Update app.py

app.py

@@ -1,9 +1,8 @@
-import numpy as np
 import random
 import subprocess
-import tempfile
 import torch
 
+import numpy as np
 import gradio as gr
 
 from transformers import AutoProcessor, MusicgenForConditionalGeneration
@@ -14,12 +13,11 @@ COLORS = [
     ["#0000ff", "#ff0000"],
 ]
 
-path = "facebook/musicgen-small"
+path = "facebook/musicgen-large"
 processor = AutoProcessor.from_pretrained(path)
 model = MusicgenForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16).to("cuda")
 
 def predict(text):
-    
     inputs = processor(
         text=[text],
         padding=True,
@@ -27,14 +25,13 @@ def predict(text):
     
     with torch.autocast("cuda"):
         outputs = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=512)
+        data = outputs[0].cpu().numpy().astype(np.float16)
 
-    with tempfile.NamedTemporaryFile(suffix='.mp4') as tmp:
-        video_path = gr.make_waveform((32000, outputs[0].cpu().numpy().astype(np.float16).ravel()), bars_color=random.choice(COLORS), bar_count=75)
+        video_path = gr.make_waveform((32000, data.ravel()), bars_color=random.choice(COLORS), bar_count=75)
         command = f'ffmpeg -y -i {video_path} -vf "scale=250:150" result.mp4'
         subprocess.run(command, shell=True) 
 
-        return (32000, outputs[0][0].cpu().numpy().astype(np.float16)), "result.mp4"
-
+        return (32000, data), "result.mp4"
 
 title = "MusicGen"