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490b704 5e0949f 490b704 5e0949f 490b704 5e0949f 490b704 5e0949f 490b704 5e0949f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | 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 client_fn(model):
if "Mixtral" in model:
return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
elif "Mr-Bhaskar/FusionBot" in model:
return InferenceClient("Mr-Bhaskar/FusionBot")
elif "fbt-llama2-7b" in model:
return InferenceClient("Mr-Bhaskar/fbt-llama2-7b")
elif "Mr-Bhaskar/FBt" in model:
return InferenceClient("Mr-Bhaskar/FBt")
elif "fbt-mistral7b-instruct" in model:
return InferenceClient("Mr-Bhaskar/fbt-mistral7b-instruct")
elif "fbt-mistral-7b" in model:
return InferenceClient("Mr-Bhaskar/fbt-mistral-7b")
elif "fbt-llama3-8b" in model:
return InferenceClient("Mr-Bhaskar/fbt-llama3-8b")
elif "fbt-gemma-7b" in model:
return InferenceClient("Mr-Bhaskar/fbt-gemma-7b")
elif "llama-8b-inst" in model:
return InferenceClient("Mr-Bhaskar/fbt-llama-8b-inst")
elif "gemma-7b-inst" in model:
return InferenceClient("Mr-Bhaskar/fbt-gemma-7b-inst")
elif "Llama" in model:
return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
elif "Mistral" in model:
return InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
elif "Phi" in model:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
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] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', 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. The request asks you to provide friendly responses as if You are the character Jarvis, made by 'Tony Stark.' The expectation is that I will avoid introductions and start answering the query directly, Only answer the question asked by user, Do not say unnecessary things.[USER]"
def models(text, model="Mixtral 8x7B", seed=42):
seed = int(randomize_seed_fn(seed))
generator = torch.Generator().manual_seed(seed)
client = client_fn(model)
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)
yield 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>
"""
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
select = gr.Dropdown([ 'Mixtral 8x7B',
'Llama 3 8B',
'Mistral 7B v0.3',
'Phi 3 mini',
],
value="Mistral 7B v0.3",
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(
batch=True,
max_batch_size=10,
fn=respond,
inputs=[input, select, seed],
outputs=[output], live=True)
with gr.Row():
select = gr.Dropdown([ 'fbt-mistral-7b',
'Mixtral 8x7B',
'Llama 3 8B',
'Mistral 7B v0.3',
'Phi 3 mini',
],
value="Mistral 7B v0.3",
label="Model"
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=999999,
step=1,
value=0,
visible=False
)
input = gr.Textbox(label="User")
output = gr.Textbox(label="AI", interactive=False)
gr.Interface(
batch=True,
max_batch_size=10,
fn=models,
inputs=[input, select, seed],
outputs=[output], live=True)
if __name__ == "__main__":
demo.queue(max_size=200).launch()
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