2 arrows

.gitignore

@@ -1 +1,3 @@
 rename.sh
+test.*
+*__pycache__*

README.md

@@ -13,7 +13,95 @@ tags:
 pretty_name: Acapella Evaluation Dataset
 size_categories:
 - n<1K
-viewer: false
+dataset_info:
+  - config_name: default
+    features:
+      - name: audio
+        dtype:
+          audio:
+            sampling_rate: 48000
+      - name: mel
+        dtype: image
+      - name: singer_id
+        dtype:
+          class_label:
+            names:
+              '0': singer1
+              '1': singer2
+              '2': singer3
+              '3': singer4
+              '4': singer5
+              '5': singer6
+              '6': singer7
+              '7': singer8
+              '8': singer9
+              '9': singer10
+              '10': singer11
+              '11': singer12
+              '12': singer13
+              '13': singer14
+              '14': singer15
+              '15': singer16
+              '16': singer17
+              '17': singer18
+              '18': singer19
+              '19': singer20
+              '20': singer21
+              '21': singer22
+      - name: pitch
+        dtype: float32
+      - name: rhythm
+        dtype: float32
+      - name: vocal_range
+        dtype: float32
+      - name: timbre
+        dtype: float32
+      - name: pronunciation
+        dtype: float32
+      - name: vibrato
+        dtype: float32
+      - name: dynamic
+        dtype: float32
+      - name: breath_control
+        dtype: float32
+      - name: overall_performance
+        dtype: float32
+    splits:
+      - name: song1
+        num_bytes: 8700
+        num_examples: 22
+      - name: song2
+        num_bytes: 8700
+        num_examples: 22
+      - name: song1
+        num_bytes: 8700
+        num_examples: 22
+      - name: song2
+        num_bytes: 8700
+        num_examples: 22
+      - name: song1
+        num_bytes: 8700
+        num_examples: 22
+      - name: song2
+        num_bytes: 8700
+        num_examples: 22
+    download_size: 1385286751
+    dataset_size: 52200
+configs:
+  - config_name: default
+    data_files:
+      - split: song1
+        path: default/song1/data-*.arrow
+      - split: song2
+        path: default/song2/data-*.arrow
+      - split: song3
+        path: default/song3/data-*.arrow
+      - split: song4
+        path: default/song4/data-*.arrow
+      - split: song5
+        path: default/song5/data-*.arrow
+      - split: song6
+        path: default/song6/data-*.arrow
 ---
 
 # Dataset Card for Acapella Evaluation
@@ -65,12 +153,6 @@ Acapella evaluation/scoring
 ### Languages
 Chinese, English
 
-## Maintenance
-```bash
-git clone git@hf.co:datasets/ccmusic-database/acapella
-cd acapella
-```
-
 ## Usage
 ```python
 from datasets import load_dataset
@@ -81,6 +163,15 @@ for i in range(1, 7):
         print(item)
 ```
 
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/acapella
+cd acapella
+```
+
+## Mirror
+<https://www.modelscope.cn/datasets/ccmusic-database/acapella>
+
 ## Dataset Creation
 ### Curation Rationale
 Lack of a training dataset for the acapella scoring system
@@ -122,11 +213,11 @@ Zijin Li
 ### Citation Information
 ```bibtex
 @article{Li2022SingingVoiceTE,
-  title={Singing-Voice Timbre Evaluations Based on Transfer Learning},
-  author={Rongfeng Li and Mingtong Zhang},
-  journal={Applied Sciences},
-  year={2022},
-  url={https://api.semanticscholar.org/CorpusID:252766951}
+  title   = {Singing-Voice Timbre Evaluations Based on Transfer Learning},
+  author  = {Rongfeng Li and Mingtong Zhang},
+  journal = {Applied Sciences},
+  year    = {2022},
+  url     = {https://api.semanticscholar.org/CorpusID:252766951}
 }
 ```
 ### Contributions

acapella.py

@@ -1,119 +0,0 @@
-import os
-import datasets
-import pandas as pd
-from datasets.tasks import AudioClassification
-
-
-_NAMES = {
-    "songs": [f"song{i}" for i in range(1, 7)],
-    "singers": [f"singer{i}" for i in range(1, 23)],
-}
-
-_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"
-
-_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"
-
-_URLS = {
-    "audio": f"{_DOMAIN}/audio.zip",
-    "mel": f"{_DOMAIN}/mel.zip",
-}
-
-
-class acapella(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "audio": datasets.Audio(sampling_rate=48000),
-                    "mel": datasets.Image(),
-                    "singer_id": datasets.features.ClassLabel(names=_NAMES["singers"]),
-                    "pitch": datasets.Value("float32"),
-                    "rhythm": datasets.Value("float32"),
-                    "vocal_range": datasets.Value("float32"),
-                    "timbre": datasets.Value("float32"),
-                    "pronunciation": datasets.Value("float32"),
-                    "vibrato": datasets.Value("float32"),
-                    "dynamic": datasets.Value("float32"),
-                    "breath_control": datasets.Value("float32"),
-                    "overall_performance": datasets.Value("float32"),
-                }
-            ),
-            supervised_keys=("audio", "singer_id"),
-            homepage=_HOMEPAGE,
-            license="CC-BY-NC-ND",
-            version="1.2.0",
-            task_templates=[
-                AudioClassification(
-                    task="audio-classification",
-                    audio_column="audio",
-                    label_column="singer_id",
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        songs = {}
-        for index in _NAMES["songs"]:
-            csv_files = dl_manager.download(f"{_DOMAIN}/{index}.csv")
-            song_eval = pd.read_csv(csv_files, index_col="singer_id")
-            scores = []
-            for i in range(len(_NAMES["singers"])):
-                scores.append(
-                    {
-                        "pitch": song_eval.iloc[i]["pitch"],
-                        "rhythm": song_eval.iloc[i]["rhythm"],
-                        "vocal_range": song_eval.iloc[i]["vocal_range"],
-                        "timbre": song_eval.iloc[i]["timbre"],
-                        "pronunciation": song_eval.iloc[i]["pronunciation"],
-                        "vibrato": song_eval.iloc[i]["vibrato"],
-                        "dynamic": song_eval.iloc[i]["dynamic"],
-                        "breath_control": song_eval.iloc[i]["breath_control"],
-                        "overall_performance": song_eval.iloc[i]["overall_performance"],
-                    }
-                )
-
-            songs[index] = scores
-
-        audio_files = dl_manager.download_and_extract(_URLS["audio"])
-        for fpath in dl_manager.iter_files([audio_files]):
-            fname: str = os.path.basename(fpath)
-            if fname.endswith(".wav"):
-                song_id = os.path.basename(os.path.dirname(fpath))
-                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
-                songs[song_id][singer_id]["audio"] = fpath
-
-        mel_files = dl_manager.download_and_extract(_URLS["mel"])
-        for fpath in dl_manager.iter_files([mel_files]):
-            fname = os.path.basename(fpath)
-            if fname.endswith(".jpg"):
-                song_id = os.path.basename(os.path.dirname(fpath))
-                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
-                songs[song_id][singer_id]["mel"] = fpath
-
-        split_generator = []
-        for key in songs.keys():
-            split_generator.append(
-                datasets.SplitGenerator(
-                    name=key,
-                    gen_kwargs={"files": songs[key]},
-                )
-            )
-
-        return split_generator
-
-    def _generate_examples(self, files):
-        for i, item in enumerate(files):
-            yield i, {
-                "audio": item["audio"],
-                "mel": item["mel"],
-                "singer_id": i,
-                "pitch": item["pitch"],
-                "rhythm": item["rhythm"],
-                "vocal_range": item["vocal_range"],
-                "timbre": item["timbre"],
-                "pronunciation": item["pronunciation"],
-                "vibrato": item["vibrato"],
-                "dynamic": item["dynamic"],
-                "breath_control": item["breath_control"],
-                "overall_performance": item["overall_performance"],
-            }

default/dataset_dict.json

@@ -0,0 +1 @@
+{"splits": ["song1", "song2", "song3", "song4", "song5", "song6"]}

default/song1/data-00000-of-00001.arrow

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default/song1/dataset_info.json

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+  "builder_name": "acapella",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "acapella",
+  "dataset_size": 52200,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/song1.csv": {
+      "num_bytes": 939,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/song2.csv": {
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+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/song3.csv": {
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+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/song4.csv": {
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+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/song5.csv": {
+      "num_bytes": 966,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/song6.csv": {
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+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/audio.zip": {
+      "num_bytes": 1361863391,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/acapella/resolve/master/data/mel.zip": {
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+      "checksum": null
+    }
+  },
+  "download_size": 1385286751,
+  "features": {
+    "audio": {
+      "sampling_rate": 48000,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "singer_id": {
+      "names": [
+        "singer1",
+        "singer2",
+        "singer3",
+        "singer4",
+        "singer5",
+        "singer6",
+        "singer7",
+        "singer8",
+        "singer9",
+        "singer10",
+        "singer11",
+        "singer12",
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+        "singer17",
+        "singer18",
+        "singer19",
+        "singer20",
+        "singer21",
+        "singer22"
+      ],
+      "_type": "ClassLabel"
+    },
+    "pitch": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "rhythm": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "vocal_range": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "timbre": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "pronunciation": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "vibrato": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "dynamic": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "breath_control": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "overall_performance": {
+      "dtype": "float32",
+      "_type": "Value"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/acapella",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 1385338951,
+  "splits": {
+    "song1": {
+      "name": "song1",
+      "num_bytes": 8700,
+      "num_examples": 22,
+      "dataset_name": "acapella"
+    },
+    "song2": {
+      "name": "song2",
+      "num_bytes": 8700,
+      "num_examples": 22,
+      "dataset_name": "acapella"
+    },
+    "song3": {
+      "name": "song3",
+      "num_bytes": 8700,
+      "num_examples": 22,
+      "dataset_name": "acapella"
+    },
+    "song4": {
+      "name": "song4",
+      "num_bytes": 8700,
+      "num_examples": 22,
+      "dataset_name": "acapella"
+    },
+    "song5": {
+      "name": "song5",
+      "num_bytes": 8700,
+      "num_examples": 22,
+      "dataset_name": "acapella"
+    },
+    "song6": {
+      "name": "song6",
+      "num_bytes": 8700,
+      "num_examples": 22,
+      "dataset_name": "acapella"
+    }
+  },
+  "supervised_keys": {
+    "input": "audio",
+    "output": "singer_id"
+  },
+  "task_templates": [
+    {
+      "task": "audio-classification",
+      "label_column": "singer_id"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

default/song1/state.json

@@ -0,0 +1,13 @@
+{
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+      "filename": "data-00000-of-00001.arrow"
+    }
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+  "_format_kwargs": {},
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+  "_output_all_columns": false,
+  "_split": "song1"
+}

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default/song2/dataset_info.json

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+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "acapella",
+  "dataset_size": 52200,
+  "description": "",
+  "download_checksums": {
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+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 1385338951,
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+      "dataset_name": "acapella"
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+      "dataset_name": "acapella"
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+  ],
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+  }
+}

default/song2/state.json

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default/song3/dataset_info.json

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+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "acapella",
+  "dataset_size": 52200,
+  "description": "",
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