Create app.py

spaces python file

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModel
+from diffusers import DiffusionPipeline
+import soundfile as sf
+import numpy as np
+
+# Load text tokenizer and embedding model (umt5-base)
+def load_text_processor():
+    tokenizer = AutoTokenizer.from_pretrained("./umt5-base")
+    text_model = AutoModel.from_pretrained(
+        "./umt5-base",
+        use_safetensors=True,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    return tokenizer, text_model
+
+# Load the transformer backbone (phantomstep_transformer)
+def load_transformer():
+    transformer = DiffusionPipeline.from_pretrained(
+        "./phantomstep_transformer",
+        use_safetensors=True,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    return transformer
+
+# Load the DCAE for audio encoding/decoding (phantomstep_dcae)
+def load_dcae():
+    dcae = DiffusionPipeline.from_pretrained(
+        "./phantomstep_dcae",
+        use_safetensors=True,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    return dcae
+
+# Load the vocoder for audio synthesis (phantomstep_vocoder)
+def load_vocoder():
+    vocoder = DiffusionPipeline.from_pretrained(
+        "./phantomstep_vocoder",
+        use_safetensors=True,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    return vocoder
+
+# Generate music from a text prompt
+def generate_music(prompt, duration=20, seed=42):
+    torch.manual_seed(seed)
+    
+    # Load all components
+    tokenizer, text_model = load_text_processor()
+    transformer = load_transformer()
+    dcae = load_dcae()
+    vocoder = load_vocoder()
+    
+    # Step 1: Process text prompt to embeddings
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    inputs = {k: v.to(text_model.device) for k, v in inputs.items()}
+    with torch.no_grad():
+        embeddings = text_model(**inputs).last_hidden_state.mean(dim=1)
+    
+    # Step 2: Pass embeddings through transformer
+    transformer_output = transformer(
+        embeddings,
+        num_inference_steps=50,
+        audio_length_in_s=duration
+    ).audios[0]
+    
+    # Step 3: Decode audio features with DCAE
+    dcae_output = dcae(
+        transformer_output,
+        num_inference_steps=50,
+        audio_length_in_s=duration
+    ).audios[0]
+    
+    # Step 4: Synthesize final audio with vocoder
+    audio = vocoder(
+        dcae_output,
+        num_inference_steps=50,
+        audio_length_in_s=duration
+    ).audios[0]
+    
+    # Save audio to a file
+    output_path = "output.wav"
+    sf.write(output_path, audio, 22050)  # 22kHz sample rate
+    return output_path
+
+# Gradio interface
+with gr.Blocks(title="PhantomStep: Text-to-Music Generation 🎵") as demo:
+    gr.Markdown("# PhantomStep by GhostAI 🚀")
+    gr.Markdown("Enter a text prompt to generate music! 🎶")
+    
+    prompt_input = gr.Textbox(label="Text Prompt", placeholder="A jazzy piano melody with a fast tempo")
+    duration_input = gr.Slider(label="Duration (seconds)", minimum=10, maximum=60, value=20, step=1)
+    seed_input = gr.Number(label="Random Seed", value=42, precision=0)
+    generate_button = gr.Button("Generate Music")
+    
+    audio_output = gr.Audio(label="Generated Music")
+    
+    generate_button.click(
+        fn=generate_music,
+        inputs=[prompt_input, duration_input, seed_input],
+        outputs=audio_output
+    )
+
+demo.launch()