Add dataset loader script

jam-alt.py

@@ -0,0 +1,124 @@
+"""HuggingFace loading script for the JamALT dataset."""
+
+
+import csv
+from dataclasses import dataclass
+import json
+import os
+from pathlib import Path
+from typing import Optional
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+_CITATION = """\
+"""
+
+# TODO: Add description of the dataset here
+_DESCRIPTION = """\
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here
+_LICENSE = ""
+
+_METADATA_FILENAME = "metadata.csv"
+
+
+_LANGUAGE_NAME_TO_CODE = {
+    "English": "en",
+    "French": "fr",
+    "German": "de",
+    "Spanish": "es",
+}
+
+
+@dataclass
+class JamAltBuilderConfig(datasets.BuilderConfig):
+    language: Optional[str] = None
+    with_audio: bool = False
+    decode_audio: bool = True
+    sampling_rate: Optional[int] = None
+    mono: bool = True
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class JamAltDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.0.0")
+    BUILDER_CONFIG_CLASS = JamAltBuilderConfig
+    BUILDER_CONFIGS = [JamAltBuilderConfig("default")]
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        feat_dict = {
+            "name": datasets.Value("string"),
+            "text": datasets.Value("string"),
+            "language": datasets.Value("string"),
+        }
+        if self.config.with_audio:
+            feat_dict["audio"] = datasets.Audio(
+                decode=self.config.decode_audio,
+                sampling_rate=self.config.sampling_rate,
+                mono=self.config.mono,
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(feat_dict),
+            supervised_keys=("audio", "text") if "audio" in feat_dict else None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        metadata_path = dl_manager.download(_METADATA_FILENAME)
+
+        audio_paths, text_paths, metadata = [], [], []
+        with open(metadata_path, encoding="utf-8") as f:
+            for row in csv.DictReader(f):
+                if (
+                    self.config.language is None
+                    or _LANGUAGE_NAME_TO_CODE[row["Language"]] == self.config.language
+                ):
+                    audio_paths.append("audio/" + row["Filepath"])
+                    text_paths.append(
+                        "lyrics/" + os.path.splitext(row["Filepath"])[0] + ".txt"
+                    )
+                    metadata.append(row)
+
+        text_paths = dl_manager.download(text_paths)
+        audio_paths = (
+            dl_manager.download(audio_paths) if self.config.with_audio else None
+        )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs=dict(
+                    text_paths=text_paths,
+                    audio_paths=audio_paths,
+                    metadata=metadata,
+                ),
+            ),
+        ]
+
+    def _generate_examples(self, text_paths, audio_paths, metadata):
+        if audio_paths is None:
+            audio_paths = [None] * len(text_paths)
+        for text_path, audio_path, meta in zip(text_paths, audio_paths, metadata):
+            name = os.path.splitext(os.path.basename(text_path))[0]
+            with open(text_path, encoding="utf-8") as text_f:
+                record = {
+                    "name": name,
+                    "text": text_f.read(),
+                    "language": _LANGUAGE_NAME_TO_CODE[meta["Language"]],
+                }
+            if audio_path is not None:
+                record["audio"] = audio_path
+            yield name, record