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import streamlit as st
import edge_tts
import asyncio
import tempfile
import os
from huggingface_hub import InferenceClient
import re
from streaming_stt_nemo import Model
import torch
import random
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
HF_TOKEN = os.environ.get("HF_TOKEN", None)
def randomize_seed_fn(seed: int) -> int:
seed = random.randint(0, 999999)
return seed
system_instructions1 = """
[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Tony Stark.'
Keep conversation friendly, short, clear, and concise.
Avoid unnecessary introductions and answer the user's questions directly.
Respond in a normal, conversational manner while being friendly and helpful.
[USER]
"""
def models(text, seed=42):
seed = int(randomize_seed_fn(seed))
generator = torch.Generator().manual_seed(seed)
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
generate_kwargs = dict(
max_new_tokens=300,
seed=seed
)
formatted_prompt = system_instructions1 + text + "[JARVIS]"
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
async def respond(audio, model, seed):
user = transcribe(audio)
reply = models(user, model, seed)
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
DESCRIPTION = """ # <center><b>JARVIS⚡</b></center>
### <center>A personal Assistant of Tony Stark for YOU
### <center>Voice Chat with your personal Assistant</center>
"""
st.markdown(DESCRIPTION)
st.title("JARVIS")
uploaded_file = st.file_uploader("Upload audio file", type=["wav"])
seed = st.slider("Seed", min_value=0, max_value=999999, value=0)
if uploaded_file is not None:
# Convert the uploaded file to a BytesIO object
audio_bytes = uploaded_file.read()
# Process the audio using the respond function
response_path = asyncio.run(respond(audio_bytes, models, seed))
# Display the audio response
st.audio(response_path, format="audio/wav")
os.remove(response_path) |