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      "source": [
        "<a href=\"https://colab.research.google.com/github/UmarIgan/Machine-Learning/blob/master/gtzan_fineitune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        "!pip install datasets\n",
        "!pip install evaluate"
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        "outputId": "e4974619-9698-431f-da40-736dd8e895ea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "gtzan.py:   0%|          | 0.00/3.35k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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          "data": {
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              "README.md:   0%|          | 0.00/4.42k [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for marsyas/gtzan contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/marsyas/gtzan.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "genres.tar.gz:   0%|          | 0.00/1.23G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6c05657ceeb41638710bd884985d73f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c6e54ea26e94ebf9b3a782778cfdac8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['file', 'audio', 'genre'],\n",
              "        num_rows: 999\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "gtzan = load_dataset(\"marsyas/gtzan\", \"all\")\n",
        "gtzan"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan = gtzan[\"train\"].train_test_split(seed=42, shuffle=True, test_size=0.1)\n",
        "gtzan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqs9fei_yTT4",
        "outputId": "41ffbb8e-ebbe-4289-8650-33160ff315f6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['file', 'audio', 'genre'],\n",
              "        num_rows: 899\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['file', 'audio', 'genre'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ku4s-WACyWD9",
        "outputId": "381b2765-2f17-4617-da34-30da096d5c51"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'file': '/root/.cache/huggingface/datasets/downloads/extracted/3b204381d6c029312e4f9c569c6b1130af3041dd36ca38ca53d4e20f585e39c6/genres/pop/pop.00098.wav',\n",
              " 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/3b204381d6c029312e4f9c569c6b1130af3041dd36ca38ca53d4e20f585e39c6/genres/pop/pop.00098.wav',\n",
              "  'array': array([ 0.10720825,  0.16122437,  0.28585815, ..., -0.22924805,\n",
              "         -0.20629883, -0.11334229]),\n",
              "  'sampling_rate': 22050},\n",
              " 'genre': 7}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label_fn = gtzan[\"train\"].features[\"genre\"].int2str\n",
        "id2label_fn(gtzan[\"train\"][0][\"genre\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "u7gwJyb-yZbC",
        "outputId": "579b8767-44ac-4bbd-af60-ff8358060812"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'pop'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoFeatureExtractor\n",
        "\n",
        "#model_id = \"ntu-spml/distilhubert\"\n",
        "model_id = \"sandychoii/distilhubert-finetuned-gtzan-audio-classification\"\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\n",
        "    model_id, do_normalize=True, return_attention_mask=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        "id": "7YlxJi1zydIU",
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      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/212 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sampling_rate = feature_extractor.sampling_rate\n",
        "sampling_rate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ckfO5YMyg0-",
        "outputId": "445df4a5-084a-48fb-b6b8-947050371611"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Audio\n",
        "\n",
        "gtzan = gtzan.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))"
      ],
      "metadata": {
        "id": "Gm_z0XpgyjqP"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbYnByjSyl9q",
        "outputId": "a7ccd439-0247-4e07-e1d8-c6ce271c5b09"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'file': '/root/.cache/huggingface/datasets/downloads/extracted/3b204381d6c029312e4f9c569c6b1130af3041dd36ca38ca53d4e20f585e39c6/genres/pop/pop.00098.wav',\n",
              " 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/3b204381d6c029312e4f9c569c6b1130af3041dd36ca38ca53d4e20f585e39c6/genres/pop/pop.00098.wav',\n",
              "  'array': array([ 0.0873509 ,  0.20183384,  0.4790867 , ..., -0.18743178,\n",
              "         -0.23294401, -0.13517427]),\n",
              "  'sampling_rate': 16000},\n",
              " 'genre': 7}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "sample = gtzan[\"train\"][0][\"audio\"]\n",
        "\n",
        "print(f\"Mean: {np.mean(sample['array']):.3}, Variance: {np.var(sample['array']):.3}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZfHzLx9yoUs",
        "outputId": "1281d48e-1186-4e73-ac18-d2304f87012a"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 0.000185, Variance: 0.0493\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = feature_extractor(sample[\"array\"], sampling_rate=sample[\"sampling_rate\"])\n",
        "\n",
        "print(f\"inputs keys: {list(inputs.keys())}\")\n",
        "\n",
        "print(\n",
        "    f\"Mean: {np.mean(inputs['input_values']):.3}, Variance: {np.var(inputs['input_values']):.3}\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ASxS_59IyqFt",
        "outputId": "0361a63e-ec6b-4459-bbca-146947bcc3e6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inputs keys: ['input_values', 'attention_mask']\n",
            "Mean: -7.45e-09, Variance: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_duration = 30.0\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    audio_arrays = [x[\"array\"] for x in examples[\"audio\"]]\n",
        "    inputs = feature_extractor(\n",
        "        audio_arrays,\n",
        "        sampling_rate=feature_extractor.sampling_rate,\n",
        "        max_length=int(feature_extractor.sampling_rate * max_duration),\n",
        "        truncation=True,\n",
        "        return_attention_mask=True,\n",
        "    )\n",
        "    return inputs"
      ],
      "metadata": {
        "id": "QErfBYHVyvOl"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan_encoded = gtzan.map(\n",
        "    preprocess_function,\n",
        "    remove_columns=[\"audio\", \"file\"],\n",
        "    batched=True,\n",
        "    batch_size=100,\n",
        "    num_proc=1,\n",
        ")\n",
        "gtzan_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255,
          "referenced_widgets": [
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        },
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        "outputId": "3f0f04d2-18f2-4f47-c293-bcb93581184d"
      },
      "execution_count": 50,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/899 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c4c3d92494a446992b954410cc9d9d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['genre', 'input_values', 'attention_mask'],\n",
              "        num_rows: 899\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['genre', 'input_values', 'attention_mask'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan_encoded = gtzan_encoded.rename_column(\"genre\", \"label\")"
      ],
      "metadata": {
        "id": "-MuFRJAdzgu5"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {\n",
        "    str(i): id2label_fn(i)\n",
        "    for i in range(len(gtzan_encoded[\"train\"].features[\"label\"].names))\n",
        "}\n",
        "label2id = {v: k for k, v in id2label.items()}\n",
        "\n",
        "id2label[\"7\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OPztnHMZy0IA",
        "outputId": "bc9eefbc-f5ea-45c2-a56f-b6411598709c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'pop'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForAudioClassification\n",
        "\n",
        "num_labels = len(id2label)\n",
        "model = AutoModelForAudioClassification.from_pretrained(\n",
        "    model_id,\n",
        "    num_labels=num_labels,\n",
        "    label2id=label2id,\n",
        "    id2label=id2label,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "ca012cc7bc8a493899e69a73c1726e0c",
            "c487a44829d3454986902037cb8c52d9",
            "8d2e593f606a4aa7895354b58ce8ea9f",
            "64e725d213804d1bafe5a03b5b69492f",
            "7fd52ab54d1b42d6996a7ff48dc72723",
            "13391526906e4b1c9c29190dbfd300a6",
            "c9daed251a4e4e2cb8f7dcc1f3be8984",
            "38ee577015d643cc8c2d934c3c469a1a",
            "255400d223d3425189f874bd9ef20586",
            "e52c093eb1b54b76a2cc1ee777d67e5c",
            "db8e12af576940a8b395dd4e68c6be41",
            "446c28bf4c0a42c0b0a4b163803d21f8",
            "428b6d33903449a6a15d3ebdf65c2002",
            "965239f2f83549519a9925f94f8d0a14",
            "15375b9ec8f14e36b140de2e69dc349c",
            "9de4ec929bf04fb6b5998fd55bc56ff5",
            "df0df29402be4850b422e4039c7b0ac2",
            "8a41b7a759434558838f47f2aa211376",
            "db9f542575144a91a7f2ab47e6dea8e7",
            "14300839dc25472383720939f355c983",
            "d2dd6765b1a04485af97e96b33e9b968",
            "00d1280680584688a2ffc4dc6da9724f"
          ]
        },
        "id": "vdYBDlqRy3AA",
        "outputId": "c49036cd-79b2-4e70-e943-91c8de32bb44"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.89k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca012cc7bc8a493899e69a73c1726e0c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/94.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "446c28bf4c0a42c0b0a4b163803d21f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at sandychoii/distilhubert-finetuned-gtzan-audio-classification were not used when initializing HubertForSequenceClassification: ['hubert.encoder.pos_conv_embed.conv.weight_g', 'hubert.encoder.pos_conv_embed.conv.weight_v']\n",
            "- This IS expected if you are initializing HubertForSequenceClassification 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 HubertForSequenceClassification 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 HubertForSequenceClassification were not initialized from the model checkpoint at sandychoii/distilhubert-finetuned-gtzan-audio-classification and are newly initialized: ['hubert.encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'hubert.encoder.pos_conv_embed.conv.parametrizations.weight.original1']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "model_name = model_id.split(\"/\")[-1]\n",
        "batch_size = 8\n",
        "gradient_accumulation_steps = 1\n",
        "num_train_epochs = 5\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    f\"{model_name}-finetuned-gtzan\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=5e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    warmup_ratio=0.1,\n",
        "    logging_steps=5,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"accuracy\",\n",
        "    fp16=True,\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I94kOs2Ny5Wi",
        "outputId": "45b8b823-0224-40e4-eda2-3b51c6df04ad"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "import numpy as np\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
        "    predictions = np.argmax(eval_pred.predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "95093221ae664c439a8559866330066a",
            "bb84fae36aa84a34b02a26bf3e4d4aed",
            "92c3e1dcb6cb46bb9cccf6b239665b9d",
            "4490255081944b0b99622928ef4243ae",
            "6c15041068a447b58eff962be9e86d26",
            "100d0cfcd6c2475bbedc12a7ae464285",
            "b6aeaab96288450e9ed50fcaa1c15272",
            "be90f21a826340b49c28d05e7b76cd70",
            "0f25a02a96b54761ae9a7f8260d9e56c",
            "7341a2eb8d4d4cbba51432b277e86fc4",
            "0f111886b28540839cf20f8f612beccc"
          ]
        },
        "id": "9LSY068ky9ld",
        "outputId": "b25fd9f1-87fe-4f84-e170-0e6844217b3b"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95093221ae664c439a8559866330066a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=gtzan_encoded[\"train\"],\n",
        "    eval_dataset=gtzan_encoded[\"test\"],\n",
        "    tokenizer=feature_extractor,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "Ytg9kJv8y_l3",
        "outputId": "5d5c8c55-86ea-4f30-ef7f-0421c0198ba2"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='565' max='565' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [565/565 46:39, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.084000</td>\n",
              "      <td>0.826217</td>\n",
              "      <td>0.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.024800</td>\n",
              "      <td>0.732492</td>\n",
              "      <td>0.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.003900</td>\n",
              "      <td>0.462734</td>\n",
              "      <td>0.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.002100</td>\n",
              "      <td>0.458600</td>\n",
              "      <td>0.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.002000</td>\n",
              "      <td>0.493614</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=565, training_loss=0.03801786177091103, metrics={'train_runtime': 2809.3878, 'train_samples_per_second': 1.6, 'train_steps_per_second': 0.201, 'total_flos': 3.066994137312e+17, 'train_loss': 0.03801786177091103, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = {\n",
        "    \"dataset_tags\": \"marsyas/gtzan\",\n",
        "    \"dataset\": \"GTZAN\",\n",
        "    \"model_name\": f\"distilhubert-finetuned-gtzan-v1\",\n",
        "    \"finetuned_from\": model_id,\n",
        "    \"tasks\": \"audio-classification\",\n",
        "}\n",
        "\n",
        "trainer.push_to_hub(**kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "8MhuwFx1AqRE",
        "outputId": "e4a11869-292d-4121-ca98-778db5337f59"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/umarigan/distilhubert-finetuned-gtzan-audio-classification-finetuned-gtzan/commit/1ac980004e0fbc648019cbaa476d419b8dc494b5', commit_message='End of training', commit_description='', oid='1ac980004e0fbc648019cbaa476d419b8dc494b5', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    }
  ]
}