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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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