

.gitignore

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+*venv/
+create_dummy_datasets_for_preview.py

data/train-00000-of-00001.parquet

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-version https://git-lfs.github.com/spec/v1
-oid sha256:819e266d6630bc5159deac488172424889507700953b1d2e37cd471d4c43574e
-size 74363964

nst_sv.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.
+
+""" Load swedish NST dataset provided by National Library of Norway | Språkbanken.
+
+Documentation with full description of the data: https://www.nb.no/sbfil/talegjenkjenning/16kHz_2020/se_2020/se-16khz_reorganized.pdf
+
+TODO:
+* add multi channel option
+* add train-validation-test split option
+"""
+
+import csv
+import json
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+This database was created by Nordic Language Technology for the development 
+of automatic speech recognition and dictation in Swedish. 
+In this updated version, the organization of the data have been altered to improve the usefulness of the database. 
+In the original version of the material, 
+the files were organized in a specific folder structure where the folder names were meaningful. 
+However, the file names were not meaningful, and there were also cases of files with identical names in different folders. 
+This proved to be impractical, since users had to keep the original folder structure in order to use the data. 
+The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. 
+The original metadata files were in spl format. These have been converted to JSON format. 
+The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8. 
+See the documentation file for a full description of the data and the changes made to the database."""
+
+_HOMEPAGE = "https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-56/"
+
+_LICENSE = "CC0 1.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "close_channel": "https://www.nb.no/sbfil/talegjenkjenning/16kHz_2020/se_2020/lydfiler_16_1.tar.gz",
+    "distant_channel": "https://www.nb.no/sbfil/talegjenkjenning/16kHz_2020/se_2020/lydfiler_16_2.tar.gz",
+    # TODO: add handling of multi channel
+    # "multi_channel": "https://www.nb.no/sbfil/talegjenkjenning/16kHz_2020/se_2020/lydfiler_16_begge.tar.gz",
+}
+
+_ANNOTATIONS_URL = "https://www.nb.no/sbfil/talegjenkjenning/16kHz_2020/se_2020/ADB_SWE_0467.tar.gz"
+
+
+class NstSV(datasets.GeneratorBasedBuilder):
+    """Audio dataset for Swedish ASR provided by National Library of Norawy.
+
+    Originally, recordings have been made on two channels: a close one and a distant one.
+    Channels have been separated and can be loaded independently.
+
+    TODO: enable and validate multi_channel
+    Two configurations available:
+    - close_channel
+    - distant_channel
+
+    Main data and metadata available:
+    - audio file (bytes)
+    - manually annotated transcription (str)
+    - age (str)
+    - gender (str)
+    - region of birth (str)
+    - region of youth (str)
+    - recording session info (object)
+    - recording system (object)
+    - "type" of recording (see detailed documentatin)
+    - common_voice-like structured information
+      (info mentioned above with object structure like common voice dataset for ease of merging)
+
+    """
+
+    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
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="close_channel", version=VERSION, description="Close channel recordings"),
+        datasets.BuilderConfig(name="distant_channel", version=VERSION, description="Distant channel recordings"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "close_channel"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features_dict = {
+            "info": dict,
+            "metadata": dict,
+            "pid": datasets.Value("string"),
+            "session": dict,
+            "system": dict,
+            "val_recordings": list,
+            "audio": datasets.features.Audio(sampling_rate=16000),
+            'client_id': datasets.Value("string"),
+            'path': datasets.Value("string"),
+            'sentence': datasets.Value("string"),
+            'up_votes': datasets.Value("int32"),
+            'down_votes': datasets.Value("int32"),
+            'age': datasets.Value("string"),
+            'gender': datasets.Value("string"),
+            'accent': datasets.Value("string"),
+            'locale': datasets.Value("string"),
+            'segment': datasets.Value("string"),
+            'channel': datasets.Value("string")
+        }
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(features_dict),
+            # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # 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
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        annotations_dir = dl_manager.download_and_extract(_ANNOTATIONS_URL)
+        
+        return [
+            datasets.SplitGenerator(
+                name="all",
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "annotations_dir": annotations_dir
+                },
+            ),
+            # TODO: add split handling
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TRAIN,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test"
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test"
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
+            #         "split": "dev",
+            #     },
+            # ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, data_dir, annotations_dir):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+
+        if self.config.name == "close_channel":
+            channel_ext = "-1"
+        else:
+            channel_ext = "-2"
+
+        for annotation_filename in os.listdir(annotations_dir):
+
+            annotations_filepath = os.path.join(annotations_dir, annotation_filename)
+            with open(annotations_filepath, "r") as f:
+                annotation = json.load(f)
+
+            for recording in annotation["val_recordings"]:
+                # channel_ext in can either be "-1" "-2"
+                # so if file is "123456.wav"
+                # close channel file is "123456-1.wav"
+                # distant channel file is "123456-2.wav"
+                rel_filepath = f'se/{annotation["pid"]}/{annotation["pid"]}_{recording["file"]}'.replace(".wav", f"{channel_ext}.wav")
+                audio_filepath = f"{data_dir}/{rel_filepath}"
+                if os.path.exists(audio_filepath):
+                    with open(audio_filepath, "rb") as f:
+                        audio_bytes = f.read()
+                    result = {
+                        "info": annotation["info"],
+                        "metadata": annotation["metadata"],
+                        "pid": annotation["pid"],
+                        "session": annotation["session"],
+                        "system": annotation["system"],
+                        "val_recordings": annotation["val_recordings"],
+                        "client_id": annotation["info"]["Speaker_ID"],
+                        'path': rel_filepath,
+                        'audio': {"path": rel_filepath, "bytes": audio_bytes},
+                        'sentence': recording["text"],
+                        'up_votes': 0,
+                        'down_votes': 0,
+                        'age': annotation["info"]["Age"],
+                        'gender': annotation["info"]["Sex"],
+                        'accent': "",
+                        'locale': "sv",
+                        'segment': ""
+                    }
+
+                    yield rel_filepath, result
+                    
+
+