docs/WhisperSeg_Training_Pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nianlonggu/WhisperSeg/blob/master/docs/WhisperSeg_Training_Pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVUIMPBquH8c"
      },
      "source": [
        "# Training Pipeline of WhisperSeg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INywgUC4J98F"
      },
      "source": [
        "## Clone GitHub repository and install environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rTzvEsrt67h",
        "outputId": "03d80817-0475-4230-be4c-6acb55a09776"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'WhisperSeg'...\n",
            "remote: Enumerating objects: 831, done.\u001b[K\n",
            "remote: Counting objects: 100% (111/111), done.\u001b[K\n",
            "remote: Compressing objects: 100% (60/60), done.\u001b[K\n",
            "remote: Total 831 (delta 71), reused 68 (delta 48), pack-reused 720\u001b[K\n",
            "Receiving objects: 100% (831/831), 214.18 MiB | 13.96 MiB/s, done.\n",
            "Resolving deltas: 100% (338/338), done.\n",
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            "\u001b[?25h\u001b[33mWARNING: The candidate selected for download or install is a yanked version: 'scipy' candidate (version 1.11.0 at https://files.pythonhosted.org/packages/5a/55/ec296aa9399d00f5d6794b90d96b5cce5eeb46c62450173fedb2b3b7ef72/scipy-1.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/scipy/) (requires-python:<3.13,>=3.9))\n",
            "Reason for being yanked: License Violation\u001b[0m\u001b[33m\n",
            "\u001b[0m\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",
            "google-colab 1.0.0 requires ipykernel==5.5.6, but you have ipykernel 6.29.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/nianlonggu/WhisperSeg.git\n",
        "!cd WhisperSeg; pip install -r requirements.txt --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "w6yFhka_AyNj"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir(\"WhisperSeg\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dZNy2PiKaov"
      },
      "source": [
        "## Dataset Processing\n",
        "Here we download the song bird dataset for demonstration purpose. For the creating you own dataset and complete datasets used in this paper, please refer our GitHub repository:\n",
        "https://github.com/nianlonggu/WhisperSeg/blob/master/docs/DatasetProcessing.md#dataset-processing"
      ]
    },
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e558125b431e4715b4b524d422ef2530"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "67c3325990614372b235c2b8b3fe145e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6de5f8b0c69e4159818970a2850a79ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04f8b8f6a1d246d1b940b9ff06bedf12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2d2535bd1d6422fa93df6b3dae7269b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import os\n",
        "from huggingface_hub import snapshot_download\n",
        "os.makedirs(\"data/datasets/\", exist_ok=True)\n",
        "snapshot_download('nccratliri/bengalese-finch-subset-with-csv-label', local_dir = \"data/datasets/bengalese-finch\", repo_type=\"dataset\" )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8X91a1s_LUoP"
      },
      "source": [
        "## Model Training\n",
        "We train a WhisperSeg-base model one the example dataset.\n",
        "\n",
        "For more details on training WhisperSeg, please refer to our GitHub repository: https://github.com/nianlonggu/WhisperSeg/blob/master/docs/ModelTraining.md#model-training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVccffg2MYAu"
      },
      "source": [
        "First we need to login wandb to log the training process. Please refer to https://wandb.ai/ for more information."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "y_lsFG1PMXyT",
        "outputId": "09132c1e-b3ec-4ed7-9931-384d140dd9ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import wandb\n",
        "wandb.login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AUjdWAUeMx_H"
      },
      "source": [
        "Start training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mezMnWFLT8l",
        "outputId": "c3dad8d0-1b23-4ee0-8bc8-d544bcc57e71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n",
            "\r0it [00:00, ?it/s]\r0it [00:00, ?it/s]\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnianlonggu\u001b[0m (\u001b[33mnccratliri\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.17.2\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/WhisperSeg/wandb/run-20240621_114847-cgfpfdey\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mlogical-energy-533\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/nccratliri/whisperseg-multi-species\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/nccratliri/whisperseg-multi-species/runs/cgfpfdey\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "config.json: 100% 1.98k/1.98k [00:00<00:00, 4.50MB/s]\n",
            "model.safetensors: 100% 290M/290M [00:05<00:00, 54.9MB/s]\n",
            "generation_config.json: 100% 3.81k/3.81k [00:00<00:00, 10.2MB/s]\n",
            "tokenizer_config.json: 100% 805/805 [00:00<00:00, 2.70MB/s]\n",
            "vocab.json: 100% 1.04M/1.04M [00:00<00:00, 4.80MB/s]\n",
            "merges.txt: 100% 494k/494k [00:00<00:00, 1.09MB/s]\n",
            "normalizer.json: 100% 52.7k/52.7k [00:00<00:00, 47.3MB/s]\n",
            "added_tokens.json: 100% 2.08k/2.08k [00:00<00:00, 6.38MB/s]\n",
            "special_tokens_map.json: 100% 2.08k/2.08k [00:00<00:00, 4.67MB/s]\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "  0% 0/163 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "2024-06-21 11:49:20.548432: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:20.548437: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:20.548515: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:20.548515: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:20.548575: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:20.548614: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:20.548608: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:20.548635: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:20.665968: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:20.668449: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:20.668955: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:20.669017: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:25.506932: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:49:25.509927: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:49:25.511354: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:49:25.516370: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " 60% 98/163 [00:23<00:06,  9.99it/s]Epoch: 0, current_step: 100, learning rate: 0.000003, Loss: 4.3506\n",
            "100% 163/163 [00:30<00:00,  5.35it/s]\n",
            "  0% 0/163 [00:00<?, ?it/s]2024-06-21 11:49:49.222146: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:49.224640: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:49.226519: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:49.228409: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:49.234622: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:49.236212: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:49.319357: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:49.319414: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:49.324425: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:49:49.324474: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:49:49.326058: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:49.329009: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:49:56.128103: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:49:56.289520: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:49:56.289891: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:49:56.291131: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " 21% 35/163 [00:14<00:12, 10.12it/s]Epoch: 1, current_step: 200, learning rate: 0.000002, Loss: 1.6277\n",
            " 83% 136/163 [00:26<00:03,  7.61it/s]Epoch: 1, current_step: 300, learning rate: 0.000001, Loss: 1.2892\n",
            "100% 163/163 [00:29<00:00,  5.61it/s]\n",
            "  0% 0/163 [00:00<?, ?it/s]2024-06-21 11:50:18.154162: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:50:18.154242: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:50:18.156431: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:50:18.195990: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:50:18.200637: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:50:18.202267: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:50:18.204037: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:50:18.204083: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:50:18.205603: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:50:18.249320: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-21 11:50:18.252639: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-21 11:50:18.254255: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-06-21 11:50:22.141628: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:50:22.375743: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:50:22.483073: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-06-21 11:50:22.515385: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " 45% 73/163 [00:16<00:08, 10.09it/s]Epoch: 2, current_step: 400, learning rate: 0.000001, Loss: 1.1924\n",
            " 99% 162/163 [00:26<00:00,  9.14it/s]Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 448, 'suppress_tokens': [1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50358, 50359, 50360, 50361, 50362], 'begin_suppress_tokens': [220, 50257]}\n",
            " 99% 162/163 [00:34<00:00,  4.76it/s]\n",
            "The best checkpoint on validation set is: model/whisperseg-base-bengalese-finch/checkpoint-489,\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 448, 'suppress_tokens': [1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50358, 50359, 50360, 50361, 50362], 'begin_suppress_tokens': [220, 50257]}\n",
            "All Done!\n"
          ]
        }
      ],
      "source": [
        "!python train.py --initial_model_path openai/whisper-base --train_dataset_folder data/datasets/bengalese-finch/train/  --model_folder model/whisperseg-base-bengalese-finch --gpu_list 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGHBtswbNfB_"
      },
      "source": [
        "## Evaluation\n",
        "The trained model checkpoint is stored at \"model/whisperseg-base-mouse/final_checkpoint\" and \"model/whisperseg-base-mouse/final_checkpoint_ct2\" (CTranslate2 version). We evaluate the performance of the trained model on Mouse's test set.\n",
        "\n",
        "For more detailed information of evaluation, please refer to our GitHub repository: https://github.com/nianlonggu/WhisperSeg/blob/master/docs/Evaluation.md#evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpWoeC2KObcj"
      },
      "source": [
        "### Evaluate the model using the frame-wise F1 score ($F1_\\text{frame}$) and segment-wise F1 score ($F1_\\text{seg}$)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4RKi5c-OO4N",
        "outputId": "db7c7b2a-a671-491f-c6fa-92b266caef13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "100%|██████████| 100/100 [00:19<00:00,  5.11it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'segment_wise_scores': {'N-true-positive': 651,\n",
              "  'N-positive-in-prediction': 667,\n",
              "  'N-positive-in-ground-truth': 686,\n",
              "  'precision': 0.9760119940029985,\n",
              "  'recall': 0.9489795918367347,\n",
              "  'F1': 0.9623059866962306},\n",
              " 'frame_wise_scores': {'N-true-positive': 45015,\n",
              "  'N-positive-in-prediction': 46318,\n",
              "  'N-positive-in-ground-truth': 46744,\n",
              "  'precision': 0.9718683880996589,\n",
              "  'recall': 0.9630112955673455,\n",
              "  'F1': 0.9674195697491994}}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "from evaluate import evaluate_dataset\n",
        "evaluate_dataset( \"data/datasets/bengalese-finch/test/\", \"model/whisperseg-base-bengalese-finch/final_checkpoint_ct2/\", num_trials =3 )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iLiJ-opOiE6"
      },
      "source": [
        "### Evaluate the model by visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "1tIElZ0UOlaw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8182702b-9033-42d3-e6b5-b22f0d0023c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "from model import WhisperSegmenterFast\n",
        "from audio_utils import SpecViewer\n",
        "import pandas as pd\n",
        "import librosa\n",
        "segmenter = WhisperSegmenterFast( \"model/whisperseg-base-bengalese-finch/final_checkpoint_ct2\", device=\"cuda\" )\n",
        "spec_viewer = SpecViewer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
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        "colab": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='offset', max=0.0, step=0.25), Output()), _dom_classe…"
            ],
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              "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "audio_file = \"data/datasets/bengalese-finch/test/gr41rd51__3part_SYLc_th4191_belowhits_220612_1956.16442_0.wav\"\n",
        "label_file = audio_file[:-4] + \".csv\"\n",
        "audio, sr = librosa.load( audio_file, sr = None )\n",
        "label = pd.read_csv( label_file )\n",
        "\n",
        "## by default the min_frequency parameter when calling the segment function (used to compute Log-Mel spectrogram) is 0.\n",
        "## For certain species, like mouse, set the parameter min_frequency to a proper value, e.g., 35000\n",
        "prediction = segmenter.segment( audio, sr = sr )\n",
        "spec_viewer.visualize( audio = audio, sr = sr, prediction = prediction, label=label )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xrlWDqcPHvR"
      },
      "source": [
        "For more details please refer to our paper:\n",
        "> [**Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection**]()\n",
        ">\n",
        "> Nianlong Gu, Kanghwi Lee, Maris Basha, Sumit Kumar Ram, Guanghao You, Richard H. R. Hahnloser <br>\n",
        "> University of Zurich and ETH Zurich\n",
        "\n",
        "GitHub: https://github.com/nianlonggu/WhisperSeg\n",
        "\n",
        "## Contact:\n",
        "nianlong.gu@uzh.ch"
      ]
    }
  ],
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\n"
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