Wave2Vec.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ElUnrast/GermanWave2Vec/blob/main/Wave2Vec.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dEKhPSO82y85"
      },
      "source": [
        "## 0. Configuartion"
      ],
      "id": "dEKhPSO82y85"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "worthy-resolution"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "\n",
        "device = 'cuda'\n",
        "wait_for_v100 = False\n",
        "ds_to_us = []\n",
        "home = os.getenv(\"HOME\")\n",
        "use_gdrive = False\n",
        "trained_model_directory = 'trained_model'\n",
        "WANDB_API_KEY='6cec6a71175dec59b32a9067a0664dec76208f2a'\n",
        "WANDB_NAME='GermanWaveToVec Training'\n",
        "WANDB_ENTITY='wandb.ai/elunrast'\n",
        "WANDB_PROJECT='GermanWaveToVec'\n",
        "# Use Model from Checkpoint. \n",
        "# The Value must be a Sub-Directory of {trained_model_path} (Example: 'checkpoint-3600')\n",
        "checkpoint = None\n",
        "\n",
        "## ----- Training arguments  ----- ##\n",
        "per_device_train_batch_size = 5      # TODO: aus freiem Grafikkarten Speicher ableiten (Graphikkarte mit 11GB)\n",
        "per_device_eval_batch_size  = per_device_train_batch_size // 2\n",
        "gradient_accumulation_steps = 2\n",
        "logging_steps               = 100\n",
        "max_steps                   = per_device_train_batch_size * logging_steps\n",
        "max_trainingset_size        = max_steps * per_device_train_batch_size # TODO freiem Grafikkarten Speicher ableiten\n",
        "max_steps                   = max_trainingset_size // per_device_train_batch_size # Schritte pro Epoche\n",
        "warmup_steps                = max_steps // 4\n",
        "save_steps                  = max_steps // 2\n",
        "eval_steps                  = save_steps\n",
        "max_sample_size             = 1200   # None -> no restriction\n",
        "max_trainingset_size        = per_device_train_batch_size * max_steps   # None -> no restriction\n",
        "fixed_training_set_size     = 100 * per_device_eval_batch_size          # None -> 20% der Originaldatensatz Größe \n",
        "num_train_epochs            = 3  # Anzahl Durchläufe für ein Dataset\n",
        "max_rounds                  = 4  # Anzahl Trainingsrunden über alle used_datasets\n",
        "\n",
        "learning_rate = per_device_train_batch_size * ((3e-4)/16)  # 3e-4/4 "
      ],
      "id": "worthy-resolution",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "motivated-budapest"
      },
      "source": [
        "## 1. Setup Directories"
      ],
      "id": "motivated-budapest"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stuffed-silicon",
        "outputId": "022a280e-ada5-49f2-fe80-2bd02775ed95"
      },
      "source": [
        "local_nlp_base_dir = f'{home}/shared'\n",
        "local_data_base_dir = f'{local_nlp_base_dir}/NLP-Data'\n",
        "local_data_dir = f'{local_data_base_dir}/audio'\n",
        "extern_nlp_base_dir = None\n",
        "extern_data_base_dir = None\n",
        "\n",
        "runs_on_colab = (home == '/root')\n",
        "print( f'runs on colab: {runs_on_colab}')\n",
        "\n",
        "if not runs_on_colab:\n",
        "    os.environ['http_proxy'] = 'http://192.168.8.50:3128'\n",
        "    os.environ['https_proxy'] = 'http://192.168.8.50:3128'\n",
        "else:\n",
        "    # to get access to the datasets we use gdrive\n",
        "    use_gdrive = True\n",
        "    # install packages\n",
        "    !pip install datasets==1.4.1\n",
        "    !pip install transformers==4.4.0\n",
        "    !pip install jiwer\n",
        "    !pip install torchaudio\n",
        "    !pip install librosa\n",
        "    # create local directories\n",
        "    !mkdir $local_nlp_base_dir\n",
        "    !mkdir $local_data_base_dir\n",
        "\n",
        "if use_gdrive:\n",
        "    gdrive_base = '/content/gdrive'    \n",
        "    extern_nlp_base_dir = f'{gdrive_base}/MyDrive'\n",
        "    extern_data_base_dir = f'{extern_nlp_base_dir}/NLP-Data'\n",
        "    extern_data_dir = f'{extern_data_base_dir}/audio'\n",
        "\n",
        "    if not os.path.isdir(gdrive_base):\n",
        "        from google.colab import drive\n",
        "        drive.mount(gdrive_base)\n",
        "\n",
        "if not os.path.isdir(local_data_dir):\n",
        "    !mkdir $local_data_dir\n",
        "\n",
        "if extern_nlp_base_dir:\n",
        "    model_dir = f'{extern_nlp_base_dir}/NLP-Models/GermanWave2Vec'\n",
        "else:\n",
        "    model_dir = f'{local_nlp_base_dir}/NLP-Models/GermanWave2Vec'\n",
        "\n",
        "# Use the Model from this Directory (Base: .../NLP-Models/GermanWave2Vec/)\n",
        "# None -> Start from 'facebook/wav2vec2-large-xlsr-53-german'\n",
        "trained_model_path = f'{model_dir}/{trained_model_directory}'\n",
        "git_views_dir = f'{local_nlp_base_dir}/gitviews'\n",
        "\n",
        "if not os.path.isdir(git_views_dir):\n",
        "    !mkdir $git_views_dir\n",
        "    !cd $git_views_dir; git clone https://github.com/ElUnrast/GermanWave2Vec.git"
      ],
      "id": "stuffed-silicon",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "runs on colab: True\n",
            "Collecting datasets==1.4.1\n",
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            "\u001b[K     |████████████████████████████████| 194kB 16.3MB/s \n",
            "\u001b[?25hCollecting xxhash\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/4f/0a862cad26aa2ed7a7cd87178cbbfa824fc1383e472d63596a0d018374e7/xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 23.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets==1.4.1) (2.23.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets==1.4.1) (0.3.3)\n",
            "Collecting fsspec\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e9/91/2ef649137816850fa4f4c97c6f2eabb1a79bf0aa2c8ed198e387e373455e/fsspec-2021.4.0-py3-none-any.whl (108kB)\n",
            "\u001b[K     |████████████████████████████████| 112kB 25.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets==1.4.1) (1.19.5)\n",
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            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from datasets==1.4.1) (3.10.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets==1.4.1) (0.70.11.1)\n",
            "Collecting huggingface-hub==0.0.2\n",
            "  Downloading https://files.pythonhosted.org/packages/b5/93/7cb0755c62c36cdadc70c79a95681df685b52cbaf76c724facb6ecac3272/huggingface_hub-0.0.2-py3-none-any.whl\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets==1.4.1) (1.1.5)\n",
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            "Installing collected packages: xxhash, fsspec, huggingface-hub, datasets\n",
            "Successfully installed datasets-1.4.1 fsspec-2021.4.0 huggingface-hub-0.0.2 xxhash-2.0.2\n",
            "Collecting transformers==4.4.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/51/bb/52c052a01bfd62d4f8038a29c15612eb144b99c11d1f082a7adb5510c22b/transformers-4.4.0-py3-none-any.whl (2.1MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n",
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            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.45 tokenizers-0.10.2 transformers-4.4.0\n",
            "Collecting jiwer\n",
            "  Downloading https://files.pythonhosted.org/packages/8c/cc/fb9d3132cba1f6d393b7d5a9398d9d4c8fc033bc54668cf87e9b197a6d7a/jiwer-2.2.0-py3-none-any.whl\n",
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            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from python-Levenshtein->jiwer) (56.1.0)\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=149819 sha256=ee8fb6ee8bc6899d577dac00dc49258bca9f36c1d0e407d6fac436bb76df7e09\n",
            "  Stored in directory: /root/.cache/pip/wheels/b3/26/73/4b48503bac73f01cf18e52cd250947049a7f339e940c5df8fc\n",
            "Successfully built python-Levenshtein\n",
            "Installing collected packages: python-Levenshtein, jiwer\n",
            "Successfully installed jiwer-2.2.0 python-Levenshtein-0.12.2\n",
            "Collecting torchaudio\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/aa/55/01ad9244bcd595e39cea5ce30726a7fe02fd963d07daeb136bfe7e23f0a5/torchaudio-0.8.1-cp37-cp37m-manylinux1_x86_64.whl (1.9MB)\n",
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            "Installing collected packages: torchaudio\n",
            "Successfully installed torchaudio-0.8.1\n",
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            "Mounted at /content/gdrive\n",
            "Cloning into 'GermanWave2Vec'...\n",
            "remote: Enumerating objects: 43, done.\u001b[K\n",
            "remote: Counting objects: 100% (43/43), done.\u001b[K\n",
            "remote: Compressing objects: 100% (29/29), done.\u001b[K\n",
            "remote: Total 43 (delta 24), reused 28 (delta 13), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (43/43), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wLJpgjxOFRM",
        "outputId": "48670cda-fe17-40f7-d7ad-967f07b28354"
      },
      "source": [
        "if runs_on_colab:\n",
        "    git_view_path = f'{git_views_dir}/GermanWave2Vec'\n",
        "    !cd $git_view_path; git fetch --all; git reset --hard origin/main\n",
        "\n",
        "script_path = f'{git_views_dir}/GermanWave2Vec/python'\n",
        "sys.path.insert(0, script_path)"
      ],
      "id": "4wLJpgjxOFRM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching origin\n",
            "HEAD is now at 32f6bfb bugfix\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "better-footwear"
      },
      "source": [
        "## 2. Check Runtime Properties"
      ],
      "id": "better-footwear"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "accompanied-wright",
        "outputId": "a6984eb8-cb57-4fa0-8dae-f817850837ae"
      },
      "source": [
        "if 'cuda' == device:\n",
        "    gpu_info = !nvidia-smi\n",
        "    gpu_info = '\\n'.join(gpu_info)\n",
        "\n",
        "    if runs_on_colab:\n",
        "        if wait_for_v100 and not gpu_info.find('V100') >= 0:\n",
        "            print('The current GPU is not a V100')\n",
        "            print('Since you want to wait for a V100 the current session is aborted')\n",
        "            raise ValueError\n",
        "\n",
        "        if gpu_info.find('failed') >= 0:\n",
        "            print('For training, please use a VM with GPU!')\n",
        "            raise ValueError\n",
        "            \n",
        "        from psutil import virtual_memory\n",
        "        ram_gb = virtual_memory().total / 1e9\n",
        "        print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "        if ram_gb < 20:\n",
        "            print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n",
        "            print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n",
        "            print('re-execute this cell.')\n",
        "        else:\n",
        "            print('You are using a high-RAM runtime!')\n",
        "            \n",
        "    print(gpu_info)"
      ],
      "id": "accompanied-wright",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your runtime has 13.6 gigabytes of available RAM\n",
            "\n",
            "To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"\n",
            "menu, and then select High-RAM in the Runtime shape dropdown. Then, \n",
            "re-execute this cell.\n",
            "Wed May 12 09:37:47 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "connected-liver"
      },
      "source": [
        "## 3 Install packages and do Imports"
      ],
      "id": "connected-liver"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "copyrighted-peeing"
      },
      "source": [
        "from dataclasses import dataclass, field\n",
        "from typing import Any, Dict, List, Optional, Union\n",
        "\n",
        "import torch\n",
        "import torchaudio\n",
        "from torch import nn\n",
        "from torch.cuda.amp import autocast\n",
        "\n",
        "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from transformers.trainer_utils import get_last_checkpoint, is_main_process\n",
        "from transformers.trainer_pt_utils import LengthGroupedSampler, DistributedLengthGroupedSampler\n",
        "\n",
        "import json\n",
        "import collections\n",
        "import librosa\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import jiwer\n",
        "from jiwer import wer\n",
        "from datasets import load_metric\n",
        "from tqdm.notebook import tqdm_notebook\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from SnippetDatasets import SnippetDatasets\n",
        "from GermanSpeechToTextTranslater import GermanSpeechToTextTranslater"
      ],
      "id": "copyrighted-peeing",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "certified-singing"
      },
      "source": [
        "## 4 Initialize Helper Classes"
      ],
      "id": "certified-singing"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unexpected-ideal",
        "scrolled": true
      },
      "source": [
        "my_datasets = SnippetDatasets(\n",
        "    runs_on_colab, \n",
        "    local_audio_base_dir=local_data_dir, \n",
        "    extern_audio_base_dir=extern_data_dir\n",
        ")"
      ],
      "id": "unexpected-ideal",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151,
          "referenced_widgets": [
            "7b46e89a13764745915b7802aaedc5ee",
            "7d8e46c71b3e45cfa775157a66ac5a52",
            "78f2db221c504bfeb80d08123dde7808",
            "03c3d21fb26343a8b6fec43119e854ce",
            "449bce20528d4f0fa84a533edf31868f",
            "f6212268701e4b5485254734029c7eda",
            "da0a96e9b4194da4b5b242dc2d716bd9",
            "03ea4a943dfa478e9a1a87e251c05af6"
          ]
        },
        "id": "measured-craps",
        "scrolled": true,
        "outputId": "e7a3968e-7107-4634-eab1-9ec5a4a2e424"
      },
      "source": [
        "model_path = None\n",
        "\n",
        "if checkpoint:\n",
        "    model_path = f'{trained_model_path}/{checkpoint}'\n",
        "elif os.path.isfile(f'{trained_model_path}/pytorch_model.bin'):\n",
        "    model_path = trained_model_path\n",
        "\n",
        "print(f'Initialize Model from Path: {model_path}')\n",
        "translator = GermanSpeechToTextTranslater(model_name=model_path)"
      ],
      "id": "measured-craps",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initialize Model from Path: /content/gdrive/MyDrive/NLP-Models/GermanWave2Vec/trained_model\n",
            "Using Model: /content/gdrive/MyDrive/NLP-Models/GermanWave2Vec/trained_model\n",
            "Loading processor\n",
            "Loading metric\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b46e89a13764745915b7802aaedc5ee",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1764.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Loading model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "internal-rider",
        "scrolled": false
      },
      "source": [
        "def train():\n",
        "  training_args = TrainingArguments(\n",
        "      output_dir=trained_model_path,\n",
        "      group_by_length=True,\n",
        "      per_device_train_batch_size=per_device_train_batch_size,\n",
        "      per_device_eval_batch_size=per_device_eval_batch_size, \n",
        "      gradient_accumulation_steps=gradient_accumulation_steps, \n",
        "      evaluation_strategy=\"steps\",\n",
        "      max_steps=max_steps,         \n",
        "      fp16=True,\n",
        "      save_steps=save_steps,     \n",
        "      eval_steps=eval_steps,     \n",
        "      logging_steps=logging_steps, \n",
        "      learning_rate=learning_rate,\n",
        "      warmup_steps=warmup_steps,   \n",
        "      save_total_limit=2\n",
        "  )\n",
        "\n",
        "  ds_to_train = []\n",
        "\n",
        "  for ds in get_used_datasets():\n",
        "      ds_to_train.append(ds)\n",
        "      \n",
        "  calc_wer_after_training = False  # Wegen OutOfMemory ausgeschaltet \n",
        "\n",
        "  for runde in range(max_rounds):\n",
        "      print(f'Starting round {runde} of {max_rounds}')\n",
        "\n",
        "      for ds_id in ds_to_train:\n",
        "          if not my_datasets.has_translation_with_original(ds_id):\n",
        "              ds_to_train.remove(ds_id)\n",
        "              continue\n",
        "\n",
        "          WANDB_NOTES=ds_id\n",
        "          stop = False\n",
        "          pandas_df = my_datasets.load_ds_content_translated_with_original(ds_id)\n",
        "          print(f'Dataset - {ds_id} loaded with {pandas_df.shape[0]} Entries')\n",
        "          mp3_dir = my_datasets.get_snippet_directory(ds_id)\n",
        "          \n",
        "          for epoche in range(num_train_epochs):\n",
        "              print(f'Starting round {runde} of {max_rounds}, epoche {epoche} of {num_train_epochs}')\n",
        "              print(f'Splitting Dataset {ds_id} with {pandas_df.shape[0]} Entries')\n",
        "              train_pandas_ds, test_pandas_ds = translator.split_dataset(\n",
        "                  pandas_df, \n",
        "                  max_trainingset_size=max_trainingset_size, \n",
        "                  max_sample_size=max_sample_size\n",
        "              )\n",
        "              \n",
        "              if train_pandas_ds.shape[0] > pandas_df.shape[0] * 0.02:            \n",
        "                  print(f'Creating Trainer for {ds_id}')\n",
        "                  trainer = translator.get_trainer(\n",
        "                      training_args, \n",
        "                      mp3_dir,             \n",
        "                      train_pandas_ds,\n",
        "                      test_pandas_ds,\n",
        "                      use_grouped_legth_trainer=False\n",
        "                  )\n",
        "                  print(f'Training of Dataset: {ds_id}')\n",
        "                  train_result = trainer.train()\n",
        "\n",
        "                  print(f'Save Model')\n",
        "                  trainer.save_model()\n",
        "                  metrics = train_result.metrics\n",
        "                  max_train_samples = train_pandas_ds.shape[0]\n",
        "                  metrics[\"train_samples\"] = min(max_train_samples, train_pandas_ds.shape[0])\n",
        "                  trainer.log_metrics(\"train\", metrics)\n",
        "                  trainer.save_metrics(\"train\", metrics)\n",
        "                  trainer.save_state()\n",
        "                  torch.cuda.empty_cache()\n",
        "\n",
        "                  last_checkpoint = get_last_checkpoint(trained_model_path)\n",
        "                  print( f'last Checkpoint: {last_checkpoint}')\n",
        "                  \n",
        "                  # print(f'Reload from last Checkpoint: {last_checkpoint}')\n",
        "                  # translator.reload_from_checkpoint(last_checkpoint)\n",
        "                  # {last_checkpoint}-\n",
        "                  bad_translation_ds, truncated_ds, wer_result = translator.test(\n",
        "                      ds_id, \n",
        "                      pandas_df, \n",
        "                      mp3_dir, \n",
        "                      diff_file_extension=f'{epoche}-{runde}', \n",
        "                      diff_calc_wer=calc_wer_after_training\n",
        "                  )\n",
        "                  \n",
        "                  if (bad_translation_ds.shape[0] < (truncated_ds.shape[0] * 0.02)) or (wer_result < 0.02):\n",
        "                      # mindestens 98% der Sätze wurde korrekt übersetzt. Überprüfung der Problemfälle ist angebracht.\n",
        "                      # Es hat sich gezeigt, dass das Ergebnis wieder schlechter werden kann.\n",
        "                      stop = True\n",
        "              else:\n",
        "                  stop = True\n",
        "\n",
        "              if stop:\n",
        "                  print('Early stopping!')\n",
        "                  ds_to_train.remove(ds_id)\n",
        "                  break\n",
        "\n",
        "  print('Training finisched!')"
      ],
      "id": "internal-rider",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHvPPiWru_IC"
      },
      "source": [
        "## 4. Choose Dataset"
      ],
      "id": "XHvPPiWru_IC"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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            "47be42299dac4ec6bd5130f577a528c1",
            "e5a7b8f2fa304a709b57eab43887db56"
          ]
        },
        "id": "A2FODbnsFmoC",
        "outputId": "6fd31956-0033-42fd-b510-a37889843eb1"
      },
      "source": [
        "all_ds_ids = set()\n",
        "all_ds_ids.update(list(my_datasets.local_datasets.keys()))\n",
        "all_ds_ids.update(list(my_datasets.extern_datasets.keys()))\n",
        "\n",
        "import difflib\n",
        "from functools import partial\n",
        "\n",
        "import ipywidgets as widgets\n",
        "from IPython.display import display, Audio\n",
        "\n",
        "ds_checkbox_items = []\n",
        "\n",
        "for ds_id in sorted(all_ds_ids):\n",
        "    ds_checkbox_items.append(widgets.Checkbox(\n",
        "        value=ds_id in ds_to_us, \n",
        "        description=ds_id, \n",
        "        disabled=False, \n",
        "        indent=False\n",
        "    ))\n",
        "\n",
        "def get_used_datasets(checkbox_items=ds_checkbox_items):\n",
        "    result = []\n",
        "\n",
        "    for item in checkbox_items:\n",
        "        if item.value:\n",
        "            result.append(item.description)\n",
        "\n",
        "    return result\n",
        "\n",
        "print('Please choose Datasets to use')\n",
        "ds_checkboxes_widget = widgets.Box(ds_checkbox_items)\n",
        "display(ds_checkboxes_widget)"
      ],
      "id": "A2FODbnsFmoC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Please choose Datasets to use\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "29190a709eab414d8d97b3c028050dab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Box(children=(Checkbox(value=False, description='HP1-FvM', indent=False), Checkbox(value=False, description='H…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
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      "source": [
        "def on_train_action_button_clicked(b):\n",
        "    print('Train')\n",
        "    train()\n",
        "\n",
        "def on_translate_action_button_clicked(b):\n",
        "    print('Translate')\n",
        "    for ds_id in get_used_datasets():\n",
        "        translator.translate_and_extend_dataset_from_directory(my_datasets, ds_id)    \n",
        "\n",
        "class InvisibleAudio(Audio):\n",
        "    def _repr_html_(self):\n",
        "        audio = super()._repr_html_()\n",
        "        audio = audio.replace('<audio', f'<audio onended=\"this.parentNode.removeChild(this)\"')\n",
        "        return f'<div style=\"display:none\">{audio}</div>'\n",
        "\n",
        "def show_diff(text, n_text):\n",
        "    \"\"\"\n",
        "    http://stackoverflow.com/a/788780\n",
        "    Unify operations between two compared strings seqm is a difflib.\n",
        "    SequenceMatcher instance whose a & b are strings\n",
        "    \"\"\"\n",
        "    seqm = difflib.SequenceMatcher(None, text, n_text)\n",
        "    output= []\n",
        "    for opcode, a0, a1, b0, b1 in seqm.get_opcodes():\n",
        "        if opcode == 'equal':\n",
        "            output.append(seqm.a[a0:a1])\n",
        "        elif opcode == 'insert':\n",
        "            output.append(\"<font color=red>^\" + seqm.b[b0:b1] + \"</font>\")\n",
        "        elif opcode == 'delete':\n",
        "            output.append(\"<font color=blue>^\" + seqm.a[a0:a1] + \"</font>\")\n",
        "        elif opcode == 'replace':\n",
        "            # seqm.a[a0:a1] -> seqm.b[b0:b1]\n",
        "            output.append(\"<font color=green>^\" + seqm.b[b0:b1] + \"</font>\")\n",
        "        else:\n",
        "            raise RuntimeError(\"unexpected opcode\")\n",
        "    return ''.join(output)\n",
        "    \n",
        "def create_diff_row(audio_file, original_text, translated_text ):\n",
        "    diff_widget = widgets.HTML(value=show_diff(original_text, translated_text))\n",
        "    button_widget = widgets.Button(description='Play')\n",
        "\n",
        "    def on_play_button_clicked(f, b):\n",
        "        display(InvisibleAudio(url=audio_file, autoplay=True))\n",
        "\n",
        "    button_widget.on_click(partial(on_play_button_clicked, 1))\n",
        "    return widgets.HBox([button_widget, diff_widget])\n",
        "\n",
        "def create_diff_content(ds_id):\n",
        "    ds = my_datasets.load_ds_content_translated_with_original(ds_id)\n",
        "    snipped_directory = my_datasets.get_snippet_directory(ds_id)\n",
        "    print(f'Use Snipped Directory: {snipped_directory}')\n",
        "    translation_row = 'Translated1' if 'Translated1' in ds.columns else 'Translated0'\n",
        "    ds = ds[ds['OriginalText'] != ds[translation_row]]\n",
        "    rows = []\n",
        "    max_len = min(20, len(ds))\n",
        "\n",
        "    for idx in tqdm_notebook(range(max_len)):\n",
        "        rows.append(create_diff_row(\n",
        "            f'{snipped_directory}/{ds.iloc[idx][\"Datei\"]}', \n",
        "            ds.iloc[idx]['OriginalText'], \n",
        "            ds.iloc[idx][translation_row]\n",
        "        ))\n",
        "\n",
        "    return widgets.VBox(rows)\n",
        "\n",
        "def on_validate_action_button_clicked(b):\n",
        "    print('Validate')\n",
        "    tab = widgets.Tab()\n",
        "    tab_titles = []\n",
        "    tab_children = []\n",
        "\n",
        "    for ds_id in get_used_datasets():\n",
        "        tab_titles.append(ds_id)\n",
        "        tab_children.append(create_diff_content(ds_id))\n",
        "\n",
        "    tab.children = tab_children\n",
        "  \n",
        "    for idx, name in enumerate(tab_titles):\n",
        "        tab.set_title(idx, name)\n",
        "  \n",
        "    display(tab)\n",
        "\n",
        "\n",
        "train_action_button = widgets.Button(description='Train')\n",
        "train_action_button.on_click(on_train_action_button_clicked)\n",
        "translate_action_button = widgets.Button(description='Translate')\n",
        "translate_action_button.on_click(on_translate_action_button_clicked)\n",
        "validate_action_button = widgets.Button(description='Validate')\n",
        "validate_action_button.on_click(on_validate_action_button_clicked)\n",
        "\n",
        "action_buttons = widgets.HBox([\n",
        "    train_action_button, \n",
        "    translate_action_button, \n",
        "    validate_action_button\n",
        "])\n",
        "display(action_buttons)"
      ],
      "id": "yrLdZDzVthSj",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "141cf9a0ee314a71a50e557977e3d9e3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(Button(description='Train', style=ButtonStyle()), Button(description='Translate', style=ButtonS…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Validate\n",
            "Loading Dataset: HP1-RB - content-translated-with_original.csv\n",
            "Download and extract /content/gdrive/MyDrive/NLP-Data/audio/HP1-RB.zip from gdrive\n",
            "Pruning Dataset HP1-RB with 10555 Entries\n",
            " - 10555 Entries left after Length Cut (min=31, max=4000)\n",
            " - 5162 Entries left after Action Cut\n",
            "Dataset was truncated from 10555 to 5162 Entries. Saving Backup.\n",
            "Use Snipped Directory: /root/shared/NLP-Data/audio/HP1-RB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "78088053d4a643f8ba3ec5b4fd57d83f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=20.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b743b53734ed418a933c453a31a8aa68",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Tab(children=(VBox(children=(HBox(children=(Button(description='Play', style=ButtonStyle()), HTML(value='<font…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "organized-valve"
      },
      "source": [
        "## 5. Translate if nessecary only on local Notebook"
      ],
      "id": "organized-valve"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "indonesian-timeline",
        "scrolled": true
      },
      "source": [
        "# ds_id = 'HP1-FvM'\n",
        "# ds = my_datasets.load_ds_content_translated_with_original(ds_id)\n",
        "# ds = ds[ds['OriginalText'] != ds['Translated0']]"
      ],
      "id": "indonesian-timeline",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "persistent-irish"
      },
      "source": [
        "## 6. Train"
      ],
      "id": "persistent-irish"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuiQST1QU7Oi"
      },
      "source": [
        "# for i in range(len(ds)):\n",
        "#   print(ds.iloc[i]['Translated0'])"
      ],
      "id": "tuiQST1QU7Oi",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egyptian-departure"
      },
      "source": [
        "# translator.reload_from_checkpoint(last_checkpoint)\n",
        "# predictions, _ = translator.translate_dataset(mp3_dir, test_dataset)\n",
        "# labels = []\n",
        "\n",
        "# for datei in test_dataset.paths:\n",
        "#     labels.extend( pandas_df[pandas_df['Datei'] == datei]['OriginalText'].tolist())\n",
        "    \n",
        "# print(wer(labels, predictions))"
      ],
      "id": "egyptian-departure",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "compressed-fence"
      },
      "source": [
        "# labels = []\n",
        "\n",
        "# for datei in test_dataset.paths:\n",
        "#     labels.extend( pandas_df[pandas_df['Datei'] == datei]['OriginalText'].tolist())\n",
        "\n",
        "# print(wer(labels, predictions))"
      ],
      "id": "compressed-fence",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stainless-atmosphere"
      },
      "source": [
        "# orig_file = open(f'{model_dir}/orig-{use_datasets[0]}.txt', 'w')\n",
        "# cp = os.path.basename(last_checkpoint)\n",
        "# translated_file = open(f'{model_dir}/orig-{use_datasets[0]}-{cp}.txt', 'w')\n",
        "\n",
        "# for file_name, label, prediction in zip(test_dataset.paths, labels, predictions):\n",
        "#         orig_file.write(f'{file_name}, {label}\\n')    \n",
        "#         translated_file.write(f'{file_name}, {prediction}\\n')    \n",
        "\n",
        "# orig_file.close()\n",
        "# translated_file.close()"
      ],
      "id": "stainless-atmosphere",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diverse-testimony"
      },
      "source": [
        "# translator.my_processor.tokenizer"
      ],
      "id": "diverse-testimony",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fossil-pipeline"
      },
      "source": [
        "# translator.my_processor.tokenizer.get_vocab()"
      ],
      "id": "fossil-pipeline",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuzzy-relative"
      },
      "source": [
        "# big_df = pandas_df[pandas_df.Length > 1200]\n",
        "# print(big_df.shape[0])"
      ],
      "id": "fuzzy-relative",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "novel-former"
      },
      "source": [
        "# big_df"
      ],
      "id": "novel-former",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "invisible-telling"
      },
      "source": [
        "# small_df = pandas_df[pandas_df.OriginalText.str.len() < 10]"
      ],
      "id": "invisible-telling",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elementary-porter"
      },
      "source": [
        "# small_df"
      ],
      "id": "elementary-porter",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "expressed-diversity"
      },
      "source": [
        "#ds = my_datasets.load_ds_content_translated_with_original(use_datasets[0])\n",
        "#del ds['Translation0']\n",
        "#ds.to_csv(f'{my_datasets.get_snippet_directory(use_datasets[0])}/content-translated-with_original.csv', sep=';')"
      ],
      "id": "expressed-diversity",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exposed-license"
      },
      "source": [
        "# truncated_ds = pandas_df[\n",
        "#     ((pandas_df.Action == 'train') | (pandas_df.Action == 'translate')) & (pandas_df.Length < 1200)\n",
        "# ]\n",
        "# truncated_ds"
      ],
      "id": "exposed-license",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "super-official"
      },
      "source": [
        "# id_or_directory = use_datasets[0]\n",
        "# mp3_dir = my_datasets._get_directory(id_or_directory)\n",
        "# pandas_df = pd.read_csv(f'{mp3_dir}/content-translated-with_original.csv', sep=';')\n",
        "# pandas_df[~pandas_df.Action.str.startswith('exclude')]"
      ],
      "id": "super-official",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "compact-violence"
      },
      "source": [
        ""
      ],
      "id": "compact-violence",
      "execution_count": null,
      "outputs": []
    }
  ]
}