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import gradio as gr
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TextStreamer
# Whisper Model for Transcription
WHISPER_MODEL = "openai/whisper-large-v3"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
transcriber = pipeline(
task="automatic-speech-recognition",
model=WHISPER_MODEL,
chunk_length_s=30,
device=DEVICE,
)
# LLaMA Model for Generating Meeting Minutes
LLAMA = "meta-llama/Llama-2-7b-chat-hf" # Change to your preferred model
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4"
)
# Load Model & Tokenizer
tokenizer = AutoTokenizer.from_pretrained(LLAMA)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map="auto", quantization_config=quant_config)
# Function to Transcribe & Generate Minutes
def process_audio(audio_file):
if audio_file is None:
return "Error: No audio provided!"
# Transcribe audio
transcript = transcriber(audio_file)["text"]
# Generate meeting minutes
system_message = "You are an assistant that produces minutes of meetings from transcripts, with summary, key discussion points, takeaways and action items with owners, in markdown."
user_prompt = f"Below is an extract transcript of a Denver council meeting. Please write minutes in markdown, including a summary with attendees, location and date; discussion points; takeaways; and action items with owners.\n{transcript}"
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_prompt}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(DEVICE)
streamer = TextStreamer(tokenizer)
outputs = model.generate(inputs, max_new_tokens=2000, streamer=streamer)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Gradio Interface
interface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(sources=["upload", "microphone"], type="filepath"),
outputs="text",
title="Meeting Minutes Generator",
description="Upload or record an audio file to get structured meeting minutes in Markdown.",
)
# Launch App
interface.launch()
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