NLP/Notebooks/11_speech_command_recognition_with_torchaudio.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42EYyPrAFSG1"
      },
      "source": [
        "# Reconocimiento de comandos de voz con torchaudio\n",
        "******************************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NxkiOsH18zmO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4fb5a0a-c87d-4337-ea8b-116cb4da7415"
      },
      "source": [
        "!pip install pydub\n",
        "!pip install torchaudio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Installing collected packages: pydub\n",
            "Successfully installed pydub-0.25.1\n",
            "Collecting torchaudio\n",
            "  Downloading torchaudio-0.9.0-cp37-cp37m-manylinux1_x86_64.whl (1.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9 MB 8.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch==1.9.0 in /usr/local/lib/python3.7/dist-packages (from torchaudio) (1.9.0+cu102)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.9.0->torchaudio) (3.7.4.3)\n",
            "Installing collected packages: torchaudio\n",
            "Successfully installed torchaudio-0.9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4jtRMoV8kF5"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-m1XPXF8kF-"
      },
      "source": [
        "Este tutorial le mostrará cómo formatear correctamente un conjunto de datos de audio y luego entrenar / probar una red de clasificador de audio en el conjunto de datos.\n",
        "\n",
        "Colab tiene la opción de GPU disponible. En las pestañas del menú, seleccione \"Tiempo de ejecución\" y luego \"Cambiar tipo de tiempo de ejecución\". En la ventana emergente que aparece a continuación, puede elegir GPU. Después del cambio, su tiempo de ejecución debería reiniciarse automáticamente (lo que significa que la información de las celdas ejecutadas desaparece).\n",
        "\n",
        "Primero, importemos los paquetes de Pytorch comunes, como torchaudio.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyXN4VW48kGB"
      },
      "source": [
        "# Uncomment the following line to run in Google Colab\n",
        "\n",
        "# CPU:\n",
        "# !pip install torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "\n",
        "# GPU:\n",
        "# !pip install torch==1.7.0+cu101 torchvision==0.8.1+cu101 torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "\n",
        "# For interactive demo at the end:\n",
        "# !pip install pydub\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torchaudio\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import IPython.display as ipd\n",
        "from tqdm.notebook import tqdm\n",
        "import librosa\n",
        "import librosa.display"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHRMYstF8kGD"
      },
      "source": [
        "Verifiquemos si hay una GPU CUDA disponible y seleccionemos nuestro dispositivo. Ejecutar la red en una GPU disminuirá en gran medida el tiempo de ejecución de entrenamiento / prueba.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pI1fm-Ow8kGE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6cfebb1-106f-48d8-d612-be89a0826e9b"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yNDAvSz8kGF"
      },
      "source": [
        "Importar el conjunto de datos\n",
        "---------------------\n",
        "\n",
        "Usamos torchaudio para descargar y representar el conjunto de datos. Aquí usamos [SpeechCommands](https://arxiv.org/abs/1804.03209), que es un conjunto de datos de 35 comandos hablados por diferentes personas. El conjunto de datos ` SPEECHCOMMANDS` forma parte de `torch.utils.data.Dataset`. En este conjunto de datos, todos los archivos de audio tienen una duración de aproximadamente 1 segundo (y por lo tanto, aproximadamente 16000 frames de tiempo).\n",
        "\n",
        "Los pasos reales de carga y formateo ocurren cuando se accede a un datapoint, y torchaudio se encarga de convertir los archivos de audio a tensores. Si uno quiere cargar un archivo de audio directamente en su lugar, se puede utilizar `torchaudio.load()`. Esto devuelve una tupla que contiene el tensor recién creado junto con la frecuencia de muestreo del archivo de audio (16 kHz para SpeechCommands).\n",
        "\n",
        "Volviendo al conjunto de datos, aquí creamos una subclase que lo divide en subconjuntos estándar de entrenamiento, validación y prueba.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxA9Ic8c8kGI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "cd72fe84fd8445e5beb296c52b6014b1",
            "f8d07bb06caf4ef89286ab450b108d37",
            "7c3bc5f72b994fc998cdead80c51daf9",
            "f49cc1cf839b491797b7a3733328fa09",
            "d8fc553b115845759c9915a6ca3e1eea",
            "aeb9d19082814b24830bd033b8fd8240",
            "db688cb02cb1401886ff466db2320114",
            "12593b13672547ff97a115a16ef0b9e3"
          ]
        },
        "outputId": "12cc1d28-e84e-4052-da80-c312b27839e8"
      },
      "source": [
        "from torchaudio.datasets import SPEECHCOMMANDS\n",
        "import os\n",
        "\n",
        "\n",
        "class SubsetSC(SPEECHCOMMANDS):\n",
        "    def __init__(self, subset: str = None):\n",
        "        super().__init__(\"./\", download=True)\n",
        "\n",
        "        def load_list(filename):\n",
        "            filepath = os.path.join(self._path, filename)\n",
        "            with open(filepath) as fileobj:\n",
        "                return [os.path.join(self._path, line.strip()) for line in fileobj]\n",
        "\n",
        "        if subset == \"validation\":\n",
        "            self._walker = load_list(\"validation_list.txt\")\n",
        "        elif subset == \"testing\":\n",
        "            self._walker = load_list(\"testing_list.txt\")\n",
        "        elif subset == \"training\":\n",
        "            excludes = load_list(\"validation_list.txt\") + load_list(\"testing_list.txt\")\n",
        "            excludes = set(excludes)\n",
        "            self._walker = [w for w in self._walker if w not in excludes]\n",
        "\n",
        "\n",
        "# Cree una división de entrenamiento y prueba de los datos. No usamos validación en este tutorial.\n",
        "train_set = SubsetSC(\"training\")\n",
        "test_set = SubsetSC(\"testing\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd72fe84fd8445e5beb296c52b6014b1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2428923189.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPIlyO_Z8kGL"
      },
      "source": [
        "Un datapoint en el conjunto de datos SPEECHCOMMANDS es una tupla hecha de una forma de onda (la señal de audio), la frecuencia de muestreo, la expresión (etiqueta), la identificación del hablante, el número de la expresión.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiDKPOln8kGM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a6fba46c-7673-4a68-a8e4-3f70b0602a75"
      },
      "source": [
        "waveform, sample_rate, label, speaker_id, utterance_number = train_set[66]\n",
        "\n",
        "print(\"Forma de la onda: {}\".format(waveform.size()))\n",
        "print(\"Sample rate de la onda: {}\".format(sample_rate))\n",
        "\n",
        "plt.plot(waveform.t().numpy());"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Forma de la onda: torch.Size([1, 16000])\n",
            "Sample rate de la onda: 16000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75YZfjKOB0T4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "821f4701-b328-4975-8ead-0587c140a933"
      },
      "source": [
        "specgram = torchaudio.transforms.MelSpectrogram()(waveform)\n",
        "\n",
        "print(\"Forma espectrograma: {}\".format(specgram.size()))\n",
        "\n",
        "plt.figure()\n",
        "p = plt.imshow(specgram.log2()[0,:,:].detach().numpy())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Forma espectrograma: torch.Size([1, 128, 81])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchaudio/functional/functional.py:433: UserWarning: At least one mel filterbank has all zero values. The value for `n_mels` (128) may be set too high. Or, the value for `n_freqs` (201) may be set too low.\n",
            "  \"At least one mel filterbank has all zero values. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVmCxR-4DYgW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "outputId": "94813af3-8355-446c-d54e-a24fe51b54a2"
      },
      "source": [
        "x, sr = waveform.t().numpy().squeeze(1), sample_rate\n",
        "\n",
        "plt.figure(figsize=(14, 5))\n",
        "\n",
        "\n",
        "\n",
        "librosa.display.waveplot(x, sr=sr)\n",
        "X = librosa.stft(x)\n",
        "Xdb = librosa.amplitude_to_db(abs(X))\n",
        "plt.figure(figsize=(14, 5))\n",
        "#librosa.display.specshow(Xdb, sr=sample_rate, x_axis='time', y_axis='hz')\n",
        "Xdb.shape\n",
        "\n",
        "S = librosa.feature.melspectrogram(x, sr=sample_rate, n_mels=128)\n",
        "log_S = librosa.power_to_db(S, ref=np.max)\n",
        "MFCC = librosa.feature.mfcc(S=log_S, n_mfcc=23)\n",
        "delta2_mfcc = librosa.feature.delta(MFCC, order=2)\n",
        "\n",
        "#MFCC = librosa.feature.mfcc(y=x, sr=sample_rate,n_mfcc=23,dct_type=2)\n",
        "librosa.display.specshow(log_S)\n",
        "#print(np.max(MFCC),np.min(MFCC))\n",
        "#MFCC = (MFCC+200)/500\n",
        "#print(np.max(MFCC),np.min(MFCC))\n",
        "plt.colorbar()\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAeeEjkT8kGN"
      },
      "source": [
        "Busquemos la lista de etiquetas disponibles en el conjunto de datos.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hu7DLuOf8kGO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f68cc4ab-d0aa-4fc4-9c55-ac7badd50827"
      },
      "source": [
        "labels = sorted(list(set(datapoint[2] for datapoint in train_set)))\n",
        "labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['backward',\n",
              " 'bed',\n",
              " 'bird',\n",
              " 'cat',\n",
              " 'dog',\n",
              " 'down',\n",
              " 'eight',\n",
              " 'five',\n",
              " 'follow',\n",
              " 'forward',\n",
              " 'four',\n",
              " 'go',\n",
              " 'happy',\n",
              " 'house',\n",
              " 'learn',\n",
              " 'left',\n",
              " 'marvin',\n",
              " 'nine',\n",
              " 'no',\n",
              " 'off',\n",
              " 'on',\n",
              " 'one',\n",
              " 'right',\n",
              " 'seven',\n",
              " 'sheila',\n",
              " 'six',\n",
              " 'stop',\n",
              " 'three',\n",
              " 'tree',\n",
              " 'two',\n",
              " 'up',\n",
              " 'visual',\n",
              " 'wow',\n",
              " 'yes',\n",
              " 'zero']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4AaG15e8kGP"
      },
      "source": [
        "Las 35 etiquetas de audio son comandos que dicen los usuarios. Los primeros archivos son personas que dicen \"backward\".\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PY2kzg3b8kGR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "d5bba26d-b141-4f07-b40b-dd1686198e86"
      },
      "source": [
        "waveform_first, *_ = train_set[0]\n",
        "ipd.Audio(waveform_first.numpy(), rate=sample_rate)\n",
        "\n",
        "waveform_second, *_ = train_set[1]\n",
        "ipd.Audio(waveform_second.numpy(), rate=sample_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1p3zNWSQ8kGS"
      },
      "source": [
        "El último archivo es alguien que dice \"zero\".\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNgD2q-68kGT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "80145a3d-6a8c-4446-a3cf-46ed7da91856"
      },
      "source": [
        "waveform_last, *_ = train_set[-1]\n",
        "ipd.Audio(waveform_last.numpy(), rate=sample_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LyNDIMso8kGU"
      },
      "source": [
        "Formatear los datos\n",
        "-------------------\n",
        "\n",
        "Este es un buen lugar para aplicar transformaciones a los datos. Para la forma de onda, reducimos la resolución del audio para un procesamiento más rápido sin perder demasiado poder de clasificación.\n",
        "\n",
        "No necesitamos aplicar otras transformaciones aquí. Sin embargo, es común que algunos conjuntos de datos tengan que reducir el número de canales (por ejemplo, de estéreo a mono) tomando la media a lo largo de la dimensión del canal o simplemente manteniendo solo uno de los canales. Dado que SpeechCommands usa un solo canal para audio, esto no es necesario aquí.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RqFPl4E8kGW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "7751220d-9a7c-4216-ed83-d0d0197626b0"
      },
      "source": [
        "new_sample_rate = 8000\n",
        "transform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=new_sample_rate)\n",
        "transformed = transform(waveform)\n",
        "\n",
        "ipd.Audio(transformed.numpy(), rate=new_sample_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdRIyi288kGX"
      },
      "source": [
        "Estamos codificando cada palabra usando su índice en la lista de etiquetas.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ltwokqV8kGY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39be1726-e679-4109-972a-2ab0de7e718a"
      },
      "source": [
        "def label_to_index(word):\n",
        "    # Devuelve la posición de la palabra en las etiquetas\n",
        "    return torch.tensor(labels.index(word))\n",
        "\n",
        "\n",
        "def index_to_label(index):\n",
        "    # Devuelve la palabra correspondiente al índice en las etiquetas\n",
        "    # Esta es la inversa de label_to_index\n",
        "    return labels[index]\n",
        "\n",
        "\n",
        "word_start = \"yes\"\n",
        "index = label_to_index(word_start)\n",
        "word_recovered = index_to_label(index)\n",
        "\n",
        "print(word_start, \"-->\", index, \"-->\", word_recovered)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "yes --> tensor(33) --> yes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTU6CRQG8kGZ"
      },
      "source": [
        "Para convertir una lista de datapoints formada por grabaciones de audio y enunciados en dos tensores por lotes para el modelo, implementamos una función de clasificación que es utilizada por PyTorch DataLoader que nos permite iterar sobre un conjunto de datos por lotes. Consulte la [documentación](https://pytorch.org/docs/stable/data.html#working-with-collate-fn) para obtener más información sobre cómo trabajar con una función de clasificación.\n",
        "\n",
        "En la función de clasificación, también aplicamos el remuestreo y el texto codificación.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhcjybUg8kGa"
      },
      "source": [
        "def pad_sequence(batch):\n",
        "    # Haga que todos los tensores de un lote tengan la misma longitud rellenando con ceros\n",
        "    batch = [item.t() for item in batch]\n",
        "    batch = torch.nn.utils.rnn.pad_sequence(batch, batch_first=True, padding_value=0.)\n",
        "    return batch.permute(0, 2, 1)\n",
        "\n",
        "\n",
        "def collate_fn(batch):\n",
        "\n",
        "    # Una tupla de datos tiene la forma:\n",
        "    # waveform, sample_rate, label, speaker_id, utterance_number\n",
        "\n",
        "    tensors, targets = [], []\n",
        "\n",
        "    # Reúna en listas y codifique etiquetas como índices\n",
        "    for waveform, _, label, *_ in batch:\n",
        "        tensors += [waveform]\n",
        "        targets += [label_to_index(label)]\n",
        "\n",
        "    # Agrupe la lista de tensores en un tensor por lotes\n",
        "    tensors = pad_sequence(tensors)\n",
        "    targets = torch.stack(targets) \n",
        "    \n",
        "    return tensors, targets\n",
        "\n",
        "\n",
        "batch_size = 256\n",
        "\n",
        "if device == \"cuda\":\n",
        "    num_workers = 1\n",
        "    pin_memory = True\n",
        "else:\n",
        "    num_workers = 0\n",
        "    pin_memory = False\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    train_set,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True,\n",
        "    collate_fn=collate_fn,\n",
        "    num_workers=num_workers,\n",
        "    pin_memory=pin_memory,\n",
        ")\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    test_set,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False,\n",
        "    drop_last=False,\n",
        "    collate_fn=collate_fn,\n",
        "    num_workers=num_workers,\n",
        "    pin_memory=pin_memory,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24_Q0n3f8kGc"
      },
      "source": [
        "Definir la red\n",
        "------------------\n",
        "\n",
        "Para este tutorial usaremos una red neuronal convolucional para procesar los datos de audio sin procesar. Por lo general, se aplican transformaciones más avanzadas a los datos de audio, sin embargo, las CNN se pueden usar para procesar con precisión los datos sin procesar.\n",
        "La arquitectura específica se modela a partir de la arquitectura de red M5 descrita en [este paper](https://arxiv.org/pdf/1610.00087.pdf). Un aspecto importante de los modelos que procesan datos de audio sin procesar es el campo receptivo de los filtros de su primera capa. El primer filtro de nuestro modelo tiene una longitud de 80, por lo que cuando se procesa audio muestreado a 8 kHz, el campo receptivo es de alrededor de 10 ms (y a 4 kHz, alrededor de 20 ms). Este tamaño es similar a las aplicaciones de procesamiento de voz que a menudo utilizan campos receptivos que van desde 20 ms hasta 40 ms.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHPniBjK8kGd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90f3721c-d5c8-4616-b5b1-75a965d15c28"
      },
      "source": [
        "class M5(nn.Module):\n",
        "    def __init__(self, n_input=1, n_output=35, stride=16, n_channel=32):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=stride)\n",
        "        self.bn1 = nn.BatchNorm1d(n_channel)\n",
        "        self.pool1 = nn.MaxPool1d(4)\n",
        "        self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3)\n",
        "        self.bn2 = nn.BatchNorm1d(n_channel)\n",
        "        self.pool2 = nn.MaxPool1d(4)\n",
        "        self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3)\n",
        "        self.bn3 = nn.BatchNorm1d(2 * n_channel)\n",
        "        self.pool3 = nn.MaxPool1d(4)\n",
        "        self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3)\n",
        "        self.bn4 = nn.BatchNorm1d(2 * n_channel)\n",
        "        self.pool4 = nn.MaxPool1d(4)\n",
        "        self.fc1 = nn.Linear(2 * n_channel, n_output)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = F.relu(self.bn1(x))\n",
        "        x = self.pool1(x)\n",
        "        x = self.conv2(x)\n",
        "        x = F.relu(self.bn2(x))\n",
        "        x = self.pool2(x)\n",
        "        x = self.conv3(x)\n",
        "        x = F.relu(self.bn3(x))\n",
        "        x = self.pool3(x)\n",
        "        x = self.conv4(x)\n",
        "        x = F.relu(self.bn4(x))\n",
        "        x = self.pool4(x)\n",
        "        x = F.avg_pool1d(x, x.shape[-1])\n",
        "        x = x.permute(0, 2, 1)\n",
        "        x = self.fc1(x)\n",
        "        return F.log_softmax(x, dim=2)\n",
        "\n",
        "\n",
        "model = M5(n_input=transformed.shape[0], n_output=len(labels))\n",
        "model.to(device)\n",
        "print(model)\n",
        "\n",
        "\n",
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "\n",
        "n = count_parameters(model)\n",
        "print(\"Número de parámetros: %s\" % n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "M5(\n",
            "  (conv1): Conv1d(1, 32, kernel_size=(80,), stride=(16,))\n",
            "  (bn1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (pool1): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
            "  (conv2): Conv1d(32, 32, kernel_size=(3,), stride=(1,))\n",
            "  (bn2): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (pool2): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
            "  (conv3): Conv1d(32, 64, kernel_size=(3,), stride=(1,))\n",
            "  (bn3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (pool3): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
            "  (conv4): Conv1d(64, 64, kernel_size=(3,), stride=(1,))\n",
            "  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (pool4): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
            "  (fc1): Linear(in_features=64, out_features=35, bias=True)\n",
            ")\n",
            "Number of parameters: 26915\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uh1Vr-218kGe"
      },
      "source": [
        "Usaremos la misma técnica de optimización utilizada en el artículo, un optimizador de Adam con una disminución de peso establecida en 0,0001. Al principio, entrenaremos con una tasa de aprendizaje de 0.01, pero usaremos un `scheduler` para disminuirlo a 0,001 durante el entrenamiento después de 20 épocas.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wOFf6q88kGf"
      },
      "source": [
        "optimizer = optim.Adam(model.parameters(), lr=0.01, weight_decay=0.0001)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.1)  # reduce the learning after 20 epochs by a factor of 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fthPbq8A8kGf"
      },
      "source": [
        "Training and Testing the Network\n",
        "--------------------------------\n",
        "\n",
        "Now let’s define a training function that will feed our training data\n",
        "into the model and perform the backward pass and optimization steps. For\n",
        "training, the loss we will use is the negative log-likelihood. The\n",
        "network will then be tested after each epoch to see how the accuracy\n",
        "varies during the training.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJmdR5rL8kGg"
      },
      "source": [
        "def train(model, epoch, log_interval):\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "\n",
        "        data = data.to(device)\n",
        "        target = target.to(device)\n",
        "\n",
        "        # apply transform and model on whole batch directly on device\n",
        "        data = transform(data)\n",
        "        output = model(data)\n",
        "\n",
        "        # negative log-likelihood for a tensor of size (batch x 1 x n_output)\n",
        "        loss = F.nll_loss(output.squeeze(), target)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # print training stats\n",
        "        if batch_idx % log_interval == 0:\n",
        "            print(f\"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} ({100. * batch_idx / len(train_loader):.0f}%)]\\tLoss: {loss.item():.6f}\")\n",
        "\n",
        "        # update progress bar\n",
        "        pbar.update(pbar_update)\n",
        "        # record loss\n",
        "        losses.append(loss.item())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHUKczGT8kGh"
      },
      "source": [
        "Ahora que tenemos una función de entrenamiento, necesitamos crear una para probar la precisión de las redes. Configuraremos el modelo en modo `eval()` y luego ejecutaremos la inferencia en el conjunto de datos de prueba. Llamar a `eval()` establece la variable de entrenamiento en todos los módulos de la red en falso. Ciertas capas, como la normalización de lotes y las capas de abandono, se comportan de manera diferente durante el entrenamiento, por lo que este paso es crucial para obtener resultados correctos.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfmO1IS08kGh"
      },
      "source": [
        "def number_of_correct(pred, target):\n",
        "    # count number of correct predictions\n",
        "    return pred.squeeze().eq(target).sum().item()\n",
        "\n",
        "\n",
        "def get_likely_index(tensor):\n",
        "    # encontrar el índice de etiqueta más probable para cada elemento del lote\n",
        "    return tensor.argmax(dim=-1)\n",
        "\n",
        "\n",
        "def test(model, epoch):\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    for data, target in test_loader:\n",
        "\n",
        "        data = data.to(device)\n",
        "        target = target.to(device)\n",
        "\n",
        "        # apply transform and model on whole batch directly on device\n",
        "        data = transform(data)\n",
        "        output = model(data)\n",
        "\n",
        "        pred = get_likely_index(output)\n",
        "        correct += number_of_correct(pred, target)\n",
        "\n",
        "        # update progress bar\n",
        "        pbar.update(pbar_update)\n",
        "\n",
        "    print(f\"\\nTest Epoch: {epoch}\\tAccuracy: {correct}/{len(test_loader.dataset)} ({100. * correct / len(test_loader.dataset):.0f}%)\\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUJyvK-S8kGi"
      },
      "source": [
        "Finalmente, podemos entrenar y probar la red. Entrenaremos a la red\n",
        "durante diez épocas, luego reduce la tasa de aprendizaje y entrena durante diez épocas más.\n",
        "La red se probará después de cada época para ver cómo la precisión\n",
        "varía durante el entrenamiento.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_GaqCC-8kGj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 966,
          "referenced_widgets": [
            "7e766ca445524e45b31ca40f1e876fbf",
            "55581573d3d04691970278f09be3634a",
            "0d4668dd872e44278fe33d6ca49956f5",
            "df638f7243934b06bd5512a01f650fc8",
            "9165a876930e44d4ad9da371c35c5532",
            "9edcadf7363b4f48b12c490c1e5a4741",
            "d8f1580998ed416c9716a439938be1c5",
            "46c8499673054d3fa611ff38df04aaf3"
          ]
        },
        "outputId": "6843ad07-cad4-471b-f585-ecf2e7753c3f"
      },
      "source": [
        "log_interval = 20\n",
        "n_epoch = 2\n",
        "\n",
        "pbar_update = 1 / (len(train_loader) + len(test_loader))\n",
        "losses = []\n",
        "\n",
        "# La transformación debe vivir en el mismo dispositivo que el modelo y los datos.\n",
        "transform = transform.to(device)\n",
        "with tqdm(total=n_epoch) as pbar:\n",
        "    for epoch in range(1, n_epoch + 1):\n",
        "        train(model, epoch, log_interval)\n",
        "        test(model, epoch)\n",
        "        scheduler.step()\n",
        "\n",
        "# Let's plot the training loss versus the number of iteration.\n",
        "# plt.plot(losses);\n",
        "# plt.title(\"training loss\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e766ca445524e45b31ca40f1e876fbf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:652: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/105829 (0%)]\tLoss: 3.830327\n",
            "Train Epoch: 1 [5120/105829 (5%)]\tLoss: 3.125507\n",
            "Train Epoch: 1 [10240/105829 (10%)]\tLoss: 2.614277\n",
            "Train Epoch: 1 [15360/105829 (14%)]\tLoss: 2.302894\n",
            "Train Epoch: 1 [20480/105829 (19%)]\tLoss: 2.154182\n",
            "Train Epoch: 1 [25600/105829 (24%)]\tLoss: 1.983730\n",
            "Train Epoch: 1 [30720/105829 (29%)]\tLoss: 1.998342\n",
            "Train Epoch: 1 [35840/105829 (34%)]\tLoss: 1.800250\n",
            "Train Epoch: 1 [40960/105829 (39%)]\tLoss: 1.651220\n",
            "Train Epoch: 1 [46080/105829 (43%)]\tLoss: 1.516853\n",
            "Train Epoch: 1 [51200/105829 (48%)]\tLoss: 1.477048\n",
            "Train Epoch: 1 [56320/105829 (53%)]\tLoss: 1.539280\n",
            "Train Epoch: 1 [61440/105829 (58%)]\tLoss: 1.179756\n",
            "Train Epoch: 1 [66560/105829 (63%)]\tLoss: 1.465654\n",
            "Train Epoch: 1 [71680/105829 (68%)]\tLoss: 1.275938\n",
            "Train Epoch: 1 [76800/105829 (72%)]\tLoss: 1.338970\n",
            "Train Epoch: 1 [81920/105829 (77%)]\tLoss: 1.180194\n",
            "Train Epoch: 1 [87040/105829 (82%)]\tLoss: 1.116816\n",
            "Train Epoch: 1 [92160/105829 (87%)]\tLoss: 1.277471\n",
            "Train Epoch: 1 [97280/105829 (92%)]\tLoss: 1.096063\n",
            "Train Epoch: 1 [102400/105829 (97%)]\tLoss: 0.924626\n",
            "\n",
            "Test Epoch: 1\tAccuracy: 7414/11005 (67%)\n",
            "\n",
            "Train Epoch: 2 [0/105829 (0%)]\tLoss: 1.108763\n",
            "Train Epoch: 2 [5120/105829 (5%)]\tLoss: 1.122094\n",
            "Train Epoch: 2 [10240/105829 (10%)]\tLoss: 0.841758\n",
            "Train Epoch: 2 [15360/105829 (14%)]\tLoss: 0.951563\n",
            "Train Epoch: 2 [20480/105829 (19%)]\tLoss: 1.043768\n",
            "Train Epoch: 2 [25600/105829 (24%)]\tLoss: 0.904824\n",
            "Train Epoch: 2 [30720/105829 (29%)]\tLoss: 0.998351\n",
            "Train Epoch: 2 [35840/105829 (34%)]\tLoss: 0.984552\n",
            "Train Epoch: 2 [40960/105829 (39%)]\tLoss: 0.711796\n",
            "Train Epoch: 2 [46080/105829 (43%)]\tLoss: 1.012239\n",
            "Train Epoch: 2 [51200/105829 (48%)]\tLoss: 0.908815\n",
            "Train Epoch: 2 [56320/105829 (53%)]\tLoss: 0.938893\n",
            "Train Epoch: 2 [61440/105829 (58%)]\tLoss: 0.865497\n",
            "Train Epoch: 2 [66560/105829 (63%)]\tLoss: 0.869133\n",
            "Train Epoch: 2 [71680/105829 (68%)]\tLoss: 0.950803\n",
            "Train Epoch: 2 [76800/105829 (72%)]\tLoss: 0.879451\n",
            "Train Epoch: 2 [81920/105829 (77%)]\tLoss: 0.776939\n",
            "Train Epoch: 2 [87040/105829 (82%)]\tLoss: 0.852080\n",
            "Train Epoch: 2 [92160/105829 (87%)]\tLoss: 0.815082\n",
            "Train Epoch: 2 [97280/105829 (92%)]\tLoss: 0.778758\n",
            "Train Epoch: 2 [102400/105829 (97%)]\tLoss: 0.779046\n",
            "\n",
            "Test Epoch: 2\tAccuracy: 8304/11005 (75%)\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFBIcZrZ-by0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "a67fc0b8-02b2-4661-ecef-95a95486c2f2"
      },
      "source": [
        "# Let's plot the training loss versus the number of iteration.\n",
        "plt.plot(losses);\n",
        "plt.title(\"training loss\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pMrPXNvqCkkqpBKIB3WbGTWO8t08a0C7gfHF5pc/9yYu21XeXlFIqYhrQbfJzm3tvD+vWiguP6upzvuhQhc99g+GtuRu5b8qyBumfUkqFogHdz/1nH05qstAqM43MVN+Uxi+WbPe5bwxM+uAXnvt2bUN2USmlHCV8lou/80d05fwR7pG5/7aid3261Oe+bjuqlGpMdIQewnF9cn22pwukEV0p1XhoQA9hbN+2rLxnQtDzB0pdDdgbpZQKTQN6GKnJSbTNTnc8t21/qfd2E9hCVSkV5zSgR2Di6B6Ox7ftO+S9bd9Uek9xeb33SSml/GlAj0B5kI2jdxwo82lTWlHJX99dyBF3T2Pq4u2Oj1FKqfoSNqCLSBcRmS4iS0VkiYhc59BGRORxEVktIotE5Ij66W5sBNs42l7/payiirs+XcK78zYDMHfdngbpm1JKeUQyQncBNxpj+gMjgatFpL9fmwlAb+tvIvBMVHsZY64I8hPLK6t8gvi78zZ5b5eUu8ifNJkXvtN8daVU/Qkb0I0x24wx863bB4BlQCe/ZmcCrxq32UCOiHSIem9j5LJju4dtU1ZRydZ91RdJD5S6KK1wlwrYas21P/b1KnYUlTLlFy0ZoJSKvhrNoYtIPjAUmON3qhOwyXZ/M4FBv8lq2SyVD686GoA/Ht+Da8b2YmzfPJ82W/YdCijBW1llWL3zAOMf+RZwB/nfvTCHq96Y7w32SikVLRGvFBWRLOB94C/GmKLavJiITMQ9JUPXrl3DtG5chnZtxafXHEv/ji1IThKufmO+z/mLXpwb8JiKyioWbtrvc2y7lepYXOYiIzX89ndKKRWpiEboIpKKO5i/YYz5wKHJFqCL7X5n65gPY8zzxpjhxpjheXl5/qcbvcM7t/TWTU+LYAOMikrjk84IkG4F8YNluihJKRVdkWS5CPAisMwY80iQZp8Av7eyXUYC+40xcT1RvLckfK55RWUVL/+wzns/My3ZuxPS8Q/N4KnpqwEorajk4wVbdHGSUqpOIplyOQa4CPhFRBZYx24BugIYY54FpgCnAquBEuDS6He1cdluWyUaTLmripU7Dnrvl5RXUlJevRjpoS9WcPXYXtz/+XJe/mE9eVnpHN0rt176q5SKf2EDujFmFhC6RJV7aHl1tDoVL3YUhQ/6W/YdYtOeEiBwAw2llKoJXSlaS4ciyFJZv7s4bJtj7v/Gm+eekhzye1MppULSgF5LxWXhA3rB+r0RPVeFVVogJUkDulKq9jSg19L5R3YJ28ZTBiCceRvcgT9ZA7pSqg40oNfSjSf18d7Oy07nN8M7O7ZrkRH+urMntTHJ2k1j9trd7C+pCPUQpZQKoAG9lkSEVGvO+6e/j+fBcwez+t7AzTC6tsmM+DmrqgylFZWc//xsrnitIGp9VUolBt1TtA4W3nES9tTxlOTA78eOLZuxeEtkC2svfGEOf7Rqry/Zsp/l24tonpZCl9aRfykopRKXjtDrIDMthebpgd+JQ7vmeG8f17tmeeXPfeuuyFhRZTjl399x5lPf162TSqmEoQE9ytbffxr3nX04AJ1ymnHRqPxaPU+5Na++p7icB6cuDyj8pZRS/nTKpR54gm+LZqlReb6nZ6yhykBRaQX/POvwqDynUir+6Ai9HnhG15EU8IrUszPX8L85G3FZOeuuyire+WkTUxfHdckcpVQN6Ai9Hgzs1JIzh3Tkz+N6A/C/y4+izFXF96t38cKsdWEeHdry7QcY2Kklt328hDfnbgTc0zxKKaUj9HqQmpzEY+cPpVfbLACO7pXL2H5tufV09859I7q35oYT+4R6iqBOf2IWM1bs9AbzYIwx7CkOXxFSKRU/dITewNb881QEmGPtP/rBVUfTNjudYx+YHvFzXPLfn8K2efXHDdzxyRLOG9aZd+dt1lG8UglAA3oD8yzvH9WzDavunUBqclJEpXhravqKnUB1+YGqKkOSlhZQKq7plEsMpVoLkaJZw6Wk3MXNH/zCXr/ploqqqoC2hQfKova6SqnY04DeCESzyuKbczfx5tyNLNzsu5dpud9WeF8v28GR937FrFW7ovbaSqnY0oDeCISaCmkZQS67J5Vx894S7v5sqWMb/4DuqfD488bISvwqpRo/DeiNgP8I/YFzqhcPffvXsdz964EhH9/r758DsMhvVG5XUem70tRT2VEXoCoVPzSgNwL2OfSV90zgN8Ora62npSRxxuCOET1PaYhdlH5av8fnvuclq3RjaqXihgb0RsA+Qk9LSUKk+n5qspDmUMXRSaht8f785s9s3Ve9QbXnNYxDQF+4aR/5kyazfHtkVSKVUo2DBvRGIFSWS3JSdd31UP7w8k+889OmkG3GPDSDl79f5/OaTlMuU35xlxOYvrww7OsqpRoPDeiNgH1E7nQukrTGb5bv9Ga25GQ6X0gtr6zizk/dF02rA7pvRH/sq1XeEr4GnY5RqikJG9BF5CUR2Skii4OcHyMi+0VkgfV3e/S7mRjOHVa9jd314/t4M1xCBXwnOWEyY2auLOShL1YA1SP0h75YzuF3fsGjX630ttPpdaWalkhWir4MPAm8GqLNd8aY06PSowS18p4JPnPp143vzXXje9fquTJSk0Oef2r6au9tYwzFZS6emr4moJ2rUiO6Uk1J2BG6MeZbYE+4dqpu0lKSarQ0/+2JI+nWJpOzj+jk+Fyh2DfLcFUZxj48w7FdUWkFL3y3Nmhuu1KqcYnWHPooEVkoIp+LyIBgjURkoogUiEhBYaFecKuN9fefxoLbT+SoHm2Y+dexjD+sXUCbcFkxLltAL3NVsjNICYCDpS7umbyMF20lf/83ZyMbdhfXsvdKqfoUjYA+H+hmjBkMPAF8FKyhMeZ5Y8xwY8zwvLy8KLx0Yslv494sOiczzXusuMwV0C7UREl+m0zvylJwB+1gyit9V5ceKq/klg9/4YR/zQzZT2OMYzqkUqp+1TmgG2OKjDEHrdtTgFQRqdnOyCqs+bedyJTrjgs43iYrzef+u1eOYv+hiqDPs7u4nCVbq/PLP1qwNWjbMld1Xrsxhr0l7oJfrjDLS5+esYbuN09x/LJRStWfOgd0EWkvVhqGiIywnnN3XZ9X+WrdPI3MtMBr2GP7tmXKtdWB/sj81t5UxJP6t+Pacb182h8IMSL3V1ZhG8mXubjy9XkRPe712RsA2Bfii0UpFX1hs1xE5E1gDJArIpuBO4BUAGPMs8C5wJ9ExAUcAs43+nu7wYgI/Tu24MvrR1fnllsj6HOHdeakAe0pKnXx8g/rfR43pEsOCzbtC/ncXy/f6b09fUVhyFoxThrrx2D1zoN0ad2M9JTQ2UBKNTWRZLlcYIzpYIxJNcZ0Nsa8aIx51grmGGOeNMYMMMYMNsaMNMb8UP/dVv76tMumZ557yzvPjIgnffEvDumPb00cSf8OLSJ+/t0HfS+c7jxQvSnHla/N48GpywMe41/hsTHYU1zO+EdmcuuHjssqlGrSdKVoHPJMuXgCek5mGg+dO8i7j+nIHq3JSE3m1MPbR/ycRYd8p2pOeLj6wujUJdt5esYavlyy3adNWSMM6J6LwLPX6aygij8a0OOQZ8olI7X6/97zhneha2t3lkybrHQArh7bi8/+fCwA7VtkhHzO79f4boRxwLrgab9wunCz7xROYwzoWs5AxTMN6HGo0m+E7lFhpSGmW3nqIkKfdtkM7pLDfbYa7E7mrgtcW2aMoe+tU7332zRP9zlfFqT6Y1VV7NIaPS8r6P6qKv7oJtFxyDOH3swvoHtWiGakVR9PS0ni46uPqVWAnWxVZfTwz1ufs24PvdpmeX8RvDRrHYO7tOS2j5ZQ6qrkmxvH1Pg1o6WG5XGUahI0oMchz5RLul8JgDOGdKRgw15utObS7WpaAAzgmv/97HPfnuYI8Mi0lXy0YAupSUk8eO4g/lHLEgIzVuykZ14WXawpo7rQDT1UPNOAHoc8Qcu/7G5mWgoPnze43l730a9W0q2Nb9BdW+guE3D/54FZMJG65L8/0SIjhUV3nlyn/kH1e6MDdBWPdA49Dr3yhxFcMKILrTLTwjcOI1yhL3/Xv7OAbftLA47bL556VFUZZqzYGXBu/sa9bNl3iNU7D3i31SsqdfH416sCpoaKSiu48rV5bNpTArjTEgfd+UXQza/DrXJVqinTgB6HBnXO4b6zB9WoemMwVx7fs0btg81ozN8YuIjpvs+Xccl/f6LvrVO9F2yrqgxnP/0Dx9z/DeMf+ZZdtvz3R6at5KBfOYH5G/Yydcl2/vSGexXrnLW7KSp18cyMwHLAUF0SuDZTTEo1dhrQVUjFZS4eOOdw3p44MurP/Z/vqqs4rth+AIASv8wY/3l6V6U7Q+azRVspraikpNzd/mCpi7fmbuRPb8wP+ZqeC8MazlU80oCuQiouc/HbI7tyVI82PscvPKprnZ+7d9ss720RcFVWcecnS3za+JcnKK+s4se1u7nmfz/T77aprN55EIDm6SlM+uAXb7tgEyuhplxueHuB44pXpZoKDejK65HfDObE/u766rlWqmHr5s7z8J1bNfPePq533YtrlruqmLmykPfmbQ7bbvfBcu/9R6a5t8zzvwAcbOrHu7mHwxD9g5+38HSQqRqlmgLNclFeZx/RmeIyF9OW7mB0n1xO6NeO8f3bes/3bpvFKmtEnCTCnFtOoNxVRZfWmeRPmhzx6xzRNYf1u0t8Kj+WlFeycsfBsI+9d/Iyxh3WNuC4/9x6sLx6V5V7rl6nXFQ80hG68pGcZK0iRThtUAefioTTbjieK47rDkCSQLsWGRHlhnvy4dNSklh17wQ+uOoYMlKSfIJwSXklJeXhS/tOXbI9oGYMwN7icp/7TuH8l837eXbmWgDWFBY3yGrVbfsPscevb6pudh0sY3+JlmZ2ogFd+fBsVJ0SJENmWLdWAAzs1NLn+Lh+gaNmj+bp7h+C7105ilSr7ECqX0C/4tUCnvhmtePj/W3YXRJwbG8E/4H/6slZfLuyeuvDR79axbpd9bud3qj7vuGof35Vr6+RaIbf8xXD750W6240ShrQlY+je7Xh3GGduWJ0D8fzpwzswNxbTuDonr7z5i9dciRnDQ3csBog0yo1kGRLFQz2heHEf45+y75DYR9jjGFfSbn3oqmTx79eFXSDbIB1u4pZH4WAX1Gpue/Rpu+pMw3oykfnVpk8fN5getkyUPy1DVKZ8dHfDvHe/uqG0d7b5w7rDEBednXxrtQQG1mP95sj96+r7klVDKXKwGmPz2L8I6H3P/WotGW/fPTzFgDGPjyDMQ4Bf0dRKfmTJvPevM0Bc/cN5aIX53DRi3Ni8tqq8dKArqLq4fMG8+k1x9Krbbb32LXjerPg9hNpZ/sisNdU+cMx3TltUAcAeuQ2Z3h+a5/njCSA+zNENpL3sK9WffjLFT7nSisqKTzgXuC080Apny5078N607sLOf3x72rct2j4btUuvlu1K3xDS3GZy2dz8KbIVVnluOJYVdOArqLq3GGdObyz7/x6UpKQ41eGoLis+j/M204/jCGdc7xt/Qtohdts2lP33T6LY58rj+TiZ6mtsNjmvYd85tYvf6WAI+91z4OPe3gm90xe5j23fncJHy/Ywnerql/PwxP47XYeKOWlWeswxrB4y37u/GRJg1ycHXDHF9zwzsJ6f536dNbTP/iUa1aBNKCrmPBktNx62mGICOm2oOypFjmsWyve/9PR7A6TJeKZLQm2ZujatxaEDZrrd/vOldvn1metdo+EjTGOUyzXvbWAi16cS/6kyfxzSnWw//ObPwe0ver1+fzjs6Vs3FPCb577kZd/WO/dLCScH9fsdszwCcfzb//E4QumsRj3rxn833uLQrb5ZUvN9rRNRBrQVUwUW9Mofdq5p2Y8qY1JIt7APKpHG4Z1a8X+Q74ZLPf8eqDPfc8cu3+5YI9PF271vp6T/EmTOfvp8FvhllaEn7J4/tu1Ic9vsIqIuaqMdyqp1KFv8zbsYU2h7wXdC/4zm4mvzfOZ749EQxck+9eXK7inhqWS1xYW83bBpnrqUeLQgK7qzYVHdSUr3XntmicIt2yWCuDNd09NTvIGLE9xsRYZ1c9x8oB2/G5kN8fnfO2yo4L25cP5oVegRkbJgM4AABYHSURBVOKGdxbU+Tk800eHbEHc82Wzt7jcO5o+55kfOeFfzhd0i2xfcNv3uy/Q/rhmN6UVld5fN3a13ax754FS7vlsaY3n3p/4ZjUvzFoXvqGKOg3oqt7886zDWXxX6BrmOZmegO7+KGakJnHmkI6kJSfx6yEdAfjkmmN58eLh/PeSI30yafzZyxT0a5/tc+62j5f4N6+xzxdHNt0xfcVOZjlcsDSmelTuu6jKxfTlOxl69zS+Wb7Tp/xB/qTJPGqVN/Ao2FBdGvin9e6tAV/6fh39bpvKLR/+gr+KIAH57s+W8sJ3wX9R3PjOQl6Ytc6xUqZqnMIu/ReRl4DTgZ3GmIEO5wV4DDgVKAEuMcaELnmnlMUzQk/zBvRkeuRlsfLeCd42+bnNyc9tHva5WllfDgDH9spluVXBsaFd+t+fAo4VlVawaFP1HLB9VWxJeSVvzNkAwModB3nAr0DYY1+v4nrbLlP2uWTPr5mdRe4a9G/9tInuuc35o63scbAR+ovWKPry45zXHGze684SykxLdjxf3zbuLqHMVUnvdtnhGysgshH6y8ApIc5PAHpbfxOBZ+reLZUosjPcQdizUMR/Y+tw3p44kq9uOJ6/ntyX1s3TuONX/WnfIoP2LZ1z5WNl0J1f8jtb3vj1b1dnnJSUV7LeWv16sMx5xav9ou4O2wYi+0rcF4ztWTn3+e0O5b/Xa6Q8qZrPzFhDn1s/r3E2jtP0T02Mfmg6Jz76bZ2eoyHtKynnuAe/YenWopj1IWxAN8Z8CwRu+V7tTOBV4zYbyBGRDtHqoIpvniqJnvzimgb0o3q0oVfbLK4e2wsR4dJjujP7lhPqHNAHdGxRp8eHY7/QW1LmYpuVM//UdOdqj/bSBvaLh9uL3EG3qDR4pozTqspI5sU900KTf9lGuauKMmuk/+zMNUzx2yDcSaTZO5GIpM5P/qTJ3PHx4qi9JsC7BZu48rV5YdvNWLGTj37ewqY9h3hqemQlLOpDNKotdgLsl6c3W8cC/h8XkYm4R/F07Vr3etqq6Zo0oR8zVuz03m9hTb30zAs/tRKJSLbfu3ZcLx4PUj/GUz64IewuLg+ZhQMwNcj8vWeqxcl9U5axdX+pYz68/Qtl/6EK79QXuBdSOa3k3VtSzowVhd79Ydfff1rIPu8rKfd5Xie7D5bx+NerQrYBuO2jwGsgS7bup3OrTFo2S/X+enjlxw3cdWbAzHCt/TVMKiW4vxwvcZhmi4UGvShqjHneGDPcGDM8Ly+vIV9aNTJXHt+TtyaO8t4f0yeP5y8axjVje0X0+IfPG8wz/++IoOftgaRHkPn35kEycMC94UY4/umTtfVFBLnlny92HhHvOBA8oD/37dqAYD5t6Q7yJ01mrW2KZvBdX/L1sh1c/NJcqqoM/W6byvVvB2b0PDR1BTfbNhHZtj/0StydB6q3D3RVVvHirHXePWI97p28jFd+3OBzrMxV6TPS/mLJdtbuCqzJc9rjs7jg+dkAHLI9b/6kyQGvU1dOK1SfnrGanzfubVR1ZaIR0LcAXWz3O1vHlIqYiHDSgPakhKjxYnfusM5MODz4zJ4noIvAB1cdzW2n9w9oE2oUP7RLK5/7TlMwNd1AOxjPEv5gxc3sbfxt2escVINNqfzPuvj69bKdPscve6WAmSsLmb12N+C8COlnv92jrnsrMOjb59ntm4V/MH8Ld3+2lGdn+k4pOc3vf7lkh0+Q/+Nr8/g5SKbN0m3u+Wr/tQpFh6JbXvdAaWC9/QenruCsp3+gosr332BsxZtdlVXkT5rMI37lJOpLND6RnwC/F7eRwH5jTPgJNqXqUUsr48UYyMlM47Jju3vPHd2zDSvvmeBt4+FJe1x818l0ad3M51x7h4JkwRYyBZOWksRnfz426Pm+7WuezeFZqORve5CpmE7WTlP+gdXjzk+Dp3faN+wG95fJY1+torSikg/mb2bngVKfAO1z8faQ++LtQb/A6P/FArXLmy865Pu8r/64IeAi7tMzVofdESuYgvV7fe7bM6gqgvT35417WbbN3e7ZMAvOoiXsJ1JE3gR+BPqKyGYRuUxErhSRK60mU4C1wGrgP8BV9dZbpSKU7TCd8uA5gwB3IE5LSaKvXzrcVWN6sureCWSlpwRcnB3SJSfg+dIi/DXhcerA9gzs1NJxfv60wztwuF+N+VA8F5ODJZ6sClI2+PXZG0M+r2fXqOYOqYr+o9Qt+w7x6FcreW7mWm54ZyFXvDrPe+EUfL9UPHG+1FXJxwvcP+C37T/kM1UC8NzMNVTWoraN/wj9yemrmbPON5fjwakruOndhSzesp/8SZO9G5M72b6/1Oe6xZWvz+Pl79d5yzJPeKy6KNvcdc45I2c9/QO/enIW4P6S8vz6qU9hL4oaYy4Ic94AV0etR0pFgYhw3Qm9OdZWS90zIvfsypSf25yvbhjNWU//4A1WnouBzWwB/a4zBnD2EZ34l98Cn1Bz8E48UzRTrjuWEfd+7XPuyQuHMn/jXqeHOWqWmhyydO/qCLbzC6UmIbWkwt2PrfsO+cxd+wZ0d0R/ffZGXp+9kdU7DzpuaHLf58t54JzDa9TXl79fx/QVgcXRKqsM//1+HXtLKjh5QDvv8dOfcAfZ71YVBvwq+mb5DgZ2bMnI+3z//wG489Ol9O/Ykt8896PP8T+94bvs5vvVux23ZDz/+dlhLyTXla4UVXHr+hP7cKStFK8nL9q+uUavttncbWVF9LGN2PvY/kM/Z1hnsjNSefyCoT7PP6pnmxr154YT+wLQPC3wi0BEfLb7s3O6+Nomq3r+32nE//SMuqXO1aRksWdqpfBAGWUVzlMu/vVk/hNihWpZDadc7vx0KTNXBgb071bt4q5Pl/L416s47fFZAedb+GXgVFYZ/vByQUDAtgtX+RMCfy00JA3oKmF4VhyeMrC9z/FfD+3E9JvGMLpPdeZVp5xmrLxnAuvuO9Vbj2ZkD9867anJSdx95oCA13n2d9XZN8f0cgf920/v782ND5ZrH+y4U8Ae3q26L51b+c73Z6WnRLQln78R3VuHb2S5/+zqUbR9UdM8qyxBcpJQsGEv63YVc9/ny/j3V76piaEKnd0ehTINAJuCXF/wSPJLZSq2ct3XO2xx6FHbRVoNRQO6Shi92max/O5T+LVDNkl3h9TGtJQkxPYffdvsDKZdP5o/ju7hDdrikN9oDxSeYJxlKzCWHGT7PU9dd4A+7ap3jLIXJ/O4/fT+3HSSuxxApxx3QB/QsQUzbhrDx9ccE9A+kuX7Qx2uE3g88pvBPvc75FR/iSy2rYz8i5Xu6LmIPPbhGTw3s2EuCPrbESJHH+C1H9dTXOaisspw/+fLeSGCC5e1LXRmF8kiqdrSgK4SSk1Xovrr3S6bm089jFMGulMm/Ud5AM3Sknn/T6N4/qJh3syQdg5ZMt1zm5OTmepTx8bjnT+O8mbaJPl9AYzpm0fLzFQmju7J/Wcfzvkj3FnDVcZ9XaBnXvWXwSdWcLcH9Nws53TNQZ2DB/TOrTJ97tszfJzK+eZmR2dh1t11yPUPlaMPsHDzfo5/aDo9b5nCszPXBF1kZndbHVeivj9vM/1v/4K1hXW7xhGMBnSl6sB/sH3raYdxbK9chnVrzUkD2rP7oDtdzz+ILrzjJKZdP5o5t5zAojtOAvBO7fx+VDdyMtPo0NJ3KgVg3q3jee6iYYD7F8T5I7qSac3JO9Va8eTj25/rbyf3c/y3eLYBdGLfD/auMwaEzcFvlhqd0NKmue/75pQ+GsymPeG3INx1MPjmKXf+KnDtwr5aTGV5dG2dyaeL3Pn966Kw+bgTDehK1UGe30i0X/sWPtMwngtv/u1aNkslJTmJ9JRk78g8IzWZBbefyJ2/8p2XN8a9Mva5i4bRJis94OKpZ6rGPlKec8sJTLt+NF1bZ3LraYfx/O+Hec+dPjh8qaWrxvTkdttiLM+XTW5WGhcfnR82Bz/UJuAerZsH/lI4b1hnxvTN8z6/fyaR02M82kbpV4FHdkYqi+86mUkTAr8A7esaInH2EZ3YuKeEDdb8fLQWpfnTgK5UHYzr15ZnfzeMUT3cFz/9c6ifvHAoT1wwlLbZkY0sczLTvFMsnq8Fg+HcYZ05eUB7x8d48uHtr92uRQa922UjIlx+XA+fEXqo/HnPnP/fTunHH2xBq03zNC4a2Y2XLx0BhF9UFcmcfedWzQJq99z964G8fOkI7xdCVrrv86QkO19/GNM3j69uPJ6WzVL542jncsA1lZWRQlZ6ivcahV2XVoHHQuliTVl5RubRmIt3ogFdqToQEU4Z2N474vIvGds2O4NfDe5Yr31ICrPIyF+o8goz/jqGn2870fE17v71QAZai5/SkkMH7D+P6+2Tyw/Vi72y0lOYOLoHN5zYhy/+Mtp7/vwju3h/rXgCt32EnpIkQS8oZ2ek0iIjlYV3nMTNpx7G3089LGT/IpFtXYx2qty5x2Gf22d/N4wfbx4XcDwrPYW/jO/t8yVXk7TQmtCArlQU5Ldxj8CyHTJSassT3JwuvNolW+eDBTuPu88cwGuXjQjZJis9hVa2aY3bTu/PGQ5fSOlh5shzs9JZaF0b8Diml3uR18EyF7ecehhj+rYlJTnJu7vUuH5tvW09I3R7zv73k8Z5N93w+PCqo/l/R3XlH2f4TlNdEWSU/vHVgRlAwbSwavXb5+1f+YP7/fPPYQfIz830+SX0x+N7cPvp/Zl323hrnUH1e1ZfmS7R+/QplcBuPvUwRvXMZXh+5Lnc4Tx07iBe+n49I8I8Z7c2mVx5fE9+e2SXkO0uGpXvc39Yt1bevHFPCqS/YHPF4coeeMor2B3fN4+pDpUlB3RsyfLtB9hhq844tEsOXy7dQTPbqDY1OYkHzxnEZ4u2cdmx3SlzVTK0ayuGdm0V8JzgLnb24c++dQK7tM50bOvEc92go23K5fg+ebw1cSTDurXinsnLAPcvjwNW+qPdzRN8fyXYv5jra4SuAV2pKMhITQ5YsFRXbVtkOF6Q8yciEbWzW3//aazYfoCT//0tvdpmcc243jV6fLgRulN6aEqQXxAXHtWV9+dv9qlo+ehvh7Bka5HPoqq0lCTG9mvLWNtIPpRHfzvEG9AvOTqf4/vk+XzJdM9tHpBtkpuV5s188fza8v/lM7KH7wrhbrmZLN5ShKfo4pRrj+NQReAI/Oheud5yxhrQlVJR1aq5e9rg2F65YVoGCjVCv/W0w7wj6zF985hh1VlxWoQF7l8Ka/95qk++ffP0lICVqzUthmZ3pzUlY98we/pNY/jw5818vGCrt4/2LyL7YrDXLhtBsB31Hj9/KNNXFDKwk/sLqX+Q3a4ePGcQlx6Tz3nP/hhRCYHa0Dl0pRJU2+wMZv51DH8/reYXEFOSk+jaOtOxkJZ90+mXLx3B2dbKXAHGH9aWk/q3C3iM/+Ipu//8fjjH9solNUiGSyh52ek+G5x4fiVMsH5NnTW0s7eWD/iOxu3pocf1zuP4Ps6b8rRtkcFlx3YP+oXl0SwtmSO6tuIvJ/Tm6J41/xKNhI7QlUpg3drUfsu/b/82FoD/e9+9i9GfxvTkGIdA5RnYJiXBCxcfWePXObF/O050+BKIxNxbTvC5LyIU3Dree8ET3PPqM24aw5iHZzCyextvrnikavpF8+cTaja9VRMa0JVSUfF/pzjP408c3YPvV+9idO+G33bSadTsVOwsP7c5b00cyZAuOT6bcEeiLlNB0aYBXSlVrw7r0IK5fx8f626E5bnYef/Zh9OzbVaY1tXCTbU0JA3oSillc/6IrrHuQq1pQFdK1ckTFwyNaKm/qn8a0JVSdVLfpQ0aqw+vOpql24rCN2xAGtCVUqoWQq1SjZXGc3lWKaVUnUQU0EXkFBFZISKrRWSSw/lLRKRQRBZYf5dHv6tKKaVCCTvlIiLJwFPAicBm4CcR+cQYs9Sv6dvGmGvqoY9KKaUiEMkIfQSw2hiz1hhTDrwFnFm/3VJKKVVTkQT0ToB96dRm65i/c0RkkYi8JyKh63gqpZSKumhdFP0UyDfGDAKmAa84NRKRiSJSICIFhYWFUXpppZRSEFlA3wLYR9ydrWNexpjdxhhPdfoXgGE4MMY8b4wZbowZnpfX8HUdlFIqnkUS0H8CeotIdxFJA84HPrE3EBH7NuJnAMui10WllFKRCJvlYoxxicg1wBdAMvCSMWaJiPwDKDDGfAJcKyJnAC5gD3BJuOedN2/eLhHZUMt+5wK7avnYRKLvU2T0fQpP36PINMT71C3YCTGRbhXeiIhIgTFmeKz70djp+xQZfZ/C0/coMrF+n3SlqFJKxQkN6EopFSeaakB/PtYdaCL0fYqMvk/h6XsUmZi+T01yDl0ppVSgpjpCV0op5UcDulJKxYkmF9DDlfJNFCLSRUSmi8hSEVkiItdZx1uLyDQRWWX9byvruIjI49b7tkhEjojtv6BhiUiyiPwsIp9Z97uLyBzr/XjbWjSHiKRb91db5/Nj2e+GJCI5Vi2m5SKyTERG6efJl4hcb/33tlhE3hSRjMb0WWpSAd1WyncC0B+4QET6x7ZXMeMCbjTG9AdGAldb78Uk4GtjTG/ga+s+uN+z3tbfROCZhu9yTF2H7wrmB4BHjTG9gL3AZdbxy4C91vFHrXaJ4jFgqjGmHzAY9/ulnyeLiHQCrgWGG2MG4l5oeT6N6bNkjGkyf8Ao4Avb/ZuBm2Pdr8bwB3yMu2b9CqCDdawDsMK6/Rxwga29t128/+GuP/Q1MA74DBDcq/lSrPPezxXuFdGjrNspVjuJ9b+hAd6jlsA6/3+rfp583gtP5dnW1mfjM+DkxvRZalIjdCIv5ZtQrJ9yQ4E5QDtjzDbr1HagnXU7kd+7fwN/A6qs+22AfcYYl3Xf/l543yfr/H6rfbzrDhQC/7Wmpl4Qkebo58nLGLMFeBjYCGzD/dmYRyP6LDW1gK78iEgW8D7wF2OMzxbkxj00SOi8VBE5HdhpjJkX6740cinAEcAzxpihQDHV0yuAfp6s6wdn4v7y6wg0B06Jaaf8NLWAHraUbyIRkVTcwfwNY8wH1uEdnuqX1v/utI4n6nt3DHCGiKzHvdvWONxzxTki4ilOZ38vvO+Tdb4lsLshOxwjm4HNxpg51v33cAd4/TxVGw+sM8YUGmMqgA9wf74azWepqQX0sKV8E4WICPAisMwY84jt1CfAxdbti3HPrXuO/97KThgJ7Lf9lI5bxpibjTGdjTH5uD8v3xhj/h8wHTjXaub/Pnnev3Ot9nE/KjXGbAc2iUhf69AJwFL082S3ERgpIpnWf3+e96jxfJZifaGhFhcmTgVWAmuAv8e6PzF8H47F/fN3EbDA+jsV9xzd18Aq4CugtdVecGcIrQF+wX2lPub/jgZ+z8YAn1m3ewBzgdXAu0C6dTzDur/aOt8j1v1uwPdnCFBgfaY+Alrp5yngPboLWA4sBl4D0hvTZ0mX/iulVJxoalMuSimlgtCArpRScUIDulJKxQkN6EopFSc0oCulVJzQgK6UUnFCA7pSSsWJ/w+s9VBebDJkygAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQ5yPTOD8kGk"
      },
      "source": [
        "La red debe tener una precisión superior al 65% en el conjunto de prueba después de 2 épocas y al 85% después de 21 épocas. Veamos las últimas palabras del conjunto de entrenamiento y veamos cómo le fue al modelo.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-SR0FEW8kGl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1b35fa9-d5da-4c50-a653-c70f6ecc0266"
      },
      "source": [
        "def predict(tensor):\n",
        "    # Use the model to predict the label of the waveform\n",
        "    tensor = tensor.to(device)\n",
        "    tensor = transform(tensor)\n",
        "    tensor = model(tensor.unsqueeze(0))\n",
        "    tensor = get_likely_index(tensor)\n",
        "    tensor = index_to_label(tensor.squeeze())\n",
        "    return tensor\n",
        "\n",
        "\n",
        "waveform, sample_rate, utterance, *_ = train_set[-1]\n",
        "ipd.Audio(waveform.numpy(), rate=sample_rate)\n",
        "\n",
        "print(f\"Esperado: {utterance}. Predicción: {predict(waveform)}.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Expected: zero. Predicted: zero.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8RV6r6-8kGl"
      },
      "source": [
        "Busquemos un ejemplo que no esté clasificado correctamente, si lo hay.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMQ_IpU28kGm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9afa8c9f-fb4f-4f54-b361-d2ecb15a9024"
      },
      "source": [
        "for i, (waveform, sample_rate, utterance, *_) in enumerate(test_set):\n",
        "    output = predict(waveform)\n",
        "    if output != utterance:\n",
        "        ipd.Audio(waveform.numpy(), rate=sample_rate)\n",
        "        print(f\"Data point #{i}. Esperado: {utterance}. Predicción: {output}.\")\n",
        "        break\n",
        "else:\n",
        "    print(\"Todos los ejemplos de este conjunto de datos se clasificaron correctamente!\")\n",
        "    print(\"En este caso, veamos el último data point\")\n",
        "    ipd.Audio(waveform.numpy(), rate=sample_rate)\n",
        "    print(f\"Data point #{i}. Esperado: {utterance}. Predicción: {output}.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data point #1. Expected: right. Predicted: nine.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUOYcWI78kGn"
      },
      "source": [
        "¡No dudes en probar con una de tus propias grabaciones de una de las etiquetas!\n",
        "Por ejemplo, si usa Colab, diga \"Go\" mientras ejecuta la celda a continuación. Esto grabará un segundo de audio e intentará clasificarlo.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vUWG3Ll8kGn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "086da91e-b607-48fe-bcb8-f90ff22353d2"
      },
      "source": [
        "from google.colab import output as colab_output\n",
        "from base64 import b64decode\n",
        "from io import BytesIO\n",
        "from pydub import AudioSegment\n",
        "\n",
        "\n",
        "RECORD = \"\"\"\n",
        "const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\n",
        "const b2text = blob => new Promise(resolve => {\n",
        "  const reader = new FileReader()\n",
        "  reader.onloadend = e => resolve(e.srcElement.result)\n",
        "  reader.readAsDataURL(blob)\n",
        "})\n",
        "var record = time => new Promise(async resolve => {\n",
        "  stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n",
        "  recorder = new MediaRecorder(stream)\n",
        "  chunks = []\n",
        "  recorder.ondataavailable = e => chunks.push(e.data)\n",
        "  recorder.start()\n",
        "  await sleep(time)\n",
        "  recorder.onstop = async ()=>{\n",
        "    blob = new Blob(chunks)\n",
        "    text = await b2text(blob)\n",
        "    resolve(text)\n",
        "  }\n",
        "  recorder.stop()\n",
        "})\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "def record(seconds=1):\n",
        "    display(ipd.Javascript(RECORD))\n",
        "    print(f\"La grabación comenzó durante {seconds} segundos.\")\n",
        "    s = colab_output.eval_js(\"record(%d)\" % (seconds * 1000))\n",
        "    print(\"Grabación terminada.\")\n",
        "    b = b64decode(s.split(\",\")[1])\n",
        "\n",
        "    fileformat = \"wav\"\n",
        "    filename = f\"_audio.{fileformat}\"\n",
        "    AudioSegment.from_file(BytesIO(b)).export(filename, format=fileformat)\n",
        "    return torchaudio.load(filename)\n",
        "\n",
        "\n",
        "waveform, sample_rate = record()\n",
        "print(f\"Predicción: {predict(waveform)}.\")\n",
        "ipd.Audio(waveform.numpy(), rate=sample_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\n",
              "const b2text = blob => new Promise(resolve => {\n",
              "  const reader = new FileReader()\n",
              "  reader.onloadend = e => resolve(e.srcElement.result)\n",
              "  reader.readAsDataURL(blob)\n",
              "})\n",
              "var record = time => new Promise(async resolve => {\n",
              "  stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n",
              "  recorder = new MediaRecorder(stream)\n",
              "  chunks = []\n",
              "  recorder.ondataavailable = e => chunks.push(e.data)\n",
              "  recorder.start()\n",
              "  await sleep(time)\n",
              "  recorder.onstop = async ()=>{\n",
              "    blob = new Blob(chunks)\n",
              "    text = await b2text(blob)\n",
              "    resolve(text)\n",
              "  }\n",
              "  recorder.stop()\n",
              "})\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Recording started for 1 seconds.\n",
            "Recording ended.\n",
            "Predicted: two.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HG4hZ31q8kGo"
      },
      "source": [
        "Conclusión\n",
        "----------\n",
        "\n",
        "En este tutorial, usamos torchaudio para cargar un conjunto de datos y volver a muestrear la señal. Luego hemos definido una red neuronal que entrenamos para\n",
        "reconocer un comando dado. También existen otros métodos de preprocesamiento de datos, como encontrar los coeficientes cepstrales de frecuencia mel (MFCC),\n",
        "que puede reducir el tamaño del conjunto de datos. Esta transformación también está disponible en torchaudio como `torchaudio.transforms.MFCC`.\n",
        "\n",
        "\n"
      ]
    }
  ]
}