Gradio_App_Inferencing.ipynb

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      "metadata": {
        "id": "NWRphL5kJlYK"
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      "source": [
        "!pip install transformers "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A7Oz9L2q7yYY"
      },
      "outputs": [],
      "source": [
        "!pip install gradio \n",
        "!pip install happytransformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kM6S1vLG9Hs5"
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      "source": [
        "## Install dependencies\n",
        "!pip install wget\n",
        "!apt-get install sox libsndfile1 ffmpeg\n",
        "!pip install text-unidecode\n",
        "!pip install matplotlib>=3.3.2\n",
        "\n",
        "## Install NeMo\n",
        "BRANCH = 'r1.13.0'\n",
        "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n",
        "\n",
        "## Grab the config we'll use in this example\n",
        "!mkdir configs\n",
        "!wget -P configs/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/asr/conf/config.yaml\n",
        "\n",
        "\"\"\"\n",
        "Remember to restart the runtime for the kernel to pick up any upgraded packages \n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3Vf3uqW-Rm3"
      },
      "source": [
        "\n",
        "Remember to restart the runtime for the kernel to pick up any upgraded packages \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CtuGm-517BxW"
      },
      "outputs": [],
      "source": [
        "import nemo.collections.asr as nemo_asr\n",
        "asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_en_citrinet_1024\")"
      ]
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      "source": [
        "from happytransformer import HappyTextToText, TTSettings\n",
        "happy_tt = HappyTextToText(\"T5\", \"vennify/t5-base-grammar-correction\")\n",
        "args = TTSettings(num_beams=5, min_length=1)"
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "All TF 2.0 model weights were used when initializing DistilBertForTokenClassification.\n",
            "\n",
            "All the weights of DistilBertForTokenClassification were initialized from the TF 2.0 model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForTokenClassification for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Aditya02/Speech_Analyzer_Model\")\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"Aditya02/Speech_Analyzer_Model\", from_tf = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "pIr7B_FeJv0E"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "classifier = pipeline(\"ner\",model = model, tokenizer = tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "m7FPTqeV77Tr"
      },
      "outputs": [],
      "source": [
        "def transcribe(audio):\n",
        "  \"\"\"Speech to text using Nvidia Nemo\"\"\"\n",
        "  text = asr_model.transcribe(paths2audio_files=[audio])[0]\n",
        "  #Disfluency Detection\n",
        "  output = classifier(text)\n",
        "  # Add the prefix \"grammar: \" before each input \n",
        "  correct = happy_tt.generate_text(\"grammar: \" + text, args=args)\n",
        "  return text, {\"text\": text, \"entities\": output},  correct.text "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "u88LY6ko76DV",
        "outputId": "1c058464-4b07-4a00-8b7d-49c0620d09b3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[NeMo W 2023-01-18 09:01:38 nemo_logging:349] /usr/local/lib/python3.8/dist-packages/gradio/components.py:3370: UserWarning: The 'color_map' parameter has been moved from the constructor to `HighlightedText.style()` \n",
            "      warnings.warn(\n",
            "    \n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
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          },
          "execution_count": 25,
          "metadata": {},
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        }
      ],
      "source": [
        "import gradio as gr\n",
        "gr.Interface(\n",
        "    fn=transcribe,\n",
        "    inputs=[\n",
        "        gr.Audio(source=\"microphone\", type=\"filepath\"),\n",
        "    ],\n",
        "    outputs=[\n",
        "        \"textbox\",\n",
        "        gr.HighlightedText(combine_adjacent = True,color_map = {'Fluent':'green','Disfluent':'red'},label = \"Disfluencies in the text\"),\n",
        "        \"textbox\"\n",
        "\n",
        "    ]).launch()"
      ]
    }
  ],
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