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            "\u001b[K     |████████████████████████████████| 197 kB 24.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.9 MB 52.4 MB/s \n",
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            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "! pip install -Uqq fastai gradio torchaudio torchvision huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio\n",
        "import torchaudio\n",
        "from fastai.vision.all import *\n",
        "from fastai.learner import load_learner\n",
        "from torchvision.utils import save_image\n",
        "from huggingface_hub import hf_hub_download"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": 2,
      "outputs": [
        {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/paramiko/transport.py:236: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
            "  \"class\": algorithms.Blowfish,\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "model = load_learner(\n",
        "    hf_hub_download(\"kurianbenoy/music_genre_classification_baseline\", \"model.pkl\")\n",
        ")\n",
        "\n",
        "labels = model.dls.vocab"
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    {
      "cell_type": "code",
      "source": [
        "N_FFT = 2048\n",
        "HOP_LEN = 1024\n",
        "\n",
        "\n",
        "def create_spectrogram(filename):\n",
        "    audio, sr = torchaudio.load(filename)\n",
        "    specgram = torchaudio.transforms.MelSpectrogram(\n",
        "        sample_rate=sr,\n",
        "        n_fft=N_FFT,\n",
        "        win_length=N_FFT,\n",
        "        hop_length=HOP_LEN,\n",
        "        center=True,\n",
        "        pad_mode=\"reflect\",\n",
        "        power=2.0,\n",
        "        norm=\"slaney\",\n",
        "        onesided=True,\n",
        "        n_mels=224,\n",
        "        mel_scale=\"htk\",\n",
        "    )(audio).mean(axis=0)\n",
        "    specgram = torchaudio.transforms.AmplitudeToDB()(specgram)\n",
        "    specgram = specgram - specgram.min()\n",
        "    specgram = specgram / specgram.max()\n",
        "\n",
        "    return specgram"
      ],
      "metadata": {
        "id": "J1Goh5DHnm59"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_image(filename):\n",
        "    specgram = create_spectrogram(filename)\n",
        "    dest = Path(\"temp.png\")\n",
        "    save_image(specgram, \"temp.png\")"
      ],
      "metadata": {
        "id": "-J15K2MmqADm"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(img):\n",
        "    img = PILImage.create(img)\n",
        "    _pred, _pred_w_idx, probs = model.predict(img)\n",
        "    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}\n",
        "    return labels_probs"
      ],
      "metadata": {
        "id": "1cOhBdp0m2Q5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def end2endpipeline(filename):\n",
        "    create_image(filename)\n",
        "    return predict(\"temp.png\")"
      ],
      "metadata": {
        "id": "eXIEc4blqOWQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interface_options = {\n",
        "    \"title\": \"Music Genre Classification\",\n",
        "    \"description\": \"A simple baseline model for classifying music genres with fast.ai on [Kaggle competition data](https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/data)\",\n",
        "    \"interpretation\": \"default\",\n",
        "    \"layout\": \"horizontal\",\n",
        "    \"theme\": \"default\",\n",
        "}\n",
        "\n",
        "demo = gradio.Interface(\n",
        "    fn=end2endpipeline,\n",
        "    inputs=gradio.inputs.Audio(source=\"microphone\", type=\"filepath\", label=\"Record/ Drop audio\"),\n",
        "    outputs=gradio.outputs.Label(num_top_classes=5),\n",
        "    **interface_options,\n",
        ")\n",
        "\n",
        "launch_options = {\n",
        "    \"enable_queue\": True,\n",
        "    \"share\": False,\n",
        "}\n",
        "demo.launch(**launch_options)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "uMO5CynZm_pL",
        "outputId": "0cff073a-293d-4c3d-d3bf-a924d63cdfec"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
            "Your interface requires microphone or webcam permissions - this may cause issues in Colab. Use the External URL in case of issues.\n",
            "Running on public URL: https://55955.gradio.app\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
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              "<IPython.lib.display.IFrame at 0x7f647af3b450>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"900\"\n",
              "            height=\"500\"\n",
              "            src=\"https://55955.gradio.app\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<fastapi.applications.FastAPI at 0x7f65bf011ed0>,\n",
              " 'http://127.0.0.1:7862/',\n",
              " 'https://55955.gradio.app')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_audio = [gradio.inputs.Audio(source=\"microphone\", type=\"filepath\", label=\"Record/ Drop audio\")]\n",
        "input_audio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ptz2HMdnAE7",
        "outputId": "2cac3314-07eb-4c91-d5ed-770e2ed6ae0d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Audio(label=\"Record/ Drop audio\")]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_wHGGfNfnufy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}