add loading script

nb_samtale.py

@@ -0,0 +1,202 @@
+# 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.
+
+# Lint as: python3
+"""NB Samtale: Norwegian conversation speech corpus"""
+
+
+from collections import defaultdict
+from email.policy import default
+from importlib import metadata
+import json
+import os
+from re import split
+
+from huggingface_hub import hf_hub_url
+import datasets
+
+from datasets.packaged_modules.parquet.parquet import Parquet
+from datasets.tasks import AutomaticSpeechRecognition
+from datasets import ClassLabel
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+
+
+_DESCRIPTION = """\
+NB Samtale is a speech corpus made by the Language Bank at the National Library of Norway.
+The corpus contains orthographically transcribed speech from podcasts and recordings of live events at the National Library.
+The corpus is intended as an open source dataset for Automatic Speech Recognition (ASR) development,
+and is specifically aimed at improving ASR systems’ handle on conversational speech.
+"""
+
+_HOMEPAGE = "https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-85/"
+
+_LICENSE = "CC-ZERO-license"
+
+_CITATION = """\
+"""
+
+# 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)
+
+# Source data: https://www.nb.no/sbfil/taledata/nb_samtale.zip
+_DATA_URL= "https://huggingface.co/datasets/Sprakbanken/nb_samtale/resolve/main/data"
+
+#_DATA_SPLITS = {
+#    "train": ["train_bm_1.tar.gz", "train_nn_1.tar.gz"],
+#    "dev": ["dev_bm_1.tar.gz", "dev_nn_1.tar.gz"],
+#    "test": ["test_bm_1.tar.gz", "test_nn_1.tar.gz"],
+#}
+
+class NBSamtaleConfig(datasets.BuilderConfig):
+    """BuilderConfig for NBSamtale"""
+
+    def __init__(self, **kwargs):
+        # Version history:
+        # 1.0.0: Initial version.
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+        #self.language = language
+
+
+class NBSamtale(datasets.GeneratorBasedBuilder):
+    """Norwegian conversational speech audio dataset with a total of 24 hours transcribed speech from 69 speakers. """
+
+    # 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 = NBSamtaleConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('Sprakbanken/nb_samtale', 'normalized')
+    # data = datasets.load_dataset('Sprakbanken/nb_samtale', 'verbatim')
+    BUILDER_CONFIGS = [
+        NBSamtaleConfig(name="annotations", description="Transcriptions contain original annotations, including hesitations, laughter, interruptions etc. See https://www.nb.no/sbfil/taledata/NB_Samtale_About_the_corpus.pdf section 'Transcriptions' for more information."),
+        NBSamtaleConfig(name="orthographic",  description="Transcriptions have been normalized and word forms that comply with the orthographic standard are chosen, even for dialect specific words, e.g. 'korsen'/'kossen' is replaced with 'hvordan' in bokmål, or 'korleis' in nynorsk."),
+        NBSamtaleConfig(name="verbatim", description="Transcriptions are closer to the spoken words, dialectal word forms have been chosen instead of the standard orthographic word form. E.g. 'korsen' or 'kossen' would be kept, instead of the orthographic bokmål 'hvordan', or nynorsk 'korleis'."),
+        #NBSamtaleConfig(name="bm", language="bokmål", description="Normalized bokmål transcriptions. Word forms that comply with the orthographic standard are chosen, e.g. 'korsen' is replaced with 'hvordan'."),
+        #NBSamtaleConfig(name="nn", language="nynorsk", description="Normalized nynorsk transcriptions. Word forms that comply with the orthographic standard are chosen, e.g. 'kossen' is replaced with 'korleis'."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "annotations"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        """This method specifies the datasets.DatasetInfo object
+        which contains informations and typings for the dataset.
+        """
+
+        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(
+                {
+                    'source_file_id': datasets.Value(dtype='string'),
+                    'segment_id': datasets.Value(dtype='string'),
+                    'segment_order': datasets.Value(dtype='int64'),
+                    'duration': datasets.Value(dtype='float64'),
+                    'overlap_previous': datasets.Value(dtype='bool'),
+                    'overlap_next': datasets.Value(dtype='bool'),
+                    'speaker_id': datasets.Value(dtype='string'),
+                    'gender': ClassLabel(names=['f', 'm']),
+                    'dialect': ClassLabel(names=['e', 'n', 'sw', 't', 'w']),
+                    'orthography': ClassLabel(names=['bm', 'nn']),
+                    'source_type': ClassLabel(names=['live-event', 'podcast']),
+                    'file_name': datasets.Value(dtype='string'),
+                    'transcription': datasets.Value(dtype='string'),
+                    'audio': datasets.Audio(sampling_rate=16000, mono=True, decode=True),
+                }
+            ),
+            # 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=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+
+    def _split_generator(self, dl_manager) -> datasets.SplitGenerator:
+        """Download data and extract to datasets.Splits"""
+        dl_manager.download_config.ignore_url_params = True
+        audio_path = {}
+        local_extracted_archive = {}
+        metadata_path = {}
+        split_type = {
+            "train": datasets.Split.TRAIN,
+            "test": datasets.Split.TEST,
+            "dev": datasets.Split.VALIDATION
+            }
+        for split in split_type:
+            audio_path[split] = dl_manager.download([f"{_DATA_URL}/{split}_{lang}_1.tar.gz" for lang in ["bm", "nn"]])
+            local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
+            metadata_path[split] = dl_manager.download_and_extract(f"{_DATA_URL}/{split}_metadata.jsonl")
+
+        return [
+            datasets.SplitGenerator(
+            name=dsplit,
+            gen_kwargs={
+                "local_extracted_archive": local_extracted_archive[split],
+                "audio_files": dl_manager.iter_archive(audio_path[split]),
+                "metadata": metadata_path[split],
+                }
+            ) for split, dsplit in split_type.items()
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, local_extracted_archive, audio_files, metadata):
+        """Loads the data files and extract the features."""
+        data_fields = list(self._info().features.keys())
+        # 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.
+        meta = {}
+        with open(metadata, encoding="utf-8") as mf:
+            for row in mf.read().splitlines():
+                data = json.loads(row)
+                audio_path = data["file_name"]
+                # if data is incomplete, fill with empty values
+                data["transcription"] = normalize_transcription(data["transcription"], config=self.config.name)
+                for field in data_fields:
+                    if field not in data:
+                        data[field] = ""
+
+                meta[audio_path] = data
+
+        id_ = 0
+        for path, audiofile in audio_files:
+            if path in meta:
+                result = dict(meta[path])
+                # set the audio feature and the path to the extracted file
+                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
+                result["audio"] = {"path": path, "bytes": audiofile.read()}
+                result["path"] = path
+                yield id_, result
+                id_ += 1
+
+
+def normalize_transcription(transcription: str, config="annotations"):
+    """Normalize transcriptions according to orthographic standards, or verbatim."""
+    # TODO: Implement normalization
+    if config == "orthographic":
+        return transcription
+    elif config == "verbatim":
+        return transcription
+    return transcription