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+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/vanderbilt-data-science/lo-achievement/blob/main/basic_UI_design_oral_exam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Project IO Achievement - UI Design (Oral Exam)"
+ ],
+ "metadata": {
+ "id": "PIbogPXyM0wr"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Problem Definition\n",
+ "\n",
+ "In this issue, we'll create a gradio app (through Google Colab) which implements most of the \"oral exam\" component of this project. Namely:\n",
+ "\n",
+ "- The user should be able to start/stop the recording. In other words, they press some button to start recording, record their answer, and then press stop recording.\n",
+ "\n",
+ "- This should be transcribed by Whisper\n",
+ "\n",
+ "- This should be sent to GPT4 for some sort of analysis.\n",
+ "\n",
+ "We will integrate this with the rest of the functionality in the repo to fill out part (3) in a later issue."
+ ],
+ "metadata": {
+ "id": "x_Vp8SiKM4p1"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Libraries\n",
+ "\n",
+ "This section will install and import some important libraries such as Langchain, openai, Gradio, and so on"
+ ],
+ "metadata": {
+ "id": "o_60X8H3NEne"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# install libraries here\n",
+ "# -q flag for \"quiet\" install\n",
+ "%%capture\n",
+ "!pip install -q langchain\n",
+ "!pip install -q openai\n",
+ "!pip install -q gradio\n",
+ "!pip install -q transformers\n",
+ "!pip install -q datasets\n",
+ "!pip install -q huggingsound\n",
+ "!pip install -q torchaudio\n",
+ "!pip install -q git+https://github.com/openai/whisper.git"
+ ],
+ "metadata": {
+ "id": "pxcqXgg2aAN7"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "pEjM1tLsMZBq"
+ },
+ "outputs": [],
+ "source": [
+ "# import libraries here\n",
+ "from langchain.llms import OpenAI\n",
+ "from langchain.prompts import PromptTemplate\n",
+ "from langchain.document_loaders import TextLoader\n",
+ "from langchain.indexes import VectorstoreIndexCreator\n",
+ "from langchain import ConversationChain, LLMChain, PromptTemplate\n",
+ "from langchain.chat_models import ChatOpenAI\n",
+ "from langchain.memory import ConversationBufferWindowMemory\n",
+ "from langchain.prompts import ChatPromptTemplate\n",
+ "from langchain.text_splitter import CharacterTextSplitter\n",
+ "from langchain.embeddings import OpenAIEmbeddings\n",
+ "import openai\n",
+ "import os\n",
+ "from getpass import getpass\n",
+ "from IPython.display import display, Javascript, HTML\n",
+ "from google.colab.output import eval_js\n",
+ "from base64 import b64decode\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import clear_output\n",
+ "import time\n",
+ "import requests\n",
+ "from transformers import WhisperProcessor, WhisperForConditionalGeneration\n",
+ "from datasets import load_dataset\n",
+ "# from torchaudio.transforms import Resample\n",
+ "import whisper\n",
+ "from huggingsound import SpeechRecognitionModel\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import librosa\n",
+ "from datasets import load_dataset\n",
+ "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n",
+ "from jiwer import wer\n",
+ "import pandas as pd\n",
+ "from IPython.display import display, HTML\n",
+ "import gradio as gr\n",
+ "from transformers import pipeline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## API Keys\n",
+ "\n",
+ "Use these cells to load the API keys required for this notebook. The below code cell uses the `getpass` library."
+ ],
+ "metadata": {
+ "id": "03KLZGI_a5W5"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "openai_api_key = getpass()\n",
+ "os.environ[\"OPENAI_API_KEY\"] = openai_api_key\n",
+ "openai.api_key = openai_api_key"
+ ],
+ "metadata": {
+ "id": "5smcWj4DbFgy",
+ "outputId": "9036e377-25bf-4c04-92a6-e5845742314d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "··········\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Prompt Design\n",
+ "\n",
+ "To be added"
+ ],
+ "metadata": {
+ "id": "pMo9x8u4AEV1"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "chat = ChatOpenAI(temperature=0.0, model_name='gpt-4')\n",
+ "chat"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "UgnCZRMhADvo",
+ "outputId": "a6171443-093f-4522-d583-413eba9a379a"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "ChatOpenAI(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, model_name='gpt-4', temperature=0.0, model_kwargs={}, openai_api_key='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', openai_api_base='', openai_organization='', openai_proxy='', request_timeout=None, max_retries=6, streaming=False, n=1, max_tokens=None, tiktoken_model_name=None)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "template_string = \"\"\"\n",
+ "You are an expert in {expertise}.\n",
+ "\n",
+ "Please translate the following text into {language} language in {style} style. \\\n",
+ "\n",
+ "The text that need to be transcribed is shown below: {transcribed_text}. \\\n",
+ "\"\"\""
+ ],
+ "metadata": {
+ "id": "KaaRReDNALer"
+ },
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "prompt_template = ChatPromptTemplate.from_template(template_string)"
+ ],
+ "metadata": {
+ "id": "oij6W5rwAaGb"
+ },
+ "execution_count": 6,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# prompt_template.messages[0].prompt\n",
+ "prompt_template.messages[0].prompt.input_variables"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C1YRmL46AaJA",
+ "outputId": "c294ffce-326e-42d2-beff-f00153f935da"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "['expertise', 'language', 'style', 'transcribed_text']"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## UI Design\n",
+ "\n",
+ "https://colab.research.google.com/github/petewarden/openai-whisper-webapp/blob/main/OpenAI_Whisper_ASR_Demo.ipynb"
+ ],
+ "metadata": {
+ "id": "M6IzVTjz5cex"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Whisper Small"
+ ],
+ "metadata": {
+ "id": "B1jctBZsExES"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "p = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-small\")\n",
+ "\n",
+ "# def transcribe(audio):\n",
+ "# text = openai.Audio.transcribe(\"whisper-1\", audio)['text']\n",
+ "# return text\n",
+ "\n",
+ "def transcribe(audio):\n",
+ " text = p(audio)[\"text\"]\n",
+ " test_input1 = prompt_template.format_messages(\n",
+ " expertise='Language Translation',\n",
+ " language='Japanese',\n",
+ " style='romantic',\n",
+ " transcribed_text=text)\n",
+ "\n",
+ " response = chat.predict_messages(test_input1)\n",
+ " return text, response.content"
+ ],
+ "metadata": {
+ "id": "zS3Rd9sSFw5h",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 369,
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+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/1.97k [00:00<?, ?B/s]"
+ ],
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ ],
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+ },
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+ "output_type": "display_data",
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+ ],
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+ ],
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+ ],
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+ ],
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "gr.Interface(\n",
+ " fn=transcribe,\n",
+ " inputs=gr.Audio(source=\"microphone\", type=\"filepath\"),\n",
+ " outputs=[\n",
+ " gr.Textbox(label=\"Transcription\"),\n",
+ " gr.Textbox(label=\"Translation\")\n",
+ " ],\n",
+ " title=\"Speech to Text using OpenAI Whisper\",\n",
+ " description=\"Speak into the microphone and have the spoken words transcribed into text and translated\"\n",
+ ").launch()\n"
+ ],
+ "metadata": {
+ "id": "WjaDxO32tz5_",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 616
+ },
+ "outputId": "a097954c-57e7-49da-9d9a-67400163ccdb"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "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"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ],
+ "application/javascript": [
+ "(async (port, path, width, height, cache, element) => {\n",
+ " if (!google.colab.kernel.accessAllowed && !cache) {\n",
+ " return;\n",
+ " }\n",
+ " element.appendChild(document.createTextNode(''));\n",
+ " const url = await google.colab.kernel.proxyPort(port, {cache});\n",
+ "\n",
+ " const external_link = document.createElement('div');\n",
+ " external_link.innerHTML = `\n",
+ " <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
+ " Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
+ " https://localhost:${port}${path}\n",
+ " </a>\n",
+ " </div>\n",
+ " `;\n",
+ " element.appendChild(external_link);\n",
+ "\n",
+ " const iframe = document.createElement('iframe');\n",
+ " iframe.src = new URL(path, url).toString();\n",
+ " iframe.height = height;\n",
+ " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
+ " iframe.width = width;\n",
+ " iframe.style.border = 0;\n",
+ " element.appendChild(iframe);\n",
+ " })(7860, \"/\", \"100%\", 500, false, window.element)"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": []
+ },
+ "metadata": {},
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Whisper API"
+ ],
+ "metadata": {
+ "id": "l4o8R5eUE1n8"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def transcribe(audio_file_path):\n",
+ " with open(audio_file_path, \"rb\") as audio_file:\n",
+ " # Call OpenAI's Whisper model for transcription\n",
+ " transcript = openai.Audio.transcribe(\"whisper-1\", audio_file)\n",
+ " transcribed_text = transcript[\"text\"]\n",
+ " return transcribed_text\n",
+ "\n",
+ "def translate(text):\n",
+ " # Create a prompt template (This will be changed later to fit the actual task)\n",
+ " # Here translation will be a filler task of GPT\n",
+ " test_input1 = prompt_template.format_messages(\n",
+ " expertise='Language Translation',\n",
+ " language='Japanese',\n",
+ " style='romantic',\n",
+ " transcribed_text=text)\n",
+ "\n",
+ " response = chat.predict_messages(test_input1)\n",
+ " return response.content"
+ ],
+ "metadata": {
+ "id": "ABN0X9xQHeii"
+ },
+ "execution_count": 21,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "with gr.Blocks() as demo:\n",
+ " gr.Markdown(\"# Oral Exam App\")\n",
+ " with gr.Box():\n",
+ " gr.HTML(\"\"\"Embed your OpenAI API key below; if you haven't created one already, visit\n",
+ " platform.openai.com/account/api-keys\n",
+ " to sign up for an account and get your personal API key\"\"\",\n",
+ " elem_classes=\"textbox_label\")\n",
+ " input = gr.Textbox(show_label=False, type=\"password\", container=False,\n",
+ " placeholder=\"●●●●●●●●●●●●●●●●●\")\n",
+ "\n",
+ " with gr.Blocks():\n",
+ " gr.Markdown(\"## Upload your audio file or start recording\")\n",
+ "\n",
+ " with gr.Row():\n",
+ "\n",
+ "\n",
+ " with gr.Column():\n",
+ " file_input = gr.Files(label=\"Load a mp3 file\",\n",
+ " file_types=['.mp3'], type=\"file\",\n",
+ " elem_classes=\"short-height\")\n",
+ " record_inputs = gr.Audio(source=\"microphone\", type=\"filepath\")\n",
+ "\n",
+ " with gr.Column():\n",
+ " outputs_transcribe=gr.Textbox(label=\"Transcription\")\n",
+ "\n",
+ " with gr.Row():\n",
+ " btn1 = gr.Button(value=\"Transcribe recorded audio\")\n",
+ " btn1.click(transcribe, inputs=record_inputs, outputs=outputs_transcribe)\n",
+ " btn2 = gr.Button(value=\"Transcribe uploaded audio\")\n",
+ " btn2.click(transcribe, inputs=file_input, outputs=outputs_transcribe)\n",
+ "\n",
+ " outputs_translate=gr.Textbox(label=\"Translation\")\n",
+ " btn3 = gr.Button(value=\"Translate\")\n",
+ " btn3.click(translate, inputs=outputs_transcribe, outputs=outputs_translate)\n",
+ "\n",
+ " demo.launch()\n"
+ ],
+ "metadata": {
+ "id": "ZkfJXCGDFhdw",
+ "outputId": "25fff3ea-7395-49e6-8b22-e288af96c648",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 616
+ }
+ },
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "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"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ],
+ "application/javascript": [
+ "(async (port, path, width, height, cache, element) => {\n",
+ " if (!google.colab.kernel.accessAllowed && !cache) {\n",
+ " return;\n",
+ " }\n",
+ " element.appendChild(document.createTextNode(''));\n",
+ " const url = await google.colab.kernel.proxyPort(port, {cache});\n",
+ "\n",
+ " const external_link = document.createElement('div');\n",
+ " external_link.innerHTML = `\n",
+ " <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
+ " Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
+ " https://localhost:${port}${path}\n",
+ " </a>\n",
+ " </div>\n",
+ " `;\n",
+ " element.appendChild(external_link);\n",
+ "\n",
+ " const iframe = document.createElement('iframe');\n",
+ " iframe.src = new URL(path, url).toString();\n",
+ " iframe.height = height;\n",
+ " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
+ " iframe.width = width;\n",
+ " iframe.style.border = 0;\n",
+ " element.appendChild(iframe);\n",
+ " })(7867, \"/\", \"100%\", 500, false, window.element)"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ }
+ ]
+}