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'''
This script calls the ada model from openai api to predict the next few words.
'''
import os
#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 conversation. Predict a few nouns, verbs, or adjectives that may be used next. Predict the next few words 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, A Restaurant, A Baseball Game, The Theater, A Party
Transcript: I would like to order a cheeseburger with a side of
Next: Fries, Milkshake, Apples, Salad, Katsup
"""
text = PROMPT + result.text
openai.api_key = os.environ["Openai_APIkey"]
response = openai.Completion.create(
model="text-ada-001",
prompt=text,
temperature=1,
max_tokens=4,
n=4)
for i in range(4):
print(response['choices'][i]['text'])
# result.text
#return getText, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
return result.text, state, response
# 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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