two epochs only

Generate.ipynb

@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 54,
    "id": "71351cf4-6d00-40ae-89cc-cedb87073625",
    "metadata": {},
    "outputs": [
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
    "id": "208eac7d-9fdd-4c82-b46f-25c1a1f246ee",
    "metadata": {},
    "outputs": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 56,
    "id": "d58f6b8c-441c-4fa9-a308-e687948875e1",
    "metadata": {},
    "outputs": [
@@ -421,11 +421,11 @@
       "The following columns in the training set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
       "***** Running training *****\n",
       "  Num examples = 1\n",
-      "  Num Epochs = 3\n",
+      "  Num Epochs = 30\n",
       "  Instantaneous batch size per device = 8\n",
       "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
       "  Gradient Accumulation steps = 1\n",
-      "  Total optimization steps = 3\n"
+      "  Total optimization steps = 30\n"
      ]
     },
     {
@@ -434,8 +434,8 @@
        "\n",
        "    <div>\n",
        "      \n",
-       "      <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [3/3 00:02, Epoch 3/3]\n",
+       "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [30/30 00:28, Epoch 30/30]\n",
        "    </div>\n",
        "    <table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -470,10 +470,10 @@
     {
      "data": {
       "text/plain": [
-       "TrainOutput(global_step=3, training_loss=15.702210744222006, metrics={'train_runtime': 3.157, 'train_samples_per_second': 0.95, 'train_steps_per_second': 0.95, 'total_flos': 94374986431680.0, 'train_loss': 15.702210744222006, 'epoch': 3.0})"
+       "TrainOutput(global_step=30, training_loss=16.291970825195314, metrics={'train_runtime': 29.1768, 'train_samples_per_second': 1.028, 'train_steps_per_second': 1.028, 'total_flos': 943749864316800.0, 'train_loss': 16.291970825195314, 'epoch': 30.0})"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 57,
    "id": "333d43cf-add3-4d78-bbca-b44c638519fe",
    "metadata": {},
    "outputs": [
@@ -505,7 +505,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [47]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhub_model_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharpcoder/wav2vec2_bjorn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Input \u001b[0;32mIn [57]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhub_model_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharpcoder/wav2vec2_bjorn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/trainer.py:2677\u001b[0m, in \u001b[0;36mTrainer.push_to_hub\u001b[0;34m(self, commit_message, blocking, **kwargs)\u001b[0m\n\u001b[1;32m   2674\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_world_process_zero():\n\u001b[1;32m   2675\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m-> 2677\u001b[0m git_head_commit_url \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[38;5;241m.\u001b[39mpush_to_hub(commit_message\u001b[38;5;241m=\u001b[39mcommit_message, blocking\u001b[38;5;241m=\u001b[39mblocking)\n\u001b[1;32m   2678\u001b[0m \u001b[38;5;66;03m# push separately the model card to be independant from the rest of the model\u001b[39;00m\n\u001b[1;32m   2679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mshould_save:\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'Trainer' object has no attribute 'repo'"
      ]

pytorch_model.bin

@@ -1,3 +1,3 @@
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training_args.bin

@@ -1,3 +1,3 @@
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 size 2735