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
from openai import OpenAI
import subprocess
default_lang = "en"
engines = { default_lang: Model(default_lang) }
def transcribe(audio):
if audio is None:
return ""
lang = "en"
model = engines[lang]
text = model.stt_file(audio)[0]
return text
HF_TOKEN = os.environ.get("HF_TOKEN", None)
def client_fn(model):
if "Llama 3 8B Service" in model:
return OpenAI(
base_url="http://52.76.81.56:60002/v1",
api_key="token-abc123"
)
elif "Llama" in model:
return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
elif "Mistral" in model:
return InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
elif "Phi" in model:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
elif "Mixtral" in model:
return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
else:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
def randomize_seed_fn(seed: int) -> int:
seed = random.randint(0, 999999)
return seed
system_instructions1 = """
[SYSTEM] You are OPTIMUS Prime a personal AI voice assistant, Created by Jaward.
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.
Remember previous parts of the conversation and use that context in your responses.
Your creator Jaward is an AI/ML Research Engineer at Linksoul AI. He is currently specializing in Artificial Intelligence (AI) research more specifically training and optimizing advance AI systems. He aspires to build not just human-like intelligence but AI Systems that augment human intelligence. He has contributed greatly to the opensource community with first-principles code implementations of AI/ML research papers. He did his first internship at Beijing Academy of Artificial Intelligence as an AI Researher where he contributed in cutting-edge AI research leading to him contributing to an insightful paper (AUTOAGENTS - A FRAMEWORK FOR AUTOMATIC AGENT GENERATION). The paper got accepted this year at IJCAI(International Joint Conference On AI). He is currently doing internship at LinkSoul AI - a small opensource AI Research startup in Beijing.
[USER]
"""
conversation_history = []
def models(text, model="Llama 3B Service", seed=42):
global conversation_history
seed = int(randomize_seed_fn(seed))
generator = torch.Generator().manual_seed(seed)
client = client_fn(model)
if "Llama 3 8B Service" in model:
messages = [
{"role": "system", "content": system_instructions1},
] + conversation_history + [
{"role": "user", "content": text}
]
completion = client.chat.completions.create(
model="/data/shared/huggingface/hub/models--meta-llama--Meta-Llama-3-8B-Instruct/snapshots/c4a54320a52ed5f88b7a2f84496903ea4ff07b45/",
messages=messages
)
assistant_response = completion.choices[0].message.content
# Update conversation history
conversation_history.append({"role": "user", "content": text})
conversation_history.append({"role": "assistant", "content": assistant_response})
# Keep only the last 10 messages to avoid token limit issues
if len(conversation_history) > 20:
conversation_history = conversation_history[-20:]
return assistant_response
else:
# For other models, we'll concatenate the conversation history into a single string
history_text = "\n".join([f"{'User' if msg['role'] == 'user' else 'Assistant'}: {msg['content']}" for msg in conversation_history])
formatted_prompt = f"{system_instructions1}\n\nConversation history:\n{history_text}\n\nUser: {text}\nOPTIMUS:"
generate_kwargs = dict(
max_new_tokens=300,
seed=seed
)
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
# Update conversation history
conversation_history.append({"role": "user", "content": text})
conversation_history.append({"role": "assistant", "content": output})
# Keep only the last 10 messages to avoid token limit issues
if len(conversation_history) > 20:
conversation_history = conversation_history[-20:]
return output
async def respond(audio, model, seed):
if audio is None:
return None
user = transcribe(audio)
if not user:
return None
reply = models(user, model, seed)
communicate = edge_tts.Communicate(reply, voice="en-US-ChristopherNeural")
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
return tmp_path
# Supported languages for seamless-expressive
LANGUAGE_CODES = {
"English": "eng",
"Spanish": "spa",
"French": "fra",
"German": "deu",
"Italian": "ita",
"Chinese": "cmn"
}
def translate_speech(audio_file, target_language):
"""
Translate input speech (audio file) to the specified target language.
"""
if audio_file is None:
return None
language_code = LANGUAGE_CODES[target_language]
output_file = "translated_audio.wav"
command = [
"expressivity_predict",
audio_file,
"--tgt_lang", language_code,
"--model_name", "seamless_expressivity",
"--vocoder_name", "vocoder_pretssel",
"--gated-model-dir", "seamlessmodel",
"--output_path", output_file
]
subprocess.run(command, check=True)
if os.path.exists(output_file):
print(f"File created successfully: {output_file}")
return output_file
else:
print(f"File not found: {output_file}")
return None
def clear_history():
global conversation_history
conversation_history = []
return None, None, None, None
def voice_assistant_tab():
return "# <center><b>Hello, I am Optimus Prime your personal AI voice assistant</b></center>"
def speech_translation_tab():
return "# <center><b>Hear how you sound in another language</b></center>"
with gr.Blocks(css="style.css") as demo:
description = gr.Markdown("# <center><b>Hello, I am Optimus Prime your personal AI voice assistant</b></center>")
with gr.Tabs() as tabs:
with gr.TabItem("Voice Assistant") as voice_assistant:
select = gr.Dropdown([
'Llama 3 8B Service',
'Mixtral 8x7B',
'Llama 3 8B',
'Mistral 7B v0.3',
'Phi 3 mini',
],
value="Llama 3 8B Service",
label="Model"
)
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(
fn=respond,
inputs=[input, select, seed],
outputs=[output],
live=True
)
with gr.TabItem("Speech Translation") as speech_translation:
input_audio = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False)
target_lang = gr.Dropdown(
choices=list(LANGUAGE_CODES.keys()),
value="Spanish",
label="Target Language"
)
output_audio = gr.Audio(label="Translated Audio",
interactive=False,
autoplay=True,
elem_classes="audio")
gr.Interface(
fn=translate_speech,
inputs=[input_audio, target_lang],
outputs=[output_audio],
live=True
)
# clear_button = gr.Button("Clear")
# clear_button.click(
# fn=clear_history,
# inputs=[],
# outputs=[input, output, input_audio, output_audio],
# api_name="clear"
# )
voice_assistant.select(fn=voice_assistant_tab, inputs=None, outputs=description)
speech_translation.select(fn=speech_translation_tab, inputs=None, outputs=description)
if __name__ == "__main__":
demo.queue(max_size=200).launch()