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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"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=, 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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},
"outputId": "c0cf487b-f277-4667-eb69-a25ad722815f"
},
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/1.97k [00:00"
],
"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",
" \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": [
""
],
"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",
" \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": {}
}
]
}
]
}