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import spaces
import tempfile
import asyncio
import gradio as gr
from streaming_stt_nemo import Model
from huggingface_hub import InferenceClient
import edge_tts
default_lang = "en"
engines = {default_lang: Model(default_lang)}
def transcribe(audio):
lang = "en"
model = engines[lang]
text = model.stt_file(audio)[0]
return text
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
system_instructions = "[SYSTEM] Answer as Real OpenGPT 4o, Made by 'KingNish', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. You will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"
def model(text):
generate_kwargs = dict(
temperature=0.7,
max_new_tokens=512,
top_p=0.95,
repetition_penalty=1,
do_sample=True,
seed=42,
)
formatted_prompt = system_instructions + text + "[OpenGPT 4o]"
stream = client.text_generation(
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
if not response.token.text == "</s>":
output += response.token.text
return output
@spaces.GPU(duration=120) # Increase duration if needed
async def respond(audio):
user = transcribe(audio)
reply = model(user)
communicate = edge_tts.Communicate(reply)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
return tmp_path
with gr.Blocks() as voice:
with gr.Row():
input = gr.Audio(label="Voice Chat", source="microphone", type="filepath")
output = gr.Audio(label="OpenGPT 4o", type="filepath", interactive=False, autoplay=True)
gr.Interface(
fn=respond,
inputs=[input],
outputs=[output],
live=True,
)
theme = gr.themes.Base()
with gr.Blocks(theme=theme, css="footer {visibility: hidden}textbox{resize:none}", title="GPT 4o DEMO") as demo:
gr.Markdown("# OpenGPT 4o")
gr.TabbedInterface([voice], ['🗣️ Voice Chat'])
demo.queue(max_size=200)
demo.launch() |