79 lines
2.7 KiB
Plaintext
79 lines
2.7 KiB
Plaintext
import gradio as gr
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import torch
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TextStreamer, AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
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from huggingface_hub import login
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import os
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# Use the secret stored in the Hugging Face space
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token = os.getenv("HF_TOKEN")
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login(token=token)
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# Whisper Model Optimization
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model = "openai/whisper-tiny"
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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processor = AutoProcessor.from_pretrained(model)
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transcriber = pipeline(
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"automatic-speech-recognition",
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model=model,
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tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor,
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device=0 if torch.cuda.is_available() else "cpu",
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)
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# Function to Transcribe & Generate Minutes
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def process_audio(audio_file):
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if audio_file is None:
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return "Error: No audio provided!"
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# Transcribe audio
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transcript = transcriber(audio_file)["text"]
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del transcriber
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del processor
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# LLaMA Model Optimization
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LLAMA = "meta-llama/Llama-3.2-3B-Instruct"
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llama_quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4"
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)
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tokenizer = AutoTokenizer.from_pretrained(LLAMA)
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tokenizer.pad_token = tokenizer.eos_token
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model = AutoModelForCausalLM.from_pretrained(
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LLAMA,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto"
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)
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# Generate meeting minutes
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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."
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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}"
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messages = [
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_prompt}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(DEVICE)
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streamer = TextStreamer(tokenizer)
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outputs = model.generate(inputs, max_new_tokens=2000, streamer=streamer)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Gradio Interface
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interface = gr.Interface(
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fn=process_audio,
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inputs=gr.Audio(sources=["upload", "microphone"], type="filepath"),
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outputs="text",
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title="Meeting Minutes Generator",
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description="Upload or record an audio file to get structured meeting minutes in Markdown.",
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)
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# Launch App
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interface.launch()
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