Add dataset loader attempt

vystadial2016_asr.py

@@ -0,0 +1,138 @@
+# coding=utf-8
+# Copyright 2022 Vojtěch Drábek
+
+# 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
+"""Vystadial 2016 Czech automatic speech recognition dataset."""
+
+
+import os
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_CITATION = """\
+ @misc{11234/1-1740,
+ title = {Vystadial 2016 – Czech data},
+ author = {Pl{\'a}tek, Ond{\v r}ej and Du{\v s}ek, Ond{\v r}ej and Jur{\v c}{\'{\i}}{\v c}ek, Filip},
+ url = {http://hdl.handle.net/11234/1-1740},
+ note = {{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University},
+ copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
+ year = {2016} }
+"""
+
+_DESCRIPTION = """\
+This is the Czech data collected during the `VYSTADIAL` project. It is an extension of the 'Vystadial 2013' Czech part data release. The dataset comprises of telephone conversations in Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems.
+"""
+
+_URL = "https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1740"
+_DL_URL = "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-1740/data_voip_cs_2016.tar.gz?sequence=1&isAllowed=y"
+
+
+class Vystadial2016ASRConfig(datasets.BuilderConfig):
+    """BuilderConfig for Vysadial 2016."""
+
+    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(Vystadial2016ASRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class Vystadial2016ASR(datasets.GeneratorBasedBuilder):
+    """Vystadial 2016 dataset."""
+
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    DEFAULT_CONFIG_NAME = "all"
+    BUILDER_CONFIGS = [
+        Vystadial2016ASRConfig(name="all", description="All samples."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("file", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_DL_URL)
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+
+             return [ datasets.SplitGenerator(
+                    name="test",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("?"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.100"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.clean.360"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.360"]),
+                    },
+                ), ]
+
+    def _generate_examples(self, files, local_extracted_archive):
+        """Generate examples from a Vystadial2016 archive_path."""
+        key = 0
+        audio_data = {}
+        transcripts = []
+        for path, f in files:
+            id_ = path.split("/")[-1][:-4]
+            if path.endswith(".wav"):
+                audio_data[id_] = f.read()
+            elif path.endswith(".trs"):
+                for line in f:
+                    if line:
+                        line = line.decode("utf-8").strip()
+                        id_, transcript = line.split(" ", 1)
+                        audio_file = f"{id_}.wav"
+                        audio_file = (
+                            os.path.join(local_extracted_archive, audio_file)
+                            if local_extracted_archive
+                            else audio_file
+                        )
+                        transcripts.append(
+                            {
+                                "id": id_,
+                                "file": audio_file,
+                                "text": transcript,
+                            }
+                        )
+            if audio_data and len(audio_data) == len(transcripts):
+                for transcript in transcripts:
+                    audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
+                    yield key, {"audio": audio, **transcript}
+                    key += 1
+                audio_data = {}
+                transcripts = []
+