Template initialization file

README.md

@@ -10,7 +10,7 @@ arxiv: https://doi.org/10.1007/978-3-031-05981-0_14
 
 # Beatbox Dataset
 
-Dataset consisting of isolated beatbox samples from the paper **[BaDumTss: Multi-task
+Dataset consisting of isolated beatbox samples. Reimplementation of a dataset from the paper **[BaDumTss: Multi-task
 Learning for Beatbox
 Transcription](https://link.springer.com/chapter/10.1007/978-3-031-05981-0_14])**
 

beatbox.py

@@ -0,0 +1,122 @@
+import datasets
+import csv
+import os
+
+# For a future citation perhaps?
+# _CITATION = """\
+# @inproceedings{luong-vu-2016-non,
+#     title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
+#     author = "Luong, Hieu-Thi  and
+#       Vu, Hai-Quan",
+#     booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
+#     month = dec,
+#     year = "2016",
+#     address = "Osaka, Japan",
+#     publisher = "The COLING 2016 Organizing Committee",
+#     url = "https://aclanthology.org/W16-5207",
+#     pages = "51--55",
+# }
+# """
+
+_DESCRIPTION = """\
+    Dataset consisting of isolated beatbox samples ,
+    reimplementation of the dataset from the following 
+    paper: BaDumTss: Multi-task Learning for Beatbox Transcription
+"""
+
+_HOMEPAGE = "https://doi.org/10.1007/978-3-031-05981-0_14"
+
+_LICENSE = "MIT"
+
+_DATA_URL = "https://huggingface.co/datasets/maxardito/beatbox/tree/main/dataset/"
+
+
+class BeatboxDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "path":
+                datasets.Value("string"),
+                "class":
+                datasets.Value("string"),
+                "audio":
+                datasets.Audio(sampling_rate=16_000),
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            # citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        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
+        }
+        for split in split_type:
+            audio_path[split] = dl_manager.download(
+                f"{_DATA_URL}/audio_{split}.tgz")
+            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}/metadata_{split}.csv.gz")
+        path_to_clips = "beatbox"
+
+        return [
+            datasets.SplitGenerator(
+                name=split_type[split],
+                gen_kwargs={
+                    "local_extracted_archive":
+                    local_extracted_archive[split],
+                    "audio_files":
+                    dl_manager.iter_archive(audio_path[split]),
+                    "metadata_path":
+                    dl_manager.download_and_extract(metadata_path[split]),
+                    "path_to_clips":
+                    path_to_clips,
+                },
+            ) for split in split_type
+        ]
+
+    def _generate_examples(
+        self,
+        local_extracted_archive,
+        audio_files,
+        metadata_path,
+        path_to_clips,
+    ):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(metadata_path, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                if self.config.name == "_all_" or self.config.name == row[
+                        "language"]:
+                    row["path"] = os.path.join(path_to_clips, row["path"])
+                    # if data is incomplete, fill with empty values
+                    for field in data_fields:
+                        if field not in row:
+                            row[field] = ""
+                    metadata[row["path"]] = row
+        id_ = 0
+        for path, f in audio_files:
+            if path in metadata:
+                result = dict(metadata[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": f.read()}
+                result["path"] = path
+                yield id_, result
+                id_ += 1

dataset/metadata.csv

@@ -1,4 +1,4 @@
-file_name,class
+path,class
 kick-001.wav,0
 kick-001-a-1.wav,0
 kick-001-a-2.wav,0