Updated script

speechdat.py

@@ -0,0 +1,129 @@
+# coding=utf-8
+# Copyright 2021 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.
+"""Speech Dat dataset"""
+
+import datasets
+import json
+import os
+from datasets.tasks import AutomaticSpeechRecognition
+from pathlib import Path
+
+_DESCRIPTION = """\
+Speechdat dataset
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+class SpeechDatConfig(datasets.BuilderConfig):
+    """BuilderConfig for CommonVoice."""
+
+    def __init__(self, name, **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.
+        """
+
+        self.date_of_snapshot = kwargs.pop("date", None)
+        self.size = kwargs.pop("size", None)
+        description = f"Speech Dat dataset"
+        super(SpeechDatConfig, self).__init__(
+            name=name, **kwargs
+        )
+
+
+class SpeechDat(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+    BUILDER_CONFIGS = [
+        SpeechDatConfig(
+            name="SpeechDat",
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "path": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "sentence": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            task_templates=[
+                AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="sentence")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        path_to_data = "/".join(["wav"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": manual_dir
+                },
+            )
+        ]
+
+    def _generate_examples(self, data_dir):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+
+        def get_single_line(path):
+            lines = []
+            with open(path, 'r', encoding="utf-8") as f:
+                for line in f:
+                    line = line.strip()
+                    lines.append(line)
+            if len(lines) == 1:
+                return lines[0]
+            elif len(lines) == 0:
+                return None
+            else:
+                return " ".join(lines)
+
+        data_path = Path(data_dir)
+        for wav_file in data_path.glob("*.wav"):
+            text_file = Path(str(wav_file).replace(".wav", ".svo"))
+            if not text_file.is_file():
+                continue
+            text_line = get_single_line(text_file)
+            if text_line is None or text_line == "":
+                continue
+            with open(wav_file, "rb") as wav_data:
+                yield str(wav_file), {
+                    "path": str(wav_file),
+                    "sentence": text_line,
+                    "audio": {
+                        "path": str(wav_file),
+                        "bytes": wav_data.read()
+                    }
+                }