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import torch
import spaces
import numpy as np
import gradio as gr
from gtts import gTTS
from transformers import pipeline
from huggingface_hub import InferenceClient
ASR_MODEL_NAME = "openai/whisper-small"
LLM_MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.2"
system_prompt = """"<s>[INST] You are Friday, a helpful and conversational AI assistant and You respond with one to two sentences. [/INST] Hello there! I'm friday how can I help you?</s>"""
chat_history = system_prompt + """"""
formatted_history = """"""
client = InferenceClient(LLM_MODEL_NAME)
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=ASR_MODEL_NAME,
device=device,
)
def generate(user_prompt, temperature=0.1, max_new_tokens=128, top_p=0.95, repetition_penalty=1.0):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
chat_history += f"""<s>[INST] {user_prompt} [/INST] """
output = client.text_generation(
chat_history, **generate_kwargs, stream=False, details=False, return_full_text=False)
print(output)
return output
@spaces.GPU(duration=60)
def transcribe(audio):
sr, y = audio
y = y.astype(np.float32)
y /= np.max(np.abs(y))
inputs = pipe({"sampling_rate": sr, "raw": y})["text"]
formatted_history += f"""Human: {inputs}\n"""
llm_response = generate(inputs)
chat_history += f""" {llm_response}</s>"""
formatted_history += f"""Friday: {llm_response}\n"""
audio_response = gTTS(llm_response)
audio_response.save("response.mp3")
print(chat_history)
return "response.mp3", formatted_history
with gr.Blocks() as demo:
gr.HTML("<center><h1>Friday: AI Virtual Assistant<h1><center>")
with gr.Row():
audio_input = gr.Audio(label="Human", sources="microphone")
output_audio = gr.Audio(label="Friday", type="filepath",
interactive=False,
autoplay=True,
elem_classes="audio")
transcribe_btn = gr.Button("Transcribe")
transcription_box = gr.Textbox(
formatted_history, label="Transcription")
transcribe_btn.click(fn=transcribe, inputs=audio_input,
outputs=[output_audio, transcription_box])
demo.queue()
demo.launch()
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