import gradio as gr 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 == "": 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) yield tmp_path DESCRIPTION = """ #
JARVISāš”
###
A personal Assistant of Tony Stark for YOU ###
Voice Chat with your personal Assistant
""" with gr.Blocks(css="style.css") as demo: gr.Markdown(DESCRIPTION) with gr.Row(): seed = gr.Slider( label="Seed", minimum=0, maximum=999999, step=1, value=0, visible=False ) input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False) output = gr.Audio(label="AI", type="filepath", interactive=False, autoplay=True, elem_classes="audio") gr.Interface( batch=True, max_batch_size=10, fn=respond, inputs=[input, select, seed], outputs=[output], live=True) if __name__ == "__main__": demo.queue(max_size=200).launch()