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@@ -52,3 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
\ No newline at end of file
diff --git a/README.md b/README.md
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+++ b/README.md
@@ -1,3 +1,13 @@
 ---
+language: sv
 license: cc-by-4.0
+tags: 
+- audio
+- speech-recognition
+task_categories:
+  - automatic-speech-recognition
 ---
+
+## RixVox
+
+A Swedish speech corpus with 5500 hours of speeches from debates in the Riksdag (the Swedish Parliament). The speeches have been aligned with transcripts from written protocols. 
\ No newline at end of file
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+# Copyright 2023 KBLab and 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.
+
+import json
+import os
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+@misc{rekathati2023finding,
+  author = {Rekathati, Faton},
+  title = {The KBLab Blog: Finding Speeches in the Riksdag's Debates},
+  url = {https://kb-labb.github.io/posts/2023-02-15-finding-speeches-in-the-riksdags-debates/},
+  year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+RixVox is a speech dataset comprised of speeches from the Swedish Parliament (the Riksdag). Audio from speeches have been aligned with official transcripts, on the sentence level, using aeneas. 
+Speaker metadata is available for each observation, including the speaker's name, gender, party, birth year and electoral district. The dataset contains a total of 5493 hours of speech. 
+An observation may consist of one or several sentences (up to 30 seconds in duration).
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+_LICENSE = "CC BY 4.0"
+
+_N_SHARDS = {"train": 126, "dev": 2, "test": 2}
+
+_BASE_PATH = "data/"
+_META_URL = _BASE_PATH + "{split}_metadata.parquet"
+_DATA_URL = _BASE_PATH + "{split}/{split}_{shard_idx}.tar.gz"
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class Rixvox(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    DEFAULT_CONFIG_NAME = "all"
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="train", version=VERSION, description="Training set of the RixVox dataset. 5383 hours of speech."
+        ),
+        datasets.BuilderConfig(
+            name="dev", version=VERSION, description="Development set of the RixVox dataset. 52 hours of speech."
+        ),
+        datasets.BuilderConfig(
+            name="test", version=VERSION, description="Test set of the RixVox dataset. 59 hours of speech."
+        ),
+    ]
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "dokid": datasets.Value("string"),
+                "anforande_nummer": datasets.Value("int16"),
+                "observation_nr": datasets.Value("int16"),
+                "audio": datasets.features.Audio(sampling_rate=16_000),
+                "text": datasets.Value("string"),
+                "debatedate": datasets.Value("date64"),
+                "speaker": datasets.Value("string"),
+                "party": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "birthyear": datasets.Value("int64"),
+                "electoral_district": datasets.Value("string"),
+                "intressent_id": datasets.Value("string"),
+                "speaker_from_id": datasets.Value("bool"),
+                "speaker_audio_meta": datasets.Value("string"),
+                "start": datasets.Value("float64"),
+                "end": datasets.Value("float64"),
+                "duration": datasets.Value("float64"),
+                "bleu_score": datasets.Value("float64"),
+                "filename": datasets.Value("string"),
+                # "path": datasets.Value("string"),
+                "speaker_total_hours": datasets.Value("float64"),
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        splits = ["train", "dev", "test"]
+
+        if self.config.name == "all":
+            archive_urls = {
+                split: [_DATA_URL.format(split=split, shard_idx=idx) for idx in range(0, _N_SHARDS[split])]
+                for split in splits
+            }
+            meta_urls = {split: [_META_URL.format(split=split)] for split in splits}
+
+        else:
+            archive_urls = {
+                self.config.name: [
+                    _DATA_URL.format(split=self.config.name, shard_idx=idx)
+                    for idx in range(0, _N_SHARDS[self.config.name])
+                ]
+            }
+            meta_urls = {self.config.name: _META_URL[self.config.name]}
+
+        archive_paths = dl_manager.download(archive_urls)
+        local_extracted_archives = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+        meta_paths = dl_manager.download(meta_urls)
+
+        split_generators = []
+        split_names = {
+            "train": datasets.Split.TRAIN,
+            "dev": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+
+        if self.config.name == "all":
+            for split in splits:
+                split_generators.append(
+                    datasets.SplitGenerator(
+                        name=split_names.get(split),
+                        gen_kwargs={
+                            "local_extracted_archive_paths": local_extracted_archives.get(split),
+                            "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
+                            "meta_paths": meta_paths[split],
+                        },
+                    ),
+                )
+        else:
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=split_names.get(self.config.name),
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archives.get(self.config.name),
+                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(self.config.name)],
+                        "meta_paths": meta_paths[self.config.name],
+                    },
+                ),
+            )
+
+        return split_generators
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(
+        self,
+        local_extracted_archives,
+        archive_iters,
+        meta_paths,
+    ):
+
+        if self.config.name == "all":
+            data = []
+            for meta_path in meta_paths.values():
+                data.append(pd.read_parquet(meta_path))
+
+            df_meta = pd.concat(data)
+        else:
+            df_meta = pd.read_parquet(meta_path[self.config.name])
+
+        df_meta = df_meta.set_index("filename")
+
+        for i, audio_archive in enumerate(archive_iters):
+            for filename, file in audio_archive:
+                if filename not in df_meta.index:
+                    continue
+
+                result = dict(df_meta.loc[filename])
+                path = (
+                    os.path.join(local_extracted_archives[i], filename)
+                    if local_extracted_archives is not None
+                    else filename
+                )
+                result["audio"] = {"path": path, "bytes": file.read()}
+                result["path"] = path if local_extracted_archives else filename
+
+                yield path, result