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import gradio as gr |
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from transformers import pipeline |
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import requests |
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import json |
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import edge_tts |
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import asyncio |
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import tempfile |
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import os |
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import inflect |
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from huggingface_hub import InferenceClient |
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import re |
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import time |
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from streaming_stt_nemo import Model |
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default_lang = "en" |
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engines = { default_lang: Model(default_lang) } |
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def transcribe(audio): |
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lang = "en" |
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model = engines[lang] |
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text = model.stt_file(audio)[0] |
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return text |
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client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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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]" |
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def model(text): |
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generate_kwargs = dict( |
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temperature=1.0, |
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max_new_tokens=128, |
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top_p=0.95, |
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repetition_penalty=0.9, |
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do_sample=True, |
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seed=42, |
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) |
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formatted_prompt = system_instructions1 + text + "[SASHA]" |
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stream = client1.text_generation( |
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formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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if not response.token.text == "</s>": |
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output += response.token.text |
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return output |
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async def respond(audio): |
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user = transcribe(audio) |
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reply = model(user) |
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communicate = edge_tts.Communicate(reply) |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: |
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tmp_path = tmp_file.name |
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await communicate.save(tmp_path) |
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yield tmp_path |
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DESCRIPTION = """ # <center><b>SASHA ✨</b></center> |
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### <center>Your AI Assistant, Sasha Bingham</center> |
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### <center>Let's chat!</center> |
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""" |
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MORE = """ ## TRY Other Models |
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### Instant Video: Create Amazing Videos in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Video |
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### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image |
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""" |
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BETA = """ ### Voice Chat (BETA)""" |
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FAST = """## Fastest Model""" |
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Complex = """## Best in Complex Question""" |
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Detail = """## Best for Detailed Generation or Long Answers""" |
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base_loaded = "mistralai/Mixtral-8x7B-Instruct-v0.1" |
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client1 = InferenceClient(base_loaded) |
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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]" |
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async def generate1(prompt): |
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generate_kwargs = dict( |
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temperature=1.0, |
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max_new_tokens=128, |
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top_p=0.95, |
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repetition_penalty=0.9, |
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do_sample=True, |
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seed=42, |
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) |
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formatted_prompt = system_instructions1 + prompt + "[SASHA]" |
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stream = client1.text_generation( |
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formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) |
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output = "" |
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for response in stream: |
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if not response.token.text == "</s>": |
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output += response.token.text |
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communicate = edge_tts.Communicate(output) |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: |
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tmp_path = tmp_file.name |
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await communicate.save(tmp_path) |
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yield tmp_path |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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input = gr.Audio(label="Voice Chat (BETA)", sources="microphone", type="filepath", waveform_options=False) |
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output = gr.Audio(label="SASHA", type="filepath", |
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interactive=False, |
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autoplay=True, |
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elem_classes="audio") |
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gr.Interface( |
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fn=respond, |
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inputs=[input], |
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outputs=[output], live=True) |
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gr.Markdown(FAST) |
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with gr.Row(): |
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user_input = gr.Textbox(label="Prompt", value="What is Wikipedia") |
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input_text = gr.Textbox(label="Input Text", elem_id="important") |
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output_audio = gr.Audio(label="SASHA", type="filepath", |
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interactive=False, |
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autoplay=True, |
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elem_classes="audio") |
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with gr.Row(): |
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translate_btn = gr.Button("Response") |
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translate_btn.click(fn=generate1, inputs=user_input, |
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outputs=output_audio, api_name="translate") |
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gr.Markdown(MORE) |
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if __name__ == "__main__": |
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demo.queue(max_size=200).launch() |