add script

clarinpl_sejmsenat.py

@@ -0,0 +1,127 @@
+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+# Copyright 2021 Jim O'Regan
+#
+# 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
+"""ClarinPL Sejm/Senat automatic speech recognition dataset."""
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{marasek2014system,
+  title={System for automatic transcription of sessions of the {P}olish {S}enate},
+  author={Marasek, Krzysztof and Kor{\v{z}}inek, Danijel and Brocki, {\L}ukasz},
+  journal={Archives of Acoustics},
+  volume={39},
+  number={4},
+  pages={501--509},
+  year={2014}
+}
+"""
+
+_DESCRIPTION = """\
+A collection of 97 hours of parliamentary speeches published on the ClarinPL website
+
+Note that in order to limit the required storage for preparing this dataset, the audio
+is stored in the .wav format and is not converted to a float32 array. To convert the audio
+file to a float32 array, please make use of the `.map()` function as follows:
+
+```python
+import soundfile as sf
+
+def map_to_array(batch):
+    speech_array, _ = sf.read(batch["file"])
+    batch["speech"] = speech_array
+    return batch
+
+dataset = dataset.map(map_to_array, remove_columns=["file"])
+```
+"""
+
+_URL = "https://mowa.clarin-pl.eu/"
+_DS_URL = "http://mowa.clarin-pl.eu/korpusy/parlament/parlament.tar.gz"
+
+class ClarinPLSejmSenatASRConfig(datasets.BuilderConfig):
+    """BuilderConfig for ClarinPLSejmSenatASR."""
+
+    def __init__(self, **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.
+        """
+        super(ClarinPLSejmSenatASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
+
+
+class ClarinPLSejmSenat(datasets.GeneratorBasedBuilder):
+    """ClarinPL Sejm/Senat dataset."""
+
+    BUILDER_CONFIGS = [
+        ClarinPLSejmSenatASRConfig(name="clean", description="'Clean' speech."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("file", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(_DS_URL)
+        archive_path = os.path.join(archive_path, "SejmSenat")
+        audio_path = os.path.join(archive_path, "audio")
+        return [
+            datasets.SplitGenerator(name="train", gen_kwargs={
+                "archive_path": os.path.join(archive_path, "train"),
+                "audio_path": audio_path
+                }),
+            datasets.SplitGenerator(name="test", gen_kwargs={
+                "archive_path": os.path.join(archive_path, "test"),
+                "audio_path": audio_path
+                }),
+        ]
+
+    def _generate_examples(self, archive_path, audio_path):
+        """Generate examples from a ClarinPL Sejm/Senat archive_path."""
+        with open(os.path.join(archive_path, "text"), "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                key, transcript = line.split(" ", 1)
+                parts = key.split('-')
+                dir = '-'.join(parts[0:2])
+                audio_file = f'{parts[2]}.wav'
+                example = {
+                    "id": key,
+                    "speaker_id": parts[0],
+                    "file": os.path.join(audio_path, dir, audio_file),
+                    "text": transcript,
+                }
+                yield key, example