initial commit

Dockerfile

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+# Use Python 3.11
+FROM python:3.11
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    libsndfile1 \
+    fluid-soundfont-gm \
+    && rm -rf /var/lib/apt/lists/*
+
+# Upgrade pip before installing dependencies
+RUN pip install --upgrade pip
+
+# Copy and install Python dependencies first (before cloning anticipation)
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Clone and install Anticipation Music Transformer (AMT)
+RUN git clone https://github.com/jthickstun/anticipation.git 
+
+# Install AMT dependencies separately to avoid conflicts
+RUN pip install -r anticipation/requirements.txt && \
+    pip install ./anticipation 
+
+# Debugging: Check installed packages
+RUN pip list
+
+# Copy the application files into the container
+COPY . .
+
+# Run the Gradio app
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import spaces  # Enables ZeroGPU on Hugging Face
+from transformers import AutoModelForCausalLM
+from anticipation.sample import generate
+from anticipation.convert import events_to_midi, midi_to_events
+from anticipation import ops
+from anticipation.tokenize import extract_instruments
+import torch
+from pyharp import *
+from safetensors.torch import load_file
+import os
+
+
+#Model Choices
+SMALL_MODEL = "stanford-crfm/music-small-800k"
+MEDIUM_MODEL = "stanford-crfm/music-medium-800k"
+LARGE_MODEL = "stanford-crfm/music-large-800k"
+
+# === Model Card ===
+model_card = ModelCard(
+    name="Anticipatory Music Transformer",
+    description="Using Anticipatory Music Transformer (AMT) to generate accompaniment for a given MIDI file with selected melody.",
+    author="John Thickstun, David Hall, Chris Donahue, Percy Liang",
+    tags=["midi", "generation", "accompaniment"],
+    midi_in=True,
+    midi_out=True
+)
+
+model_cache = {}
+
+'''
+def load_amt_model(model_choice):
+    """Loads and caches the AMT model inside the worker process."""
+    if model_choice in model_cache:
+        return model_cache[model_choice]  
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = AutoModelForCausalLM.from_pretrained(model_choice).to(device)
+    
+    model_cache[model_choice] = model  
+    return model
+    '''
+    
+def load_amt_model(model_choice):
+    """Loads and caches the AMT model inside the worker process."""
+    if model_choice in model_cache:
+        return model_cache[model_choice]  
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    if model_choice == LARGE_MODEL:
+        # Large model uses safetensors
+        model_dir = "./tmp_music_large"
+        os.makedirs(model_dir, exist_ok=True)
+
+        print(f"Loading {LARGE_MODEL} from safetensors format...")
+        model = AutoModelForCausalLM.from_pretrained(
+            LARGE_MODEL, 
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            low_cpu_mem_usage=True
+        ).to(device)
+    else:
+        # Small and medium use standard PyTorch .bin format
+        print(f"Loading {model_choice} from standard format...")
+        model = AutoModelForCausalLM.from_pretrained(model_choice).to(device)
+    
+    model_cache[model_choice] = model  
+    return model
+
+
+
+
+@spaces.GPU
+def generate_accompaniment(midi_file, model_choice, selected_midi_program, history_length):
+    """Generates accompaniment for the entire MIDI input, conditioned on the user-selected history length."""
+    
+    model = load_amt_model(model_choice)  
+    events = midi_to_events(midi_file.name)
+    total_time = round(ops.max_time(events, seconds=True))
+    
+    # Extract melody line using the selected MIDI program number
+    events, melody = extract_instruments(events, [selected_midi_program])
+
+    if not melody:  
+        return None, "⚠️ Please select a valid MIDI program that contains events."
+
+    history = ops.clip(events, 0, history_length, clip_duration=False)
+
+    # Generate accompaniment for the remaining duration
+    accompaniment = generate(
+        model,
+        history_length,  # Start generating after user-defined history length
+        total_time,  # Generate for the full remaining duration
+        inputs=history,
+        controls=melody,
+        top_p=0.95,
+        debug=False
+    )
+
+    # Combine the accompaniment with the melody
+    output_events = ops.clip(ops.combine(accompaniment, melody), 0, total_time, clip_duration=True)
+    
+    # Convert back to MIDI
+    output_midi = "generated_accompaniment_huggingface.mid"
+    mid = events_to_midi(output_events)
+    mid.save(output_midi)
+
+    return output_midi, None  
+
+
+def process_fn(input_midi, model_choice, selected_midi_program, history_length):
+    """Processes the input and runs AMT to generate accompaniment for the full MIDI file."""   
+    output_midi, error_message = generate_accompaniment(input_midi, model_choice, selected_midi_program, history_length)
+
+    if error_message:  
+        return None, {"message": error_message}  
+
+    output_labels = LabelList()
+    return output_midi, output_labels
+
+
+# === Build HARP gradioEndpoint ===
+with gr.Blocks() as demo:
+    components = [
+        gr.Dropdown(
+          choices=[SMALL_MODEL, MEDIUM_MODEL, LARGE_MODEL], 
+            value=MEDIUM_MODEL, 
+            label="Select AMT Model (Faster vs. Higher Quality)"
+        ),
+        gr.Slider(0, 127, step=1, value=1, label="Select Melody Instrument (MIDI Program Number)"),
+        gr.Slider(1, 10, step=1, value=5, label="Select History Length (seconds)")
+    ]
+
+        # Wrap in PyHARP
+    app = build_endpoint(
+        model_card=model_card,
+        components=components,
+        process_fn=process_fn)
+
+# Launch PyHARP App
+demo.launch(share=True, show_error=True, debug=True)
+
+

requirements.txt

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+-e git+https://github.com/TEAMuP-dev/pyharp.git#egg=pyharp
+demucs
+dora-search
+einops
+julius>=0.2.3
+lameenc>=1.2
+openunmix
+pyyaml
+tqdm
+torch>=1.8.1, <2.1
+torchaudio>=0.8, <2.1
+diffq>=0.2.1  
+ffmpeg  
+numpy<2
+scipy  
+soundfile  
+hydra-core>=1.1