File size: 2,517 Bytes
eaf61fe 9a41897 3d1304c eaf61fe 9804570 3d1304c dc6e6db 3d1304c 9804570 f2c31e0 3d1304c f2c31e0 3d1304c f2c31e0 3d1304c 9804570 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import spaces
import gradio as gr
import io
from urllib.request import urlopen
import soundfile as sf
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
MODEL_ID = "microsoft/Phi-4-multimodal-instruct"
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="cuda" if torch.cuda.is_available() else "cpu",
torch_dtype="auto",
_attn_implementation="flash_attention_2",
)
model.load_adapter(MODEL_ID, adapter_name="speech", device_map="cuda" if torch.cuda.is_available() else "cpu", adapter_kwargs={"subfolder": 'speech-lora'})
model.set_adapter("speech")
generation_config = GenerationConfig.from_pretrained(MODEL_ID)
@spaces.GPU
def run_phi4(audio_path: str, instruction: str) -> str:
if not audio_path:
return "Please upload an audio file."
audio, samplerate = sf.read(audio_path)
user_prompt = "<|user|>"
assistant_prompt = "<|assistant|>"
prompt_suffix = "<|end|>"
prompt = f"{user_prompt}<|audio_1|>{instruction}{prompt_suffix}{assistant_prompt}"
inputs = processor(text=prompt, audios=[(audio, samplerate)], return_tensors="pt").to(model.device)
output_ids = model.generate(
**inputs,
max_new_tokens=4096,
generation_config=generation_config,
)
output_ids = output_ids[:, inputs["input_ids"].shape[1]:]
response = processor.batch_decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
return response
with gr.Blocks(title="Phi-4 Multimodal Audio Demo") as demo:
gr.Markdown("# Phi-4 Multimodal (Audio) Demo")
gr.Markdown("Upload an audio file and run instructions with Phi-4.")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(type="filepath", label="Upload Audio")
instruction = gr.Textbox(
label="Instruction",
value=(
"Transcribe the audio to text, and then translate the audio to French. "
"Use <sep> as a separator between the original transcript and the translation."
),
)
submit_btn = gr.Button("Run", variant="primary")
with gr.Column():
output_text = gr.Textbox(label="Model Response", lines=14)
submit_btn.click(run_phi4, [audio_input, instruction], output_text)
if __name__ == "__main__":
demo.queue().launch(share=False, ssr_mode=False) |