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'''
This script calls the ada model from openai api to predict the next few words.
'''
import os
os.system("pip install git+https://github.com/openai/openai-python.git")
import openai
import os
from pprint import pprint
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper
from transformers import pipeline
import torch
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
import time
# import streaming.py
# from next_word_prediction import GPT2
#gpt2 = AutoModelForCausalLM.from_pretrained("gpt2", return_dict_in_generate=True)
#tokenizer = AutoTokenizer.from_pretrained("gpt2")
### /code snippet
# get gpt2 model
generator = pipeline('text-generation', model='gpt2')
# whisper model specification
model = whisper.load_model("tiny")
def inference(audio, state=""):
#time.sleep(2)
#text = p(audio)["text"]
#state += text + " "
# load audio data
audio = whisper.load_audio(audio)
# ensure sample is in correct format for inference
audio = whisper.pad_or_trim(audio)
# generate a log-mel spetrogram of the audio data
mel = whisper.log_mel_spectrogram(audio).to(model.device)
_, probs = model.detect_language(mel)
# decode audio data
options = whisper.DecodingOptions(fp16 = False)
# transcribe speech to text
result = whisper.decode(model, mel, options)
PROMPT = """The following is a transcript of a verbal conversation. Predict the next few words in the conversation as a list of options.
A few examples are provided below and then the current transcript is provided.
Examples:
Transcript: Tomorrow night we're going out to
Next: The Movies to watch a blockbuster, A Restaurant for dinner, A Baseball Game, The Theater, A Party for a friend
Transcript: I would like to order a cheeseburger with a side of
Next: Frnech fries, Milkshake, Apple slices, Side salad, Extra katsup
Transcript: My friend Savanah is
Next: An elecrical engineer, A marine biologist, A classical musician
Transcript: I need to buy a birthday
Next: Present, Gift, Cake, Card
"""
text = PROMPT + result.text
openai.api_key = os.environ["Openai_APIkey"]
response = openai.Completion.create(
model="text-ada-001",
#model="text-curie-001",
prompt=text,
temperature=1,
max_tokens=6,
n=5)
infers =[]
for i in range(5):
print("print1 ", response['choices'][i]['text'])
infers.append(response['choices'][i]['text'])
print("print2: infers ", infers)
print("print3: Responses ", response)
print("Object type of response: ", type(response))
# result.text
#return getText, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
return result.text, state, infers
# get audio from microphone
gr.Interface(
fn=inference,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath"),
"state"
],
outputs=[
"textbox",
"state",
"textbox"
],
live=True).launch()
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