try use arrow

range/dataset_dict.json

@@ -0,0 +1 @@
+{"splits": ["train", "validation", "test"]}

range/test/data-00000-of-00001.arrow

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b39b3b910e3048adad1b46e6c40f415650d82468f5961affbebf4456ca1cf32
+size 8062128

range/test/dataset_info.json

@@ -0,0 +1,75 @@
+{
+  "builder_name": "timbre_range",
+  "citation": "",
+  "config_name": "range",
+  "dataset_name": "timbre_range",
+  "dataset_size": 262914,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/timbre_range/resolve/master/data/range.zip": {
+      "num_bytes": 65303363,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/timbre_range/resolve/master/data/range.csv": {
+      "num_bytes": 5801,
+      "checksum": null
+    }
+  },
+  "download_size": 65309164,
+  "features": {
+    "audio": {
+      "sampling_rate": 44100,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "label": {
+      "names": [
+        "Narrow",
+        "Moderate",
+        "Wide"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 65572078,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 210052,
+      "num_examples": 580,
+      "dataset_name": "timbre_range"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 26462,
+      "num_examples": 73,
+      "dataset_name": "timbre_range"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 26400,
+      "num_examples": 73,
+      "dataset_name": "timbre_range"
+    }
+  },
+  "supervised_keys": {
+    "input": "audio",
+    "output": "label"
+  },
+  "task_templates": [
+    {
+      "task": "audio-classification",
+      "label_column": "label"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

range/test/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "3220c984179e0991",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "test"
+}

range/train/data-00000-of-00001.arrow

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8dfce9431caaaa917c3cca055a3479e80b72412d375c468032eaf49a1c6fca9
+size 62968128

range/train/dataset_info.json

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+{
+  "builder_name": "timbre_range",
+  "citation": "",
+  "config_name": "range",
+  "dataset_name": "timbre_range",
+  "dataset_size": 262914,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/timbre_range/resolve/master/data/range.zip": {
+      "num_bytes": 65303363,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/timbre_range/resolve/master/data/range.csv": {
+      "num_bytes": 5801,
+      "checksum": null
+    }
+  },
+  "download_size": 65309164,
+  "features": {
+    "audio": {
+      "sampling_rate": 44100,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "label": {
+      "names": [
+        "Narrow",
+        "Moderate",
+        "Wide"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 65572078,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 210052,
+      "num_examples": 580,
+      "dataset_name": "timbre_range"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 26462,
+      "num_examples": 73,
+      "dataset_name": "timbre_range"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 26400,
+      "num_examples": 73,
+      "dataset_name": "timbre_range"
+    }
+  },
+  "supervised_keys": {
+    "input": "audio",
+    "output": "label"
+  },
+  "task_templates": [
+    {
+      "task": "audio-classification",
+      "label_column": "label"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

range/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "9378e1980f3bcdfe",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "train"
+}

range/validation/data-00000-of-00001.arrow

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1f4345cc0f0ac4d80c999ca6ad77003840a783957d843ec3a1a586c177e4f6d
+size 8025600

range/validation/dataset_info.json

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+{
+  "builder_name": "timbre_range",
+  "citation": "",
+  "config_name": "range",
+  "dataset_name": "timbre_range",
+  "dataset_size": 262914,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/timbre_range/resolve/master/data/range.zip": {
+      "num_bytes": 65303363,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/timbre_range/resolve/master/data/range.csv": {
+      "num_bytes": 5801,
+      "checksum": null
+    }
+  },
+  "download_size": 65309164,
+  "features": {
+    "audio": {
+      "sampling_rate": 44100,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "label": {
+      "names": [
+        "Narrow",
+        "Moderate",
+        "Wide"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 65572078,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 210052,
+      "num_examples": 580,
+      "dataset_name": "timbre_range"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 26462,
+      "num_examples": 73,
+      "dataset_name": "timbre_range"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 26400,
+      "num_examples": 73,
+      "dataset_name": "timbre_range"
+    }
+  },
+  "supervised_keys": {
+    "input": "audio",
+    "output": "label"
+  },
+  "task_templates": [
+    {
+      "task": "audio-classification",
+      "label_column": "label"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

range/validation/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "a7a0a2d87e4d0cb9",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "validation"
+}

timbre_range.py

@@ -1,107 +0,0 @@
-import os
-import random
-import datasets
-import pandas as pd
-from datasets.tasks import AudioClassification
-
-
-_NAMES = {
-    "timbre": ["Base", "Split", "Short"],
-    "range": ["Narrow", "Moderate", "Wide"],
-}
-
-_URL = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}/resolve/master/data"
-
-
-class timbre_range(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        if self.config.name == "default":
-            self.config.name = "range"
-
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "audio": datasets.Audio(sampling_rate=44_100),
-                    "mel": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=_NAMES["timbre"]),
-                    "score1": datasets.Value("float64"),
-                    "score2": datasets.Value("float64"),
-                    "avg_score": datasets.Value("float64"),
-                }
-                if self.config.name == "timbre"
-                else {
-                    "audio": datasets.Audio(sampling_rate=44_100),
-                    "mel": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=_NAMES["range"]),
-                }
-            ),
-            supervised_keys=("audio", "label"),
-            license="CC-BY-NC-ND",
-            version="1.2.0",
-            task_templates=[
-                AudioClassification(
-                    task="audio-classification",
-                    audio_column="audio",
-                    label_column="label",
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        dataset = []
-        data_files = dl_manager.download_and_extract(f"{_URL}/{self.config.name}.zip")
-        for fpath in dl_manager.iter_files([data_files]):
-            if os.path.basename(fpath).endswith(".wav"):
-                dataset.append(fpath)
-
-        random.shuffle(dataset)
-        count = len(dataset)
-        p80 = int(0.8 * count)
-        p90 = int(0.9 * count)
-
-        csv_file = dl_manager.download(f"{_URL}/{self.config.name}.csv")
-        labels = pd.read_csv(csv_file, index_col="id")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"files": dataset[:p80], "labels": labels},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"files": dataset[p80:p90], "labels": labels},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"files": dataset[p90:], "labels": labels},
-            ),
-        ]
-
-    def _generate_examples(self, files, labels):
-        if self.config.name == "timbre":
-            for i, fpath in enumerate(files):
-                id: str = os.path.basename(fpath)[:-4]
-                if "-" in id:
-                    id = id.split("-")[0]
-
-                yield i, {
-                    "audio": fpath,
-                    "mel": fpath.replace("/audios/", "/images/")
-                    .replace("\\audios\\", "\\images\\")
-                    .replace(".wav", ".jpg"),
-                    "label": os.path.basename(os.path.dirname(fpath)).capitalize(),
-                    "score1": float(labels.loc[id]["score1"]),
-                    "score2": float(labels.loc[id]["score2"]),
-                    "avg_score": float(labels.loc[id]["avg_score"]),
-                }
-
-        else:
-            for i, fpath in enumerate(files):
-                id = os.path.basename(fpath)[:-4]
-                yield i, {
-                    "audio": fpath,
-                    "mel": fpath.replace("/audios/", "/images/")
-                    .replace("\\audios\\", "\\images\\")
-                    .replace(".wav", ".jpg"),
-                    "label": _NAMES["range"][int(labels.loc[id]["range"])],
-                }