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        "<a href=\"https://colab.research.google.com/github/FrenchKrab/IS2023-powerset-diarization/blob/master/notebook_ps_diarization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Powerset multi-class cross entropy loss for neural speaker diarization - Example notebook"
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      "source": [
        "This is the example notebook of the paper \"*Powerset multi-class cross entropy loss for neural speaker diarization*\", published at Interspeech 2023.\n",
        "\n",
        "The corresponding official repository is available here"
      ],
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      "source": [
        "## Installing dependencies\n",
        "\n",
        "Make sure you're **using a GPU** (Runtime -> Change runtime type) before starting the notebook.\n",
        "\n",
        "Please download the ckpt file at https://github.com/FrenchKrab/IS2023-powerset-diarization/blob/master/models/powerset/powerset_pretrained.ckpt and place it in `/content/`.\n",
        "\n",
        "**Restart the runtime once this part is complete** (Runtime -> Restart runtime) and then continue to next part, else you will encounter a pyannote.database error."
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.5/60.5 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.6/41.6 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.4/51.4 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.2/112.2 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.0/519.0 kB\u001b[0m \u001b[31m53.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.9/137.9 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m390.6/390.6 kB\u001b[0m \u001b[31m44.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m75.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.6/59.6 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m887.5/887.5 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m557.1/557.1 MB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.0/21.0 MB\u001b[0m \u001b[31m71.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m100.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m237.5/237.5 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.1/51.1 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.6/109.6 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.7/78.7 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.6/485.6 kB\u001b[0m \u001b[31m48.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pyannote.audio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for julius (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchvision 0.15.2+cu118 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyannote.database (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pyannote-audio 2.1.1 requires pyannote.database<5.0,>=4.1.1, but you have pyannote-database 5.0.1+1.gbac3225 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# Note: pyannote.audio's version is the oldest compatible version tested with this method.\n",
        "# pyannote.database version 5.1 (newer) is used for convenience and ease of use.\n",
        "!pip install -qq rich\n",
        "!pip install -qq git+https://github.com/pyannote/pyannote-audio.git@e3dc7d68cc60c7d4f89df005b58674aa936b0882\n",
        "!pip install -qq git+https://github.com/pyannote/pyannote-database.git@bac3225ee76333b4e8332ff309ea554f062660ff"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# download AMI-SDM mini corpus\n",
        "%cd /content/\n",
        "!git clone https://github.com/pyannote/AMI-diarization-setup\n",
        "%cd /content/AMI-diarization-setup/pyannote/\n",
        "!bash download_ami_sdm_mini.sh\n",
        "!cd /"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKkayWCsAsTW",
        "outputId": "577edde3-ed97-4184-85a0-3300251c1a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'AMI-diarization-setup'...\n",
            "remote: Enumerating objects: 977, done.\u001b[K\n",
            "remote: Counting objects: 100% (977/977), done.\u001b[K\n",
            "remote: Compressing objects: 100% (429/429), done.\u001b[K\n",
            "remote: Total 977 (delta 390), reused 949 (delta 369), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (977/977), 1.67 MiB | 14.26 MiB/s, done.\n",
            "Resolving deltas: 100% (390/390), done.\n",
            "/content/AMI-diarization-setup/pyannote\n",
            "ES2002a.Array1-01.w 100%[===================>]  38.84M  46.2MB/s    in 0.8s    \n",
            "2023-06-07 14:49:38 URL:https://groups.inf.ed.ac.uk/ami/AMICorpusMirror/amicorpus/ES2002a/audio/ES2002a.Array1-01.wav [40727594/40727594] -> \"amicorpus/ES2002a/audio/ES2002a.Array1-01.wav\" [1]\n",
            "ES2003a.Array1-01.w 100%[===================>]  34.78M  41.2MB/s    in 0.8s    \n",
            "2023-06-07 14:49:39 URL:https://groups.inf.ed.ac.uk/ami/AMICorpusMirror/amicorpus/ES2003a/audio/ES2003a.Array1-01.wav [36472536/36472536] -> \"amicorpus/ES2003a/audio/ES2003a.Array1-01.wav\" [1]\n",
            "ES2004a.Array1-01.w 100%[===================>]  32.02M  46.4MB/s    in 0.7s    \n",
            "2023-06-07 14:49:39 URL:https://groups.inf.ed.ac.uk/ami/AMICorpusMirror/amicorpus/ES2004a/audio/ES2004a.Array1-01.wav [33579394/33579394] -> \"amicorpus/ES2004a/audio/ES2004a.Array1-01.wav\" [1]\n",
            "ES2005a.Array1-01.w 100%[===================>]  14.58M  36.6MB/s    in 0.4s    \n",
            "2023-06-07 14:49:40 URL:https://groups.inf.ed.ac.uk/ami/AMICorpusMirror/amicorpus/ES2005a/audio/ES2005a.Array1-01.wav [15292120/15292120] -> \"amicorpus/ES2005a/audio/ES2005a.Array1-01.wav\" [1]\n",
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            "2023-06-07 14:50:09 URL:https://groups.inf.ed.ac.uk/ami/AMICorpusMirror/amicorpus/TS3003a/audio/TS3003a.Array1-01.wav [48183678/48183678] -> \"amicorpus/TS3003a/audio/TS3003a.Array1-01.wav\" [1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading data and adapting the segmentation model to AMI mini"
      ],
      "metadata": {
        "id": "aBhaWEuWpSS8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyannote.database import registry, FileFinder\n",
        "\n",
        "registry.load_database(\"/content/AMI-diarization-setup/pyannote/database.yml\")\n",
        "protocol = registry.get_protocol(\"AMI-SDM.SpeakerDiarization.mini\", preprocessors={\"audio\": FileFinder()})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "575fIo6VcVHj",
        "outputId": "9a2bf2bf-bc89-4017-ce15-a0c7e6dd0d76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'AMI-SDM.SpeakerDiarization.only_words' found in /content/AMI-diarization-setup/pyannote/database.yml does not define the 'scope' of speaker labels (file, database, or global). Setting it to 'file'.\n",
            "'AMI-SDM.SpeakerDiarization.mini' found in /content/AMI-diarization-setup/pyannote/database.yml does not define the 'scope' of speaker labels (file, database, or global). Setting it to 'file'.\n",
            "'AMI.SpeakerDiarization.only_words' found in /content/AMI-diarization-setup/pyannote/database.yml does not define the 'scope' of speaker labels (file, database, or global). Setting it to 'file'.\n",
            "'AMI.SpeakerDiarization.mini' found in /content/AMI-diarization-setup/pyannote/database.yml does not define the 'scope' of speaker labels (file, database, or global). Setting it to 'file'.\n",
            "'AMI.SpeakerDiarization.word_and_vocalsounds' found in /content/AMI-diarization-setup/pyannote/database.yml does not define the 'scope' of speaker labels (file, database, or global). Setting it to 'file'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fix eventual locale errors with colab (happens when you want to use a command such as cd)\n",
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "6T9qMPTs8F8O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyannote.audio import Model\n",
        "from pyannote.audio.tasks import Segmentation\n",
        "# The task Segmentation has been renamed to SpeakerDiarization in newer versions\n",
        "\n",
        "PRETRAINED_PATH = \"/content/powerset_pretrained.ckpt\"\n",
        "\n",
        "segmentation_model = Model.from_pretrained(PRETRAINED_PATH)\n",
        "segmentation_model.task = Segmentation(protocol, duration=5.0, max_speakers_per_chunk=3, max_speakers_per_frame=2)\n",
        "segmentation_model.setup(\"fit\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t49LpVn8hzZW",
        "outputId": "baba6683-a92d-4282-9c7e-b520129a316c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c104cuda20CUDACachingAllocator9allocatorE'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Protocol AMI-SDM.SpeakerDiarization.mini does not precompute the output of torchaudio.info(): adding a 'torchaudio.info' preprocessor for you to speed up dataloaders. See pyannote.database documentation on how to do that yourself.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The training should take a bit less than 15 minutes on a Google Colab T4 GPU\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "IKkI2U9eBxXK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from types import MethodType\n",
        "from torch.optim import Adam\n",
        "from pytorch_lightning.callbacks import (\n",
        "    EarlyStopping,\n",
        "    ModelCheckpoint,\n",
        "    RichProgressBar,\n",
        ")\n",
        "from pytorch_lightning import Trainer\n",
        "\n",
        "# we use Adam optimizer with 1e-4 learning rate\n",
        "def configure_optimizers(self):\n",
        "    return Adam(self.parameters(), lr=1e-4)\n",
        "\n",
        "segmentation_model.configure_optimizers = MethodType(configure_optimizers, segmentation_model)\n",
        "\n",
        "# we monitor diarization error rate on the validation set\n",
        "# and use to keep the best checkpoint and stop early\n",
        "monitor, direction = segmentation_model.task.val_monitor\n",
        "checkpoint = ModelCheckpoint(\n",
        "    monitor=monitor,\n",
        "    mode=direction,\n",
        "    save_top_k=1,\n",
        "    every_n_epochs=1,\n",
        "    save_last=False,\n",
        "    save_weights_only=False,\n",
        "    filename=\"{epoch}\",\n",
        "    verbose=False,\n",
        ")\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor=monitor,\n",
        "    mode=direction,\n",
        "    min_delta=0.0,\n",
        "    patience=10,\n",
        "    strict=True,\n",
        "    verbose=False,\n",
        ")\n",
        "\n",
        "callbacks = [RichProgressBar(), checkpoint, early_stopping]\n",
        "\n",
        "# we train for at most 20 epochs (might be shorter in case of early stopping)\n",
        "from pytorch_lightning import Trainer\n",
        "trainer = Trainer(accelerator=\"gpu\",\n",
        "                  callbacks=callbacks,\n",
        "                  max_epochs=20,\n",
        "                  gradient_clip_val=0.5)\n",
        "trainer.fit(segmentation_model)"
      ],
      "metadata": {
        "id": "VyFhq4ivlf7z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598,
          "referenced_widgets": [
            "aa6eae2edbb14a45aebecedd1b7697f3",
            "b100e98cba894747980a7a20bc20be2d"
          ]
        },
        "outputId": "adb4452a-a921-4433-cbad-8ee1e00d2d40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName             \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType            \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35m      In sizes\u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35m                                 Out sizes\u001b[0m\u001b[1;35m \u001b[0m┃\n",
              "┡━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ sincnet           │ SincNet          │ 42.6 K │\u001b[37m \u001b[0m\u001b[37m[32, 1, 80000]\u001b[0m\u001b[37m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m                             [32, 60, 293]\u001b[0m\u001b[37m \u001b[0m│\n",
              "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ lstm              │ LSTM             │  1.4 M │\u001b[37m \u001b[0m\u001b[37m [32, 293, 60]\u001b[0m\u001b[37m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m   [[32, 293, 256], [[8, 32, 128], [8, 32,\u001b[0m\u001b[37m \u001b[0m│\n",
              "│\u001b[2m   \u001b[0m│                   │                  │        │\u001b[37m                \u001b[0m│\u001b[37m \u001b[0m\u001b[37m                                    128]]]\u001b[0m\u001b[37m \u001b[0m│\n",
              "│\u001b[2m \u001b[0m\u001b[2m2\u001b[0m\u001b[2m \u001b[0m│ linear            │ ModuleList       │ 49.4 K │\u001b[37m \u001b[0m\u001b[37m             ?\u001b[0m\u001b[37m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m                                         ?\u001b[0m\u001b[37m \u001b[0m│\n",
              "│\u001b[2m \u001b[0m\u001b[2m3\u001b[0m\u001b[2m \u001b[0m│ classifier        │ Linear           │    903 │\u001b[37m \u001b[0m\u001b[37m[32, 293, 128]\u001b[0m\u001b[37m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m                              [32, 293, 7]\u001b[0m\u001b[37m \u001b[0m│\n",
              "│\u001b[2m \u001b[0m\u001b[2m4\u001b[0m\u001b[2m \u001b[0m│ activation        │ LogSoftmax       │      0 │\u001b[37m \u001b[0m\u001b[37m  [32, 293, 7]\u001b[0m\u001b[37m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m                              [32, 293, 7]\u001b[0m\u001b[37m \u001b[0m│\n",
              "│\u001b[2m \u001b[0m\u001b[2m5\u001b[0m\u001b[2m \u001b[0m│ powerset          │ Powerset         │      0 │\u001b[37m \u001b[0m\u001b[37m             ?\u001b[0m\u001b[37m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m                                         ?\u001b[0m\u001b[37m \u001b[0m│\n",
              "│\u001b[2m \u001b[0m\u001b[2m6\u001b[0m\u001b[2m \u001b[0m│ validation_metric │ MetricCollection │      0 │\u001b[37m \u001b[0m\u001b[37m             ?\u001b[0m\u001b[37m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m                                         ?\u001b[0m\u001b[37m \u001b[0m│\n",
              "└───┴───────────────────┴──────────────────┴────────┴────────────────┴────────────────────────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name              </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type             </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">       In sizes </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">                                  Out sizes </span>┃\n",
              "┡━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ sincnet           │ SincNet          │ 42.6 K │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> [32, 1, 80000] </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                              [32, 60, 293] </span>│\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ lstm              │ LSTM             │  1.4 M │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">  [32, 293, 60] </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">    [[32, 293, 256], [[8, 32, 128], [8, 32, </span>│\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">   </span>│                   │                  │        │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                                     128]]] </span>│\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 2 </span>│ linear            │ ModuleList       │ 49.4 K │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">              ? </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                                          ? </span>│\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 3 </span>│ classifier        │ Linear           │    903 │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> [32, 293, 128] </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                               [32, 293, 7] </span>│\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 4 </span>│ activation        │ LogSoftmax       │      0 │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">   [32, 293, 7] </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                               [32, 293, 7] </span>│\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 5 </span>│ powerset          │ Powerset         │      0 │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">              ? </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                                          ? </span>│\n",
              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 6 </span>│ validation_metric │ MetricCollection │      0 │<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">              ? </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">                                          ? </span>│\n",
              "└───┴───────────────────┴──────────────────┴────────┴────────────────┴────────────────────────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mTrainable params\u001b[0m: 1.5 M                                                                                            \n",
              "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
              "\u001b[1mTotal params\u001b[0m: 1.5 M                                                                                                \n",
              "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 5                                                                          \n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 1.5 M                                                                                            \n",
              "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
              "<span style=\"font-weight: bold\">Total params</span>: 1.5 M                                                                                                \n",
              "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 5                                                                          \n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa6eae2edbb14a45aebecedd1b7697f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=20` reached.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using the adapted segmentation model\n",
        "## Segmentation model output"
      ],
      "metadata": {
        "id": "HWxsF45OnAsD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Path to the best checkpoint obtained from the adaption training."
      ],
      "metadata": {
        "id": "UbYoukayB0hv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint.best_model_path"
      ],
      "metadata": {
        "id": "Ker1wx1npFtx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "720d039f-330f-4d36-b102-cfb36142182c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/lightning_logs/version_0/checkpoints/epoch=19.ckpt'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compute and display the local segmentation models (pretrained and adapted) outputs over a sliding window."
      ],
      "metadata": {
        "id": "Ma6GmvjEuTQG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyannote.audio import Inference\n",
        "from pyannote.audio.pipelines.utils import get_devices\n",
        "\n",
        "STEP = 5.0\n",
        "\n",
        "(device,) = get_devices(needs=1)\n",
        "file1 = next(protocol.test())\n",
        "\n",
        "inf_pretrained = Inference(model=PRETRAINED_PATH, step=STEP, device=device)\n",
        "inf_adapted = Inference(model=checkpoint.best_model_path, step=STEP, device=device)\n",
        "\n",
        "local_out_pretrained = inf_pretrained(file1)\n",
        "local_out_adapted = inf_adapted(file1)"
      ],
      "metadata": {
        "id": "Z7OToarInRri"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyannote.core import notebook, Segment\n",
        "notebook.crop = Segment(120,160)\n",
        "\n",
        "local_out_pretrained"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "EtSAEsmZt7xp",
        "outputId": "02a7b7de-271f-4416-a61b-abc80a70949b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyannote.core.feature.SlidingWindowFeature at 0x7fc259008c70>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "local_out_adapted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Am03TVfrt9_e",
        "outputId": "63a9def1-52d0-44e6-e148-888368ecf417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyannote.core.feature.SlidingWindowFeature at 0x7fc259008a00>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file1[\"annotation\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "0NO5d1Ayt_TA",
        "outputId": "d62176a2-f9bf-4e52-c92c-2d24270e4360"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyannote.core.annotation.Annotation at 0x7fc259008af0>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adapted pipeline output\n",
        "Now apply the full speaker diarization pipeline using the pretrained/adapted models to compare the outputs.\n",
        "We can then observe the difference between pretrained/adapted/reference."
      ],
      "metadata": {
        "id": "RPZ-A1-LCu84"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyannote.audio.pipelines import SpeakerDiarization as SpeakerDiarizationPipeline\n",
        "\n",
        "STEP=5.0\n",
        "\n",
        "# hyperparameters used for AMI\n",
        "PIPELINE_PARAMS = {\n",
        "    \"clustering\": {\n",
        "        \"method\": \"centroid\",\n",
        "        \"min_cluster_size\": 15,\n",
        "        \"threshold\": 0.6285824248662424,\n",
        "    },\n",
        "    \"segmentation\": {\n",
        "        \"min_duration_off\": 0.0,\n",
        "    },\n",
        "}\n",
        "\n",
        "adapted_model = Model.from_pretrained(checkpoint.best_model_path)\n",
        "pipeline_adapted = SpeakerDiarizationPipeline(adapted_model, embedding=\"speechbrain/spkrec-ecapa-voxceleb\", clustering=\"AgglomerativeClustering\").to(device)\n",
        "pipeline_adapted.instantiate(PIPELINE_PARAMS)\n",
        "out_adapted = pipeline_adapted(next(protocol.test()))\n",
        "\n",
        "pipeline_pretrained = SpeakerDiarizationPipeline(PRETRAINED_PATH, embedding=\"speechbrain/spkrec-ecapa-voxceleb\", clustering=\"AgglomerativeClustering\").to(device)\n",
        "pipeline_pretrained.instantiate(PIPELINE_PARAMS)\n",
        "out_pretrained = pipeline_pretrained(next(protocol.test()))"
      ],
      "metadata": {
        "id": "wR5unD9sqwzI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "3ed0b9ea8f4e49dcb8c2c5512d28f2af",
            "29bc644872804ccaa7f40b92c0a9b891",
            "b9b495d562e94946a80e682b07b001fe",
            "e1b1123e24f84f518a1dac518ba8f893",
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            "df25d8a8186e4d51a7526b8fba1061e3",
            "a52c22c37cb44e4b9beb875ee7eb0d97"
          ]
        },
        "outputId": "1f2606bf-1146-45f4-8317-295886f7fc83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ain/hyperparams.yaml:   0%|          | 0.00/1.92k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ed0b9ea8f4e49dcb8c2c5512d28f2af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading embedding_model.ckpt:   0%|          | 0.00/83.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bdf83e6a9c324e24b48ad5a381dea054"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)an_var_norm_emb.ckpt:   0%|          | 0.00/1.92k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af911865c1fb4cf78951708854dea71a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading classifier.ckpt:   0%|          | 0.00/5.53M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9170b2d8954f4b4b941aed85efd14e23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/label_encoder.txt:   0%|          | 0.00/129k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b16f03d0a174983826ea4630ec5151e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "notebook.crop = Segment(120,160)\n",
        "\n",
        "out_pretrained"
      ],
      "metadata": {
        "id": "x4CJL-1J9DKe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "86514d3e-e589-4bd7-a7e5-656f24951e5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyannote.core.annotation.Annotation at 0x7fc24f21e740>"
            ],
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          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out_adapted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "dDacDq_e8nJs",
        "outputId": "5335f19e-05b6-4a88-c752-9d9e725013bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyannote.core.annotation.Annotation at 0x7fc24f410460>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file1[\"annotation\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "QpAEffto8r1C",
        "outputId": "07c7d1a0-3dd4-4125-cef4-dcb7c0f0ab6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyannote.core.annotation.Annotation at 0x7fc259008af0>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating the Diarization Error Rate (DER)"
      ],
      "metadata": {
        "id": "HfUL1_UjaSD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyannote.metrics.diarization import DiarizationErrorRate\n",
        "\n",
        "def get_der(predictions, files):\n",
        "  dermetric = DiarizationErrorRate()\n",
        "\n",
        "  for file, pred in zip(files, predictions):\n",
        "      # update der, computed between ground truth annotation and predicted\n",
        "      # speech, on the file's uem.\n",
        "      _ = dermetric(file[\"annotation\"], pred, uem=file[\"annotated\"])\n",
        "\n",
        "  false_alarm = dermetric[\"false alarm\"] / dermetric[\"total\"]\n",
        "  missed = dermetric[\"missed detection\"] / dermetric[\"total\"]\n",
        "  confusion = dermetric[\"confusion\"] / dermetric[\"total\"]\n",
        "  der = abs(dermetric)\n",
        "\n",
        "  return false_alarm, missed, confusion, der\n",
        "\n",
        "\n",
        "res_pt = {}\n",
        "res_pt[\"fa\"], res_pt[\"miss\"], res_pt[\"conf\"], res_pt[\"der\"] = get_der([out_pretrained], [file1])\n",
        "\n",
        "res_ad = {}\n",
        "res_ad[\"fa\"], res_ad[\"miss\"], res_ad[\"conf\"], res_ad[\"der\"] = get_der([out_adapted], [file1])\n",
        "\n",
        "print(f\"On the first file of AMI mini ({file1['uri']}):\")\n",
        "print(f\"{'Title':10} | {'FA%':>6} | {'MISS%':>6} | {'CONF%':>6} | {'DER%':>6}\")\n",
        "print(f\"{'Pretrained':10} | {res_pt['fa']*100:6.2f} | {res_pt['miss']*100:6.2f} | {res_pt['conf']*100:6.2f} | {res_pt['der']*100:6.2f}\")\n",
        "print(f\"{'Adapted':10} | {res_ad['fa']*100:6.2f} | {res_ad['miss']*100:6.2f} | {res_ad['conf']*100:6.2f} | {res_ad['der']*100:6.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAHZCZv9NSm7",
        "outputId": "50ff3fa3-fc07-4db5-dbde-3e8aa87b0592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On the first file of AMI mini (IS1009a):\n",
            "Title      |    FA% |  MISS% |  CONF% |   DER%\n",
            "Pretrained |   8.32 |   7.36 |  15.56 |  31.23\n",
            "Adapted    |   7.72 |   6.66 |   6.75 |  21.12\n"
          ]
        }
      ]
    }
  ]
}