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| ''' | |
| This script calls the model from openai api to predict the next few words in a conversation. | |
| ''' | |
| import os | |
| import sys | |
| import openai | |
| import gradio as gr | |
| os.system("pip install git+https://github.com/openai/whisper.git") | |
| import whisper | |
| from transformers import pipeline | |
| import torch | |
| from transformers import AutoModelForCausalLM | |
| from transformers import AutoTokenizer | |
| import time | |
| EXAMPLE_PROMPT = """This is a tool for helping someone with memory issues remember the next word. | |
| The predictions follow a few rules: | |
| 1) The predictions are suggestions of ways to continue the transcript as if someone forgot what the next word was. | |
| 2) The predictions do not repeat themselves. | |
| 3) The predictions focus on suggesting nouns, adjectives, and verbs. | |
| 4) The predictions are related to the context in the transcript. | |
| EXAMPLES: | |
| Transcript: Tomorrow night we're going out to | |
| Prediction: The Movies, A Restaurant, A Baseball Game, The Theater, A Party for a friend | |
| Transcript: I would like to order a cheeseburger with a side of | |
| Prediction: Frnech fries, Milkshake, Apple slices, Side salad, Extra katsup | |
| Transcript: My friend Savanah is | |
| Prediction: An elecrical engineer, A marine biologist, A classical musician | |
| Transcript: I need to buy a birthday | |
| Prediction: Present, Gift, Cake, Card | |
| Transcript: """ | |
| # whisper model specification | |
| asr_model = whisper.load_model("tiny") | |
| openai.api_key = os.environ["Openai_APIkey"] | |
| # Transcribe function | |
| def transcribe(audio_file): | |
| print("Transcribing") | |
| transcription = asr_model.transcribe(audio_file)["text"] | |
| return transcription | |
| def inference(audio, prompt, model, temperature): | |
| # Transcribe with Whisper | |
| print("The audio is:", audio) | |
| transcript = transcribe(audio) | |
| text = prompt + transcript + "\nPrediction: " | |
| response = openai.Completion.create( | |
| model=model, | |
| prompt=text, | |
| temperature=temperature, | |
| max_tokens=8, | |
| n=5) | |
| infers = [] | |
| temp = [] | |
| #infered=[] | |
| for i in range(5): | |
| print("print1 ", response['choices'][i]['text']) | |
| temp.append(response['choices'][i]['text']) | |
| print("print2: infers ", infers) | |
| print("print3: Responses ", response) | |
| print("Object type of response: ", type(response)) | |
| #infered = list(map(lambda x: x.split(',')[0], infers)) | |
| #print("Infered type is: ", type(infered)) | |
| infers = list(map(lambda x: x.replace("\n", ""), temp)) | |
| #infered = list(map(lambda x: x.split(','), infers)) | |
| return transcript, infers | |
| # get audio from microphone | |
| with gr.Blocks() as face: | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio = gr.Audio(source="microphone", type="filepath") | |
| promptText = gr.inputs.Textbox(lines=15, placeholder="Enter a prompt here"), | |
| dropChoice = gr.inputs.Dropdown(["text-ada-001", "text-davinci-002", "text-davinci-003", "gpt-3.5-turbo"], label="Model"), | |
| sliderChoice = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.8, step=0.1, label="Temperature") | |
| transcribe_btn = gr.Button(value="Transcribe") | |
| with gr.Column(): | |
| script = gr.Textbox(label="text...") | |
| options = gr.Textbox(label="predictions...") | |
| transcribe_btn.click(inference, inputs=[audio, promptText, dropChoice, sliderChoice], outputs=[script, options]) | |
| examples = gr.Examples(examples=["Sedan, Truck, SUV", "Dalmaion, Shepherd, Lab, Mutt"], | |
| inputs=[options]) | |
| face.launch() |