Processed data from AutoTrain data processor ([2023-06-16 14:26 ]

README.md

@@ -22,26 +22,12 @@ A sample from this dataset looks as follows:
 ```json
 [
   {
-    "feat_ytid": "XILyHZyyCik",
-    "feat_start_s": 140,
-    "feat_end_s": 150,
-    "feat_audioset_positive_labels": "/m/015lz1,/m/028sqc,/m/04rlf",
-    "text": "['pop', 'low quality', 'live performance', 'flat male vocal', 'passionate female vocal', 'wide harmonizing vocals', 'punchy kick', 'punchy snare', 'shimmering hi hats', 'groovy bass', 'crowd cheering', 'crowd singing', 'energetic', 'groovy', 'emotional', 'passionate']",
-    "target": "The low quality recording features a live performance of a pop song that consists of flat male vocal talking, passionate female vocal, alongside harmonizing wide female vocals, singing over punchy kick and snare hits, shimmering hi hats and groovy bass. There are crowd singing and cheering sounds in the background. It sounds groovy, emotional, energetic and passionate.",
-    "feat_author_id": 4,
-    "feat_is_balanced_subset": false,
-    "feat_is_audioset_eval": false
+    "text": "['instrumental', 'medium tempo', 'electric guitar lead', 'ambient', 'steady drumming', 'groovy bass line', 'trumpets', 'melodic', 'pleasant', 'funky', 'groovy', 'soft rock', 'pop rock', 'funk rock', 'youthful', 'atmospheric', 'brass band', 'soul', 'neo soul', 'soothing', 'rhythmic acoustic guitar']",
+    "target": "This music is a melodic instrumental. The tempo is medium  with a captivating electric guitar lead, rhythmic acoustic guitar, funky bass line, keyboard accompaniment, steady drumming and trumpets. The music is soothing, atmospheric, euphonious, youthful, and soulful. This instrumental is a Soft Rock/Funk pop."
   },
   {
-    "feat_ytid": "t9aSL2MwEDM",
-    "feat_start_s": 30,
-    "feat_end_s": 40,
-    "feat_audioset_positive_labels": "/m/015lz1,/m/04rlf,/m/04wptg,/m/05w3f,/m/064t9,/m/06rqw",
-    "text": "['pop', 'amateur recording', 'e-bass', 'e-guitar', 'acoustic drums', 'synth', 'female voice singing', 'mid range', 'mideum to uptempo']",
-    "target": "This song contains a female singer singing with a lower voice. An acoustic drum is playing a simple groove with a catchy bassline. An e-guitar is playing the notes along with the bass. A keyboard is emulating an e-guitar sound by playing a little lick. This song may be playing at a live concert.",
-    "feat_author_id": 6,
-    "feat_is_balanced_subset": false,
-    "feat_is_audioset_eval": false
+    "text": "['pianomusic/meditation', 'water soundsample', 'acoustic piano', 'reverb']",
+    "target": "This song contains a piano-composition with a lot of reverb playing a relaxing melody while running a waterdrippling sample. This song may be playing at home for meditation or sleeping."
   }
 ]
 ```
@@ -52,15 +38,8 @@ The dataset has the following fields (also called "features"):
 
 ```json
 {
-  "feat_ytid": "Value(dtype='string', id=None)",
-  "feat_start_s": "Value(dtype='int64', id=None)",
-  "feat_end_s": "Value(dtype='int64', id=None)",
-  "feat_audioset_positive_labels": "Value(dtype='string', id=None)",
   "text": "Value(dtype='string', id=None)",
-  "target": "Value(dtype='string', id=None)",
-  "feat_author_id": "Value(dtype='int64', id=None)",
-  "feat_is_balanced_subset": "Value(dtype='bool', id=None)",
-  "feat_is_audioset_eval": "Value(dtype='bool', id=None)"
+  "target": "Value(dtype='string', id=None)"
 }
 ```
 
@@ -70,5 +49,5 @@ This dataset is split into a train and validation split. The split sizes are as
 
 | Split name   | Num samples         |
 | ------------ | ------------------- |
-| train        | 4416 |
-| valid        | 1105 |
+| train        | 2159 |
+| valid        | 540 |

processed/dataset_dict.json

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

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97341d8d4c5081079547ca8ca36f7d03a6d2b8745a288343f289c12bece3838f
+size 1028920

processed/train/dataset_info.json

@@ -0,0 +1,24 @@
+{
+  "citation": "",
+  "description": "AutoTrain generated dataset",
+  "features": {
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "target": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 1027832,
+      "num_examples": 2159,
+      "dataset_name": null
+    }
+  }
+}

processed/train/state.json

@@ -0,0 +1,16 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "f069687702b8683f",
+  "_format_columns": [
+    "target",
+    "text"
+  ],
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

processed/valid/data-00000-of-00001.arrow

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:777f6263155e6814107684088677518f3cf456799e1e2e23c9cb6f3b0f1e79f3
+size 254728

processed/valid/dataset_info.json

@@ -0,0 +1,24 @@
+{
+  "citation": "",
+  "description": "AutoTrain generated dataset",
+  "features": {
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "target": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "splits": {
+    "valid": {
+      "name": "valid",
+      "num_bytes": 254129,
+      "num_examples": 540,
+      "dataset_name": null
+    }
+  }
+}

processed/valid/state.json

@@ -0,0 +1,16 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "84efb11c178761eb",
+  "_format_columns": [
+    "target",
+    "text"
+  ],
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}