Update README

README.md

@@ -6,7 +6,7 @@ tags:
 - music
 - spectrogram
 size_categories:
-- n<1K
+- 10K<n<100K
 ---
 
 ## Google/MusicCapsをスペクトログラムにしたデータ。
@@ -29,7 +29,7 @@ size_categories:
   </tbody>
 </table>
 
-### データ作った方法
+### How this dataset was made
 
 * コード：https://colab.research.google.com/drive/13m792FEoXszj72viZuBtusYRUL1z6Cu2?usp=sharing
 * 参考にしたKaggle Notebook : https://www.kaggle.com/code/osanseviero/musiccaps-explorer
@@ -48,7 +48,140 @@ image = Image.fromarray(np.uint8(D), mode='L')  # 'L'は1チャンネルのグ
 image.save('spectrogram_{}.png')
 ```
 
-### ♪復元方法
+## How to use this
+* <font color="red">Subset <b>data 1300-1600</b> and <b>data 3400-3600</b> are not working now, so please get subset_name_list</n>
+  those were removed first</font>.
+### get information about this dataset:
+```python
+# Extract dataset's information using huggingface API
+import requests
+headers = {"Authorization": f"Bearer {API token}"}
+API_URL = "https://datasets-server.huggingface.co/info?dataset=mb23%2FGraySpectrogram"
+def query():
+    response = requests.get(API_URL, headers=headers)
+    return response.json()
+data = query()
+
+# Make subset name list.
+subset__name_list = list()
+for dic in data["failed"]:
+    subset_name_list.append(dic["config"])
+    # print(dic["config"])
+subset_name_list = sorted(subset_list, key=natural_keys)
+
+
+remove_list = [
+    "data 1300-1600",
+    "data 3400-3600"
+]
+for remove_dataset in remove_list:
+  if remove_dataset in subset_list:
+      subset_list.remove(remove_dataset)
+  else:
+      pass
+subset_list
+
+'''
+return subset name list. for example, 
+['data 0-200',
+ 'data 200-600',
+ 'data 600-1000',
+ 'data 1000-1300',
+ 'data 1600-2000',
+ 'data 2000-2200',
+ 'data 2200-2400',
+ 'data 2400-2600',
+ 'data 2600-2800',
+ 'data 3000-3200',
+ 'data 3200-3400',
+ 'data 3600-3800',
+ 'data 3800-4000',
+ 'data 4000-4200',
+ 'data 4200-4400',
+ 'data 4400-4600',
+ 'data 4600-4800',
+ 'data 4800-5000',
+ 'data 5000-5200',
+ 'data 5200-5520']
+'''
+```
+
+
+### load dataset and change to dataloader:
+* You can use the code below:
+* <font color="red">...but (;・∀・)I don't know whether this code works efficiently, because I haven't tried this code so far</color>
+```python
+import datasets
+from datasets import load_dataset, DatasetDict
+from torchvision import transforms
+from torch.utils.data import DataLoader
+BATCH_SIZE = ??? 
+IMAGE_SIZE = ???
+TRAIN_SIZE = ??? # the number of training data
+TEST_SIZE = ??? # the number of test data
+
+def load_datasets():
+
+    # Define data transforms
+    data_transforms = [
+        transforms.Resize((IMG_SIZE, IMG_SIZE)),
+        transforms.ToTensor(), # Scales data into [0,1]
+        transforms.Lambda(lambda t: (t * 2) - 1) # Scale between [-1, 1]
+    ]
+    data_transform = transforms.Compose(data_transforms)
+
+    data = load_dataset("mb23/GraySpectrogram", subset_list[0])
+    for subset in subset_list:
+        # Confirm subset_list doesn't include "remove_list" datasets in the above cell.
+        print(subset)
+        new_ds = load_dataset("mb23/GraySpectrogram", subset)
+        new_dataset_train = datasets.concatenate_datasets([data["train"], new_ds["train"]])
+        new_dataset_test = datasets.concatenate_datasets([data["test"], new_ds["test"]])
+
+        # take place of data[split]
+        data["train"] = new_dataset_train
+        data["test"] = new_dataset_test
+
+    # memo:
+    # 特徴量上手く抽出する方法が...わからん。これは力づく。
+    # 本当はload_dataset()の時点で抽出したかったけど、無理そう
+    # リポジトリ作り直してpush_to_hub()したほうがいいかもしれない。
+
+    new_dataset = dict()
+    new_dataset["train"] = Dataset.from_dict({
+        "image" : data["train"]["image"],
+        "caption" : data["train"]["caption"]
+    })
+    
+    new_dataset["test"] = Dataset.from_dict({
+        "image" : data["test"]["image"],
+        "caption" : data["test"]["caption"]
+    })
+    data = datasets.DatasetDict(new_dataset)
+    train = data["train"]
+    test = data["test"]
+
+    for idx in range(len(train["image"])):
+        train["image"][idx] = data_transform(train["image"][idx])
+        test["image"][idx] = data_transform(test["image"][idx])
+    
+    train = Dataset.from_dict(train)
+    train = train.with_format("torch") # リスト型回避
+    test = Dataset.from_dict(train)
+    test = test.with_format("torch") # リスト型回避
+
+    # or
+    train_loader = DataLoader(train, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
+    test_loader = DataLoader(test, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
+    return train_loader, test_loader
+
+```
+* then try this?
+```
+train_loader, test_loader = load_datasets()
+```
+
+### Recover music(wave form) from sprctrogram
 ```python
 im = Image.open("pngファイル")
 db_ud = np.uint8(np.array(im))
@@ -60,4 +193,4 @@ print(amp.shape)
 
 y_inv = librosa.griffinlim(amp*200)
 display(IPython.display.Audio(y_inv, rate=sr))
-```
+```