{"cells":[{"cell_type":"markdown","metadata":{"id":"RYGnI-EZp_nK"},"source":["# Getting Started: Sample Conversational AI application\n","This notebook shows how to use NVIDIA NeMo (https://github.com/NVIDIA/NeMo) to construct a toy demo which translate Mandarin audio file into English one.\n","\n","The demo demonstrates how to: \n","\n","* Instantiate pre-trained NeMo models from NVIDIA NGC.\n","* Transcribe audio with (Mandarin) speech recognition model.\n","* Translate text with machine translation model.\n","* Generate audio with text-to-speech models."]},{"cell_type":"markdown","metadata":{"id":"V72HXYuQ_p9a"},"source":["## Installation\n","NeMo can be installed via simple pip command.\n","This will take about 4 minutes.\n","\n","(The installation method below should work inside your new Conda environment or in an NVIDIA docker container.)"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"efDmTWf1_iYK","outputId":"002bd9aa-be20-480c-f8b4-aa21655582a0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667020892332,"user_tz":-330,"elapsed":187555,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting nemo_toolkit[all]\n","  Cloning https://github.com/NVIDIA/NeMo.git (to revision r1.12.0) to /tmp/pip-install-3axyo1cx/nemo-toolkit_dae21620f6af47c88b786d77c1dec33c\n","  Running command git clone -q https://github.com/NVIDIA/NeMo.git /tmp/pip-install-3axyo1cx/nemo-toolkit_dae21620f6af47c88b786d77c1dec33c\n","  Running command git checkout -b r1.12.0 --track origin/r1.12.0\n","  Switched to a new branch 'r1.12.0'\n","  Branch 'r1.12.0' set up to track remote branch 'r1.12.0' from 'origin'.\n","Collecting setuptools==59.5.0\n","  Downloading setuptools-59.5.0-py3-none-any.whl (952 kB)\n","\u001b[K     |████████████████████████████████| 952 kB 17.0 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.21 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.21.6)\n","Collecting onnx>=1.7.0\n","  Downloading onnx-1.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n","\u001b[K     |████████████████████████████████| 13.1 MB 57.2 MB/s \n","\u001b[?25hRequirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (2.8.2)\n","Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.12.1+cu113)\n","Requirement already satisfied: wrapt in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.14.1)\n","Collecting ruamel.yaml\n","  Downloading ruamel.yaml-0.17.21-py3-none-any.whl (109 kB)\n","\u001b[K     |████████████████████████████████| 109 kB 56.4 MB/s \n","\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.0.2)\n","Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (4.64.1)\n","Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.56.3)\n","Collecting wget\n","  Downloading wget-3.2.zip (10 kB)\n","Collecting frozendict\n","  Downloading frozendict-2.3.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (99 kB)\n","\u001b[K     |████████████████████████████████| 99 kB 10.1 MB/s \n","\u001b[?25hCollecting unidecode\n","  Downloading Unidecode-1.3.6-py3-none-any.whl (235 kB)\n","\u001b[K     |████████████████████████████████| 235 kB 64.0 MB/s \n","\u001b[?25hCollecting huggingface_hub\n","  Downloading huggingface_hub-0.10.1-py3-none-any.whl (163 kB)\n","\u001b[K     |████████████████████████████████| 163 kB 64.2 MB/s \n","\u001b[?25hCollecting black==19.10b0\n","  Downloading black-19.10b0-py36-none-any.whl (97 kB)\n","\u001b[K     |████████████████████████████████| 97 kB 7.7 MB/s \n","\u001b[?25hCollecting isort[requirements]<5\n","  Downloading isort-4.3.21-py2.py3-none-any.whl (42 kB)\n","\u001b[K     |████████████████████████████████| 42 kB 1.1 MB/s \n","\u001b[?25hCollecting parameterized\n","  Downloading parameterized-0.8.1-py2.py3-none-any.whl (26 kB)\n","Requirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.6.4)\n","Collecting pytest-runner\n","  Using cached pytest_runner-6.0.0-py3-none-any.whl (7.2 kB)\n","Requirement already satisfied: sphinx in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.8.6)\n","Collecting sphinxcontrib-bibtex\n","  Downloading sphinxcontrib_bibtex-2.5.0-py3-none-any.whl (39 kB)\n","Collecting wandb\n","  Downloading wandb-0.13.4-py2.py3-none-any.whl (1.9 MB)\n","\u001b[K     |████████████████████████████████| 1.9 MB 61.2 MB/s \n","\u001b[?25hRequirement already satisfied: inflect in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (2.1.0)\n","Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (2022.6.2)\n","Collecting pytorch-lightning<=1.7.6,>=1.7.0\n","  Downloading pytorch_lightning-1.7.6-py3-none-any.whl (707 kB)\n","\u001b[K     |████████████████████████████████| 707 kB 62.0 MB/s \n","\u001b[?25hCollecting torchmetrics>=0.4.1rc0\n","  Downloading torchmetrics-0.10.1-py3-none-any.whl (529 kB)\n","\u001b[K     |████████████████████████████████| 529 kB 64.2 MB/s \n","\u001b[?25hCollecting transformers<=4.21.2,>=4.0.1\n","  Downloading transformers-4.21.2-py3-none-any.whl (4.7 MB)\n","\u001b[K     |████████████████████████████████| 4.7 MB 62.4 MB/s \n","\u001b[?25hCollecting webdataset<=0.1.62,>=0.1.48\n","  Downloading webdataset-0.1.62-py3-none-any.whl (32 kB)\n","Collecting omegaconf<2.2,>=2.1.2\n","  Downloading omegaconf-2.1.2-py3-none-any.whl (74 kB)\n","\u001b[K     |████████████████████████████████| 74 kB 4.0 MB/s \n","\u001b[?25hCollecting hydra-core<1.2,>=1.1.0\n","  Downloading hydra_core-1.1.2-py3-none-any.whl (147 kB)\n","\u001b[K     |████████████████████████████████| 147 kB 70.9 MB/s \n","\u001b[?25hCollecting pyyaml<6\n","  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n","\u001b[K     |████████████████████████████████| 636 kB 62.1 MB/s \n","\u001b[?25hCollecting sentencepiece<1.0.0\n","  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n","\u001b[K     |████████████████████████████████| 1.3 MB 62.4 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 64.0 MB/s \n","\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.3.5)\n","Collecting sacremoses>=0.0.43\n","  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n","\u001b[K     |████████████████████████████████| 880 kB 68.0 MB/s \n","\u001b[?25hCollecting braceexpand\n","  Downloading braceexpand-0.1.7-py2.py3-none-any.whl (5.9 kB)\n","Requirement already satisfied: editdistance in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.5.3)\n","Collecting librosa>=0.9.0\n","  Downloading librosa-0.9.2-py3-none-any.whl (214 kB)\n","\u001b[K     |████████████████████████████████| 214 kB 59.3 MB/s \n","\u001b[?25hRequirement already satisfied: marshmallow in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.18.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (21.3)\n","Requirement already satisfied: soundfile in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.11.0)\n","Collecting sox\n","  Downloading sox-1.4.1-py2.py3-none-any.whl (39 kB)\n","Collecting kaldi-python-io\n","  Downloading kaldi-python-io-1.2.2.tar.gz (8.8 kB)\n","Collecting kaldiio\n","  Downloading kaldiio-2.17.2.tar.gz (24 kB)\n","Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.7.3)\n","Collecting g2p_en\n","  Downloading g2p_en-2.1.0-py3-none-any.whl (3.1 MB)\n","\u001b[K     |████████████████████████████████| 3.1 MB 52.4 MB/s \n","\u001b[?25hCollecting pydub\n","  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n","Collecting pyannote.core\n","  Downloading pyannote.core-4.5-py3-none-any.whl (60 kB)\n","\u001b[K     |████████████████████████████████| 60 kB 6.2 MB/s \n","\u001b[?25hCollecting pyannote.metrics\n","  Downloading pyannote.metrics-3.2.1-py3-none-any.whl (51 kB)\n","\u001b[K     |████████████████████████████████| 51 kB 193 kB/s \n","\u001b[?25hRequirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (7.7.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.2.2)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (7.1.2)\n","Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.13.1+cu113)\n","Collecting boto3\n","  Downloading boto3-1.25.4-py3-none-any.whl (132 kB)\n","\u001b[K     |████████████████████████████████| 132 kB 63.2 MB/s \n","\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.1.0)\n","Collecting matplotlib>=3.3.2\n","  Downloading matplotlib-3.5.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (11.2 MB)\n","\u001b[K     |████████████████████████████████| 11.2 MB 62.7 MB/s \n","\u001b[?25hCollecting rapidfuzz\n","  Downloading rapidfuzz-2.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n","\u001b[K     |████████████████████████████████| 2.2 MB 53.2 MB/s \n","\u001b[?25hRequirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (4.4.0)\n","Collecting sacrebleu[ja]\n","  Downloading sacrebleu-2.3.1-py3-none-any.whl (118 kB)\n","\u001b[K     |████████████████████████████████| 118 kB 67.1 MB/s \n","\u001b[?25hRequirement already satisfied: nltk>=3.6.5 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.7)\n","Collecting fasttext\n","  Downloading fasttext-0.9.2.tar.gz (68 kB)\n","\u001b[K     |████████████████████████████████| 68 kB 7.9 MB/s \n","\u001b[?25hCollecting opencc\n","  Downloading OpenCC-1.1.4-cp37-cp37m-manylinux1_x86_64.whl (769 kB)\n","\u001b[K     |████████████████████████████████| 769 kB 59.7 MB/s \n","\u001b[?25hCollecting pangu\n","  Downloading pangu-4.0.6.1-py3-none-any.whl (6.4 kB)\n","Requirement already satisfied: jieba in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.42.1)\n","Collecting ftfy\n","  Downloading ftfy-6.1.1-py3-none-any.whl (53 kB)\n","\u001b[K     |████████████████████████████████| 53 kB 2.1 MB/s \n","\u001b[?25hCollecting flask_restful\n","  Downloading Flask_RESTful-0.3.9-py2.py3-none-any.whl (25 kB)\n","Collecting einops\n","  Downloading einops-0.5.0-py3-none-any.whl (36 kB)\n","Collecting ijson\n","  Downloading ijson-3.1.4-cp37-cp37m-manylinux2010_x86_64.whl (126 kB)\n","\u001b[K     |████████████████████████████████| 126 kB 39.2 MB/s \n","\u001b[?25hCollecting faiss-cpu\n","  Downloading faiss_cpu-1.7.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.6 MB)\n","\u001b[K     |████████████████████████████████| 8.6 MB 61.1 MB/s \n","\u001b[?25hCollecting sentence_transformers\n","  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n","\u001b[K     |████████████████████████████████| 85 kB 5.5 MB/s \n","\u001b[?25hRequirement already satisfied: librosa in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (0.8.1)\n","Collecting phonemizer\n","  Downloading phonemizer-3.2.1-py3-none-any.whl (90 kB)\n","\u001b[K     |████████████████████████████████| 90 kB 10.0 MB/s \n","\u001b[?25hCollecting pypinyin\n","  Downloading pypinyin-0.47.1-py2.py3-none-any.whl (1.4 MB)\n","\u001b[K     |████████████████████████████████| 1.4 MB 55.3 MB/s \n","\u001b[?25hCollecting attrdict\n","  Downloading attrdict-2.0.1-py2.py3-none-any.whl (9.9 kB)\n","Collecting pystoi\n","  Downloading pystoi-0.3.3.tar.gz (7.0 kB)\n","Collecting pesq\n","  Downloading pesq-0.0.4.tar.gz (38 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: attrs>=18.1.0 in /usr/local/lib/python3.7/dist-packages (from black==19.10b0->nemo_toolkit[all]) (22.1.0)\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 pathspec<1,>=0.6\n","  Downloading pathspec-0.10.1-py3-none-any.whl (27 kB)\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","Collecting typed-ast>=1.4.0\n","  Downloading typed_ast-1.5.4-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 60.4 MB/s \n","\u001b[?25hCollecting antlr4-python3-runtime==4.8\n","  Downloading antlr4-python3-runtime-4.8.tar.gz (112 kB)\n","\u001b[K     |████████████████████████████████| 112 kB 70.4 MB/s \n","\u001b[?25hCollecting importlib-resources<5.3\n","  Downloading importlib_resources-5.2.3-py3-none-any.whl (27 kB)\n","Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources<5.3->hydra-core<1.2,>=1.1.0->nemo_toolkit[all]) (3.9.0)\n","Collecting pipreqs\n","  Downloading pipreqs-0.4.11-py2.py3-none-any.whl (32 kB)\n","Collecting pip-api\n","  Downloading pip_api-0.0.30-py3-none-any.whl (111 kB)\n","\u001b[K     |████████████████████████████████| 111 kB 62.5 MB/s \n","\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.7.0->nemo_toolkit[all]) (4.1.1)\n","Requirement already satisfied: protobuf<=3.20.1,>=3.12.2 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.7.0->nemo_toolkit[all]) (3.17.3)\n","Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf<=3.20.1,>=3.12.2->onnx>=1.7.0->nemo_toolkit[all]) (1.15.0)\n","Collecting pyDeprecate>=0.3.1\n","  Downloading pyDeprecate-0.3.2-py3-none-any.whl (10 kB)\n","Requirement already satisfied: fsspec[http]!=2021.06.0,>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (2022.10.0)\n","Requirement already satisfied: tensorboard>=2.9.1 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (2.9.1)\n","Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.7/dist-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (3.8.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (2.23.0)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.2.0)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.8.1)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.3.1)\n","Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (4.0.2)\n","Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (2.1.1)\n","Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (0.13.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (6.0.2)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->nemo_toolkit[all]) (3.0.9)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses>=0.0.43->nemo_toolkit[all]) (1.2.0)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (3.4.1)\n","Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (0.6.1)\n","Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.35.0)\n","Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.50.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.3.0)\n","Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.8.1)\n","Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.0.1)\n","Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (0.37.1)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (0.4.6)\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>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (4.9)\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>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (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>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (0.2.8)\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>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.3.1)\n","Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (4.13.0)\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>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (0.4.8)\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<=1.7.6,>=1.7.0->nemo_toolkit[all]) (2022.9.24)\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<=1.7.6,>=1.7.0->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<=1.7.6,>=1.7.0->nemo_toolkit[all]) (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<=1.7.6,>=1.7.0->nemo_toolkit[all]) (2.10)\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>=2.9.1->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (3.2.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<=4.21.2,>=4.0.1->nemo_toolkit[all]) (3.8.0)\n","Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n","  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n","\u001b[K     |████████████████████████████████| 6.6 MB 63.0 MB/s \n","\u001b[?25hCollecting botocore<1.29.0,>=1.28.4\n","  Downloading botocore-1.28.4-py3-none-any.whl (9.3 MB)\n","\u001b[K     |████████████████████████████████| 9.3 MB 55.3 MB/s \n","\u001b[?25hCollecting s3transfer<0.7.0,>=0.6.0\n","  Downloading s3transfer-0.6.0-py3-none-any.whl (79 kB)\n","\u001b[K     |████████████████████████████████| 79 kB 9.0 MB/s \n","\u001b[?25hCollecting jmespath<2.0.0,>=0.7.1\n","  Downloading jmespath-1.0.1-py3-none-any.whl (20 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 71.4 MB/s \n","\u001b[?25hCollecting pybind11>=2.2\n","  Using cached pybind11-2.10.0-py3-none-any.whl (213 kB)\n","Requirement already satisfied: Flask>=0.8 in /usr/local/lib/python3.7/dist-packages (from flask_restful->nemo_toolkit[all]) (1.1.4)\n","Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from flask_restful->nemo_toolkit[all]) (2022.5)\n","Collecting aniso8601>=0.82\n","  Downloading aniso8601-9.0.1-py2.py3-none-any.whl (52 kB)\n","\u001b[K     |████████████████████████████████| 52 kB 1.6 MB/s \n","\u001b[?25hRequirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask_restful->nemo_toolkit[all]) (2.11.3)\n","Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask_restful->nemo_toolkit[all]) (1.1.0)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask>=0.8->flask_restful->nemo_toolkit[all]) (2.0.1)\n","Requirement already satisfied: wcwidth>=0.2.5 in /usr/local/lib/python3.7/dist-packages (from ftfy->nemo_toolkit[all]) (0.2.5)\n","Collecting distance>=0.1.3\n","  Downloading Distance-0.1.3.tar.gz (180 kB)\n","\u001b[K     |████████████████████████████████| 180 kB 64.3 MB/s \n","\u001b[?25hRequirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from gdown->nemo_toolkit[all]) (4.6.3)\n","Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->nemo_toolkit[all]) (1.5.2)\n","Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (5.3.4)\n","Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (3.0.3)\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]) (7.9.0)\n","Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->nemo_toolkit[all]) (3.6.1)\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: tornado>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]) (5.1.1)\n","Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]) (6.1.12)\n","Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (2.0.10)\n","Collecting jedi>=0.10\n","  Downloading jedi-0.18.1-py2.py3-none-any.whl (1.6 MB)\n","\u001b[K     |████████████████████████████████| 1.6 MB 53.0 MB/s \n","\u001b[?25hRequirement 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: 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: backcall in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (0.2.0)\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: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from jedi>=0.10->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]) (0.8.3)\n","Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (5.5.0)\n","Requirement already satisfied: jupyter-core>=4.4.0 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (4.11.2)\n","Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (5.6.1)\n","Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (23.2.1)\n","Requirement already satisfied: nbformat in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (5.7.0)\n","Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (0.13.3)\n","Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (1.8.0)\n","Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (0.7.0)\n","Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa->nemo_toolkit[all]) (3.0.0)\n","Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa->nemo_toolkit[all]) (1.6.0)\n","Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa->nemo_toolkit[all]) (0.4.2)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba->nemo_toolkit[all]) (0.39.1)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->nemo_toolkit[all]) (3.1.0)\n","Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from soundfile->nemo_toolkit[all]) (1.15.1)\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]) (1.4.4)\n","Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (0.8.4)\n","Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->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~=3.6.0->ipywidgets->nemo_toolkit[all]) (1.5.0)\n","Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (5.0.1)\n","Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (0.4)\n","Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (0.6.0)\n","Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.7/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (2.16.2)\n","Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.7/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (4.3.3)\n","Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (0.18.1)\n","Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]) (0.5.1)\n","Collecting segments\n","  Downloading segments-2.2.1-py2.py3-none-any.whl (15 kB)\n","Collecting dlinfo\n","  Downloading dlinfo-1.2.1-py3-none-any.whl (3.6 kB)\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","Collecting yarg\n","  Downloading yarg-0.1.9-py2.py3-none-any.whl (19 kB)\n","Collecting docopt\n","  Downloading docopt-0.6.2.tar.gz (25 kB)\n","Requirement already satisfied: sortedcontainers>=2.0.4 in /usr/local/lib/python3.7/dist-packages (from pyannote.core->nemo_toolkit[all]) (2.4.0)\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 62.8 MB/s \n","\u001b[?25hCollecting pyannote.database>=4.0.1\n","  Downloading pyannote.database-4.1.3-py3-none-any.whl (41 kB)\n","\u001b[K     |████████████████████████████████| 41 kB 551 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","Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from pyannote.metrics->nemo_toolkit[all]) (0.8.10)\n","Requirement already satisfied: typer[all]>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[all]) (0.4.2)\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-1.5.0-py2.py3-none-any.whl (9.3 kB)\n","Collecting colorama<0.5.0,>=0.4.3\n","  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\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: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->nemo_toolkit[all]) (1.11.0)\n","Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->nemo_toolkit[all]) (1.4.1)\n","Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->nemo_toolkit[all]) (9.0.0)\n","Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning<=1.7.6,>=1.7.0->nemo_toolkit[all]) (1.7.1)\n","Collecting ruamel.yaml.clib>=0.2.6\n","  Downloading ruamel.yaml.clib-0.2.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (500 kB)\n","\u001b[K     |████████████████████████████████| 500 kB 61.2 MB/s \n","\u001b[?25hCollecting portalocker\n","  Downloading portalocker-2.6.0-py2.py3-none-any.whl (15 kB)\n","Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from sacrebleu[ja]->nemo_toolkit[all]) (4.9.1)\n","Collecting mecab-python3==1.0.5\n","  Downloading mecab_python3-1.0.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (574 kB)\n","\u001b[K     |████████████████████████████████| 574 kB 67.2 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 56.3 MB/s \n","\u001b[?25hCollecting clldutils>=1.7.3\n","  Downloading clldutils-3.12.0-py2.py3-none-any.whl (197 kB)\n","\u001b[K     |████████████████████████████████| 197 kB 58.7 MB/s \n","\u001b[?25hCollecting csvw>=1.5.6\n","  Downloading csvw-3.1.2-py2.py3-none-any.whl (56 kB)\n","\u001b[K     |████████████████████████████████| 56 kB 5.5 MB/s \n","\u001b[?25hCollecting colorlog\n","  Downloading colorlog-6.7.0-py2.py3-none-any.whl (11 kB)\n","Requirement already satisfied: babel in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer->nemo_toolkit[all]) (2.10.3)\n","Collecting isodate\n","  Downloading isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n","\u001b[K     |████████████████████████████████| 41 kB 711 kB/s \n","\u001b[?25hRequirement already satisfied: uritemplate>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer->nemo_toolkit[all]) (3.0.1)\n","Collecting language-tags\n","  Downloading language_tags-1.1.0-py2.py3-none-any.whl (210 kB)\n","\u001b[K     |████████████████████████████████| 210 kB 64.2 MB/s \n","\u001b[?25hCollecting rfc3986<2\n","  Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n","Collecting rdflib\n","  Downloading rdflib-6.2.0-py3-none-any.whl (500 kB)\n","\u001b[K     |████████████████████████████████| 500 kB 69.8 MB/s \n","\u001b[?25hRequirement 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: imagesize in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (1.4.1)\n","Requirement already satisfied: snowballstemmer>=1.1 in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (2.2.0)\n","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: sphinxcontrib-websupport in /usr/local/lib/python3.7/dist-packages (from sphinx->nemo_toolkit[all]) (1.2.4)\n","Collecting pybtex>=0.24\n","  Downloading pybtex-0.24.0-py2.py3-none-any.whl (561 kB)\n","\u001b[K     |████████████████████████████████| 561 kB 64.1 MB/s \n","\u001b[?25hCollecting sphinx\n","  Downloading sphinx-5.3.0-py3-none-any.whl (3.2 MB)\n","\u001b[K     |████████████████████████████████| 3.2 MB 63.4 MB/s \n","\u001b[?25hCollecting pybtex-docutils>=1.0.0\n","  Downloading pybtex_docutils-1.0.2-py3-none-any.whl (6.3 kB)\n","Collecting latexcodec>=1.0.4\n","  Downloading latexcodec-2.0.1-py2.py3-none-any.whl (18 kB)\n","Collecting sphinx\n","  Downloading sphinx-5.2.3-py3-none-any.whl (3.2 MB)\n","\u001b[K     |████████████████████████████████| 3.2 MB 47.3 MB/s \n","\u001b[?25h  Downloading sphinx-5.2.2-py3-none-any.whl (3.2 MB)\n","\u001b[K     |████████████████████████████████| 3.2 MB 60.7 MB/s \n","\u001b[?25h  Downloading sphinx-5.2.1-py3-none-any.whl (3.2 MB)\n","\u001b[K     |████████████████████████████████| 3.2 MB 30.1 MB/s \n","\u001b[?25h  Downloading sphinx-5.2.0.post0-py3-none-any.whl (3.2 MB)\n","\u001b[K     |████████████████████████████████| 3.2 MB 57.6 MB/s \n","\u001b[?25h  Downloading sphinx-5.2.0-py3-none-any.whl (3.2 MB)\n","\u001b[K     |████████████████████████████████| 3.2 MB 62.4 MB/s \n","\u001b[?25h  Downloading Sphinx-5.1.1-py3-none-any.whl (3.2 MB)\n","\u001b[K     |████████████████████████████████| 3.2 MB 56.0 MB/s \n","\u001b[?25hCollecting sphinxcontrib-jsmath\n","  Downloading sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl (5.1 kB)\n","Collecting sphinxcontrib-applehelp\n","  Downloading sphinxcontrib_applehelp-1.0.2-py2.py3-none-any.whl (121 kB)\n","\u001b[K     |████████████████████████████████| 121 kB 71.7 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-qthelp\n","  Downloading sphinxcontrib_qthelp-1.0.3-py2.py3-none-any.whl (90 kB)\n","\u001b[K     |████████████████████████████████| 90 kB 12.1 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 3.5 MB/s \n","\u001b[?25hCollecting sphinxcontrib-htmlhelp>=2.0.0\n","  Downloading sphinxcontrib_htmlhelp-2.0.0-py2.py3-none-any.whl (100 kB)\n","\u001b[K     |████████████████████████████████| 100 kB 10.8 MB/s \n","\u001b[?25hCollecting GitPython>=1.0.0\n","  Downloading GitPython-3.1.29-py3-none-any.whl (182 kB)\n","\u001b[K     |████████████████████████████████| 182 kB 62.1 MB/s \n","\u001b[?25hCollecting sentry-sdk>=1.0.0\n","  Downloading sentry_sdk-1.10.1-py2.py3-none-any.whl (166 kB)\n","\u001b[K     |████████████████████████████████| 166 kB 64.4 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","Collecting setproctitle\n","  Downloading setproctitle-1.3.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n","Collecting pathtools\n","  Downloading pathtools-0.1.2.tar.gz (11 kB)\n","Collecting shortuuid>=0.5.0\n","  Downloading shortuuid-1.0.9-py3-none-any.whl (9.4 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","Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb->nemo_toolkit[all]) (5.4.8)\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","Collecting sentry-sdk>=1.0.0\n","  Downloading sentry_sdk-1.10.0-py2.py3-none-any.whl (166 kB)\n","\u001b[K     |████████████████████████████████| 166 kB 61.9 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.10-py2.py3-none-any.whl (162 kB)\n","\u001b[K     |████████████████████████████████| 162 kB 65.8 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.9-py2.py3-none-any.whl (162 kB)\n","\u001b[K     |████████████████████████████████| 162 kB 59.4 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.8-py2.py3-none-any.whl (158 kB)\n","\u001b[K     |████████████████████████████████| 158 kB 63.3 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.7-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 67.7 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.6-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 58.1 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.5-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 64.3 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.4-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 66.9 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.3-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 63.7 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.2-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 61.4 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.1-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 66.2 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.0-py2.py3-none-any.whl (156 kB)\n","\u001b[K     |████████████████████████████████| 156 kB 62.3 MB/s \n","\u001b[?25hBuilding wheels for collected packages: nemo-toolkit, antlr4-python3-runtime, sacremoses, fasttext, distance, kaldi-python-io, kaldiio, pesq, docopt, pystoi, ipadic, sentence-transformers, pathtools, wget\n","  Building wheel for nemo-toolkit (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for nemo-toolkit: filename=nemo_toolkit-1.12.0-py3-none-any.whl size=4007487 sha256=fb0f5b57374c6d839869e62b69b93e9c54fba39efe22dbdf7f182a1955c79322\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-bx4rrm5v/wheels/04/d3/ae/cbc4d4426ee484acd0fdd6d1478be2f507520a76aa393b5e97\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=152a2f9f77ca87b71f8c0cf6a81dd6b385514fbe72919aebeb514e0fd42d632e\n","  Stored in directory: /root/.cache/pip/wheels/ca/33/b7/336836125fc9bb4ceaa4376d8abca10ca8bc84ddc824baea6c\n","  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=3fc31540db0f5c76baeb09f7a54d3c7963f03a6951c6bbdb39a3aad8e50e4256\n","  Stored in directory: /root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\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=3162792 sha256=eeabe7bc5912505a78fbc8aee6cef99428a0e1d70c3d6a9574d27f3a54a13a5f\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=16276 sha256=6c88dbe8681127ff364c14484f7784b1a82840576c72f4a8e8b7ab1a05834e83\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=8969 sha256=9c3100eb178349df33143b4fc8129113e997e1def0e99d396c886827483f6925\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=24471 sha256=d83f389257455e3eb68943c521577885dca6ea1bbcaa7af2017265b405050ae8\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-0.0.4-cp37-cp37m-linux_x86_64.whl size=214577 sha256=cc83e601cc0601f1f80ea32bee6c106a6db1184e332a30f086cbc0f941b09acc\n","  Stored in directory: /root/.cache/pip/wheels/c5/3d/9c/542731f8357f7c82eb6ac2047cc5375f92c9a05b09a715aff6\n","  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13723 sha256=c97cc3b294e66ee91b63fdd880028f0629de74dab3763c64f6f6edd028fd216c\n","  Stored in directory: /root/.cache/pip/wheels/72/b0/3f/1d95f96ff986c7dfffe46ce2be4062f38ebd04b506c77c81b9\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=d40dec8a5c2a00ff154d5feecee04a82754c498909f13657cf150823ca37a2bc\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=63aef746e71c81382e555858e611c1fea11165d2108ab0d26ef2274cf4256857\n","  Stored in directory: /root/.cache/pip/wheels/33/8b/99/cf0d27191876637cd3639a560f93aa982d7855ce826c94348b\n","  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=d05ac312c6da821daa2804fe0eaf28ed57052975d2e274c97b3e6432c44aee70\n","  Stored in directory: /root/.cache/pip/wheels/bf/06/fb/d59c1e5bd1dac7f6cf61ec0036cc3a10ab8fecaa6b2c3d3ee9\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=8806 sha256=9847f55773157a0086a7eb81a65d953d84ce4ffd722fbe2a16badcbab8e01865\n","  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\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=9675 sha256=19492ecf82cb13a522086afda2bbbe3998d2f32cc89dd17d9a8ad7d3b1572020\n","  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n","Successfully built nemo-toolkit antlr4-python3-runtime sacremoses fasttext distance kaldi-python-io kaldiio pesq docopt pystoi ipadic sentence-transformers pathtools wget\n","Installing collected packages: importlib-resources, urllib3, setuptools, jedi, isodate, rfc3986, rdflib, language-tags, colorama, smmap, simplejson, shellingham, pyyaml, latexcodec, jmespath, csvw, colorlog, yarg, tokenizers, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, ruamel.yaml.clib, pybtex, pyannote.core, portalocker, huggingface-hub, gitdb, docopt, clldutils, botocore, antlr4-python3-runtime, wget, unidecode, typed-ast, transformers, torchmetrics, sphinx, shortuuid, setproctitle, sentry-sdk, sentencepiece, segments, sacrebleu, s3transfer, ruamel.yaml, pyDeprecate, pybtex-docutils, pybind11, pyannote.database, pipreqs, pip-api, pathtools, pathspec, onnx, omegaconf, mecab-python3, isort, ipadic, GitPython, frozendict, docker-pycreds, dlinfo, distance, braceexpand, aniso8601, youtokentome, webdataset, wandb, sphinxcontrib-bibtex, sox, sentence-transformers, sacremoses, rapidfuzz, pytorch-lightning, pytest-runner, pystoi, pypinyin, pydub, pyannote.metrics, phonemizer, pesq, parameterized, pangu, opencc, nemo-toolkit, kaldiio, kaldi-python-io, ijson, hydra-core, g2p-en, ftfy, flask-restful, fasttext, faiss-cpu, einops, boto3, black, attrdict\n","  Attempting uninstall: importlib-resources\n","    Found existing installation: importlib-resources 5.10.0\n","    Uninstalling importlib-resources-5.10.0:\n","      Successfully uninstalled importlib-resources-5.10.0\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: setuptools\n","    Found existing installation: setuptools 57.4.0\n","    Uninstalling setuptools-57.4.0:\n","      Successfully uninstalled setuptools-57.4.0\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","Successfully installed GitPython-3.1.29 aniso8601-9.0.1 antlr4-python3-runtime-4.8 attrdict-2.0.1 black-19.10b0 boto3-1.25.4 botocore-1.28.4 braceexpand-0.1.7 clldutils-3.12.0 colorama-0.4.6 colorlog-6.7.0 csvw-3.1.2 distance-0.1.3 dlinfo-1.2.1 docker-pycreds-0.4.0 docopt-0.6.2 einops-0.5.0 faiss-cpu-1.7.2 fasttext-0.9.2 flask-restful-0.3.9 frozendict-2.3.4 ftfy-6.1.1 g2p-en-2.1.0 gitdb-4.0.9 huggingface-hub-0.10.1 hydra-core-1.1.2 ijson-3.1.4 importlib-resources-5.2.3 ipadic-1.0.0 isodate-0.6.1 isort-4.3.21 jedi-0.18.1 jmespath-1.0.1 kaldi-python-io-1.2.2 kaldiio-2.17.2 language-tags-1.1.0 latexcodec-2.0.1 mecab-python3-1.0.5 nemo-toolkit-1.12.0 omegaconf-2.1.2 onnx-1.12.0 opencc-1.1.4 pangu-4.0.6.1 parameterized-0.8.1 pathspec-0.10.1 pathtools-0.1.2 pesq-0.0.4 phonemizer-3.2.1 pip-api-0.0.30 pipreqs-0.4.11 portalocker-2.6.0 pyDeprecate-0.3.2 pyannote.core-4.5 pyannote.database-4.1.3 pyannote.metrics-3.2.1 pybind11-2.10.0 pybtex-0.24.0 pybtex-docutils-1.0.2 pydub-0.25.1 pypinyin-0.47.1 pystoi-0.3.3 pytest-runner-6.0.0 pytorch-lightning-1.7.6 pyyaml-5.4.1 rapidfuzz-2.12.0 rdflib-6.2.0 rfc3986-1.5.0 ruamel.yaml-0.17.21 ruamel.yaml.clib-0.2.7 s3transfer-0.6.0 sacrebleu-2.3.1 sacremoses-0.0.53 segments-2.2.1 sentence-transformers-2.2.2 sentencepiece-0.1.97 sentry-sdk-1.9.0 setproctitle-1.3.2 setuptools-59.5.0 shellingham-1.5.0 shortuuid-1.0.9 simplejson-3.17.6 smmap-5.0.0 sox-1.4.1 sphinx-5.1.1 sphinxcontrib-applehelp-1.0.2 sphinxcontrib-bibtex-2.5.0 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.0 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 tokenizers-0.12.1 torchmetrics-0.10.1 transformers-4.21.2 typed-ast-1.5.4 unidecode-1.3.6 urllib3-1.25.11 wandb-0.13.4 webdataset-0.1.62 wget-3.2 yarg-0.1.9 youtokentome-1.0.6\n","--2022-10-29 05:21:13--  https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 130 [text/plain]\n","Saving to: ‘install_pynini.sh’\n","\n","install_pynini.sh   100%[===================>]     130  --.-KB/s    in 0s      \n","\n","2022-10-29 05:21:13 (4.95 MB/s) - ‘install_pynini.sh’ saved [130/130]\n","\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting pynini==2.1.5\n","  Downloading pynini-2.1.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (161.1 MB)\n","\u001b[K     |████████████████████████████████| 161.1 MB 3.3 kB/s \n","\u001b[?25hRequirement already satisfied: Cython>=0.29 in /usr/local/lib/python3.7/dist-packages (from pynini==2.1.5) (0.29.32)\n","Installing collected packages: pynini\n","Successfully installed pynini-2.1.5\n"]}],"source":["BRANCH = 'r1.12.0'\n","!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n","\n","# install Pynini for text normalization\n","! wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n","! bash install_pynini.sh"]},{"cell_type":"markdown","metadata":{"id":"EyJ5HiiPrPKA"},"source":["## Import all necessary packages"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"tdUqxeUEA8nw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667020947902,"user_tz":-330,"elapsed":10999,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}},"outputId":"8af79704-473b-437d-ccc2-6711502e18e7"},"outputs":[{"output_type":"stream","name":"stderr","text":["[NeMo W 2022-10-29 05:22:22 optimizers:55] Apex was not found. Using the lamb or fused_adam optimizer will error out.\n","[NeMo W 2022-10-29 05:22:27 experimental:28] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n","[NeMo W 2022-10-29 05:22:28 experimental:28] Module <class 'nemo.collections.nlp.models.text_normalization_as_tagging.thutmose_tagger.ThutmoseTaggerModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n","[NeMo W 2022-10-29 05:22:28 experimental:28] Module <class 'nemo_text_processing.g2p.modules.IPAG2P'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n","[NeMo W 2022-10-29 05:22:29 experimental:28] Module <class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n","[NeMo W 2022-10-29 05:22:29 experimental:28] Module <class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.PhonemizerTokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n","[NeMo W 2022-10-29 05:22:29 experimental:28] Module <class 'nemo.collections.tts.models.radtts.RadTTSModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n"]}],"source":["# Import NeMo and it's ASR, NLP and TTS collections\n","import nemo\n","# Import Speech Recognition collection\n","import nemo.collections.asr as nemo_asr\n","# Import Natural Language Processing colleciton\n","import nemo.collections.nlp as nemo_nlp\n","# Import Speech Synthesis collection\n","import nemo.collections.tts as nemo_tts\n","# We'll use this to listen to audio\n","import IPython"]},{"cell_type":"markdown","metadata":{"id":"bt2EZyU3A1aq"},"source":["## Instantiate pre-trained NeMo models\n","\n","Every NeMo model has these methods:\n","\n","* ``list_available_models()`` - it will list all models currently available on NGC and their names.\n","\n","* ``from_pretrained(...)`` API downloads and initialized model directly from the NGC using model name.\n"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"YNNHs5Xjr8ox","scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667021015015,"user_tz":-330,"elapsed":496,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}},"outputId":"9434c759-1fbb-443d-bffc-dc3f30d2581b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[PretrainedModelInfo(\n"," \tpretrained_model_name=QuartzNet15x5Base-En,\n"," \tdescription=QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. Please visit https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels for further details.,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_en_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_quartznet15x5/versions/1.0.0rc1/files/stt_en_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_en_jasper10x5dr,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_jasper10x5dr,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_jasper10x5dr/versions/1.0.0rc1/files/stt_en_jasper10x5dr.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_ca_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_quartznet15x5/versions/1.0.0rc1/files/stt_ca_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_it_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_quartznet15x5/versions/1.0.0rc1/files/stt_it_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_fr_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_quartznet15x5/versions/1.0.0rc1/files/stt_fr_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_es_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_quartznet15x5/versions/1.0.0rc1/files/stt_es_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_de_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_quartznet15x5/versions/1.0.0rc1/files/stt_de_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_pl_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_pl_quartznet15x5/versions/1.0.0rc1/files/stt_pl_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_ru_quartznet15x5,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_quartznet15x5,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_quartznet15x5/versions/1.0.0rc1/files/stt_ru_quartznet15x5.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_zh_citrinet_512,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_512,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_512/versions/1.0.0rc1/files/stt_zh_citrinet_512.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_zh_citrinet_1024_gamma_0_25,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=stt_zh_citrinet_1024_gamma_0_25,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo\n"," ), PretrainedModelInfo(\n"," \tpretrained_model_name=asr_talknet_aligner,\n"," \tdescription=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:asr_talknet_aligner,\n"," \tlocation=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/asr_talknet_aligner/versions/1.0.0rc1/files/qn5x5_libri_tts_phonemes.nemo\n"," )]"]},"metadata":{},"execution_count":3}],"source":["# Here is an example of all CTC-based models:\n","nemo_asr.models.EncDecCTCModel.list_available_models()\n","# More ASR Models are available - see: nemo_asr.models.ASRModel.list_available_models()"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"1h9nhICjA5Dk","scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667021388588,"user_tz":-330,"elapsed":204198,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}},"outputId":"a5a2babc-9eb5-4b57-890d-aa6c250e5963"},"outputs":[{"output_type":"stream","name":"stdout","text":["[NeMo I 2022-10-29 05:26:26 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo to /root/.cache/torch/NeMo/NeMo_1.12.0/stt_zh_citrinet_1024_gamma_0_25/e4a8b1119971335507d9672e03bc80f4/stt_zh_citrinet_1024_gamma_0_25.nemo\n","[NeMo I 2022-10-29 05:26:59 common:910] Instantiating model from pre-trained checkpoint\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","    - 佯\n","    - 佰\n","    - 佳\n","    - 佶\n","    - 佻\n","    - 佼\n","    - 使\n","    - 侃\n","    - 侄\n","    - 侈\n","    - 例\n","    - 侍\n","    - 侏\n","    - 侑\n","    - 侗\n","    - 供\n","    - 依\n","    - 侠\n","    - 侣\n","    - 侥\n","    - 侦\n","    - 侧\n","    - 侨\n","    - 侬\n","    - 侮\n","    - 侯\n","    - 侵\n","    - 便\n","    - 促\n","    - 俄\n","    - 俊\n","    - 俎\n","    - 俏\n","    - 俐\n","    - 俑\n","    - 俗\n","    - 俘\n","    - 俚\n","    - 保\n","    - 俞\n","    - 俟\n","    - 信\n","    - 俨\n","    - 俩\n","    - 俪\n","    - 俭\n","    - 修\n","    - 俯\n","    - 俱\n","    - 俸\n","    - 俺\n","    - 俾\n","    - 倌\n","    - 倍\n","    - 倒\n","    - 倔\n","    - 倘\n","    - 候\n","    - 倚\n","    - 倜\n","    - 借\n","    - 倡\n","    - 倦\n","    - 倩\n","    - 倪\n","    - 倭\n","    - 债\n","    - 值\n","    - 倾\n","    - 偃\n","    - 假\n","    - 偈\n","    - 偌\n","    - 偎\n","    - 偏\n","    - 偓\n","    - 偕\n","    - 做\n","    - 停\n","    - 健\n","    - 偶\n","    - 偷\n","    - 偻\n","    - 偿\n","    - 傀\n","    - 傅\n","    - 傍\n","    - 傣\n","    - 傥\n","    - 储\n","    - 催\n","    - 傲\n","    - 傻\n","    - 像\n","    - 僚\n","    - 僧\n","    - 僮\n","    - 僵\n","    - 僻\n","    - 儋\n","    - 儒\n","    - 儡\n","    - 儿\n","    - 兀\n","    - 允\n","    - 元\n","    - 兄\n","    - 充\n","    - 兆\n","    - 先\n","    - 光\n","    - 克\n","    - 免\n","    - 兑\n","    - 兔\n","    - 兖\n","    - 党\n","    - 兜\n","    - 兢\n","    - 入\n","    - 全\n","    - 八\n","    - 公\n","    - 六\n","    - 兮\n","    - 兰\n","    - 共\n","    - 关\n","    - 兴\n","    - 兵\n","    - 其\n","    - 具\n","    - 典\n","    - 兹\n","    - 养\n","    - 兼\n","    - 兽\n","    - 冀\n","    - 内\n","    - 冈\n","    - 冉\n","    - 册\n","    - 再\n","    - 冒\n","    - 冕\n","    - 冗\n","    - 写\n","    - 军\n","    - 农\n","    - 冠\n","    - 冢\n","    - 冤\n","    - 冥\n","    - 冬\n","    - 冯\n","    - 冰\n","    - 冲\n","    - 决\n","    - 况\n","    - 冶\n","    - 冷\n","    - 冻\n","    - 冼\n","    - 冽\n","    - 净\n","    - 凄\n","    - 准\n","    - 凇\n","    - 凉\n","    - 凋\n","    - 凌\n","    - 减\n","    - 凑\n","    - 凛\n","    - 凝\n","    - 几\n","    - 凡\n","    - 凤\n","    - 凭\n","    - 凯\n","    - 凰\n","    - 凳\n","    - 凶\n","    - 凸\n","    - 凹\n","    - 出\n","    - 击\n","    - 函\n","    - 凿\n","    - 刀\n","    - 刁\n","    - 刃\n","    - 分\n","    - 切\n","    - 刊\n","    - 刍\n","    - 刎\n","    - 刑\n","    - 划\n","    - 列\n","    - 刘\n","    - 则\n","    - 刚\n","    - 创\n","    - 初\n","    - 删\n","    - 判\n","    - 刨\n","    - 利\n","    - 别\n","    - 刮\n","    - 到\n","    - 制\n","    - 刷\n","    - 券\n","    - 刹\n","    - 刺\n","    - 刻\n","    - 刽\n","    - 剁\n","    - 剂\n","    - 剃\n","    - 削\n","    - 剌\n","    - 前\n","    - 剐\n","    - 剑\n","    - 剔\n","    - 剖\n","    - 剜\n","    - 剥\n","    - 剧\n","    - 剩\n","    - 剪\n","    - 副\n","    - 割\n","    - 剽\n","    - 剿\n","    - 劈\n","    - 力\n","    - 劝\n","    - 办\n","    - 功\n","    - 加\n","    - 务\n","    - 劣\n","    - 动\n","    - 助\n","    - 努\n","    - 劫\n","    - 劭\n","    - 励\n","    - 劲\n","    - 劳\n","    - 劵\n","    - 劾\n","    - 势\n","    - 勃\n","    - 勇\n","    - 勉\n","    - 勋\n","    - 勐\n","    - 勒\n","    - 勘\n","    - 募\n","    - 勤\n","    - 勺\n","    - 勾\n","    - 勿\n","    - 匀\n","    - 包\n","    - 匆\n","    - 匈\n","    - 匏\n","    - 匕\n","    - 化\n","    - 北\n","    - 匙\n","    - 匝\n","    - 匠\n","    - 匡\n","    - 匣\n","    - 匪\n","    - 匮\n","    - 匹\n","    - 区\n","    - 医\n","    - 匾\n","    - 匿\n","    - 十\n","    - 千\n","    - 升\n","    - 午\n","    - 卉\n","    - 半\n","    - 华\n","    - 协\n","    - 卑\n","    - 卒\n","    - 卓\n","    - 单\n","    - 卖\n","    - 南\n","    - 博\n","    - 卜\n","    - 卞\n","    - 占\n","    - 卡\n","    - 卢\n","    - 卤\n","    - 卦\n","    - 卧\n","    - 卫\n","    - 卯\n","    - 印\n","    - 危\n","    - 卲\n","    - 即\n","    - 却\n","    - 卵\n","    - 卷\n","    - 卸\n","    - 卿\n","    - 厂\n","    - 厄\n","    - 厅\n","    - 历\n","    - 厉\n","    - 压\n","    - 厌\n","    - 厕\n","    - 厘\n","    - 厚\n","    - 厝\n","    - 原\n","    - 厢\n","    - 厥\n","    - 厦\n","    - 厨\n","    - 厩\n","    - 厮\n","    - 去\n","    - 县\n","    - 叁\n","    - 参\n","    - 又\n","    - 叉\n","    - 及\n","    - 友\n","    - 双\n","    - 反\n","    - 发\n","    - 叔\n","    - 取\n","    - 受\n","    - 变\n","    - 叙\n","    - 叛\n","    - 叠\n","    - 口\n","    - 古\n","    - 句\n","    - 另\n","    - 叨\n","    - 叩\n","    - 只\n","    - 叫\n","    - 召\n","    - 叭\n","    - 叮\n","    - 可\n","    - 台\n","    - 叱\n","    - 史\n","    - 右\n","    - 叵\n","    - 叶\n","    - 号\n","    - 司\n","    - 叹\n","    - 叼\n","    - 叽\n","    - 吁\n","    - 吃\n","    - 各\n","    - 吆\n","    - 合\n","    - 吉\n","    - 吊\n","    - 吋\n","    - 同\n","    - 名\n","    - 后\n","    - 吏\n","    - 吐\n","    - 向\n","    - 吒\n","    - 吓\n","    - 吕\n","    - 吖\n","    - 吗\n","    - 君\n","    - 吝\n","    - 吞\n","    - 吟\n","    - 吠\n","    - 否\n","    - 吧\n","    - 吨\n","    - 吩\n","    - 含\n","    - 听\n","    - 吭\n","    - 吮\n","    - 启\n","    - 吱\n","    - 吴\n","    - 吵\n","    - 吸\n","    - 吹\n","    - 吻\n","    - 吼\n","    - 吾\n","    - 呀\n","    - 呃\n","    - 呆\n","    - 呈\n","    - 告\n","    - 呐\n","    - 呕\n","    - 呗\n","    - 员\n","    - 呛\n","    - 呜\n","    - 呢\n","    - 呦\n","    - 周\n","    - 呱\n","    - 呲\n","    - 味\n","    - 呵\n","    - 呷\n","    - 呸\n","    - 呻\n","    - 呼\n","    - 命\n","    - 咀\n","    - 咂\n","    - 咄\n","    - 咆\n","    - 咋\n","    - 和\n","    - 咎\n","    - 咏\n","    - 咐\n","    - 咒\n","    - 咔\n","    - 咕\n","    - 咖\n","    - 咘\n","    - 咙\n","    - 咚\n","    - 咝\n","    - 咣\n","    - 咤\n","    - 咦\n","    - 咧\n","    - 咨\n","    - 咩\n","    - 咪\n","    - 咫\n","    - 咬\n","    - 咭\n","    - 咯\n","    - 咱\n","    - 咳\n","    - 咸\n","    - 咻\n","    - 咽\n","    - 哀\n","    - 品\n","    - 哂\n","    - 哄\n","    - 哆\n","    - 哇\n","    - 哈\n","    - 哉\n","    - 响\n","    - 哎\n","    - 哐\n","    - 哑\n","    - 哒\n","    - 哔\n","    - 哕\n","    - 哗\n","    - 哟\n","    - 哥\n","    - 哦\n","    - 哨\n","    - 哩\n","    - 哪\n","    - 哭\n","    - 哮\n","    - 哲\n","    - 哺\n","    - 哼\n","    - 哽\n","    - 唁\n","    - 唆\n","    - 唇\n","    - 唉\n","    - 唏\n","    - 唐\n","    - 唑\n","    - 唛\n","    - 唠\n","    - 唢\n","    - 唤\n","    - 唧\n","    - 唬\n","    - 售\n","    - 唯\n","    - 唰\n","    - 唱\n","    - 唳\n","    - 唷\n","    - 唾\n","    - 啃\n","    - 啄\n","    - 商\n","    - 啊\n","    - 啕\n","    - 啖\n","    - 啜\n","    - 啡\n","    - 啤\n","    - 啥\n","    - 啦\n","    - 啧\n","    - 啪\n","    - 啬\n","    - 啰\n","    - 啲\n","    - 啵\n","    - 啶\n","    - 啸\n","    - 啼\n","    - 啾\n","    - 喀\n","    - 喁\n","    - 喂\n","    - 喃\n","    - 善\n","    - 喆\n","    - 喇\n","    - 喉\n","    - 喊\n","    - 喋\n","    - 喔\n","    - 喘\n","    - 喜\n","    - 喝\n","    - 喟\n","    - 喧\n","    - 喱\n","    - 喳\n","    - 喵\n","    - 喷\n","    - 喻\n","    - 喽\n","    - 嗄\n","    - 嗅\n","    - 嗑\n","    - 嗒\n","    - 嗓\n","    - 嗔\n","    - 嗖\n","    - 嗜\n","    - 嗝\n","    - 嗡\n","    - 嗣\n","    - 嗤\n","    - 嗦\n","    - 嗨\n","    - 嗪\n","    - 嗫\n","    - 嗬\n","    - 嗯\n","    - 嗲\n","    - 嗷\n","    - 嗽\n","    - 嘀\n","    - 嘈\n","    - 嘉\n","    - 嘎\n","    - 嘏\n","    - 嘘\n","    - 嘛\n","    - 嘞\n","    - 嘟\n","    - 嘣\n","    - 嘭\n","    - 嘱\n","    - 嘲\n","    - 嘴\n","    - 嘶\n","    - 嘹\n","    - 嘻\n","    - 嘿\n","    - 噌\n","    - 噎\n","    - 噗\n","    - 噘\n","    - 噙\n","    - 噜\n","    - 噢\n","    - 噤\n","    - 器\n","    - 噩\n","    - 噪\n","    - 噬\n","    - 噱\n","    - 噶\n","    - 噻\n","    - 噼\n","    - 嚎\n","    - 嚏\n","    - 嚓\n","    - 嚣\n","    - 嚷\n","    - 嚼\n","    - 囊\n","    - 囍\n","    - 囔\n","    - 囗\n","    - 囚\n","    - 四\n","    - 回\n","    - 因\n","    - 团\n","    - 囤\n","    - 囧\n","    - 囫\n","    - 园\n","    - 囯\n","    - 困\n","    - 囱\n","    - 围\n","    - 囵\n","    - 囹\n","    - 固\n","    - 国\n","    - 图\n","    - 圃\n","    - 圄\n","    - 圆\n","    - 圈\n","    - 土\n","    - 圣\n","    - 在\n","    - 圩\n","    - 圪\n","    - 圭\n","    - 地\n","    - 圳\n","    - 圹\n","    - 场\n","    - 圻\n","    - 圾\n","    - 址\n","    - 坂\n","    - 均\n","    - 坊\n","    - 坍\n","    - 坎\n","    - 坏\n","    - 坐\n","    - 坑\n","    - 块\n","    - 坚\n","    - 坛\n","    - 坝\n","    - 坞\n","    - 坟\n","    - 坠\n","    - 坡\n","    - 坤\n","    - 坦\n","    - 坨\n","    - 坩\n","    - 坪\n","    - 坭\n","    - 坯\n","    - 坳\n","    - 坷\n","    - 坻\n","    - 垂\n","    - 垃\n","    - 垄\n","    - 垅\n","    - 型\n","    - 垌\n","    - 垒\n","    - 垚\n","    - 垛\n","    - 垡\n","    - 垢\n","    - 垣\n","    - 垤\n","    - 垦\n","    - 垩\n","    - 垫\n","    - 垭\n","    - 垮\n","    - 埂\n","    - 埃\n","    - 埇\n","    - 埋\n","    - 城\n","    - 埔\n","    - 埕\n","    - 埚\n","    - 埝\n","    - 域\n","    - 埠\n","    - 埭\n","    - 埸\n","    - 培\n","    - 基\n","    - 堀\n","    - 堂\n","    - 堃\n","    - 堆\n","    - 堇\n","    - 堕\n","    - 堡\n","    - 堤\n","    - 堪\n","    - 堰\n","    - 堵\n","    - 堺\n","    - 塌\n","    - 塍\n","    - 塑\n","    - 塔\n","    - 塘\n","    - 塞\n","    - 填\n","    - 塬\n","    - 塾\n","    - 境\n","    - 墅\n","    - 墉\n","    - 墓\n","    - 増\n","    - 墙\n","    - 增\n","    - 墟\n","    - 墨\n","    - 墩\n","    - 壁\n","    - 壑\n","    - 壕\n","    - 壤\n","    - 士\n","    - 壬\n","    - 壮\n","    - 声\n","    - 壳\n","    - 壶\n","    - 壹\n","    - 处\n","    - 备\n","    - 复\n","    - 夏\n","    - 夔\n","    - 夕\n","    - 外\n","    - 夙\n","    - 多\n","    - 夜\n","    - 够\n","    - 大\n","    - 天\n","    - 太\n","    - 夫\n","    - 夭\n","    - 央\n","    - 夯\n","    - 失\n","    - 头\n","    - 夷\n","    - 夸\n","    - 夹\n","    - 夺\n","    - 奁\n","    - 奂\n","    - 奄\n","    - 奇\n","    - 奈\n","    - 奉\n","    - 奋\n","    - 奎\n","    - 奏\n","    - 契\n","    - 奔\n","    - 奕\n","    - 奖\n","    - 套\n","    - 奘\n","    - 奚\n","    - 奠\n","    - 奢\n","    - 奥\n","    - 女\n","    - 奴\n","    - 奶\n","    - 奸\n","    - 她\n","    - 好\n","    - 如\n","    - 妃\n","    - 妄\n","    - 妆\n","    - 妇\n","    - 妈\n","    - 妊\n","    - 妍\n","    - 妒\n","    - 妓\n","    - 妖\n","    - 妙\n","    - 妞\n","    - 妤\n","    - 妥\n","    - 妨\n","    - 妩\n","    - 妪\n","    - 妫\n","    - 妮\n","    - 妯\n","    - 妲\n","    - 妹\n","    - 妻\n","    - 妾\n","    - 姆\n","    - 姊\n","    - 始\n","    - 姐\n","    - 姑\n","    - 姓\n","    - 委\n","    - 姗\n","    - 姚\n","    - 姜\n","    - 姝\n","    - 姣\n","    - 姥\n","    - 姨\n","    - 姬\n","    - 姻\n","    - 姿\n","    - 威\n","    - 娃\n","    - 娄\n","    - 娅\n","    - 娆\n","    - 娇\n","    - 娈\n","    - 娉\n","    - 娌\n","    - 娓\n","    - 娘\n","    - 娜\n","    - 娟\n","    - 娠\n","    - 娣\n","    - 娥\n","    - 娩\n","    - 娱\n","    - 娲\n","    - 娴\n","    - 娶\n","    - 娼\n","    - 婀\n","    - 婆\n","    - 婉\n","    - 婊\n","    - 婕\n","    - 婚\n","    - 婢\n","    - 婧\n","    - 婪\n","    - 婴\n","    - 婵\n","    - 婶\n","    - 婷\n","    - 婺\n","    - 婿\n","    - 媒\n","    - 媚\n","    - 媛\n","    - 媞\n","    - 媲\n","    - 媳\n","    - 嫁\n","    - 嫂\n","    - 嫉\n","    - 嫌\n","    - 嫒\n","    - 嫔\n","    - 嫖\n","    - 嫚\n","    - 嫡\n","    - 嫣\n","    - 嫦\n","    - 嫩\n","    - 嫫\n","    - 嬅\n","    - 嬉\n","    - 嬗\n","    - 嬛\n","    - 嬴\n","    - 嬷\n","    - 孀\n","    - 子\n","    - 孑\n","    - 孔\n","    - 孕\n","    - 字\n","    - 存\n","    - 孙\n","    - 孚\n","    - 孛\n","    - 孜\n","    - 孝\n","    - 孟\n","    - 孢\n","    - 季\n","    - 孤\n","    - 学\n","    - 孩\n","    - 孪\n","    - 孬\n","    - 孰\n","    - 孱\n","    - 孳\n","    - 孵\n","    - 孺\n","    - 孽\n","    - 宁\n","    - 它\n","    - 宅\n","    - 宇\n","    - 守\n","    - 安\n","    - 宋\n","    - 完\n","    - 宏\n","    - 宓\n","    - 宕\n","    - 宗\n","    - 官\n","    - 宙\n","    - 定\n","    - 宛\n","    - 宜\n","    - 宝\n","    - 实\n","    - 宠\n","    - 审\n","    - 客\n","    - 宣\n","    - 室\n","    - 宥\n","    - 宦\n","    - 宪\n","    - 宫\n","    - 宰\n","    - 害\n","    - 宴\n","    - 宵\n","    - 家\n","    - 宸\n","    - 容\n","    - 宽\n","    - 宾\n","    - 宿\n","    - 寂\n","    - 寄\n","    - 寅\n","    - 密\n","    - 寇\n","    - 富\n","    - 寐\n","    - 寒\n","    - 寓\n","    - 寝\n","    - 寞\n","    - 察\n","    - 寡\n","    - 寥\n","    - 寨\n","    - 寮\n","    - 寰\n","    - 寸\n","    - 对\n","    - 寺\n","    - 寻\n","    - 导\n","    - 寿\n","    - 封\n","    - 射\n","    - 尅\n","    - 将\n","    - 尉\n","    - 尊\n","    - 小\n","    - 少\n","    - 尔\n","    - 尕\n","    - 尖\n","    - 尘\n","    - 尚\n","    - 尝\n","    - 尤\n","    - 尧\n","    - 尬\n","    - 就\n","    - 尴\n","    - 尸\n","    - 尹\n","    - 尺\n","    - 尼\n","    - 尽\n","    - 尾\n","    - 尿\n","    - 局\n","    - 屁\n","    - 层\n","    - 居\n","    - 屈\n","    - 屉\n","    - 届\n","    - 屋\n","    - 屌\n","    - 屎\n","    - 屏\n","    - 屐\n","    - 屑\n","    - 展\n","    - 属\n","    - 屠\n","    - 屡\n","    - 履\n","    - 屯\n","    - 山\n","    - 屹\n","    - 屿\n","    - 岁\n","    - 岂\n","    - 岌\n","    - 岐\n","    - 岑\n","    - 岔\n","    - 岖\n","    - 岗\n","    - 岚\n","    - 岛\n","    - 岩\n","    - 岬\n","    - 岭\n","    - 岱\n","    - 岳\n","    - 岷\n","    - 岸\n","    - 峁\n","    - 峋\n","    - 峒\n","    - 峙\n","    - 峡\n","    - 峥\n","    - 峦\n","    - 峨\n","    - 峪\n","    - 峭\n","    - 峰\n","    - 峻\n","    - 崂\n","    - 崃\n","    - 崆\n","    - 崇\n","    - 崎\n","    - 崔\n","    - 崖\n","    - 崛\n","    - 崧\n","    - 崩\n","    - 崭\n","    - 崮\n","    - 崴\n","    - 崽\n","    - 嵇\n","    - 嵊\n","    - 嵋\n","    - 嵌\n","    - 嵘\n","    - 嵛\n","    - 嵩\n","    - 嵬\n","    - 嶂\n","    - 嶙\n","    - 嶝\n","    - 巅\n","    - 巍\n","    - 川\n","    - 州\n","    - 巡\n","    - 巢\n","    - 工\n","    - 左\n","    - 巧\n","    - 巨\n","    - 巩\n","    - 巫\n","    - 差\n","    - 己\n","    - 已\n","    - 巳\n","    - 巴\n","    - 巷\n","    - 巾\n","    - 币\n","    - 市\n","    - 布\n","    - 帅\n","    - 帆\n","    - 师\n","    - 希\n","    - 帐\n","    - 帕\n","    - 帖\n","    - 帘\n","    - 帚\n","    - 帛\n","    - 帜\n","    - 帝\n","    - 带\n","    - 帧\n","    - 席\n","    - 帮\n","    - 帷\n","    - 常\n","    - 帼\n","    - 帽\n","    - 幂\n","    - 幄\n","    - 幅\n","    - 幌\n","    - 幔\n","    - 幕\n","    - 幡\n","    - 幢\n","    - 干\n","    - 平\n","    - 年\n","    - 并\n","    - 幸\n","    - 幺\n","    - 幻\n","    - 幼\n","    - 幽\n","    - 广\n","    - 庄\n","    - 庆\n","    - 庇\n","    - 床\n","    - 序\n","    - 庐\n","    - 库\n","    - 应\n","    - 底\n","    - 庖\n","    - 店\n","    - 庙\n","    - 庚\n","    - 府\n","    - 庞\n","    - 废\n","    - 度\n","    - 座\n","    - 庭\n","    - 庵\n","    - 庶\n","    - 康\n","    - 庸\n","    - 庹\n","    - 庾\n","    - 廉\n","    - 廊\n","    - 廓\n","    - 廖\n","    - 延\n","    - 廷\n","    - 建\n","    - 开\n","    - 异\n","    - 弃\n","    - 弄\n","    - 弈\n","    - 弊\n","    - 弋\n","    - 式\n","    - 弑\n","    - 弓\n","    - 引\n","    - 弗\n","    - 弘\n","    - 弛\n","    - 弟\n","    - 张\n","    - 弥\n","    - 弦\n","    - 弧\n","    - 弩\n","    - 弭\n","    - 弯\n","    - 弱\n","    - 弹\n","    - 强\n","    - 弼\n","    - 归\n","    - 当\n","    - 录\n","    - 彗\n","    - 彝\n","    - 形\n","    - 彤\n","    - 彦\n","    - 彩\n","    - 彪\n","    - 彬\n","    - 彭\n","    - 彰\n","    - 影\n","    - 彷\n","    - 役\n","    - 彻\n","    - 彼\n","    - 往\n","    - 征\n","    - 径\n","    - 待\n","    - 徇\n","    - 很\n","    - 徉\n","    - 徊\n","    - 律\n","    - 徐\n","    - 徒\n","    - 得\n","    - 徘\n","    - 徙\n","    - 徜\n","    - 御\n","    - 徨\n","    - 循\n","    - 微\n","    - 德\n","    - 徽\n","    - 心\n","    - 必\n","    - 忆\n","    - 忌\n","    - 忍\n","    - 忏\n","    - 忐\n","    - 忑\n","    - 忒\n","    - 忖\n","    - 志\n","    - 忘\n","    - 忙\n","    - 忠\n","    - 忡\n","    - 忤\n","    - 忧\n","    - 忪\n","    - 快\n","    - 忱\n","    - 念\n","    - 忻\n","    - 忽\n","    - 忿\n","    - 怀\n","    - 态\n","    - 怂\n","    - 怄\n","    - 怅\n","    - 怆\n","    - 怎\n","    - 怒\n","    - 怕\n","    - 怖\n","    - 怜\n","    - 思\n","    - 怠\n","    - 怡\n","    - 急\n","    - 怦\n","    - 性\n","    - 怨\n","    - 怪\n","    - 怫\n","    - 怯\n","    - 怵\n","    - 总\n","    - 怼\n","    - 怿\n","    - 恁\n","    - 恃\n","    - 恋\n","    - 恍\n","    - 恐\n","    - 恒\n","    - 恕\n","    - 恙\n","    - 恢\n","    - 恣\n","    - 恤\n","    - 恨\n","    - 恩\n","    - 恪\n","    - 恬\n","    - 恭\n","    - 息\n","    - 恰\n","    - 恳\n","    - 恶\n","    - 恸\n","    - 恺\n","    - 恻\n","    - 恼\n","    - 恿\n","    - 悄\n","    - 悉\n","    - 悌\n","    - 悍\n","    - 悔\n","    - 悖\n","    - 悚\n","    - 悟\n","    - 悠\n","    - 患\n","    - 悦\n","    - 您\n","    - 悬\n","    - 悭\n","    - 悯\n","    - 悱\n","    - 悲\n","    - 悴\n","    - 悸\n","    - 悻\n","    - 悼\n","    - 情\n","    - 惆\n","    - 惊\n","    - 惋\n","    - 惑\n","    - 惕\n","    - 惚\n","    - 惜\n","    - 惟\n","    - 惠\n","    - 惦\n","    - 惧\n","    - 惨\n","    - 惩\n","    - 惫\n","    - 惬\n","    - 惭\n","    - 惮\n","    - 惯\n","    - 惰\n","    - 想\n","    - 惶\n","    - 惹\n","    - 惺\n","    - 愁\n","    - 愈\n","    - 愉\n","    - 意\n","    - 愕\n","    - 愚\n","    - 感\n","    - 愣\n","    - 愤\n","    - 愧\n","    - 愫\n","    - 愿\n","    - 慈\n","    - 慌\n","    - 慎\n","    - 慑\n","    - 慕\n","    - 慢\n","    - 慧\n","    - 慨\n","    - 慰\n","    - 慵\n","    - 慷\n","    - 憋\n","    - 憎\n","    - 憔\n","    - 憧\n","    - 憨\n","    - 憩\n","    - 憬\n","    - 憷\n","    - 憾\n","    - 懂\n","    - 懈\n","    - 懊\n","    - 懋\n","    - 懑\n","    - 懒\n","    - 懦\n","    - 懵\n","    - 懿\n","    - 戈\n","    - 戊\n","    - 戌\n","    - 戍\n","    - 戎\n","    - 戏\n","    - 成\n","    - 我\n","    - 戒\n","    - 或\n","    - 戗\n","    - 战\n","    - 戚\n","    - 戛\n","    - 戟\n","    - 截\n","    - 戬\n","    - 戮\n","    - 戳\n","    - 戴\n","    - 户\n","    - 戾\n","    - 房\n","    - 所\n","    - 扁\n","    - 扇\n","    - 扈\n","    - 扉\n","    - 手\n","    - 才\n","    - 扎\n","    - 扑\n","    - 扒\n","    - 打\n","    - 扔\n","    - 托\n","    - 扛\n","    - 扞\n","    - 扣\n","    - 扦\n","    - 执\n","    - 扩\n","    - 扪\n","    - 扫\n","    - 扬\n","    - 扭\n","    - 扮\n","    - 扯\n","    - 扰\n","    - 扳\n","    - 扶\n","    - 批\n","    - 扼\n","    - 找\n","    - 承\n","    - 技\n","    - 抄\n","    - 抉\n","    - 把\n","    - 抑\n","    - 抒\n","    - 抓\n","    - 投\n","    - 抖\n","    - 抗\n","    - 折\n","    - 抚\n","    - 抛\n","    - 抠\n","    - 抡\n","    - 抢\n","    - 护\n","    - 报\n","    - 抨\n","    - 披\n","    - 抬\n","    - 抱\n","    - 抵\n","    - 抹\n","    - 押\n","    - 抽\n","    - 抿\n","    - 拂\n","    - 拄\n","    - 担\n","    - 拆\n","    - 拇\n","    - 拈\n","    - 拉\n","    - 拌\n","    - 拍\n","    - 拎\n","    - 拐\n","    - 拒\n","    - 拓\n","    - 拔\n","    - 拖\n","    - 拗\n","    - 拘\n","    - 拙\n","    - 拚\n","    - 招\n","    - 拜\n","    - 拟\n","    - 拢\n","    - 拣\n","    - 拥\n","    - 拦\n","    - 拧\n","    - 拨\n","    - 择\n","    - 括\n","    - 拭\n","    - 拮\n","    - 拯\n","    - 拱\n","    - 拳\n","    - 拴\n","    - 拷\n","    - 拼\n","    - 拽\n","    - 拾\n","    - 拿\n","    - 持\n","    - 挂\n","    - 指\n","    - 按\n","    - 挎\n","    - 挑\n","    - 挖\n","    - 挚\n","    - 挛\n","    - 挝\n","    - 挞\n","    - 挟\n","    - 挠\n","    - 挡\n","    - 挣\n","    - 挤\n","    - 挥\n","    - 挨\n","    - 挪\n","    - 挫\n","    - 振\n","    - 挺\n","    - 挽\n","    - 捂\n","    - 捅\n","    - 捆\n","    - 捉\n","    - 捋\n","    - 捌\n","    - 捍\n","    - 捎\n","    - 捏\n","    - 捐\n","    - 捕\n","    - 捞\n","    - 损\n","    - 捡\n","    - 换\n","    - 捣\n","    - 捧\n","    - 据\n","    - 捶\n","    - 捷\n","    - 捺\n","    - 捻\n","    - 掀\n","    - 掂\n","    - 掇\n","    - 授\n","    - 掉\n","    - 掌\n","    - 掏\n","    - 掐\n","    - 排\n","    - 掖\n","    - 掘\n","    - 掠\n","    - 探\n","    - 掣\n","    - 接\n","    - 控\n","    - 推\n","    - 掩\n","    - 措\n","    - 掬\n","    - 掮\n","    - 掰\n","    - 掳\n","    - 掴\n","    - 掷\n","    - 掸\n","    - 掺\n","    - 揄\n","    - 揉\n","    - 揍\n","    - 描\n","    - 提\n","    - 插\n","    - 握\n","    - 揣\n","    - 揩\n","    - 揪\n","    - 揭\n","    - 援\n","    - 揶\n","    - 揽\n","    - 搀\n","    - 搁\n","    - 搂\n","    - 搅\n","    - 搏\n","    - 搐\n","    - 搓\n","    - 搔\n","    - 搜\n","    - 搞\n","    - 搡\n","    - 搧\n","    - 搪\n","    - 搬\n","    - 搭\n","    - 携\n","    - 搽\n","    - 摁\n","    - 摄\n","    - 摆\n","    - 摇\n","    - 摈\n","    - 摊\n","    - 摒\n","    - 摔\n","    - 摘\n","    - 摞\n","    - 摧\n","    - 摩\n","    - 摸\n","    - 摹\n","    - 撂\n","    - 撅\n","    - 撇\n","    - 撑\n","    - 撒\n","    - 撕\n","    - 撞\n","    - 撤\n","    - 撩\n","    - 撬\n","    - 播\n","    - 撮\n","    - 撰\n","    - 撵\n","    - 撸\n","    - 撺\n","    - 撼\n","    - 擀\n","    - 擂\n","    - 擅\n","    - 操\n","    - 擎\n","    - 擒\n","    - 擘\n","    - 擞\n","    - 擢\n","    - 擦\n","    - 攀\n","    - 攒\n","    - 攘\n","    - 攥\n","    - 攫\n","    - 支\n","    - 收\n","    - 攸\n","    - 改\n","    - 攻\n","    - 放\n","    - 政\n","    - 故\n","    - 效\n","    - 敌\n","    - 敏\n","    - 救\n","    - 敕\n","    - 敖\n","    - 教\n","    - 敛\n","    - 敝\n","    - 敞\n","    - 敢\n","    - 散\n","    - 敦\n","    - 敬\n","    - 数\n","    - 敲\n","    - 整\n","    - 敷\n","    - 文\n","    - 斋\n","    - 斌\n","    - 斐\n","    - 斑\n","    - 斓\n","    - 斗\n","    - 料\n","    - 斛\n","    - 斜\n","    - 斟\n","    - 斡\n","    - 斤\n","    - 斥\n","    - 斧\n","    - 斩\n","    - 断\n","    - 斯\n","    - 新\n","    - 方\n","    - 施\n","    - 旁\n","    - 旅\n","    - 旋\n","    - 旌\n","    - 族\n","    - 旖\n","    - 旗\n","    - 无\n","    - 既\n","    - 日\n","    - 旦\n","    - 旧\n","    - 旨\n","    - 早\n","    - 旬\n","    - 旭\n","    - 旮\n","    - 旯\n","    - 旱\n","    - 时\n","    - 旷\n","    - 旺\n","    - 旻\n","    - 昀\n","    - 昂\n","    - 昆\n","    - 昊\n","    - 昌\n","    - 明\n","    - 昏\n","    - 易\n","    - 昔\n","    - 昕\n","    - 昙\n","    - 昝\n","    - 星\n","    - 映\n","    - 春\n","    - 昧\n","    - 昨\n","    - 昭\n","    - 是\n","    - 昱\n","    - 昴\n","    - 昵\n","    - 昶\n","    - 昼\n","    - 显\n","    - 晃\n","    - 晋\n","    - 晌\n","    - 晏\n","    - 晒\n","    - 晓\n","    - 晔\n","    - 晕\n","    - 晖\n","    - 晗\n","    - 晚\n","    - 晞\n","    - 晟\n","    - 晤\n","    - 晦\n","    - 晨\n","    - 普\n","    - 景\n","    - 晰\n","    - 晴\n","    - 晶\n","    - 晷\n","    - 智\n","    - 晾\n","    - 暂\n","    - 暄\n","    - 暇\n","    - 暌\n","    - 暑\n","    - 暖\n","    - 暗\n","    - 暧\n","    - 暨\n","    - 暮\n","    - 暴\n","    - 暹\n","    - 暾\n","    - 曈\n","    - 曙\n","    - 曜\n","    - 曝\n","    - 曦\n","    - 曰\n","    - 曲\n","    - 曳\n","    - 更\n","    - 曹\n","    - 曼\n","    - 曾\n","    - 替\n","    - 最\n","    - 月\n","    - 有\n","    - 朋\n","    - 服\n","    - 朐\n","    - 朔\n","    - 朕\n","    - 朗\n","    - 望\n","    - 朝\n","    - 期\n","    - 朦\n","    - 木\n","    - 未\n","    - 末\n","    - 本\n","    - 札\n","    - 术\n","    - 朱\n","    - 朴\n","    - 朵\n","    - 机\n","    - 朽\n","    - 杀\n","    - 杂\n","    - 权\n","    - 杆\n","    - 杈\n","    - 杉\n","    - 李\n","    - 杏\n","    - 材\n","    - 村\n","    - 杓\n","    - 杖\n","    - 杜\n","    - 杞\n","    - 束\n","    - 杠\n","    - 条\n","    - 来\n","    - 杨\n","    - 杭\n","    - 杯\n","    - 杰\n","    - 杳\n","    - 杵\n","    - 杷\n","    - 松\n","    - 板\n","    - 极\n","    - 构\n","    - 枇\n","    - 枉\n","    - 枋\n","    - 析\n","    - 枕\n","    - 林\n","    - 枚\n","    - 果\n","    - 枝\n","    - 枞\n","    - 枢\n","    - 枣\n","    - 枥\n","    - 枪\n","    - 枫\n","    - 枭\n","    - 枯\n","    - 枰\n","    - 枳\n","    - 架\n","    - 枷\n","    - 枸\n","    - 柃\n","    - 柄\n","    - 柏\n","    - 某\n","    - 柑\n","    - 柒\n","    - 染\n","    - 柔\n","    - 柘\n","    - 柚\n","    - 柜\n","    - 柞\n","    - 柠\n","    - 查\n","    - 柩\n","    - 柬\n","    - 柯\n","    - 柱\n","    - 柳\n","    - 柴\n","    - 柿\n","    - 栀\n","    - 栅\n","    - 标\n","    - 栈\n","    - 栋\n","    - 栌\n","    - 栎\n","    - 栏\n","    - 树\n","    - 栓\n","    - 栖\n","    - 栗\n","    - 校\n","    - 栩\n","    - 株\n","    - 样\n","    - 核\n","    - 根\n","    - 格\n","    - 栽\n","    - 栾\n","    - 桁\n","    - 桂\n","    - 桃\n","    - 框\n","    - 案\n","    - 桉\n","    - 桌\n","    - 桎\n","    - 桐\n","    - 桑\n","    - 桓\n","    - 桔\n","    - 桠\n","    - 桢\n","    - 档\n","    - 桥\n","    - 桦\n","    - 桨\n","    - 桩\n","    - 桴\n","    - 桶\n","    - 桷\n","    - 梁\n","    - 梅\n","    - 梆\n","    - 梏\n","    - 梓\n","    - 梗\n","    - 梢\n","    - 梦\n","    - 梧\n","    - 梨\n","    - 梭\n","    - 梯\n","    - 械\n","    - 梳\n","    - 梵\n","    - 检\n","    - 棂\n","    - 棉\n","    - 棋\n","    - 棍\n","    - 棒\n","    - 棕\n","    - 棘\n","    - 棚\n","    - 棠\n","    - 棣\n","    - 森\n","    - 棱\n","    - 棵\n","    - 棺\n","    - 椁\n","    - 椅\n","    - 椋\n","    - 植\n","    - 椎\n","    - 椒\n","    - 椟\n","    - 椤\n","    - 椭\n","    - 椰\n","    - 椴\n","    - 椹\n","    - 椿\n","    - 楂\n","    - 楔\n","    - 楚\n","    - 楞\n","    - 楠\n","    - 楣\n","    - 楷\n","    - 楸\n","    - 楼\n","    - 概\n","    - 榄\n","    - 榆\n","    - 榈\n","    - 榉\n","    - 榔\n","    - 榕\n","    - 榛\n","    - 榜\n","    - 榨\n","    - 榫\n","    - 榭\n","    - 榴\n","    - 榷\n","    - 榻\n","    - 槃\n","    - 槌\n","    - 槎\n","    - 槐\n","    - 槛\n","    - 槟\n","    - 槭\n","    - 槽\n","    - 槿\n","    - 樊\n","    - 樟\n","    - 模\n","    - 樨\n","    - 横\n","    - 樯\n","    - 樱\n","    - 樵\n","    - 樽\n","    - 樾\n","    - 橄\n","    - 橇\n","    - 橐\n","    - 橘\n","    - 橙\n","    - 橡\n","    - 橱\n","    - 檀\n","    - 檐\n","    - 檗\n","    - 檬\n","    - 欠\n","    - 次\n","    - 欢\n","    - 欣\n","    - 欧\n","    - 欲\n","    - 欸\n","    - 欺\n","    - 款\n","    - 歆\n","    - 歇\n","    - 歉\n","    - 歌\n","    - 歙\n","    - 止\n","    - 正\n","    - 此\n","    - 步\n","    - 武\n","    - 歧\n","    - 歩\n","    - 歪\n","    - 歹\n","    - 死\n","    - 歼\n","    - 殁\n","    - 殃\n","    - 殆\n","    - 殇\n","    - 殉\n","    - 殊\n","    - 残\n","    - 殒\n","    - 殓\n","    - 殖\n","    - 殚\n","    - 殡\n","    - 殴\n","    - 段\n","    - 殷\n","    - 殿\n","    - 毁\n","    - 毂\n","    - 毅\n","    - 毋\n","    - 母\n","    - 每\n","    - 毒\n","    - 毓\n","    - 比\n","    - 毕\n","    - 毗\n","    - 毙\n","    - 毛\n","    - 毡\n","    - 毫\n","    - 毯\n","    - 毽\n","    - 氏\n","    - 民\n","    - 氓\n","    - 气\n","    - 氚\n","    - 氛\n","    - 氟\n","    - 氢\n","    - 氤\n","    - 氦\n","    - 氧\n","    - 氨\n","    - 氪\n","    - 氮\n","    - 氯\n","    - 氰\n","    - 氲\n","    - 水\n","    - 永\n","    - 汀\n","    - 汁\n","    - 求\n","    - 汇\n","    - 汉\n","    - 汊\n","    - 汐\n","    - 汕\n","    - 汗\n","    - 汛\n","    - 汝\n","    - 汞\n","    - 江\n","    - 池\n","    - 污\n","    - 汤\n","    - 汨\n","    - 汩\n","    - 汪\n","    - 汰\n","    - 汲\n","    - 汴\n","    - 汶\n","    - 汹\n","    - 汽\n","    - 汾\n","    - 沁\n","    - 沂\n","    - 沃\n","    - 沅\n","    - 沈\n","    - 沉\n","    - 沌\n","    - 沏\n","    - 沐\n","    - 沓\n","    - 沙\n","    - 沛\n","    - 沟\n","    - 没\n","    - 沢\n","    - 沣\n","    - 沥\n","    - 沦\n","    - 沧\n","    - 沪\n","    - 沫\n","    - 沭\n","    - 沮\n","    - 沱\n","    - 河\n","    - 沸\n","    - 油\n","    - 治\n","    - 沼\n","    - 沽\n","    - 沾\n","    - 沿\n","    - 泄\n","    - 泉\n","    - 泊\n","    - 泌\n","    - 泓\n","    - 泔\n","    - 法\n","    - 泖\n","    - 泗\n","    - 泛\n","    - 泞\n","    - 泠\n","    - 泡\n","    - 波\n","    - 泣\n","    - 泥\n","    - 注\n","    - 泪\n","    - 泫\n","    - 泮\n","    - 泯\n","    - 泰\n","    - 泱\n","    - 泳\n","    - 泵\n","    - 泷\n","    - 泸\n","    - 泺\n","    - 泻\n","    - 泼\n","    - 泽\n","    - 泾\n","    - 洁\n","    - 洋\n","    - 洒\n","    - 洗\n","    - 洙\n","    - 洛\n","    - 洞\n","    - 津\n","    - 洪\n","    - 洮\n","    - 洱\n","    - 洲\n","    - 洵\n","    - 洹\n","    - 洺\n","    - 活\n","    - 洼\n","    - 洽\n","    - 派\n","    - 流\n","    - 浃\n","    - 浅\n","    - 浆\n","    - 浇\n","    - 浈\n","    - 浊\n","    - 测\n","    - 济\n","    - 浏\n","    - 浐\n","    - 浑\n","    - 浒\n","    - 浓\n","    - 浔\n","    - 浙\n","    - 浚\n","    - 浜\n","    - 浠\n","    - 浣\n","    - 浦\n","    - 浩\n","    - 浪\n","    - 浮\n","    - 浴\n","    - 海\n","    - 浸\n","    - 涂\n","    - 涅\n","    - 消\n","    - 涉\n","    - 涌\n","    - 涎\n","    - 涑\n","    - 涓\n","    - 涕\n","    - 涛\n","    - 涝\n","    - 涞\n","    - 涟\n","    - 涠\n","    - 涡\n","    - 涣\n","    - 涤\n","    - 润\n","    - 涧\n","    - 涨\n","    - 涩\n","    - 涪\n","    - 涮\n","    - 涯\n","    - 液\n","    - 涵\n","    - 涸\n","    - 涿\n","    - 淀\n","    - 淄\n","    - 淅\n","    - 淆\n","    - 淇\n","    - 淋\n","    - 淌\n","    - 淑\n","    - 淖\n","    - 淘\n","    - 淝\n","    - 淞\n","    - 淡\n","    - 淤\n","    - 淦\n","    - 淫\n","    - 淬\n","    - 淮\n","    - 深\n","    - 淳\n","    - 混\n","    - 淹\n","    - 添\n","    - 淼\n","    - 清\n","    - 渊\n","    - 渌\n","    - 渍\n","    - 渎\n","    - 渐\n","    - 渑\n","    - 渔\n","    - 渗\n","    - 渚\n","    - 渝\n","    - 渠\n","    - 渡\n","    - 渣\n","    - 渤\n","    - 渥\n","    - 温\n","    - 渭\n","    - 港\n","    - 渲\n","    - 渴\n","    - 游\n","    - 渺\n","    - 湃\n","    - 湄\n","    - 湉\n","    - 湍\n","    - 湎\n","    - 湖\n","    - 湘\n","    - 湛\n","    - 湫\n","    - 湾\n","    - 湿\n","    - 溃\n","    - 溅\n","    - 溆\n","    - 溉\n","    - 溏\n","    - 源\n","    - 溜\n","    - 溟\n","    - 溢\n","    - 溥\n","    - 溧\n","    - 溪\n","    - 溯\n","    - 溶\n","    - 溺\n","    - 滁\n","    - 滇\n","    - 滋\n","    - 滑\n","    - 滔\n","    - 滕\n","    - 滘\n","    - 滚\n","    - 滞\n","    - 满\n","    - 滢\n","    - 滤\n","    - 滥\n","    - 滦\n","    - 滨\n","    - 滩\n","    - 滴\n","    - 滹\n","    - 漂\n","    - 漆\n","    - 漉\n","    - 漏\n","    - 漓\n","    - 演\n","    - 漕\n","    - 漠\n","    - 漩\n","    - 漪\n","    - 漫\n","    - 漭\n","    - 漯\n","    - 漱\n","    - 漳\n","    - 漾\n","    - 潆\n","    - 潇\n","    - 潋\n","    - 潍\n","    - 潘\n","    - 潜\n","    - 潞\n","    - 潢\n","    - 潦\n","    - 潭\n","    - 潮\n","    - 潸\n","    - 潺\n","    - 潼\n","    - 澄\n","    - 澈\n","    - 澍\n","    - 澎\n","    - 澜\n","    - 澡\n","    - 澧\n","    - 澳\n","    - 澶\n","    - 激\n","    - 濂\n","    - 濑\n","    - 濒\n","    - 濠\n","    - 濡\n","    - 濮\n","    - 濯\n","    - 瀑\n","    - 瀚\n","    - 瀛\n","    - 灌\n","    - 灏\n","    - 灞\n","    - 火\n","    - 灭\n","    - 灯\n","    - 灰\n","    - 灵\n","    - 灶\n","    - 灸\n","    - 灼\n","    - 灾\n","    - 灿\n","    - 炀\n","    - 炅\n","    - 炉\n","    - 炊\n","    - 炎\n","    - 炒\n","    - 炔\n","    - 炕\n","    - 炖\n","    - 炙\n","    - 炜\n","    - 炫\n","    - 炬\n","    - 炭\n","    - 炮\n","    - 炯\n","    - 炳\n","    - 炷\n","    - 炸\n","    - 点\n","    - 炼\n","    - 炽\n","    - 烀\n","    - 烁\n","    - 烂\n","    - 烃\n","    - 烈\n","    - 烊\n","    - 烘\n","    - 烙\n","    - 烛\n","    - 烟\n","    - 烤\n","    - 烦\n","    - 烧\n","    - 烨\n","    - 烩\n","    - 烫\n","    - 烬\n","    - 热\n","    - 烯\n","    - 烷\n","    - 烹\n","    - 烽\n","    - 焉\n","    - 焊\n","    - 焓\n","    - 焕\n","    - 焖\n","    - 焗\n","    - 焘\n","    - 焙\n","    - 焚\n","    - 焦\n","    - 焯\n","    - 焰\n","    - 焱\n","    - 然\n","    - 煊\n","    - 煌\n","    - 煎\n","    - 煜\n","    - 煞\n","    - 煤\n","    - 煦\n","    - 照\n","    - 煨\n","    - 煮\n","    - 煲\n","    - 煳\n","    - 煽\n","    - 熄\n","    - 熊\n","    - 熏\n","    - 熔\n","    - 熙\n","    - 熟\n","    - 熠\n","    - 熨\n","    - 熬\n","    - 熵\n","    - 熹\n","    - 燃\n","    - 燊\n","    - 燎\n","    - 燕\n","    - 燥\n","    - 燮\n","    - 爆\n","    - 爪\n","    - 爬\n","    - 爱\n","    - 爵\n","    - 父\n","    - 爷\n","    - 爸\n","    - 爹\n","    - 爽\n","    - 片\n","    - 版\n","    - 牌\n","    - 牍\n","    - 牒\n","    - 牙\n","    - 牛\n","    - 牟\n","    - 牠\n","    - 牡\n","    - 牢\n","    - 牧\n","    - 物\n","    - 牲\n","    - 牵\n","    - 特\n","    - 牺\n","    - 牾\n","    - 犀\n","    - 犁\n","    - 犄\n","    - 犇\n","    - 犊\n","    - 犒\n","    - 犟\n","    - 犬\n","    - 犯\n","    - 状\n","    - 犷\n","    - 犸\n","    - 犹\n","    - 狂\n","    - 狄\n","    - 狈\n","    - 狐\n","    - 狒\n","    - 狗\n","    - 狙\n","    - 狞\n","    - 狠\n","    - 狡\n","    - 狩\n","    - 独\n","    - 狭\n","    - 狮\n","    - 狰\n","    - 狱\n","    - 狸\n","    - 狼\n","    - 猁\n","    - 猎\n","    - 猖\n","    - 猛\n","    - 猜\n","    - 猝\n","    - 猥\n","    - 猩\n","    - 猪\n","    - 猫\n","    - 猬\n","    - 献\n","    - 猴\n","    - 猷\n","    - 猹\n","    - 猾\n","    - 猿\n","    - 獒\n","    - 獗\n","    - 獭\n","    - 獾\n","    - 玄\n","    - 率\n","    - 玉\n","    - 王\n","    - 玑\n","    - 玖\n","    - 玛\n","    - 玟\n","    - 玥\n","    - 玩\n","    - 玫\n","    - 玮\n","    - 环\n","    - 现\n","    - 玲\n","    - 玳\n","    - 玷\n","    - 玹\n","    - 玺\n","    - 玻\n","    - 珀\n","    - 珂\n","    - 珈\n","    - 珉\n","    - 珊\n","    - 珍\n","    - 珏\n","    - 珑\n","    - 珙\n","    - 珞\n","    - 珠\n","    - 珥\n","    - 班\n","    - 珮\n","    - 珲\n","    - 珺\n","    - 球\n","    - 琅\n","    - 理\n","    - 琉\n","    - 琊\n","    - 琏\n","    - 琐\n","    - 琛\n","    - 琢\n","    - 琤\n","    - 琥\n","    - 琦\n","    - 琨\n","    - 琪\n","    - 琬\n","    - 琮\n","    - 琰\n","    - 琳\n","    - 琴\n","    - 琵\n","    - 琶\n","    - 琼\n","    - 瑁\n","    - 瑄\n","    - 瑕\n","    - 瑙\n","    - 瑚\n","    - 瑛\n","    - 瑜\n","    - 瑞\n","    - 瑟\n","    - 瑠\n","    - 瑭\n","    - 瑰\n","    - 瑶\n","    - 瑷\n","    - 瑾\n","    - 璀\n","    - 璃\n","    - 璇\n","    - 璋\n","    - 璐\n","    - 璞\n","    - 璟\n","    - 璧\n","    - 璨\n","    - 瓜\n","    - 瓢\n","    - 瓣\n","    - 瓦\n","    - 瓮\n","    - 瓯\n","    - 瓶\n","    - 瓷\n","    - 甄\n","    - 甘\n","    - 甚\n","    - 甜\n","    - 生\n","    - 甥\n","    - 用\n","    - 甩\n","    - 甫\n","    - 甬\n","    - 甭\n","    - 田\n","    - 由\n","    - 甲\n","    - 申\n","    - 电\n","    - 男\n","    - 甸\n","    - 町\n","    - 画\n","    - 畅\n","    - 畈\n","    - 畊\n","    - 界\n","    - 畏\n","    - 畔\n","    - 留\n","    - 畜\n","    - 略\n","    - 番\n","    - 畴\n","    - 畸\n","    - 畿\n","    - 疃\n","    - 疆\n","    - 疏\n","    - 疑\n","    - 疖\n","    - 疗\n","    - 疙\n","    - 疚\n","    - 疝\n","    - 疟\n","    - 疡\n","    - 疣\n","    - 疤\n","    - 疫\n","    - 疮\n","    - 疯\n","    - 疱\n","    - 疲\n","    - 疴\n","    - 疵\n","    - 疸\n","    - 疹\n","    - 疼\n","    - 疽\n","    - 疾\n","    - 病\n","    - 症\n","    - 痉\n","    - 痊\n","    - 痍\n","    - 痒\n","    - 痔\n","    - 痕\n","    - 痘\n","    - 痛\n","    - 痞\n","    - 痢\n","    - 痣\n","    - 痧\n","    - 痨\n","    - 痪\n","    - 痫\n","    - 痰\n","    - 痱\n","    - 痴\n","    - 痹\n","    - 痼\n","    - 瘀\n","    - 瘁\n","    - 瘙\n","    - 瘟\n","    - 瘠\n","    - 瘢\n","    - 瘤\n","    - 瘦\n","    - 瘩\n","    - 瘪\n","    - 瘫\n","    - 瘳\n","    - 瘴\n","    - 瘸\n","    - 瘾\n","    - 癌\n","    - 癖\n","    - 癜\n","    - 癞\n","    - 癣\n","    - 癫\n","    - 登\n","    - 白\n","    - 百\n","    - 皂\n","    - 的\n","    - 皆\n","    - 皇\n","    - 皋\n","    - 皎\n","    - 皑\n","    - 皓\n","    - 皖\n","    - 皙\n","    - 皮\n","    - 皱\n","    - 皿\n","    - 盂\n","    - 盅\n","    - 盆\n","    - 盈\n","    - 益\n","    - 盎\n","    - 盏\n","    - 盐\n","    - 监\n","    - 盒\n","    - 盔\n","    - 盖\n","    - 盗\n","    - 盘\n","    - 盛\n","    - 盟\n","    - 目\n","    - 盯\n","    - 盱\n","    - 盲\n","    - 直\n","    - 相\n","    - 盹\n","    - 盼\n","    - 盾\n","    - 省\n","    - 眈\n","    - 眉\n","    - 看\n","    - 眙\n","    - 真\n","    - 眠\n","    - 眨\n","    - 眩\n","    - 眬\n","    - 眯\n","    - 眶\n","    - 眷\n","    - 眸\n","    - 眺\n","    - 眼\n","    - 着\n","    - 睁\n","    - 睇\n","    - 睐\n","    - 睑\n","    - 睛\n","    - 睡\n","    - 睢\n","    - 督\n","    - 睦\n","    - 睫\n","    - 睬\n","    - 睹\n","    - 睽\n","    - 睾\n","    - 睿\n","    - 瞄\n","    - 瞅\n","    - 瞌\n","    - 瞎\n","    - 瞑\n","    - 瞒\n","    - 瞟\n","    - 瞠\n","    - 瞥\n","    - 瞧\n","    - 瞩\n","    - 瞪\n","    - 瞬\n","    - 瞭\n","    - 瞰\n","    - 瞳\n","    - 瞻\n","    - 瞿\n","    - 矍\n","    - 矗\n","    - 矛\n","    - 矜\n","    - 矢\n","    - 矣\n","    - 知\n","    - 矩\n","    - 矫\n","    - 矬\n","    - 短\n","    - 矮\n","    - 石\n","    - 矶\n","    - 矸\n","    - 矽\n","    - 矾\n","    - 矿\n","    - 砀\n","    - 码\n","    - 砂\n","    - 砌\n","    - 砍\n","    - 砒\n","    - 研\n","    - 砖\n","    - 砚\n","    - 砝\n","    - 砣\n","    - 砥\n","    - 砭\n","    - 砰\n","    - 破\n","    - 砷\n","    - 砸\n","    - 砺\n","    - 砼\n","    - 砾\n","    - 础\n","    - 硅\n","    - 硌\n","    - 硒\n","    - 硕\n","    - 硖\n","    - 硚\n","    - 硝\n","    - 硫\n","    - 硬\n","    - 确\n","    - 硼\n","    - 碉\n","    - 碌\n","    - 碍\n","    - 碎\n","    - 碑\n","    - 碓\n","    - 碗\n","    - 碘\n","    - 碚\n","    - 碜\n","    - 碟\n","    - 碣\n","    - 碧\n","    - 碰\n","    - 碱\n","    - 碳\n","    - 碴\n","    - 碾\n","    - 磁\n","    - 磅\n","    - 磊\n","    - 磋\n","    - 磐\n","    - 磕\n","    - 磨\n","    - 磴\n","    - 磷\n","    - 磺\n","    - 礁\n","    - 示\n","    - 礼\n","    - 社\n","    - 祀\n","    - 祁\n","    - 祈\n","    - 祉\n","    - 祎\n","    - 祐\n","    - 祖\n","    - 祚\n","    - 祛\n","    - 祝\n","    - 神\n","    - 祟\n","    - 祠\n","    - 祢\n","    - 祥\n","    - 票\n","    - 祭\n","    - 祯\n","    - 祷\n","    - 祸\n","    - 祺\n","    - 禀\n","    - 禁\n","    - 禄\n","    - 禅\n","    - 福\n","    - 禧\n","    - 禹\n","    - 禺\n","    - 离\n","    - 禽\n","    - 禾\n","    - 秀\n","    - 私\n","    - 秃\n","    - 秆\n","    - 秉\n","    - 秋\n","    - 种\n","    - 科\n","    - 秒\n","    - 秘\n","    - 租\n","    - 秣\n","    - 秤\n","    - 秦\n","    - 秧\n","    - 秩\n","    - 积\n","    - 称\n","    - 秸\n","    - 移\n","    - 秽\n","    - 稀\n","    - 程\n","    - 稍\n","    - 税\n","    - 稔\n","    - 稚\n","    - 稞\n","    - 稠\n","    - 稣\n","    - 稳\n","    - 稷\n","    - 稹\n","    - 稻\n","    - 稼\n","    - 稽\n","    - 稿\n","    - 穆\n","    - 穗\n","    - 穴\n","    - 究\n","    - 穷\n","    - 穹\n","    - 空\n","    - 穿\n","    - 突\n","    - 窃\n","    - 窄\n","    - 窈\n","    - 窍\n","    - 窑\n","    - 窒\n","    - 窕\n","    - 窖\n","    - 窗\n","    - 窘\n","    - 窜\n","    - 窝\n","    - 窟\n","    - 窠\n","    - 窥\n","    - 窦\n","    - 窨\n","    - 窿\n","    - 立\n","    - 竖\n","    - 站\n","    - 竞\n","    - 竟\n","    - 章\n","    - 竣\n","    - 童\n","    - 竭\n","    - 端\n","    - 竹\n","    - 竺\n","    - 竽\n","    - 竿\n","    - 笃\n","    - 笆\n","    - 笈\n","    - 笋\n","    - 笑\n","    - 笔\n","    - 笙\n","    - 笛\n","    - 笠\n","    - 符\n","    - 笨\n","    - 第\n","    - 笳\n","    - 笸\n","    - 笼\n","    - 等\n","    - 筋\n","    - 筏\n","    - 筐\n","    - 筑\n","    - 筒\n","    - 答\n","    - 策\n","    - 筛\n","    - 筝\n","    - 筠\n","    - 筱\n","    - 筵\n","    - 筷\n","    - 筹\n","    - 签\n","    - 简\n","    - 箍\n","    - 箔\n","    - 箕\n","    - 算\n","    - 管\n","    - 箩\n","    - 箫\n","    - 箭\n","    - 箱\n","    - 箴\n","    - 篁\n","    - 篆\n","    - 篇\n","    - 篑\n","    - 篓\n","    - 篝\n","    - 篡\n","    - 篦\n","    - 篪\n","    - 篮\n","    - 篱\n","    - 篷\n","    - 篼\n","    - 簇\n","    - 簋\n","    - 簧\n","    - 簪\n","    - 簸\n","    - 簿\n","    - 籁\n","    - 籍\n","    - 米\n","    - 类\n","    - 籼\n","    - 籽\n","    - 粉\n","    - 粑\n","    - 粒\n","    - 粕\n","    - 粗\n","    - 粘\n","    - 粟\n","    - 粤\n","    - 粥\n","    - 粪\n","    - 粮\n","    - 粱\n","    - 粲\n","    - 粳\n","    - 粹\n","    - 粼\n","    - 粽\n","    - 精\n","    - 糊\n","    - 糕\n","    - 糖\n","    - 糗\n","    - 糙\n","    - 糟\n","    - 糠\n","    - 糯\n","    - 系\n","    - 紊\n","    - 素\n","    - 索\n","    - 紧\n","    - 紫\n","    - 累\n","    - 絮\n","    - 綦\n","    - 繁\n","    - 纂\n","    - 纠\n","    - 纡\n","    - 红\n","    - 纣\n","    - 纤\n","    - 约\n","    - 级\n","    - 纨\n","    - 纪\n","    - 纫\n","    - 纬\n","    - 纭\n","    - 纯\n","    - 纰\n","    - 纱\n","    - 纲\n","    - 纳\n","    - 纵\n","    - 纶\n","    - 纷\n","    - 纸\n","    - 纹\n","    - 纺\n","    - 纽\n","    - 纾\n","    - 线\n","    - 绀\n","    - 练\n","    - 组\n","    - 绅\n","    - 细\n","    - 织\n","    - 终\n","    - 绉\n","    - 绊\n","    - 绋\n","    - 绌\n","    - 绍\n","    - 绎\n","    - 经\n","    - 绑\n","    - 绒\n","    - 结\n","    - 绔\n","    - 绕\n","    - 绘\n","    - 给\n","    - 绚\n","    - 绛\n","    - 络\n","    - 绝\n","    - 绞\n","    - 统\n","    - 绢\n","    - 绣\n","    - 绥\n","    - 继\n","    - 绩\n","    - 绪\n","    - 绫\n","    - 续\n","    - 绮\n","    - 绯\n","    - 绰\n","    - 绳\n","    - 维\n","    - 绵\n","    - 绷\n","    - 绸\n","    - 绻\n","    - 综\n","    - 绽\n","    - 绿\n","    - 缀\n","    - 缄\n","    - 缅\n","    - 缆\n","    - 缇\n","    - 缉\n","    - 缎\n","    - 缓\n","    - 缔\n","    - 缕\n","    - 编\n","    - 缘\n","    - 缙\n","    - 缚\n","    - 缛\n","    - 缜\n","    - 缝\n","    - 缠\n","    - 缢\n","    - 缤\n","    - 缨\n","    - 缩\n","    - 缪\n","    - 缬\n","    - 缭\n","    - 缮\n","    - 缰\n","    - 缱\n","    - 缴\n","    - 缸\n","    - 缺\n","    - 罂\n","    - 罄\n","    - 罐\n","    - 网\n","    - 罔\n","    - 罕\n","    - 罗\n","    - 罚\n","    - 罡\n","    - 罢\n","    - 罩\n","    - 罪\n","    - 置\n","    - 署\n","    - 罹\n","    - 羁\n","    - 羊\n","    - 羌\n","    - 美\n","    - 羔\n","    - 羚\n","    - 羞\n","    - 羡\n","    - 群\n","    - 羧\n","    - 羯\n","    - 羲\n","    - 羸\n","    - 羹\n","    - 羽\n","    - 羿\n","    - 翁\n","    - 翅\n","    - 翊\n","    - 翌\n","    - 翎\n","    - 翔\n","    - 翘\n","    - 翟\n","    - 翠\n","    - 翡\n","    - 翩\n","    - 翰\n","    - 翱\n","    - 翻\n","    - 翼\n","    - 耀\n","    - 老\n","    - 考\n","    - 耄\n","    - 者\n","    - 耆\n","    - 耋\n","    - 而\n","    - 耍\n","    - 耐\n","    - 耒\n","    - 耕\n","    - 耗\n","    - 耘\n","    - 耙\n","    - 耜\n","    - 耪\n","    - 耳\n","    - 耶\n","    - 耷\n","    - 耸\n","    - 耻\n","    - 耽\n","    - 耿\n","    - 聂\n","    - 聆\n","    - 聊\n","    - 聋\n","    - 职\n","    - 联\n","    - 聘\n","    - 聚\n","    - 聪\n","    - 肃\n","    - 肆\n","    - 肇\n","    - 肉\n","    - 肋\n","    - 肌\n","    - 肖\n","    - 肘\n","    - 肚\n","    - 肛\n","    - 肝\n","    - 肠\n","    - 股\n","    - 肢\n","    - 肤\n","    - 肥\n","    - 肩\n","    - 肪\n","    - 肮\n","    - 肯\n","    - 肱\n","    - 育\n","    - 肴\n","    - 肺\n","    - 肾\n","    - 肿\n","    - 胀\n","    - 胁\n","    - 胃\n","    - 胆\n","    - 背\n","    - 胎\n","    - 胖\n","    - 胗\n","    - 胚\n","    - 胛\n","    - 胜\n","    - 胞\n","    - 胡\n","    - 胤\n","    - 胥\n","    - 胧\n","    - 胫\n","    - 胭\n","    - 胯\n","    - 胰\n","    - 胱\n","    - 胳\n","    - 胶\n","    - 胸\n","    - 胺\n","    - 能\n","    - 脂\n","    - 脆\n","    - 脉\n","    - 脊\n","    - 脍\n","    - 脏\n","    - 脐\n","    - 脑\n","    - 脓\n","    - 脖\n","    - 脚\n","    - 脯\n","    - 脱\n","    - 脸\n","    - 脾\n","    - 腆\n","    - 腈\n","    - 腊\n","    - 腋\n","    - 腌\n","    - 腐\n","    - 腑\n","    - 腓\n","    - 腔\n","    - 腕\n","    - 腥\n","    - 腩\n","    - 腭\n","    - 腮\n","    - 腰\n","    - 腱\n","    - 腴\n","    - 腹\n","    - 腺\n","    - 腻\n","    - 腼\n","    - 腾\n","    - 腿\n","    - 膀\n","    - 膈\n","    - 膊\n","    - 膏\n","    - 膑\n","    - 膛\n","    - 膜\n","    - 膝\n","    - 膨\n","    - 膳\n","    - 膺\n","    - 臀\n","    - 臂\n","    - 臃\n","    - 臆\n","    - 臊\n","    - 臣\n","    - 臧\n","    - 自\n","    - 臬\n","    - 臭\n","    - 至\n","    - 致\n","    - 臻\n","    - 臼\n","    - 舀\n","    - 舅\n","    - 舆\n","    - 舌\n","    - 舍\n","    - 舐\n","    - 舒\n","    - 舔\n","    - 舛\n","    - 舜\n","    - 舞\n","    - 舟\n","    - 航\n","    - 舫\n","    - 般\n","    - 舰\n","    - 舱\n","    - 舵\n","    - 舶\n","    - 舷\n","    - 舸\n","    - 船\n","    - 艇\n","    - 艋\n","    - 艘\n","    - 艮\n","    - 良\n","    - 艰\n","    - 色\n","    - 艳\n","    - 艺\n","    - 艾\n","    - 艿\n","    - 节\n","    - 芊\n","    - 芋\n","    - 芍\n","    - 芒\n","    - 芗\n","    - 芙\n","    - 芜\n","    - 芝\n","    - 芥\n","    - 芦\n","    - 芩\n","    - 芪\n","    - 芬\n","    - 芭\n","    - 芮\n","    - 芯\n","    - 花\n","    - 芳\n","    - 芷\n","    - 芸\n","    - 芹\n","    - 芽\n","    - 芾\n","    - 苇\n","    - 苋\n","    - 苍\n","    - 苏\n","    - 苑\n","    - 苓\n","    - 苔\n","    - 苗\n","    - 苛\n","    - 苞\n","    - 苟\n","    - 苡\n","    - 苣\n","    - 若\n","    - 苦\n","    - 苫\n","    - 苯\n","    - 英\n","    - 苷\n","    - 苹\n","    - 茁\n","    - 茂\n","    - 范\n","    - 茄\n","    - 茅\n","    - 茆\n","    - 茉\n","    - 茌\n","    - 茎\n","    - 茗\n","    - 茛\n","    - 茜\n","    - 茧\n","    - 茨\n","    - 茫\n","    - 茬\n","    - 茯\n","    - 茱\n","    - 茳\n","    - 茴\n","    - 茵\n","    - 茶\n","    - 茸\n","    - 茹\n","    - 茼\n","    - 荀\n","    - 荃\n","    - 荆\n","    - 荇\n","    - 草\n","    - 荏\n","    - 荐\n","    - 荒\n","    - 荔\n","    - 荚\n","    - 荛\n","    - 荞\n","    - 荟\n","    - 荠\n","    - 荡\n","    - 荣\n","    - 荤\n","    - 荧\n","    - 荨\n","    - 荫\n","    - 药\n","    - 荷\n","    - 荸\n","    - 荻\n","    - 荼\n","    - 莅\n","    - 莆\n","    - 莉\n","    - 莎\n","    - 莒\n","    - 莓\n","    - 莘\n","    - 莜\n","    - 莞\n","    - 莠\n","    - 莪\n","    - 莫\n","    - 莱\n","    - 莲\n","    - 莴\n","    - 获\n","    - 莹\n","    - 莺\n","    - 莽\n","    - 菀\n","    - 菁\n","    - 菅\n","    - 菇\n","    - 菊\n","    - 菌\n","    - 菏\n","    - 菖\n","    - 菘\n","    - 菜\n","    - 菠\n","    - 菡\n","    - 菩\n","    - 菱\n","    - 菲\n","    - 萃\n","    - 萄\n","    - 萋\n","    - 萌\n","    - 萍\n","    - 萎\n","    - 萝\n","    - 萤\n","    - 营\n","    - 萦\n","    - 萧\n","    - 萨\n","    - 萱\n","    - 萸\n","    - 落\n","    - 葆\n","    - 著\n","    - 葚\n","    - 葛\n","    - 葡\n","    - 董\n","    - 葩\n","    - 葫\n","    - 葬\n","    - 葱\n","    - 葳\n","    - 葵\n","    - 葺\n","    - 蒂\n","    - 蒋\n","    - 蒙\n","    - 蒜\n","    - 蒯\n","    - 蒲\n","    - 蒸\n","    - 蒿\n","    - 蓁\n","    - 蓄\n","    - 蓉\n","    - 蓓\n","    - 蓝\n","    - 蓟\n","    - 蓥\n","    - 蓦\n","    - 蓬\n","    - 蓼\n","    - 蔑\n","    - 蔓\n","    - 蔗\n","    - 蔚\n","    - 蔡\n","    - 蔫\n","    - 蔬\n","    - 蔷\n","    - 蔺\n","    - 蔻\n","    - 蔼\n","    - 蔽\n","    - 蕃\n","    - 蕉\n","    - 蕊\n","    - 蕙\n","    - 蕨\n","    - 蕲\n","    - 蕴\n","    - 蕾\n","    - 薄\n","    - 薇\n","    - 薏\n","    - 薛\n","    - 薪\n","    - 薯\n","    - 薰\n","    - 薷\n","    - 藁\n","    - 藉\n","    - 藏\n","    - 藐\n","    - 藓\n","    - 藕\n","    - 藜\n","    - 藠\n","    - 藤\n","    - 藩\n","    - 藻\n","    - 藿\n","    - 蘑\n","    - 蘸\n","    - 虎\n","    - 虏\n","    - 虐\n","    - 虑\n","    - 虔\n","    - 虚\n","    - 虞\n","    - 虫\n","    - 虱\n","    - 虹\n","    - 虻\n","    - 虽\n","    - 虾\n","    - 蚀\n","    - 蚁\n","    - 蚂\n","    - 蚊\n","    - 蚌\n","    - 蚓\n","    - 蚕\n","    - 蚝\n","    - 蚣\n","    - 蚤\n","    - 蚪\n","    - 蚬\n","    - 蚯\n","    - 蚱\n","    - 蚴\n","    - 蛀\n","    - 蛆\n","    - 蛇\n","    - 蛉\n","    - 蛊\n","    - 蛋\n","    - 蛎\n","    - 蛐\n","    - 蛔\n","    - 蛙\n","    - 蛛\n","    - 蛟\n","    - 蛤\n","    - 蛮\n","    - 蛰\n","    - 蛳\n","    - 蛹\n","    - 蛾\n","    - 蜀\n","    - 蜂\n","    - 蜃\n","    - 蜇\n","    - 蜈\n","    - 蜊\n","    - 蜍\n","    - 蜒\n","    - 蜓\n","    - 蜕\n","    - 蜗\n","    - 蜘\n","    - 蜚\n","    - 蜜\n","    - 蜡\n","    - 蜢\n","    - 蜥\n","    - 蜱\n","    - 蜴\n","    - 蜷\n","    - 蜻\n","    - 蜿\n","    - 蝇\n","    - 蝈\n","    - 蝉\n","    - 蝌\n","    - 蝎\n","    - 蝗\n","    - 蝙\n","    - 蝠\n","    - 蝮\n","    - 蝴\n","    - 蝶\n","    - 蝽\n","    - 螂\n","    - 螃\n","    - 螈\n","    - 融\n","    - 螨\n","    - 螳\n","    - 螺\n","    - 蟀\n","    - 蟆\n","    - 蟊\n","    - 蟋\n","    - 蟑\n","    - 蟒\n","    - 蟠\n","    - 蟹\n","    - 蟾\n","    - 蠊\n","    - 蠕\n","    - 蠡\n","    - 蠢\n","    - 血\n","    - 衅\n","    - 行\n","    - 衍\n","    - 衔\n","    - 街\n","    - 衙\n","    - 衡\n","    - 衢\n","    - 衣\n","    - 补\n","    - 表\n","    - 衩\n","    - 衫\n","    - 衬\n","    - 衮\n","    - 衰\n","    - 衲\n","    - 衷\n","    - 袁\n","    - 袂\n","    - 袄\n","    - 袅\n","    - 袈\n","    - 袋\n","    - 袍\n","    - 袒\n","    - 袖\n","    - 袜\n","    - 被\n","    - 袭\n","    - 袱\n","    - 裁\n","    - 裂\n","    - 装\n","    - 裆\n","    - 裔\n","    - 裕\n","    - 裘\n","    - 裙\n","    - 裟\n","    - 裤\n","    - 裨\n","    - 裱\n","    - 裳\n","    - 裴\n","    - 裸\n","    - 裹\n","    - 褂\n","    - 褐\n","    - 褒\n","    - 褓\n","    - 褔\n","    - 褚\n","    - 褛\n","    - 褥\n","    - 褪\n","    - 褴\n","    - 褶\n","    - 襁\n","    - 襄\n","    - 襟\n","    - 西\n","    - 要\n","    - 覃\n","    - 覆\n","    - 见\n","    - 观\n","    - 规\n","    - 觅\n","    - 视\n","    - 览\n","    - 觉\n","    - 觊\n","    - 觎\n","    - 觐\n","    - 觑\n","    - 角\n","    - 觞\n","    - 解\n","    - 觥\n","    - 触\n","    - 言\n","    - 訾\n","    - 詹\n","    - 誉\n","    - 誓\n","    - 警\n","    - 譬\n","    - 计\n","    - 订\n","    - 讣\n","    - 认\n","    - 讥\n","    - 讧\n","    - 讨\n","    - 让\n","    - 讪\n","    - 训\n","    - 议\n","    - 讯\n","    - 记\n","    - 讲\n","    - 讳\n","    - 讴\n","    - 讶\n","    - 讷\n","    - 许\n","    - 讹\n","    - 论\n","    - 讼\n","    - 讽\n","    - 设\n","    - 访\n","    - 诀\n","    - 证\n","    - 诃\n","    - 评\n","    - 诅\n","    - 识\n","    - 诈\n","    - 诉\n","    - 诊\n","    - 诋\n","    - 词\n","    - 诏\n","    - 译\n","    - 诓\n","    - 试\n","    - 诗\n","    - 诘\n","    - 诙\n","    - 诚\n","    - 诛\n","    - 话\n","    - 诞\n","    - 诟\n","    - 诠\n","    - 诡\n","    - 询\n","    - 诣\n","    - 诤\n","    - 该\n","    - 详\n","    - 诧\n","    - 诩\n","    - 诫\n","    - 诬\n","    - 语\n","    - 误\n","    - 诱\n","    - 诲\n","    - 说\n","    - 诵\n","    - 诶\n","    - 请\n","    - 诸\n","    - 诹\n","    - 诺\n","    - 读\n","    - 诽\n","    - 课\n","    - 诿\n","    - 谀\n","    - 谁\n","    - 调\n","    - 谅\n","    - 谆\n","    - 谈\n","    - 谊\n","    - 谋\n","    - 谌\n","    - 谍\n","    - 谎\n","    - 谏\n","    - 谐\n","    - 谑\n","    - 谓\n","    - 谕\n","    - 谖\n","    - 谘\n","    - 谙\n","    - 谚\n","    - 谛\n","    - 谜\n","    - 谟\n","    - 谢\n","    - 谣\n","    - 谤\n","    - 谦\n","    - 谧\n","    - 谨\n","    - 谩\n","    - 谬\n","    - 谭\n","    - 谮\n","    - 谯\n","    - 谱\n","    - 谴\n","    - 谶\n","    - 谷\n","    - 豁\n","    - 豆\n","    - 豇\n","    - 豉\n","    - 豌\n","    - 豚\n","    - 象\n","    - 豢\n","    - 豪\n","    - 豫\n","    - 豹\n","    - 豺\n","    - 貂\n","    - 貅\n","    - 貉\n","    - 貌\n","    - 貔\n","    - 贝\n","    - 贞\n","    - 负\n","    - 贡\n","    - 财\n","    - 责\n","    - 贤\n","    - 败\n","    - 账\n","    - 货\n","    - 质\n","    - 贩\n","    - 贪\n","    - 贫\n","    - 贬\n","    - 购\n","    - 贮\n","    - 贯\n","    - 贰\n","    - 贱\n","    - 贲\n","    - 贴\n","    - 贵\n","    - 贷\n","    - 贸\n","    - 费\n","    - 贺\n","    - 贻\n","    - 贼\n","    - 贾\n","    - 贿\n","    - 赁\n","    - 赂\n","    - 赃\n","    - 资\n","    - 赅\n","    - 赈\n","    - 赉\n","    - 赊\n","    - 赋\n","    - 赌\n","    - 赎\n","    - 赏\n","    - 赐\n","    - 赓\n","    - 赔\n","    - 赖\n","    - 赘\n","    - 赚\n","    - 赛\n","    - 赝\n","    - 赞\n","    - 赠\n","    - 赡\n","    - 赢\n","    - 赣\n","    - 赤\n","    - 赦\n","    - 赫\n","    - 走\n","    - 赳\n","    - 赴\n","    - 赵\n","    - 赶\n","    - 起\n","    - 趁\n","    - 超\n","    - 越\n","    - 趋\n","    - 趟\n","    - 趣\n","    - 足\n","    - 趴\n","    - 趵\n","    - 趸\n","    - 趺\n","    - 趾\n","    - 跃\n","    - 跄\n","    - 跆\n","    - 跋\n","    - 跌\n","    - 跎\n","    - 跑\n","    - 跚\n","    - 跛\n","    - 距\n","    - 跟\n","    - 跤\n","    - 跨\n","    - 跪\n","    - 跬\n","    - 路\n","    - 跳\n","    - 践\n","    - 跶\n","    - 跷\n","    - 跹\n","    - 跺\n","    - 跻\n","    - 踉\n","    - 踊\n","    - 踌\n","    - 踏\n","    - 踝\n","    - 踞\n","    - 踢\n","    - 踩\n","    - 踪\n","    - 踮\n","    - 踯\n","    - 踱\n","    - 踵\n","    - 踹\n","    - 踺\n","    - 蹁\n","    - 蹂\n","    - 蹄\n","    - 蹈\n","    - 蹉\n","    - 蹊\n","    - 蹋\n","    - 蹒\n","    - 蹚\n","    - 蹦\n","    - 蹩\n","    - 蹬\n","    - 蹭\n","    - 蹲\n","    - 蹴\n","    - 蹶\n","    - 蹼\n","    - 蹿\n","    - 躁\n","    - 躅\n","    - 躇\n","    - 躏\n","    - 身\n","    - 躬\n","    - 躯\n","    - 躲\n","    - 躺\n","    - 车\n","    - 轧\n","    - 轨\n","    - 轩\n","    - 轫\n","    - 转\n","    - 轮\n","    - 软\n","    - 轰\n","    - 轱\n","    - 轲\n","    - 轳\n","    - 轴\n","    - 轶\n","    - 轸\n","    - 轻\n","    - 轼\n","    - 载\n","    - 轿\n","    - 较\n","    - 辄\n","    - 辅\n","    - 辆\n","    - 辈\n","    - 辉\n","    - 辊\n","    - 辍\n","    - 辐\n","    - 辑\n","    - 输\n","    - 辕\n","    - 辖\n","    - 辗\n","    - 辘\n","    - 辙\n","    - 辛\n","    - 辜\n","    - 辞\n","    - 辟\n","    - 辣\n","    - 辨\n","    - 辩\n","    - 辫\n","    - 辰\n","    - 辱\n","    - 边\n","    - 辽\n","    - 达\n","    - 迁\n","    - 迂\n","    - 迄\n","    - 迅\n","    - 过\n","    - 迈\n","    - 迎\n","    - 运\n","    - 近\n","    - 返\n","    - 还\n","    - 这\n","    - 进\n","    - 远\n","    - 违\n","    - 连\n","    - 迟\n","    - 迢\n","    - 迥\n","    - 迦\n","    - 迩\n","    - 迪\n","    - 迫\n","    - 迭\n","    - 述\n","    - 迷\n","    - 迸\n","    - 迹\n","    - 追\n","    - 退\n","    - 送\n","    - 适\n","    - 逃\n","    - 逅\n","    - 逆\n","    - 选\n","    - 逊\n","    - 逋\n","    - 逍\n","    - 透\n","    - 逐\n","    - 逑\n","    - 递\n","    - 途\n","    - 逗\n","    - 通\n","    - 逛\n","    - 逝\n","    - 逞\n","    - 速\n","    - 造\n","    - 逡\n","    - 逢\n","    - 逮\n","    - 逯\n","    - 逵\n","    - 逸\n","    - 逻\n","    - 逼\n","    - 逾\n","    - 遁\n","    - 遂\n","    - 遇\n","    - 遍\n","    - 遏\n","    - 遐\n","    - 遑\n","    - 道\n","    - 遗\n","    - 遛\n","    - 遢\n","    - 遣\n","    - 遥\n","    - 遨\n","    - 遭\n","    - 遮\n","    - 遴\n","    - 遵\n","    - 避\n","    - 邀\n","    - 邂\n","    - 邃\n","    - 邋\n","    - 邑\n","    - 邓\n","    - 邕\n","    - 邙\n","    - 邛\n","    - 邝\n","    - 邡\n","    - 邢\n","    - 那\n","    - 邦\n","    - 邪\n","    - 邬\n","    - 邮\n","    - 邯\n","    - 邰\n","    - 邱\n","    - 邳\n","    - 邵\n","    - 邸\n","    - 邹\n","    - 邺\n","    - 邻\n","    - 郁\n","    - 郅\n","    - 郇\n","    - 郊\n","    - 郎\n","    - 郑\n","    - 郓\n","    - 郜\n","    - 郝\n","    - 郡\n","    - 郧\n","    - 部\n","    - 郫\n","    - 郭\n","    - 郯\n","    - 郴\n","    - 郸\n","    - 都\n","    - 鄂\n","    - 鄙\n","    - 鄞\n","    - 鄢\n","    - 鄱\n","    - 酉\n","    - 酊\n","    - 酋\n","    - 酌\n","    - 配\n","    - 酐\n","    - 酒\n","    - 酗\n","    - 酚\n","    - 酝\n","    - 酞\n","    - 酣\n","    - 酥\n","    - 酩\n","    - 酪\n","    - 酬\n","    - 酮\n","    - 酯\n","    - 酰\n","    - 酱\n","    - 酵\n","    - 酶\n","    - 酷\n","    - 酸\n","    - 酿\n","    - 醇\n","    - 醉\n","    - 醋\n","    - 醍\n","    - 醐\n","    - 醒\n","    - 醛\n","    - 醺\n","    - 采\n","    - 釉\n","    - 释\n","    - 里\n","    - 重\n","    - 野\n","    - 量\n","    - 金\n","    - 釜\n","    - 鉴\n","    - 銮\n","    - 鏖\n","    - 鑫\n","    - 钇\n","    - 针\n","    - 钉\n","    - 钊\n","    - 钎\n","    - 钏\n","    - 钐\n","    - 钒\n","    - 钓\n","    - 钗\n","    - 钙\n","    - 钛\n","    - 钜\n","    - 钝\n","    - 钞\n","    - 钟\n","    - 钠\n","    - 钢\n","    - 钣\n","    - 钥\n","    - 钦\n","    - 钧\n","    - 钨\n","    - 钩\n","    - 钮\n","    - 钯\n","    - 钰\n","    - 钱\n","    - 钲\n","    - 钳\n","    - 钴\n","    - 钵\n","    - 钻\n","    - 钼\n","    - 钾\n","    - 钿\n","    - 铀\n","    - 铁\n","    - 铂\n","    - 铃\n","    - 铄\n","    - 铅\n","    - 铆\n","    - 铉\n","    - 铋\n","    - 铍\n","    - 铎\n","    - 铐\n","    - 铑\n","    - 铖\n","    - 铛\n","    - 铜\n","    - 铝\n","    - 铟\n","    - 铠\n","    - 铡\n","    - 铣\n","    - 铤\n","    - 铧\n","    - 铨\n","    - 铩\n","    - 铬\n","    - 铭\n","    - 铮\n","    - 铰\n","    - 铲\n","    - 银\n","    - 铷\n","    - 铸\n","    - 铺\n","    - 链\n","    - 铿\n","    - 销\n","    - 锁\n","    - 锂\n","    - 锄\n","    - 锅\n","    - 锆\n","    - 锈\n","    - 锉\n","    - 锋\n","    - 锌\n","    - 锏\n","    - 锐\n","    - 锑\n","    - 锒\n","    - 错\n","    - 锚\n","    - 锟\n","    - 锡\n","    - 锢\n","    - 锣\n","    - 锤\n","    - 锥\n","    - 锦\n","    - 锨\n","    - 锭\n","    - 键\n","    - 锯\n","    - 锰\n","    - 锲\n","    - 锴\n","    - 锵\n","    - 锷\n","    - 锹\n","    - 锻\n","    - 镀\n","    - 镁\n","    - 镂\n","    - 镇\n","    - 镉\n","    - 镊\n","    - 镌\n","    - 镍\n","    - 镏\n","    - 镐\n","    - 镑\n","    - 镔\n","    - 镕\n","    - 镖\n","    - 镜\n","    - 镣\n","    - 镭\n","    - 镯\n","    - 镰\n","    - 镳\n","    - 镶\n","    - 长\n","    - 门\n","    - 闩\n","    - 闪\n","    - 闫\n","    - 闭\n","    - 问\n","    - 闯\n","    - 闰\n","    - 闲\n","    - 闳\n","    - 间\n","    - 闵\n","    - 闷\n","    - 闸\n","    - 闹\n","    - 闺\n","    - 闻\n","    - 闽\n","    - 闾\n","    - 阀\n","    - 阁\n","    - 阂\n","    - 阄\n","    - 阅\n","    - 阆\n","    - 阉\n","    - 阎\n","    - 阐\n","    - 阑\n","    - 阔\n","    - 阕\n","    - 阖\n","    - 阙\n","    - 阚\n","    - 阜\n","    - 队\n","    - 阡\n","    - 阪\n","    - 阮\n","    - 阱\n","    - 防\n","    - 阳\n","    - 阴\n","    - 阵\n","    - 阶\n","    - 阻\n","    - 阿\n","    - 陀\n","    - 陂\n","    - 附\n","    - 际\n","    - 陆\n","    - 陇\n","    - 陈\n","    - 陉\n","    - 陋\n","    - 陌\n","    - 降\n","    - 限\n","    - 陕\n","    - 陛\n","    - 陡\n","    - 院\n","    - 除\n","    - 陨\n","    - 险\n","    - 陪\n","    - 陬\n","    - 陵\n","    - 陶\n","    - 陷\n","    - 隅\n","    - 隆\n","    - 隋\n","    - 隍\n","    - 随\n","    - 隐\n","    - 隔\n","    - 隗\n","    - 隘\n","    - 隙\n","    - 障\n","    - 隧\n","    - 隶\n","    - 隼\n","    - 隽\n","    - 难\n","    - 雀\n","    - 雁\n","    - 雄\n","    - 雅\n","    - 集\n","    - 雇\n","    - 雉\n","    - 雌\n","    - 雍\n","    - 雏\n","    - 雒\n","    - 雕\n","    - 雨\n","    - 雪\n","    - 雯\n","    - 雳\n","    - 零\n","    - 雷\n","    - 雹\n","    - 雾\n","    - 需\n","    - 霁\n","    - 霄\n","    - 霆\n","    - 震\n","    - 霈\n","    - 霉\n","    - 霍\n","    - 霎\n","    - 霏\n","    - 霓\n","    - 霖\n","    - 霜\n","    - 霞\n","    - 霪\n","    - 露\n","    - 霸\n","    - 霹\n","    - 霾\n","    - 靑\n","    - 青\n","    - 靓\n","    - 靖\n","    - 静\n","    - 靛\n","    - 非\n","    - 靠\n","    - 靡\n","    - 面\n","    - 革\n","    - 靳\n","    - 靴\n","    - 靶\n","    - 鞅\n","    - 鞋\n","    - 鞍\n","    - 鞑\n","    - 鞘\n","    - 鞠\n","    - 鞭\n","    - 韦\n","    - 韧\n","    - 韩\n","    - 韫\n","    - 韬\n","    - 韭\n","    - 音\n","    - 韵\n","    - 韶\n","    - 页\n","    - 顶\n","    - 顷\n","    - 项\n","    - 顺\n","    - 须\n","    - 顽\n","    - 顾\n","    - 顿\n","    - 颀\n","    - 颁\n","    - 颂\n","    - 预\n","    - 颅\n","    - 领\n","    - 颇\n","    - 颈\n","    - 颊\n","    - 颌\n","    - 颍\n","    - 颐\n","    - 频\n","    - 颓\n","    - 颖\n","    - 颗\n","    - 题\n","    - 颚\n","    - 颜\n","    - 额\n","    - 颠\n","    - 颢\n","    - 颤\n","    - 颦\n","    - 颧\n","    - 风\n","    - 飒\n","    - 飓\n","    - 飘\n","    - 飙\n","    - 飚\n","    - 飞\n","    - 食\n","    - 飧\n","    - 餍\n","    - 餐\n","    - 餮\n","    - 饕\n","    - 饥\n","    - 饨\n","    - 饪\n","    - 饭\n","    - 饮\n","    - 饯\n","    - 饰\n","    - 饱\n","    - 饲\n","    - 饴\n","    - 饵\n","    - 饶\n","    - 饷\n","    - 饺\n","    - 饼\n","    - 饽\n","    - 饿\n","    - 馀\n","    - 馁\n","    - 馄\n","    - 馅\n","    - 馆\n","    - 馈\n","    - 馊\n","    - 馋\n","    - 馍\n","    - 馏\n","    - 馑\n","    - 馒\n","    - 馕\n","    - 首\n","    - 馗\n","    - 香\n","    - 馥\n","    - 馨\n","    - 马\n","    - 驭\n","    - 驮\n","    - 驯\n","    - 驰\n","    - 驱\n","    - 驳\n","    - 驴\n","    - 驶\n","    - 驷\n","    - 驸\n","    - 驹\n","    - 驻\n","    - 驼\n","    - 驾\n","    - 驿\n","    - 骁\n","    - 骂\n","    - 骄\n","    - 骅\n","    - 骆\n","    - 骇\n","    - 骈\n","    - 骊\n","    - 骋\n","    - 验\n","    - 骏\n","    - 骐\n","    - 骑\n","    - 骓\n","    - 骗\n","    - 骚\n","    - 骛\n","    - 骜\n","    - 骝\n","    - 骞\n","    - 骠\n","    - 骡\n","    - 骤\n","    - 骥\n","    - 骨\n","    - 骰\n","    - 骷\n","    - 骸\n","    - 骺\n","    - 骼\n","    - 髂\n","    - 髅\n","    - 髋\n","    - 髌\n","    - 髓\n","    - 高\n","    - 髦\n","    - 髯\n","    - 鬃\n","    - 鬓\n","    - 鬟\n","    - 鬼\n","    - 魁\n","    - 魂\n","    - 魄\n","    - 魅\n","    - 魇\n","    - 魉\n","    - 魍\n","    - 魏\n","    - 魔\n","    - 魟\n","    - 鱼\n","    - 鱿\n","    - 鲁\n","    - 鲅\n","    - 鲈\n","    - 鲍\n","    - 鲑\n","    - 鲜\n","    - 鲟\n","    - 鲠\n","    - 鲢\n","    - 鲤\n","    - 鲨\n","    - 鲫\n","    - 鲭\n","    - 鲳\n","    - 鲶\n","    - 鲷\n","    - 鲸\n","    - 鲼\n","    - 鳃\n","    - 鳄\n","    - 鳅\n","    - 鳌\n","    - 鳍\n","    - 鳕\n","    - 鳖\n","    - 鳗\n","    - 鳝\n","    - 鳞\n","    - 鳟\n","    - 鸟\n","    - 鸠\n","    - 鸡\n","    - 鸢\n","    - 鸣\n","    - 鸥\n","    - 鸦\n","    - 鸩\n","    - 鸪\n","    - 鸫\n","    - 鸭\n","    - 鸯\n","    - 鸳\n","    - 鸵\n","    - 鸽\n","    - 鸾\n","    - 鸿\n","    - 鹁\n","    - 鹂\n","    - 鹃\n","    - 鹅\n","    - 鹉\n","    - 鹊\n","    - 鹌\n","    - 鹏\n","    - 鹑\n","    - 鹜\n","    - 鹞\n","    - 鹤\n","    - 鹦\n","    - 鹧\n","    - 鹫\n","    - 鹭\n","    - 鹰\n","    - 鹳\n","    - 鹿\n","    - 麂\n","    - 麋\n","    - 麒\n","    - 麓\n","    - 麝\n","    - 麟\n","    - 麦\n","    - 麸\n","    - 麻\n","    - 麾\n","    - 黄\n","    - 黍\n","    - 黎\n","    - 黏\n","    - 黑\n","    - 黔\n","    - 默\n","    - 黛\n","    - 黝\n","    - 黟\n","    - 黯\n","    - 鼎\n","    - 鼓\n","    - 鼠\n","    - 鼬\n","    - 鼹\n","    - 鼻\n","    - 鼾\n","    - 齐\n","    - 齿\n","    - 龃\n","    - 龄\n","    - 龅\n","    - 龈\n","    - 龉\n","    - 龊\n","    - 龌\n","    - 龙\n","    - 龚\n","    - 龟\n","    - 𫖯\n","    - 𫚉\n","    batch_size: 32\n","    shuffle: false\n","    use_start_end_token: false\n","    \n"]},{"output_type":"stream","name":"stdout","text":["[NeMo I 2022-10-29 05:27:19 features:225] PADDING: 16\n","[NeMo I 2022-10-29 05:27:24 audio_preprocessing:491] Numba CUDA SpecAugment kernel is being used\n","[NeMo I 2022-10-29 05:27:31 save_restore_connector:243] Model EncDecCTCModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.12.0/stt_zh_citrinet_1024_gamma_0_25/e4a8b1119971335507d9672e03bc80f4/stt_zh_citrinet_1024_gamma_0_25.nemo.\n","[NeMo I 2022-10-29 05:27:31 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_zh_en_transformer6x6/versions/1.0.0rc1/files/nmt_zh_en_transformer6x6.nemo to /root/.cache/torch/NeMo/NeMo_1.12.0/nmt_zh_en_transformer6x6/eff3792e6f4420ba83436be889e92d79/nmt_zh_en_transformer6x6.nemo\n","[NeMo I 2022-10-29 05:28:25 common:910] Instantiating model from pre-trained checkpoint\n","[NeMo I 2022-10-29 05:28:33 tokenizer_utils:179] Getting YouTokenToMeTokenizer with model: /tmp/tmp3e6cn4fn/tokenizer.decoder.32000.BPE.model with r2l: False.\n","[NeMo I 2022-10-29 05:28:34 tokenizer_utils:179] Getting YouTokenToMeTokenizer with model: /tmp/tmp3e6cn4fn/tokenizer.encoder.32000.BPE.model with r2l: False.\n"]},{"output_type":"stream","name":"stderr","text":["[NeMo W 2022-10-29 05:28:34 modelPT:143] 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","    src_file_name: /raid/tarred_data_accaligned_16k_tokens_32k_vocab_cov_0.999/batches.tokens.16000._OP_1..144_CL_.tar\n","    tgt_file_name: /raid/tarred_data_accaligned_16k_tokens_32k_vocab_cov_0.999/batches.tokens.16000._OP_1..144_CL_.tar\n","    tokens_in_batch: 16000\n","    clean: true\n","    max_seq_length: 512\n","    cache_ids: false\n","    cache_data_per_node: false\n","    use_cache: false\n","    shuffle: true\n","    num_samples: -1\n","    drop_last: false\n","    pin_memory: false\n","    num_workers: 8\n","    load_from_cached_dataset: false\n","    reverse_lang_direction: true\n","    load_from_tarred_dataset: true\n","    metadata_path: /raid/tarred_data_accaligned_16k_tokens_32k_vocab_cov_0.999/metadata.json\n","    tar_shuffle_n: 100\n","    \n","[NeMo W 2022-10-29 05:28:34 modelPT:150] 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","    src_file_name: /raid/wmt19-zh-en.clean.tok.src\n","    tgt_file_name: /raid/wmt19-zh-en.clean.tok.ref\n","    tokens_in_batch: 512\n","    clean: false\n","    max_seq_length: 512\n","    cache_ids: false\n","    cache_data_per_node: false\n","    use_cache: false\n","    shuffle: false\n","    num_samples: -1\n","    drop_last: false\n","    pin_memory: false\n","    num_workers: 8\n","    load_from_cached_dataset: false\n","    reverse_lang_direction: false\n","    load_from_tarred_dataset: false\n","    metadata_path: null\n","    tar_shuffle_n: 100\n","    \n","[NeMo W 2022-10-29 05:28:34 modelPT:156] 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","    src_file_name: /raid/wmt20-zh-en.clean.tok.src\n","    tgt_file_name: /raid/wmt20-zh-en.clean.tok.src\n","    tokens_in_batch: 512\n","    clean: false\n","    max_seq_length: 512\n","    cache_ids: false\n","    cache_data_per_node: false\n","    use_cache: false\n","    shuffle: false\n","    num_samples: -1\n","    drop_last: false\n","    pin_memory: false\n","    num_workers: 8\n","    load_from_cached_dataset: false\n","    reverse_lang_direction: false\n","    load_from_tarred_dataset: false\n","    metadata_path: null\n","    tar_shuffle_n: 100\n","    \n","[NeMo W 2022-10-29 05:28:34 nlp_overrides:227] Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/apex\n","    Megatron-based models require Apex to function correctly.\n"]},{"output_type":"stream","name":"stdout","text":["[NeMo I 2022-10-29 05:28:38 save_restore_connector:243] Model MTEncDecModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.12.0/nmt_zh_en_transformer6x6/eff3792e6f4420ba83436be889e92d79/nmt_zh_en_transformer6x6.nemo.\n","[NeMo I 2022-10-29 05:28:38 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_fastpitch/versions/1.8.1/files/tts_en_fastpitch_align.nemo to /root/.cache/torch/NeMo/NeMo_1.12.0/tts_en_fastpitch_align/26d7e09971f1d611e24df90c7a9d9b38/tts_en_fastpitch_align.nemo\n","[NeMo I 2022-10-29 05:28:52 common:910] Instantiating model from pre-trained checkpoint\n","[NeMo I 2022-10-29 05:28:55 tokenize_and_classify:87] Creating ClassifyFst grammars.\n"]},{"output_type":"stream","name":"stderr","text":["[NeMo W 2022-10-29 05:29:20 modules:89] apply_to_oov_word=None, This means that some of words will remain unchanged if they are not handled by any of the rules in self.parse_one_word(). This may be intended if phonemes and chars are both valid inputs, otherwise, you may see unexpected deletions in your input.\n","[NeMo W 2022-10-29 05:29:20 modelPT:143] 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","    dataset:\n","      _target_: nemo.collections.tts.torch.data.TTSDataset\n","      manifest_filepath: /ws/LJSpeech/nvidia_ljspeech_train_clean_ngc.json\n","      sample_rate: 22050\n","      sup_data_path: /raid/LJSpeech/supplementary\n","      sup_data_types:\n","      - align_prior_matrix\n","      - pitch\n","      n_fft: 1024\n","      win_length: 1024\n","      hop_length: 256\n","      window: hann\n","      n_mels: 80\n","      lowfreq: 0\n","      highfreq: 8000\n","      max_duration: null\n","      min_duration: 0.1\n","      ignore_file: null\n","      trim: false\n","      pitch_fmin: 65.40639132514966\n","      pitch_fmax: 2093.004522404789\n","      pitch_norm: true\n","      pitch_mean: 212.35873413085938\n","      pitch_std: 68.52806091308594\n","      use_beta_binomial_interpolator: true\n","    dataloader_params:\n","      drop_last: false\n","      shuffle: true\n","      batch_size: 24\n","      num_workers: 0\n","    \n","[NeMo W 2022-10-29 05:29:20 modelPT:150] 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","    dataset:\n","      _target_: nemo.collections.tts.torch.data.TTSDataset\n","      manifest_filepath: /ws/LJSpeech/nvidia_ljspeech_val_clean_ngc.json\n","      sample_rate: 22050\n","      sup_data_path: /raid/LJSpeech/supplementary\n","      sup_data_types:\n","      - align_prior_matrix\n","      - pitch\n","      n_fft: 1024\n","      win_length: 1024\n","      hop_length: 256\n","      window: hann\n","      n_mels: 80\n","      lowfreq: 0\n","      highfreq: 8000\n","      max_duration: null\n","      min_duration: null\n","      ignore_file: null\n","      trim: false\n","      pitch_fmin: 65.40639132514966\n","      pitch_fmax: 2093.004522404789\n","      pitch_norm: true\n","      pitch_mean: 212.35873413085938\n","      pitch_std: 68.52806091308594\n","      use_beta_binomial_interpolator: true\n","    dataloader_params:\n","      drop_last: false\n","      shuffle: false\n","      batch_size: 24\n","      num_workers: 0\n","    \n"]},{"output_type":"stream","name":"stdout","text":["[NeMo I 2022-10-29 05:29:20 features:225] PADDING: 1\n","[NeMo I 2022-10-29 05:29:21 save_restore_connector:243] Model FastPitchModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.12.0/tts_en_fastpitch_align/26d7e09971f1d611e24df90c7a9d9b38/tts_en_fastpitch_align.nemo.\n","[NeMo I 2022-10-29 05:29:21 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_hifigan/versions/1.0.0rc1/files/tts_hifigan.nemo to /root/.cache/torch/NeMo/NeMo_1.12.0/tts_hifigan/e6da322f0f7e7dcf3f1900a9229a7e69/tts_hifigan.nemo\n","[NeMo I 2022-10-29 05:29:45 common:910] Instantiating model from pre-trained checkpoint\n"]},{"output_type":"stream","name":"stderr","text":["[NeMo W 2022-10-29 05:29:48 modelPT:143] 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","    dataset:\n","      _target_: nemo.collections.tts.data.datalayers.MelAudioDataset\n","      manifest_filepath: /home/fkreuk/data/train_finetune.txt\n","      min_duration: 0.75\n","      n_segments: 8192\n","    dataloader_params:\n","      drop_last: false\n","      shuffle: true\n","      batch_size: 64\n","      num_workers: 4\n","    \n","[NeMo W 2022-10-29 05:29:48 modelPT:150] 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","    dataset:\n","      _target_: nemo.collections.tts.data.datalayers.MelAudioDataset\n","      manifest_filepath: /home/fkreuk/data/val_finetune.txt\n","      min_duration: 3\n","      n_segments: 66150\n","    dataloader_params:\n","      drop_last: false\n","      shuffle: false\n","      batch_size: 5\n","      num_workers: 4\n","    \n","[NeMo W 2022-10-29 05:29:48 features:203] Using torch_stft is deprecated and has been removed. The values have been forcibly set to False for FilterbankFeatures and AudioToMelSpectrogramPreprocessor. Please set exact_pad to True as needed.\n"]},{"output_type":"stream","name":"stdout","text":["[NeMo I 2022-10-29 05:29:48 features:225] PADDING: 0\n"]},{"output_type":"stream","name":"stderr","text":["[NeMo W 2022-10-29 05:29:48 features:203] Using torch_stft is deprecated and has been removed. The values have been forcibly set to False for FilterbankFeatures and AudioToMelSpectrogramPreprocessor. Please set exact_pad to True as needed.\n"]},{"output_type":"stream","name":"stdout","text":["[NeMo I 2022-10-29 05:29:48 features:225] PADDING: 0\n","[NeMo I 2022-10-29 05:29:49 save_restore_connector:243] Model HifiGanModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.12.0/tts_hifigan/e6da322f0f7e7dcf3f1900a9229a7e69/tts_hifigan.nemo.\n"]}],"source":["# Speech Recognition model - Citrinet initially trained on Multilingual LibriSpeech English corpus, and fine-tuned on the open source Aishell-2\n","asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"stt_zh_citrinet_1024_gamma_0_25\").cuda()\n","\n","# Neural Machine Translation model\n","nmt_model = nemo_nlp.models.MTEncDecModel.from_pretrained(model_name='nmt_zh_en_transformer6x6').cuda()\n","\n","# Spectrogram generator which takes text as an input and produces spectrogram\n","spectrogram_generator = nemo_tts.models.FastPitchModel.from_pretrained(model_name=\"tts_en_fastpitch\").cuda()\n","\n","# Vocoder model which takes spectrogram and produces actual audio\n","vocoder = nemo_tts.models.HifiGanModel.from_pretrained(model_name=\"tts_hifigan\").cuda()"]},{"cell_type":"markdown","metadata":{"id":"KPota-JtsqSY"},"source":["## Get an audio sample in Mandarin"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"7cGCEKkcLr52","colab":{"base_uri":"https://localhost:8080/","height":270},"executionInfo":{"status":"ok","timestamp":1667021411244,"user_tz":-330,"elapsed":1617,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}},"outputId":"8f2906ac-ea82-452b-bf4f-d2d949afffb0"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-10-29 05:30:11--  https://nemo-public.s3.us-east-2.amazonaws.com/zh-samples/common_voice_zh-CN_21347786.mp3\n","Resolving nemo-public.s3.us-east-2.amazonaws.com (nemo-public.s3.us-east-2.amazonaws.com)... 52.219.105.210\n","Connecting to nemo-public.s3.us-east-2.amazonaws.com (nemo-public.s3.us-east-2.amazonaws.com)|52.219.105.210|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 24813 (24K) [audio/mp3]\n","Saving to: ‘common_voice_zh-CN_21347786.mp3’\n","\n","common_voice_zh-CN_ 100%[===================>]  24.23K  --.-KB/s    in 0.1s    \n","\n","2022-10-29 05:30:11 (254 KB/s) - ‘common_voice_zh-CN_21347786.mp3’ saved [24813/24813]\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["<IPython.lib.display.Audio object>"],"text/html":["\n","                <audio  controls=\"controls\" >\n","                    <source src=\"data:audio/mpeg;base64,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\" type=\"audio/mpeg\" />\n","                    Your browser does not support the audio element.\n","                </audio>\n","              "]},"metadata":{},"execution_count":5}],"source":["# Download audio sample which we'll try\n","# This is a sample from MCV 6.1 Dev dataset - the model hasn't seen it before\n","# IMPORTANT: The audio must be mono with 16Khz sampling rate\n","audio_sample = 'common_voice_zh-CN_21347786.mp3'\n","!wget 'https://nemo-public.s3.us-east-2.amazonaws.com/zh-samples/common_voice_zh-CN_21347786.mp3'\n","# To listen it, click on the play button below\n","IPython.display.Audio(audio_sample)"]},{"cell_type":"markdown","metadata":{"id":"BaCdNJhhtBfM"},"source":["## Transcribe audio file\n","We will use speech recognition model to convert audio into text.\n"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"KTA7jM6sL6yC","colab":{"base_uri":"https://localhost:8080/","height":67,"referenced_widgets":["f8ef803fbe754870bb146093c1381388","d86d5fc34cfd4ca2ab9072bce661bf62","9f5f3ddd41074199ac6919075a1671de","bfbfaf95b35446bf808b2262a539369c","a2a6c468aa0e4ab9ba7bd9962c33e371","d3fe21b5fd67455792e93b9bdf10df1d","b579e85a70d3471aab67647037848378","7ac86d068bdf40c49c57312f2e1cffa4","a13815a2634042e1979f07b1b622df3e","a21dc63da1fd44f9a1dc270575978509","1777104e52004c7ab79e590525b11dac"]},"executionInfo":{"status":"ok","timestamp":1667021486617,"user_tz":-330,"elapsed":9425,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}},"outputId":"fd0b3a6f-a1fd-4678-ceff-814335c49931"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Transcribing:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f8ef803fbe754870bb146093c1381388"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["['我们尽了最大努力']\n"]}],"source":["transcribed_text = asr_model.transcribe([audio_sample])\n","print(transcribed_text)"]},{"cell_type":"markdown","metadata":{"id":"BjYb2TMtttCc"},"source":["## Translate Chinese text into English\n","NeMo's NMT models have a handy ``.translate()`` method."]},{"cell_type":"code","execution_count":7,"metadata":{"id":"kQTdE4b9Nm9O","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667021524405,"user_tz":-330,"elapsed":1258,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}},"outputId":"4480671b-b48b-4f2f-8c60-30a8295319b5"},"outputs":[{"output_type":"stream","name":"stderr","text":["[NeMo W 2022-10-29 05:32:05 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/nlp/modules/common/transformer/transformer_generators.py:363: 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","      mems_ids = indices_i.unsqueeze(2).unsqueeze(3).repeat(1, 1, p_len - 1, hidden_size) // self.beam_size\n","    \n"]},{"output_type":"stream","name":"stdout","text":["['We tried our best']\n"]}],"source":["english_text = nmt_model.translate(transcribed_text)\n","print(english_text)"]},{"cell_type":"markdown","metadata":{"id":"9Rppc59Ut7uy"},"source":["## Generate English audio from text\n","Speech generation from text typically has two steps:\n","* Generate spectrogram from the text. In this example we will use FastPitch model for this.\n","* Generate actual audio from the spectrogram. In this example we will use HifiGan model for this.\n"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"wpMYfufgNt15","executionInfo":{"status":"ok","timestamp":1667021543962,"user_tz":-330,"elapsed":794,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}}},"outputs":[],"source":["# A helper function which combines FastPitch and HifiGan to go directly from \n","# text to audio\n","def text_to_audio(text):\n","  parsed = spectrogram_generator.parse(text)\n","  spectrogram = spectrogram_generator.generate_spectrogram(tokens=parsed)\n","  audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)\n","  return audio.to('cpu').detach().numpy()"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"yJl3kKaJdcwI","colab":{"base_uri":"https://localhost:8080/","height":110},"executionInfo":{"status":"ok","timestamp":1667021558132,"user_tz":-330,"elapsed":1083,"user":{"displayName":"Amit kumar","userId":"18403776620162725384"}},"outputId":"b7bab95e-599c-4f38-f4e2-7c911ad6a2e2"},"outputs":[{"output_type":"stream","name":"stderr","text":["[NeMo W 2022-10-29 05:32:38 fastpitch:256] parse() is meant to be called in eval mode.\n","[NeMo W 2022-10-29 05:32:38 fastpitch:318] generate_spectrogram() is meant to be called in eval mode.\n"]},{"output_type":"execute_result","data":{"text/plain":["<IPython.lib.display.Audio object>"],"text/html":["\n","                <audio  controls=\"controls\" >\n","                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n","                    Your browser does not support the audio element.\n","                </audio>\n","              "]},"metadata":{},"execution_count":9}],"source":["# Listen to generated audio in English\n","IPython.display.Audio(text_to_audio(english_text[0]), rate=22050)"]},{"cell_type":"markdown","metadata":{"id":"LiQ_GQpcBYUs"},"source":["## Next steps\n","A demo like this is great for prototyping and experimentation. However, for real production deployment, you would want to use a service like [NVIDIA Riva](https://developer.nvidia.com/riva).\n","\n","**NeMo is built for training.** You can fine-tune, or train from scratch on your data all models used in this example. We recommend you checkout the following, more in-depth, tutorials next:\n","\n","* [NeMo fundamentals](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/00_NeMo_Primer.ipynb)\n","* [NeMo models](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/01_NeMo_Models.ipynb)\n","* [Speech Recognition](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_with_NeMo.ipynb)\n","* [Punctuation and Capitalization](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb)\n","* [Speech Synthesis](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/tts/Inference_ModelSelect.ipynb)\n","\n","\n","You can find scripts for training and fine-tuning ASR, NLP and TTS models [here](https://github.com/NVIDIA/NeMo/tree/main/examples). "]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"https://github.com/NVIDIA/NeMo/blob/stable/tutorials/AudioTranslationSample.ipynb","timestamp":1666975244458}],"toc_visible":true},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.12"},"gpuClass":"standard","widgets":{"application/vnd.jupyter.widget-state+json":{"f8ef803fbe754870bb146093c1381388":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_d86d5fc34cfd4ca2ab9072bce661bf62","IPY_MODEL_9f5f3ddd41074199ac6919075a1671de","IPY_MODEL_bfbfaf95b35446bf808b2262a539369c"],"layout":"IPY_MODEL_a2a6c468aa0e4ab9ba7bd9962c33e371"}},"d86d5fc34cfd4ca2ab9072bce661bf62":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_d3fe21b5fd67455792e93b9bdf10df1d","placeholder":"​","style":"IPY_MODEL_b579e85a70d3471aab67647037848378","value":"Transcribing: 100%"}},"9f5f3ddd41074199ac6919075a1671de":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_7ac86d068bdf40c49c57312f2e1cffa4","max":1,"min":0,"orientation":"horizontal","style":"IPY_MODEL_a13815a2634042e1979f07b1b622df3e","value":1}},"bfbfaf95b35446bf808b2262a539369c":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_a21dc63da1fd44f9a1dc270575978509","placeholder":"​","style":"IPY_MODEL_1777104e52004c7ab79e590525b11dac","value":" 1/1 [00:08&lt;00:00,  8.05s/it]"}},"a2a6c468aa0e4ab9ba7bd9962c33e371":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"d3fe21b5fd67455792e93b9bdf10df1d":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"b579e85a70d3471aab67647037848378":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"7ac86d068bdf40c49c57312f2e1cffa4":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"a13815a2634042e1979f07b1b622df3e":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"ProgressStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","bar_color":null,"description_width":""}},"a21dc63da1fd44f9a1dc270575978509":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","model_module_version":"1.2.0","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"1777104e52004c7ab79e590525b11dac":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}}},"nbformat":4,"nbformat_minor":0}