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import whisper
import gradio as gr
import time
import warnings
import json
import openai
import os
from gtts import gTTS
warnings.filterwarnings("ignore")
openai.api_key = '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'
model = whisper.load_model("base")
model.device
!ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 10 -q:a 9 -acodec libmp3lame Temp.mp3
def chatgpt_api(input_text):
messages = [
{"role": "system", "content": "You are a helpful assistant."}]
if input_text:
messages.append(
{"role": "user", "content": input_text},
)
chat_completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo", messages=messages
)
reply = chat_completion.choices[0].message.content
return reply
def transcribe(audio):
language = 'en'
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(model.device)
_, probs = model.detect_language(mel)
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
result_text = result.text
out_result = chatgpt_api(result_text)
audioobj = gTTS(text = out_result,
lang = language,
slow = False)
audioobj.save("Temp.mp3")
return [result_text, out_result, "Temp.mp3"]
output_1 = gr.Textbox(label="Speech to Text")
output_2 = gr.Textbox(label="ChatGPT Output")
output_3 = gr.Audio("Temp.mp3")
gr.Interface(
title = 'OpenAI Whisper and ChatGPT ASR Gradio Web UI',
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath")
],
outputs=[
output_1, output_2, output_3
],
live=True).launch() |