Update app.py

app.py

@@ -1,41 +0,0 @@
-import gradio as gr
-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
-import torch
-import soundfile as sf
-
-model_name = "facebook/musicgen-small"
-config = AutoConfig.from_pretrained(model_name)
-
-if not hasattr(config, 'dropout'):
-    config.dropout = 0.1 
-if not hasattr(config, 'layerdrop'):
-    config.layerdrop = 0.1
-if not hasattr(config, 'max_position_embeddings'):
-    config.max_position_embeddings = 2048 
-if not hasattr(config, 'num_attention_heads'):
-    config.num_attention_heads = 16 
-if not hasattr(config, 'num_hidden_layers'):
-    config.num_hidden_layers = 24
-if not hasattr(config, 'scale_embedding'):
-    config.scale_embedding = False
-if not hasattr(config, 'hidden_size'):
-    config.hidden_size = 1024
-if not hasattr(config, 'num_codebooks'):
-    config.num_codebooks = 4
-
-model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-def text_to_audio(prompt):
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
-
-    with torch.no_grad(): 
-        output = model.generate(input_ids)
-        
-    audio_data = output[0].cpu().numpy()
-    audio_file = "generated_audio.wav"
-    sf.write(audio_file, audio_data, 22050)
-
-    return audio_file
-
-gr.Interface(fn=text_to_audio, inputs="text", outputs="audio").launch()