changing and deleting files

Generate_metadata.ipynb

@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "id": "aa925968",
    "metadata": {
     "scrolled": true
@@ -28,22 +28,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[]\n",
-      "[]\n",
-      "['kotiria000263.wav', 'kotiria000265.wav', 'kotiria000273.wav', 'kotiria000285.wav', 'kotiria000289.wav', 'kotiria000291.wav', 'kotiria000294.wav', 'kotiria000295.wav', 'kotiria000297.wav', 'kotiria000300.wav', 'kotiria000306.wav', 'kotiria000308.wav']\n",
-      "[]\n",
-      "['waikhana000740.wav', 'waikhana000745.wav', 'waikhana000746.wav']\n"
+      "[]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15adf9d48a44440dac871ce9f432294c",
+       "model_id": "86aedd302b3041d9b4bf80a3c60c096a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Uploading the dataset shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -52,12 +48,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aa491992d4fa43688c71ea1e09b25ca0",
+       "model_id": "d40686210f1b49cf9a2b980964f32c34",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Map:   0%|          | 0/1583 [00:00<?, ? examples/s]"
+       "Map:   0%|          | 0/1097 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
@@ -66,12 +62,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f560606c9094daf92d9f5328f18b2dd",
+       "model_id": "6e86ed4b25894e37837cdb6cb46e662a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Creating parquet from Arrow format:   0%|          | 0/16 [00:00<?, ?ba/s]"
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
       ]
      },
      "metadata": {},
@@ -80,26 +76,33 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "538b15ad0bbe4684a09ae610fce7ab8c",
+       "model_id": "d138c6b6f3b548298986710d9dbea010",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Map:   0%|          | 0/1583 [00:00<?, ? examples/s]"
+       "README.md:   0%|          | 0.00/1.64k [00:00<?, ?B/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "391fb889ea15447ca8ec509a04de2ebe",
+       "model_id": "41c0e3b2a72f4ce690a5d068ad6ee76e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Creating parquet from Arrow format:   0%|          | 0/16 [00:00<?, ?ba/s]"
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -108,12 +111,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "960a0088fc564383a32d2f6f0816b215",
+       "model_id": "c4c9e9d1a0c64e728527636022468fb0",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Map:   0%|          | 0/1583 [00:00<?, ? examples/s]"
+       "Map:   0%|          | 0/1097 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
@@ -122,34 +125,565 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3355cb1c65d84a24b1a146a17e43b1c4",
+       "model_id": "de8c8d8db3704cbe9e86266166e898fa",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Creating parquet from Arrow format:   0%|          | 0/16 [00:00<?, ?ba/s]"
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "ename": "ValueError",
-     "evalue": "Features of the new split don't match the features of the existing splits on the hub: {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None), 'source_processed': Value(dtype='string', id=None), 'source_raw': Value(dtype='string', id=None), 'target_raw': Value(dtype='string', id=None)} != {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None)}",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 38>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m a \u001b[38;5;241m=\u001b[39m flatten(a)\n\u001b[1;32m     32\u001b[0m audio_dataset \u001b[38;5;241m=\u001b[39m Dataset\u001b[38;5;241m.\u001b[39mfrom_dict({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile_name\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     33\u001b[0m                                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     34\u001b[0m                                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     35\u001b[0m                                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     36\u001b[0m                                   },\n\u001b[1;32m     37\u001b[0m                                  )\u001b[38;5;241m.\u001b[39mcast_column(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m, Audio())\n\u001b[0;32m---> 38\u001b[0m \u001b[43maudio_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mivangtorre/second_americas_nlp_2022\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m     42\u001b[0m df \u001b[38;5;241m=\u001b[39m generate_df(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquechua\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdev\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:5707\u001b[0m, in \u001b[0;36mDataset.push_to_hub\u001b[0;34m(self, repo_id, config_name, set_default, split, data_dir, commit_message, commit_description, private, token, revision, branch, create_pr, max_shard_size, num_shards, embed_external_files)\u001b[0m\n\u001b[1;32m   5705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m repo_info\u001b[38;5;241m.\u001b[39msplits \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(repo_info\u001b[38;5;241m.\u001b[39msplits) \u001b[38;5;241m!=\u001b[39m [split]:\n\u001b[1;32m   5706\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mfeatures \u001b[38;5;241m!=\u001b[39m repo_info\u001b[38;5;241m.\u001b[39mfeatures:\n\u001b[0;32m-> 5707\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   5708\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeatures of the new split don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt match the features of the existing splits on the hub: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m != \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_info\u001b[38;5;241m.\u001b[39mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   5709\u001b[0m         )\n\u001b[1;32m   5711\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m repo_info\u001b[38;5;241m.\u001b[39msplits:\n\u001b[1;32m   5712\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39mdownload_size \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m deleted_size\n",
-      "\u001b[0;31mValueError\u001b[0m: Features of the new split don't match the features of the existing splits on the hub: {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None), 'source_processed': Value(dtype='string', id=None), 'source_raw': Value(dtype='string', id=None), 'target_raw': Value(dtype='string', id=None)} != {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None)}"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30139cf5070d40d69db2bc1aa691e422",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/1.64k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91e76dd77c394f10a6316e2cbc738f53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd557f750be248b8900132ec09bd3ea9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1097 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82d08a53de1f42f8a8e2a98686f15e02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c86ca04ca6d46b7a4637153831843c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/1.75k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1105fc397b7942569e2b6e7263dadb3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2464a4420284e2fa43352d21cc0849d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1097 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4b5a5308c584bfeb9933074e70cfa0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eefe4a691e4e4d1db3f8ea7db47b068e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/2.22k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75a9f81c1ee843c397590f4d156df530",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1097 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f455a25b8ec4686b36569e323b93e3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "780547210b7e4db588220b112c7958f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d7eaf59c4eb4bcb9aab6dd38fd0f101",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1097 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ebec41677a04747ae02d1645a19bf8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ed3f65de03c48f488ca3f518d063d0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/2.80k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
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+      ]
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/2.90k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d805ff2d7b43482cb7d9ffe19a473bf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f89273f09ed34a67bb905bfa80a82628",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "563022c1f761483fbb63a3e95d92e5ed",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "466c0109b16d4a14a7587bf395e20270",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/3.37k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6564df13321c4447bf713efb5dd7c0d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/3.47k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75d0a41d05334cba889bd566cd746f96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4223d1e159f6432d98a95a4868c31af5",
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "model_id": "32685d9c0ba54e8288b35d8c6b56369e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa33e335c997460b8047df3d2550fa8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/3.95k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/datasets/ivangtorre/second_americas_nlp_2022/commit/edd8ca4dc1e477443d98f7eace86ee02daf62347', commit_message='Upload dataset', commit_description='', oid='edd8ca4dc1e477443d98f7eace86ee02daf62347', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "import pandas as pd\n",
     "from datasets import Dataset, Audio\n",
     "\n",
+    "def flatten(xss):\n",
+    "    return [x for xs in xss for x in xs]\n",
+    "\n",
+    "def create_dataset(df):\n",
+    "    audio_dataset = Dataset.from_dict({\"audio\": flatten(df[\"file_name\"].values.tolist()),\n",
+    "                                   \"subset\": flatten(df[\"subset\"].values.tolist()),\n",
+    "                                   \"source_processed\": flatten(df[\"source_processed\"].values.tolist()),\n",
+    "                                   \"source_raw\": flatten(df[\"source_raw\"].values.tolist()),\n",
+    "                                   \"target_raw\": flatten(df[\"target_raw\"].values.tolist()),\n",
+    "                                   \"split\": flatten(df[\"split\"].values.tolist()),\n",
+    "                                  },\n",
+    "                                 ).cast_column(\"audio\", Audio())\n",
+    "    return(audio_dataset)\n",
+    "\n",
+    "\n",
     "def generate_df(language, split):\n",
     "    # QUECHUA TRAIN\n",
     "    with open(\"./../\"+language +\"_\"+split+\".tsv\") as f:\n",
@@ -162,276 +696,35 @@
     "    df1 = df1.assign(subset=[language]*df1.shape[0])\n",
     "    df1 = df1.rename(columns={'wav': 'file_name'})\n",
     "    df1['file_name'] = 'data/' + language + '/' + split +'/' + df1['file_name'].astype(str)\n",
-    "    return df1\n",
+    "    audio_dataset = create_dataset(df)\n",
+    "    return audio_dataset\n",
     "\n",
-    "df = generate_df(\"quechua\", \"train\")\n",
-    "df = pd.concat([df, generate_df(\"guarani\", \"train\")])\n",
-    "df = pd.concat([df, generate_df(\"kotiria\", \"train\")])\n",
-    "df = pd.concat([df, generate_df(\"bribri\", \"train\")])\n",
-    "df = pd.concat([df, generate_df(\"waikhana\", \"train\")])\n",
-    "cols = df.columns.tolist()\n",
-    "cols = cols[-1:] + cols[:-1]\n",
-    "df = df[cols]\n",
     "\n",
-    "def flatten(xss):\n",
-    "    return [x for xs in xss for x in xs]\n",
     "\n",
-    "a = flatten(df[\"file_name\"].values.tolist())\n",
-    "a = flatten(a)\n",
-    "audio_dataset = Dataset.from_dict({\"audio\": flatten(df[\"file_name\"].values.tolist()),\n",
-    "                                   \"source_processed\": flatten(df[\"source_processed\"].values.tolist()),\n",
-    "                                   \"source_raw\": flatten(df[\"source_raw\"].values.tolist()),\n",
-    "                                   \"target_raw\": flatten(df[\"target_raw\"].values.tolist()),\n",
-    "                                  },\n",
-    "                                 ).cast_column(\"audio\", Audio())\n",
-    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", split=\"train\")\n",
+    "audio_dataset = generate_df(\"quechua\", \"train\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"quechua\", split=\"train\")\n",
+    "audio_dataset = generate_df(\"quechua\", \"dev\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"quechua\", split=\"dev\")\n",
     "\n",
-    "df.to_csv(\"train.csv\", sep='\\t', index=None)\n",
+    "audio_dataset = generate_df(\"guarani\", \"train\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"guarani\", split=\"train\")\n",
+    "audio_dataset = generate_df(\"guarani\", \"dev\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"guarani\", split=\"dev\")\n",
     "\n",
-    "df = generate_df(\"quechua\", \"dev\")\n",
-    "df = pd.concat([df, generate_df(\"guarani\", \"dev\")])\n",
-    "df = pd.concat([df, generate_df(\"kotiria\", \"dev\")])\n",
-    "df = pd.concat([df, generate_df(\"bribri\", \"dev\")])\n",
-    "df = pd.concat([df, generate_df(\"waikhana\", \"dev\")])\n",
-    "cols = df.columns.tolist()\n",
-    "cols = cols[-1:] + cols[:-1]\n",
-    "df = df[cols]\n",
-    "df.to_csv(\"dev.csv\", sep='\\t', index=None)\n",
+    "audio_dataset = generate_df(\"kotiria\", \"dev\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"kotiria\", split=\"train\")\n",
+    "audio_dataset = generate_df(\"kotiria\", \"dev\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"kotiria\", split=\"dev\")\n",
     "\n",
-    "a = df[\"file_name\"].values.tolist()\n",
-    "a = flatten(a)\n",
-    "#audio_dataset = Dataset.from_dict({\"audio\": a}).cast_column(\"audio\", Audio())\n",
-    "#audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", split=\"dev\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "4ce2eeb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'audio': {'path': 'data/quechua/train/quechua000000.wav',\n",
-       "  'array': array([0.00045776, 0.00042725, 0.00018311, ..., 0.00286865, 0.00186157,\n",
-       "         0.00253296]),\n",
-       "  'sampling_rate': 16000}}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "audio_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "bd39f2f4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>subset</th>\n",
-       "      <th>file_name</th>\n",
-       "      <th>source_processed</th>\n",
-       "      <th>source_raw</th>\n",
-       "      <th>target_raw</th>\n",
-       "      <th>split</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>quechua</td>\n",
-       "      <td>data/quechua/train/quechua000000.wav</td>\n",
-       "      <td>wañuchisunchu kay suwakunata</td>\n",
-       "      <td>wañuchisunchu kay suwakunata</td>\n",
-       "      <td>matemos a esos ladrones</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>quechua</td>\n",
-       "      <td>data/quechua/train/quechua000001.wav</td>\n",
-       "      <td>imaninkichikmi qamkuna</td>\n",
-       "      <td>imaninkichikmi qamkuna</td>\n",
-       "      <td>que dicen ustedes</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>quechua</td>\n",
-       "      <td>data/quechua/train/quechua000002.wav</td>\n",
-       "      <td>hatun urqukunapi kunturkunapas uyarirqan</td>\n",
-       "      <td>hatun urqukunapi kunturkunapas uyarirqan</td>\n",
-       "      <td>en grandes montañas hasta los condores escuchaban</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>quechua</td>\n",
-       "      <td>data/quechua/train/quechua000003.wav</td>\n",
-       "      <td>ninsi winsislaw maqtaqa tumpa machasqaña</td>\n",
-       "      <td>ninsi winsislaw maqtaqa tumpa machasqaña</td>\n",
-       "      <td>dice el joven wessceslao cuando ya estaba borr...</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>quechua</td>\n",
-       "      <td>data/quechua/train/quechua000004.wav</td>\n",
-       "      <td>huk qilli chuspi chuspi misapi kimsantin suwak...</td>\n",
-       "      <td>huk qilli chuspi chuspi misapi kimsantin suwak...</td>\n",
-       "      <td>una sucia mosca en la mesa con los tres ladron...</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1411</th>\n",
-       "      <td>waikhana</td>\n",
-       "      <td>data/waikhana/train/waikhana001414.wav</td>\n",
-       "      <td>masiaha malia masinapea</td>\n",
-       "      <td>masiaha malia masinapea, ()</td>\n",
-       "      <td>Nos tambem sabemos (as historias antigas)</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1412</th>\n",
-       "      <td>waikhana</td>\n",
-       "      <td>data/waikhana/train/waikhana001415.wav</td>\n",
-       "      <td>a'lide mu:sale ya'uaha yu:'u:</td>\n",
-       "      <td>a'lide mu:sale ya'uaha yu:'u:</td>\n",
-       "      <td>Tudo isso estou explicando para voces.</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1413</th>\n",
-       "      <td>waikhana</td>\n",
-       "      <td>data/waikhana/train/waikhana001416.wav</td>\n",
-       "      <td>a'lide tina a'likodo pekasonoko a'li gravaka'a...</td>\n",
-       "      <td>a'lide tina a'likodo pekasonoko a'li gravaka'a...</td>\n",
-       "      <td>Tudo isso essa branca vai gravar.</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1414</th>\n",
-       "      <td>waikhana</td>\n",
-       "      <td>data/waikhana/train/waikhana001417.wav</td>\n",
-       "      <td>sayeotha ninokata mipe</td>\n",
-       "      <td>sayeotha ninokata mipe</td>\n",
-       "      <td>Ela disse que vai fazer tudo isso,</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1415</th>\n",
-       "      <td>waikhana</td>\n",
-       "      <td>data/waikhana/train/waikhana001418.wav</td>\n",
-       "      <td>yu:'u:le ~o'o ihide yu:'u: akaye</td>\n",
-       "      <td>yu:'u:le ~o'o ihide yu:'u: akaye</td>\n",
-       "      <td>Para mim, e' ate aqui, meus irmaos.</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4749 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        subset                               file_name  \\\n",
-       "0      quechua    data/quechua/train/quechua000000.wav   \n",
-       "1      quechua    data/quechua/train/quechua000001.wav   \n",
-       "2      quechua    data/quechua/train/quechua000002.wav   \n",
-       "3      quechua    data/quechua/train/quechua000003.wav   \n",
-       "4      quechua    data/quechua/train/quechua000004.wav   \n",
-       "...        ...                                     ...   \n",
-       "1411  waikhana  data/waikhana/train/waikhana001414.wav   \n",
-       "1412  waikhana  data/waikhana/train/waikhana001415.wav   \n",
-       "1413  waikhana  data/waikhana/train/waikhana001416.wav   \n",
-       "1414  waikhana  data/waikhana/train/waikhana001417.wav   \n",
-       "1415  waikhana  data/waikhana/train/waikhana001418.wav   \n",
-       "\n",
-       "                                       source_processed  \\\n",
-       "0                          wañuchisunchu kay suwakunata   \n",
-       "1                                imaninkichikmi qamkuna   \n",
-       "2              hatun urqukunapi kunturkunapas uyarirqan   \n",
-       "3              ninsi winsislaw maqtaqa tumpa machasqaña   \n",
-       "4     huk qilli chuspi chuspi misapi kimsantin suwak...   \n",
-       "...                                                 ...   \n",
-       "1411                            masiaha malia masinapea   \n",
-       "1412                      a'lide mu:sale ya'uaha yu:'u:   \n",
-       "1413  a'lide tina a'likodo pekasonoko a'li gravaka'a...   \n",
-       "1414                             sayeotha ninokata mipe   \n",
-       "1415                   yu:'u:le ~o'o ihide yu:'u: akaye   \n",
-       "\n",
-       "                                             source_raw  \\\n",
-       "0                          wañuchisunchu kay suwakunata   \n",
-       "1                                imaninkichikmi qamkuna   \n",
-       "2              hatun urqukunapi kunturkunapas uyarirqan   \n",
-       "3              ninsi winsislaw maqtaqa tumpa machasqaña   \n",
-       "4     huk qilli chuspi chuspi misapi kimsantin suwak...   \n",
-       "...                                                 ...   \n",
-       "1411                        masiaha malia masinapea, ()   \n",
-       "1412                      a'lide mu:sale ya'uaha yu:'u:   \n",
-       "1413  a'lide tina a'likodo pekasonoko a'li gravaka'a...   \n",
-       "1414                             sayeotha ninokata mipe   \n",
-       "1415                   yu:'u:le ~o'o ihide yu:'u: akaye   \n",
-       "\n",
-       "                                             target_raw  split  \n",
-       "0                               matemos a esos ladrones  train  \n",
-       "1                                     que dicen ustedes  train  \n",
-       "2     en grandes montañas hasta los condores escuchaban  train  \n",
-       "3     dice el joven wessceslao cuando ya estaba borr...  train  \n",
-       "4     una sucia mosca en la mesa con los tres ladron...  train  \n",
-       "...                                                 ...    ...  \n",
-       "1411          Nos tambem sabemos (as historias antigas)  train  \n",
-       "1412             Tudo isso estou explicando para voces.  train  \n",
-       "1413                  Tudo isso essa branca vai gravar.  train  \n",
-       "1414                 Ela disse que vai fazer tudo isso,  train  \n",
-       "1415                Para mim, e' ate aqui, meus irmaos.  train  \n",
-       "\n",
-       "[4749 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
+    "audio_dataset = generate_df(\"bribri\", \"train\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"bribri\", split=\"train\")\n",
+    "audio_dataset = generate_df(\"bribri\", \"dev\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"bribri\", split=\"dev\")\n",
+    "\n",
+    "audio_dataset = generate_df(\"waikhana\", \"dev\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"waikhana\", split=\"train\")\n",
+    "audio_dataset = generate_df(\"waikhana\", \"dev\")\n",
+    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"waikhana\", split=\"dev\")\n"
    ]
   },
   {

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