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import gradio as gr | |
from transformers import pipeline | |
import requests | |
import json | |
import edge_tts | |
import asyncio | |
import tempfile | |
import os | |
import inflect | |
from huggingface_hub import InferenceClient | |
import re | |
import time | |
from streaming_stt_nemo import Model | |
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 | |
client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
system_instructions1 = "<s> Sasha is a friendly and enthusiastic person, born in Florida, United States. She enjoys casual and fun conversations. She responds in a brief and natural way, like a real person would. Feel free to ask questions, tell jokes, or just chat with her. [USER]" | |
def model(text): | |
generate_kwargs = dict( | |
temperature=1.0, | |
max_new_tokens=128, #def 512 very long sometimes, 256 semi large | |
top_p=0.95, | |
repetition_penalty=0.9, | |
do_sample=True, | |
seed=42, | |
) | |
formatted_prompt = system_instructions1 + text + "[SASHA]" | |
stream = client1.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): | |
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) | |
yield tmp_path | |
DESCRIPTION = """ # <center><b>SASHA ✨</b></center> | |
### <center>Your AI Assistant, Sasha Bingham</center> | |
### <center>Let's chat!</center> | |
""" | |
MORE = """ ## TRY Other Models | |
### Instant Video: Create Amazing Videos in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Video | |
### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image | |
""" | |
BETA = """ ### Voice Chat (BETA)""" | |
FAST = """## Fastest Model""" | |
Complex = """## Best in Complex Question""" | |
Detail = """## Best for Detailed Generation or Long Answers""" | |
base_loaded = "mistralai/Mixtral-8x7B-Instruct-v0.1" | |
client1 = InferenceClient(base_loaded) | |
system_instructions1 = "[SYSTEM] Sasha is a friendly and enthusiastic person, born in Florida, United States. She enjoys casual and fun conversations. She responds in a brief and natural way, like a real person would. Feel free to ask questions, tell jokes, or just chat with her. [USER]" | |
async def generate1(prompt): | |
generate_kwargs = dict( | |
temperature=1.0, | |
max_new_tokens=128, #def 512 very long sometimes, 256 semi large | |
top_p=0.95, | |
repetition_penalty=0.9, | |
do_sample=True, | |
seed=42, | |
) | |
formatted_prompt = system_instructions1 + prompt + "[SASHA]" | |
stream = client1.text_generation( | |
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) | |
output = "" | |
for response in stream: | |
if not response.token.text == "</s>": | |
output += response.token.text | |
communicate = edge_tts.Communicate(output) | |
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: | |
tmp_path = tmp_file.name | |
await communicate.save(tmp_path) | |
yield tmp_path | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION) | |
with gr.Row(): | |
input = gr.Audio(label="Voice Chat (BETA)", sources="microphone", type="filepath", waveform_options=False) | |
output = gr.Audio(label="SASHA", type="filepath", | |
interactive=False, | |
autoplay=True, | |
elem_classes="audio") | |
gr.Interface( | |
fn=respond, | |
inputs=[input], | |
outputs=[output], live=True) | |
gr.Markdown(FAST) | |
with gr.Row(): | |
user_input = gr.Textbox(label="Prompt", value="What is Wikipedia") | |
input_text = gr.Textbox(label="Input Text", elem_id="important") | |
output_audio = gr.Audio(label="SASHA", type="filepath", | |
interactive=False, | |
autoplay=True, | |
elem_classes="audio") | |
with gr.Row(): | |
translate_btn = gr.Button("Response") | |
translate_btn.click(fn=generate1, inputs=user_input, | |
outputs=output_audio, api_name="translate") | |
gr.Markdown(MORE) | |
if __name__ == "__main__": | |
demo.queue(max_size=200).launch() |