Smirnova_Anna, HW2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "test.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "5a2290d4e8cd4f73a2cb627ba39cde2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_8891f5f0c83e40bf9ec335be0c142f42",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_27deb8ec4b534ceab15b94f2a73d0988",
              "IPY_MODEL_e9807929f8a244a490923fe0764bf7cf",
              "IPY_MODEL_d51639994c4b463ea437e082deebb1e5"
            ]
          }
        },
        "8891f5f0c83e40bf9ec335be0c142f42": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": "row wrap",
            "width": "100%",
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": "inline-flex",
            "left": null
          }
        },
        "27deb8ec4b534ceab15b94f2a73d0988": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_208e4d2e2b384d35b0296e12e6f1f589",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "Validation sanity check:   0%",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_3566df0f94784517833033172ed5d8d6"
          }
        },
        "e9807929f8a244a490923fe0764bf7cf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_2ab874885c044f97aef6a15de5dc5df6",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "danger",
            "max": 2,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 0,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_325315d4d24f44338bad5138e7c7819b"
          }
        },
        "d51639994c4b463ea437e082deebb1e5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_758cd14653534f75884c8a42d4f3a9d4",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 0/2 [00:02&lt;?, ?it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d1b8a45314884117811614de92da2141"
          }
        },
        "208e4d2e2b384d35b0296e12e6f1f589": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "3566df0f94784517833033172ed5d8d6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "2ab874885c044f97aef6a15de5dc5df6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "325315d4d24f44338bad5138e7c7819b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": "2",
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "758cd14653534f75884c8a42d4f3a9d4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d1b8a45314884117811614de92da2141": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "e4418e9f42ce4bcf817cbf39a01c879b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_50d6c130beef4dc69b012d78c23f9781",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_e48d004a5d8b4c9bb08c40a051f5cb78",
              "IPY_MODEL_75ddc201e46946b999b83b91d6462d2e",
              "IPY_MODEL_0a9916a8b4304818b32e341e774dc8df"
            ]
          }
        },
        "50d6c130beef4dc69b012d78c23f9781": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": "row wrap",
            "width": "100%",
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": "inline-flex",
            "left": null
          }
        },
        "e48d004a5d8b4c9bb08c40a051f5cb78": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_b9d456045d934617a710657dc219c58c",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "Epoch 2: 100%",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6c936d2b797b4c91a9084d8c3ce022b8"
          }
        },
        "75ddc201e46946b999b83b91d6462d2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_8814dad08fc6456f8df611906db249b9",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 158,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 158,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_196e15fc79674055acbfbedaf0e494a9"
          }
        },
        "0a9916a8b4304818b32e341e774dc8df": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_c9da53a509e541c7977ca0117f6850c7",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 158/158 [05:01&lt;00:00,  1.91s/it, loss=492, v_num=0]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_1bcf776fae1b48cdb4089320ebe87639"
          }
        },
        "b9d456045d934617a710657dc219c58c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6c936d2b797b4c91a9084d8c3ce022b8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "8814dad08fc6456f8df611906db249b9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "196e15fc79674055acbfbedaf0e494a9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": "2",
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "c9da53a509e541c7977ca0117f6850c7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "1bcf776fae1b48cdb4089320ebe87639": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "47326950474448db9a9b87f21955fa12": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_4b5e3033427b4587a735d6aa7116e817",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_50fecad6071e4ed98b17997df3f023b7",
              "IPY_MODEL_9e12e4cfa83f446095a8e251a0f4741a",
              "IPY_MODEL_c5487c8b1a724258ab1416e76232659c"
            ]
          }
        },
        "4b5e3033427b4587a735d6aa7116e817": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": "row wrap",
            "width": "100%",
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": "inline-flex",
            "left": null
          }
        },
        "50fecad6071e4ed98b17997df3f023b7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_6724e9d299864072b28556a1c744c633",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "Validating: 100%",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_9bb1ed7eff3b4d5985c9da8484989265"
          }
        },
        "9e12e4cfa83f446095a8e251a0f4741a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_211767a2ad934e43ba7d1674c8ec1ec0",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "",
            "max": 120,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 120,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_a7de94d853214904b935e1a40650904b"
          }
        },
        "c5487c8b1a724258ab1416e76232659c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_c951b3d393384332ac9d73604e8b885b",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 120/120 [00:41&lt;00:00,  3.05it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_9b1057310edd417aa0ddf0e12f6bbee8"
          }
        },
        "6724e9d299864072b28556a1c744c633": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "9bb1ed7eff3b4d5985c9da8484989265": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "211767a2ad934e43ba7d1674c8ec1ec0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "a7de94d853214904b935e1a40650904b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": "2",
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "c951b3d393384332ac9d73604e8b885b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "9b1057310edd417aa0ddf0e12f6bbee8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "fb0b8ec8d3e54c34854e1201695c591b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_43d79bec0b53458b81ff5bb0b3874220",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_610eae6d0eb545a88aa6395a74216f50",
              "IPY_MODEL_6548e9a8324241acacc10189aa06993c",
              "IPY_MODEL_eddf2fe1ea8e40cc8af5b7b2eed5d01f"
            ]
          }
        },
        "43d79bec0b53458b81ff5bb0b3874220": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": "row wrap",
            "width": "100%",
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": "inline-flex",
            "left": null
          }
        },
        "610eae6d0eb545a88aa6395a74216f50": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_1ae93db089034465b8970f6b188c0bb1",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "Validating: 100%",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_10722be3f5a84b2d82f861961ba001ca"
          }
        },
        "6548e9a8324241acacc10189aa06993c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_672c0e80355c407ca4229af47d1aa7b1",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "",
            "max": 120,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 120,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6d79ba8e036841499ad09752205f80e5"
          }
        },
        "eddf2fe1ea8e40cc8af5b7b2eed5d01f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_34c668aa7cb64c869424e3f1c66f5702",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 120/120 [00:41&lt;00:00,  3.05it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6d018bdfb761484ea34cbaa7fefba2ca"
          }
        },
        "1ae93db089034465b8970f6b188c0bb1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "10722be3f5a84b2d82f861961ba001ca": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "672c0e80355c407ca4229af47d1aa7b1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6d79ba8e036841499ad09752205f80e5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": "2",
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "34c668aa7cb64c869424e3f1c66f5702": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6d018bdfb761484ea34cbaa7fefba2ca": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "309f897963e841d09f904481d28d14b3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_b8dfe26d843b410b9fed7b5c39dd7367",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_fbbb760b1aaf43768a575429a252da3a",
              "IPY_MODEL_76fe424dc179414db31470481cb8ec84",
              "IPY_MODEL_b44da4a466d44622b6f87d9ece772648"
            ]
          }
        },
        "b8dfe26d843b410b9fed7b5c39dd7367": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": "row wrap",
            "width": "100%",
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": "inline-flex",
            "left": null
          }
        },
        "fbbb760b1aaf43768a575429a252da3a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_0ce09a467ddf4203aaa209d3465322ff",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "Validating: 100%",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_5cef72311c0643c6a50358ec9b9a3894"
          }
        },
        "76fe424dc179414db31470481cb8ec84": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_d83f912131964a1eac9455f5ef1ff7d3",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "",
            "max": 120,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 120,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_f31282bf76b54b45894301b1e4417198"
          }
        },
        "b44da4a466d44622b6f87d9ece772648": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_597943f73acc475fa28ba87956e0979e",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 120/120 [00:41&lt;00:00,  3.05it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_437e98294a0a4e9d8be61d3ced7424d1"
          }
        },
        "0ce09a467ddf4203aaa209d3465322ff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "5cef72311c0643c6a50358ec9b9a3894": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "d83f912131964a1eac9455f5ef1ff7d3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "f31282bf76b54b45894301b1e4417198": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": "2",
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "597943f73acc475fa28ba87956e0979e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "437e98294a0a4e9d8be61d3ced7424d1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "791a95fe91864c65b0b4f4ba8217b622": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_553beab772fe4fccb05814eddfb000fa",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_5f0b4ae2ca81475ba83382d21f99ccd6",
              "IPY_MODEL_057404950b324585b259c3de2726cea2",
              "IPY_MODEL_265c8c5b7c7d40fbab0e71a08816de37"
            ]
          }
        },
        "553beab772fe4fccb05814eddfb000fa": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "5f0b4ae2ca81475ba83382d21f99ccd6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_035b66dd200f49068289dc903f6fcbde",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "Transcribing: 100%",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_cecaa9e4ddd2498f98751bcdbaa1ecdb"
          }
        },
        "057404950b324585b259c3de2726cea2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_92b491a7c20f467c8280b67e7e9b06a7",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 2564,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 2564,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_b345fa1c3a954c7ebf8d9b04b57646a0"
          }
        },
        "265c8c5b7c7d40fbab0e71a08816de37": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_bc77f798a7b946469bbaf18633cfa657",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 2564/2564 [1:13:06&lt;00:00,  1.45s/it]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_fe0cd375675c4f30bc91dfd1f2579fe1"
          }
        },
        "035b66dd200f49068289dc903f6fcbde": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "cecaa9e4ddd2498f98751bcdbaa1ecdb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "92b491a7c20f467c8280b67e7e9b06a7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "b345fa1c3a954c7ebf8d9b04b57646a0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "bc77f798a7b946469bbaf18633cfa657": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "fe0cd375675c4f30bc91dfd1f2579fe1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sw87cmMylKNs",
        "outputId": "b4c19433-2390-4db7-fc0e-b503b74f4be4"
      },
      "source": [
        "# If you're using Google Colab and not running locally, run this cell.\n",
        "\n",
        "## Install dependencies\n",
        "!pip install wget\n",
        "!apt-get install sox libsndfile1 ffmpeg\n",
        "!pip install unidecode\n",
        "!pip install pytorch_lightning\n",
        "!pip install python-Levenshtein\n",
        "\n",
        "\n",
        "## Install NeMo\n",
        "!python -m pip install nemo_toolkit[all]\n",
        "\n",
        "# !pip install torchtext==0.10.1\n",
        "# !pip install matplotlib==3.1.3\n",
        "\"\"\"\n",
        "Remember to restart the runtime for the kernel to pick up any upgraded packages (e.g. matplotlib)!\n",
        "Alternatively, you can uncomment the exit() below to crash and restart the kernel, in the case\n",
        "that you want to use the \"Run All Cells\" (or similar) option.\n",
        "\"\"\"\n",
        "exit() "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9672 sha256=be1e44fe57a7fee2a41c0e0e859573afd475152a01273e0a2687cc06131fbb16\n",
            "  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "libsndfile1 is already the newest version (1.0.28-4ubuntu0.18.04.2).\n",
            "ffmpeg is already the newest version (7:3.4.8-0ubuntu0.2).\n",
            "The following additional packages will be installed:\n",
            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
            "  libsox-fmt-base libsox3\n",
            "Suggested packages:\n",
            "  file libsox-fmt-all\n",
            "The following NEW packages will be installed:\n",
            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
            "  libsox-fmt-base libsox3 sox\n",
            "0 upgraded, 8 newly installed, 0 to remove and 37 not upgraded.\n",
            "Need to get 760 kB of archives.\n",
            "After this operation, 6,717 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrnb0 amd64 0.1.3-2.1 [92.0 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrwb0 amd64 0.1.3-2.1 [45.8 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic-mgc amd64 1:5.32-2ubuntu0.4 [184 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic1 amd64 1:5.32-2ubuntu0.4 [68.6 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox3 amd64 14.4.2-3ubuntu0.18.04.1 [226 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-alsa amd64 14.4.2-3ubuntu0.18.04.1 [10.6 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-base amd64 14.4.2-3ubuntu0.18.04.1 [32.1 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 sox amd64 14.4.2-3ubuntu0.18.04.1 [101 kB]\n",
            "Fetched 760 kB in 1s (697 kB/s)\n",
            "Selecting previously unselected package libopencore-amrnb0:amd64.\n",
            "(Reading database ... 155222 files and directories currently installed.)\n",
            "Preparing to unpack .../0-libopencore-amrnb0_0.1.3-2.1_amd64.deb ...\n",
            "Unpacking libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
            "Selecting previously unselected package libopencore-amrwb0:amd64.\n",
            "Preparing to unpack .../1-libopencore-amrwb0_0.1.3-2.1_amd64.deb ...\n",
            "Unpacking libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
            "Selecting previously unselected package libmagic-mgc.\n",
            "Preparing to unpack .../2-libmagic-mgc_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
            "Unpacking libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
            "Selecting previously unselected package libmagic1:amd64.\n",
            "Preparing to unpack .../3-libmagic1_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
            "Unpacking libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
            "Selecting previously unselected package libsox3:amd64.\n",
            "Preparing to unpack .../4-libsox3_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package libsox-fmt-alsa:amd64.\n",
            "Preparing to unpack .../5-libsox-fmt-alsa_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package libsox-fmt-base:amd64.\n",
            "Preparing to unpack .../6-libsox-fmt-base_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package sox.\n",
            "Preparing to unpack .../7-sox_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking sox (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
            "Setting up libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
            "Setting up libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
            "Setting up libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
            "Setting up libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up sox (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Collecting unidecode\n",
            "  Downloading Unidecode-1.3.2-py3-none-any.whl (235 kB)\n",
            "\u001b[K     |████████████████████████████████| 235 kB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: unidecode\n",
            "Successfully installed unidecode-1.3.2\n",
            "Collecting pytorch_lightning\n",
            "  Downloading pytorch_lightning-1.5.3-py3-none-any.whl (523 kB)\n",
            "\u001b[K     |████████████████████████████████| 523 kB 5.3 MB/s \n",
            "\u001b[?25hCollecting pyDeprecate==0.3.1\n",
            "  Downloading pyDeprecate-0.3.1-py3-none-any.whl (10 kB)\n",
            "Collecting fsspec[http]!=2021.06.0,>=2021.05.0\n",
            "  Downloading fsspec-2021.11.1-py3-none-any.whl (132 kB)\n",
            "\u001b[K     |████████████████████████████████| 132 kB 49.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (21.3)\n",
            "Requirement already satisfied: torch>=1.6 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (1.10.0+cu111)\n",
            "Collecting torchmetrics>=0.4.1\n",
            "  Downloading torchmetrics-0.6.0-py3-none-any.whl (329 kB)\n",
            "\u001b[K     |████████████████████████████████| 329 kB 41.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorboard>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (2.7.0)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (4.62.3)\n",
            "Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (1.19.5)\n",
            "Collecting future>=0.17.1\n",
            "  Downloading future-0.18.2.tar.gz (829 kB)\n",
            "\u001b[K     |████████████████████████████████| 829 kB 32.6 MB/s \n",
            "\u001b[?25hCollecting PyYAML>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 46.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (3.10.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (2.23.0)\n",
            "Collecting aiohttp\n",
            "  Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 41.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=17.0->pytorch_lightning) (3.0.6)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (0.37.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (0.4.6)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (0.12.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (1.8.0)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (1.42.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (1.35.0)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (3.17.3)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (3.3.6)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (57.4.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (0.6.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py>=0.4->tensorboard>=2.2.0->pytorch_lightning) (1.15.0)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch_lightning) (4.2.4)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch_lightning) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch_lightning) (4.7.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch_lightning) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch_lightning) (4.8.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.2.0->pytorch_lightning) (3.6.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch_lightning) (0.4.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch_lightning) (3.1.1)\n",
            "Collecting multidict<7.0,>=4.5\n",
            "  Downloading multidict-5.2.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (160 kB)\n",
            "\u001b[K     |████████████████████████████████| 160 kB 45.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (21.2.0)\n",
            "Collecting frozenlist>=1.1.1\n",
            "  Downloading frozenlist-1.2.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (192 kB)\n",
            "\u001b[K     |████████████████████████████████| 192 kB 46.7 MB/s \n",
            "\u001b[?25hCollecting asynctest==0.13.0\n",
            "  Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
            "Collecting async-timeout<5.0,>=4.0.0a3\n",
            "  Downloading async_timeout-4.0.1-py3-none-any.whl (5.7 kB)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch_lightning) (2.0.7)\n",
            "Collecting aiosignal>=1.1.2\n",
            "  Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n",
            "Collecting yarl<2.0,>=1.0\n",
            "  Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
            "\u001b[K     |████████████████████████████████| 271 kB 45.6 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: future\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491070 sha256=afffb61741429585a2e65288a5ba999e2695900fab11be17b390db0267fd96ec\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
            "Successfully built future\n",
            "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, fsspec, aiohttp, torchmetrics, PyYAML, pyDeprecate, future, pytorch-lightning\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: future\n",
            "    Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "Successfully installed PyYAML-6.0 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.1 asynctest-0.13.0 frozenlist-1.2.0 fsspec-2021.11.1 future-0.18.2 multidict-5.2.0 pyDeprecate-0.3.1 pytorch-lightning-1.5.3 torchmetrics-0.6.0 yarl-1.7.2\n",
            "Collecting python-Levenshtein\n",
            "  Downloading python-Levenshtein-0.12.2.tar.gz (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 2.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from python-Levenshtein) (57.4.0)\n",
            "Building wheels for collected packages: python-Levenshtein\n",
            "  Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149861 sha256=1b665c6c28ebc33bfe364e32910f40d294463da04188631857ce86f7d91cdcb1\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/5f/ca/7c4367734892581bb5ff896f15027a932c551080b2abd3e00d\n",
            "Successfully built python-Levenshtein\n",
            "Installing collected packages: python-Levenshtein\n",
            "Successfully installed python-Levenshtein-0.12.2\n",
            "Collecting nemo_toolkit[all]\n",
            "  Downloading nemo_toolkit-1.5.0-py3-none-any.whl (2.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.7 MB 5.3 MB/s \n",
            "\u001b[?25hCollecting ruamel.yaml\n",
            "  Downloading ruamel.yaml-0.17.17-py3-none-any.whl (109 kB)\n",
            "\u001b[K     |████████████████████████████████| 109 kB 46.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.13.3)\n",
            "Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.19.5)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.51.2)\n",
            "Requirement already satisfied: wget in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.2)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.10.0+cu111)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (2.8.2)\n",
            "Collecting sentencepiece<1.0.0\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 34.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: unidecode in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.3.2)\n",
            "Collecting onnx>=1.7.0\n",
            "  Downloading onnx-1.10.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (12.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 12.7 MB 36.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.0.1)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (4.62.3)\n",
            "Collecting frozendict\n",
            "  Downloading frozendict-2.1.1-py3-none-any.whl (12 kB)\n",
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Requirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (7.6.5)\n",
            "Collecting attrdict\n",
            "  Downloading attrdict-2.0.1-py2.py3-none-any.whl (9.9 kB)\n",
            "Collecting pytest-runner\n",
            "  Downloading pytest_runner-5.3.1-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: soundfile in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.10.3.post1)\n",
            "Requirement already satisfied: pytorch-lightning>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.5.3)\n",
            "Collecting hydra-core>=1.1.0\n",
            "  Downloading hydra_core-1.1.1-py3-none-any.whl (145 kB)\n",
            "\u001b[K     |████████████████████████████████| 145 kB 45.8 MB/s \n",
            "\u001b[?25hCollecting sacremoses>=0.0.43\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 45.5 MB/s \n",
            "\u001b[?25hCollecting pangu\n",
            "  Downloading pangu-4.0.6.1-py3-none-any.whl (6.4 kB)\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.6.4)\n",
            "Collecting isort[requirements]<5\n",
            "  Downloading isort-4.3.21-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[K     |████████████████████████████████| 42 kB 976 kB/s \n",
            "\u001b[?25hCollecting boto3\n",
            "  Downloading boto3-1.20.16-py3-none-any.whl (131 kB)\n",
            "\u001b[K     |████████████████████████████████| 131 kB 45.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (2019.12.20)\n",
            "Collecting braceexpand\n",
            "  Downloading braceexpand-0.1.7-py2.py3-none-any.whl (5.9 kB)\n",
            "Collecting rapidfuzz\n",
            "  Downloading rapidfuzz-1.8.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (854 kB)\n",
            "\u001b[K     |████████████████████████████████| 854 kB 48.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: editdistance in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.5.3)\n",
            "Collecting black==19.10b0\n",
            "  Downloading black-19.10b0-py36-none-any.whl (97 kB)\n",
            "\u001b[K     |████████████████████████████████| 97 kB 7.0 MB/s \n",
            "\u001b[?25hCollecting kaldi-python-io\n",
            "  Downloading kaldi-python-io-1.2.2.tar.gz (8.8 kB)\n",
            "Collecting transformers>=4.0.1\n",
            "  Downloading transformers-4.12.5-py3-none-any.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 46.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (21.3)\n",
            "Collecting wordninja==2.0.0\n",
            "  Downloading wordninja-2.0.0.tar.gz (541 kB)\n",
            "\u001b[K     |████████████████████████████████| 541 kB 43.0 MB/s \n",
            "\u001b[?25hCollecting pyannote.metrics\n",
            "  Downloading pyannote.metrics-3.1-py3-none-any.whl (49 kB)\n",
            "\u001b[K     |████████████████████████████████| 49 kB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.11.1+cu111)\n",
            "Collecting nltk>=3.6.5\n",
            "  Downloading nltk-3.6.5-py3-none-any.whl (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 44.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (7.1.2)\n",
            "Collecting pypinyin\n",
            "  Downloading pypinyin-0.44.0-py2.py3-none-any.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 47.4 MB/s \n",
            "\u001b[?25hCollecting torch-stft\n",
            "  Downloading torch_stft-0.1.4-py3-none-any.whl (6.2 kB)\n",
            "Requirement already satisfied: torchmetrics>=0.4.1rc0 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.6.0)\n",
            "Collecting g2p-en\n",
            "  Downloading g2p_en-2.1.0-py3-none-any.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 44.0 MB/s \n",
            "\u001b[?25hCollecting wandb\n",
            "  Downloading wandb-0.12.7-py2.py3-none-any.whl (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 34.4 MB/s \n",
            "\u001b[?25hCollecting sox\n",
            "  Downloading sox-1.4.1-py2.py3-none-any.whl (39 kB)\n",
            "Collecting kaldi-io\n",
            "  Downloading kaldi_io-0.9.4-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.2.5)\n",
            "Collecting sphinxcontrib-bibtex\n",
            "  Downloading sphinxcontrib_bibtex-2.4.1-py3-none-any.whl (38 kB)\n",
            "Collecting marshmallow\n",
            "  Downloading marshmallow-3.14.1-py3-none-any.whl (47 kB)\n",
            "\u001b[K     |████████████████████████████████| 47 kB 4.5 MB/s \n",
            "\u001b[?25hCollecting kaldiio\n",
            "  Downloading kaldiio-2.17.2.tar.gz (24 kB)\n",
            "Collecting pyannote.core\n",
            "  Downloading pyannote.core-4.3-py3-none-any.whl (59 kB)\n",
            "\u001b[K     |████████████████████████████████| 59 kB 7.1 MB/s \n",
            "\u001b[?25hCollecting sacrebleu[ja]\n",
            "  Downloading sacrebleu-2.0.0-py3-none-any.whl (90 kB)\n",
            "\u001b[K     |████████████████████████████████| 90 kB 9.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.4.1)\n",
            "Requirement already satisfied: inflect in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (2.1.0)\n",
            "Collecting pystoi\n",
            "  Downloading pystoi-0.3.3.tar.gz (7.0 kB)\n",
            "Collecting pyyaml<6\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 27.4 MB/s \n",
            "\u001b[?25hCollecting opencc\n",
            "  Downloading OpenCC-1.1.3-cp37-cp37m-manylinux1_x86_64.whl (766 kB)\n",
            "\u001b[K     |████████████████████████████████| 766 kB 26.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.1.5)\n",
            "Requirement already satisfied: librosa in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.8.1)\n",
            "Collecting pesq\n",
            "  Downloading pesq-0.0.3.tar.gz (35 kB)\n",
            "Requirement already satisfied: jieba in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.42.1)\n",
            "Requirement already satisfied: sphinx in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.8.6)\n",
            "Collecting matplotlib>=3.3.2\n",
            "  Downloading matplotlib-3.5.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (11.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 11.2 MB 22.4 MB/s \n",
            "\u001b[?25hCollecting fasttext\n",
            "  Downloading fasttext-0.9.2.tar.gz (68 kB)\n",
            "\u001b[K     |████████████████████████████████| 68 kB 6.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.6.4)\n",
            "Collecting parameterized\n",
            "  Downloading parameterized-0.8.1-py2.py3-none-any.whl (26 kB)\n",
            "Collecting webdataset<=0.1.62,>=0.1.48\n",
            "  Downloading webdataset-0.1.62-py3-none-any.whl (32 kB)\n",
            "Collecting omegaconf>=2.1.0\n",
            "  Downloading omegaconf-2.1.1-py3-none-any.whl (74 kB)\n",
            "\u001b[K     |████████████████████████████████| 74 kB 3.4 MB/s \n",
            "\u001b[?25hCollecting youtokentome>=1.0.5\n",
            "  Downloading youtokentome-1.0.6-cp37-cp37m-manylinux2010_x86_64.whl (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 31.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.1.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.2.2)\n",
            "Requirement already satisfied: appdirs in /usr/local/lib/python3.7/dist-packages (from black==19.10b0->nemo_toolkit[all]) (1.4.4)\n",
            "Collecting typed-ast>=1.4.0\n",
            "  Downloading typed_ast-1.5.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (843 kB)\n",
            "\u001b[K     |████████████████████████████████| 843 kB 23.5 MB/s \n",
            "\u001b[?25hCollecting pathspec<1,>=0.6\n",
            "  Downloading pathspec-0.9.0-py2.py3-none-any.whl (31 kB)\n",
            "Requirement already satisfied: click>=6.5 in /usr/local/lib/python3.7/dist-packages (from black==19.10b0->nemo_toolkit[all]) (7.1.2)\n",
            "Requirement already satisfied: toml>=0.9.4 in /usr/local/lib/python3.7/dist-packages (from black==19.10b0->nemo_toolkit[all]) (0.10.2)\n",
            "Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.7/dist-packages (from black==19.10b0->nemo_toolkit[all]) (21.2.0)\n",
            "Collecting antlr4-python3-runtime==4.8\n",
            "  Downloading antlr4-python3-runtime-4.8.tar.gz (112 kB)\n",
            "\u001b[K     |████████████████████████████████| 112 kB 48.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from hydra-core>=1.1.0->nemo_toolkit[all]) (5.4.0)\n",
            "Collecting pip-api\n",
            "  Downloading pip_api-0.0.23-py3-none-any.whl (107 kB)\n",
            "\u001b[K     |████████████████████████████████| 107 kB 46.4 MB/s \n",
            "\u001b[?25hCollecting pipreqs\n",
            "  Downloading pipreqs-0.4.11-py2.py3-none-any.whl (32 kB)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.7.0->nemo_toolkit[all]) (3.10.0.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx>=1.7.0->nemo_toolkit[all]) (1.15.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx>=1.7.0->nemo_toolkit[all]) (3.17.3)\n",
            "Requirement already satisfied: pyDeprecate==0.3.1 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.3.1)\n",
            "Requirement already satisfied: fsspec[http]!=2021.06.0,>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]) (2021.11.1)\n",
            "Requirement already satisfied: future>=0.17.1 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.18.2)\n",
            "Requirement already satisfied: tensorboard>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]) (2.7.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (2.23.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (3.8.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->nemo_toolkit[all]) (3.0.6)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses>=0.0.43->nemo_toolkit[all]) (1.1.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.37.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.4.6)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.12.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (57.4.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.35.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.8.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (3.3.6)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.6.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.0.1)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.42.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (4.7.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (4.2.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (4.8.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (3.6.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.4.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.24.3)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (3.1.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers>=4.0.1->nemo_toolkit[all]) (3.4.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.1.2-py3-none-any.whl (59 kB)\n",
            "\u001b[K     |████████████████████████████████| 59 kB 6.7 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 46.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.7.2)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (5.2.0)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (2.0.7)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.2.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (1.2.0)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (0.13.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]) (4.0.1)\n",
            "Collecting botocore<1.24.0,>=1.23.16\n",
            "  Downloading botocore-1.23.16-py3-none-any.whl (8.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 8.3 MB 34.0 MB/s \n",
            "\u001b[?25hCollecting s3transfer<0.6.0,>=0.5.0\n",
            "  Downloading s3transfer-0.5.0-py3-none-any.whl (79 kB)\n",
            "\u001b[K     |████████████████████████████████| 79 kB 7.9 MB/s \n",
            "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1\n",
            "  Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 46.8 MB/s \n",
            "\u001b[?25hCollecting pybind11>=2.2\n",
            "  Using cached pybind11-2.8.1-py2.py3-none-any.whl (208 kB)\n",
            "Collecting distance>=0.1.3\n",
            "  Downloading Distance-0.1.3.tar.gz (180 kB)\n",
            "\u001b[K     |████████████████████████████████| 180 kB 43.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->nemo_toolkit[all]) (1.5.2)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (5.1.1)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (5.5.0)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (0.2.0)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (4.10.1)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (5.1.3)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (1.0.2)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (3.5.2)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]) (5.3.5)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]) (5.1.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (4.4.2)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (4.8.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (0.7.5)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (2.6.1)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (0.8.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (1.0.18)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets->nemo_toolkit[all]) (4.9.1)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets->nemo_toolkit[all]) (2.6.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (0.2.5)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (5.3.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (1.8.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (2.11.3)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (5.6.1)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (0.12.1)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]) (22.3.0)\n",
            "Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (0.7.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (2.0.1)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa->nemo_toolkit[all]) (2.1.9)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa->nemo_toolkit[all]) (1.5.2)\n",
            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa->nemo_toolkit[all]) (0.2.2)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->nemo_toolkit[all]) (0.34.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->nemo_toolkit[all]) (3.0.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from soundfile->nemo_toolkit[all]) (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->soundfile->nemo_toolkit[all]) (2.21)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->nemo_toolkit[all]) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->nemo_toolkit[all]) (1.3.2)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (0.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (4.1.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (0.5.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (0.7.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (1.5.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (0.8.4)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->nemo_toolkit[all]) (0.5.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->nemo_toolkit[all]) (2018.9)\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.7/dist-packages (from pip-api->isort[requirements]<5->nemo_toolkit[all]) (21.1.3)\n",
            "Requirement already satisfied: docopt in /usr/local/lib/python3.7/dist-packages (from pipreqs->isort[requirements]<5->nemo_toolkit[all]) (0.6.2)\n",
            "Collecting yarg\n",
            "  Downloading yarg-0.1.9-py2.py3-none-any.whl (19 kB)\n",
            "Collecting simplejson>=3.8.1\n",
            "  Downloading simplejson-3.17.6-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (130 kB)\n",
            "\u001b[K     |████████████████████████████████| 130 kB 45.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: sortedcontainers>=2.0.4 in /usr/local/lib/python3.7/dist-packages (from pyannote.core->nemo_toolkit[all]) (2.4.0)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from pyannote.metrics->nemo_toolkit[all]) (0.8.9)\n",
            "Collecting pyannote.database>=4.0.1\n",
            "  Downloading pyannote.database-4.1.1-py3-none-any.whl (41 kB)\n",
            "\u001b[K     |████████████████████████████████| 41 kB 252 kB/s \n",
            "\u001b[?25hRequirement already satisfied: sympy>=1.1 in /usr/local/lib/python3.7/dist-packages (from pyannote.metrics->nemo_toolkit[all]) (1.7.1)\n",
            "Collecting typer[all]>=0.2.1\n",
            "  Downloading typer-0.4.0-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy>=1.1->pyannote.metrics->nemo_toolkit[all]) (1.2.1)\n",
            "Collecting shellingham<2.0.0,>=1.3.0\n",
            "  Downloading shellingham-1.4.0-py2.py3-none-any.whl (9.4 kB)\n",
            "Collecting colorama<0.5.0,>=0.4.3\n",
            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->nemo_toolkit[all]) (1.4.0)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.7/dist-packages (from pytest->nemo_toolkit[all]) (0.7.1)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->nemo_toolkit[all]) (8.11.0)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->nemo_toolkit[all]) (1.11.0)\n",
            "Collecting ruamel.yaml.clib>=0.1.2\n",
            "  Downloading ruamel.yaml.clib-0.2.6-cp37-cp37m-manylinux1_x86_64.whl (546 kB)\n",
            "\u001b[K     |████████████████████████████████| 546 kB 46.5 MB/s \n",
            "\u001b[?25hCollecting portalocker\n",
            "  Downloading portalocker-2.3.2-py2.py3-none-any.whl (15 kB)\n",
            "Collecting mecab-python3==1.0.3\n",
            "  Downloading mecab_python3-1.0.3-cp37-cp37m-manylinux1_x86_64.whl (487 kB)\n",
            "\u001b[K     |████████████████████████████████| 487 kB 45.0 MB/s \n",
            "\u001b[?25hCollecting ipadic<2.0,>=1.0\n",
            "  Downloading ipadic-1.0.0.tar.gz (13.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.4 MB 48.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: babel!=2.0,>=1.3 in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (2.9.1)\n",
            "Requirement already satisfied: alabaster<0.8,>=0.7 in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (0.7.12)\n",
            "Requirement already satisfied: sphinxcontrib-websupport in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (1.2.4)\n",
            "Requirement already satisfied: imagesize in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (1.3.0)\n",
            "Requirement already satisfied: docutils<0.18,>=0.11 in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (0.17.1)\n",
            "Requirement already satisfied: snowballstemmer>=1.1 in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (2.2.0)\n",
            "Collecting pybtex-docutils>=1.0.0\n",
            "  Downloading pybtex_docutils-1.0.1-py3-none-any.whl (4.8 kB)\n",
            "Collecting pybtex>=0.20\n",
            "  Downloading pybtex-0.24.0-py2.py3-none-any.whl (561 kB)\n",
            "\u001b[K     |████████████████████████████████| 561 kB 39.5 MB/s \n",
            "\u001b[?25hCollecting sphinx\n",
            "  Downloading Sphinx-4.3.1-py3-none-any.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 33.1 MB/s \n",
            "\u001b[?25hCollecting latexcodec>=1.0.4\n",
            "  Downloading latexcodec-2.0.1-py2.py3-none-any.whl (18 kB)\n",
            "Collecting sphinxcontrib-jsmath\n",
            "  Downloading sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl (5.1 kB)\n",
            "Collecting sphinxcontrib-qthelp\n",
            "  Downloading sphinxcontrib_qthelp-1.0.3-py2.py3-none-any.whl (90 kB)\n",
            "\u001b[K     |████████████████████████████████| 90 kB 9.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: sphinxcontrib-serializinghtml>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (1.1.5)\n",
            "Collecting sphinxcontrib-htmlhelp>=2.0.0\n",
            "  Downloading sphinxcontrib_htmlhelp-2.0.0-py2.py3-none-any.whl (100 kB)\n",
            "\u001b[K     |████████████████████████████████| 100 kB 9.9 MB/s \n",
            "\u001b[?25hCollecting sphinxcontrib-devhelp\n",
            "  Downloading sphinxcontrib_devhelp-1.0.2-py2.py3-none-any.whl (84 kB)\n",
            "\u001b[K     |████████████████████████████████| 84 kB 3.6 MB/s \n",
            "\u001b[?25hCollecting sphinxcontrib-applehelp\n",
            "  Downloading sphinxcontrib_applehelp-1.0.2-py2.py3-none-any.whl (121 kB)\n",
            "\u001b[K     |████████████████████████████████| 121 kB 41.6 MB/s \n",
            "\u001b[?25hCollecting subprocess32>=3.5.3\n",
            "  Downloading subprocess32-3.5.4.tar.gz (97 kB)\n",
            "\u001b[K     |████████████████████████████████| 97 kB 7.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb->nemo_toolkit[all]) (5.4.8)\n",
            "Collecting configparser>=3.8.1\n",
            "  Downloading configparser-5.1.0-py3-none-any.whl (19 kB)\n",
            "Collecting pathtools\n",
            "  Downloading pathtools-0.1.2.tar.gz (11 kB)\n",
            "Collecting yaspin>=1.0.0\n",
            "  Downloading yaspin-2.1.0-py3-none-any.whl (18 kB)\n",
            "Collecting sentry-sdk>=1.0.0\n",
            "  Downloading sentry_sdk-1.5.0-py2.py3-none-any.whl (140 kB)\n",
            "\u001b[K     |████████████████████████████████| 140 kB 41.7 MB/s \n",
            "\u001b[?25hCollecting shortuuid>=0.5.0\n",
            "  Downloading shortuuid-1.0.8-py3-none-any.whl (9.5 kB)\n",
            "Collecting GitPython>=1.0.0\n",
            "  Downloading GitPython-3.1.24-py3-none-any.whl (180 kB)\n",
            "\u001b[K     |████████████████████████████████| 180 kB 45.7 MB/s \n",
            "\u001b[?25hCollecting docker-pycreds>=0.4.0\n",
            "  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n",
            "Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb->nemo_toolkit[all]) (2.3)\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "  Downloading gitdb-4.0.9-py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.6 MB/s \n",
            "\u001b[?25hCollecting smmap<6,>=3.0.1\n",
            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: termcolor<2.0.0,>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from yaspin>=1.0.0->wandb->nemo_toolkit[all]) (1.1.0)\n",
            "Building wheels for collected packages: wordninja, antlr4-python3-runtime, fasttext, distance, kaldi-python-io, kaldiio, pesq, pystoi, ipadic, subprocess32, pathtools\n",
            "  Building wheel for wordninja (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wordninja: filename=wordninja-2.0.0-py3-none-any.whl size=541551 sha256=244a21a778271225772938e89e7738c1e031d44664aa7646efe28b00ac6afc9a\n",
            "  Stored in directory: /root/.cache/pip/wheels/dd/3f/eb/a2692e3d2b9deb1487b09ba4967dd6920bd5032bfd9ff7acfc\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141230 sha256=2f1a1e2d8ab1838fc2e6e42405ca88d1adab2c265f323072758b840ac8867ed8\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/33/b7/336836125fc9bb4ceaa4376d8abca10ca8bc84ddc824baea6c\n",
            "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fasttext: filename=fasttext-0.9.2-cp37-cp37m-linux_x86_64.whl size=3122675 sha256=55a86c869f5ac287efb9d8e8768ece0f905383b747ef702ccd1f2775b8f45170\n",
            "  Stored in directory: /root/.cache/pip/wheels/4e/ca/bf/b020d2be95f7641801a6597a29c8f4f19e38f9c02a345bab9b\n",
            "  Building wheel for distance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for distance: filename=Distance-0.1.3-py3-none-any.whl size=16275 sha256=b1d029e846281a00c89333ff8aee7e8c0eafd53bdbab00153c0c0542b0056dcb\n",
            "  Stored in directory: /root/.cache/pip/wheels/b2/10/1b/96fca621a1be378e2fe104cfb0d160bb6cdf3d04a3d35266cc\n",
            "  Building wheel for kaldi-python-io (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaldi-python-io: filename=kaldi_python_io-1.2.2-py3-none-any.whl size=8968 sha256=d4852036671948d464f2ad300fe2d89961642ff276947e4eb7df43d9d1d8c8de\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/26/38/7678d1ff6cd1bbcbfc0d80b0a29d94d917dfa9ad790b4a85a9\n",
            "  Building wheel for kaldiio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaldiio: filename=kaldiio-2.17.2-py3-none-any.whl size=24472 sha256=3e44ba7c43b563f3585f55d123d2a623ff8939bfe03b78764d7289de17f5698b\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/07/e8/45641287c59bf6ce41e22259f8680b521c31e6306cb88392ac\n",
            "  Building wheel for pesq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pesq: filename=pesq-0.0.3-cp37-cp37m-linux_x86_64.whl size=209963 sha256=0cfe488912bce61e13ca8de96ac594882c2ba938ec5e05b6b6d5ad311e26a815\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/67/5b/aa7cf31fe0c7199e35c604bb7bc91c629a13726bf221fedba0\n",
            "  Building wheel for pystoi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pystoi: filename=pystoi-0.3.3-py2.py3-none-any.whl size=7793 sha256=31642bbd1e5441898e35508e2879806646e5994c4b66d5a4c23a4d9488489b10\n",
            "  Stored in directory: /root/.cache/pip/wheels/46/4a/ad/3ab460193ed0535430b4b1575f255aa6bae69df17453628e86\n",
            "  Building wheel for ipadic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipadic: filename=ipadic-1.0.0-py3-none-any.whl size=13556723 sha256=87c4a0951d10cb2b4ffe906ef7eb37c842c5228db3b195f920e4921d22cf6b9d\n",
            "  Stored in directory: /root/.cache/pip/wheels/33/8b/99/cf0d27191876637cd3639a560f93aa982d7855ce826c94348b\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-py3-none-any.whl size=6502 sha256=f966bae5636860a50f16edb2128274e3f0fdb2541f969f872b0781cafea1a957\n",
            "  Stored in directory: /root/.cache/pip/wheels/50/ca/fa/8fca8d246e64f19488d07567547ddec8eb084e8c0d7a59226a\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8807 sha256=07940a16c2a1198882bd6e992d0d349e5436426de71e81288cb758ec3f4824c8\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n",
            "Successfully built wordninja antlr4-python3-runtime fasttext distance kaldi-python-io kaldiio pesq pystoi ipadic subprocess32 pathtools\n",
            "Installing collected packages: urllib3, typer, smmap, simplejson, shellingham, pyyaml, latexcodec, jmespath, colorama, yarg, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, ruamel.yaml.clib, pybtex, pyannote.core, portalocker, gitdb, botocore, antlr4-python3-runtime, yaspin, typed-ast, tokenizers, subprocess32, sphinx, shortuuid, sentry-sdk, sentencepiece, sacremoses, sacrebleu, s3transfer, ruamel.yaml, pybtex-docutils, pybind11, pyannote.database, pipreqs, pip-api, pathtools, pathspec, onnx, omegaconf, mecab-python3, isort, ipadic, huggingface-hub, GitPython, frozendict, docker-pycreds, distance, configparser, braceexpand, youtokentome, wordninja, webdataset, wandb, transformers, torch-stft, sphinxcontrib-bibtex, sox, rapidfuzz, pytest-runner, pystoi, pypinyin, pydub, pyannote.metrics, pesq, parameterized, pangu, opencc, nemo-toolkit, marshmallow, kaldiio, kaldi-python-io, kaldi-io, hydra-core, g2p-en, fasttext, boto3, black, attrdict\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "  Attempting uninstall: sphinx\n",
            "    Found existing installation: Sphinx 1.8.6\n",
            "    Uninstalling Sphinx-1.8.6:\n",
            "      Successfully uninstalled Sphinx-1.8.6\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",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed GitPython-3.1.24 antlr4-python3-runtime-4.8 attrdict-2.0.1 black-19.10b0 boto3-1.20.16 botocore-1.23.16 braceexpand-0.1.7 colorama-0.4.4 configparser-5.1.0 distance-0.1.3 docker-pycreds-0.4.0 fasttext-0.9.2 frozendict-2.1.1 g2p-en-2.1.0 gitdb-4.0.9 huggingface-hub-0.1.2 hydra-core-1.1.1 ipadic-1.0.0 isort-4.3.21 jmespath-0.10.0 kaldi-io-0.9.4 kaldi-python-io-1.2.2 kaldiio-2.17.2 latexcodec-2.0.1 marshmallow-3.14.1 mecab-python3-1.0.3 nemo-toolkit-1.5.0 omegaconf-2.1.1 onnx-1.10.2 opencc-1.1.3 pangu-4.0.6.1 parameterized-0.8.1 pathspec-0.9.0 pathtools-0.1.2 pesq-0.0.3 pip-api-0.0.23 pipreqs-0.4.11 portalocker-2.3.2 pyannote.core-4.3 pyannote.database-4.1.1 pyannote.metrics-3.1 pybind11-2.8.1 pybtex-0.24.0 pybtex-docutils-1.0.1 pydub-0.25.1 pypinyin-0.44.0 pystoi-0.3.3 pytest-runner-5.3.1 pyyaml-5.4.1 rapidfuzz-1.8.3 ruamel.yaml-0.17.17 ruamel.yaml.clib-0.2.6 s3transfer-0.5.0 sacrebleu-2.0.0 sacremoses-0.0.46 sentencepiece-0.1.96 sentry-sdk-1.5.0 shellingham-1.4.0 shortuuid-1.0.8 simplejson-3.17.6 smmap-5.0.0 sox-1.4.1 sphinx-4.3.1 sphinxcontrib-applehelp-1.0.2 sphinxcontrib-bibtex-2.4.1 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.0 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 subprocess32-3.5.4 tokenizers-0.10.3 torch-stft-0.1.4 transformers-4.12.5 typed-ast-1.5.0 typer-0.4.0 urllib3-1.25.11 wandb-0.12.7 webdataset-0.1.62 wordninja-2.0.0 yarg-0.1.9 yaspin-2.1.0 youtokentome-1.0.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I44Tnm3o8irr",
        "outputId": "cf0cb9a3-2629-4d2f-d8d3-39d85831c8a7"
      },
      "source": [
        "# все import'ы\n",
        "# NeMo's \"core\" package\n",
        "import nemo\n",
        "# NeMo's ASR collection - this collections contains complete ASR models and\n",
        "# building blocks (modules) for ASR\n",
        "import nemo.collections.asr as nemo_asr\n",
        "import glob\n",
        "import os\n",
        "import subprocess\n",
        "import tarfile\n",
        "import wget\n",
        "import json\n",
        "import pytorch_lightning as pl\n",
        "from Levenshtein import distance\n",
        "import zipfile\n",
        "import pandas as pd\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 12:30:14 optimizers:50] Apex was not found. Using the lamb or fused_adam optimizer will error out.\n",
            "################################################################################\n",
            "### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk\n",
            "###          (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)\n",
            "###          (or run as: KALDI_ROOT=<your_path> python <your_script>.py)\n",
            "################################################################################\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlXd41xgo2oD"
      },
      "source": [
        "data_dir = '/content'"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDBvNW39pU74",
        "outputId": "01f7590c-523c-45ff-fb30-ef7269801b4d"
      },
      "source": [
        "! ls /content"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drive\t  farfield_dropped.csv\ttest\t  train_manifest.jsonl\n",
            "farfield  sample_data\t\ttest.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EV-vcyyOpZOO",
        "outputId": "e17a6898-f2c6-475f-9236-f25d592d31fa"
      },
      "source": [
        "# Загрузка датасета Golos\n",
        "def load(golos_url, fname):\n",
        "  if not os.path.exists(os.path.join(data_dir, fname)):\n",
        "      golos_path = wget.download(golos_url, os.path.join(data_dir, fname))\n",
        "      print(f\"Dataset downloaded at: {golos_path}\")\n",
        "  else:\n",
        "      print(\"Tarfile already exists.\")\n",
        "      golos_path = os.path.join(data_dir, fname)\n",
        "  return golos_path\n",
        "\n",
        "golos_path = load(\"https://sc.link/Kqr\", \"test.tar\")\n",
        "\n",
        "if not os.path.exists(os.path.join(data_dir, '/test/')):\n",
        "    tar = tarfile.open(golos_path)\n",
        "    tar.extractall(path=data_dir)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/test.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAlg_Ek45P0q",
        "outputId": "49ea15e8-2219-473d-d271-7f2cb21d68f2"
      },
      "source": [
        "# --- Building Manifest Files --- #\n",
        "\n",
        "# Function to build a manifest\n",
        "def build_manifest(manifest_rel, manifest_abs):\n",
        "    manifest_path = os.path.split(os.path.abspath(manifest_rel))[0]\n",
        "    with open(manifest_rel, 'r') as fin:\n",
        "        with open(manifest_abs, 'w') as fout:\n",
        "            for line in fin:\n",
        "                metadata = json.loads(line)\n",
        "                metadata[\"audio_filepath\"]=os.path.join(manifest_path, metadata[\"audio_filepath\"])\n",
        "                json.dump(metadata, fout)\n",
        "                fout.write('\\n')\n",
        "                \n",
        "# Building Manifests\n",
        "print(\"******\")\n",
        "train_rel = os.path.join(data_dir, 'test/farfield/manifest.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'test/farfield/farfield.jsonl')\n",
        "if not os.path.isfile(train_abs):\n",
        "  build_manifest(train_rel, train_abs)\n",
        "test_manifest = train_abs\n",
        "print(\"test_manifest\", test_manifest)\n",
        "\n",
        "train_rel = os.path.join(data_dir, 'test/crowd/manifest.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'test/crowd/crowd.jsonl')\n",
        "if not os.path.isfile(train_abs):\n",
        "  build_manifest(train_rel, train_abs)\n",
        "train_manifest = train_abs\n",
        "print(\"train_manifest\", train_manifest)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "******\n",
            "test_manifest /content/test/farfield/farfield.jsonl\n",
            "train_manifest /content/test/crowd/crowd.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VK1Z3ReI8NMR",
        "outputId": "e151ac3c-6b5d-484c-baea-83ad356c15c5"
      },
      "source": [
        "#загрузка модели\n",
        "def load(golos_url, fname):\n",
        "  if not os.path.exists(os.path.join(data_dir, fname)):\n",
        "      golos_path = wget.download(golos_url, os.path.join(data_dir, fname))\n",
        "      print(f\"Dataset downloaded at: {golos_path}\")\n",
        "  else:\n",
        "      print(\"Tarfile already exists.\")\n",
        "      golos_path = os.path.join(data_dir, fname)\n",
        "  return golos_path\n",
        "  \n",
        "load(\"https://sc.link/ZMv\", \"QuartzNet15x5_golos.nemo\")\n",
        "asr_model = nemo_asr.models.EncDecCTCModel.restore_from(os.path.join(data_dir, \"QuartzNet15x5_golos.nemo\"))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/QuartzNet15x5_golos.nemo\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 12:33:29 modelPT:131] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
            "    Train config : \n",
            "    manifest_filepath: train/golos_and_mcv.jsonl\n",
            "    sample_rate: 16000\n",
            "    labels:\n",
            "    - ' '\n",
            "    - а\n",
            "    - б\n",
            "    - в\n",
            "    - г\n",
            "    - д\n",
            "    - е\n",
            "    - ж\n",
            "    - з\n",
            "    - и\n",
            "    - й\n",
            "    - к\n",
            "    - л\n",
            "    - м\n",
            "    - н\n",
            "    - о\n",
            "    - п\n",
            "    - р\n",
            "    - с\n",
            "    - т\n",
            "    - у\n",
            "    - ф\n",
            "    - х\n",
            "    - ц\n",
            "    - ч\n",
            "    - ш\n",
            "    - щ\n",
            "    - ъ\n",
            "    - ы\n",
            "    - ь\n",
            "    - э\n",
            "    - ю\n",
            "    - я\n",
            "    batch_size: 64\n",
            "    trim_silence: false\n",
            "    max_duration: 20.0\n",
            "    min_duration: 0.1\n",
            "    num_workers: 20\n",
            "    shuffle: true\n",
            "    is_tarred: false\n",
            "    tarred_audio_filepaths: null\n",
            "    tarred_shard_strategy: scatter\n",
            "    parser: ru\n",
            "    \n",
            "[NeMo W 2021-11-30 12:33:29 modelPT:138] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
            "    Validation config : \n",
            "    manifest_filepath:\n",
            "    - test/mcv/dev_ru.jsonl\n",
            "    - test/mcv/test_ru.jsonl\n",
            "    - test/crowd/crowd.jsonl\n",
            "    - test/farfield/farfield.jsonl\n",
            "    sample_rate: 16000\n",
            "    labels:\n",
            "    - ' '\n",
            "    - а\n",
            "    - б\n",
            "    - в\n",
            "    - г\n",
            "    - д\n",
            "    - е\n",
            "    - ж\n",
            "    - з\n",
            "    - и\n",
            "    - й\n",
            "    - к\n",
            "    - л\n",
            "    - м\n",
            "    - н\n",
            "    - о\n",
            "    - п\n",
            "    - р\n",
            "    - с\n",
            "    - т\n",
            "    - у\n",
            "    - ф\n",
            "    - х\n",
            "    - ц\n",
            "    - ч\n",
            "    - ш\n",
            "    - щ\n",
            "    - ъ\n",
            "    - ы\n",
            "    - ь\n",
            "    - э\n",
            "    - ю\n",
            "    - я\n",
            "    batch_size: 64\n",
            "    num_workers: 20\n",
            "    shuffle: false\n",
            "    parser: ru\n",
            "    \n",
            "[NeMo W 2021-11-30 12:33:29 modelPT:144] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
            "    Test config : \n",
            "    manifest_filepath: test/mcv/test_ru.jsonl\n",
            "    sample_rate: 16000\n",
            "    labels:\n",
            "    - ' '\n",
            "    - а\n",
            "    - б\n",
            "    - в\n",
            "    - г\n",
            "    - д\n",
            "    - е\n",
            "    - ж\n",
            "    - з\n",
            "    - и\n",
            "    - й\n",
            "    - к\n",
            "    - л\n",
            "    - м\n",
            "    - н\n",
            "    - о\n",
            "    - п\n",
            "    - р\n",
            "    - с\n",
            "    - т\n",
            "    - у\n",
            "    - ф\n",
            "    - х\n",
            "    - ц\n",
            "    - ч\n",
            "    - ш\n",
            "    - щ\n",
            "    - ъ\n",
            "    - ы\n",
            "    - ь\n",
            "    - э\n",
            "    - ю\n",
            "    - я\n",
            "    batch_size: 64\n",
            "    shuffle: false\n",
            "    parser: ru\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 12:33:29 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-30 12:33:29 features:282] STFT using torch\n",
            "[NeMo I 2021-11-30 12:33:43 save_restore_connector:149] Model EncDecCTCModel was successfully restored from /content/QuartzNet15x5_golos.nemo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJ85uxhD5_MO",
        "outputId": "9db87afc-0515-4dc8-a04b-e5e671b7b0ca"
      },
      "source": [
        "#создание обучающего объекта\n",
        "trainer = pl.Trainer(gpus=1, max_epochs=3, precision=16) "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using 16bit native Automatic Mixed Precision (AMP)\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3b86mN8RACYJ"
      },
      "source": [
        "#изменение параметров\n",
        "\n",
        "params_model = asr_model._cfg\n",
        "\n",
        "# --- Config Information ---#\n",
        "# try:\n",
        "#     from ruamel.yaml import YAML\n",
        "# except ModuleNotFoundError:\n",
        "#     from ruamel_yaml import YAML\n",
        "# config_path = '/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/models/configs/'\n",
        "\n",
        "# yaml = YAML(typ='safe')\n",
        "# with open(config_path) as f:\n",
        "#     params = yaml.load(f)\n",
        "# print(params)\n",
        "params_model['train_ds']['parser']='base'\n",
        "params_model['validation_ds']['parser']='base'\n",
        "params_model['test_ds']['parser']='base'\n",
        "params_model['train_ds']['manifest_filepath'] = 'train_manifest.jsonl'\n",
        "params_model['train_ds']['batch_size']=16\n",
        "params_model['validation_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['validation_ds']['batch_size']=16\n",
        "params_model['test_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['test_ds']['batch_size']=8\n",
        "params_model['optim']['lr'] = 0.0001"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEf752TAE3A6",
        "outputId": "4cec224f-4ea0-4c37-9a2e-7fc28045ad60"
      },
      "source": [
        "#создание экземпляра\n",
        "from omegaconf import DictConfig\n",
        "\n",
        "\n",
        "first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params_model), trainer=trainer)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 12:37:16 collections:173] Dataset loaded with 601 files totalling 0.64 hours\n",
            "[NeMo I 2021-11-30 12:37:16 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 12:37:16 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 20 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",
            "      cpuset_checked))\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 12:37:16 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 12:37:16 collections:174] 0 files were filtered totalling 0.00 hours\n",
            "[NeMo I 2021-11-30 12:37:16 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 12:37:16 collections:174] 0 files were filtered totalling 0.00 hours\n",
            "[NeMo I 2021-11-30 12:37:16 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-30 12:37:16 features:282] STFT using torch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 934,
          "referenced_widgets": [
            "5a2290d4e8cd4f73a2cb627ba39cde2e",
            "8891f5f0c83e40bf9ec335be0c142f42",
            "27deb8ec4b534ceab15b94f2a73d0988",
            "e9807929f8a244a490923fe0764bf7cf",
            "d51639994c4b463ea437e082deebb1e5",
            "208e4d2e2b384d35b0296e12e6f1f589",
            "3566df0f94784517833033172ed5d8d6",
            "2ab874885c044f97aef6a15de5dc5df6",
            "325315d4d24f44338bad5138e7c7819b",
            "758cd14653534f75884c8a42d4f3a9d4",
            "d1b8a45314884117811614de92da2141",
            "e4418e9f42ce4bcf817cbf39a01c879b",
            "50d6c130beef4dc69b012d78c23f9781",
            "e48d004a5d8b4c9bb08c40a051f5cb78",
            "75ddc201e46946b999b83b91d6462d2e",
            "0a9916a8b4304818b32e341e774dc8df",
            "b9d456045d934617a710657dc219c58c",
            "6c936d2b797b4c91a9084d8c3ce022b8",
            "8814dad08fc6456f8df611906db249b9",
            "196e15fc79674055acbfbedaf0e494a9",
            "c9da53a509e541c7977ca0117f6850c7",
            "1bcf776fae1b48cdb4089320ebe87639",
            "47326950474448db9a9b87f21955fa12",
            "4b5e3033427b4587a735d6aa7116e817",
            "50fecad6071e4ed98b17997df3f023b7",
            "9e12e4cfa83f446095a8e251a0f4741a",
            "c5487c8b1a724258ab1416e76232659c",
            "6724e9d299864072b28556a1c744c633",
            "9bb1ed7eff3b4d5985c9da8484989265",
            "211767a2ad934e43ba7d1674c8ec1ec0",
            "a7de94d853214904b935e1a40650904b",
            "c951b3d393384332ac9d73604e8b885b",
            "9b1057310edd417aa0ddf0e12f6bbee8",
            "fb0b8ec8d3e54c34854e1201695c591b",
            "43d79bec0b53458b81ff5bb0b3874220",
            "610eae6d0eb545a88aa6395a74216f50",
            "6548e9a8324241acacc10189aa06993c",
            "eddf2fe1ea8e40cc8af5b7b2eed5d01f",
            "1ae93db089034465b8970f6b188c0bb1",
            "10722be3f5a84b2d82f861961ba001ca",
            "672c0e80355c407ca4229af47d1aa7b1",
            "6d79ba8e036841499ad09752205f80e5",
            "34c668aa7cb64c869424e3f1c66f5702",
            "6d018bdfb761484ea34cbaa7fefba2ca",
            "309f897963e841d09f904481d28d14b3",
            "b8dfe26d843b410b9fed7b5c39dd7367",
            "fbbb760b1aaf43768a575429a252da3a",
            "76fe424dc179414db31470481cb8ec84",
            "b44da4a466d44622b6f87d9ece772648",
            "0ce09a467ddf4203aaa209d3465322ff",
            "5cef72311c0643c6a50358ec9b9a3894",
            "d83f912131964a1eac9455f5ef1ff7d3",
            "f31282bf76b54b45894301b1e4417198",
            "597943f73acc475fa28ba87956e0979e",
            "437e98294a0a4e9d8be61d3ced7424d1"
          ]
        },
        "id": "8MhGWZfWFFf7",
        "outputId": "69ffb051-7713-42a9-e3cb-f05d45150202"
      },
      "source": [
        "# Start training!!!\n",
        "trainer.fit(first_asr_model)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "[NeMo W 2021-11-30 12:37:19 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7f475ae35510>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 12:37:19 modelPT:561] Optimizer config = Novograd (\n",
            "    Parameter Group 0\n",
            "        amsgrad: False\n",
            "        betas: [0.9, 0.98]\n",
            "        eps: 1e-08\n",
            "        grad_averaging: False\n",
            "        lr: 0.0001\n",
            "        weight_decay: 0.001\n",
            "    )\n",
            "[NeMo I 2021-11-30 12:37:19 lr_scheduler:752] Scheduler \"<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f4759c8a390>\" \n",
            "    will be used during training (effective maximum steps = 114) - \n",
            "    Parameters : \n",
            "    (warmup_steps: 500\n",
            "    warmup_ratio: null\n",
            "    min_lr: 0.0\n",
            "    last_epoch: -1\n",
            "    max_steps: 114\n",
            "    )\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "  | Name              | Type                              | Params\n",
            "------------------------------------------------------------------------\n",
            "0 | preprocessor      | AudioToMelSpectrogramPreprocessor | 0     \n",
            "1 | encoder           | ConvASREncoder                    | 18.9 M\n",
            "2 | decoder           | ConvASRDecoder                    | 34.9 K\n",
            "3 | loss              | CTCLoss                           | 0     \n",
            "4 | spec_augmentation | SpectrogramAugmentation           | 0     \n",
            "5 | _wer              | WER                               | 0     \n",
            "------------------------------------------------------------------------\n",
            "18.9 M    Trainable params\n",
            "0         Non-trainable params\n",
            "18.9 M    Total params\n",
            "37.859    Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a2290d4e8cd4f73a2cb627ba39cde2e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validation sanity check: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 12:37:20 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 20 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",
            "      cpuset_checked))\n",
            "    \n",
            "[NeMo W 2021-11-30 12:37:21 patch_utils:50] torch.stft() signature has been updated for PyTorch 1.7+\n",
            "    Please update PyTorch to remain compatible with later versions of NeMo.\n",
            "[NeMo W 2021-11-30 12:37:22 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/jasper.py:280: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "      ) // self.conv.stride[0] + 1\n",
            "    \n",
            "[NeMo W 2021-11-30 12:37:23 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/data_loading.py:408: UserWarning: The number of training samples (38) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
            "      f\"The number of training samples ({self.num_training_batches}) is smaller than the logging interval\"\n",
            "    \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e4418e9f42ce4bcf817cbf39a01c879b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 12:39:34 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "      \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
            "    \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47326950474448db9a9b87f21955fa12",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb0b8ec8d3e54c34854e1201695c591b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "309f897963e841d09f904481d28d14b3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "791a95fe91864c65b0b4f4ba8217b622",
            "553beab772fe4fccb05814eddfb000fa",
            "5f0b4ae2ca81475ba83382d21f99ccd6",
            "057404950b324585b259c3de2726cea2",
            "265c8c5b7c7d40fbab0e71a08816de37",
            "035b66dd200f49068289dc903f6fcbde",
            "cecaa9e4ddd2498f98751bcdbaa1ecdb",
            "92b491a7c20f467c8280b67e7e9b06a7",
            "b345fa1c3a954c7ebf8d9b04b57646a0",
            "bc77f798a7b946469bbaf18633cfa657",
            "fe0cd375675c4f30bc91dfd1f2579fe1"
          ]
        },
        "id": "feaRFKFumYKF",
        "outputId": "4dc3eaff-2f15-4f3b-b398-c3958a990320"
      },
      "source": [
        "df = pd.read_json('prob.jsonl', lines = True)\n",
        "paths = list(df['audio_filepath'])\n",
        "hypo = asr_model.transcribe(paths2audio_files=paths)\n",
        "df['hypothesis'] = hypo"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "791a95fe91864c65b0b4f4ba8217b622",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Transcribing:   0%|          | 0/2564 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-29 19:01:59 patch_utils:50] torch.stft() signature has been updated for PyTorch 1.7+\n",
            "    Please update PyTorch to remain compatible with later versions of NeMo.\n",
            "[NeMo W 2021-11-29 19:01:59 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/jasper.py:280: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "      ) // self.conv.stride[0] + 1\n",
            "    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bq94LSyfgsoM",
        "outputId": "4ed96cff-203a-4615-c267-0648a943cadd"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "Mde32Ltt43IN",
        "outputId": "78af07f4-365b-4b75-9514-2ce8f17991fa"
      },
      "source": [
        "leve = []\n",
        "\n",
        "for i in range(len(list(df['text']))):\n",
        "  leve.append(distance( list(df['text'])[i], list(df['hypothesis'])[i] ))\n",
        "\n",
        "df['leve'] = leve\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>audio_filepath</th>\n",
              "      <th>text</th>\n",
              "      <th>duration</th>\n",
              "      <th>hypothesis</th>\n",
              "      <th>leve</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b99cc98e6ebdd2975af0050682a948f4</td>\n",
              "      <td>farfield/b99cc98e6ebdd2975af0050682a948f4.opus</td>\n",
              "      <td>фильм бамблби смотреть онлайн в хорошем качестве</td>\n",
              "      <td>3.206875</td>\n",
              "      <td>фильм бамблби смотреть онлайн в хорошем качестве</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ada90cee7a7c273bb6331dd3a3e972ea</td>\n",
              "      <td>farfield/ada90cee7a7c273bb6331dd3a3e972ea.opus</td>\n",
              "      <td>включи другую музыку</td>\n",
              "      <td>2.282625</td>\n",
              "      <td>включи другую музыку</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>96a3206b07a50ba90f85d11a3604b0c1</td>\n",
              "      <td>farfield/96a3206b07a50ba90f85d11a3604b0c1.opus</td>\n",
              "      <td>афина что такое инвестиционный пай</td>\n",
              "      <td>3.284000</td>\n",
              "      <td>афина что такое инвестиционный пай</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9f22684c432c13fcf6fc96cf16bf6cf7</td>\n",
              "      <td>farfield/9f22684c432c13fcf6fc96cf16bf6cf7.opus</td>\n",
              "      <td>сбер какое число будет в следующую среду</td>\n",
              "      <td>2.852062</td>\n",
              "      <td>сбер какое число будет в следующую среду</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70d05224987f66a014a4bb4fda0ace2e</td>\n",
              "      <td>farfield/70d05224987f66a014a4bb4fda0ace2e.opus</td>\n",
              "      <td>на каком основании вы считаете что вы обезьяна...</td>\n",
              "      <td>4.623375</td>\n",
              "      <td>на каком основании вы считаете что вы обезьяна...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10249</th>\n",
              "      <td>30cf1a197021534389ed971ce5164417</td>\n",
              "      <td>farfield/30cf1a197021534389ed971ce5164417.opus</td>\n",
              "      <td>афина когда сбербанк был основан</td>\n",
              "      <td>6.866812</td>\n",
              "      <td>афина когда сбербанк был основан</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10250</th>\n",
              "      <td>83fd94cd83001e30c8e3f8c3ffdf7f09</td>\n",
              "      <td>farfield/83fd94cd83001e30c8e3f8c3ffdf7f09.opus</td>\n",
              "      <td>джой когда я смогу забрать новую карту которую...</td>\n",
              "      <td>3.622250</td>\n",
              "      <td>джой когда я смогу забрать новую карту которую...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10251</th>\n",
              "      <td>dc2bfb03b0a1902fc0421067a4408c78</td>\n",
              "      <td>farfield/dc2bfb03b0a1902fc0421067a4408c78.opus</td>\n",
              "      <td>сколько занимает бон</td>\n",
              "      <td>2.138813</td>\n",
              "      <td>сколько занимает бонн</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10252</th>\n",
              "      <td>ba1cff3411a64594e37fdf8b00335543</td>\n",
              "      <td>farfield/ba1cff3411a64594e37fdf8b00335543.opus</td>\n",
              "      <td>джой в какой битве прославился британский флот</td>\n",
              "      <td>4.987875</td>\n",
              "      <td>джой в какой битве прославился британский флот</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10253</th>\n",
              "      <td>1f9d89fe42e25834c3767e0b8b00a5a7</td>\n",
              "      <td>farfield/1f9d89fe42e25834c3767e0b8b00a5a7.opus</td>\n",
              "      <td>афина откуда вы узнали</td>\n",
              "      <td>2.797562</td>\n",
              "      <td>афина откуда вы узнали</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10254 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     id  ... leve\n",
              "0      b99cc98e6ebdd2975af0050682a948f4  ...    0\n",
              "1      ada90cee7a7c273bb6331dd3a3e972ea  ...    0\n",
              "2      96a3206b07a50ba90f85d11a3604b0c1  ...    0\n",
              "3      9f22684c432c13fcf6fc96cf16bf6cf7  ...    0\n",
              "4      70d05224987f66a014a4bb4fda0ace2e  ...    0\n",
              "...                                 ...  ...  ...\n",
              "10249  30cf1a197021534389ed971ce5164417  ...    0\n",
              "10250  83fd94cd83001e30c8e3f8c3ffdf7f09  ...    0\n",
              "10251  dc2bfb03b0a1902fc0421067a4408c78  ...    1\n",
              "10252  ba1cff3411a64594e37fdf8b00335543  ...    0\n",
              "10253  1f9d89fe42e25834c3767e0b8b00a5a7  ...    0\n",
              "\n",
              "[10254 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Tfkz7yA6Y5R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "7c155dba-c5b8-4bcd-f7e4-84b770f532d2"
      },
      "source": [
        "df = df.sort_values(by='leve', ascending=False) \n",
        "#del df['Unnamed: 0'] <- возникало при чтении файла из сохраненного csv\n",
        "del df['hypothesis']\n",
        "del df['leve']\n",
        "df.to_json('train_manifest.jsonl', orient='records', lines=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>audio_filepath</th>\n",
              "      <th>text</th>\n",
              "      <th>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5070</th>\n",
              "      <td>02431856f67b9c4b0d56d39947cf4ef3</td>\n",
              "      <td>farfield/02431856f67b9c4b0d56d39947cf4ef3.opus</td>\n",
              "      <td>салют хочу посмотреть интересную веселую смешн...</td>\n",
              "      <td>5.059500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4527</th>\n",
              "      <td>3e03ebf8c56841662d47a9c088f84c3d</td>\n",
              "      <td>farfield/3e03ebf8c56841662d47a9c088f84c3d.opus</td>\n",
              "      <td>салют для шестнадцати семнадцатилетних детей ж...</td>\n",
              "      <td>6.027188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8833</th>\n",
              "      <td>0c116246523fdd4a7823bcc0fa29a7e8</td>\n",
              "      <td>farfield/0c116246523fdd4a7823bcc0fa29a7e8.opus</td>\n",
              "      <td>сбер переключить на канал телевидения пятый канал</td>\n",
              "      <td>4.554875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2552</th>\n",
              "      <td>059020210f4e155a2501ccc38ab7578f</td>\n",
              "      <td>farfield/059020210f4e155a2501ccc38ab7578f.opus</td>\n",
              "      <td>сбер какие новгородская земля ты знаешь</td>\n",
              "      <td>4.223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5553</th>\n",
              "      <td>14396ba930c88527a3302cad541a3c7b</td>\n",
              "      <td>farfield/14396ba930c88527a3302cad541a3c7b.opus</td>\n",
              "      <td>джой запиши меня на стрижку в ближайшей к рабо...</td>\n",
              "      <td>4.511500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7148</th>\n",
              "      <td>2b9f591da8d6f4c5a8087c4aafd60af2</td>\n",
              "      <td>farfield/2b9f591da8d6f4c5a8087c4aafd60af2.opus</td>\n",
              "      <td>сбер братан мне сказали что ты женщина это правда</td>\n",
              "      <td>4.373875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3445</th>\n",
              "      <td>f8c964f44adba934f245359f0d780e1f</td>\n",
              "      <td>farfield/f8c964f44adba934f245359f0d780e1f.opus</td>\n",
              "      <td>афина хочу записаться на маник</td>\n",
              "      <td>3.346687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9394</th>\n",
              "      <td>34bbe26093a949754581ae8f3fbf94f6</td>\n",
              "      <td>farfield/34bbe26093a949754581ae8f3fbf94f6.opus</td>\n",
              "      <td>сбер организации государственный секретарь сша</td>\n",
              "      <td>3.250437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>a8fcbfc2000bee72bd81627e1c7ffadb</td>\n",
              "      <td>farfield/a8fcbfc2000bee72bd81627e1c7ffadb.opus</td>\n",
              "      <td>сбер что нужно сделать чтобы быстрее забеременеть</td>\n",
              "      <td>4.801063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9972</th>\n",
              "      <td>4c8d7a983b03defa10899061180c8bc9</td>\n",
              "      <td>farfield/4c8d7a983b03defa10899061180c8bc9.opus</td>\n",
              "      <td>в чем суть эмиссия</td>\n",
              "      <td>2.323812</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>601 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    id  ...  duration\n",
              "5070  02431856f67b9c4b0d56d39947cf4ef3  ...  5.059500\n",
              "4527  3e03ebf8c56841662d47a9c088f84c3d  ...  6.027188\n",
              "8833  0c116246523fdd4a7823bcc0fa29a7e8  ...  4.554875\n",
              "2552  059020210f4e155a2501ccc38ab7578f  ...  4.223500\n",
              "5553  14396ba930c88527a3302cad541a3c7b  ...  4.511500\n",
              "...                                ...  ...       ...\n",
              "7148  2b9f591da8d6f4c5a8087c4aafd60af2  ...  4.373875\n",
              "3445  f8c964f44adba934f245359f0d780e1f  ...  3.346687\n",
              "9394  34bbe26093a949754581ae8f3fbf94f6  ...  3.250437\n",
              "916   a8fcbfc2000bee72bd81627e1c7ffadb  ...  4.801063\n",
              "9972  4c8d7a983b03defa10899061180c8bc9  ...  2.323812\n",
              "\n",
              "[601 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMM-LQikVcGF"
      },
      "source": [
        "df = df.drop(df[df.leve == 0].index)\n",
        "#удалить все те, которые без ошибок; сильных выбросов не было, поэтому верхнюю границу не ставила"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2y7FcO2QRWSH"
      },
      "source": [
        "df.to_csv('drive/MyDrive/farfield_dropped.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "MJ_JGF-mzT2U",
        "outputId": "57f8bc0d-38e6-4c59-a7d5-749dbe70a2fc"
      },
      "source": [
        "#уже загрузила с диска в /content\n",
        "df = pd.read_csv('farfield_dropped.csv')\n",
        "del df['Unnamed: 0']\n",
        "del df['hypothesis']\n",
        "del df['leve']\n",
        "df.to_json('train_manifest.jsonl', orient='records', lines=True)\n",
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>audio_filepath</th>\n",
              "      <th>text</th>\n",
              "      <th>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>02431856f67b9c4b0d56d39947cf4ef3</td>\n",
              "      <td>farfield/02431856f67b9c4b0d56d39947cf4ef3.opus</td>\n",
              "      <td>салют хочу посмотреть интересную веселую смешн...</td>\n",
              "      <td>5.059500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3e03ebf8c56841662d47a9c088f84c3d</td>\n",
              "      <td>farfield/3e03ebf8c56841662d47a9c088f84c3d.opus</td>\n",
              "      <td>салют для шестнадцати семнадцатилетних детей ж...</td>\n",
              "      <td>6.027188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0c116246523fdd4a7823bcc0fa29a7e8</td>\n",
              "      <td>farfield/0c116246523fdd4a7823bcc0fa29a7e8.opus</td>\n",
              "      <td>сбер переключить на канал телевидения пятый канал</td>\n",
              "      <td>4.554875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>059020210f4e155a2501ccc38ab7578f</td>\n",
              "      <td>farfield/059020210f4e155a2501ccc38ab7578f.opus</td>\n",
              "      <td>сбер какие новгородская земля ты знаешь</td>\n",
              "      <td>4.223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14396ba930c88527a3302cad541a3c7b</td>\n",
              "      <td>farfield/14396ba930c88527a3302cad541a3c7b.opus</td>\n",
              "      <td>джой запиши меня на стрижку в ближайшей к рабо...</td>\n",
              "      <td>4.511500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>7f861804e11e4270032896e5931d71d2</td>\n",
              "      <td>farfield/7f861804e11e4270032896e5931d71d2.opus</td>\n",
              "      <td>салют по чем нынче валюта в анапе по курсу цб</td>\n",
              "      <td>4.807563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>6e78786f511b2ee9a55636f801afdc0d</td>\n",
              "      <td>farfield/6e78786f511b2ee9a55636f801afdc0d.opus</td>\n",
              "      <td>найти банкомат в радиусе один километр</td>\n",
              "      <td>4.172313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>346fc79aa3becd3d6da6a261b55b3dd5</td>\n",
              "      <td>farfield/346fc79aa3becd3d6da6a261b55b3dd5.opus</td>\n",
              "      <td>афина малефисенту смотрел в высоком качестве о...</td>\n",
              "      <td>4.210750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>7ccd492d73298a3edf50bb8e669c4b9b</td>\n",
              "      <td>farfield/7ccd492d73298a3edf50bb8e669c4b9b.opus</td>\n",
              "      <td>джой найти фильм список шиндлера</td>\n",
              "      <td>4.233562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>4c8d7a983b03defa10899061180c8bc9</td>\n",
              "      <td>farfield/4c8d7a983b03defa10899061180c8bc9.opus</td>\n",
              "      <td>в чем суть эмиссия</td>\n",
              "      <td>2.323812</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>601 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   id  ...  duration\n",
              "0    02431856f67b9c4b0d56d39947cf4ef3  ...  5.059500\n",
              "1    3e03ebf8c56841662d47a9c088f84c3d  ...  6.027188\n",
              "2    0c116246523fdd4a7823bcc0fa29a7e8  ...  4.554875\n",
              "3    059020210f4e155a2501ccc38ab7578f  ...  4.223500\n",
              "4    14396ba930c88527a3302cad541a3c7b  ...  4.511500\n",
              "..                                ...  ...       ...\n",
              "596  7f861804e11e4270032896e5931d71d2  ...  4.807563\n",
              "597  6e78786f511b2ee9a55636f801afdc0d  ...  4.172313\n",
              "598  346fc79aa3becd3d6da6a261b55b3dd5  ...  4.210750\n",
              "599  7ccd492d73298a3edf50bb8e669c4b9b  ...  4.233562\n",
              "600  4c8d7a983b03defa10899061180c8bc9  ...  2.323812\n",
              "\n",
              "[601 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6xWGquqn3JN",
        "outputId": "7fcd880b-52e2-4bda-ed57-a994c70b0fe7"
      },
      "source": [
        "#загрузила из golos_opus в /content\n",
        "#заменила относительные пути на абсолютные с помощью notepad++\n",
        "#оставляем только farfield\n",
        "df = pd.read_json('100hours.jsonl', lines = True)\n",
        "paths = df['audio_filepath']\n",
        "for p in range(len(paths)):\n",
        "  if paths[p].startswith('crowd'):\n",
        "    paths[p] = 0\n",
        "df = df.drop(df[df.audio_filepath == 0].index)\n",
        "df.to_json('prob.jsonl', orient='records', lines=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Txmu2Iho8VLP"
      },
      "source": [
        "#кусок архива golos_opus\n",
        "\n",
        "with zipfile.ZipFile('/content/drive/MyDrive/farfield_opus/farfield.zip', 'r') as zip_ref:\n",
        "    zip_ref.extractall('')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "454BpXx10jIA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5b87666-9fdd-4266-e879-b9e96b532611"
      },
      "source": [
        "first_asr_model.cuda()\n",
        "\n",
        "# We will be computing Word Error Rate (WER) metric between our hypothesis and predictions.\n",
        "# WER is computed as numerator/denominator.\n",
        "# We'll gather all the test batches' numerators and denominators.\n",
        "wer_nums = []\n",
        "wer_denoms = []\n",
        "\n",
        "# Loop over all test batches.\n",
        "# Iterating over the model's `test_dataloader` will give us:\n",
        "# (audio_signal, audio_signal_length, transcript_tokens, transcript_length)\n",
        "# See the AudioToCharDataset for more details.\n",
        "for test_batch in first_asr_model.test_dataloader():\n",
        "        test_batch = [x.cuda() for x in test_batch]\n",
        "        targets = test_batch[2]\n",
        "        targets_lengths = test_batch[3]        \n",
        "        log_probs, encoded_len, greedy_predictions = first_asr_model(\n",
        "            input_signal=test_batch[0], input_signal_length=test_batch[1]\n",
        "        )\n",
        "        # Notice the model has a helper object to compute WER\n",
        "        first_asr_model._wer.update(greedy_predictions, targets, targets_lengths)\n",
        "        _, wer_num, wer_denom = first_asr_model._wer.compute()\n",
        "        first_asr_model._wer.reset()\n",
        "        wer_nums.append(wer_num.detach().cpu().numpy())\n",
        "        wer_denoms.append(wer_denom.detach().cpu().numpy())\n",
        "\n",
        "        # Release tensors from GPU memory\n",
        "        del test_batch, log_probs, targets, targets_lengths, encoded_len, greedy_predictions\n",
        "\n",
        "# We need to sum all numerators and denominators first. Then divide.\n",
        "print(f\"WER = {sum(wer_nums)/sum(wer_denoms)}\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 12:52:31 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/jasper.py:280: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "      ) // self.conv.stride[0] + 1\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WER = 1.0003579098067288\n"
          ]
        }
      ]
    }
  ]
}