AudioCourse_MusicGenreClassifier_P2.ipynb

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          ]
        }
      ],
      "source": [
        "!pip install --upgrade transformers\n",
        "!pip install datasets\n",
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "gtzan = load_dataset(\"marsyas/gtzan\", \"all\")\n",
        "gtzan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrTlAZVufKeV",
        "outputId": "126f2db7-90be-4dda-a600-f771b5321e98"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1454: FutureWarning: 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 message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GTZAN does not provide a split in the dataset, so we are creating one\n",
        "# ourselves\n",
        "gtzan = gtzan['train'].train_test_split(test_size=0.1, shuffle=True, seed=42)\n",
        "gtzan\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LcqFIO3fgzP",
        "outputId": "0950e2f7-6296-4caf-cc3c-55be65b4ebfa"
      },
      "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": "Sbt2VLI_hsST",
        "outputId": "82195ca4-97aa-4c84-c37e-e636e46e5007"
      },
      "execution_count": 4,
      "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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# genre is represented as an integer, so let’s use the int2str() method of\n",
        "# the genre feature to map these integers to human-readable names\n",
        "id2label_fn = gtzan['train'].features['genre'].int2str\n",
        "id2label_fn(gtzan['train'][0]['genre'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "fE6G0CGgmzg5",
        "outputId": "5bd97299-1683-4de7-db6c-2d75b044dee1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'pop'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let’s now listen to a few more examples by using Gradio to create a simple\n",
        "# interface with the Blocks API\n",
        "\n",
        "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": 719
        },
        "id": "oap4dmy3n6My",
        "outputId": "50e78e31-1eee-48e1-b7e0-92e89dbfbffb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/processing_utils.py:342: 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": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "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",
            "Running on public URL: https://d1afc3d4e7ad871caa.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://d1afc3d4e7ad871caa.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://d1afc3d4e7ad871caa.gradio.live\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "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": {
        "id": "I5B0AY8kriV2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# As we have seen above, the sampling rate of the audio samples\n",
        "# in the dataset is = 22KHz(approx.) let's find the sampling rate accepted by the\n",
        "# model\n",
        "sampling_rate = feature_extractor.sampling_rate\n",
        "sampling_rate\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrxOveu7rjye",
        "outputId": "e25e58a1-7b02-4264-c23c-cb70293a0075"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The model needs 16Khz samples so we can use the cast_column() method to\n",
        "# downsample the examples to match the requirements of the model.\n",
        "\n",
        "from datasets import Audio\n",
        "\n",
        "gtzan = gtzan.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))"
      ],
      "metadata": {
        "id": "esPmL4iGsZ8g"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's verify if the changes were successful.\n",
        "gtzan['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcaWMHNbs0ET",
        "outputId": "f84b8f21-ba25-4b8d-927a-e0fc706a2327"
      },
      "execution_count": 10,
      "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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Works! However, I noticed that the 1-D NP Array of the audio has changed.\n",
        "# What exactly did the feature extractor do?\n",
        "# Looking into this\n",
        "\n",
        "import numpy as np\n",
        "test_sample = gtzan['train'][0]['audio']\n",
        "print(f\"Mean: {np.mean(test_sample['array']):.3},\\n\",\n",
        "      f\"Variance: {np.var(test_sample['array']):.3}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkBfHSRSs6YE",
        "outputId": "74b594f1-471e-4e84-cb1b-1a88ad9d5922"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 0.000185,\n",
            " Variance: 0.0493\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = feature_extractor(test_sample[\"array\"],\n",
        "                           sampling_rate=test_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": "tggdC8iJyQFI",
        "outputId": "bb405a17-4fd0-49aa-f735-dc8fc442c48e"
      },
      "execution_count": 12,
      "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": [
        "# the model cannot process audio samples above 30secs,\n",
        "# therefore, wee need to trucate examples that have longer durations.\n",
        "# Let's define a method to set the max_duration and use the feature_extractor\n",
        "# class on a single sample.\n",
        "# Later, we can use the .map() method to apply the same for all samples.\n",
        "\n",
        "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": {
        "id": "osKqngJ5zrPq"
      },
      "execution_count": 13,
      "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": 260,
          "referenced_widgets": [
            "43f3e104b19f4c66bbe3ea0051a73f8b",
            "3b54b0edc0e9490cb29415380aca9b20",
            "2b538e894362418d8f728d79c5896134",
            "8c568e71e2504564bc0838ee8da4c939",
            "3c9ad2ffa59542e79c021e83d06b22a0",
            "c98123f58ac84597b0a73dab1d979fde",
            "a85a8095b6d742ffbbf6f9d3d9c5b92a",
            "68843970766d4329ad86695100134e94",
            "8c21d637574e4f3ea16ca38b6ca4c011",
            "c8de6a018fd94edfaea606ce011c8269",
            "e5eaabc025f64e9e9514ec57d3dc6237",
            "01d1971703454bacac6b3527cce2d820",
            "8f222d62382d44c29a5b5bf92157e9e3",
            "6ac1a58e2be34eb59aec010e54c1addd",
            "a0321f7073fb4ab9a721db5eb0935a5b",
            "2f62ca15159f48ab8d083b2033d01919",
            "f61112901f6f410db12b70e0aaf28cda",
            "eb53ee188a7c4140991577e60bc73f2c",
            "2b04b741fa5f4ec5bc6359eb4d26ace6",
            "dc6056981fd8482a92aa4f1890eba36c",
            "680c2489ca0c4bbf92b34f523934da79",
            "0ff7ce08485445e0bc2edd5936ef20f5"
          ]
        },
        "id": "GlrqUCnD0b5v",
        "outputId": "a76936cf-93a7-4c70-e7a3-f79d49727d86"
      },
      "execution_count": 14,
      "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": "43f3e104b19f4c66bbe3ea0051a73f8b"
            }
          },
          "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": "01d1971703454bacac6b3527cce2d820"
            }
          },
          "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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtzan_encoded = gtzan_encoded.rename_column(\"genre\", \"label\")"
      ],
      "metadata": {
        "id": "g9ZStVvs1bUb"
      },
      "execution_count": 15,
      "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": "rt801Q6N1gob",
        "outputId": "70424296-3347-4eaf-d021-3d713f6227c3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'pop'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Begin fine tuning the model\n",
        "\n",
        "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TdQV60Jg648N",
        "outputId": "a4d15d86-23c5-4e32-fb33-e79daed3e430"
      },
      "execution_count": 17,
      "outputs": [
        {
          "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: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\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 huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "739ae350d09042e784eaec4095ad9e87",
            "cb68b21793a54790b2b95b4784801d34",
            "3a7eb1df05034c9ba9a673b6bf9cba54",
            "a750e712f3284417b4c95484b892165e",
            "3f0eb8b654ae4c91805d226c354d5bf1",
            "8ce7ccccb71049ffaa738c8cf1553bc5",
            "5e3295a339f541dfbe9209948a9b8efd",
            "fab48b6a8b2d453aa3879bf0e9d42e37",
            "90d46cef2a30418aa1388f77925bcce3",
            "db6344b770ab4bfcbcbeb40e1161735c",
            "9e7c7998857f48f0a9307deca7276283",
            "35ffca75e99146ae8c6577d29c528152",
            "f7d93b7ab71f4273ae080cb53d37d0ef",
            "e9bbc75862e14ce29fbb5aa3422c36ea",
            "5c190d00e06b461fa843bcf8426c97d6",
            "0240e32c03d14f0ba7f939ebf02d61ce",
            "05ac4041b92e49b7993ceabe8a49e4ad",
            "91fb8d53119b4be894141700cd16b908",
            "87c958830dfe4ecb91ff679ba24d8763",
            "1c8ab8deb37b4d4e98ce5281949a0494",
            "4f6254527d974abebc22fdff4f1b5eef",
            "156591cf468f475488f6e60edd710b5f",
            "68d6eb725c274297831e3648de86009c",
            "1a904a89a9434eacadb08b28490b9ac3",
            "5d0592c5f77e4f3d884a2c665773124b",
            "01af6a1d1f7b48a48a6457e020197f99",
            "0d4c11080fa74e2d86ba3d2517ff134d",
            "3740c85110c9440c97d78745c6dd3da1",
            "46a0ce27078a4a5d90f1446174e04b3b",
            "4ad65300d4874917965c8bb73460ec2e",
            "0efb648beee34538907cafc237409fce",
            "d8b3f7ebd07347f6a58785d0b6e32135"
          ]
        },
        "id": "oTr0vYV3_ST_",
        "outputId": "8c0f2e28-a6b1-424f-cd39-36b274663a40"
      },
      "execution_count": 18,
      "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": "739ae350d09042e784eaec4095ad9e87"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[torch]\n",
        "!pip install accelerate -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztvSuE9eBNJH",
        "outputId": "64ecff0d-7298-4392-9b98-a9a812373432"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.37.2)\n",
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            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n",
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            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.15.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2024.2.2)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
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            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.20.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.9.0)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
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          ]
        }
      ]
    },
    {
      "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 = 10\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=False,\n",
        ")"
      ],
      "metadata": {
        "id": "CoHN69wJ_3ex"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYQUGUOPCdU2",
        "outputId": "39a82580-b480-4ecb-c2c2-5d183bf113be"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.2/84.1 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.1 responses-0.18.0\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": "QgsRbceLCtFq"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we have got all the required pieces.\n",
        "# Instantiating the Trainer class and training the model.\n",
        "\n",
        "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": 443
        },
        "id": "MCLuD65ODA5P",
        "outputId": "28074493-1bec-4fb5-a84e-15a4f1542a57"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1130' max='1130' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1130/1130 1:34:36, Epoch 10/10]\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>1.955600</td>\n",
              "      <td>1.801055</td>\n",
              "      <td>0.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.330400</td>\n",
              "      <td>1.269688</td>\n",
              "      <td>0.630000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.951800</td>\n",
              "      <td>0.964875</td>\n",
              "      <td>0.730000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.813400</td>\n",
              "      <td>0.890248</td>\n",
              "      <td>0.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.473000</td>\n",
              "      <td>0.770490</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.368100</td>\n",
              "      <td>0.704531</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.450000</td>\n",
              "      <td>0.576809</td>\n",
              "      <td>0.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.141600</td>\n",
              "      <td>0.526543</td>\n",
              "      <td>0.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.273600</td>\n",
              "      <td>0.505022</td>\n",
              "      <td>0.860000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.145000</td>\n",
              "      <td>0.558684</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1130, training_loss=0.7378938935499275, metrics={'train_runtime': 5693.3561, 'train_samples_per_second': 1.579, 'train_steps_per_second': 0.198, 'total_flos': 6.133988274624e+17, 'train_loss': 0.7378938935499275, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface-cli"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZzFsDWqs_5g",
        "outputId": "790dbbe6-4588-48f5-a33e-345516f5fd1e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting huggingface-cli\n",
            "  Downloading huggingface_cli-0.1-py3-none-any.whl (1.0 kB)\n",
            "Installing collected packages: huggingface-cli\n",
            "Successfully installed huggingface-cli-0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XN4XHx5FtOUY",
        "outputId": "a1fdc340-3487-416e-bc53-ef71ac7f2d19"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = {\n",
        "    \"dataset_tags\": \"marsyas/gtzan\",\n",
        "    \"dataset\": \"GTZAN\",\n",
        "    \"model_name\": f\"{model_name}-finetuned-gtzan\",\n",
        "    \"finetuned_from\": model_id,\n",
        "    \"tasks\": \"audio-classification\",\n",
        "}"
      ],
      "metadata": {
        "id": "YQrmGJQ9X1wM"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 828
        },
        "id": "XBi_EkTFX-q1",
        "outputId": "dfd91c1b-66c0-4d61-b5e5-5317f6ba7819"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RepositoryNotFoundError",
          "evalue": "404 Client Error. (Request ID: Root=1-65c69536-0fe5f1b8053570bd6bb0da39;a885cbd5-70d8-4207-91e0-d30bf58ae6f4)\n\nRepository Not Found for url: https://huggingface.co/api/models/distilhubert-finetuned-gtzan.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    285\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mHfHubHTTPError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3178\u001b[0;31m             \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3179\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    332\u001b[0m         \u001b[0;31m# as well (request id and/or server error message)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mHfHubHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHfHubHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create (Request ID: Root=1-65c69536-49f7696c2cca288e5ca2fecc;3f301dba-bb01-4626-ae73-0d658bc6e932)\n\nYou don't have the rights to create a model under this namespace",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    285\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/api/models/distilhubert-finetuned-gtzan",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-ce0a97cc110b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, commit_message, blocking, **kwargs)\u001b[0m\n\u001b[1;32m   3725\u001b[0m         \u001b[0;31m# In case the user calls this method with args.push_to_hub = False\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3726\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3727\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_hf_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3729\u001b[0m         \u001b[0;31m# Needs to be executed on all processes for TPU training, but will only save on the processed determined by\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36minit_hf_repo\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3558\u001b[0m             \u001b[0mrepo_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3560\u001b[0;31m         \u001b[0mrepo_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_private_repo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3561\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrepo_url\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3562\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_in_progress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3184\u001b[0m                 \u001b[0;31m# No write permission on the namespace but repo might already exist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3185\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3186\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepo_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3187\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mrepo_type\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mrepo_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mREPO_TYPE_MODEL\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3188\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0mRepoUrl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.endpoint}/{repo_id}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mrepo_info\u001b[0;34m(self, repo_id, revision, repo_type, timeout, files_metadata, token)\u001b[0m\n\u001b[1;32m   2273\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2274\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unsupported repo type.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2275\u001b[0;31m         return method(\n\u001b[0m\u001b[1;32m   2276\u001b[0m             \u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2277\u001b[0m             \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mmodel_info\u001b[0;34m(self, repo_id, revision, timeout, securityStatus, files_metadata, token)\u001b[0m\n\u001b[1;32m   2083\u001b[0m             \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"blobs\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2085\u001b[0;31m         \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2086\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2087\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mModelInfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    321\u001b[0m                 \u001b[0;34m\" make sure you are authenticated.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m             )\n\u001b[0;32m--> 323\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRepositoryNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-65c69536-0fe5f1b8053570bd6bb0da39;a885cbd5-70d8-4207-91e0-d30bf58ae6f4)\n\nRepository Not Found for url: https://huggingface.co/api/models/distilhubert-finetuned-gtzan.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "model_id = \"sanchit-gandhi/distilhubert-finetuned-gtzan\"\n",
        "pipe = pipeline(\"audio-classification\", model=model_id)"
      ],
      "metadata": {
        "id": "we5JztBCnSbb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classify_audio(filepath):\n",
        "    preds = pipe(filepath)\n",
        "    outputs = {}\n",
        "    for p in preds:\n",
        "        outputs[p[\"label\"]] = p[\"score\"]\n",
        "    return outputs"
      ],
      "metadata": {
        "id": "vIboUhssnT6n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "demo = gr.Interface(\n",
        "    fn=classify_audio, inputs=gr.Audio(type=\"filepath\"), outputs=gr.outputs.Label()\n",
        ")\n",
        "demo.launch(debug=True)"
      ],
      "metadata": {
        "id": "5TGZP_A4nWWd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}