unit4/distilhubert_finetuned_gtzan_bs_16.ipynb

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            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.9->speechbrain) (16.0.6)\n",
            "Collecting ruamel.yaml<=0.17.28,>=0.17.8 (from hyperpyyaml->speechbrain)\n",
            "  Downloading ruamel.yaml-0.17.28-py3-none-any.whl (109 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.6/109.6 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Collecting ruamel.yaml.clib>=0.2.7 (from ruamel.yaml<=0.17.28,>=0.17.8->hyperpyyaml->speechbrain)\n",
            "  Downloading ruamel.yaml.clib-0.2.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (485 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.6/485.6 kB\u001b[0m \u001b[31m45.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.9->speechbrain) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.9->speechbrain) (1.3.0)\n",
            "Installing collected packages: sentencepiece, xxhash, ruamel.yaml.clib, dill, ruamel.yaml, multiprocess, huggingface-hub, hyperpyyaml, datasets, speechbrain, accelerate\n",
            "Successfully installed accelerate-0.21.0 datasets-2.14.3 dill-0.3.7 huggingface-hub-0.16.4 hyperpyyaml-1.2.1 multiprocess-0.70.15 ruamel.yaml-0.17.28 ruamel.yaml.clib-0.2.7 sentencepiece-0.1.99 speechbrain-0.5.15 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install soundfile speechbrain accelerate datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ipython-autotime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxZ2SzCHv5jT",
        "outputId": "1f1cd9af-eda5-4af0-8d62-e21b522216ce"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ipython-autotime\n",
            "  Downloading ipython_autotime-0.3.1-py2.py3-none-any.whl (6.8 kB)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from ipython-autotime) (7.34.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (67.7.2)\n",
            "Collecting jedi>=0.16 (from ipython->ipython-autotime)\n",
            "  Downloading jedi-0.19.0-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (3.0.39)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (2.14.0)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->ipython-autotime) (4.8.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->ipython-autotime) (0.8.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->ipython-autotime) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ipython-autotime) (0.2.6)\n",
            "Installing collected packages: jedi, ipython-autotime\n",
            "Successfully installed ipython-autotime-0.3.1 jedi-0.19.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext autotime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBUyechGwA0X",
        "outputId": "c88463af-57ca-4a5b-e777-c58389677f00"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 236 µs (started: 2023-08-07 23:26:39 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2cXtCFfwL5W",
        "outputId": "30150113-849c-45bd-b340-2e13d0e8a6bb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m60.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m74.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "time: 28.2 s (started: 2023-08-07 23:26:39 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q git+https://github.com/huggingface/accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWH6UBQXwPC_",
        "outputId": "69c08ce5-302e-46d1-af7f-31db70669cc9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "time: 13.9 s (started: 2023-08-07 23:27:08 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OmIQLaHwUYo",
        "outputId": "a827db5e-3749-4eae-edb3-b1a770a96903"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Aug  7 23:27:22 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P0    45W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n",
            "time: 203 ms (started: 2023-08-07 23:27:22 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
            "93f12797936946b4adb7f968ebecf0b1",
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            "e88fb938d82b4ed18ca7ff0144abc7dd",
            "cc55be6bfe284723926b64c86ebbeb7c",
            "6018b92610914618ae4fa7933b720c14",
            "e1596ff21c0c404fbea171a5ce0e9aa2",
            "f7d6b5533fc547b0af68a1cd5d934073",
            "efb8e8fc34174267a4936c4995abbcd9",
            "3bd487595519481c9ec3efe31db26942",
            "3a0116c28d92436980e71a8d15825798",
            "b80635572fa74728859b12acaab2cef9",
            "242636f0ffb946f89d6d1e96cca37c1b",
            "f390f7a432e64fa385f0aa053f4319e5",
            "b822d3538c28423f97f7454c59984d80",
            "a385a3c2c74945398caae06c32b78c65",
            "06163a43152046138a17f45b911e11a5",
            "e55f2d15b22948e4b787c2293a2dd249",
            "bc199dd293b8486597c583627d3630b4",
            "4e5ccda04fd54dc5a631f8b244a7ddb7",
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            "aba391cab9284039ad5eae4236337b03",
            "1e6366c7baca4400983c03af1f21f345",
            "572ff5aece8740cdb54df84cc25f05d1",
            "7c33c46a7b584b81a067cafbdd073744",
            "cfcff710d3214294a13974af9505beef",
            "264df4c313ad4a418360b1e0e0da4169",
            "4748dec5e7bc4ea2b12b616487b6a968",
            "895e990cbeb34f49a12a7933f4bb7216",
            "208dacc888a54d838a6f8d963db83401",
            "e2f7eb9701ee4f6b9f50144c83a74213",
            "de490a15163e4957a72bb1665809c847",
            "2ab5bb385bca45d3b631428d703c0b12"
          ]
        },
        "id": "u2FbJnC1wYME",
        "outputId": "080ba69a-fbcd-4840-fd62-6501f0b431db"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93f12797936946b4adb7f968ebecf0b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 190 ms (started: 2023-08-07 23:27:22 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import set_seed\n",
        "set_seed(42)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFnWacmhRZTx",
        "outputId": "133c4c25-fd11-4762-8753-f6e68390a4c9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 6.65 s (started: 2023-08-07 23:28:13 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "gtzan = load_dataset(\"marsyas/gtzan\", split=\"train\")\n",
        "gtzan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218,
          "referenced_widgets": [
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            "0b51a00023184bc8a8fb892c92050f19",
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            "760f6bb2c32f4c95bbbfca9d21dd98b3",
            "ede829e747ee46d6b5151f3b6db7f769",
            "0994e514a4cd4b77bbfa1a5d6c89b73a",
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            "4cf63be99a004d91b2547eaae185d88f"
          ]
        },
        "id": "4zBGhdgJxpvk",
        "outputId": "e43432ed-5f05-4083-e35d-4b3430056f9d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.38k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f1eac263eac43fa900e5eec955f7703"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/4.42k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cee57202bae24be5b85ffc417b11e061"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.23G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b0f07a078674b82b80d0c497d431590"
            }
          },
          "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": "0b51a00023184bc8a8fb892c92050f19"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['file', 'audio', 'genre'],\n",
              "    num_rows: 999\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 28.1 s (started: 2023-08-07 23:28:19 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hF2hnwjWxp6i",
        "outputId": "14531c1d-e910-428c-d2c5-eb47883021df"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['file', 'audio', 'genre'],\n",
              "    num_rows: 999\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 2.69 ms (started: 2023-08-07 23:28:47 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan = gtzan.train_test_split(seed=42, shuffle=True, test_size=0.1)\n",
        "gtzan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1ljlRa0x2F4",
        "outputId": "1a52fa05-aed7-4b5f-8895-d52054338939"
      },
      "execution_count": 11,
      "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": 11
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 7.99 ms (started: 2023-08-07 23:28:47 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "911N5IfLyTKv",
        "outputId": "548c6431-0411-4e65-aa1e-2353ff0986d4"
      },
      "execution_count": 12,
      "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": 12
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1.88 ms (started: 2023-08-07 23:28:47 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jM-vGyx3aN9",
        "outputId": "c01fe926-d4c5-4081-a92c-93ee36df830b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'file': '/root/.cache/huggingface/datasets/downloads/extracted/5022b0984afa7334ff9a3c60566280b08b5179d4ac96a628052bada7d8940244/genres/pop/pop.00098.wav',\n",
              " 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/5022b0984afa7334ff9a3c60566280b08b5179d4ac96a628052bada7d8940244/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": 13
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 8.21 s (started: 2023-08-07 23:28:47 +00:00)\n"
          ]
        }
      ]
    },
    {
      "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": 36
        },
        "id": "QBztQ0QQ3e9m",
        "outputId": "338bd945-dd9d-44fb-ba3f-100ebd26507e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'pop'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 7.67 ms (started: 2023-08-07 23:28:56 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ft1TXvWl_OiE",
        "outputId": "31e222eb-a86f-46a5-91c9-52870a9b50e9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.9/19.9 MB\u001b[0m \u001b[31m82.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/41.0 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "time: 10.9 s (started: 2023-08-07 23:29:06 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "\n",
        "def generate_audio():\n",
        "    example = gtzan[\"train\"].shuffle()[0]\n",
        "    audio = example[\"audio\"]\n",
        "    return (\n",
        "        audio[\"sampling_rate\"],\n",
        "        audio[\"array\"],\n",
        "    ), id2label_fn(example[\"genre\"])\n",
        "\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "    with gr.Column():\n",
        "        for _ in range(4):\n",
        "            audio, label = generate_audio()\n",
        "            output = gr.Audio(audio, label=label)\n",
        "\n",
        "demo.launch(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "Se6_jJKY_Kem",
        "outputId": "41bc7af1-d483-4c8c-a28b-ffafd21b0e41"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/processing_utils.py:183: UserWarning: Trying to convert audio automatically from float64 to 16-bit int format.\n",
            "  warnings.warn(warning.format(data.dtype))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 19 s (started: 2023-08-07 23:29:17 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoFeatureExtractor\n",
        "\n",
        "model_id = \"ntu-spml/distilhubert\"\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": 67,
          "referenced_widgets": [
            "0356a82466c84549ba76430d26289df9",
            "7d556705bf9f49b18ddcac012f3c5bcb",
            "9a1068ef4ddf4e6fb8278947273ee266",
            "6fd355d14ed64610a02ded85ddf64537",
            "4670ec7ee9d04a279fefdc2f25eeda8b",
            "a7eac1e622624bcab14eaedc4c5b4bfa",
            "4bbd2418623d41288aef7164b032f469",
            "60fc875b5c9645dda9d57c2fd6d558f8",
            "c288f9eb3b2e4e65ae60649da5f5b4a0",
            "845da7857916413480d4706d2a30e740",
            "fc02070f3fdc4fbb9f053ce13c1ce1a5"
          ]
        },
        "id": "3w-IXUIb_K9a",
        "outputId": "34613282-a180-4aac-d767-88f2f0cf95ca"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/214 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0356a82466c84549ba76430d26289df9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 614 ms (started: 2023-08-07 23:29:40 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sampling_rate = feature_extractor.sampling_rate\n",
        "sampling_rate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9v3K8SbG_mR7",
        "outputId": "8b220d77-ed86-4555-fb2e-19934ed6a0b8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 2.83 ms (started: 2023-08-07 23:29:42 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Audio\n",
        "\n",
        "gtzan = gtzan.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gPSwe6e_uGC",
        "outputId": "62dbd5d4-110d-4099-dc9a-c232732d6b0c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 8.16 ms (started: 2023-08-07 23:29:44 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymmN8pOkAsh3",
        "outputId": "eeadc002-2dbf-480f-af03-170b36614bc9"
      },
      "execution_count": 20,
      "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": 20
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 2.12 ms (started: 2023-08-07 23:29:46 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "II5wJ-A7Axx1",
        "outputId": "a6094a01-4b22-4cb6-86e8-61f48eb35f1f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'file': '/root/.cache/huggingface/datasets/downloads/extracted/5022b0984afa7334ff9a3c60566280b08b5179d4ac96a628052bada7d8940244/genres/pop/pop.00098.wav',\n",
              " 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/5022b0984afa7334ff9a3c60566280b08b5179d4ac96a628052bada7d8940244/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": 21
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 33.3 ms (started: 2023-08-07 23:29:51 +00:00)\n"
          ]
        }
      ]
    },
    {
      "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": "ah2nc8M3A2nC",
        "outputId": "5673c089-45db-4736-828c-5a5ccea87277"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 0.000185, Variance: 0.0493\n",
            "time: 32.3 ms (started: 2023-08-07 23:29:53 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_duration = 30.0\n",
        "\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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAvZNmGuGYt8",
        "outputId": "23da8b8c-ba32-4e06-c7ef-4ef6eeaff97f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 526 µs (started: 2023-08-07 23:30:01 +00:00)\n"
          ]
        }
      ]
    },
    {
      "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": {
        "id": "_ZTiFioLLetW",
        "outputId": "cceffbd3-8263-4815-dbb1-1b0751f7c03f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263,
          "referenced_widgets": [
            "ff74a85e31d84abfaa754b87a000e153",
            "ec2d589febc44346a9781a13368eda39",
            "89d1bea9dada4d14991aad37f9a92882",
            "82a486791d6747dd8866b12a3bd197aa",
            "7ab07439ed3547ada10394378aae6e7b",
            "771bda901c1649c483c1e94049e4b053",
            "feca0f975c4c4e62b0ad858b6dbaa65c",
            "a8232a93771649cdb9a2fcde4ac9848c",
            "dc1677e089614dfb90562410474433bc",
            "f8f63bf1808749aaa6c7aa8dfa926d39",
            "d42d4fefc5f04b9882a04caffad60a30",
            "136518b2ef744d8298187462719b91de",
            "90032e9875d749b181863ea8bb417758",
            "50af66aee1734f908005618f41aadfff",
            "46f30e45181f423bb97fe38f74fdb076",
            "45dcffd8a2e7461f878d041d4b2cd67c",
            "74076a39de4544e7869b6aeea1d6ddc8",
            "9c6bb8b212d84e47a9efe05c5d6d67a6",
            "33427999961a4f8281902295db2a7091",
            "44c6e579febf4884b411b7935b79a6ac",
            "ee015d70a25c44b39054ddebeb1678af",
            "94ad1f17e1474d4e934b7bcd3e502f0f"
          ]
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/899 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff74a85e31d84abfaa754b87a000e153"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "136518b2ef744d8298187462719b91de"
            }
          },
          "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": 24
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 30.7 s (started: 2023-08-07 23:30:03 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan_encoded = gtzan_encoded.rename_column(\"genre\", \"label\")"
      ],
      "metadata": {
        "id": "7E2etQ29LlSZ",
        "outputId": "3fe69be8-beaf-436d-b3ea-322d7333a294",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 4.67 ms (started: 2023-08-07 23:30:34 +00:00)\n"
          ]
        }
      ]
    },
    {
      "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": {
        "id": "vGlgwNzeLn-5",
        "outputId": "566662a6-d79e-46cd-f210-b852115d54bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'pop'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 2.47 ms (started: 2023-08-07 23:30:34 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForAudioClassification\n",
        "\n",
        "num_labels = len(id2label)\n",
        "\n",
        "model = AutoModelForAudioClassification.from_pretrained(\n",
        "    model_id,\n",
        "    num_labels=num_labels,\n",
        "    label2id=label2id,\n",
        "    id2label=id2label,\n",
        ")"
      ],
      "metadata": {
        "id": "gk22oRsCLqo6",
        "outputId": "d7d4163b-92c7-4417-fdc4-05fdd3344dd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "d4fb5a9e06954b4dbb999be56adde967",
            "c42059e851ed48049364b0e9f63bcdc7",
            "429fb448299c4cf1b54e8937797a8ce8",
            "8125d7819a884212a6f0528ed017b401",
            "ec1bff618a9b4293b59177ff67fb784e",
            "513cfd85be624956a825b605b541d22b",
            "e27c75a733f049dd9b61e4bf902d1bd8",
            "dfe378f2021c40cfbccc9c15f79d088e",
            "278e46cc704f4cef98df5586cd7ab380",
            "51db84f0512d43ad827d414234214f31",
            "3c9614d8ca42449ebe11ec962118af8d",
            "61be9bacf49f41dfbfbd10726a66b71f",
            "27405fc9a5ed4042a876f33963403a38",
            "24af705c645d4ba080b91e064cce3026",
            "205be6254bca46a2b9064a7227a21346",
            "1998bda94e6e40e4ac74331303b3bd0f",
            "22dd0064e99649508c919a7cc0f8a873",
            "9affd9f0787647eead0805658431703d",
            "5da8e9c5e66c43509925436b27ef45e3",
            "7a1f6bab668b41f18f183e37c101cb9c",
            "b124f00605544791861b217d22796a6b",
            "8c9ab203b7514594a466ca29ff20f5c9"
          ]
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.30k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4fb5a9e06954b4dbb999be56adde967"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/94.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61be9bacf49f41dfbfbd10726a66b71f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['projector.weight', 'classifier.weight', 'projector.bias', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1.57 s (started: 2023-08-07 23:30:34 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "model_name = model_id.split(\"/\")[-1]\n",
        "batch_size = 16\n",
        "gradient_accumulation_steps = 1\n",
        "num_train_epochs = 15\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    f\"{model_name}-finetuned-gtzan-bs-{batch_size}\",\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": {
        "id": "0IbvHPxeLzGR",
        "outputId": "75ee626f-580c-4cde-df4a-e5e063af6bc5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 3.91 ms (started: 2023-08-07 23:30:55 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate"
      ],
      "metadata": {
        "id": "fAigksuYMSrA",
        "outputId": "6c9e177d-54d4-49c9-e046-ef8564e61cb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/81.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.14.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.22.4)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.3.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.27.1)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from evaluate) (4.65.0)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from evaluate) (3.3.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.70.15)\n",
            "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2023.6.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.16.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.1)\n",
            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (9.0.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.8.5)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.7.0->evaluate) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.7.0->evaluate) (4.7.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2023.7.22)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (3.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2022.7.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n",
            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.0 responses-0.18.0\n",
            "time: 5.13 s (started: 2023-08-07 23:30:56 +00:00)\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": {
        "id": "7V2Ym_SVMELR",
        "outputId": "329b2122-8cc5-44ca-f857-fdba30b10d63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "049cadd4671d44aaa6f581131fcd2088",
            "9fd6e98a793f465d94ba24685d96f8a7",
            "02c8b9deac1744a8bd1e186661076b87",
            "4a4e68a81f704f0e87adfb1b84a1a228",
            "c61999a02d38464c842308f7a2b01454",
            "5cfe5e790966401d8f611c5094483c5e",
            "a5698988fb6b4750bf4bc15c3e9cbc14",
            "dea49e8b61b641c98df36440184d74e4",
            "1c51a2e08d194dd1832a341c752243b2",
            "8c5a21ef552d47afb1ab107e821bd07f",
            "023e196da0634017a76aff71d1a004e9"
          ]
        }
      },
      "execution_count": 32,
      "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": "049cadd4671d44aaa6f581131fcd2088"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 2.42 s (started: 2023-08-07 23:31:01 +00:00)\n"
          ]
        }
      ]
    },
    {
      "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": {
        "id": "uV7yYSxgMKw5",
        "outputId": "6963ea90-0d26-40e9-e0bc-ff10b550e7b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='286' max='855' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [286/855 36:22 < 1:12:52, 0.13 it/s, Epoch 5/15]\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>2.195500</td>\n",
              "      <td>2.111934</td>\n",
              "      <td>0.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.691600</td>\n",
              "      <td>1.597314</td>\n",
              "      <td>0.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.180500</td>\n",
              "      <td>1.184939</td>\n",
              "      <td>0.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.092400</td>\n",
              "      <td>0.977072</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='855' max='855' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [855/855 1:50:50, Epoch 15/15]\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>2.195500</td>\n",
              "      <td>2.111934</td>\n",
              "      <td>0.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.691600</td>\n",
              "      <td>1.597314</td>\n",
              "      <td>0.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.180500</td>\n",
              "      <td>1.184939</td>\n",
              "      <td>0.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.092400</td>\n",
              "      <td>0.977072</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.779400</td>\n",
              "      <td>0.820123</td>\n",
              "      <td>0.780000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.633500</td>\n",
              "      <td>0.696918</td>\n",
              "      <td>0.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.617800</td>\n",
              "      <td>0.663172</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.423200</td>\n",
              "      <td>0.584108</td>\n",
              "      <td>0.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.313500</td>\n",
              "      <td>0.595969</td>\n",
              "      <td>0.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.555742</td>\n",
              "      <td>0.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.165100</td>\n",
              "      <td>0.595671</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.119100</td>\n",
              "      <td>0.564030</td>\n",
              "      <td>0.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.126700</td>\n",
              "      <td>0.560362</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.078400</td>\n",
              "      <td>0.523277</td>\n",
              "      <td>0.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.107600</td>\n",
              "      <td>0.522883</td>\n",
              "      <td>0.870000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=855, training_loss=0.7135471267309802, metrics={'train_runtime': 6672.5152, 'train_samples_per_second': 2.021, 'train_steps_per_second': 0.128, 'total_flos': 9.200982411936e+17, 'train_loss': 0.7135471267309802, 'epoch': 15.0})"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1h 51min 29s (started: 2023-08-07 23:31:04 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = {\n",
        "    \"dataset_tags\": \"marsyas/gtzan\",\n",
        "    \"dataset\": \"GTZAN\",\n",
        "    \"model_name\": f\"{model_name}-finetuned-gtzan-bs-{batch_size}\",\n",
        "    \"finetuned_from\": model_id,\n",
        "    \"tasks\": \"audio-classification\",\n",
        "}"
      ],
      "metadata": {
        "id": "VPP0LM7vMeR6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbee3efb-4b74-40e8-a678-b77125354093"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 426 µs (started: 2023-08-08 01:26:35 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ],
      "metadata": {
        "id": "ZxZj7e4afS8T",
        "outputId": "6a59b206-69b7-4de5-ec89-e775403bb6fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/DrishtiSharma/distilhubert-finetuned-gtzan-bs-16/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1.74 s (started: 2023-08-08 01:27:30 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yeahAhYRfTft"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}