diff --git "a/train_kh.ipynb" "b/train_kh.ipynb"
--- "a/train_kh.ipynb"
+++ "b/train_kh.ipynb"
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "bff05704",
+   "id": "a88514f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9637cdfd",
+   "id": "2d955148",
    "metadata": {
     "collapsed": true,
     "jupyter": {
@@ -19160,14 +19160,14 @@
     }
    ],
    "source": [
-    "%%bash \n",
-    "wget https://www.openslr.org/resources/42/km_kh_male.zip\n",
-    "unzip km_kh_male.zip"
+    "# %%bash \n",
+    "# wget https://www.openslr.org/resources/42/km_kh_male.zip\n",
+    "# unzip km_kh_male.zip"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b11b1d53",
+   "id": "54b0e493",
    "metadata": {},
    "source": [
     "### Load KH Data"
@@ -19175,153 +19175,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "f35b6d68",
+   "execution_count": 6,
+   "id": "1f31e61b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "import pandas as pd\n",
-    "from datasets import load_dataset\n",
+    "# from sklearn.model_selection import train_test_split\n",
+    "# import pandas as pd\n",
+    "# from datasets import load_dataset\n",
     " \n",
-    "colnames=['path','drop','text'] \n",
-    "df  = pd.read_csv('km_kh_male/line_index.tsv',sep='\\t',header=None,names=colnames)\n",
-    "df['path'] = '/workspace/xls-r-300m-km/km_kh_male/wavs/'+df['path'] +'.wav'\n",
+    "# colnames=['path','drop','text'] \n",
+    "# df  = pd.read_csv('km_kh_male/line_index.tsv',sep='\\t',header=None,names=colnames)\n",
+    "# df['path'] = '/workspace/xls-r-300m-km/km_kh_male/wavs/'+df['path'] +'.wav'\n",
     "\n",
-    "train, test = train_test_split(df, test_size=0.1)\n",
+    "# train_valid, test = train_test_split(df, test_size=0.1)\n",
+    "# train, valid = train_test_split(train_valid, test_size=0.1)\n",
     "\n",
-    "train.to_csv('./km_kh_male/line_index_train.csv')\n",
-    "test.to_csv('./km_kh_male/line_index_test.csv')"
+    "# train.to_csv('./km_kh_male/line_index_train.csv')\n",
+    "# valid.to_csv('./km_kh_male/line_index_valid.csv')\n",
+    "# test.to_csv('./km_kh_male/line_index_test.csv')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "a0b561cb",
+   "execution_count": 2,
+   "id": "63b2d9b0",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using custom data configuration default-9437ac7a59e13b5b\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading and preparing dataset csv/default to /workspace/.cache/huggingface/datasets/csv/default-9437ac7a59e13b5b/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cb75a7b737324ce98def82472b0823f3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb54d69ef0b44b12a291e97c65e6acb5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset csv downloaded and prepared to /workspace/.cache/huggingface/datasets/csv/default-9437ac7a59e13b5b/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e. Subsequent calls will reuse this data.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using custom data configuration default-fbad308ab5a03eb2\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading and preparing dataset csv/default to /workspace/.cache/huggingface/datasets/csv/default-fbad308ab5a03eb2/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "21208a6e24e4475eb1035cde32ca9cf8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9b4cbf41b1f44239a225984dd271ef38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset csv downloaded and prepared to /workspace/.cache/huggingface/datasets/csv/default-fbad308ab5a03eb2/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e. Subsequent calls will reuse this data.\n"
+      "Using custom data configuration default-decaf49f8e8b5be8\n",
+      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n",
+      "Using custom data configuration default-2ae3784a8d52f12b\n",
+      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-2ae3784a8d52f12b/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n"
      ]
     }
    ],
    "source": [
-    "common_voice_train = load_dataset('csv', data_files='km_kh_male/line_index_train.csv', split= 'train')\n",
-    "common_voice_test  = load_dataset('csv', data_files='km_kh_male/line_index_test.csv', split = 'train')"
+    "common_voice_train  = load_dataset('csv', data_files='km_kh_male/line_index_train.csv', split= 'train')\n",
+    "common_voice_valid  = load_dataset('csv', data_files='km_kh_male/line_index_valid.csv', split = 'train')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "c8ae4532",
+   "execution_count": 3,
+   "id": "dbb54220",
    "metadata": {},
    "outputs": [],
    "source": [
     "common_voice_train = (common_voice_train\n",
     "                      .remove_columns([\"Unnamed: 0\", \"drop\"])\n",
     "                      .rename_column('text', 'sentence'))\n",
-    "common_voice_test  = (common_voice_test\n",
+    "common_voice_valid  = (common_voice_valid\n",
     "                      .remove_columns([\"Unnamed: 0\", \"drop\"])\n",
     "                      .rename_column('text', 'sentence'))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4649ca2b",
+   "id": "3bb5808a",
    "metadata": {},
    "source": [
     "### Clean Up the Text"
@@ -19329,90 +19243,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "363283a2",
+   "execution_count": 4,
+   "id": "8d407f91",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Remove character\n",
-    "import re\n",
-    "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\']'\n",
+    "# # Remove character\n",
+    "# import re\n",
+    "# chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\']'\n",
     "\n",
-    "def remove_special_characters(batch):\n",
-    "    batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"])\n",
-    "    return batch"
+    "# def remove_special_characters(batch):\n",
+    "#     batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"])\n",
+    "#     return batch"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "51f70aa8",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-00e6eaf49d7a0a72.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-a5c6e0729d04ce41.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-ce6c54a59fe05e35.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-29d8049f5c2dba0e.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-1df366b8d10cfb25.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-c45845836398934b.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-7903962493b874e7.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-f2e999a6f044abd3.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-dd4571ca2fa9c4a1.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-a257464bb7803111.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-e1c125052aa48f5b.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-f664da64f417b5a5.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-02bd05008dc04374.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-6fad30701b26812e.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-dad4e12ac7d6d186.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-5fd0baa6aaaebb3e.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-6f779778f437a26a.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-0efd47ea8a8177b6.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-8a88d75b0505fbb2.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-4bfa4af95a8ec66f.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-9cad1e7ec046a602.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-1a35866972ad6ff0.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-851089e09a0300d5.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-b7841fcfb395ed79.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-fa5f9aa61c7ae7c7.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-af520a73d2804e0d.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-ab7efcf946800b88.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-a300b8c2ffe734b5.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-0be434c6760de4ea.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-6688f4c58a7ac42a.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-61e7bc46c7a41532.arrow\n",
-      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd/cache-f500a4728b69e2aa.arrow\n"
-     ]
-    }
-   ],
+   "execution_count": 5,
+   "id": "9fb25eaf",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# TODO: somehow this break thigns. WHY?\n",
-    "common_voice_train = common_voice_train.map(remove_special_characters, num_proc=16)\n",
-    "common_voice_test = common_voice_test.map(remove_special_characters, num_proc=16)"
+    "# # TODO: somehow this break thigns. WHY?\n",
+    "# common_voice_train = common_voice_train.map(remove_special_characters, num_proc=16)\n",
+    "# common_voice_valid = common_voice_test.map(remove_special_characters, num_proc=16)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "fbc089d7",
+   "execution_count": 6,
+   "id": "84c7300e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'path': '/workspace/xls-r-300m-km/km_kh_male/wavs/khm_6753_2783635929.wav',\n",
-       " 'sentence': 'កុងដ្វាណា មាន មូលដ្ឋាន នៅ ប្រទេស ស៊ីរី'}"
+       "{'path': '/workspace/xls-r-300m-km/km_kh_male/wavs/khm_1443_4015603856.wav',\n",
+       " 'sentence': 'ទេសចរណ៍ នៅ ខេត្ត ព្រះ សីហនុ នា រដូវ បុណ្យ ភ្ជុំ បិណ្ឌ នេះ មាន ការ កើន ឡើង យ៉ាង ខ្លាំង'}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -19423,7 +19293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af02801f",
+   "id": "66dfb9ff",
    "metadata": {},
    "source": [
     "### Build Character"
@@ -19431,19 +19301,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "a9e58b43",
+   "execution_count": 7,
+   "id": "64329ebd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "43a0dab848324962b6dd24d047f2d1e2",
+       "model_id": "75af178afb3347c8ad58898236df7ad7",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/2353 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -19452,12 +19322,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98aa49d7ca8c4edd970cdd7e51488b08",
+       "model_id": "8092fc6f10db42bf9da801757fdd8ba8",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/262 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -19472,15 +19342,15 @@
     "    sentence = batch['sentence']\n",
     "    vocab_train.extend(list(set(list(sentence))))\n",
     "    \n",
-    "for batch in tqdm(common_voice_test):\n",
+    "for batch in tqdm(common_voice_valid):\n",
     "    sentence = batch['sentence']\n",
     "    vocab_test.extend(list(set(list(sentence))))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "4480543c",
+   "execution_count": 8,
+   "id": "78297789",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19490,8 +19360,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "99857f4d",
+   "execution_count": 9,
+   "id": "d66aebea",
    "metadata": {},
    "outputs": [
     {
@@ -19508,8 +19378,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "bec53215",
+   "execution_count": 10,
+   "id": "7c085935",
    "metadata": {},
    "outputs": [
     {
@@ -19518,7 +19388,7 @@
        "73"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -19535,8 +19405,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "cf58f8a4",
+   "execution_count": 11,
+   "id": "fba33316",
    "metadata": {},
    "outputs": [
     {
@@ -19553,8 +19423,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "0c621a15",
+   "execution_count": 12,
+   "id": "5376a5b4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19565,7 +19435,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb8b5aa3",
+   "id": "aec637e0",
    "metadata": {},
    "source": [
     "# Tokenizer"
@@ -19573,8 +19443,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "dc1c1984",
+   "execution_count": 13,
+   "id": "781094bc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19585,25 +19455,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "id": "6324377d",
+   "execution_count": 14,
+   "id": "3a3eb52f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Didn't find file ./tokenizer.json. We won't load it.\n",
-      "loading file ./vocab.json\n",
-      "loading file ./tokenizer_config.json\n",
-      "loading file ./added_tokens.json\n",
-      "loading file ./special_tokens_map.json\n",
-      "loading file None\n",
-      "Adding <s> to the vocabulary\n",
-      "Adding </s> to the vocabulary\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")  # './' load vocab.json in the current directory\n",
     "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)  \n",
@@ -19613,7 +19468,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "f971580d",
+   "id": "2711ed79",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19630,7 +19485,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "d0368c7a",
+   "id": "2772b591",
    "metadata": {},
    "outputs": [
     {
@@ -19669,32 +19524,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "62e9d0c6",
+   "execution_count": 15,
+   "id": "db2af48f",
    "metadata": {},
    "outputs": [],
    "source": [
     "common_voice_train = common_voice_train.cast_column(\"path\", Audio(sampling_rate=16_000)).rename_column('path', 'audio')\n",
-    "common_voice_test  = common_voice_test.cast_column(\"path\", Audio(sampling_rate=16_000)).rename_column('path', 'audio')"
+    "common_voice_valid  = common_voice_valid.cast_column(\"path\", Audio(sampling_rate=16_000)).rename_column('path', 'audio')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "f642a861",
+   "execution_count": 16,
+   "id": "b7f42c6a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'audio': {'path': '/workspace/xls-r-300m-km/km_kh_male/wavs/khm_6753_2783635929.wav',\n",
-       "  'array': array([ 3.3284457e-05,  5.5497538e-05,  2.6061889e-05, ...,\n",
-       "          2.0593125e-06, -5.3043197e-05,  0.0000000e+00], dtype=float32),\n",
+       "{'audio': {'path': '/workspace/xls-r-300m-km/km_kh_male/wavs/khm_1443_4015603856.wav',\n",
+       "  'array': array([-1.3359112e-06,  1.5759380e-06, -2.0205737e-06, ...,\n",
+       "         -7.6091878e-06,  9.0511895e-07,  0.0000000e+00], dtype=float32),\n",
        "  'sampling_rate': 16000},\n",
-       " 'sentence': 'កុងដ្វាណា មាន មូលដ្ឋាន នៅ ប្រទេស ស៊ីរី'}"
+       " 'sentence': 'ទេសចរណ៍ នៅ ខេត្ត ព្រះ សីហនុ នា រដូវ បុណ្យ ភ្ជុំ បិណ្ឌ នេះ មាន ការ កើន ឡើង យ៉ាង ខ្លាំង'}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -19705,16 +19560,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "0c756a07",
+   "execution_count": 17,
+   "id": "42b525d0",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Target text: ផ្សារ អូឡាំពិក មាន ក្រណាត់ និង សម្លៀកបំពាក់ បោះ ដុំ និង លក់ រាយ\n",
-      "Input array shape: (81920,)\n",
+      "Target text: បញ្ជី ឈ្មោះ បោះឆ្នោត ខេត្ត កំពង់ចាម\n",
+      "Input array shape: (65536,)\n",
       "Sampling rate: 16000\n"
      ]
     },
@@ -19722,8 +19577,8 @@
      "data": {
       "text/html": [
        "\n",
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/uH+oQLVB/wMhhLSGF0hiykwKcUbDQuYAisEdQlSCKQATfo++VX14uyw4w7dvt1E5wryEvc2+jb8efx4/TQBvgLxA3kINAsVCZ0ET/3288TuPu4X777xB/bF+En7jP/jA+QHzg/qGvoiPCSLHnYXNxSwFEMUag93C5AJEgap/YD0te1C6KzlkeVo6J/saPEu8gHwBfG09ZP5Qftx/Of96wApA4wAzvk/9RX1R/fP+Vj7I/3hAPcELgZeB4gMhBbuIF8kzB60FkgUqxSKE7AO5AkcCRwK4QTY99Lt4OkF6XTo6eiJ6kbux/Kw8irw8PBs9Q/5ovub/ZP+HP8Z/nD6AvZd9ej37fpE/KP88P7oA/kI+gqyDCAU1B43IxUcOBKoELYVxxhkE88LZQqHDUkJBfrf7KbppuwG7mjsXOvo7WXySvG66+fp7+6I9e75HP0BAecEhgS4/Wn1z/Nl+Cf97/3h/G3+EAKtAwoBEwDJBVUQMRtaHzQc6hWyEwcUMxLHDlcMeA4tEVAP3ASu+F3yA/FF7/Xrl+tT7/3zivT879bqsur+7jrzJ/Vs90X7D/5E/Bn3SfO09A76nf2n/fT9aAJ2CM4KlgmTCjMUVCEWJTUc8xEdEgIXjhbJDtkJrg1nEi4NaP3x8JHuPfEM7zHplOhI7x71R/K76hfnU+uY8W/0ufQp+QMBVQQd/4r2xvP59+/9Pf+Z/VL/IATQBmcE3gFUBXsPwRtkIM4b5BOiEXATwBEvDEcJGQ9zFe4SKgYS+QD0CvSE8ffqB+kZ8DH4ofcF777nVeiw7ZTwMfBz89z8kQRRAir4EfGV8x77rf4K/Vf9rQKCB9QFaADx/7UIMRUlHsYeBRrqFO0TRRNWDlgJFQsBFOMXAxECA+H4M/Z19Fru9ueI6nb0rPqs9dbqXeWo6HrtqO1I7QP0BgCOBjABgvZf8r/3a/4x//38+f5dBasIMQTm/fL+IQnJE1EaIxzdGhEXjhLDD8ALFwmSCi8SOxegEvQG//t29330r++T6hnsxPTH+334p+045oTnNeyL7XPtnvIV/hgG/wHi9Uzv7PN0/D//xfyt/bUEegnEBBD9Xv32B6oTcBpcHH8b0Bh1FAwRtgw3CVIKRhKmGBsUAAhF/d74efXx70jqd+se9EL7Qfjo7WrmoObW6tXsdu1d8oL9SgYTBHv58vGL9Gr8vACU//T/7ARXCMwDa/uE+V4BGQ2PEw8XOxs4HQkXMw2SCesJ8Qp1DEQTZhhIFAMID/yE9p7zofCL7X3vtvb2/EX5W+3/47Tk+uqC7tjuYPLq/GUFpgE/9K/sBvMr/jsCU/7G/Q8EfwjaAkX6TfujBqYS+he9GmAcHxs9FLUO0AwvC5UKiw/ZGAQZBw4UAPn4g/bs8oPtI+sP8Q/6K/x38h3mEeLo5+XtPu5L7mf2EgSSCLf+TvFt8BD7PwNwAdD8aAD4BwEI2/1u9sL7/gjeEXcSuxTCGhocMRInCRsJDwytDLgO6xUBGE8QugJ4+RH2dPRY8fvub/I8+sn9AfV157vh3eeg7rzvve4g9WgC3AcR/szuqO0f+lkEEAKe+y7/5wg+Ccr8h/Mq+ncJ0hJeEUYRiBrFIOoWjAcUBZgMzhABEaoTvxXFEh8H1vpZ9FLzmPRB9OPzGvV198H0q+qc4gjmIu4C88rxJfLm+bsCuQE29rPx4/nNA4EDrvsX+tQB0Ad+Amz5t/vbBrcP7A4/DKMTCB16GgEOswrpD+sQ9AwKDbkRshIHDNIBPPva+NL2D/Je7ibvWPUy+Frx4uc+5+/tffDn7efscfXNApUGe/ze8vT2QQCHASX7xPi4AEcJ3AWq+o33DAGIC5oNOApPD3QcrB9FElwHaAwGEzcQQQuFDvkUDBQ+CJr7mvea+eD3HvEP7aXxYfkt95HrCOQp6cvwjPGi7UjvcfsqByEE8fbv8nb8iQTgAFD5ofpUBeoJfAAP9gr5lAXmC64IDgUJDyIfDx+dDcADFg5jFlIQxwbvCisXAxluCUr4XvbX/Mn7FfEX6hbw6vub+nDrt+Du5hvytPMZ7VHs+vmUCOoFwPUY72P5uQSiAhH5nfh+BHQLewE/9DT2yQSkDY0KKwV4Dcge/yA9D80CvAzmFxsT0QeCCU4WjRqKC2v4XPUT/Tv9Z/LU6cjtAfqc+zjtc+CY5Zvy7/Xl7sfrpPf2B2YIrvgQ8N75lgZjBS76Pfd/ApkLQgPi9H/0qwEzC14I7wFNBucY6iK/FV0DaQdXF8QXNAlvA/YRZx9+Fln+IPQ+/CwCbPh56ifpgfbx//z0Z+Ki3+Lt8PbF8QDpsu6wAYkMJwHO79bwuQAFCdj/mfWj+/QKwwul+szu7PZSB9gLcwOI/hwKMh5jIeoOp/8tCqgbXxeVBOoBKxYqI2UU8vnt8t3+sgRx93Pn0Oi5+fwBnPM94PPfbPAL+YDx+ueW70MD5gzTAOfvpfHKAU0J3P6z9Bz7DgplC0X7E+9s9qwGDAuaAkj+cQkdHYwi9RGlAWoKIxxoGIkFcwJuFlYkOxeE/Kfz0P7hBOL3kOdB6Dr4zAEM9RDh/t4F71j4g/HX5w3uvQGQDTID0PDL8NgAyQnr/5v0xfmyCeMMmfzl7c/zWQTkCXwBZPuMBEMXsyE2F/8FgQZwGDkdCwsc/nMNnCTMIXAG7/Mb+6MGDv/f6hnk0vExArL9ruj23GnnifZW9Z3pwed4+JoKUwpZ+QnvRfjLBl4GUPng9PgAoQ10By31Te25+FUGoAWE+1/6GAgxGHwd3RNRCFEK8xgoG1kJJ/6jDs8lASJOByT2ev0oBiX8xOnh5WHzLAH4/Zrr7d+k52/0kvNN6lHqBfmeCAUJLvs18Sz3MgGMAAD4K/cyAuAKzQMQ9G3uP/iyA2QFcQHIA7gPuxvLI94luB8hFLARZRNFCMr50QDLFdMW/QAe7kbtZO7D5dncJ+KE8n4ASgPy+sLz2/Zv/54A6P2dAroPQBr8FxkLwPzZ9XnzNe9m6Ojmv+0K9D7wfOU34S7oi/Pt+4AB7AgXEyYdPyGmJHg0tUcEQpkgegmvB/cCxPPb8Hb6Svtt8IXid9ZPy+vLIdtX71b+rgrgFiMbiRVJDmQMWA1tEQAWABfrESwJbfvX5WPTmczd0GbXEt8A6RnxdfVo9Tn3mP5MCl0VOx0HIrojtSUoKJAyl0Y9TOIlce5+2iHhQNtQz03dpfSJ+TTuxeJX2v/aNvAOD8EjNimXLkYzQSlFEPn4L/AX8pz38vcT8l/t+OyE5/HXUs661s/sE/9iB/YIhQigCL0E0P9R/A4DShBWGvoW3w8vFMAmoERVUK0rxuaJyCTZW+MP13nc0fzkELMIaPHu4IDiufxYH1gvsiiMIcokdRpA+P/W/dDZ4MjwmvZf84X1gP9hAvnz5eMh5z75OAieB8b+p/gO+FL2P/Dt7lj3oAmiFrQW1g/FEmshmTE9SSVZ0D4T9Ja8b7ofxnbE6NF9/CYaTxdQBEj3h/NFAaIfxTTKL9EeXBb2B9Xlr8N+vN3QS+vs/z8JQw6GE5kR4gGy7UToL/F2+iT4Je/o6F/pcO4D9RP+8gnkGGciGh4CEgUM1BReITI1tk9FTY8LZLbpnFK0Pced0/v8ayxVN3Ehegbi9H7wvAXUIs8oPxcuCWwDWeskyOW5Ks/W8uUOth0VH3wdJhWK/33ipNQn3Vbs6/Ny8sHw4fJ6+fwBLQpQEgYZxxvgEvsCM/n6/y0SpiTbQZVa8kAJ6Hqgp6Q+yePcFfFaHWM6Hy09CUXtEeGE66gNOisfKtUWiwk4+/XcRMFoxT3laQaEGzUgVRknEO8Bjewx2t/bFeoO9ab39vV19sD47P0VBgENixCkDnQJGABz+KL71wofHoQuhUqJX0498t/Km3Ck58lB3930/BsPNIAnXwaH6zLkwPZ8GBIxTC6BFtX9VOld0ui/fcbT6AYQ3SReJJ8YPg5UAn7vdOCf4MrpwO6T76fv8PBO9uYBHA2jEooRBg0+BI/5n/PF+SEKCR2FKkI4UlEjUvoUOrsTmZqzr9St6+YJXyaPK6IbXwGG6rvqgghEJ+stDx0uBKjqMtTmxTzGT9zDApIlDjCaIxURdQMI9j3l8d3P4rboqOjU6DfuRPjWBWoTexmxFWIL8f9l8uLqb/EJBAMXHSRhLLE02UkuTRAYZsMJoLe4iNSk4vz+UyPALD8bdgYk9y/zXwchJEYrARbA+lblidMYx4jK2eMpCggpey8IIHkMpQCB9gXnQd5e4azlKeXV57ryPwBODZUXtBidDuAAuvZ87ifrSvVrCDwaiCLBJeonkDlnUK862u0crSutbcVN0Cvk3Q9pL2Et6Rt0CFH23PgMEwMl3RjK/hDrm9szztLK5Nl5+QYdBzEMK+sWeQYu/GjsXdvW13ne0eQm6hL0Uv/yCZETkBelD5kBJ/aW8Gvt+fKcAWMRFRtiHhIiFCqHRAFWVjCn2xSmyaw9v9DJ3Or0Hw87FDI5HfEG0PM7+dIRvxzsDVv6nuvM2r3LE8zE4OkDrybsNV0qyRMqAlf0QOH+03vYVuTw6/nwFPqYAzoMZhLDEmwJJ/7m9XLxT+4k9F0CqhFFHLshnyadLRdFw1POLXraY6RaqNW6B8mH7i0lmj+ANcwf4Qhd8230kgmVFUYLuft/7rDdms6/zm/izwMiJc8zTigDEuUA2vJZ3xfSM9iN5pHvwfRU/RwFKApaDX8NhQbM/fr3G/QD8IzylP9vD1scLiK8JcUpkDsVUJo8I/T7rsqh8bPHxCXj3RjZPrU9zChfEsL4sesb+cMMCQ4HA5H46ekT19zOcNvY9mQWVyv6Kh0a8giJ+QDkaNCI0l7kAvL691n/dwYRCPwG3gVtAoT9JPx++jf2wfKJ+m4INRboHaUhuSWyLhNEf0vbIX3Ty6O5qOa8N9BP+pkuiUOLNq8hkAnm7I3l4fnLDBIMXwQ2+qfnfNXn1HfkM/wPFgMoxCb9FzYHIPJK2DLNP9pX63X0rP3UCYcM7QRV/rT8Qvxa/WD/Tf1t+MX47QHICwgUEhqlITApXjTTRo5EchEhxxekqq2Iv2jXSQgyNwVCyTKFIJsEK+Qb4Vn6iww4C7IFFf0Y6uLY5deQ4h72YBEOJ/wmZxhBBmLusNSXzjXcFeqF9dIFMxKoDAP/LPmE+W75IPwSAbwBO/7+/pkEiglcD6QXWSInKyg4n0kQQJwGUsETp0exzsBX3NwPHDrAQJIzwiB//grdOt8T+v8IjQgKCqMFP/Gz3IfXrd177kwKqyEJJOMZqgyQ9RXZYs932UDmw/ONB7cUHg6UAGP68/YI8+f2vADfBEMDZwVCCSMJPwrzEXgdaicYNgJK7EMhD4bMGa65sHq77tWHCYA2XEMlO7QnuQGj20jYWO6p/ZwEYxB1E8QAfeik21vXSt539kkSOx4lHh4ZhwU25s/T9dOx2gHoEQELFrYVvgkoASD4k+0v7ez3uwFiB/4OfhPdDrgJoA2fFyUhKjBqRQVDPBWg2SW7CLZfuLHN5/54LPM9oDx3L00N8uV02iPmI/D3+EML0RbcC8v2oOeH2/7Wf+TD/O8NeBjvHygXv/sV5B/aHdYq2hXvHgmvExsRXQ2LBEL0Vep/7uL3zwA4DQsYwxcoEQoQsRRfGZ0iUDdYQWsmRvRF0pTFmb0nwmPkoBB8Kg004zXDJLIB6+qN6dXqEur29mYKYg4LA8L4de704CvcLuco9qYCjhEYG1US1//J8eDlutqM3KvuAwBYBxsN/BBJCC/4E/Db8Xr1Lf2RC+AW5hctF/saix3qHLMlrTSaLpALkOna2irRHsi208jzRg3cGHYjRyifGG8Dffw/+3byze59+a4C5v7N+eP4NvKe5oPjN+pI8e/5mwagDn8LKgQR/WPzyel56aXxl/hJ/VYEEAmYBPv7pPim+aT67v4vCBoQahM+FxAdlR+aIE8oGC6/IBEGU/ON6ibhqtxE6Bj5zv95AtEJdwx2AwX7jPtm/ez62PtjAhMGAgRNAwkEaP4I81fsQO3S7njuJ/Do9KD30Pbj9SH2tfWr9sn6Zf8AAfkBbwRNBaECYwBVAfYCbQN3BHAHRwoWDBgOSxA/EpMUsRQoD9cE7vvC+E37uwFfCfEOVRFGEkISew5EBcj6zvQl86jxE/Au8Uf1NPpl/uX/6fyS9xf13PXS9fLzQfQT+Cb7Mvt6+oD6Z/pd+gD7N/sc+nv5UPqv+lX5UPgd+Yn6RPsz/J/++QEJBbQHZQo0DdUQ4BT9FS4RpAnoBScIfg1ZE5MYUxzWHgEhbiBRGfoNWAXyABT8vPUz8gnzcPW895P4TPXo7pPqpunm56zjUeEu4ybmAug66iPtDe9E8APySvPh8rXyGPW8+FD7ZP2QACEEOwZFBykJGwxND/USchdbHHAgviFhHnMXXRG4D0wSRBYMGV8aBxyeHvoelxn4DzoH0ABQ+m3zse5M7WvuLvEb8/jw3+sz6M3mOuRa3+LbEdw03t3ggOSi6N/rUO6T8LjxOvH78BHzyfZm+sT9wwHkBbMIbwo2DLoOuhGhFWIaZB9fJBEogScCIXQYPRPCEqwU8xb6GAEbYR1PHzwdRRUYCxoDcPxZ9H/sW+hR6Erqkuwn7d/qn+fe5XPk1eAj3A/aDNsr3enfZeQj6hXvNPK48wn05fO19Bn3VPrD/ekBoAZSCvIL0gynDqMR6BRkGGMcjyGPJ1UrQymeIVsZsxSzE00USRWIFtEYMBwJHp8aURKMCX0Czfpe8WTpqeWd5VTnLulh6cDnM+Zv5ZLjbd8H2wPZRNnh2mTeduRq667we/O79Fz1w/We9m74bPuP/5gEdgmfDPQNJA9IEf0ToBaOGZwdLyMrKR0sMCmqIUMazxX7E6gTJhRbFaEXvBoDHGcYsRDYCOUBm/nl7zvomOQV5DLl+ubJ5xXnPubG5eHjw97l2PHV5tZN2rjfrOYw7Ujxe/P19G/12fRA9Tz40fxcAfgF5QpYDgEQThGGE9MVGhgrG7UfQyVLKkYs9iiUIUAaLhbeFE0UwhNIFJYWthlZGkIW2A6FB6EAGPg87jbmaOIk4tLjn+Vg5j7mi+aC5v3iyNuf1ZzUz9dq3MTh8eea7cXxUvQl9S/0xvMY9lj6Mf4JAm4HEw2MEPURfxPHFS0YlxqHHTkhKyZOKyot6CgUIbgazhdCFnkUARNBE6oVdxhXGKQTtwxQBqL/U/bY6w3ku+Co4OrhMuMC5KnkKuVZ48ndM9d21HzWStrZ3Qjiiece7RXxwfKz8s7y6vSI+Oj7Hf8wBL0KFBCiEggUFBbkGH0bnx2+HzsjzCi9Lb8tFSgnIZ8cJRqGF74UDxOXE94VvBdYFk0RKgtfBRv+/PPM6Q7jbuDv32bgS+Ew4m/iP+EC3n/ZW9Z61uzYeNsC3gfiiudq7AzvL/CV8Qf0FvcZ+mT96gHhB+ENLBKNFJcWbBlwHIweByBKIocm3SsSL3ot/ycoIucduRqyF/QUVBNsE9YUohWPE5YO2QjHAtX61vC358bh19653Z7d49283S7dGNxT2hXYC9cL2AzamNtu3fjg0OUF6tXsPe8x8sT1Yfnx/L4AngVNC7gQhBQGF4IZgRxnH1chwiIzJQAqny5CLtIocyMcIUcfBRxbGFgVfxPOExoVORMZDZ4H/QQmABr2E+zi5iPkHOGb3mrdMNyV25Hcz9xE2uzXRNn620fcydtu3onjm+cj6vbsb/Dt85r3hPvX/mYCngerDdkRzRMcFvIZSB25HpUflCFcJHMnvioMLUAsZCfsIKsbshjsFo0VyhN0EbUPfA/4Dh8LQwSq/Yj4j/L76kLkeOCl3sPdWd3Y3KvbItso3AHdK9xk267cxd4P4KHhO+Wj6UPtafDy82/3uPqU/g0DHwfOChgPXRMRFpEXsRmMHJseXh8fILghESTFJsooeChiJU0hnB3sGZwVnBHjDksNJAz8ClsJwQZ5A8f/BPvM9CLuWujh41zgyt043KLbw9uI3I3dSN6v3gzfg98B4OPglOIv5XboJuwG8BD0Efjy+7z/lQOSB48LKw9KEisV2hdKGlwcFR5OH00gdCEfIyAlZSb9JdEjiyDBHPYYXRXaEYEO1wsvCgEJPweuBMQBcv79+Wv0lu4t6WHkoOBQ3u7cKdxK3Ibdzd5b37Tfc+Ah4WPh9uGj4yfmMOnQ7Nvw2vS8+OT83gBeBJwHOAvVDsYRNBSrFiQZMBvAHP4d8B6rH3sgmyGeIoUiKCHhHvEbZRjfFMARug6uCx0JUgeZBWYDCAG0/pb7SPeB8v3t1Ok/5rvjAuKy4Bzgp+DD4YTi+OK344nkF+Wy5eLmmOi+6qrtN/HA9Cr48PvN/xkD/gUTCSgM0g4HETcTQBX2FnwY4xnJGh4bYxvDG98buxuRG+MaIhlVFkETVhClDdMKCAhBBd0CFQGu/wT+yPut+Z73OfU28g7vK+yz6ebnrOba5ZnlTubd50fpJerV6s7r5uzX7c3u/++y8Q/07PbN+Xn8Of8vAv4EUgdBCSML9gyXDvMPExESEv4SqhPvE9ITghMtE9QSKhImETkQYQ87DnEMRgomCCsGWAS5AhQBUf/r/QX9N/wO+9n58fj296X2MvXq85vyavHR8LfwyfAC8bzxqvJR8+fzo/Rf9ej1kfaW97747/lj+xn9sP4eAKEBHAM7BBIFAAbWBnsHFQiyCDMJgQneCR0KNgr9CboJXQnyCIwI+gdVB5wGCAZlBZMEsAPEAuEBBgFkANX/Hv+X/jf+7/19/fT8ePz4+4z7KPvL+nb6Lvom+if6JPo2+kD6WPpw+qX60/oB+1T7svsK/Hn8Cv2m/Rn+i/4E/3D/2v9KAOYAWAHRAVkC3QJJA4cD1AMTBCwEJQQjBCoEPQQ3BC0EJgQRBPcDxwOLAzkD5QJ7AhkCuAFRARMBygCKAEkA/P+z/2H//P6e/jv+6/2z/W/9L/30/Nf8xfy4/Kn8qPyp/Lj8z/zk/Af9Nf1r/aH92v0W/mv+t/4H/1T/pf/9/0IAlQDoAC8BbAGhAdMB+AEVAjQCUwJbAlsCZAJpAl4CSAImAgAC0gGjAYEBRwESAeIAvQCcAG0ARwAWAOX/u/+Q/1z/Jv/2/tL+u/6l/ov+i/6G/oT+f/5+/oP+fP6D/o3+mv6l/sn+8/4b/0P/Z/+S/6//z//u/woAKwBRAIIAqgDVAP8AIQFEAVoBbwF/AYEBhgGNAYoBhgGIAYkBgQFvAVcBPgEgAQMB3wC2AJMAdABgAD0AHQABAOb/zP+n/4X/Xf8//yn/Ev8C/+3+3/7P/sH+v/62/rT+tv67/sj+0/7n/vv+Ff8n/z//Wf9z/4r/nv+4/9j//P8XADoAYAB/AJ4ArAC6AM8A3gDnAPEA/gAJARABFgEbARcBAwH4APEA2wDIALgAqACVAIEAbABSAD8AJwAMAPX/2v/A/63/lf9//3D/Zf9Y/07/Rv86/zf/Mv8p/yj/KP8r/zD/M/8+/0f/U/9c/2X/eP+H/53/sP/E/97/9f8MACcANgBAAFIAbQB4AIYAkwCiAK8AswC+AL0AvwC5ALsAvACuAKQAlQCNAIEAdABkAE8AOQAiAAsA/f/q/9r/yf+1/6P/jP99/3P/Yf9N/0f/OP8y/y3/Jv8r/yv/MP8x/zX/OP9F/07/Xf9x/4L/k/+f/7T/yP/e//D/AAARACQAOQBFAFcAaQB1AIMAiQCTAJwAmwCjAKYAqACmAJsAlwCOAIgAfABvAGMAVABMAD0AMgAhABMABwD5/+z/2P/L/73/rP+f/5L/jP+E/33/e/96/3j/ev+A/4T/if+Q/5j/pP+v/7b/vv/Q/9v/7P/7/wwAIwAuAEEATgBgAG8AegCGAIoAkgCWAJYAmACZAJkAkwCMAIYAewB1AGsAYQBTAEYAPQAvACEADwACAPb/5v/X/8v/vf+t/6D/lP+I/37/dv9u/2z/bP9q/23/bP9w/3j/fv+F/43/mP+j/6//tv/B/8v/1//r//j/AAAN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type=\"audio/wav\" />\n",
        "                    Your browser does not support the audio element.\n",
        "                </audio>\n",
        "              "
@@ -19732,7 +19587,7 @@
        "<IPython.lib.display.Audio object>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -19747,13 +19602,13 @@
     "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n",
     "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n",
     "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])\n",
-    "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)"
+    "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=False, rate=16000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "d2a5374c",
+   "execution_count": 18,
+   "id": "1db1a77c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19774,19 +19629,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "9c3697ba",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "id": "b0a33568",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-a8ad7f3bec152712.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-5802da9af6ac9ac7.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-418585d4baf07152.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-c7e5028c91005615.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-b96a6332cc5af3be.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-6a1544fcabe8e1c5.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-4926c7991e987d55.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-b607d477202e12db.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-a04833a515432724.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-bee3e39e2b69f652.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-ad833922c61a3f31.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-ecf0c779c655274d.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-2ba17aaff236f685.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-5ab7f29ea26c63ef.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-240348bb32bdbb06.arrow\n",
+      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-decaf49f8e8b5be8/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-38e1126fb0ce94f8.arrow\n"
+     ]
+    }
+   ],
    "source": [
     "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names, num_proc=16)\n",
-    "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, num_proc=16)"
+    "common_voice_valid = common_voice_valid.map(prepare_dataset, remove_columns=common_voice_valid.column_names, num_proc=16)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "d5bd0662",
+   "execution_count": 20,
+   "id": "ca8be265",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19797,8 +19680,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "639dd5a7",
+   "execution_count": 21,
+   "id": "53a815bf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19857,8 +19740,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "c4fe1643",
+   "execution_count": 22,
+   "id": "1d0cbdf6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19867,8 +19750,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "9fb388e3",
+   "execution_count": 23,
+   "id": "e26e68a2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19878,8 +19761,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "96611455",
+   "execution_count": 24,
+   "id": "f347bb3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19891,10 +19774,6 @@
     "\n",
     "    pred_str = tokenizer.batch_decode(pred_ids)\n",
     "    label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)\n",
-    "\n",
-    "#     print(\"pred : \", pred_ids[0])\n",
-    "#     print(\"label: \", pred.label_ids[0])\n",
-    "#     print(\"-----------------\")\n",
     "    \n",
     "    wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
     "\n",
@@ -19903,134 +19782,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "id": "bb429520",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
+   "execution_count": 25,
+   "id": "aff51ef4",
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "loading configuration file checkpoint-4000/config.json\n",
-      "Model config Wav2Vec2Config {\n",
-      "  \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n",
-      "  \"activation_dropout\": 0.0,\n",
-      "  \"adapter_kernel_size\": 3,\n",
-      "  \"adapter_stride\": 2,\n",
-      "  \"add_adapter\": false,\n",
-      "  \"apply_spec_augment\": true,\n",
-      "  \"architectures\": [\n",
-      "    \"Wav2Vec2ForCTC\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"bos_token_id\": 1,\n",
-      "  \"classifier_proj_size\": 256,\n",
-      "  \"codevector_dim\": 768,\n",
-      "  \"contrastive_logits_temperature\": 0.1,\n",
-      "  \"conv_bias\": true,\n",
-      "  \"conv_dim\": [\n",
-      "    512,\n",
-      "    512,\n",
-      "    512,\n",
-      "    512,\n",
-      "    512,\n",
-      "    512,\n",
-      "    512\n",
-      "  ],\n",
-      "  \"conv_kernel\": [\n",
-      "    10,\n",
-      "    3,\n",
-      "    3,\n",
-      "    3,\n",
-      "    3,\n",
-      "    2,\n",
-      "    2\n",
-      "  ],\n",
-      "  \"conv_stride\": [\n",
-      "    5,\n",
-      "    2,\n",
-      "    2,\n",
-      "    2,\n",
-      "    2,\n",
-      "    2,\n",
-      "    2\n",
-      "  ],\n",
-      "  \"ctc_loss_reduction\": \"mean\",\n",
-      "  \"ctc_zero_infinity\": false,\n",
-      "  \"diversity_loss_weight\": 0.1,\n",
-      "  \"do_stable_layer_norm\": true,\n",
-      "  \"eos_token_id\": 2,\n",
-      "  \"feat_extract_activation\": \"gelu\",\n",
-      "  \"feat_extract_dropout\": 0.0,\n",
-      "  \"feat_extract_norm\": \"layer\",\n",
-      "  \"feat_proj_dropout\": 0.0,\n",
-      "  \"feat_quantizer_dropout\": 0.0,\n",
-      "  \"final_dropout\": 0.0,\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"hidden_act\": \"gelu\",\n",
-      "  \"hidden_dropout\": 0.1,\n",
-      "  \"hidden_size\": 1024,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 4096,\n",
-      "  \"layer_norm_eps\": 1e-05,\n",
-      "  \"layerdrop\": 0.0,\n",
-      "  \"mask_feature_length\": 64,\n",
-      "  \"mask_feature_min_masks\": 0,\n",
-      "  \"mask_feature_prob\": 0.25,\n",
-      "  \"mask_time_length\": 10,\n",
-      "  \"mask_time_min_masks\": 2,\n",
-      "  \"mask_time_prob\": 0.75,\n",
-      "  \"model_type\": \"wav2vec2\",\n",
-      "  \"num_adapter_layers\": 3,\n",
-      "  \"num_attention_heads\": 16,\n",
-      "  \"num_codevector_groups\": 2,\n",
-      "  \"num_codevectors_per_group\": 320,\n",
-      "  \"num_conv_pos_embedding_groups\": 16,\n",
-      "  \"num_conv_pos_embeddings\": 128,\n",
-      "  \"num_feat_extract_layers\": 7,\n",
-      "  \"num_hidden_layers\": 24,\n",
-      "  \"num_negatives\": 100,\n",
-      "  \"output_hidden_size\": 1024,\n",
-      "  \"pad_token_id\": 72,\n",
-      "  \"proj_codevector_dim\": 768,\n",
-      "  \"tdnn_dilation\": [\n",
-      "    1,\n",
-      "    2,\n",
-      "    3,\n",
-      "    1,\n",
-      "    1\n",
-      "  ],\n",
-      "  \"tdnn_dim\": [\n",
-      "    512,\n",
-      "    512,\n",
-      "    512,\n",
-      "    512,\n",
-      "    1500\n",
-      "  ],\n",
-      "  \"tdnn_kernel\": [\n",
-      "    5,\n",
-      "    3,\n",
-      "    3,\n",
-      "    1,\n",
-      "    1\n",
-      "  ],\n",
-      "  \"torch_dtype\": \"float32\",\n",
-      "  \"transformers_version\": \"4.17.0.dev0\",\n",
-      "  \"use_weighted_layer_sum\": false,\n",
-      "  \"vocab_size\": 75,\n",
-      "  \"xvector_output_dim\": 512\n",
-      "}\n",
-      "\n",
-      "loading weights file checkpoint-4000/pytorch_model.bin\n",
-      "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n",
-      "\n",
-      "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at checkpoint-4000.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n"
+      "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_hid.bias', 'quantizer.codevectors', 'quantizer.weight_proj.weight', 'project_q.weight', 'project_hid.weight', 'quantizer.weight_proj.bias', 'project_q.bias']\n",
+      "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
      ]
     }
    ],
@@ -20038,8 +19802,7 @@
     "from transformers import Wav2Vec2ForCTC\n",
     "\n",
     "model = Wav2Vec2ForCTC.from_pretrained(\n",
-    "#     \"facebook/wav2vec2-xls-r-300m\", \n",
-    "    \"checkpoint-4000\",\n",
+    "    \"facebook/wav2vec2-xls-r-300m\", \n",
     "    attention_dropout=0.1,\n",
     "    layerdrop=0.0,\n",
     "    feat_proj_dropout=0.0,\n",
@@ -20055,8 +19818,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "id": "ffcd9012",
+   "execution_count": 26,
+   "id": "6e363fc8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20065,19 +19828,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "id": "b07418cf",
+   "execution_count": 27,
+   "id": "447dfc3e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "PyTorch: setting up devices\n",
-      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from transformers import TrainingArguments\n",
     "\n",
@@ -20089,12 +19843,12 @@
     "  evaluation_strategy=\"steps\",\n",
     "  gradient_checkpointing=True,\n",
     "  fp16=True,\n",
-    "  num_train_epochs=50,\n",
+    "  num_train_epochs=100,\n",
     "  save_steps=400,\n",
     "  eval_steps=400,\n",
     "  logging_steps=100,\n",
     "  learning_rate=5e-5,\n",
-    "  warmup_steps=100,\n",
+    "  warmup_steps=1000,\n",
     "  save_total_limit=3,\n",
     "  load_best_model_at_end=True\n",
     ")"
@@ -20102,8 +19856,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "id": "7776cd7d",
+   "execution_count": 29,
+   "id": "eeda7f6e",
    "metadata": {},
    "outputs": [
     {
@@ -20123,15 +19877,15 @@
     "    args=training_args,\n",
     "    compute_metrics=compute_metrics,\n",
     "    train_dataset=common_voice_train,\n",
-    "    eval_dataset=common_voice_test,\n",
+    "    eval_dataset=common_voice_valid,\n",
     "    tokenizer=processor.feature_extractor,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "id": "ac33ed4c",
+   "execution_count": 30,
+   "id": "af09f9f9",
    "metadata": {},
    "outputs": [
     {
@@ -20142,12 +19896,12 @@
       "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
       "  warnings.warn(\n",
       "***** Running training *****\n",
-      "  Num examples = 2615\n",
-      "  Num Epochs = 50\n",
+      "  Num examples = 2353\n",
+      "  Num Epochs = 100\n",
       "  Instantaneous batch size per device = 8\n",
       "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
       "  Gradient Accumulation steps = 4\n",
-      "  Total optimization steps = 4050\n"
+      "  Total optimization steps = 7300\n"
      ]
     },
     {
@@ -20156,8 +19910,8 @@
        "\n",
        "    <div>\n",
        "      \n",
-       "      <progress value='4050' max='4050' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [4050/4050 2:16:09, Epoch 49/50]\n",
+       "      <progress value='7300' max='7300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [7300/7300 4:05:50, Epoch 99/100]\n",
        "    </div>\n",
        "    <table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -20171,63 +19925,111 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>400</td>\n",
-       "      <td>1.382900</td>\n",
-       "      <td>0.429020</td>\n",
-       "      <td>0.479627</td>\n",
+       "      <td>5.079500</td>\n",
+       "      <td>4.412057</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>800</td>\n",
-       "      <td>1.315600</td>\n",
-       "      <td>0.385632</td>\n",
-       "      <td>0.447419</td>\n",
+       "      <td>3.565800</td>\n",
+       "      <td>3.520337</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1200</td>\n",
-       "      <td>1.239600</td>\n",
-       "      <td>0.359977</td>\n",
-       "      <td>0.430733</td>\n",
+       "      <td>3.368900</td>\n",
+       "      <td>2.898376</td>\n",
+       "      <td>0.999596</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1600</td>\n",
-       "      <td>1.144400</td>\n",
-       "      <td>0.342276</td>\n",
-       "      <td>0.417928</td>\n",
+       "      <td>2.010000</td>\n",
+       "      <td>1.004076</td>\n",
+       "      <td>0.728814</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2000</td>\n",
-       "      <td>1.097900</td>\n",
-       "      <td>0.337029</td>\n",
-       "      <td>0.388436</td>\n",
+       "      <td>1.678300</td>\n",
+       "      <td>0.694064</td>\n",
+       "      <td>0.598870</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2400</td>\n",
-       "      <td>1.071400</td>\n",
-       "      <td>0.323725</td>\n",
-       "      <td>0.370974</td>\n",
+       "      <td>1.527000</td>\n",
+       "      <td>0.559943</td>\n",
+       "      <td>0.528249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2800</td>\n",
-       "      <td>1.044200</td>\n",
-       "      <td>0.333624</td>\n",
-       "      <td>0.368258</td>\n",
+       "      <td>1.427800</td>\n",
+       "      <td>0.482676</td>\n",
+       "      <td>0.480630</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3200</td>\n",
-       "      <td>1.049200</td>\n",
-       "      <td>0.316629</td>\n",
-       "      <td>0.352736</td>\n",
+       "      <td>1.345800</td>\n",
+       "      <td>0.442869</td>\n",
+       "      <td>0.453188</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3600</td>\n",
-       "      <td>1.028400</td>\n",
-       "      <td>0.317763</td>\n",
-       "      <td>0.356616</td>\n",
+       "      <td>1.289300</td>\n",
+       "      <td>0.415609</td>\n",
+       "      <td>0.433010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4000</td>\n",
-       "      <td>1.030200</td>\n",
-       "      <td>0.314151</td>\n",
-       "      <td>0.351184</td>\n",
+       "      <td>1.244100</td>\n",
+       "      <td>0.402046</td>\n",
+       "      <td>0.403955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4400</td>\n",
+       "      <td>1.188000</td>\n",
+       "      <td>0.377747</td>\n",
+       "      <td>0.386602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4800</td>\n",
+       "      <td>1.162800</td>\n",
+       "      <td>0.360746</td>\n",
+       "      <td>0.385795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5200</td>\n",
+       "      <td>1.132400</td>\n",
+       "      <td>0.353398</td>\n",
+       "      <td>0.360371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5600</td>\n",
+       "      <td>1.096900</td>\n",
+       "      <td>0.342845</td>\n",
+       "      <td>0.362389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6000</td>\n",
+       "      <td>1.089700</td>\n",
+       "      <td>0.338743</td>\n",
+       "      <td>0.356739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6400</td>\n",
+       "      <td>1.062500</td>\n",
+       "      <td>0.333874</td>\n",
+       "      <td>0.349879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6800</td>\n",
+       "      <td>1.060100</td>\n",
+       "      <td>0.328815</td>\n",
+       "      <td>0.344633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7200</td>\n",
+       "      <td>1.047400</td>\n",
+       "      <td>0.328094</td>\n",
+       "      <td>0.346247</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>"
@@ -20245,35 +20047,33 @@
      "text": [
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-400\n",
       "Configuration saved in ./checkpoint-400/config.json\n",
       "Model weights saved in ./checkpoint-400/pytorch_model.bin\n",
       "Configuration saved in ./checkpoint-400/preprocessor_config.json\n",
-      "Deleting older checkpoint [checkpoint-3200] due to args.save_total_limit\n",
-      "Deleting older checkpoint [checkpoint-3600] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-800\n",
       "Configuration saved in ./checkpoint-800/config.json\n",
       "Model weights saved in ./checkpoint-800/pytorch_model.bin\n",
       "Configuration saved in ./checkpoint-800/preprocessor_config.json\n",
-      "Deleting older checkpoint [checkpoint-4000-prev-best] due to args.save_total_limit\n",
+      "Deleting older checkpoint [checkpoint-3200] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-1200\n",
       "Configuration saved in ./checkpoint-1200/config.json\n",
       "Model weights saved in ./checkpoint-1200/pytorch_model.bin\n",
       "Configuration saved in ./checkpoint-1200/preprocessor_config.json\n",
-      "Deleting older checkpoint [checkpoint-4000] due to args.save_total_limit\n",
+      "Deleting older checkpoint [checkpoint-3600] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-1600\n",
       "Configuration saved in ./checkpoint-1600/config.json\n",
@@ -20282,7 +20082,7 @@
       "Deleting older checkpoint [checkpoint-400] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-2000\n",
       "Configuration saved in ./checkpoint-2000/config.json\n",
@@ -20291,7 +20091,7 @@
       "Deleting older checkpoint [checkpoint-800] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-2400\n",
       "Configuration saved in ./checkpoint-2400/config.json\n",
@@ -20300,7 +20100,7 @@
       "Deleting older checkpoint [checkpoint-1200] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-2800\n",
       "Configuration saved in ./checkpoint-2800/config.json\n",
@@ -20309,7 +20109,7 @@
       "Deleting older checkpoint [checkpoint-1600] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-3200\n",
       "Configuration saved in ./checkpoint-3200/config.json\n",
@@ -20318,7 +20118,7 @@
       "Deleting older checkpoint [checkpoint-2000] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-3600\n",
       "Configuration saved in ./checkpoint-3600/config.json\n",
@@ -20327,28 +20127,100 @@
       "Deleting older checkpoint [checkpoint-2400] due to args.save_total_limit\n",
       "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running Evaluation *****\n",
-      "  Num examples = 291\n",
+      "  Num examples = 262\n",
       "  Batch size = 8\n",
       "Saving model checkpoint to ./checkpoint-4000\n",
       "Configuration saved in ./checkpoint-4000/config.json\n",
       "Model weights saved in ./checkpoint-4000/pytorch_model.bin\n",
       "Configuration saved in ./checkpoint-4000/preprocessor_config.json\n",
       "Deleting older checkpoint [checkpoint-2800] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-4400\n",
+      "Configuration saved in ./checkpoint-4400/config.json\n",
+      "Model weights saved in ./checkpoint-4400/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-4400/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-3200] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-4800\n",
+      "Configuration saved in ./checkpoint-4800/config.json\n",
+      "Model weights saved in ./checkpoint-4800/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-4800/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-3600] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-5200\n",
+      "Configuration saved in ./checkpoint-5200/config.json\n",
+      "Model weights saved in ./checkpoint-5200/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-5200/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-4000] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-5600\n",
+      "Configuration saved in ./checkpoint-5600/config.json\n",
+      "Model weights saved in ./checkpoint-5600/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-5600/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-4400] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-6000\n",
+      "Configuration saved in ./checkpoint-6000/config.json\n",
+      "Model weights saved in ./checkpoint-6000/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-6000/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-4800] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-6400\n",
+      "Configuration saved in ./checkpoint-6400/config.json\n",
+      "Model weights saved in ./checkpoint-6400/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-6400/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-5200] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-6800\n",
+      "Configuration saved in ./checkpoint-6800/config.json\n",
+      "Model weights saved in ./checkpoint-6800/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-6800/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-5600] due to args.save_total_limit\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 262\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to ./checkpoint-7200\n",
+      "Configuration saved in ./checkpoint-7200/config.json\n",
+      "Model weights saved in ./checkpoint-7200/pytorch_model.bin\n",
+      "Configuration saved in ./checkpoint-7200/preprocessor_config.json\n",
+      "Deleting older checkpoint [checkpoint-6000] due to args.save_total_limit\n",
       "\n",
       "\n",
       "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
       "\n",
       "\n",
-      "Loading best model from ./checkpoint-4000 (score: 0.3141506016254425).\n"
+      "Loading best model from ./checkpoint-7200 (score: 0.3280937969684601).\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "TrainOutput(global_step=4050, training_loss=1.1567209813624253, metrics={'train_runtime': 8173.6251, 'train_samples_per_second': 15.997, 'train_steps_per_second': 0.495, 'total_flos': 1.9735608328149316e+19, 'train_loss': 1.1567209813624253, 'epoch': 49.99})"
+       "TrainOutput(global_step=7300, training_loss=2.0282830110314776, metrics={'train_runtime': 14754.0737, 'train_samples_per_second': 15.948, 'train_steps_per_second': 0.495, 'total_flos': 3.5572390287970673e+19, 'train_loss': 2.0282830110314776, 'epoch': 99.99})"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -20359,8 +20231,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "19b3350f",
+   "execution_count": 31,
+   "id": "e9563734",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4c8fe67e",
    "metadata": {},
    "outputs": [
     {
@@ -20369,8 +20262,21 @@
      "text": [
       "tokenizer config file saved in vitouphy/xls-r-300m-km/tokenizer_config.json\n",
       "Special tokens file saved in vitouphy/xls-r-300m-km/special_tokens_map.json\n",
-      "added tokens file saved in vitouphy/xls-r-300m-km/added_tokens.json\n"
+      "added tokens file saved in vitouphy/xls-r-300m-km/added_tokens.json\n",
+      "To https://huggingface.co/vitouphy/xls-r-300m-km\n",
+      "   3ef5dfc..cb4f72c  main -> main\n",
+      "\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'https://huggingface.co/vitouphy/xls-r-300m-km/commit/cb4f72cb420eee8ca1f44b582a9d3cfbcd258f3d'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -20379,8 +20285,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "724e14ef",
+   "execution_count": 34,
+   "id": "dc64c376",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20388,7 +20294,7 @@
     "    \"finetuned_from\": \"facebook/wav2vec2-xls-r-300m\",\n",
     "    \"tasks\": \"speech-recognition\",\n",
     "    \"tags\": [\"automatic-speech-recognition\", \"openslr\", \"robust-speech-event\", \"km\"],\n",
-    "    \"dataset_args\": f\"Config: km, Training split: train+validation, Eval split: test\",\n",
+    "    \"dataset_args\": f\"Config: km, Training split: train, Eval split: validation\",\n",
     "    \"dataset\": \"openslr\",\n",
     "    \"language\": \"km\"\n",
     "}"
@@ -20396,8 +20302,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "75b87f11",
+   "execution_count": 35,
+   "id": "9f9d87c3",
    "metadata": {},
    "outputs": [
     {
@@ -20415,75 +20321,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "9e4a2ec9",
+   "execution_count": 36,
+   "id": "4b50cbfe",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py:1001: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
-      "  warnings.warn(\n",
-      "Cloning https://huggingface.co/vitouphy/xls-r-300m-km into local empty directory.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae4aa0641113454c801089fa2dbd6777",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Download file pytorch_model.bin:   0%|          | 2.83k/1.18G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9a3129d18855473ba7da0f290f26419b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Download file training_args.bin:  63%|######2   | 1.84k/2.92k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dabccfa9f14045919cf70a905afb5506",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Clean file training_args.bin:  34%|###4      | 1.00k/2.92k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee7e633b1e784625b2d3695176f6c0f2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Clean file pytorch_model.bin:   0%|          | 1.00k/1.18G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -20495,7 +20336,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9738e4743ca3470f863dfd4d85f6e411",
+       "model_id": "69dc015463b64e3c946ccfbe017d1828",
        "version_major": 2,
        "version_minor": 0
       },
@@ -20511,17 +20352,17 @@
      "output_type": "stream",
      "text": [
       "To https://huggingface.co/vitouphy/xls-r-300m-km\n",
-      "   6f203d5..74be6ec  main -> main\n",
+      "   cb4f72c..8fe8876  main -> main\n",
       "\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'https://huggingface.co/vitouphy/xls-r-300m-km/commit/74be6ece8cca85ef00972b1f3f88460217d0acf5'"
+       "'https://huggingface.co/vitouphy/xls-r-300m-km/commit/8fe88762a9fca1dce5e056605465042b5700b69e'"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -20532,8 +20373,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "id": "8c70b0b9",
+   "execution_count": 37,
+   "id": "33a99751",
    "metadata": {},
    "outputs": [
     {
@@ -20554,7 +20395,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "96cd8308",
+   "id": "b9482eed",
    "metadata": {},
    "outputs": [],
    "source": []