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import spaces |
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import gradio as gr |
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import soundfile as sf |
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import torch |
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from transformers import AutoModelForCausalLM, AutoProcessor, infer_device |
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model_path = "microsoft/Phi-4-multimodal-instruct" |
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device = f"{infer_device()}:0" |
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processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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device_map=device, |
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dtype=torch.float16, |
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) |
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model.load_adapter( |
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model_path, |
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adapter_name="speech", |
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device_map=device, |
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adapter_kwargs={"subfolder": 'speech-lora'} |
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) |
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model.set_adapter("speech") |
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@spaces.GPU |
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def run_phi4(audio_path: str, instruction: str) -> str: |
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if not audio_path: |
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return "Please upload an audio file." |
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audio, samplerate = sf.read(audio_path) |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "audio", "url": audio_path}, |
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{"type": "text", "text": instruction}, |
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], |
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} |
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] |
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chat_text = processor.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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tokenize=False, |
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return_dict=False, |
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) |
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inputs = processor( |
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text=chat_text, |
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audios=[(audio, samplerate)], |
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return_tensors="pt", |
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).to(model.device) |
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generate_ids = model.generate( |
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**inputs, |
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max_new_tokens=1000, |
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do_sample=False, |
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) |
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generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] |
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response = processor.batch_decode( |
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generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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)[0] |
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return response |
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with gr.Blocks(title="Phi-4 Multimodal Audio Demo") as demo: |
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gr.Markdown("# Phi-4 Multimodal (Audio) Demo") |
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gr.Markdown("Upload an audio file and run instructions with Phi-4.") |
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with gr.Row(): |
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with gr.Column(): |
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audio_input = gr.Audio(type="filepath", label="Upload Audio") |
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instruction = gr.Textbox( |
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label="Instruction", |
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value=( |
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"Transcribe the audio to text, and then translate the audio to French. " |
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"Use <sep> as a separator between the original transcript and the translation." |
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), |
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) |
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submit_btn = gr.Button("Run", variant="primary") |
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with gr.Column(): |
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output_text = gr.Textbox(label="Model Response", lines=14) |
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submit_btn.click(run_phi4, [audio_input, instruction], output_text) |
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if __name__ == "__main__": |
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demo.queue().launch(share=False, ssr_mode=False) |
