asr-model.ipynb

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   "source": [
    "!pip install wget\n",
    "!apt-get -y install sox libsndfile1 ffmpeg\n",
    "!pip install unidecode\n",
    "BRANCH = 'r1.4.0'\n",
    "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n",
    "!mkdir configs\n",
    "!wget -P configs/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/asr/conf/config.yaml\n",
    "!pip install matplotlib==3.1.3\n",
    "!pip install torchtext==0.10.1\n",
    "!pip install torchaudio==0.9.1"
   ]
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   "source": [
    "data_dir = '/kaggle/working'"
   ]
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     "text": [
      "Dataset downloaded at: /kaggle/working/test.tar\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import wget\n",
    "\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)"
   ]
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   "source": [
    "import nemo\n",
    "import nemo.collections.asr as nemo_asr\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\"))"
   ]
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     "text": [
      "******\n",
      "test_manifest /kaggle/working/test/farfield/farfield.jsonl\n",
      "train_manifest /kaggle/working/test/crowd/crowd.jsonl\n"
     ]
    }
   ],
   "source": [
    "# --- Building Manifest Files --- #\n",
    "import json\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)"
   ]
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   "source": [
    "# --- Config Information ---#\n",
    "try:\n",
    "    from ruamel.yaml import YAML\n",
    "except ModuleNotFoundError:\n",
    "    from ruamel_yaml import YAML\n",
    "config_path = './configs/config.yaml'\n",
    "\n",
    "yaml = YAML(typ='safe')\n",
    "with open(config_path) as f:\n",
    "    params = yaml.load(f)"
   ]
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   "source": [
    "params[\"labels\"] = asr_model.decoder.vocabulary\n",
    "params[\"model\"][\"train_ds\"][\"labels\"] = asr_model.decoder.vocabulary\n",
    "params[\"model\"][\"validation_ds\"][\"labels\"] = asr_model.decoder.vocabulary\n",
    "params[\"model\"][\"decoder\"][\"vocabulary\"] = asr_model.decoder.vocabulary\n",
    "params[\"model\"][\"decoder\"][\"num_classes\"] = len(asr_model.decoder.vocabulary)"
   ]
  },
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   "source": [
    "params = asr_model._cfg\n",
    "params['train_ds']['parser'] = 'base'\n",
    "params['validation_ds']['parser'] = 'base'\n",
    "params['test_ds']['parser'] = 'base'\n",
    "params['train_ds']['manifest_filepath'] = train_manifest\n",
    "params['train_ds']['batch_size'] = 32\n",
    "params['validation_ds']['manifest_filepath'] = test_manifest\n",
    "params['validation_ds']['batch_size'] = 32\n",
    "params['test_ds']['manifest_filepath'] = test_manifest\n",
    "params['test_ds']['batch_size'] = 32"
   ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2021-11-30 20:00:01 modelPT:436] Trainer wasn't specified in model constructor. Make sure that you really wanted it.\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2021-11-30 20:00:01 modelPT:544] 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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2021-11-30 20:00:01 lr_scheduler:605] Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, cannot compute effective `max_steps` !\n",
      "    Scheduler will not be instantiated !\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(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",
       " None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copy\n",
    "from omegaconf import DictConfig\n",
    "\n",
    "new_opt = copy.deepcopy(params['optim'])\n",
    "new_opt['lr'] = 0.0001\n",
    "asr_model.setup_optimization(optim_config=DictConfig(new_opt))"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "[NeMo I 2021-11-30 20:00:02 collections:173] Dataset loaded with 9990 files totalling 11.17 hours\n",
      "[NeMo I 2021-11-30 20:00:02 collections:174] 4 files were filtered totalling 0.03 hours\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2021-11-30 20:00:02 nemo_logging:349] /opt/conda/lib/python3.7/site-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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2021-11-30 20:00:02 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
      "[NeMo I 2021-11-30 20:00:02 collections:174] 0 files were filtered totalling 0.00 hours\n",
      "[NeMo I 2021-11-30 20:00:02 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
      "[NeMo I 2021-11-30 20:00:02 collections:174] 0 files were filtered totalling 0.00 hours\n"
     ]
    }
   ],
   "source": [
    "asr_model.setup_training_data(train_data_config=params['train_ds'])\n",
    "asr_model.setup_test_data(test_data_config=params['test_ds'])\n",
    "asr_model.setup_validation_data(val_data_config=params['validation_ds'])"
   ]
  },
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     "name": "stderr",
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     "text": [
      "[NeMo W 2021-11-30 20:00:02 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 20:00:04 nemo_logging:349] /opt/conda/lib/python3.7/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed 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.\n",
      "    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'). (Triggered internally at  /usr/local/src/pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)\n",
      "      return torch.floor_divide(self, other)\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WER = 0.6125029825817228\n"
     ]
    }
   ],
   "source": [
    "asr_model.cuda()\n",
    "\n",
    "wer_nums = []\n",
    "wer_denoms = []\n",
    "\n",
    "for test_batch in 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 = asr_model(\n",
    "        input_signal=test_batch[0], input_signal_length=test_batch[1]\n",
    "    )\n",
    "    asr_model._wer.update(greedy_predictions, targets, targets_lengths)\n",
    "    _, wer_num, wer_denom = asr_model._wer.compute()\n",
    "    asr_model._wer.reset()\n",
    "    wer_nums.append(wer_num.detach().cpu().numpy())\n",
    "    wer_denoms.append(wer_denom.detach().cpu().numpy())\n",
    "\n",
    "    del test_batch, log_probs, targets, targets_lengths, encoded_len, greedy_predictions\n",
    "\n",
    "print(f\"WER = {sum(wer_nums)/sum(wer_denoms)}\")"
   ]
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      "[NeMo W 2021-11-30 20:00:21 nemo_logging:349] /opt/conda/lib/python3.7/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:446: UserWarning: Checkpoint directory /kaggle/working exists and is not empty.\n",
      "      rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "    \n",
      "[NeMo W 2021-11-30 20:00:21 modelPT:436] Trainer wasn't specified in model constructor. Make sure that you really wanted it.\n"
     ]
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     "text": [
      "[NeMo I 2021-11-30 20:00:21 modelPT:544] 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"
     ]
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     "text": [
      "[NeMo W 2021-11-30 20:00:21 lr_scheduler:605] Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, cannot compute effective `max_steps` !\n",
      "    Scheduler will not be instantiated !\n"
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      "      cpuset_checked))\n",
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   "source": [
    "import copy\n",
    "from omegaconf import DictConfig\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint\n",
    "\n",
    "\n",
    "trainer = pl.Trainer(gpus=1, max_epochs=5, val_check_interval=0.2,\n",
    "                     precision=16, callbacks=[ModelCheckpoint(dirpath=data_dir)])   \n",
    "trainer.fit(asr_model)"
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      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_loss': 6.480802059173584, 'test_wer': 0.1621331423521042}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
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