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import spaces |
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import gradio as gr |
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import io |
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from urllib.request import urlopen |
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import soundfile as sf |
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import torch |
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from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig |
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MODEL_ID = "microsoft/Phi-4-multimodal-instruct" |
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processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_ID, |
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device_map="cuda" if torch.cuda.is_available() else "cpu", |
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torch_dtype="auto", |
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trust_remote_code=True |
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) |
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model.load_adapter(MODEL_ID, adapter_name="speech", device_map="cuda" if torch.cuda.is_available() else "cpu", adapter_kwargs={"subfolder": 'speech-lora'}) |
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model.set_adapter("speech") |
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generation_config = GenerationConfig.from_pretrained(MODEL_ID) |
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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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user_prompt = "<|user|>" |
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assistant_prompt = "<|assistant|>" |
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prompt_suffix = "<|end|>" |
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prompt = f"{user_prompt}<|audio_1|>{instruction}{prompt_suffix}{assistant_prompt}" |
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inputs = processor(text=prompt, audios=[(audio, samplerate)], return_tensors="pt").to(model.device) |
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output_ids = model.generate( |
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**inputs, |
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max_new_tokens=4096, |
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generation_config=generation_config, |
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) |
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output_ids = output_ids[:, inputs["input_ids"].shape[1]:] |
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response = processor.batch_decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[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) |
